diff --git a/docs/_static/florencestheme.css b/docs/_static/florencestheme.css
index 7b7d13a4..37c52f8e 100644
--- a/docs/_static/florencestheme.css
+++ b/docs/_static/florencestheme.css
@@ -266,6 +266,13 @@ a:active {
display: inline-block;
}
+.wy-menu-vertical li.toctree-l4.current li.toctree-l5 > a {
+ /* background: #cdf8be; */
+ /*background: #ffe7fb;*/
+ background: var(--lightpink);
+ display: inline-block;
+}
+
.wy-menu-vertical {
overflow-x: scroll;
/*background-color: #ffe7fb; !* Background color for the side scroll *!*/
diff --git a/docs/_static/fusilli_pipeline_diagram.png b/docs/_static/fusilli_pipeline_diagram.png
new file mode 100644
index 00000000..60ae2bb5
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diff --git a/docs/_static/modify_thumbnail.png b/docs/_static/modify_thumbnail.png
new file mode 100644
index 00000000..cf7a0d12
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diff --git a/docs/auto_examples/auto_examples_jupyter.zip b/docs/auto_examples/auto_examples_jupyter.zip
index 9234ac5b..d4e092b8 100644
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diff --git a/docs/auto_examples/auto_examples_python.zip b/docs/auto_examples/auto_examples_python.zip
index 704a0c2e..e5eb074e 100644
Binary files a/docs/auto_examples/auto_examples_python.zip and b/docs/auto_examples/auto_examples_python.zip differ
diff --git a/docs/auto_examples/customising_behaviour/customising_training_parameters.ipynb b/docs/auto_examples/customising_behaviour/customising_training_parameters.ipynb
index 99770534..c4d941ff 100644
--- a/docs/auto_examples/customising_behaviour/customising_training_parameters.ipynb
+++ b/docs/auto_examples/customising_behaviour/customising_training_parameters.ipynb
@@ -6,6 +6,17 @@
"source": [
"\n# How to customise the training in Fusilli\n\nThis tutorial will show you how to customise the training of your fusion model.\n\nWe will cover the following topics:\n\n* Early stopping\n* Batch size\n* Number of epochs\n* Checkpoint suffix modification\n\n## Early stopping\n\nEarly stopping is implemented in Fusilli using the PyTorch Lightning\n[EarlyStopping](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.EarlyStopping.html#lightning.pytorch.callbacks.EarlyStopping)\ncallback. This callback can be passed to the\n:func:`~fusilli.model_utils.train_and_save_models` function using the\n``early_stopping_callback`` argument. For example:\n\n```python\nfrom fusilli.data import get_data_module\nfrom fusilli.train import train_and_save_models\n\nfrom lightning.pytorch.callbacks import EarlyStopping\n\nmodified_early_stopping_callback = EarlyStopping(\n monitor=\"val_loss\",\n min_delta=0.00,\n patience=3,\n verbose=True,\n mode=\"min\",\n)\n\ndatamodule = get_data_module(\n fusion_model=example_model,\n params=params,\n own_early_stopping_callback=modified_early_stopping_callback,\n )\n\ntrained_model_list = train_and_save_models(\n data_module=datamodule,\n params=params,\n fusion_model=example_model,\n )\n```\nNote that you only need to pass the callback to the :func:`~.fusilli.data.get_data_module` and **not** to the :func:`~.fusilli.train.train_and_save_models` function. The new early stopping measure will be saved within the data module and accessed during training.\n\n\n-----\n\n## Batch size\n\nThe batch size can be set using the ``batch_size`` argument in the :func:`~.fusilli.data.get_data_module` function. By default, the batch size is 8.\n\n```python\nfrom fusilli.data import get_data_module\nfrom fusilli.train import train_and_save_models\n\ndatamodule = get_data_module(\n fusion_model=example_model,\n params=params,\n batch_size=32,\n )\n\ntrained_model_list = train_and_save_models(\n data_module=datamodule,\n params=params,\n fusion_model=example_model,\n batch_size=32,\n )\n```\n-----\n\n## Number of epochs\n\nYou can change the maximum number of epochs using the ``max_epochs`` argument in the :func:`~.fusilli.data.get_data_module` and :func:`~.fusilli.train.train_and_save_models` functions. By default, the maximum number of epochs is 1000.\n\nYou also pass it to the :func:`~.fusilli.data.get_data_module` function because some of the fusion models require pre-training.\n\nChanging the ``max_epochs`` parameter is especially useful when wanting to run a quick test of your model. For example, you can set ``max_epochs=5`` to run a quick test of your model.\n\n```python\nfrom fusilli.data import get_data_module\nfrom fusilli.train import train_and_save_models\n\ndatamodule = get_data_module(\n fusion_model=example_model,\n params=params,\n max_epochs=5,\n )\n\ntrained_model_list = train_and_save_models(\n data_module=datamodule,\n params=params,\n fusion_model=example_model,\n max_epochs=5,\n )\n```\nSetting ``max_epochs`` to -1 will train the model until early stopping is triggered.\n\n-----\n\n## Checkpoint suffix modification\n\nBy default, Fusilli saves the model checkpoints in the following format:\n\n ``{fusion_model.__name__}_epoch={epoch_n}.ckpt``\n\nIf the checkpoint is for a pre-trained model, then the following format is used:\n\n ``subspace_{fusion_model.__name__}_{pretrained_model.__name__}.ckpt``\n\nYou can add suffixes to the checkpoint names by passing a string to the ``extra_log_string_dict`` argument in the :func:`~.fusilli.data.get_data_module` and :func:`~.fusilli.train.train_and_save_models` functions. For example, I could add a suffix to denote that I've changed the batch size for this particular run:\n\n```python\nfrom fusilli.data import get_data_module\nfrom fusilli.train import train_and_save_models\n\nextra_suffix_dict = {\"batchsize\": 32}\n\ndatamodule = get_data_module(\n fusion_model=example_model,\n params=params,\n batch_size=32,\n extra_log_string_dict=extra_suffix_dict,\n )\n\ntrained_model_list = train_and_save_models(\n data_module=datamodule,\n params=params,\n fusion_model=example_model,\n batch_size=32,\n extra_log_string_dict=extra_suffix_dict,\n )\n```\nThe checkpoint name would then be (if the model trained for 100 epochs):\n\n ``ExampleModel_epoch=100_batchsize_32.ckpt``\n\n\n
diff --git a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_001.png b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_001.png
index 25c0a0ef..d343a9a3 100644
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index 018fcf87..2e15c71a 100644
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diff --git a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_006.png b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_006.png
index a9654639..03f1689f 100644
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diff --git a/docs/auto_examples/training_and_testing/images/thumb/sphx_glr_plot_one_model_binary_kfold_thumb.png b/docs/auto_examples/training_and_testing/images/thumb/sphx_glr_plot_one_model_binary_kfold_thumb.png
index 709ffeea..54b6e51c 100644
Binary files a/docs/auto_examples/training_and_testing/images/thumb/sphx_glr_plot_one_model_binary_kfold_thumb.png and b/docs/auto_examples/training_and_testing/images/thumb/sphx_glr_plot_one_model_binary_kfold_thumb.png differ
diff --git a/docs/auto_examples/training_and_testing/index.rst b/docs/auto_examples/training_and_testing/index.rst
index 68f67d4d..7d193a2d 100644
--- a/docs/auto_examples/training_and_testing/index.rst
+++ b/docs/auto_examples/training_and_testing/index.rst
@@ -10,6 +10,9 @@ Running Fusilli on your own data
These are examples of how to train and validate fusion models with Fusilli.
+
+
+
.. raw:: html
diff --git a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.ipynb b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.ipynb
index ad630f76..686320f9 100644
--- a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.ipynb
+++ b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.ipynb
@@ -87,7 +87,7 @@
},
"outputs": [],
"source": [
- "all_trained_models = {}\n\nfor i, fusion_model in enumerate(fusion_models):\n fusion_model_name = fusion_model.__name__\n print(f\"Running model {fusion_model_name}\")\n\n # Get data module\n data_module = get_data_module(fusion_model, params, batch_size=params[\"batch_size\"])\n\n # Train and test\n single_model_list = train_and_save_models(\n data_module=data_module,\n params=params,\n fusion_model=fusion_model,\n enable_checkpointing=False, # False for the example notebooks\n show_loss_plot=True, # True for the example notebooks\n )\n\n # Save to all_trained_models\n all_trained_models[fusion_model_name] = single_model_list"
+ "# Using %%capture to hide the progress bar and plots (there are a lot of them!)\n\nall_trained_models = {}\n\nfor i, fusion_model in enumerate(fusion_models):\n fusion_model_name = fusion_model.__name__\n print(f\"Running model {fusion_model_name}\")\n\n # Get data module\n data_module = get_data_module(fusion_model, params, batch_size=params[\"batch_size\"])\n\n # Train and test\n single_model_list = train_and_save_models(\n data_module=data_module,\n params=params,\n fusion_model=fusion_model,\n enable_checkpointing=False, # False for the example notebooks\n show_loss_plot=True, # True for the example notebooks\n )\n\n # Save to all_trained_models\n all_trained_models[fusion_model_name] = single_model_list\n\n plt.close(\"all\")"
]
},
{
diff --git a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.py b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.py
index a6dc1f95..8515a5d0 100644
--- a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.py
+++ b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.py
@@ -127,6 +127,8 @@
# In this section, we train all the fusion models using the generated data and specified parameters.
# We store the results of each model for later analysis.
+# Using %%capture to hide the progress bar and plots (there are a lot of them!)
+
all_trained_models = {}
for i, fusion_model in enumerate(fusion_models):
@@ -148,6 +150,8 @@
# Save to all_trained_models
all_trained_models[fusion_model_name] = single_model_list
+ plt.close("all")
+
# %%
# 5. Plotting the results of the individual models
# -------------------------------------------------
diff --git a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.rst b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.rst
index 176a04aa..fc451f42 100644
--- a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.rst
+++ b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.rst
@@ -208,11 +208,13 @@ This function also simulated image data which we aren't using here.
In this section, we train all the fusion models using the generated data and specified parameters.
We store the results of each model for later analysis.
-.. GENERATED FROM PYTHON SOURCE LINES 129-151
+.. GENERATED FROM PYTHON SOURCE LINES 129-155
.. code-block:: Python
+ # Using %%capture to hide the progress bar and plots (there are a lot of them!)
+
all_trained_models = {}
for i, fusion_model in enumerate(fusion_models):
@@ -234,8 +236,10 @@ We store the results of each model for later analysis.
# Save to all_trained_models
all_trained_models[fusion_model_name] = single_model_list
+ plt.close("all")
+
-.. GENERATED FROM PYTHON SOURCE LINES 152-159
+.. GENERATED FROM PYTHON SOURCE LINES 156-163
5. Plotting the results of the individual models
-------------------------------------------------
@@ -245,7 +249,7 @@ If you want to save the figures rather than show them, you can use the :meth:`~.
This will save the figures in a timestamped folder in the current working directory with the method name and plot type in the filename.
You can add an extra suffix to the filename by passing a string to the ``extra_string`` argument of the :meth:`~fusilli.eval.Plotter.save_to_local` method.
-.. GENERATED FROM PYTHON SOURCE LINES 159-164
+.. GENERATED FROM PYTHON SOURCE LINES 163-168
.. code-block:: Python
@@ -255,13 +259,13 @@ You can add an extra suffix to the filename by passing a string to the ``extra_s
plt.show()
-.. GENERATED FROM PYTHON SOURCE LINES 165-168
+.. GENERATED FROM PYTHON SOURCE LINES 169-172
6. Plotting comparison of the models
-------------------------------------
In this section, we visualize the results of each individual model.
-.. GENERATED FROM PYTHON SOURCE LINES 168-172
+.. GENERATED FROM PYTHON SOURCE LINES 172-176
.. code-block:: Python
@@ -270,13 +274,13 @@ In this section, we visualize the results of each individual model.
plt.show()
-.. GENERATED FROM PYTHON SOURCE LINES 173-176
+.. GENERATED FROM PYTHON SOURCE LINES 177-180
7. Saving the results of the models
-------------------------------------
In this section, we compare the performance of all the trained models using a violin chart, providing an overview of how each model performed as a distribution over the different cross-validation folds.
-.. GENERATED FROM PYTHON SOURCE LINES 176-179
+.. GENERATED FROM PYTHON SOURCE LINES 180-183
.. code-block:: Python
@@ -287,7 +291,7 @@ In this section, we compare the performance of all the trained models using a vi
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.006 seconds)
+ **Total running time of the script:** (0 minutes 0.080 seconds)
.. _sphx_glr_download_auto_examples_training_and_testing_plot_model_comparison_loop_kfold.py:
diff --git a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold_codeobj.pickle b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold_codeobj.pickle
index 21cc2fdb..82a9b9c7 100644
Binary files a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold_codeobj.pickle and b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold_codeobj.pickle differ
diff --git a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.ipynb b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.ipynb
index a7b8a8fc..e473d50c 100644
--- a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.ipynb
+++ b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.ipynb
@@ -15,7 +15,7 @@
},
"outputs": [],
"source": [
- "import matplotlib.pyplot as plt\nfrom tqdm.auto import tqdm\nimport os\n\nfrom docs.examples import generate_sklearn_simulated_data\nfrom fusilli.data import get_data_module\nfrom fusilli.eval import ConfusionMatrix\nfrom fusilli.train import train_and_save_models"
+ "import matplotlib.pyplot as plt\nfrom tqdm.auto import tqdm\nimport os\n\nfrom docs.examples import generate_sklearn_simulated_data\nfrom fusilli.data import get_data_module\nfrom fusilli.eval import ConfusionMatrix\nfrom fusilli.train import train_and_save_models\n\n# sphinx_gallery_thumbnail_number = -1"
]
},
{
@@ -105,7 +105,7 @@
},
"outputs": [],
"source": [
- "confusion_matrix_fig = ConfusionMatrix.from_final_val_data(\n single_model_list\n)\n\nplt.show()"
+ "confusion_matrix_fig = ConfusionMatrix.from_final_val_data(\n single_model_list\n)\nplt.show()"
]
}
],
diff --git a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py
index 995ebb84..fd75925a 100644
--- a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py
+++ b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py
@@ -24,6 +24,8 @@
from fusilli.eval import ConfusionMatrix
from fusilli.train import train_and_save_models
+# sphinx_gallery_thumbnail_number = -1
+
# %%
# 1. Import the fusion model 🔍
# --------------------------------
@@ -124,5 +126,4 @@
confusion_matrix_fig = ConfusionMatrix.from_final_val_data(
single_model_list
)
-
plt.show()
diff --git a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py.md5 b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py.md5
index 0cc15905..9510df63 100644
--- a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py.md5
+++ b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py.md5
@@ -1 +1 @@
-c6dda228d1218472134a61e603a0a2ba
\ No newline at end of file
+36b51b90e2f0212151c0237326c907e2
\ No newline at end of file
diff --git a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.rst b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.rst
index 3b66c7ef..874644da 100644
--- a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.rst
+++ b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.rst
@@ -33,7 +33,7 @@ Key Features:
- 📈 Plotting the loss curves of each fold.
- 📊 Visualising the results of a single K-Fold model using the :class:`~.ConfusionMatrix` class.
-.. GENERATED FROM PYTHON SOURCE LINES 17-27
+.. GENERATED FROM PYTHON SOURCE LINES 17-29
.. code-block:: Python
@@ -47,6 +47,7 @@ Key Features:
from fusilli.eval import ConfusionMatrix
from fusilli.train import train_and_save_models
+ # sphinx_gallery_thumbnail_number = -1
@@ -54,14 +55,15 @@ Key Features:
-.. GENERATED FROM PYTHON SOURCE LINES 28-32
+
+.. GENERATED FROM PYTHON SOURCE LINES 30-34
1. Import the fusion model 🔍
--------------------------------
We're importing only one model for this example, the :class:`~.TabularCrossmodalMultiheadAttention` model.
Instead of using the :func:`~fusilli.utils.model_chooser.import_chosen_fusion_models` function, we're importing the model directly like with any other library method.
-.. GENERATED FROM PYTHON SOURCE LINES 32-38
+.. GENERATED FROM PYTHON SOURCE LINES 34-40
.. code-block:: Python
@@ -78,7 +80,7 @@ Instead of using the :func:`~fusilli.utils.model_chooser.import_chosen_fusion_mo
-.. GENERATED FROM PYTHON SOURCE LINES 39-51
+.. GENERATED FROM PYTHON SOURCE LINES 41-53
2. Set the training parameters 🎯
-----------------------------------
@@ -93,7 +95,7 @@ For using k-fold cross validation, the necessary parameters are:
We're also setting our own batch_size for this example.
-.. GENERATED FROM PYTHON SOURCE LINES 51-68
+.. GENERATED FROM PYTHON SOURCE LINES 53-70
.. code-block:: Python
@@ -121,14 +123,14 @@ We're also setting our own batch_size for this example.
-.. GENERATED FROM PYTHON SOURCE LINES 69-73
+.. GENERATED FROM PYTHON SOURCE LINES 71-75
3. Generating simulated data 🔮
--------------------------------
Time to create some simulated data for our models to work their wonders on.
This function also simulated image data which we aren't using here.
-.. GENERATED FROM PYTHON SOURCE LINES 73-82
+.. GENERATED FROM PYTHON SOURCE LINES 75-84
.. code-block:: Python
@@ -148,7 +150,7 @@ This function also simulated image data which we aren't using here.
-.. GENERATED FROM PYTHON SOURCE LINES 83-96
+.. GENERATED FROM PYTHON SOURCE LINES 85-98
4. Training the fusion model 🏁
--------------------------------------
@@ -164,7 +166,7 @@ This function takes the following parameters:
Then we pass the data module, the parameters, and the fusion model to the :func:`~fusilli.train.train_and_save_models` function.
We're not using checkpointing for this example, so we set ``enable_checkpointing=False``. We're also setting ``show_loss_plot=True`` to plot the loss curves for each fold.
-.. GENERATED FROM PYTHON SOURCE LINES 96-117
+.. GENERATED FROM PYTHON SOURCE LINES 98-119
.. code-block:: Python
@@ -238,51 +240,51 @@ We're not using checkpointing for this example, so we set ``enable_checkpointing
method_name: Tabular Crossmodal multi-head attention
modality_type: tabular_tabular
fusion_type: attention
-
Training: | | 0/? [00:00, ?it/s]
Training: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 52.65it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 51.83it/s, v_num=ld_0]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 72.65it/s, v_num=ld_0]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 71.59it/s, v_num=ld_0]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 87.98it/s, v_num=ld_0]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 87.59it/s, v_num=ld_0]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 98.81it/s, v_num=ld_0]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 98.37it/s, v_num=ld_0]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 105.31it/s, v_num=ld_0]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 104.90it/s, v_num=ld_0]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 110.67it/s, v_num=ld_0]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 110.31it/s, v_num=ld_0]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 115.30it/s, v_num=ld_0]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 114.99it/s, v_num=ld_0]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 119.56it/s, v_num=ld_0]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 119.27it/s, v_num=ld_0]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 122.46it/s, v_num=ld_0]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 122.18it/s, v_num=ld_0]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 125.26it/s, v_num=ld_0]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 125.00it/s, v_num=ld_0]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 127.89it/s, v_num=ld_0]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 127.65it/s, v_num=ld_0]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 129.96it/s, v_num=ld_0]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 129.74it/s, v_num=ld_0]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 131.84it/s, v_num=ld_0]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 131.60it/s, v_num=ld_0]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 105.41it/s, v_num=ld_0, val_loss=0.701]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 104.88it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 0: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 141.74it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 138.14it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 149.65it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 147.75it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 140.13it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 138.86it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 142.62it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 141.74it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 151.06it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 150.14it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 153.67it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 152.95it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 157.79it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 157.15it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 162.22it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 161.70it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 165.55it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 164.93it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 167.62it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 167.07it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 168.48it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 168.03it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 170.82it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 170.41it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 170.78it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 170.43it/s, v_num=ld_0, val_loss=0.701, train_loss=0.714]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 148.40it/s, v_num=ld_0, val_loss=0.687, train_loss=0.714]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 147.61it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 184.80it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 179.14it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 187.40it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 184.07it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 183.42it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 181.71it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 190.58it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 189.38it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 195.90it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 194.67it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 197.22it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 195.94it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 195.34it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 194.48it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 195.09it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 194.27it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 195.34it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 194.70it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 196.17it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 195.58it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 196.25it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 195.74it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 197.90it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 197.34it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 197.61it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 197.14it/s, v_num=ld_0, val_loss=0.687, train_loss=0.688]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 179.08it/s, v_num=ld_0, val_loss=0.674, train_loss=0.688]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 178.19it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 195.01it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 188.80it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 195.12it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 192.16it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 201.60it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 199.67it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 203.91it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 202.30it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 204.48it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 203.04it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 204.38it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 203.32it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 198.47it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 197.54it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 198.24it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 197.33it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 197.17it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 196.46it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 197.04it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 196.41it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 196.99it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 196.42it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 196.98it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 196.55it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 198.21it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 197.65it/s, v_num=ld_0, val_loss=0.674, train_loss=0.669]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 179.10it/s, v_num=ld_0, val_loss=0.650, train_loss=0.669]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 178.20it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 198.32it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 193.35it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 203.15it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 200.21it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 208.26it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 206.19it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 209.51it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 208.13it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 211.04it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 209.72it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 210.76it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 209.80it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 211.79it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 210.85it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 212.35it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 211.63it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 214.39it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 213.78it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 215.74it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 215.15it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 206.05it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 205.56it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 207.83it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 207.42it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 208.90it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 208.48it/s, v_num=ld_0, val_loss=0.650, train_loss=0.639]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 185.56it/s, v_num=ld_0, val_loss=0.622, train_loss=0.639]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 184.77it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 212.45it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 206.23it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 207.43it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 204.27it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 203.82it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 201.68it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 199.16it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 197.44it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 196.16it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 194.89it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 195.40it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 194.36it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 195.07it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 194.19it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 194.21it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 193.50it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 194.37it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 193.74it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 196.32it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 195.79it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 197.47it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 196.99it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 198.05it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 197.61it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 199.18it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 198.75it/s, v_num=ld_0, val_loss=0.622, train_loss=0.597]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 172.98it/s, v_num=ld_0, val_loss=0.614, train_loss=0.597]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 172.16it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 200.12it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 194.36it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 204.08it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 201.27it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 204.78it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 202.81it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 204.51it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 203.10it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 204.73it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 203.49it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 204.83it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 203.78it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 205.09it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 204.25it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 207.24it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 206.33it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 205.40it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 204.69it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 205.55it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 204.88it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 205.98it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 205.48it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 206.53it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 206.07it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 208.55it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 208.18it/s, v_num=ld_0, val_loss=0.614, train_loss=0.577]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 186.58it/s, v_num=ld_0, val_loss=0.621, train_loss=0.577]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 185.46it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 200.16it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 194.36it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 202.96it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 199.60it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 197.52it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 195.38it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 198.90it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 197.51it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 200.50it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 199.31it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 201.94it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 200.90it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 202.97it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 202.11it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 204.33it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 203.52it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 204.16it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 203.47it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 201.99it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 201.34it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 202.62it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 202.04it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 201.68it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 201.11it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 201.52it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 201.05it/s, v_num=ld_0, val_loss=0.621, train_loss=0.553]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 180.81it/s, v_num=ld_0, val_loss=0.625, train_loss=0.553]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 179.78it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 187.34it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 181.78it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 191.28it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 188.27it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 194.17it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 192.23it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 198.38it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 196.84it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 198.09it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 196.76it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 199.04it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 197.26it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 196.57it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 195.70it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 198.60it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 197.85it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 198.18it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 197.34it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 195.98it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 195.06it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 189.85it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 189.01it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 188.78it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 188.30it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 189.32it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 188.70it/s, v_num=ld_0, val_loss=0.625, train_loss=0.558]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 167.09it/s, v_num=ld_0, val_loss=0.628, train_loss=0.558]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 166.18it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 175.13it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 170.27it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 184.45it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 181.87it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 188.22it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 186.41it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 186.81it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 185.38it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 186.22it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 184.96it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 186.76it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 186.04it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 187.79it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 186.96it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 188.09it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 187.50it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 190.58it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 190.07it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 192.00it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 191.41it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 192.52it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 192.03it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 193.82it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 193.37it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 195.03it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 194.64it/s, v_num=ld_0, val_loss=0.628, train_loss=0.548]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 177.63it/s, v_num=ld_0, val_loss=0.624, train_loss=0.548]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 176.88it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 216.17it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 209.52it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 219.42it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 216.10it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 219.46it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 217.20it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 213.72it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 212.03it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 215.24it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 213.83it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 205.26it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 204.32it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 205.33it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 204.49it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 203.79it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 203.07it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 203.64it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 203.08it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 204.43it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 203.82it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 204.75it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 204.23it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 205.97it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 205.50it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 207.16it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 206.73it/s, v_num=ld_0, val_loss=0.624, train_loss=0.543]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 188.77it/s, v_num=ld_0, val_loss=0.612, train_loss=0.543]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 187.97it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 225.02it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 218.00it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 226.32it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 223.19it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 225.70it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 223.53it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 225.59it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 224.00it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 224.81it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 223.64it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 223.78it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 222.76it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 224.42it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 223.58it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 224.28it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 223.52it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 224.45it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 223.74it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 223.45it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 222.81it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 223.55it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 223.00it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 223.23it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 222.69it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 222.36it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 221.88it/s, v_num=ld_0, val_loss=0.612, train_loss=0.546]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 193.93it/s, v_num=ld_0, val_loss=0.630, train_loss=0.546]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 192.98it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 227.32it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 220.79it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 225.88it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 222.59it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 228.13it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 225.90it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 226.90it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 225.20it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 223.97it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 222.69it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 222.78it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 221.76it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 221.56it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 220.58it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 220.14it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 219.32it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 219.18it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 218.52it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 218.94it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 218.35it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 218.84it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 218.31it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 219.30it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 218.75it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 218.94it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 218.46it/s, v_num=ld_0, val_loss=0.630, train_loss=0.538]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 196.03it/s, v_num=ld_0, val_loss=0.623, train_loss=0.538]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 195.10it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 219.39it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 213.04it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 223.56it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 220.48it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 226.49it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 224.45it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 224.32it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 222.69it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 222.69it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 221.49it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 221.34it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 220.32it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 221.11it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 220.22it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 219.87it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 219.06it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 219.52it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 218.83it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 219.53it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 218.86it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 217.91it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 217.37it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 217.28it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 216.77it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 216.63it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 216.15it/s, v_num=ld_0, val_loss=0.623, train_loss=0.539]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 195.16it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 194.25it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 216.12it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 210.17it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 217.60it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 214.31it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 215.00it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 212.86it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 215.52it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 213.76it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 213.37it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 212.12it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 210.53it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 209.48it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 209.41it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 208.59it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 207.60it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 206.71it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 205.76it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 205.09it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 206.15it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 205.59it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 206.50it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 205.97it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 206.87it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 206.34it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 207.48it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 207.01it/s, v_num=ld_0, val_loss=0.617, train_loss=0.539]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 154.78it/s, v_num=ld_0, val_loss=0.626, train_loss=0.539]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 153.93it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 184.43it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 178.72it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 185.15it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 182.42it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 184.97it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 183.13it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 188.15it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 186.89it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 191.50it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 190.23it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 189.22it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 188.02it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 186.42it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 185.50it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 187.68it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 186.99it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 189.26it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 188.67it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 188.84it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 188.23it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 188.46it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 187.80it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 188.70it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 188.20it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 187.28it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 186.75it/s, v_num=ld_0, val_loss=0.626, train_loss=0.534]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 167.81it/s, v_num=ld_0, val_loss=0.616, train_loss=0.534]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 166.82it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 171.11it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 166.55it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 178.63it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 176.16it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 183.88it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 182.33it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 188.30it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 186.84it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 183.90it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 182.67it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 184.26it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 183.23it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 183.97it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 183.25it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 183.92it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 183.18it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 183.59it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 182.94it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 184.99it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 184.44it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 186.67it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 186.11it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 186.19it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 185.72it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 186.57it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 186.20it/s, v_num=ld_0, val_loss=0.616, train_loss=0.531]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 167.56it/s, v_num=ld_0, val_loss=0.614, train_loss=0.531]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 166.70it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 177.46it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 172.56it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 178.48it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 176.04it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 185.98it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 184.04it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 184.71it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 183.22it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 185.31it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 184.32it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 188.65it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 187.68it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 187.80it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 187.10it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 188.71it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 188.03it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 188.57it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 188.00it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 189.85it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 189.30it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 190.85it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 190.36it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 191.28it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 190.83it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 192.82it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 192.44it/s, v_num=ld_0, val_loss=0.614, train_loss=0.535]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 176.52it/s, v_num=ld_0, val_loss=0.620, train_loss=0.535]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 175.81it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 218.53it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 212.36it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 219.64it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 216.40it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 214.35it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 212.48it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 215.51it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 213.52it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 212.47it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 211.36it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 212.77it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 211.74it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 211.04it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 210.14it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 209.59it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 208.89it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 209.43it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 208.79it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 208.47it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 207.90it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 208.38it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 207.88it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 209.06it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 208.55it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 209.10it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 208.63it/s, v_num=ld_0, val_loss=0.620, train_loss=0.532]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 188.85it/s, v_num=ld_0, val_loss=0.626, train_loss=0.532]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 188.06it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 216.82it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 210.61it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 217.63it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 214.41it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 216.22it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 214.43it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 213.34it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 211.84it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 212.80it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 211.58it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 210.82it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 209.83it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 209.70it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 208.82it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 209.31it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 208.58it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 207.57it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 206.86it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 206.21it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 205.67it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 206.18it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 205.67it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 206.47it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 206.04it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 207.57it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 207.17it/s, v_num=ld_0, val_loss=0.626, train_loss=0.531]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 187.73it/s, v_num=ld_0, val_loss=0.612, train_loss=0.531]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 186.91it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 224.98it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 218.68it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 217.37it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 214.15it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 214.81it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 212.78it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 213.34it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 211.60it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 211.93it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 210.73it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 212.50it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 211.48it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 212.37it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 211.57it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 212.25it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 211.51it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 211.75it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 211.11it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 211.58it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 211.03it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 212.04it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 211.54it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 212.51it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 212.02it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 212.88it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 212.45it/s, v_num=ld_0, val_loss=0.612, train_loss=0.529]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 189.10it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 188.29it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 223.55it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 217.29it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 219.30it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 216.12it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 218.20it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 216.17it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 217.45it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 215.93it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 215.40it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 214.06it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 215.59it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 214.65it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 215.84it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 214.96it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 215.11it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 214.31it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 213.13it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 212.31it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 212.84it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 212.24it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 212.27it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 211.73it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 209.54it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 208.94it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 208.80it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 208.27it/s, v_num=ld_0, val_loss=0.625, train_loss=0.529]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 183.33it/s, v_num=ld_0, val_loss=0.619, train_loss=0.529]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 182.39it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 203.79it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 197.86it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 207.47it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 204.48it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 205.71it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 203.50it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 202.78it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 201.24it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 203.18it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 201.99it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 202.31it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 200.95it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 199.63it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 198.84it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 200.24it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 199.58it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 200.81it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 200.26it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 199.28it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 198.71it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 199.80it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 199.35it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 200.45it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 199.98it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 201.03it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 200.58it/s, v_num=ld_0, val_loss=0.619, train_loss=0.533]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 182.46it/s, v_num=ld_0, val_loss=0.611, train_loss=0.533]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 181.66it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 204.50it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 199.12it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 208.86it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 206.10it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 207.77it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 205.92it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 206.86it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 205.09it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 202.48it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 201.39it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 203.84it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 202.88it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 204.09it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 203.24it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 202.91it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 202.11it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 202.81it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 202.23it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 202.81it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 202.24it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 202.82it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 202.40it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 203.03it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 202.57it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 203.71it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 203.27it/s, v_num=ld_0, val_loss=0.611, train_loss=0.535]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 184.39it/s, v_num=ld_0, val_loss=0.630, train_loss=0.535]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 183.54it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 209.25it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 203.22it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 210.88it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 207.95it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 207.13it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 205.26it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 207.67it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 206.21it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 207.94it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 206.84it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 207.13it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 206.19it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 206.30it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 205.49it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 205.76it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 205.09it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 204.98it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 204.33it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 206.01it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 205.45it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 207.25it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 206.75it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 207.02it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 206.54it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 207.82it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 207.37it/s, v_num=ld_0, val_loss=0.630, train_loss=0.532]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 188.53it/s, v_num=ld_0, val_loss=0.614, train_loss=0.532]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 187.66it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 208.34it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 202.43it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 208.91it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 206.25it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 210.59it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 207.73it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 205.44it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 203.84it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 201.53it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 200.39it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 199.84it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 198.85it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 199.89it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 199.22it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 200.36it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 199.69it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 200.42it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 199.93it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 201.22it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 200.66it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 200.34it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 199.87it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 201.33it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 200.90it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 201.76it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 201.34it/s, v_num=ld_0, val_loss=0.614, train_loss=0.530]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 181.83it/s, v_num=ld_0, val_loss=0.612, train_loss=0.530]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 181.00it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 208.94it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 203.21it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 208.03it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 205.05it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 205.59it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 203.25it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 200.50it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 199.12it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 199.56it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 198.44it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 199.98it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 199.12it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 199.70it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 198.86it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 198.79it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 198.15it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 199.13it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 198.50it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 198.04it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 197.48it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 198.28it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 197.79it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 197.71it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 197.25it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 199.04it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 198.63it/s, v_num=ld_0, val_loss=0.612, train_loss=0.528]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 181.04it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 180.30it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 8%|▊ | 1/13 [00:00<00:00, 205.28it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 8%|▊ | 1/13 [00:00<00:00, 199.11it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 15%|█▌ | 2/13 [00:00<00:00, 207.40it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 15%|█▌ | 2/13 [00:00<00:00, 204.52it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 23%|██▎ | 3/13 [00:00<00:00, 209.79it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 23%|██▎ | 3/13 [00:00<00:00, 207.81it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 31%|███ | 4/13 [00:00<00:00, 208.40it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 31%|███ | 4/13 [00:00<00:00, 206.99it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 38%|███▊ | 5/13 [00:00<00:00, 209.83it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 38%|███▊ | 5/13 [00:00<00:00, 208.74it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 46%|████▌ | 6/13 [00:00<00:00, 208.43it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 46%|████▌ | 6/13 [00:00<00:00, 207.40it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 54%|█████▍ | 7/13 [00:00<00:00, 201.86it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 54%|█████▍ | 7/13 [00:00<00:00, 201.08it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 62%|██████▏ | 8/13 [00:00<00:00, 201.02it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 62%|██████▏ | 8/13 [00:00<00:00, 200.32it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 69%|██████▉ | 9/13 [00:00<00:00, 200.03it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 69%|██████▉ | 9/13 [00:00<00:00, 199.41it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 77%|███████▋ | 10/13 [00:00<00:00, 200.11it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 77%|███████▋ | 10/13 [00:00<00:00, 199.44it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 85%|████████▍ | 11/13 [00:00<00:00, 197.78it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 85%|████████▍ | 11/13 [00:00<00:00, 197.27it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 92%|█████████▏| 12/13 [00:00<00:00, 197.49it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 92%|█████████▏| 12/13 [00:00<00:00, 197.07it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 198.53it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 198.10it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 180.06it/s, v_num=ld_0, val_loss=0.608, train_loss=0.528]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 179.34it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 27: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 8%|▊ | 1/13 [00:00<00:00, 214.05it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 8%|▊ | 1/13 [00:00<00:00, 208.25it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 15%|█▌ | 2/13 [00:00<00:00, 211.04it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 15%|█▌ | 2/13 [00:00<00:00, 207.95it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 23%|██▎ | 3/13 [00:00<00:00, 206.06it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 23%|██▎ | 3/13 [00:00<00:00, 204.07it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 31%|███ | 4/13 [00:00<00:00, 204.44it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 31%|███ | 4/13 [00:00<00:00, 203.06it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 38%|███▊ | 5/13 [00:00<00:00, 205.77it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 38%|███▊ | 5/13 [00:00<00:00, 204.69it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 46%|████▌ | 6/13 [00:00<00:00, 205.97it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 46%|████▌ | 6/13 [00:00<00:00, 205.04it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 54%|█████▍ | 7/13 [00:00<00:00, 205.06it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 54%|█████▍ | 7/13 [00:00<00:00, 204.21it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 62%|██████▏ | 8/13 [00:00<00:00, 204.97it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 62%|██████▏ | 8/13 [00:00<00:00, 204.30it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 69%|██████▉ | 9/13 [00:00<00:00, 203.48it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 69%|██████▉ | 9/13 [00:00<00:00, 202.72it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 77%|███████▋ | 10/13 [00:00<00:00, 201.35it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 77%|███████▋ | 10/13 [00:00<00:00, 200.71it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 85%|████████▍ | 11/13 [00:00<00:00, 201.34it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 85%|████████▍ | 11/13 [00:00<00:00, 200.88it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 92%|█████████▏| 12/13 [00:00<00:00, 201.74it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 92%|█████████▏| 12/13 [00:00<00:00, 201.26it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 203.14it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 202.69it/s, v_num=ld_0, val_loss=0.608, train_loss=0.527]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 183.89it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 183.15it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 28: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 8%|▊ | 1/13 [00:00<00:00, 218.58it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 8%|▊ | 1/13 [00:00<00:00, 212.13it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 15%|█▌ | 2/13 [00:00<00:00, 216.19it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 15%|█▌ | 2/13 [00:00<00:00, 212.41it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 23%|██▎ | 3/13 [00:00<00:00, 207.92it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 23%|██▎ | 3/13 [00:00<00:00, 206.01it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 31%|███ | 4/13 [00:00<00:00, 206.25it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 31%|███ | 4/13 [00:00<00:00, 204.85it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 38%|███▊ | 5/13 [00:00<00:00, 203.29it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 38%|███▊ | 5/13 [00:00<00:00, 202.17it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 46%|████▌ | 6/13 [00:00<00:00, 200.39it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 46%|████▌ | 6/13 [00:00<00:00, 199.40it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 54%|█████▍ | 7/13 [00:00<00:00, 197.16it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 54%|█████▍ | 7/13 [00:00<00:00, 196.31it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 62%|██████▏ | 8/13 [00:00<00:00, 196.42it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 62%|██████▏ | 8/13 [00:00<00:00, 195.70it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 69%|██████▉ | 9/13 [00:00<00:00, 195.60it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 69%|██████▉ | 9/13 [00:00<00:00, 194.98it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 77%|███████▋ | 10/13 [00:00<00:00, 195.66it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 77%|███████▋ | 10/13 [00:00<00:00, 195.16it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 85%|████████▍ | 11/13 [00:00<00:00, 196.65it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 85%|████████▍ | 11/13 [00:00<00:00, 196.22it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 92%|█████████▏| 12/13 [00:00<00:00, 197.69it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 92%|█████████▏| 12/13 [00:00<00:00, 197.29it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 198.83it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 198.39it/s, v_num=ld_0, val_loss=0.607, train_loss=0.527]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 177.94it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 177.17it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 29: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 8%|▊ | 1/13 [00:00<00:00, 219.51it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 8%|▊ | 1/13 [00:00<00:00, 213.08it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 15%|█▌ | 2/13 [00:00<00:00, 211.82it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 15%|█▌ | 2/13 [00:00<00:00, 208.59it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 23%|██▎ | 3/13 [00:00<00:00, 203.71it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 23%|██▎ | 3/13 [00:00<00:00, 201.82it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 31%|███ | 4/13 [00:00<00:00, 202.60it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 31%|███ | 4/13 [00:00<00:00, 201.35it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 38%|███▊ | 5/13 [00:00<00:00, 202.68it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 38%|███▊ | 5/13 [00:00<00:00, 201.45it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 46%|████▌ | 6/13 [00:00<00:00, 202.11it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 46%|████▌ | 6/13 [00:00<00:00, 201.21it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 54%|█████▍ | 7/13 [00:00<00:00, 200.83it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 54%|█████▍ | 7/13 [00:00<00:00, 200.08it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 62%|██████▏ | 8/13 [00:00<00:00, 201.22it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 62%|██████▏ | 8/13 [00:00<00:00, 200.58it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 69%|██████▉ | 9/13 [00:00<00:00, 201.66it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 69%|██████▉ | 9/13 [00:00<00:00, 201.01it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 77%|███████▋ | 10/13 [00:00<00:00, 201.63it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 77%|███████▋ | 10/13 [00:00<00:00, 200.99it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 85%|████████▍ | 11/13 [00:00<00:00, 200.93it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 85%|████████▍ | 11/13 [00:00<00:00, 200.43it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 92%|█████████▏| 12/13 [00:00<00:00, 201.23it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 92%|█████████▏| 12/13 [00:00<00:00, 200.74it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 201.53it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 201.14it/s, v_num=ld_0, val_loss=0.625, train_loss=0.527]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 183.21it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 182.43it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 30: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 8%|▊ | 1/13 [00:00<00:00, 210.26it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 8%|▊ | 1/13 [00:00<00:00, 203.71it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 15%|█▌ | 2/13 [00:00<00:00, 208.27it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 15%|█▌ | 2/13 [00:00<00:00, 205.45it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 23%|██▎ | 3/13 [00:00<00:00, 208.75it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 23%|██▎ | 3/13 [00:00<00:00, 207.03it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 31%|███ | 4/13 [00:00<00:00, 207.48it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 31%|███ | 4/13 [00:00<00:00, 206.11it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 38%|███▊ | 5/13 [00:00<00:00, 207.86it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 38%|███▊ | 5/13 [00:00<00:00, 206.71it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 46%|████▌ | 6/13 [00:00<00:00, 207.22it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 46%|████▌ | 6/13 [00:00<00:00, 206.21it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 54%|█████▍ | 7/13 [00:00<00:00, 205.03it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 54%|█████▍ | 7/13 [00:00<00:00, 204.13it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 62%|██████▏ | 8/13 [00:00<00:00, 204.86it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 62%|██████▏ | 8/13 [00:00<00:00, 204.20it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 69%|██████▉ | 9/13 [00:00<00:00, 205.22it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 69%|██████▉ | 9/13 [00:00<00:00, 204.64it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 77%|███████▋ | 10/13 [00:00<00:00, 205.44it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 77%|███████▋ | 10/13 [00:00<00:00, 204.92it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 85%|████████▍ | 11/13 [00:00<00:00, 205.20it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 85%|████████▍ | 11/13 [00:00<00:00, 204.72it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 92%|█████████▏| 12/13 [00:00<00:00, 205.41it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 92%|█████████▏| 12/13 [00:00<00:00, 204.96it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 205.40it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 204.97it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 183.63it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 182.90it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 31: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 8%|▊ | 1/13 [00:00<00:00, 214.00it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 8%|▊ | 1/13 [00:00<00:00, 208.07it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 15%|█▌ | 2/13 [00:00<00:00, 212.52it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 15%|█▌ | 2/13 [00:00<00:00, 209.60it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 23%|██▎ | 3/13 [00:00<00:00, 210.45it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 23%|██▎ | 3/13 [00:00<00:00, 208.67it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 31%|███ | 4/13 [00:00<00:00, 204.44it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 31%|███ | 4/13 [00:00<00:00, 203.04it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 38%|███▊ | 5/13 [00:00<00:00, 203.20it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 38%|███▊ | 5/13 [00:00<00:00, 202.19it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 46%|████▌ | 6/13 [00:00<00:00, 201.52it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 46%|████▌ | 6/13 [00:00<00:00, 200.31it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 54%|█████▍ | 7/13 [00:00<00:00, 199.89it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 54%|█████▍ | 7/13 [00:00<00:00, 199.04it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 62%|██████▏ | 8/13 [00:00<00:00, 196.65it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 62%|██████▏ | 8/13 [00:00<00:00, 195.83it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 69%|██████▉ | 9/13 [00:00<00:00, 195.31it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 69%|██████▉ | 9/13 [00:00<00:00, 194.70it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 77%|███████▋ | 10/13 [00:00<00:00, 195.39it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 77%|███████▋ | 10/13 [00:00<00:00, 194.80it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 85%|████████▍ | 11/13 [00:00<00:00, 192.82it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 85%|████████▍ | 11/13 [00:00<00:00, 192.26it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 92%|█████████▏| 12/13 [00:00<00:00, 192.09it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 92%|█████████▏| 12/13 [00:00<00:00, 191.67it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 193.43it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 193.03it/s, v_num=ld_0, val_loss=0.619, train_loss=0.527]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 174.48it/s, v_num=ld_0, val_loss=0.624, train_loss=0.527]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 173.70it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 32: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 8%|▊ | 1/13 [00:00<00:00, 184.58it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 8%|▊ | 1/13 [00:00<00:00, 178.99it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 15%|█▌ | 2/13 [00:00<00:00, 191.67it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 15%|█▌ | 2/13 [00:00<00:00, 188.79it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 23%|██▎ | 3/13 [00:00<00:00, 189.44it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 23%|██▎ | 3/13 [00:00<00:00, 187.51it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 31%|███ | 4/13 [00:00<00:00, 188.57it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 31%|███ | 4/13 [00:00<00:00, 187.28it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 38%|███▊ | 5/13 [00:00<00:00, 189.32it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 38%|███▊ | 5/13 [00:00<00:00, 188.09it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 46%|████▌ | 6/13 [00:00<00:00, 186.39it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 46%|████▌ | 6/13 [00:00<00:00, 185.45it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 54%|█████▍ | 7/13 [00:00<00:00, 186.25it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 54%|█████▍ | 7/13 [00:00<00:00, 185.62it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 62%|██████▏ | 8/13 [00:00<00:00, 188.03it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 62%|██████▏ | 8/13 [00:00<00:00, 187.39it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 69%|██████▉ | 9/13 [00:00<00:00, 189.21it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 69%|██████▉ | 9/13 [00:00<00:00, 188.56it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 77%|███████▋ | 10/13 [00:00<00:00, 186.50it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 77%|███████▋ | 10/13 [00:00<00:00, 185.88it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 85%|████████▍ | 11/13 [00:00<00:00, 186.69it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 85%|████████▍ | 11/13 [00:00<00:00, 186.21it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 92%|█████████▏| 12/13 [00:00<00:00, 187.53it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 92%|█████████▏| 12/13 [00:00<00:00, 187.03it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 100%|██████████| 13/13 [00:00<00:00, 186.84it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 100%|██████████| 13/13 [00:00<00:00, 186.45it/s, v_num=ld_0, val_loss=0.624, train_loss=0.529]
Epoch 33: 100%|██████████| 13/13 [00:00<00:00, 166.43it/s, v_num=ld_0, val_loss=0.611, train_loss=0.529]
Epoch 33: 100%|██████████| 13/13 [00:00<00:00, 165.74it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 33: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 8%|▊ | 1/13 [00:00<00:00, 196.72it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 8%|▊ | 1/13 [00:00<00:00, 191.05it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 15%|█▌ | 2/13 [00:00<00:00, 201.37it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 15%|█▌ | 2/13 [00:00<00:00, 198.47it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 23%|██▎ | 3/13 [00:00<00:00, 198.46it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 23%|██▎ | 3/13 [00:00<00:00, 196.27it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 31%|███ | 4/13 [00:00<00:00, 195.03it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 31%|███ | 4/13 [00:00<00:00, 193.55it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 38%|███▊ | 5/13 [00:00<00:00, 194.88it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 38%|███▊ | 5/13 [00:00<00:00, 193.89it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 46%|████▌ | 6/13 [00:00<00:00, 197.10it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 46%|████▌ | 6/13 [00:00<00:00, 196.24it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 54%|█████▍ | 7/13 [00:00<00:00, 197.37it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 54%|█████▍ | 7/13 [00:00<00:00, 196.33it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 62%|██████▏ | 8/13 [00:00<00:00, 194.76it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 62%|██████▏ | 8/13 [00:00<00:00, 193.91it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 69%|██████▉ | 9/13 [00:00<00:00, 193.92it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 69%|██████▉ | 9/13 [00:00<00:00, 193.32it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 77%|███████▋ | 10/13 [00:00<00:00, 194.36it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 77%|███████▋ | 10/13 [00:00<00:00, 193.71it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 85%|████████▍ | 11/13 [00:00<00:00, 194.46it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 85%|████████▍ | 11/13 [00:00<00:00, 193.94it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 92%|█████████▏| 12/13 [00:00<00:00, 193.52it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 92%|█████████▏| 12/13 [00:00<00:00, 192.97it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 100%|██████████| 13/13 [00:00<00:00, 192.97it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 100%|██████████| 13/13 [00:00<00:00, 192.55it/s, v_num=ld_0, val_loss=0.611, train_loss=0.528]
Epoch 34: 100%|██████████| 13/13 [00:00<00:00, 173.90it/s, v_num=ld_0, val_loss=0.614, train_loss=0.528]
Epoch 34: 100%|██████████| 13/13 [00:00<00:00, 173.09it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 34: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 8%|▊ | 1/13 [00:00<00:00, 195.13it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 8%|▊ | 1/13 [00:00<00:00, 188.93it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 15%|█▌ | 2/13 [00:00<00:00, 190.10it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 15%|█▌ | 2/13 [00:00<00:00, 187.45it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 23%|██▎ | 3/13 [00:00<00:00, 195.30it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 23%|██▎ | 3/13 [00:00<00:00, 193.64it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 31%|███ | 4/13 [00:00<00:00, 194.99it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 31%|███ | 4/13 [00:00<00:00, 193.57it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 38%|███▊ | 5/13 [00:00<00:00, 196.61it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 38%|███▊ | 5/13 [00:00<00:00, 195.42it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 46%|████▌ | 6/13 [00:00<00:00, 194.62it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 46%|████▌ | 6/13 [00:00<00:00, 193.45it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 54%|█████▍ | 7/13 [00:00<00:00, 190.68it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 54%|█████▍ | 7/13 [00:00<00:00, 189.96it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 62%|██████▏ | 8/13 [00:00<00:00, 189.91it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 62%|██████▏ | 8/13 [00:00<00:00, 189.13it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 69%|██████▉ | 9/13 [00:00<00:00, 189.76it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 69%|██████▉ | 9/13 [00:00<00:00, 189.23it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 77%|███████▋ | 10/13 [00:00<00:00, 189.97it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 77%|███████▋ | 10/13 [00:00<00:00, 189.36it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 85%|████████▍ | 11/13 [00:00<00:00, 187.82it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 85%|████████▍ | 11/13 [00:00<00:00, 187.23it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 92%|█████████▏| 12/13 [00:00<00:00, 188.07it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 92%|█████████▏| 12/13 [00:00<00:00, 187.54it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 100%|██████████| 13/13 [00:00<00:00, 186.11it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 100%|██████████| 13/13 [00:00<00:00, 185.58it/s, v_num=ld_0, val_loss=0.614, train_loss=0.527]
Epoch 35: 100%|██████████| 13/13 [00:00<00:00, 167.15it/s, v_num=ld_0, val_loss=0.633, train_loss=0.527]
Epoch 35: 100%|██████████| 13/13 [00:00<00:00, 166.21it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 35: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 8%|▊ | 1/13 [00:00<00:00, 168.67it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 8%|▊ | 1/13 [00:00<00:00, 163.41it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 15%|█▌ | 2/13 [00:00<00:00, 173.98it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 15%|█▌ | 2/13 [00:00<00:00, 170.10it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 23%|██▎ | 3/13 [00:00<00:00, 177.82it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 23%|██▎ | 3/13 [00:00<00:00, 176.22it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 31%|███ | 4/13 [00:00<00:00, 178.49it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 31%|███ | 4/13 [00:00<00:00, 177.33it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 38%|███▊ | 5/13 [00:00<00:00, 181.93it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 38%|███▊ | 5/13 [00:00<00:00, 180.99it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 46%|████▌ | 6/13 [00:00<00:00, 181.55it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 46%|████▌ | 6/13 [00:00<00:00, 180.71it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 54%|█████▍ | 7/13 [00:00<00:00, 184.61it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 54%|█████▍ | 7/13 [00:00<00:00, 183.94it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 62%|██████▏ | 8/13 [00:00<00:00, 186.67it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 62%|██████▏ | 8/13 [00:00<00:00, 186.12it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 69%|██████▉ | 9/13 [00:00<00:00, 188.60it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 69%|██████▉ | 9/13 [00:00<00:00, 188.10it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 77%|███████▋ | 10/13 [00:00<00:00, 188.63it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 77%|███████▋ | 10/13 [00:00<00:00, 188.10it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 85%|████████▍ | 11/13 [00:00<00:00, 190.03it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 85%|████████▍ | 11/13 [00:00<00:00, 189.52it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 92%|█████████▏| 12/13 [00:00<00:00, 186.81it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 92%|█████████▏| 12/13 [00:00<00:00, 186.30it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 100%|██████████| 13/13 [00:00<00:00, 185.58it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 100%|██████████| 13/13 [00:00<00:00, 185.17it/s, v_num=ld_0, val_loss=0.633, train_loss=0.526]
Epoch 36: 100%|██████████| 13/13 [00:00<00:00, 169.39it/s, v_num=ld_0, val_loss=0.636, train_loss=0.526]
Epoch 36: 100%|██████████| 13/13 [00:00<00:00, 168.69it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 36: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 8%|▊ | 1/13 [00:00<00:00, 199.80it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 8%|▊ | 1/13 [00:00<00:00, 194.29it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 15%|█▌ | 2/13 [00:00<00:00, 196.65it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 15%|█▌ | 2/13 [00:00<00:00, 194.06it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 23%|██▎ | 3/13 [00:00<00:00, 195.10it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 23%|██▎ | 3/13 [00:00<00:00, 193.20it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 31%|███ | 4/13 [00:00<00:00, 194.93it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 31%|███ | 4/13 [00:00<00:00, 193.54it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 38%|███▊ | 5/13 [00:00<00:00, 193.69it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 38%|███▊ | 5/13 [00:00<00:00, 192.51it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 46%|████▌ | 6/13 [00:00<00:00, 191.39it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 46%|████▌ | 6/13 [00:00<00:00, 190.53it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 54%|█████▍ | 7/13 [00:00<00:00, 190.86it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 54%|█████▍ | 7/13 [00:00<00:00, 190.14it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 62%|██████▏ | 8/13 [00:00<00:00, 191.22it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 62%|██████▏ | 8/13 [00:00<00:00, 190.57it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 69%|██████▉ | 9/13 [00:00<00:00, 191.31it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 69%|██████▉ | 9/13 [00:00<00:00, 190.73it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 77%|███████▋ | 10/13 [00:00<00:00, 191.98it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 77%|███████▋ | 10/13 [00:00<00:00, 191.49it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 85%|████████▍ | 11/13 [00:00<00:00, 192.15it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 85%|████████▍ | 11/13 [00:00<00:00, 191.73it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 92%|█████████▏| 12/13 [00:00<00:00, 193.32it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 92%|█████████▏| 12/13 [00:00<00:00, 192.93it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 100%|██████████| 13/13 [00:00<00:00, 193.90it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 100%|██████████| 13/13 [00:00<00:00, 193.55it/s, v_num=ld_0, val_loss=0.636, train_loss=0.524]
Epoch 37: 100%|██████████| 13/13 [00:00<00:00, 176.08it/s, v_num=ld_0, val_loss=0.617, train_loss=0.524]
Epoch 37: 100%|██████████| 13/13 [00:00<00:00, 175.36it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 37: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 8%|▊ | 1/13 [00:00<00:00, 213.85it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 8%|▊ | 1/13 [00:00<00:00, 208.11it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 15%|█▌ | 2/13 [00:00<00:00, 218.53it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 15%|█▌ | 2/13 [00:00<00:00, 215.61it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 23%|██▎ | 3/13 [00:00<00:00, 219.28it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 23%|██▎ | 3/13 [00:00<00:00, 217.28it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 31%|███ | 4/13 [00:00<00:00, 218.70it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 31%|███ | 4/13 [00:00<00:00, 217.14it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 38%|███▊ | 5/13 [00:00<00:00, 213.65it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 38%|███▊ | 5/13 [00:00<00:00, 212.42it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 46%|████▌ | 6/13 [00:00<00:00, 212.19it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 46%|████▌ | 6/13 [00:00<00:00, 211.14it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 54%|█████▍ | 7/13 [00:00<00:00, 210.92it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 54%|█████▍ | 7/13 [00:00<00:00, 210.09it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 62%|██████▏ | 8/13 [00:00<00:00, 209.19it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 62%|██████▏ | 8/13 [00:00<00:00, 208.37it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 69%|██████▉ | 9/13 [00:00<00:00, 207.49it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 69%|██████▉ | 9/13 [00:00<00:00, 206.88it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 77%|███████▋ | 10/13 [00:00<00:00, 207.25it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 77%|███████▋ | 10/13 [00:00<00:00, 206.70it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 85%|████████▍ | 11/13 [00:00<00:00, 205.88it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 85%|████████▍ | 11/13 [00:00<00:00, 205.34it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 92%|█████████▏| 12/13 [00:00<00:00, 205.94it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 92%|█████████▏| 12/13 [00:00<00:00, 205.51it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 206.44it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 206.03it/s, v_num=ld_0, val_loss=0.617, train_loss=0.527]
Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 186.51it/s, v_num=ld_0, val_loss=0.650, train_loss=0.527]
Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 185.71it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 38: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 8%|▊ | 1/13 [00:00<00:00, 177.33it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 8%|▊ | 1/13 [00:00<00:00, 172.80it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 15%|█▌ | 2/13 [00:00<00:00, 182.10it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 15%|█▌ | 2/13 [00:00<00:00, 179.63it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 23%|██▎ | 3/13 [00:00<00:00, 182.98it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 23%|██▎ | 3/13 [00:00<00:00, 181.40it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 31%|███ | 4/13 [00:00<00:00, 185.49it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 31%|███ | 4/13 [00:00<00:00, 184.23it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 38%|███▊ | 5/13 [00:00<00:00, 187.81it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 38%|███▊ | 5/13 [00:00<00:00, 186.86it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 46%|████▌ | 6/13 [00:00<00:00, 186.28it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 46%|████▌ | 6/13 [00:00<00:00, 185.53it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 54%|█████▍ | 7/13 [00:00<00:00, 186.65it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 54%|█████▍ | 7/13 [00:00<00:00, 186.09it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 62%|██████▏ | 8/13 [00:00<00:00, 188.10it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 62%|██████▏ | 8/13 [00:00<00:00, 187.36it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 69%|██████▉ | 9/13 [00:00<00:00, 187.31it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 69%|██████▉ | 9/13 [00:00<00:00, 186.79it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 77%|███████▋ | 10/13 [00:00<00:00, 188.01it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 77%|███████▋ | 10/13 [00:00<00:00, 187.55it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 85%|████████▍ | 11/13 [00:00<00:00, 189.29it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 85%|████████▍ | 11/13 [00:00<00:00, 188.81it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 92%|█████████▏| 12/13 [00:00<00:00, 189.79it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 92%|█████████▏| 12/13 [00:00<00:00, 189.43it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 100%|██████████| 13/13 [00:00<00:00, 191.06it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 100%|██████████| 13/13 [00:00<00:00, 190.71it/s, v_num=ld_0, val_loss=0.650, train_loss=0.528]
Epoch 39: 100%|██████████| 13/13 [00:00<00:00, 163.87it/s, v_num=ld_0, val_loss=0.609, train_loss=0.528]
Epoch 39: 100%|██████████| 13/13 [00:00<00:00, 163.21it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 39: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 8%|▊ | 1/13 [00:00<00:00, 207.13it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 8%|▊ | 1/13 [00:00<00:00, 201.33it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 15%|█▌ | 2/13 [00:00<00:00, 209.08it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 15%|█▌ | 2/13 [00:00<00:00, 205.96it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 23%|██▎ | 3/13 [00:00<00:00, 207.36it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 23%|██▎ | 3/13 [00:00<00:00, 205.54it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 31%|███ | 4/13 [00:00<00:00, 206.31it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 31%|███ | 4/13 [00:00<00:00, 204.92it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 38%|███▊ | 5/13 [00:00<00:00, 205.34it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 38%|███▊ | 5/13 [00:00<00:00, 204.21it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 46%|████▌ | 6/13 [00:00<00:00, 204.68it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 46%|████▌ | 6/13 [00:00<00:00, 203.79it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 54%|█████▍ | 7/13 [00:00<00:00, 204.40it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 54%|█████▍ | 7/13 [00:00<00:00, 203.55it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 62%|██████▏ | 8/13 [00:00<00:00, 203.97it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 62%|██████▏ | 8/13 [00:00<00:00, 203.29it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 69%|██████▉ | 9/13 [00:00<00:00, 203.79it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 69%|██████▉ | 9/13 [00:00<00:00, 203.16it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 77%|███████▋ | 10/13 [00:00<00:00, 203.04it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 77%|███████▋ | 10/13 [00:00<00:00, 202.47it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 85%|████████▍ | 11/13 [00:00<00:00, 201.80it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 85%|████████▍ | 11/13 [00:00<00:00, 201.32it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 92%|█████████▏| 12/13 [00:00<00:00, 201.75it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 92%|█████████▏| 12/13 [00:00<00:00, 201.31it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 100%|██████████| 13/13 [00:00<00:00, 201.22it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 100%|██████████| 13/13 [00:00<00:00, 200.78it/s, v_num=ld_0, val_loss=0.609, train_loss=0.531]
Epoch 40: 100%|██████████| 13/13 [00:00<00:00, 182.52it/s, v_num=ld_0, val_loss=0.623, train_loss=0.531]
Epoch 40: 100%|██████████| 13/13 [00:00<00:00, 181.78it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 40: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 8%|▊ | 1/13 [00:00<00:00, 213.09it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 8%|▊ | 1/13 [00:00<00:00, 207.22it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 15%|█▌ | 2/13 [00:00<00:00, 207.75it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 15%|█▌ | 2/13 [00:00<00:00, 203.71it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 23%|██▎ | 3/13 [00:00<00:00, 194.53it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 23%|██▎ | 3/13 [00:00<00:00, 192.56it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 31%|███ | 4/13 [00:00<00:00, 192.24it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 31%|███ | 4/13 [00:00<00:00, 191.02it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 38%|███▊ | 5/13 [00:00<00:00, 191.98it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 38%|███▊ | 5/13 [00:00<00:00, 190.94it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 46%|████▌ | 6/13 [00:00<00:00, 191.16it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 46%|████▌ | 6/13 [00:00<00:00, 190.12it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 54%|█████▍ | 7/13 [00:00<00:00, 190.81it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 54%|█████▍ | 7/13 [00:00<00:00, 190.05it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 62%|██████▏ | 8/13 [00:00<00:00, 191.70it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 62%|██████▏ | 8/13 [00:00<00:00, 191.07it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 69%|██████▉ | 9/13 [00:00<00:00, 191.56it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 69%|██████▉ | 9/13 [00:00<00:00, 190.90it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 77%|███████▋ | 10/13 [00:00<00:00, 191.26it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 77%|███████▋ | 10/13 [00:00<00:00, 190.72it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 85%|████████▍ | 11/13 [00:00<00:00, 190.28it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 85%|████████▍ | 11/13 [00:00<00:00, 189.75it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 92%|█████████▏| 12/13 [00:00<00:00, 189.66it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 92%|█████████▏| 12/13 [00:00<00:00, 189.22it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 100%|██████████| 13/13 [00:00<00:00, 184.20it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 100%|██████████| 13/13 [00:00<00:00, 183.85it/s, v_num=ld_0, val_loss=0.623, train_loss=0.527]
Epoch 41: 100%|██████████| 13/13 [00:00<00:00, 167.21it/s, v_num=ld_0, val_loss=0.629, train_loss=0.527]
Epoch 41: 100%|██████████| 13/13 [00:00<00:00, 166.48it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 41: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 8%|▊ | 1/13 [00:00<00:00, 186.85it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 8%|▊ | 1/13 [00:00<00:00, 182.18it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 15%|█▌ | 2/13 [00:00<00:00, 191.70it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 15%|█▌ | 2/13 [00:00<00:00, 188.95it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 23%|██▎ | 3/13 [00:00<00:00, 191.09it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 23%|██▎ | 3/13 [00:00<00:00, 189.07it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 31%|███ | 4/13 [00:00<00:00, 189.89it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 31%|███ | 4/13 [00:00<00:00, 188.41it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 38%|███▊ | 5/13 [00:00<00:00, 189.27it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 38%|███▊ | 5/13 [00:00<00:00, 188.08it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 46%|████▌ | 6/13 [00:00<00:00, 184.96it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 46%|████▌ | 6/13 [00:00<00:00, 183.95it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 54%|█████▍ | 7/13 [00:00<00:00, 184.81it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 54%|█████▍ | 7/13 [00:00<00:00, 184.05it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 62%|██████▏ | 8/13 [00:00<00:00, 186.44it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 62%|██████▏ | 8/13 [00:00<00:00, 185.87it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 69%|██████▉ | 9/13 [00:00<00:00, 186.13it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 69%|██████▉ | 9/13 [00:00<00:00, 185.33it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 77%|███████▋ | 10/13 [00:00<00:00, 185.54it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 77%|███████▋ | 10/13 [00:00<00:00, 185.05it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 85%|████████▍ | 11/13 [00:00<00:00, 186.02it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 85%|████████▍ | 11/13 [00:00<00:00, 185.48it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 92%|█████████▏| 12/13 [00:00<00:00, 186.17it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 92%|█████████▏| 12/13 [00:00<00:00, 185.74it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 100%|██████████| 13/13 [00:00<00:00, 186.85it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 100%|██████████| 13/13 [00:00<00:00, 186.20it/s, v_num=ld_0, val_loss=0.629, train_loss=0.529]
Epoch 42: 100%|██████████| 13/13 [00:00<00:00, 168.51it/s, v_num=ld_0, val_loss=0.641, train_loss=0.529]
Epoch 42: 100%|██████████| 13/13 [00:00<00:00, 167.75it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 42: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 8%|▊ | 1/13 [00:00<00:00, 188.14it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 8%|▊ | 1/13 [00:00<00:00, 182.71it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 15%|█▌ | 2/13 [00:00<00:00, 193.14it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 15%|█▌ | 2/13 [00:00<00:00, 190.59it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 23%|██▎ | 3/13 [00:00<00:00, 194.86it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 23%|██▎ | 3/13 [00:00<00:00, 193.11it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 31%|███ | 4/13 [00:00<00:00, 193.03it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 31%|███ | 4/13 [00:00<00:00, 191.45it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 38%|███▊ | 5/13 [00:00<00:00, 191.64it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 38%|███▊ | 5/13 [00:00<00:00, 190.64it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 46%|████▌ | 6/13 [00:00<00:00, 192.46it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 46%|████▌ | 6/13 [00:00<00:00, 191.60it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 54%|█████▍ | 7/13 [00:00<00:00, 191.57it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 54%|█████▍ | 7/13 [00:00<00:00, 190.66it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 62%|██████▏ | 8/13 [00:00<00:00, 189.26it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 62%|██████▏ | 8/13 [00:00<00:00, 188.67it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 69%|██████▉ | 9/13 [00:00<00:00, 191.27it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 69%|██████▉ | 9/13 [00:00<00:00, 190.71it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 77%|███████▋ | 10/13 [00:00<00:00, 192.29it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 77%|███████▋ | 10/13 [00:00<00:00, 191.78it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 85%|████████▍ | 11/13 [00:00<00:00, 193.07it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 85%|████████▍ | 11/13 [00:00<00:00, 192.67it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 92%|█████████▏| 12/13 [00:00<00:00, 193.95it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 92%|█████████▏| 12/13 [00:00<00:00, 193.51it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 100%|██████████| 13/13 [00:00<00:00, 193.93it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 100%|██████████| 13/13 [00:00<00:00, 193.52it/s, v_num=ld_0, val_loss=0.641, train_loss=0.532]
Epoch 43: 100%|██████████| 13/13 [00:00<00:00, 176.18it/s, v_num=ld_0, val_loss=0.638, train_loss=0.532]
Epoch 43: 100%|██████████| 13/13 [00:00<00:00, 175.49it/s, v_num=ld_0, val_loss=0.638, train_loss=0.529]
Epoch 43: 100%|██████████| 13/13 [00:00<00:00, 172.81it/s, v_num=ld_0, val_loss=0.638, train_loss=0.529]
+
Training: | | 0/? [00:00, ?it/s]
Training: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 16.70it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 16.58it/s, v_num=ld_0]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 30.06it/s, v_num=ld_0]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 29.99it/s, v_num=ld_0]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 41.53it/s, v_num=ld_0]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 41.44it/s, v_num=ld_0]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 51.19it/s, v_num=ld_0]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 51.06it/s, v_num=ld_0]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 59.69it/s, v_num=ld_0]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 59.58it/s, v_num=ld_0]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 67.44it/s, v_num=ld_0]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 67.32it/s, v_num=ld_0]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 74.32it/s, v_num=ld_0]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 74.17it/s, v_num=ld_0]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 78.63it/s, v_num=ld_0]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 78.49it/s, v_num=ld_0]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 84.04it/s, v_num=ld_0]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 83.91it/s, v_num=ld_0]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 88.74it/s, v_num=ld_0]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 88.61it/s, v_num=ld_0]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 93.13it/s, v_num=ld_0]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 93.00it/s, v_num=ld_0]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 96.71it/s, v_num=ld_0]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 96.60it/s, v_num=ld_0]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 100.60it/s, v_num=ld_0]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 100.47it/s, v_num=ld_0]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 93.79it/s, v_num=ld_0, val_loss=0.709]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 93.49it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 0: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 183.76it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 178.25it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 186.45it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 183.42it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 184.75it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 182.80it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 184.98it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 183.60it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 186.26it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 185.23it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 189.50it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 188.68it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 191.00it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 190.30it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 191.94it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 191.31it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 192.64it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 192.06it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 192.70it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 192.21it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 193.32it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 192.86it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 193.90it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 193.45it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 194.46it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 194.08it/s, v_num=ld_0, val_loss=0.709, train_loss=0.716]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 177.26it/s, v_num=ld_0, val_loss=0.673, train_loss=0.716]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 176.58it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 209.16it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 203.51it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 211.24it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 208.25it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 212.18it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 210.29it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 211.80it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 210.44it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 210.25it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 209.11it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 206.36it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 205.45it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 206.39it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 205.60it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 206.36it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 205.70it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 205.96it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 205.34it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 205.61it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 205.02it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 205.15it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 204.66it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 204.83it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 204.41it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 205.02it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 204.60it/s, v_num=ld_0, val_loss=0.673, train_loss=0.679]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 168.77it/s, v_num=ld_0, val_loss=0.617, train_loss=0.679]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 168.08it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 197.98it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 192.84it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 200.55it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 198.08it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 200.79it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 199.07it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 201.10it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 199.79it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 201.78it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 200.69it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 202.01it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 201.12it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 202.11it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 201.34it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 202.03it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 201.39it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 201.90it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 201.36it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 202.19it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 201.69it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 202.96it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 202.51it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 203.45it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 203.00it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 203.81it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 203.37it/s, v_num=ld_0, val_loss=0.617, train_loss=0.628]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 184.43it/s, v_num=ld_0, val_loss=0.605, train_loss=0.628]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 183.69it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 214.54it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 208.77it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 210.94it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 207.90it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 210.98it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 209.14it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 208.09it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 206.66it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 206.37it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 205.30it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 205.64it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 204.73it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 205.65it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 204.94it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 206.36it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 205.67it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 205.24it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 204.67it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 204.97it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 204.48it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 204.61it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 204.10it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 203.83it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 203.28it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 202.19it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 201.75it/s, v_num=ld_0, val_loss=0.605, train_loss=0.575]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 182.88it/s, v_num=ld_0, val_loss=0.622, train_loss=0.575]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 182.09it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 202.88it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 197.16it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 201.21it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 198.43it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 201.37it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 199.79it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 201.51it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 200.24it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 201.69it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 200.51it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 199.64it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 198.68it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 198.38it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 197.61it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 197.67it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 197.07it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 198.27it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 197.70it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 198.52it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 198.04it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 196.98it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 196.44it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 196.77it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 196.24it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 195.48it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 195.02it/s, v_num=ld_0, val_loss=0.622, train_loss=0.570]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 177.54it/s, v_num=ld_0, val_loss=0.607, train_loss=0.570]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 176.71it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 195.43it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 190.62it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 195.66it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 193.24it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 195.11it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 193.13it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 196.37it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 195.09it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 195.87it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 194.67it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 192.89it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 191.90it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 192.68it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 191.89it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 192.96it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 192.31it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 192.26it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 191.49it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 191.48it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 191.01it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 192.48it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 192.04it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 193.15it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 192.68it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 192.85it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 192.48it/s, v_num=ld_0, val_loss=0.607, train_loss=0.572]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 174.82it/s, v_num=ld_0, val_loss=0.604, train_loss=0.572]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 174.11it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 209.29it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 203.71it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 204.14it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 201.31it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 201.33it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 199.60it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 198.78it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 197.46it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 198.29it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 197.15it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 197.91it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 196.88it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 194.55it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 193.76it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 192.96it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 192.38it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 193.34it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 192.76it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 193.40it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 192.88it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 193.66it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 193.21it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 194.34it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 193.94it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 195.21it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 194.84it/s, v_num=ld_0, val_loss=0.604, train_loss=0.562]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 177.68it/s, v_num=ld_0, val_loss=0.611, train_loss=0.562]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 176.93it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 196.47it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 191.31it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 199.24it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 196.72it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 201.86it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 200.16it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 203.37it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 202.11it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 202.95it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 201.87it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 201.59it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 200.76it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 202.07it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 201.38it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 188.41it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 187.85it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 189.41it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 188.85it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 187.45it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 186.93it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 188.08it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 187.68it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 188.91it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 188.47it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 189.44it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 189.00it/s, v_num=ld_0, val_loss=0.611, train_loss=0.567]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 172.06it/s, v_num=ld_0, val_loss=0.609, train_loss=0.567]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 171.38it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 197.64it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 191.91it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 200.10it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 197.42it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 200.26it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 198.52it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 198.37it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 197.04it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 198.71it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 197.75it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 199.34it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 198.47it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 199.35it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 198.59it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 199.16it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 198.53it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 199.35it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 198.78it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 199.55it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 199.01it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 199.29it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 198.84it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 198.32it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 197.88it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 198.65it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 198.29it/s, v_num=ld_0, val_loss=0.609, train_loss=0.575]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 169.65it/s, v_num=ld_0, val_loss=0.619, train_loss=0.575]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 168.94it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 198.70it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 193.72it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 203.40it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 200.71it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 204.56it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 202.73it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 203.27it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 202.04it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 203.06it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 201.96it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 202.90it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 201.99it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 202.76it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 201.99it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 202.73it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 202.08it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 203.06it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 202.48it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 202.30it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 201.79it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 201.52it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 201.04it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 200.89it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 200.47it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 201.40it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 201.01it/s, v_num=ld_0, val_loss=0.619, train_loss=0.564]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 182.18it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 181.43it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 207.38it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 201.89it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 202.76it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 200.14it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 202.63it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 200.91it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 201.46it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 200.22it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 203.30it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 202.18it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 202.60it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 201.66it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 202.11it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 201.38it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 201.82it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 201.20it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 202.09it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 201.50it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 201.00it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 200.40it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 199.83it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 199.29it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 199.27it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 198.80it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 199.26it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 198.86it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 181.08it/s, v_num=ld_0, val_loss=0.595, train_loss=0.565]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 180.34it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 209.82it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 204.29it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 207.15it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 204.33it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 204.07it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 202.24it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 202.29it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 201.00it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 202.00it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 201.06it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 201.28it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 200.39it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 201.68it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 200.94it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 200.43it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 199.72it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 199.42it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 198.78it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 199.22it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 198.75it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 199.63it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 199.16it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 200.06it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 199.59it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 199.74it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 199.37it/s, v_num=ld_0, val_loss=0.595, train_loss=0.564]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 176.99it/s, v_num=ld_0, val_loss=0.601, train_loss=0.564]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 176.25it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 183.45it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 178.25it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 189.00it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 186.26it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 188.69it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 187.17it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 188.88it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 187.64it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 190.51it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 189.52it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 191.39it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 190.61it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 192.14it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 191.33it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 192.43it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 191.75it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 190.86it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 190.28it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 189.54it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 188.94it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 189.29it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 188.78it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 188.91it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 188.49it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 189.23it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 188.78it/s, v_num=ld_0, val_loss=0.601, train_loss=0.558]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 167.61it/s, v_num=ld_0, val_loss=0.602, train_loss=0.558]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 166.84it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 177.62it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 171.29it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 180.09it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 177.81it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 181.61it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 179.62it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 181.66it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 180.50it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 184.34it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 183.46it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 187.01it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 186.26it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 188.71it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 188.08it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 190.60it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 189.99it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 190.23it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 189.67it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 190.23it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 189.77it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 190.49it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 190.07it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 190.85it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 190.47it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 191.64it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 191.29it/s, v_num=ld_0, val_loss=0.602, train_loss=0.557]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 174.94it/s, v_num=ld_0, val_loss=0.603, train_loss=0.557]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 174.25it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 203.08it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 197.70it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 199.56it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 197.00it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 196.83it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 194.66it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 190.51it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 189.03it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 187.04it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 185.95it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 186.93it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 186.11it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 184.59it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 183.82it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 182.96it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 182.30it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 182.60it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 182.05it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 183.77it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 183.23it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 167.53it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 167.12it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 168.46it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 168.11it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 170.46it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 170.10it/s, v_num=ld_0, val_loss=0.603, train_loss=0.556]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 155.88it/s, v_num=ld_0, val_loss=0.611, train_loss=0.556]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 155.25it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 177.03it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 172.42it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 187.45it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 185.12it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 193.97it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 192.23it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 195.69it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 194.39it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 195.42it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 194.37it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 195.56it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 194.71it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 196.38it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 195.72it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 197.16it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 196.53it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 196.85it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 196.32it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 196.72it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 196.23it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 197.06it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 196.59it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 197.20it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 196.79it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 197.35it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 196.95it/s, v_num=ld_0, val_loss=0.611, train_loss=0.554]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 178.80it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 178.08it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 200.64it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 195.69it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 198.18it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 195.67it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 196.88it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 195.26it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 197.57it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 196.24it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 196.97it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 195.97it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 197.15it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 196.33it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 196.46it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 195.58it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 195.07it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 194.36it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 193.19it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 192.62it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 192.69it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 192.17it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 192.48it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 192.01it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 191.92it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 191.49it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 192.08it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 191.71it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 174.20it/s, v_num=ld_0, val_loss=0.607, train_loss=0.554]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 173.51it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 202.11it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 196.78it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 199.87it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 197.30it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 196.90it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 195.26it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 195.63it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 194.42it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 195.05it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 194.06it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 195.82it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 195.05it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 196.16it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 195.47it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 195.54it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 194.93it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 195.12it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 194.55it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 194.88it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 194.39it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 194.91it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 194.47it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 194.92it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 194.49it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 195.26it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 194.89it/s, v_num=ld_0, val_loss=0.607, train_loss=0.549]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 177.48it/s, v_num=ld_0, val_loss=0.619, train_loss=0.549]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 176.78it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 203.45it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 198.01it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 196.31it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 193.37it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 197.24it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 195.50it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 196.08it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 194.44it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 194.00it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 193.01it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 191.83it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 190.78it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 189.27it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 188.33it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 188.07it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 187.49it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 188.71it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 188.17it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 189.09it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 188.57it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 188.44it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 188.03it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 189.59it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 189.18it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 190.19it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 189.84it/s, v_num=ld_0, val_loss=0.619, train_loss=0.548]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 161.47it/s, v_num=ld_0, val_loss=0.611, train_loss=0.548]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 160.77it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 188.11it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 181.95it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 187.72it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 185.44it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 188.49it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 186.97it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 188.22it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 186.90it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 187.78it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 186.87it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 188.43it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 187.62it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 187.42it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 186.70it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 186.76it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 186.15it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 187.66it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 187.18it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 188.58it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 188.00it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 185.86it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 185.30it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 184.97it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 184.49it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 184.30it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 183.88it/s, v_num=ld_0, val_loss=0.611, train_loss=0.549]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 158.74it/s, v_num=ld_0, val_loss=0.615, train_loss=0.549]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 157.98it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 172.00it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 166.88it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 167.56it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 165.18it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 166.03it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 164.78it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 168.69it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 167.59it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 169.48it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 168.62it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 171.70it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 170.94it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 168.99it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 168.22it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 167.99it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 167.49it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 170.30it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 169.90it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 172.46it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 172.08it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 174.51it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 174.17it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 176.73it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 176.41it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 177.78it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 177.37it/s, v_num=ld_0, val_loss=0.615, train_loss=0.546]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 161.26it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 160.58it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 170.67it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 166.66it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 175.07it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 172.95it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 180.09it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 178.73it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 182.71it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 181.62it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 183.02it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 182.04it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 182.08it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 181.29it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 180.88it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 180.23it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 180.88it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 180.26it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 181.16it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 180.69it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 182.52it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 182.11it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 183.16it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 182.77it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 183.77it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 183.39it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 184.81it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 184.49it/s, v_num=ld_0, val_loss=0.620, train_loss=0.546]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 168.73it/s, v_num=ld_0, val_loss=0.610, train_loss=0.546]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 168.08it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 201.49it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 195.66it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 198.06it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 195.54it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 196.91it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 195.17it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 195.06it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 193.87it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 194.05it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 193.05it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 191.91it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 191.04it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 190.67it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 189.94it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 190.10it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 189.52it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 189.65it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 189.12it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 189.71it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 189.25it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 189.87it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 189.48it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 189.84it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 189.45it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 190.29it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 189.93it/s, v_num=ld_0, val_loss=0.610, train_loss=0.543]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 173.05it/s, v_num=ld_0, val_loss=0.612, train_loss=0.543]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 172.37it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 196.50it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 191.24it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 193.73it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 191.28it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 194.11it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 192.52it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 194.14it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 192.99it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 194.11it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 193.13it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 193.73it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 192.93it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 193.95it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 193.31it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 193.78it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 193.20it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 194.03it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 193.53it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 194.13it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 193.64it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 194.69it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 194.27it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 194.52it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 194.10it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 192.84it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 192.43it/s, v_num=ld_0, val_loss=0.612, train_loss=0.542]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 168.08it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 167.31it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 184.50it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 180.44it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 190.31it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 188.16it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 192.52it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 191.05it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 192.25it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 190.95it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 191.26it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 190.30it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 191.68it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 190.90it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 192.76it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 192.12it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 192.78it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 192.10it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 191.70it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 191.20it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 191.88it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 191.41it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 190.61it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 190.10it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 190.24it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 189.85it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 190.73it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 190.36it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 172.85it/s, v_num=ld_0, val_loss=0.613, train_loss=0.542]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 172.12it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 186.89it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 182.25it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 183.94it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 181.67it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 186.43it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 184.84it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 188.18it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 187.08it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 186.62it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 185.68it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 186.92it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 186.08it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 186.77it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 186.14it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 186.16it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 185.56it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 186.24it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 185.62it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 186.49it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 186.03it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 185.67it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 185.23it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 185.70it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 185.32it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 185.76it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 185.41it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 169.30it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 168.67it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 166.32it/s, v_num=ld_0, val_loss=0.613, train_loss=0.541]
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Validate metric ┃ DataLoader 0 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
- │ binary_accuracy_val │ 0.7300000190734863 │
- │ binary_auroc_val │ 0.8251201510429382 │
- │ val_loss │ 0.6381955742835999 │
+ │ binary_accuracy_val │ 0.8399999737739563 │
+ │ binary_auroc_val │ 0.8891626000404358 │
+ │ val_loss │ 0.6126667261123657 │
└───────────────────────────┴───────────────────────────┘
-
Training: | | 0/? [00:00, ?it/s]
Training: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:01, 9.38it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:01, 9.36it/s, v_num=ld_1]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 17.46it/s, v_num=ld_1]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 17.40it/s, v_num=ld_1]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 24.55it/s, v_num=ld_1]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 24.52it/s, v_num=ld_1]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 30.11it/s, v_num=ld_1]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 29.93it/s, v_num=ld_1]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 28.11it/s, v_num=ld_1]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 28.05it/s, v_num=ld_1]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 31.87it/s, v_num=ld_1]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 31.82it/s, v_num=ld_1]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 35.43it/s, v_num=ld_1]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 35.40it/s, v_num=ld_1]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 39.51it/s, v_num=ld_1]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 39.48it/s, v_num=ld_1]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 43.33it/s, v_num=ld_1]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 43.30it/s, v_num=ld_1]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 47.04it/s, v_num=ld_1]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 47.01it/s, v_num=ld_1]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 50.67it/s, v_num=ld_1]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 50.64it/s, v_num=ld_1]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 54.16it/s, v_num=ld_1]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 54.12it/s, v_num=ld_1]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 57.52it/s, v_num=ld_1]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 57.49it/s, v_num=ld_1]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 55.77it/s, v_num=ld_1, val_loss=0.683]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 55.69it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 0: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 211.95it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 204.71it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 205.27it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 202.08it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 209.06it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 206.65it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 207.06it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 205.41it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 206.18it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 205.03it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 208.37it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 207.36it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 208.19it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 207.34it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 209.24it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 208.46it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 209.56it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 208.84it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 209.82it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 209.15it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 209.53it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 208.95it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 208.92it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 208.39it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 209.85it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 209.32it/s, v_num=ld_1, val_loss=0.683, train_loss=0.725]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 190.05it/s, v_num=ld_1, val_loss=0.693, train_loss=0.725]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 189.09it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 204.45it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 198.37it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 213.49it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 210.66it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 218.02it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 215.92it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 219.27it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 217.72it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 221.75it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 220.41it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 222.05it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 220.98it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 220.29it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 219.31it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 220.86it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 220.09it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 220.82it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 220.13it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 221.30it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 220.66it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 221.28it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 220.70it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 221.23it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 220.70it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 222.04it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 221.59it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 200.92it/s, v_num=ld_1, val_loss=0.693, train_loss=0.696]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 200.05it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 233.98it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 226.93it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 235.66it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 232.00it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 235.12it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 232.87it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 233.81it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 232.11it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 231.58it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 229.98it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 229.50it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 228.32it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 227.87it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 226.91it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 225.72it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 224.97it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 224.90it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 224.17it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 223.13it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 222.44it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 221.90it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 221.34it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 221.83it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 221.33it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 221.56it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 221.05it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 200.62it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 199.73it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 214.60it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 207.60it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 147.22it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 145.36it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 159.94it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 158.50it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 164.90it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 163.80it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 171.18it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 169.87it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 174.28it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 173.62it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 177.75it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 177.08it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 178.96it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 178.24it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 179.24it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 178.73it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 182.23it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 181.73it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 184.58it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 184.21it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 186.35it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 185.84it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 187.04it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 186.60it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 170.32it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 169.54it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 207.90it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 202.68it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 215.94it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 212.95it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 208.26it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 205.72it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 206.16it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 204.62it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 207.73it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 206.66it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 210.56it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 209.03it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 210.11it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 209.27it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 209.33it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 208.31it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 206.83it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 206.08it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 206.54it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 205.92it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 205.88it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 205.30it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 206.95it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 206.46it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 207.38it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 206.88it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 187.00it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 185.93it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 205.34it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 199.52it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 207.88it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 204.60it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 200.59it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 198.22it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 200.33it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 199.10it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 204.78it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 203.58it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 204.76it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 203.51it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 202.82it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 201.61it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 201.96it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 201.25it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 202.94it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 202.29it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 202.91it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 202.29it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 203.92it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 203.40it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 204.03it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 203.48it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 204.57it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 204.06it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 177.77it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 176.95it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 194.51it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 189.10it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 201.87it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 198.71it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 196.59it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 194.60it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 198.11it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 196.00it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 195.48it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 194.50it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 198.49it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 197.52it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 199.28it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 198.31it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 198.07it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 197.41it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 199.16it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 198.36it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 198.46it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 197.87it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 198.23it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 197.74it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 200.06it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 199.62it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 201.57it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 201.12it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 181.13it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 180.18it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 217.54it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 210.93it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 215.58it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 212.31it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 211.47it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 209.34it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 211.63it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 210.29it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 213.07it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 211.89it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 210.62it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 209.37it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 209.51it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 208.76it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 211.30it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 210.49it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 210.06it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 209.14it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 207.84it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 207.16it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 207.91it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 207.32it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 208.61it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 208.10it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 209.28it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 208.80it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 161.15it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 160.32it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 200.07it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 194.24it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 201.29it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 198.51it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 204.25it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 201.94it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 203.08it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 201.67it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 204.70it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 203.42it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 205.25it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 203.94it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 204.39it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 203.57it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 206.34it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 205.50it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 207.06it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 206.48it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 207.45it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 206.85it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 207.92it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 207.37it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 208.92it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 208.38it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 209.13it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 208.47it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 188.53it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 187.44it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 207.81it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 201.94it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 211.73it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 208.83it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 215.09it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 213.19it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 215.48it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 213.93it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 214.23it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 212.59it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 209.86it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 208.65it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 207.88it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 206.89it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 204.60it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 203.86it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 203.48it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 202.76it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 203.07it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 202.47it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 202.56it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 202.02it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 202.61it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 202.19it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 202.99it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 202.53it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 184.35it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 183.46it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 192.97it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 187.02it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 203.00it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 200.28it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 207.64it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 205.69it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 206.48it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 204.86it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 202.57it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 201.03it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 202.80it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 201.98it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 203.77it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 202.86it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 202.49it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 201.76it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 203.05it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 202.20it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 201.87it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 201.28it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 202.40it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 201.76it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 202.69it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 202.27it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 204.31it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 203.83it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 183.83it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 182.90it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 219.92it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 213.40it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 216.66it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 213.29it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 209.91it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 207.44it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 205.97it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 204.35it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 206.23it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 205.02it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 206.29it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 205.22it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 205.28it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 204.45it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 204.67it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 203.83it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 203.97it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 203.29it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 204.00it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 203.38it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 204.27it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 203.76it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 204.23it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 203.63it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 202.80it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 202.26it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 181.66it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 180.88it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 214.00it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 207.07it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 209.31it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 205.99it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 206.98it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 204.83it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 205.58it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 204.18it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 193.60it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 192.50it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 191.47it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 190.36it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 188.67it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 187.71it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 188.76it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 187.96it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 189.20it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 188.58it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 189.46it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 188.87it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 189.35it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 188.69it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 188.58it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 188.06it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 187.78it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 187.31it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 170.38it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 169.66it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 189.44it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 181.95it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 186.55it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 184.30it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 190.92it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 188.95it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 193.16it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 191.78it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 192.16it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 190.71it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 191.75it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 190.70it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 190.84it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 190.13it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 191.52it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 190.74it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 193.21it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 192.55it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 193.76it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 193.25it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 194.65it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 194.16it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 195.91it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 195.50it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 196.77it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 196.32it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 175.03it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 174.33it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 219.69it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 212.23it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 217.20it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 214.27it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 219.55it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 217.31it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 218.29it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 216.69it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 214.09it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 212.85it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 214.69it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 213.71it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 214.82it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 213.99it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 214.29it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 213.33it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 212.75it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 212.08it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 213.10it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 212.46it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 212.97it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 212.46it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 211.84it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 211.26it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 210.87it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 210.37it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 187.47it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 186.61it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 183.71it/s, v_num=ld_1, val_loss=0.693, train_loss=0.693]
+
Training: | | 0/? [00:00, ?it/s]
Training: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 87.21it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 84.27it/s, v_num=ld_1]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 85.18it/s, v_num=ld_1]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 83.92it/s, v_num=ld_1]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 73.50it/s, v_num=ld_1]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 71.67it/s, v_num=ld_1]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 68.96it/s, v_num=ld_1]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 68.47it/s, v_num=ld_1]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 66.91it/s, v_num=ld_1]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 66.54it/s, v_num=ld_1]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 70.04it/s, v_num=ld_1]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 69.67it/s, v_num=ld_1]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 72.41it/s, v_num=ld_1]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 72.18it/s, v_num=ld_1]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 74.13it/s, v_num=ld_1]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 73.86it/s, v_num=ld_1]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 76.35it/s, v_num=ld_1]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 76.11it/s, v_num=ld_1]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 75.73it/s, v_num=ld_1]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 75.48it/s, v_num=ld_1]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 77.73it/s, v_num=ld_1]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 77.54it/s, v_num=ld_1]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 76.55it/s, v_num=ld_1]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 76.39it/s, v_num=ld_1]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 76.34it/s, v_num=ld_1]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 76.17it/s, v_num=ld_1]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 67.34it/s, v_num=ld_1, val_loss=0.712]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 67.01it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 0: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 137.97it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 135.24it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 160.45it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 158.69it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 172.89it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 171.63it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 180.17it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 179.13it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 184.32it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 183.38it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 184.59it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 183.78it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 185.17it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 184.49it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 186.79it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 186.18it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 187.96it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 187.45it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 189.21it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 188.75it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 190.85it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 190.39it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 191.57it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 191.15it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 192.63it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 192.27it/s, v_num=ld_1, val_loss=0.712, train_loss=0.719]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 169.64it/s, v_num=ld_1, val_loss=0.668, train_loss=0.719]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 168.99it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 209.46it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 203.96it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 213.92it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 211.01it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 212.56it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 210.72it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 212.02it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 210.49it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 210.58it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 209.49it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 210.62it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 209.72it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 208.66it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 207.65it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 204.64it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 203.89it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 203.58it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 202.98it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 203.77it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 203.24it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 203.34it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 202.82it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 202.65it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 202.22it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 203.02it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 202.57it/s, v_num=ld_1, val_loss=0.668, train_loss=0.676]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 183.79it/s, v_num=ld_1, val_loss=0.631, train_loss=0.676]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 182.98it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 205.63it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 199.92it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 199.94it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 197.25it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 199.49it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 197.70it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 199.56it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 198.25it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 199.43it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 198.38it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 200.21it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 199.30it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 199.72it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 198.94it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 200.63it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 199.92it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 200.44it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 199.91it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 200.87it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 200.36it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 201.48it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 201.02it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 202.55it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 202.11it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 203.28it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 202.88it/s, v_num=ld_1, val_loss=0.631, train_loss=0.629]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 184.58it/s, v_num=ld_1, val_loss=0.615, train_loss=0.629]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 183.81it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 208.52it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 202.60it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 211.62it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 208.84it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 213.54it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 211.59it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 211.10it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 209.67it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 210.01it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 208.85it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 209.64it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 208.74it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 209.32it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 208.54it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 208.69it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 208.04it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 208.07it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 207.47it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 207.86it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 207.32it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 207.55it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 207.08it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 207.23it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 206.75it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 207.38it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 206.97it/s, v_num=ld_1, val_loss=0.615, train_loss=0.598]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 188.14it/s, v_num=ld_1, val_loss=0.618, train_loss=0.598]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 187.34it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 210.12it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 204.33it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 209.47it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 206.70it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 208.80it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 206.97it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 208.31it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 206.94it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 206.74it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 205.68it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 205.49it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 204.60it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 204.09it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 203.18it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 202.65it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 201.80it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 199.79it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 199.14it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 199.67it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 199.04it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 198.66it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 198.07it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 196.96it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 196.51it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 197.38it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 197.00it/s, v_num=ld_1, val_loss=0.618, train_loss=0.568]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 179.50it/s, v_num=ld_1, val_loss=0.615, train_loss=0.568]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 178.78it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 190.44it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 185.56it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 195.48it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 192.97it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 196.31it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 194.50it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 194.70it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 193.31it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 195.80it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 194.75it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 196.03it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 195.19it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 195.48it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 194.70it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 196.13it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 195.48it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 195.72it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 195.18it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 195.87it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 195.32it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 196.07it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 195.62it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 196.58it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 196.17it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 197.31it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 196.95it/s, v_num=ld_1, val_loss=0.615, train_loss=0.562]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 179.22it/s, v_num=ld_1, val_loss=0.622, train_loss=0.562]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 178.51it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 214.47it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 208.60it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 212.77it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 209.69it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 208.25it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 206.34it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 206.82it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 205.55it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 207.16it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 206.09it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 206.51it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 205.64it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 206.36it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 205.63it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 205.58it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 204.92it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 205.06it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 204.48it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 204.80it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 204.28it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 204.43it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 203.97it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 203.79it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 203.34it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 204.65it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 204.27it/s, v_num=ld_1, val_loss=0.622, train_loss=0.556]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 185.39it/s, v_num=ld_1, val_loss=0.612, train_loss=0.556]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 184.63it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 209.43it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 204.00it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 209.67it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 207.04it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 208.21it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 206.43it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 209.68it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 208.37it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 209.69it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 208.62it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 207.58it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 206.59it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 204.72it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 203.87it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 201.92it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 201.14it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 199.82it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 199.24it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 199.44it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 198.93it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 199.45it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 198.94it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 198.96it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 198.54it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 198.83it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 198.45it/s, v_num=ld_1, val_loss=0.612, train_loss=0.553]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 168.69it/s, v_num=ld_1, val_loss=0.619, train_loss=0.553]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 167.97it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 194.04it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 188.58it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 185.91it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 183.54it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 187.90it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 186.28it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 190.70it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 189.60it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 190.09it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 188.98it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 191.04it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 190.22it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 191.57it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 190.82it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 191.18it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 190.51it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 191.67it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 191.16it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 191.92it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 191.43it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 191.97it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 191.47it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 192.05it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 191.67it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 193.37it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 192.97it/s, v_num=ld_1, val_loss=0.619, train_loss=0.554]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 176.07it/s, v_num=ld_1, val_loss=0.615, train_loss=0.554]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 175.28it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 192.06it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 187.45it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 196.12it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 193.65it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 193.50it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 191.82it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 194.84it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 193.63it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 196.08it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 195.07it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 195.43it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 194.57it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 195.19it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 194.48it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 195.88it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 195.25it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 196.13it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 195.59it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 196.37it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 195.91it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 196.92it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 196.49it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 196.65it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 196.26it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 197.45it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 197.07it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 179.42it/s, v_num=ld_1, val_loss=0.615, train_loss=0.560]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 178.70it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 207.13it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 201.01it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 202.10it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 199.56it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 201.41it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 199.73it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 201.28it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 200.02it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 200.50it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 199.54it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 200.43it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 199.50it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 198.67it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 197.89it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 197.94it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 197.32it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 197.77it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 197.22it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 197.93it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 197.42it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 197.07it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 196.50it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 195.19it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 194.66it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 193.97it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 193.49it/s, v_num=ld_1, val_loss=0.615, train_loss=0.557]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 176.19it/s, v_num=ld_1, val_loss=0.611, train_loss=0.557]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 175.46it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 201.19it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 195.77it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 192.03it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 189.30it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 191.45it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 189.88it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 193.78it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 192.65it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 194.95it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 193.90it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 194.55it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 193.70it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 193.26it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 192.56it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 193.33it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 192.67it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 192.69it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 192.16it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 192.64it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 192.10it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 193.10it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 192.70it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 193.87it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 193.50it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 194.22it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 193.75it/s, v_num=ld_1, val_loss=0.611, train_loss=0.558]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 166.82it/s, v_num=ld_1, val_loss=0.623, train_loss=0.558]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 166.16it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 195.77it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 189.68it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 194.53it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 191.92it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 193.56it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 191.79it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 189.39it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 187.91it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 188.06it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 187.06it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 188.05it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 187.27it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 189.73it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 189.11it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 190.86it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 190.21it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 191.09it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 190.57it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 191.85it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 191.40it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 192.23it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 191.80it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 192.77it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 192.38it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 193.38it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 193.01it/s, v_num=ld_1, val_loss=0.623, train_loss=0.561]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 174.98it/s, v_num=ld_1, val_loss=0.620, train_loss=0.561]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 174.31it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 200.00it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 194.97it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 199.23it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 196.74it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 197.33it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 195.57it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 197.19it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 195.94it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 196.46it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 195.51it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 197.24it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 196.42it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 198.03it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 197.23it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 197.30it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 196.66it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 196.47it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 195.87it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 196.39it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 195.83it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 195.41it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 194.91it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 193.99it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 193.55it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 194.60it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 194.22it/s, v_num=ld_1, val_loss=0.620, train_loss=0.555]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 177.13it/s, v_num=ld_1, val_loss=0.610, train_loss=0.555]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 176.41it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 206.66it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 201.01it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 199.52it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 197.06it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 199.59it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 197.90it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 199.69it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 198.35it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 198.89it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 197.84it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 198.74it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 197.90it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 198.88it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 198.19it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 198.75it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 198.12it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 199.31it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 198.74it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 199.40it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 198.93it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 198.87it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 198.41it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 198.70it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 198.31it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 199.36it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 198.99it/s, v_num=ld_1, val_loss=0.610, train_loss=0.559]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 181.42it/s, v_num=ld_1, val_loss=0.621, train_loss=0.559]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 180.68it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 206.06it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 200.44it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 204.40it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 201.76it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 202.80it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 201.03it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 201.88it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 200.61it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 201.57it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 200.54it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 200.32it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 199.43it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 199.86it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 199.15it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 199.44it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 198.80it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 198.95it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 198.37it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 198.14it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 197.60it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 197.83it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 197.38it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 198.25it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 197.77it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 198.04it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 197.59it/s, v_num=ld_1, val_loss=0.621, train_loss=0.555]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 179.26it/s, v_num=ld_1, val_loss=0.610, train_loss=0.555]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 178.52it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 195.20it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 190.44it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 196.23it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 193.50it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 194.82it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 193.16it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 194.43it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 193.24it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 194.29it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 193.33it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 194.96it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 194.20it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 194.95it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 194.22it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 194.81it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 194.20it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 194.74it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 194.21it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 194.99it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 194.49it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 195.32it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 194.91it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 195.37it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 194.99it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 195.98it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 195.60it/s, v_num=ld_1, val_loss=0.610, train_loss=0.551]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 178.32it/s, v_num=ld_1, val_loss=0.622, train_loss=0.551]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 177.61it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 201.79it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 196.55it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 198.67it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 196.28it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 197.23it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 195.53it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 197.18it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 195.95it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 196.34it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 195.39it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 195.91it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 195.11it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 196.01it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 195.36it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 195.44it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 194.79it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 194.88it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 194.33it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 194.59it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 194.13it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 194.93it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 194.51it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 194.71it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 194.30it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 195.10it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 194.73it/s, v_num=ld_1, val_loss=0.622, train_loss=0.553]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 176.50it/s, v_num=ld_1, val_loss=0.626, train_loss=0.553]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 175.64it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 175.69it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 171.29it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 175.32it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 173.21it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 180.79it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 179.28it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 183.92it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 182.82it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 183.47it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 182.55it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 186.08it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 185.26it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 187.09it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 186.45it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 187.76it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 187.05it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 187.30it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 186.73it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 187.69it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 187.26it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 187.83it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 187.34it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 187.80it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 187.43it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 188.39it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 188.04it/s, v_num=ld_1, val_loss=0.626, train_loss=0.552]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 171.33it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 170.60it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 194.40it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 189.39it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 195.23it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 192.62it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 195.12it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 193.50it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 194.58it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 193.41it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 193.02it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 192.14it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 193.60it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 192.81it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 193.91it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 193.22it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 194.31it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 193.75it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 194.86it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 194.34it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 195.54it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 195.03it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 195.56it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 195.13it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 195.30it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 194.91it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 195.80it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 195.42it/s, v_num=ld_1, val_loss=0.625, train_loss=0.555]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 174.74it/s, v_num=ld_1, val_loss=0.617, train_loss=0.555]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 174.06it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 203.55it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 197.95it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 201.49it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 199.02it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 201.51it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 199.89it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 199.84it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 198.61it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 198.78it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 197.80it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 197.60it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 196.77it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 196.43it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 195.74it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 196.41it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 195.79it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 196.57it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 196.03it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 196.62it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 196.10it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 197.05it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 196.61it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 197.05it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 196.66it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 197.03it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 196.65it/s, v_num=ld_1, val_loss=0.617, train_loss=0.558]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 176.90it/s, v_num=ld_1, val_loss=0.624, train_loss=0.558]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 176.19it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 197.52it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 192.36it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 195.75it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 193.11it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 192.00it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 190.25it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 191.16it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 189.94it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 191.56it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 190.61it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 191.38it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 190.50it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 191.50it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 190.86it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 191.27it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 190.66it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 191.02it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 190.39it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 190.67it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 190.24it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 191.19it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 190.74it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 191.01it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 190.43it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 190.75it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 190.41it/s, v_num=ld_1, val_loss=0.624, train_loss=0.552]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 173.75it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 173.07it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 192.13it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 186.02it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 191.19it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 188.84it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 190.65it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 189.10it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 191.67it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 190.20it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 190.45it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 189.49it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 189.50it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 188.69it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 188.63it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 187.88it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 188.06it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 187.47it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 187.90it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 187.32it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 187.11it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 186.61it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 187.64it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 187.22it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 186.74it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 186.30it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 186.92it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 186.56it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 170.54it/s, v_num=ld_1, val_loss=0.625, train_loss=0.552]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 169.88it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 196.57it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 191.20it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 192.42it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 190.13it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 191.68it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 190.04it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 191.40it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 190.24it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 190.92it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 189.94it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 190.93it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 190.14it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 189.96it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 189.33it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 189.55it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 188.95it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 190.71it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 190.23it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 191.44it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 190.99it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 191.63it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 191.23it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 192.03it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 191.64it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 193.01it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 192.68it/s, v_num=ld_1, val_loss=0.625, train_loss=0.550]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 149.45it/s, v_num=ld_1, val_loss=0.621, train_loss=0.550]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 148.87it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 183.75it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 178.79it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 182.38it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 179.92it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 180.57it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 178.82it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 176.65it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 175.33it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 176.69it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 175.70it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 175.69it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 174.87it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 174.83it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 174.12it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 174.48it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 173.83it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 174.11it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 173.60it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 173.02it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 172.47it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 172.36it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 171.95it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 173.71it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 173.36it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 172.91it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 172.54it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 156.49it/s, v_num=ld_1, val_loss=0.621, train_loss=0.548]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 155.82it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 167.53it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 162.81it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 172.22it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 169.96it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 170.46it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 169.01it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 172.21it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 170.86it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 168.26it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 167.27it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 168.11it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 167.42it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 169.77it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 169.02it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 168.08it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 167.47it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 168.55it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 168.11it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 170.04it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 169.56it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 169.02it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 168.61it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 169.54it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 169.19it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 169.58it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 169.19it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 154.35it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 153.79it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 8%|▊ | 1/13 [00:00<00:00, 186.64it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 8%|▊ | 1/13 [00:00<00:00, 182.25it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 15%|█▌ | 2/13 [00:00<00:00, 188.95it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 15%|█▌ | 2/13 [00:00<00:00, 186.71it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 23%|██▎ | 3/13 [00:00<00:00, 187.52it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 23%|██▎ | 3/13 [00:00<00:00, 186.07it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 31%|███ | 4/13 [00:00<00:00, 188.04it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 31%|███ | 4/13 [00:00<00:00, 187.00it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 38%|███▊ | 5/13 [00:00<00:00, 190.14it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 38%|███▊ | 5/13 [00:00<00:00, 189.12it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 46%|████▌ | 6/13 [00:00<00:00, 190.91it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 46%|████▌ | 6/13 [00:00<00:00, 190.14it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 54%|█████▍ | 7/13 [00:00<00:00, 191.78it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 54%|█████▍ | 7/13 [00:00<00:00, 191.14it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 62%|██████▏ | 8/13 [00:00<00:00, 191.60it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 62%|██████▏ | 8/13 [00:00<00:00, 191.06it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 69%|██████▉ | 9/13 [00:00<00:00, 190.97it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 69%|██████▉ | 9/13 [00:00<00:00, 190.46it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 77%|███████▋ | 10/13 [00:00<00:00, 190.15it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 77%|███████▋ | 10/13 [00:00<00:00, 189.53it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 85%|████████▍ | 11/13 [00:00<00:00, 189.15it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 85%|████████▍ | 11/13 [00:00<00:00, 188.73it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 92%|█████████▏| 12/13 [00:00<00:00, 189.25it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 92%|█████████▏| 12/13 [00:00<00:00, 188.90it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 190.19it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 189.85it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 168.55it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 167.93it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 27: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 8%|▊ | 1/13 [00:00<00:00, 197.31it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 8%|▊ | 1/13 [00:00<00:00, 192.57it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 15%|█▌ | 2/13 [00:00<00:00, 199.26it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 15%|█▌ | 2/13 [00:00<00:00, 196.99it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 23%|██▎ | 3/13 [00:00<00:00, 198.91it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 23%|██▎ | 3/13 [00:00<00:00, 197.28it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 31%|███ | 4/13 [00:00<00:00, 194.86it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 31%|███ | 4/13 [00:00<00:00, 193.62it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 38%|███▊ | 5/13 [00:00<00:00, 193.19it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 38%|███▊ | 5/13 [00:00<00:00, 192.34it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 46%|████▌ | 6/13 [00:00<00:00, 192.49it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 46%|████▌ | 6/13 [00:00<00:00, 191.71it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 54%|█████▍ | 7/13 [00:00<00:00, 192.12it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 54%|█████▍ | 7/13 [00:00<00:00, 191.42it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 62%|██████▏ | 8/13 [00:00<00:00, 190.24it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 62%|██████▏ | 8/13 [00:00<00:00, 189.59it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 69%|██████▉ | 9/13 [00:00<00:00, 187.90it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 69%|██████▉ | 9/13 [00:00<00:00, 187.28it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 77%|███████▋ | 10/13 [00:00<00:00, 186.09it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 77%|███████▋ | 10/13 [00:00<00:00, 185.59it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 85%|████████▍ | 11/13 [00:00<00:00, 185.62it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 85%|████████▍ | 11/13 [00:00<00:00, 185.13it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 92%|█████████▏| 12/13 [00:00<00:00, 183.79it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 92%|█████████▏| 12/13 [00:00<00:00, 183.44it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 184.91it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 184.56it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 167.39it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 166.56it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 28: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 0%| | 0/13 [00:00, ?it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 8%|▊ | 1/13 [00:00<00:00, 168.81it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 8%|▊ | 1/13 [00:00<00:00, 164.75it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 15%|█▌ | 2/13 [00:00<00:00, 177.18it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 15%|█▌ | 2/13 [00:00<00:00, 175.16it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 23%|██▎ | 3/13 [00:00<00:00, 181.27it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 23%|██▎ | 3/13 [00:00<00:00, 179.93it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 31%|███ | 4/13 [00:00<00:00, 182.67it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 31%|███ | 4/13 [00:00<00:00, 181.57it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 38%|███▊ | 5/13 [00:00<00:00, 184.12it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 38%|███▊ | 5/13 [00:00<00:00, 183.25it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 46%|████▌ | 6/13 [00:00<00:00, 185.55it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 46%|████▌ | 6/13 [00:00<00:00, 184.76it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 54%|█████▍ | 7/13 [00:00<00:00, 186.67it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 54%|█████▍ | 7/13 [00:00<00:00, 186.05it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 62%|██████▏ | 8/13 [00:00<00:00, 186.72it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 62%|██████▏ | 8/13 [00:00<00:00, 186.17it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 69%|██████▉ | 9/13 [00:00<00:00, 187.65it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 69%|██████▉ | 9/13 [00:00<00:00, 187.15it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 77%|███████▋ | 10/13 [00:00<00:00, 188.25it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 77%|███████▋ | 10/13 [00:00<00:00, 187.78it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 85%|████████▍ | 11/13 [00:00<00:00, 188.52it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 85%|████████▍ | 11/13 [00:00<00:00, 188.12it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 92%|█████████▏| 12/13 [00:00<00:00, 188.66it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 92%|█████████▏| 12/13 [00:00<00:00, 188.31it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 189.11it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 188.78it/s, v_num=ld_1, val_loss=0.621, train_loss=0.547]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 172.25it/s, v_num=ld_1, val_loss=0.622, train_loss=0.547]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 171.56it/s, v_num=ld_1, val_loss=0.622, train_loss=0.547]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 169.39it/s, v_num=ld_1, val_loss=0.622, train_loss=0.547]
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Validate metric ┃ DataLoader 0 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
- │ binary_accuracy_val │ 0.4399999976158142 │
- │ binary_auroc_val │ 0.7146915197372437 │
- │ val_loss │ 0.6931471824645996 │
+ │ binary_accuracy_val │ 0.8299999833106995 │
+ │ binary_auroc_val │ 0.9098800420761108 │
+ │ val_loss │ 0.6216896176338196 │
└───────────────────────────┴───────────────────────────┘
-
Training: | | 0/? [00:00, ?it/s]
Training: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 159.34it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 155.93it/s, v_num=ld_2]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 183.19it/s, v_num=ld_2]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 181.16it/s, v_num=ld_2]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 194.01it/s, v_num=ld_2]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 192.52it/s, v_num=ld_2]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 201.13it/s, v_num=ld_2]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 199.83it/s, v_num=ld_2]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 203.53it/s, v_num=ld_2]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 201.96it/s, v_num=ld_2]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 202.96it/s, v_num=ld_2]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 202.06it/s, v_num=ld_2]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 204.43it/s, v_num=ld_2]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 203.73it/s, v_num=ld_2]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 205.16it/s, v_num=ld_2]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 204.52it/s, v_num=ld_2]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 206.30it/s, v_num=ld_2]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 205.71it/s, v_num=ld_2]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 208.20it/s, v_num=ld_2]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 207.61it/s, v_num=ld_2]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 206.98it/s, v_num=ld_2]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 206.55it/s, v_num=ld_2]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 208.05it/s, v_num=ld_2]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 207.51it/s, v_num=ld_2]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 207.32it/s, v_num=ld_2]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 206.90it/s, v_num=ld_2]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 184.50it/s, v_num=ld_2, val_loss=0.705]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 183.62it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 0: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 199.52it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 193.87it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 212.04it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 209.25it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 213.08it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 211.07it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 216.33it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 214.55it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 217.31it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 216.03it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 217.28it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 216.11it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 215.08it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 214.07it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 214.38it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 213.65it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 214.21it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 213.58it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 213.02it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 212.34it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 212.72it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 212.11it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 213.37it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 212.91it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 215.05it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 214.53it/s, v_num=ld_2, val_loss=0.705, train_loss=0.713]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 195.22it/s, v_num=ld_2, val_loss=0.684, train_loss=0.713]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 194.11it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 189.33it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 183.19it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 196.02it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 193.30it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 202.29it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 200.08it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 204.15it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 202.85it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 207.95it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 206.75it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 208.28it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 207.08it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 206.02it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 205.13it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 207.28it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 206.62it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 210.05it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 209.38it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 210.04it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 209.19it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 209.17it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 208.56it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 209.26it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 208.73it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 210.27it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 209.84it/s, v_num=ld_2, val_loss=0.684, train_loss=0.686]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 176.04it/s, v_num=ld_2, val_loss=0.660, train_loss=0.686]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 174.81it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 206.65it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 200.47it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 202.53it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 199.20it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 202.02it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 199.68it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 201.25it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 199.44it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 198.95it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 197.74it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 197.85it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 196.89it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 198.57it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 197.63it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 196.12it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 195.23it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 194.77it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 194.13it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 196.33it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 195.76it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 197.66it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 197.08it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 199.30it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 198.86it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 201.03it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 200.59it/s, v_num=ld_2, val_loss=0.660, train_loss=0.672]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 182.04it/s, v_num=ld_2, val_loss=0.633, train_loss=0.672]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 181.24it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 214.76it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 209.24it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 214.93it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 211.35it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 207.72it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 205.63it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 202.59it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 200.90it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 203.11it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 202.03it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 203.98it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 203.04it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 205.14it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 204.12it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 205.67it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 204.85it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 206.35it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 205.55it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 206.81it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 206.19it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 206.75it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 206.14it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 206.29it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 205.72it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 206.75it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 206.23it/s, v_num=ld_2, val_loss=0.633, train_loss=0.640]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 187.11it/s, v_num=ld_2, val_loss=0.596, train_loss=0.640]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 185.91it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 181.20it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 175.56it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 188.75it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 185.82it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 187.01it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 184.77it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 188.09it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 186.60it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 191.62it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 190.62it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 193.47it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 192.44it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 193.38it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 192.59it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 195.28it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 194.52it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 193.59it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 192.88it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 193.09it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 192.44it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 191.85it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 191.34it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 192.85it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 192.34it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 193.70it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 193.22it/s, v_num=ld_2, val_loss=0.596, train_loss=0.599]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 174.47it/s, v_num=ld_2, val_loss=0.604, train_loss=0.599]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 173.59it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 178.89it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 173.15it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 186.76it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 184.07it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 196.12it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 194.41it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 201.32it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 199.85it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 202.84it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 201.77it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 204.00it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 202.96it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 205.74it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 204.80it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 207.97it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 207.29it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 206.41it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 205.72it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 206.53it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 205.93it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 207.57it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 207.04it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 206.79it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 206.20it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 206.74it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 206.20it/s, v_num=ld_2, val_loss=0.604, train_loss=0.582]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 124.90it/s, v_num=ld_2, val_loss=0.596, train_loss=0.582]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 124.21it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 145.39it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 140.75it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 148.26it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 146.08it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 155.04it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 153.61it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 161.02it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 159.78it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 166.81it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 165.52it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 167.38it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 166.39it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 171.02it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 170.30it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 174.79it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 174.20it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 178.07it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 177.55it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 180.02it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 179.45it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 181.35it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 180.91it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 182.72it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 182.24it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 183.38it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 182.91it/s, v_num=ld_2, val_loss=0.596, train_loss=0.577]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 163.54it/s, v_num=ld_2, val_loss=0.597, train_loss=0.577]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 162.47it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 205.25it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 198.21it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 205.07it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 199.50it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 203.66it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 201.41it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 198.43it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 196.92it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 197.32it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 195.79it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 196.25it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 195.24it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 194.67it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 193.67it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 192.79it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 192.06it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 193.90it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 193.40it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 195.59it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 195.06it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 196.86it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 196.37it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 198.37it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 197.89it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 197.82it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 197.31it/s, v_num=ld_2, val_loss=0.597, train_loss=0.574]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 179.51it/s, v_num=ld_2, val_loss=0.609, train_loss=0.574]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 178.71it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 218.96it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 212.09it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 210.08it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 206.01it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 205.42it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 203.55it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 207.73it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 206.14it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 205.56it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 203.92it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 201.20it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 199.88it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 201.04it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 200.26it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 202.94it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 202.21it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 200.57it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 199.93it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 200.93it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 200.32it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 197.15it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 196.46it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 194.72it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 194.08it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 194.90it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 194.43it/s, v_num=ld_2, val_loss=0.609, train_loss=0.567]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 176.82it/s, v_num=ld_2, val_loss=0.601, train_loss=0.567]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 175.71it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 92.17it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 90.65it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 123.36it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 121.71it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 138.17it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 137.14it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 145.05it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 143.94it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 151.49it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 150.58it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 156.23it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 155.61it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 160.74it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 160.13it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 162.19it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 161.43it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 163.36it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 162.88it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 165.79it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 165.33it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 168.49it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 168.04it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 169.60it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 169.18it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 171.82it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 171.47it/s, v_num=ld_2, val_loss=0.601, train_loss=0.564]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 157.78it/s, v_num=ld_2, val_loss=0.588, train_loss=0.564]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 157.07it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 198.69it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 192.23it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 200.48it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 197.77it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 203.98it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 201.98it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 201.76it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 200.40it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 204.93it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 203.83it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 206.19it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 204.93it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 204.89it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 203.78it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 203.82it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 203.09it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 203.67it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 202.92it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 201.83it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 201.11it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 200.91it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 200.41it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 202.11it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 201.60it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 202.09it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 201.60it/s, v_num=ld_2, val_loss=0.588, train_loss=0.561]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 182.71it/s, v_num=ld_2, val_loss=0.599, train_loss=0.561]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 181.93it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 209.64it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 203.25it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 212.18it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 208.55it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 213.47it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 211.43it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 214.93it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 213.32it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 214.06it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 212.71it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 211.22it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 210.12it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 210.31it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 209.50it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 212.51it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 211.83it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 212.65it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 211.93it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 213.10it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 212.48it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 213.92it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 213.39it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 214.93it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 214.42it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 215.88it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 215.43it/s, v_num=ld_2, val_loss=0.599, train_loss=0.559]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 191.27it/s, v_num=ld_2, val_loss=0.587, train_loss=0.559]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 190.46it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 226.45it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 219.88it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 229.23it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 223.76it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 213.90it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 211.09it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 206.64it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 204.88it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 202.42it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 201.01it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 197.01it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 195.93it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 193.40it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 192.19it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 190.33it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 189.57it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 188.67it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 188.03it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 188.98it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 188.35it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 188.38it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 187.78it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 186.41it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 185.85it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 186.48it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 185.97it/s, v_num=ld_2, val_loss=0.587, train_loss=0.560]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 168.56it/s, v_num=ld_2, val_loss=0.601, train_loss=0.560]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 167.67it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 173.89it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 168.24it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 179.19it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 176.28it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 169.62it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 167.85it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 177.07it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 175.86it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 178.82it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 177.80it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 181.51it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 180.48it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 179.71it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 178.49it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 179.82it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 179.07it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 178.81it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 178.12it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 178.40it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 177.76it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 179.28it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 178.72it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 179.54it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 179.02it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 179.03it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 178.49it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 163.52it/s, v_num=ld_2, val_loss=0.601, train_loss=0.554]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 162.68it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 184.70it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 178.79it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 181.93it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 179.24it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 186.85it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 185.13it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 190.43it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 189.13it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 186.58it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 185.21it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 185.64it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 184.77it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 187.03it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 186.23it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 187.37it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 186.57it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 187.48it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 186.93it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 187.53it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 186.90it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 186.46it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 185.88it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 184.48it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 184.02it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 185.35it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 185.01it/s, v_num=ld_2, val_loss=0.601, train_loss=0.553]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 167.85it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 166.93it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 187.98it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 182.52it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 199.05it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 196.58it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 201.02it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 198.90it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 197.27it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 195.80it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 194.56it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 193.45it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 195.03it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 193.95it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 194.95it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 194.06it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 193.94it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 193.24it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 192.94it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 192.17it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 192.03it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 191.48it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 192.73it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 192.21it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 192.10it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 191.64it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 193.63it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 193.12it/s, v_num=ld_2, val_loss=0.603, train_loss=0.553]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 174.66it/s, v_num=ld_2, val_loss=0.585, train_loss=0.553]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 173.94it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 202.92it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 196.11it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 204.94it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 202.06it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 205.53it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 203.57it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 206.80it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 205.37it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 201.00it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 199.41it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 197.34it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 196.44it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 198.19it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 197.43it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 198.35it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 197.73it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 198.63it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 197.92it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 197.51it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 196.97it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 198.12it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 197.49it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 197.91it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 197.45it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 199.40it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 198.96it/s, v_num=ld_2, val_loss=0.585, train_loss=0.554]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 170.85it/s, v_num=ld_2, val_loss=0.592, train_loss=0.554]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 170.00it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 204.03it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 197.94it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 198.04it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 195.17it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 200.68it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 199.03it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 201.08it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 199.74it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 199.89it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 198.77it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 199.22it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 198.17it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 197.43it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 196.61it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 198.87it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 198.22it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 200.65it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 200.04it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 202.02it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 201.51it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 203.06it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 202.56it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 204.52it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 204.10it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 206.08it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 205.69it/s, v_num=ld_2, val_loss=0.592, train_loss=0.552]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 187.31it/s, v_num=ld_2, val_loss=0.611, train_loss=0.552]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 186.54it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 229.15it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 222.37it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 229.23it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 226.04it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 225.50it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 223.30it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 223.03it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 221.45it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 222.13it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 220.89it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 221.47it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 220.43it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 219.68it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 218.66it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 214.64it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 213.72it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 210.64it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 209.84it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 208.46it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 207.84it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 207.19it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 206.65it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 206.88it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 206.45it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 207.21it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 206.81it/s, v_num=ld_2, val_loss=0.611, train_loss=0.551]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 186.15it/s, v_num=ld_2, val_loss=0.588, train_loss=0.551]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 185.04it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 185.77it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 181.06it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 196.06it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 193.74it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 201.45it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 199.67it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 200.09it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 198.44it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 199.55it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 198.46it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 199.73it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 198.89it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 200.42it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 199.44it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 198.51it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 197.85it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 199.23it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 198.61it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 198.20it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 197.38it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 196.43it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 195.90it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 197.88it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 197.42it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 198.85it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 198.44it/s, v_num=ld_2, val_loss=0.588, train_loss=0.550]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 136.27it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 135.67it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 185.22it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 179.86it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 189.90it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 186.95it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 190.72it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 188.94it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 192.01it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 190.84it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 194.52it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 193.40it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 195.20it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 194.29it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 193.90it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 193.20it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 196.44it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 195.78it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 197.34it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 196.71it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 197.63it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 197.09it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 198.50it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 198.01it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 199.36it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 198.92it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 200.92it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 200.53it/s, v_num=ld_2, val_loss=0.590, train_loss=0.550]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 176.94it/s, v_num=ld_2, val_loss=0.593, train_loss=0.550]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 176.19it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 220.42it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 213.91it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 224.17it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 220.92it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 227.34it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 225.24it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 226.95it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 225.09it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 218.80it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 217.51it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 210.52it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 209.37it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 206.23it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 205.37it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 205.64it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 204.96it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 204.54it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 203.93it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 204.96it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 204.37it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 204.88it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 204.40it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 205.25it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 204.79it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 206.04it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 205.65it/s, v_num=ld_2, val_loss=0.593, train_loss=0.560]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 158.40it/s, v_num=ld_2, val_loss=0.599, train_loss=0.560]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 157.65it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 199.96it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 194.37it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 195.18it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 192.36it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 194.75it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 192.67it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 193.27it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 191.92it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 194.83it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 193.75it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 191.82it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 190.95it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 193.84it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 193.15it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 194.70it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 194.05it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 195.41it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 194.87it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 194.27it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 193.65it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 193.53it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 192.99it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 194.16it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 193.74it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 194.88it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 194.46it/s, v_num=ld_2, val_loss=0.599, train_loss=0.577]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 176.44it/s, v_num=ld_2, val_loss=0.579, train_loss=0.577]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 175.70it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 222.27it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 215.89it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 218.70it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 215.63it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 219.73it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 217.64it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 216.84it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 215.25it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 212.35it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 210.88it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 208.60it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 207.64it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 208.58it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 207.73it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 208.46it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 207.73it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 207.42it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 206.69it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 207.11it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 206.54it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 207.30it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 206.76it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 207.73it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 207.29it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 208.61it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 208.20it/s, v_num=ld_2, val_loss=0.579, train_loss=0.558]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 188.65it/s, v_num=ld_2, val_loss=0.583, train_loss=0.558]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 187.90it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 221.35it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 215.06it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 216.71it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 213.74it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 214.63it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 212.48it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 209.25it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 207.63it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 206.54it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 205.34it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 206.19it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 205.17it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 205.01it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 204.25it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 204.70it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 203.93it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 202.95it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 202.32it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 203.10it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 202.56it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 204.40it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 203.93it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 205.55it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 205.07it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 206.38it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 205.98it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 186.11it/s, v_num=ld_2, val_loss=0.583, train_loss=0.561]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 185.35it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 227.69it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 221.14it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 221.19it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 217.82it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 217.75it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 215.64it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 213.14it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 211.62it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 213.21it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 212.14it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 213.53it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 212.56it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 213.22it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 212.31it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 211.23it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 210.31it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 208.26it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 207.54it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 205.91it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 205.32it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 206.17it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 205.67it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 204.95it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 204.43it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 204.75it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 204.26it/s, v_num=ld_2, val_loss=0.583, train_loss=0.556]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 158.43it/s, v_num=ld_2, val_loss=0.586, train_loss=0.556]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 157.63it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 8%|▊ | 1/13 [00:00<00:00, 202.39it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 8%|▊ | 1/13 [00:00<00:00, 196.73it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 15%|█▌ | 2/13 [00:00<00:00, 204.86it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 15%|█▌ | 2/13 [00:00<00:00, 201.49it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 23%|██▎ | 3/13 [00:00<00:00, 194.45it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 23%|██▎ | 3/13 [00:00<00:00, 192.38it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 31%|███ | 4/13 [00:00<00:00, 195.89it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 31%|███ | 4/13 [00:00<00:00, 194.39it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 38%|███▊ | 5/13 [00:00<00:00, 193.29it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 38%|███▊ | 5/13 [00:00<00:00, 192.10it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 46%|████▌ | 6/13 [00:00<00:00, 191.79it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 46%|████▌ | 6/13 [00:00<00:00, 190.88it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 54%|█████▍ | 7/13 [00:00<00:00, 191.27it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 54%|█████▍ | 7/13 [00:00<00:00, 190.62it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 62%|██████▏ | 8/13 [00:00<00:00, 191.29it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 62%|██████▏ | 8/13 [00:00<00:00, 190.58it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 69%|██████▉ | 9/13 [00:00<00:00, 190.58it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 69%|██████▉ | 9/13 [00:00<00:00, 189.84it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 77%|███████▋ | 10/13 [00:00<00:00, 190.67it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 77%|███████▋ | 10/13 [00:00<00:00, 190.16it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 85%|████████▍ | 11/13 [00:00<00:00, 190.39it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 85%|████████▍ | 11/13 [00:00<00:00, 189.88it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 92%|█████████▏| 12/13 [00:00<00:00, 189.73it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 92%|█████████▏| 12/13 [00:00<00:00, 189.20it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 189.16it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 188.78it/s, v_num=ld_2, val_loss=0.586, train_loss=0.548]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 172.56it/s, v_num=ld_2, val_loss=0.592, train_loss=0.548]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 171.80it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 27: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 8%|▊ | 1/13 [00:00<00:00, 194.51it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 8%|▊ | 1/13 [00:00<00:00, 188.78it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 15%|█▌ | 2/13 [00:00<00:00, 202.78it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 15%|█▌ | 2/13 [00:00<00:00, 200.04it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 23%|██▎ | 3/13 [00:00<00:00, 197.79it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 23%|██▎ | 3/13 [00:00<00:00, 195.62it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 31%|███ | 4/13 [00:00<00:00, 197.09it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 31%|███ | 4/13 [00:00<00:00, 195.46it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 38%|███▊ | 5/13 [00:00<00:00, 198.13it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 38%|███▊ | 5/13 [00:00<00:00, 197.14it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 46%|████▌ | 6/13 [00:00<00:00, 200.72it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 46%|████▌ | 6/13 [00:00<00:00, 199.79it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 54%|█████▍ | 7/13 [00:00<00:00, 199.48it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 54%|█████▍ | 7/13 [00:00<00:00, 198.37it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 62%|██████▏ | 8/13 [00:00<00:00, 197.99it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 62%|██████▏ | 8/13 [00:00<00:00, 197.32it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 69%|██████▉ | 9/13 [00:00<00:00, 199.18it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 69%|██████▉ | 9/13 [00:00<00:00, 198.60it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 77%|███████▋ | 10/13 [00:00<00:00, 199.25it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 77%|███████▋ | 10/13 [00:00<00:00, 198.65it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 85%|████████▍ | 11/13 [00:00<00:00, 197.80it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 85%|████████▍ | 11/13 [00:00<00:00, 197.15it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 92%|█████████▏| 12/13 [00:00<00:00, 195.98it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 92%|█████████▏| 12/13 [00:00<00:00, 195.44it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 196.12it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 195.72it/s, v_num=ld_2, val_loss=0.592, train_loss=0.544]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 176.43it/s, v_num=ld_2, val_loss=0.596, train_loss=0.544]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 175.63it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 28: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 8%|▊ | 1/13 [00:00<00:00, 207.03it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 8%|▊ | 1/13 [00:00<00:00, 200.36it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 15%|█▌ | 2/13 [00:00<00:00, 206.38it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 15%|█▌ | 2/13 [00:00<00:00, 202.49it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 23%|██▎ | 3/13 [00:00<00:00, 205.07it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 23%|██▎ | 3/13 [00:00<00:00, 203.25it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 31%|███ | 4/13 [00:00<00:00, 206.34it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 31%|███ | 4/13 [00:00<00:00, 204.87it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 38%|███▊ | 5/13 [00:00<00:00, 204.02it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 38%|███▊ | 5/13 [00:00<00:00, 202.83it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 46%|████▌ | 6/13 [00:00<00:00, 202.48it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 46%|████▌ | 6/13 [00:00<00:00, 201.48it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 54%|█████▍ | 7/13 [00:00<00:00, 203.22it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 54%|█████▍ | 7/13 [00:00<00:00, 202.51it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 62%|██████▏ | 8/13 [00:00<00:00, 204.35it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 62%|██████▏ | 8/13 [00:00<00:00, 203.68it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 69%|██████▉ | 9/13 [00:00<00:00, 204.75it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 69%|██████▉ | 9/13 [00:00<00:00, 204.01it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 77%|███████▋ | 10/13 [00:00<00:00, 204.37it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 77%|███████▋ | 10/13 [00:00<00:00, 203.83it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 85%|████████▍ | 11/13 [00:00<00:00, 204.35it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 85%|████████▍ | 11/13 [00:00<00:00, 203.78it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 92%|█████████▏| 12/13 [00:00<00:00, 203.25it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 92%|█████████▏| 12/13 [00:00<00:00, 202.70it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 201.82it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 201.37it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 181.97it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 181.24it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 29: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 8%|▊ | 1/13 [00:00<00:00, 205.89it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 8%|▊ | 1/13 [00:00<00:00, 200.53it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 15%|█▌ | 2/13 [00:00<00:00, 207.54it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 15%|█▌ | 2/13 [00:00<00:00, 204.73it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 23%|██▎ | 3/13 [00:00<00:00, 204.89it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 23%|██▎ | 3/13 [00:00<00:00, 203.09it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 31%|███ | 4/13 [00:00<00:00, 204.69it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 31%|███ | 4/13 [00:00<00:00, 203.38it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 38%|███▊ | 5/13 [00:00<00:00, 204.42it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 38%|███▊ | 5/13 [00:00<00:00, 203.22it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 46%|████▌ | 6/13 [00:00<00:00, 202.75it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 46%|████▌ | 6/13 [00:00<00:00, 201.82it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 54%|█████▍ | 7/13 [00:00<00:00, 203.52it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 54%|█████▍ | 7/13 [00:00<00:00, 202.74it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 62%|██████▏ | 8/13 [00:00<00:00, 203.61it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 62%|██████▏ | 8/13 [00:00<00:00, 202.89it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 69%|██████▉ | 9/13 [00:00<00:00, 203.20it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 69%|██████▉ | 9/13 [00:00<00:00, 202.58it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 77%|███████▋ | 10/13 [00:00<00:00, 203.82it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 77%|███████▋ | 10/13 [00:00<00:00, 203.30it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 85%|████████▍ | 11/13 [00:00<00:00, 202.47it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 85%|████████▍ | 11/13 [00:00<00:00, 201.97it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 92%|█████████▏| 12/13 [00:00<00:00, 202.96it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 92%|█████████▏| 12/13 [00:00<00:00, 202.55it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 203.08it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 202.69it/s, v_num=ld_2, val_loss=0.596, train_loss=0.547]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 184.62it/s, v_num=ld_2, val_loss=0.605, train_loss=0.547]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 183.84it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 30: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 8%|▊ | 1/13 [00:00<00:00, 218.88it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 8%|▊ | 1/13 [00:00<00:00, 212.77it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 15%|█▌ | 2/13 [00:00<00:00, 211.75it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 15%|█▌ | 2/13 [00:00<00:00, 209.07it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 23%|██▎ | 3/13 [00:00<00:00, 210.91it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 23%|██▎ | 3/13 [00:00<00:00, 209.03it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 31%|███ | 4/13 [00:00<00:00, 210.89it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 31%|███ | 4/13 [00:00<00:00, 209.45it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 38%|███▊ | 5/13 [00:00<00:00, 208.55it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 38%|███▊ | 5/13 [00:00<00:00, 207.43it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 46%|████▌ | 6/13 [00:00<00:00, 208.90it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 46%|████▌ | 6/13 [00:00<00:00, 207.92it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 54%|█████▍ | 7/13 [00:00<00:00, 208.14it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 54%|█████▍ | 7/13 [00:00<00:00, 207.38it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 62%|██████▏ | 8/13 [00:00<00:00, 207.29it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 62%|██████▏ | 8/13 [00:00<00:00, 206.60it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 69%|██████▉ | 9/13 [00:00<00:00, 207.38it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 69%|██████▉ | 9/13 [00:00<00:00, 206.75it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 77%|███████▋ | 10/13 [00:00<00:00, 206.15it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 77%|███████▋ | 10/13 [00:00<00:00, 205.66it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 85%|████████▍ | 11/13 [00:00<00:00, 205.57it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 85%|████████▍ | 11/13 [00:00<00:00, 205.06it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 92%|█████████▏| 12/13 [00:00<00:00, 205.42it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 92%|█████████▏| 12/13 [00:00<00:00, 204.95it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 206.74it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 206.31it/s, v_num=ld_2, val_loss=0.605, train_loss=0.543]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 187.48it/s, v_num=ld_2, val_loss=0.593, train_loss=0.543]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 186.71it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 31: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 8%|▊ | 1/13 [00:00<00:00, 222.57it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 8%|▊ | 1/13 [00:00<00:00, 216.22it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 15%|█▌ | 2/13 [00:00<00:00, 214.80it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 15%|█▌ | 2/13 [00:00<00:00, 211.96it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 23%|██▎ | 3/13 [00:00<00:00, 210.38it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 23%|██▎ | 3/13 [00:00<00:00, 208.17it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 31%|███ | 4/13 [00:00<00:00, 201.00it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 31%|███ | 4/13 [00:00<00:00, 199.59it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 38%|███▊ | 5/13 [00:00<00:00, 199.92it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 38%|███▊ | 5/13 [00:00<00:00, 198.89it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 46%|████▌ | 6/13 [00:00<00:00, 197.75it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 46%|████▌ | 6/13 [00:00<00:00, 196.89it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 54%|█████▍ | 7/13 [00:00<00:00, 196.33it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 54%|█████▍ | 7/13 [00:00<00:00, 195.63it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 62%|██████▏ | 8/13 [00:00<00:00, 196.14it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 62%|██████▏ | 8/13 [00:00<00:00, 195.39it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 69%|██████▉ | 9/13 [00:00<00:00, 196.20it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 69%|██████▉ | 9/13 [00:00<00:00, 195.60it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 77%|███████▋ | 10/13 [00:00<00:00, 195.83it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 77%|███████▋ | 10/13 [00:00<00:00, 195.30it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 85%|████████▍ | 11/13 [00:00<00:00, 194.50it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 85%|████████▍ | 11/13 [00:00<00:00, 193.99it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 92%|█████████▏| 12/13 [00:00<00:00, 194.23it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 92%|█████████▏| 12/13 [00:00<00:00, 193.80it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 194.07it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 193.70it/s, v_num=ld_2, val_loss=0.593, train_loss=0.540]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 175.49it/s, v_num=ld_2, val_loss=0.587, train_loss=0.540]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 174.72it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 32: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 8%|▊ | 1/13 [00:00<00:00, 201.70it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 8%|▊ | 1/13 [00:00<00:00, 196.00it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 15%|█▌ | 2/13 [00:00<00:00, 202.16it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 15%|█▌ | 2/13 [00:00<00:00, 199.54it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 23%|██▎ | 3/13 [00:00<00:00, 202.68it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 23%|██▎ | 3/13 [00:00<00:00, 200.82it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 31%|███ | 4/13 [00:00<00:00, 198.86it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 31%|███ | 4/13 [00:00<00:00, 196.78it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 38%|███▊ | 5/13 [00:00<00:00, 195.03it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 38%|███▊ | 5/13 [00:00<00:00, 194.05it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 46%|████▌ | 6/13 [00:00<00:00, 195.02it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 46%|████▌ | 6/13 [00:00<00:00, 194.14it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 54%|█████▍ | 7/13 [00:00<00:00, 195.24it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 54%|█████▍ | 7/13 [00:00<00:00, 194.22it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 62%|██████▏ | 8/13 [00:00<00:00, 194.82it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 62%|██████▏ | 8/13 [00:00<00:00, 194.13it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 69%|██████▉ | 9/13 [00:00<00:00, 194.77it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 69%|██████▉ | 9/13 [00:00<00:00, 194.01it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 77%|███████▋ | 10/13 [00:00<00:00, 193.79it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 77%|███████▋ | 10/13 [00:00<00:00, 193.06it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 85%|████████▍ | 11/13 [00:00<00:00, 193.90it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 85%|████████▍ | 11/13 [00:00<00:00, 193.41it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 92%|█████████▏| 12/13 [00:00<00:00, 193.95it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 92%|█████████▏| 12/13 [00:00<00:00, 193.51it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 100%|██████████| 13/13 [00:00<00:00, 193.87it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 100%|██████████| 13/13 [00:00<00:00, 193.31it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 33: 100%|██████████| 13/13 [00:00<00:00, 174.35it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 33: 100%|██████████| 13/13 [00:00<00:00, 173.61it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 33: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 8%|▊ | 1/13 [00:00<00:00, 209.82it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 8%|▊ | 1/13 [00:00<00:00, 202.07it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 15%|█▌ | 2/13 [00:00<00:00, 203.23it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 15%|█▌ | 2/13 [00:00<00:00, 199.38it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 23%|██▎ | 3/13 [00:00<00:00, 199.11it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 23%|██▎ | 3/13 [00:00<00:00, 197.05it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 31%|███ | 4/13 [00:00<00:00, 199.99it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 31%|███ | 4/13 [00:00<00:00, 198.58it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 38%|███▊ | 5/13 [00:00<00:00, 202.16it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 38%|███▊ | 5/13 [00:00<00:00, 201.10it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 46%|████▌ | 6/13 [00:00<00:00, 201.24it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 46%|████▌ | 6/13 [00:00<00:00, 200.20it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 54%|█████▍ | 7/13 [00:00<00:00, 197.89it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 54%|█████▍ | 7/13 [00:00<00:00, 197.05it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 62%|██████▏ | 8/13 [00:00<00:00, 196.55it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 62%|██████▏ | 8/13 [00:00<00:00, 195.84it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 69%|██████▉ | 9/13 [00:00<00:00, 195.74it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 69%|██████▉ | 9/13 [00:00<00:00, 195.10it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 77%|███████▋ | 10/13 [00:00<00:00, 196.05it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 77%|███████▋ | 10/13 [00:00<00:00, 195.52it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 85%|████████▍ | 11/13 [00:00<00:00, 196.12it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 85%|████████▍ | 11/13 [00:00<00:00, 195.44it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 92%|█████████▏| 12/13 [00:00<00:00, 196.05it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 92%|█████████▏| 12/13 [00:00<00:00, 195.55it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 100%|██████████| 13/13 [00:00<00:00, 196.58it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 100%|██████████| 13/13 [00:00<00:00, 196.12it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 100%|██████████| 13/13 [00:00<00:00, 174.64it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 100%|██████████| 13/13 [00:00<00:00, 173.75it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 34: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 8%|▊ | 1/13 [00:00<00:00, 186.36it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 8%|▊ | 1/13 [00:00<00:00, 181.39it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 15%|█▌ | 2/13 [00:00<00:00, 196.81it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 15%|█▌ | 2/13 [00:00<00:00, 194.25it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 23%|██▎ | 3/13 [00:00<00:00, 200.10it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 23%|██▎ | 3/13 [00:00<00:00, 198.11it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 31%|███ | 4/13 [00:00<00:00, 196.31it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 31%|███ | 4/13 [00:00<00:00, 194.74it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 38%|███▊ | 5/13 [00:00<00:00, 193.84it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 38%|███▊ | 5/13 [00:00<00:00, 192.58it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 46%|████▌ | 6/13 [00:00<00:00, 192.06it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 46%|████▌ | 6/13 [00:00<00:00, 191.18it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 54%|█████▍ | 7/13 [00:00<00:00, 190.04it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 54%|█████▍ | 7/13 [00:00<00:00, 189.23it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 62%|██████▏ | 8/13 [00:00<00:00, 190.26it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 62%|██████▏ | 8/13 [00:00<00:00, 189.63it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 69%|██████▉ | 9/13 [00:00<00:00, 191.56it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 69%|██████▉ | 9/13 [00:00<00:00, 190.96it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 77%|███████▋ | 10/13 [00:00<00:00, 189.94it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 77%|███████▋ | 10/13 [00:00<00:00, 189.31it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 85%|████████▍ | 11/13 [00:00<00:00, 189.36it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 85%|████████▍ | 11/13 [00:00<00:00, 188.85it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 92%|█████████▏| 12/13 [00:00<00:00, 189.04it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 92%|█████████▏| 12/13 [00:00<00:00, 188.57it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 100%|██████████| 13/13 [00:00<00:00, 187.97it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 100%|██████████| 13/13 [00:00<00:00, 187.51it/s, v_num=ld_2, val_loss=0.586, train_loss=0.539]
Epoch 35: 100%|██████████| 13/13 [00:00<00:00, 161.08it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 35: 100%|██████████| 13/13 [00:00<00:00, 160.46it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 35: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 8%|▊ | 1/13 [00:00<00:00, 198.01it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 8%|▊ | 1/13 [00:00<00:00, 192.67it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 15%|█▌ | 2/13 [00:00<00:00, 197.80it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 15%|█▌ | 2/13 [00:00<00:00, 195.17it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 23%|██▎ | 3/13 [00:00<00:00, 199.49it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 23%|██▎ | 3/13 [00:00<00:00, 197.80it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 31%|███ | 4/13 [00:00<00:00, 199.65it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 31%|███ | 4/13 [00:00<00:00, 198.20it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 38%|███▊ | 5/13 [00:00<00:00, 199.90it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 38%|███▊ | 5/13 [00:00<00:00, 198.86it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 46%|████▌ | 6/13 [00:00<00:00, 200.29it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 46%|████▌ | 6/13 [00:00<00:00, 199.43it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 54%|█████▍ | 7/13 [00:00<00:00, 199.13it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 54%|█████▍ | 7/13 [00:00<00:00, 198.20it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 62%|██████▏ | 8/13 [00:00<00:00, 198.06it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 62%|██████▏ | 8/13 [00:00<00:00, 197.37it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 69%|██████▉ | 9/13 [00:00<00:00, 199.39it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 69%|██████▉ | 9/13 [00:00<00:00, 198.81it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 77%|███████▋ | 10/13 [00:00<00:00, 200.87it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 77%|███████▋ | 10/13 [00:00<00:00, 200.36it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 85%|████████▍ | 11/13 [00:00<00:00, 200.86it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 85%|████████▍ | 11/13 [00:00<00:00, 200.34it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 92%|█████████▏| 12/13 [00:00<00:00, 200.01it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 92%|█████████▏| 12/13 [00:00<00:00, 199.55it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 100%|██████████| 13/13 [00:00<00:00, 200.17it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 100%|██████████| 13/13 [00:00<00:00, 199.77it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 36: 100%|██████████| 13/13 [00:00<00:00, 182.01it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 36: 100%|██████████| 13/13 [00:00<00:00, 181.19it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 36: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 8%|▊ | 1/13 [00:00<00:00, 207.94it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 8%|▊ | 1/13 [00:00<00:00, 202.17it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 15%|█▌ | 2/13 [00:00<00:00, 210.81it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 15%|█▌ | 2/13 [00:00<00:00, 207.92it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 23%|██▎ | 3/13 [00:00<00:00, 212.36it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 23%|██▎ | 3/13 [00:00<00:00, 210.50it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 31%|███ | 4/13 [00:00<00:00, 210.50it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 31%|███ | 4/13 [00:00<00:00, 209.05it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 38%|███▊ | 5/13 [00:00<00:00, 209.64it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 38%|███▊ | 5/13 [00:00<00:00, 208.45it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 46%|████▌ | 6/13 [00:00<00:00, 208.12it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 46%|████▌ | 6/13 [00:00<00:00, 207.24it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 54%|█████▍ | 7/13 [00:00<00:00, 207.65it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 54%|█████▍ | 7/13 [00:00<00:00, 206.84it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 62%|██████▏ | 8/13 [00:00<00:00, 207.50it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 62%|██████▏ | 8/13 [00:00<00:00, 206.78it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 69%|██████▉ | 9/13 [00:00<00:00, 207.35it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 69%|██████▉ | 9/13 [00:00<00:00, 206.74it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 77%|███████▋ | 10/13 [00:00<00:00, 206.76it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 77%|███████▋ | 10/13 [00:00<00:00, 206.24it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 85%|████████▍ | 11/13 [00:00<00:00, 205.60it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 85%|████████▍ | 11/13 [00:00<00:00, 205.09it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 92%|█████████▏| 12/13 [00:00<00:00, 205.43it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 92%|█████████▏| 12/13 [00:00<00:00, 204.91it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 100%|██████████| 13/13 [00:00<00:00, 204.17it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 100%|██████████| 13/13 [00:00<00:00, 203.68it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 100%|██████████| 13/13 [00:00<00:00, 182.43it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 100%|██████████| 13/13 [00:00<00:00, 181.59it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 37: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 8%|▊ | 1/13 [00:00<00:00, 196.00it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 8%|▊ | 1/13 [00:00<00:00, 190.69it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 15%|█▌ | 2/13 [00:00<00:00, 203.21it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 15%|█▌ | 2/13 [00:00<00:00, 200.64it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 23%|██▎ | 3/13 [00:00<00:00, 205.58it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 23%|██▎ | 3/13 [00:00<00:00, 203.68it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 31%|███ | 4/13 [00:00<00:00, 204.98it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 31%|███ | 4/13 [00:00<00:00, 203.55it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 38%|███▊ | 5/13 [00:00<00:00, 202.50it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 38%|███▊ | 5/13 [00:00<00:00, 201.36it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 46%|████▌ | 6/13 [00:00<00:00, 200.29it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 46%|████▌ | 6/13 [00:00<00:00, 199.42it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 54%|█████▍ | 7/13 [00:00<00:00, 200.42it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 54%|█████▍ | 7/13 [00:00<00:00, 199.69it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 62%|██████▏ | 8/13 [00:00<00:00, 201.27it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 62%|██████▏ | 8/13 [00:00<00:00, 200.64it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 69%|██████▉ | 9/13 [00:00<00:00, 201.40it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 69%|██████▉ | 9/13 [00:00<00:00, 200.84it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 77%|███████▋ | 10/13 [00:00<00:00, 202.26it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 77%|███████▋ | 10/13 [00:00<00:00, 201.75it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 85%|████████▍ | 11/13 [00:00<00:00, 202.19it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 85%|████████▍ | 11/13 [00:00<00:00, 201.67it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 92%|█████████▏| 12/13 [00:00<00:00, 201.35it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 92%|█████████▏| 12/13 [00:00<00:00, 200.86it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 201.94it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 201.52it/s, v_num=ld_2, val_loss=0.589, train_loss=0.539]
Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 181.82it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 181.10it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 153.79it/s, v_num=ld_2, val_loss=0.587, train_loss=0.539]
+
Training: | | 0/? [00:00, ?it/s]
Training: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 159.64it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 156.43it/s, v_num=ld_2]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 179.54it/s, v_num=ld_2]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 177.53it/s, v_num=ld_2]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 184.66it/s, v_num=ld_2]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 183.24it/s, v_num=ld_2]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 189.29it/s, v_num=ld_2]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 188.03it/s, v_num=ld_2]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 190.36it/s, v_num=ld_2]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 189.40it/s, v_num=ld_2]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 192.43it/s, v_num=ld_2]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 191.68it/s, v_num=ld_2]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 194.37it/s, v_num=ld_2]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 193.67it/s, v_num=ld_2]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 194.27it/s, v_num=ld_2]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 193.71it/s, v_num=ld_2]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 194.81it/s, v_num=ld_2]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 194.26it/s, v_num=ld_2]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 195.57it/s, v_num=ld_2]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 195.09it/s, v_num=ld_2]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 196.75it/s, v_num=ld_2]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 196.38it/s, v_num=ld_2]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 197.61it/s, v_num=ld_2]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 197.16it/s, v_num=ld_2]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 197.96it/s, v_num=ld_2]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 197.49it/s, v_num=ld_2]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 177.83it/s, v_num=ld_2, val_loss=0.680]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 177.03it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 0: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 198.80it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 192.94it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 189.81it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 187.58it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 191.08it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 189.40it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 191.81it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 190.53it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 194.16it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 193.32it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 195.94it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 194.87it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 193.04it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 192.13it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 191.44it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 190.87it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 192.25it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 191.62it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 192.07it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 191.61it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 193.13it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 192.75it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 194.47it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 194.00it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 194.09it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 193.68it/s, v_num=ld_2, val_loss=0.680, train_loss=0.731]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 175.53it/s, v_num=ld_2, val_loss=0.670, train_loss=0.731]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 174.68it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 178.51it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 173.58it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 187.38it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 184.98it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 187.48it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 185.93it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 188.74it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 187.46it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 190.38it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 189.44it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 191.60it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 190.76it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 190.01it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 189.35it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 191.92it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 191.20it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 190.46it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 189.95it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 191.79it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 191.17it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 190.85it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 190.42it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 190.90it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 190.51it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 191.65it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 191.28it/s, v_num=ld_2, val_loss=0.670, train_loss=0.688]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 174.22it/s, v_num=ld_2, val_loss=0.607, train_loss=0.688]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 173.48it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 197.94it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 193.00it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 202.73it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 200.16it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 204.89it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 203.10it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 202.58it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 201.17it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 201.73it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 200.54it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 200.81it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 199.96it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 198.11it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 197.14it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 196.10it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 195.37it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 193.48it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 192.76it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 191.60it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 191.04it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 191.71it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 191.29it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 192.72it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 192.34it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 193.22it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 192.83it/s, v_num=ld_2, val_loss=0.607, train_loss=0.658]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 176.87it/s, v_num=ld_2, val_loss=0.556, train_loss=0.658]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 176.14it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 204.88it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 198.88it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 205.63it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 202.88it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 204.79it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 202.93it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 204.00it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 202.72it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 201.81it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 200.53it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 200.31it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 199.45it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 199.37it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 198.62it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 196.20it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 195.28it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 194.17it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 193.62it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 194.36it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 193.81it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 195.00it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 194.56it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 194.02it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 193.60it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 194.83it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 194.46it/s, v_num=ld_2, val_loss=0.556, train_loss=0.611]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 177.19it/s, v_num=ld_2, val_loss=0.566, train_loss=0.611]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 176.35it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 190.19it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 185.88it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 195.96it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 193.31it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 195.01it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 192.61it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 192.83it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 191.67it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 194.59it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 193.63it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 195.77it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 194.87it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 193.75it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 192.99it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 193.73it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 193.11it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 194.58it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 194.06it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 194.65it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 194.17it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 194.49it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 194.04it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 194.26it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 193.86it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 194.77it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 194.37it/s, v_num=ld_2, val_loss=0.566, train_loss=0.596]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 176.27it/s, v_num=ld_2, val_loss=0.557, train_loss=0.596]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 175.47it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 201.53it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 196.00it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 199.74it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 197.12it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 203.93it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 202.06it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 202.13it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 200.75it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 200.47it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 199.40it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 199.14it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 198.17it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 199.47it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 198.75it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 200.16it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 199.52it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 200.07it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 199.50it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 199.70it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 199.07it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 199.06it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 198.64it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 199.55it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 199.13it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 199.69it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 199.29it/s, v_num=ld_2, val_loss=0.557, train_loss=0.590]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 182.11it/s, v_num=ld_2, val_loss=0.565, train_loss=0.590]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 181.38it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 199.37it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 193.96it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 200.40it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 197.91it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 201.21it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 199.47it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 198.85it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 197.24it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 197.11it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 196.10it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 196.94it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 196.08it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 196.07it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 195.08it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 193.93it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 193.24it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 192.69it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 192.11it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 191.97it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 191.24it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 191.54it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 191.11it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 192.07it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 191.64it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 192.63it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 192.17it/s, v_num=ld_2, val_loss=0.565, train_loss=0.584]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 175.16it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 174.47it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 205.82it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 200.44it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 208.22it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 205.36it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 206.88it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 205.17it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 205.05it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 203.48it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 203.41it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 202.37it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 203.09it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 202.26it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 203.10it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 202.34it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 203.28it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 202.66it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 202.92it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 202.23it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 200.71it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 200.14it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 198.90it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 198.40it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 198.39it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 197.98it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 199.41it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 199.04it/s, v_num=ld_2, val_loss=0.554, train_loss=0.584]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 181.69it/s, v_num=ld_2, val_loss=0.559, train_loss=0.584]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 180.97it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 200.52it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 195.85it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 201.45it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 199.07it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 202.77it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 201.15it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 201.32it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 200.12it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 201.18it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 200.20it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 200.63it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 199.69it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 197.80it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 196.99it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 196.48it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 195.83it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 197.00it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 196.42it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 197.12it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 196.66it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 197.22it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 196.77it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 197.34it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 196.91it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 197.81it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 197.46it/s, v_num=ld_2, val_loss=0.559, train_loss=0.583]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 180.54it/s, v_num=ld_2, val_loss=0.553, train_loss=0.583]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 179.83it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 209.63it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 203.50it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 202.40it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 199.90it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 199.55it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 197.85it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 199.89it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 198.67it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 199.45it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 198.41it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 199.25it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 198.39it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 198.46it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 197.68it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 197.07it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 196.48it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 197.53it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 197.00it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 197.53it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 197.06it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 198.04it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 197.58it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 198.53it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 198.11it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 199.15it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 198.77it/s, v_num=ld_2, val_loss=0.553, train_loss=0.576]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 181.25it/s, v_num=ld_2, val_loss=0.549, train_loss=0.576]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 180.53it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 212.18it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 206.48it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 210.32it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 207.51it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 206.81it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 205.07it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 203.34it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 201.87it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 201.93it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 200.94it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 201.80it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 201.00it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 200.53it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 199.81it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 200.23it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 199.63it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 200.17it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 199.63it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 199.46it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 198.88it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 198.42it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 197.98it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 198.12it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 197.63it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 197.60it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 197.21it/s, v_num=ld_2, val_loss=0.549, train_loss=0.574]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 179.33it/s, v_num=ld_2, val_loss=0.552, train_loss=0.574]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 178.59it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 209.56it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 204.00it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 204.73it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 202.02it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 198.42it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 196.41it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 196.63it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 195.44it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 198.13it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 197.14it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 198.27it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 197.47it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 198.19it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 197.51it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 198.59it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 197.95it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 198.67it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 198.13it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 198.50it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 197.98it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 198.06it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 197.62it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 197.78it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 197.39it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 198.11it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 197.72it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 180.00it/s, v_num=ld_2, val_loss=0.557, train_loss=0.572]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 179.27it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 198.45it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 193.35it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 195.68it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 193.27it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 198.57it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 196.90it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 198.57it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 197.36it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 198.67it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 197.68it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 199.20it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 198.37it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 200.14it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 199.47it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 199.88it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 199.20it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 198.73it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 198.13it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 198.61it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 198.13it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 198.92it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 198.46it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 197.34it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 196.87it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 197.95it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 197.54it/s, v_num=ld_2, val_loss=0.557, train_loss=0.568]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 180.37it/s, v_num=ld_2, val_loss=0.556, train_loss=0.568]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 179.67it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 193.21it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 188.44it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 197.96it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 195.43it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 194.85it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 193.24it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 192.16it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 190.88it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 192.64it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 191.70it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 189.92it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 189.05it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 188.41it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 187.72it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 188.78it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 188.14it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 188.62it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 188.09it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 188.09it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 187.54it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 188.17it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 187.71it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 188.52it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 188.16it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 189.25it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 188.92it/s, v_num=ld_2, val_loss=0.556, train_loss=0.567]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 173.24it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 172.55it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 196.00it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 190.76it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 198.06it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 195.69it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 197.13it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 195.54it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 195.96it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 194.69it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 195.38it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 194.45it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 195.71it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 194.91it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 196.27it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 195.54it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 196.57it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 195.98it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 197.25it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 196.72it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 197.23it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 196.74it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 197.15it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 196.73it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 197.15it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 196.74it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 197.21it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 196.85it/s, v_num=ld_2, val_loss=0.554, train_loss=0.567]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 170.22it/s, v_num=ld_2, val_loss=0.557, train_loss=0.567]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 169.52it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 200.21it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 194.98it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 202.08it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 199.54it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 202.39it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 200.64it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 201.51it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 200.20it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 200.67it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 199.70it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 199.43it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 198.55it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 198.78it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 198.07it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 198.68it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 198.02it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 196.85it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 196.27it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 195.36it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 194.81it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 195.19it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 194.75it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 194.94it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 194.51it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 195.20it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 194.81it/s, v_num=ld_2, val_loss=0.557, train_loss=0.579]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 164.03it/s, v_num=ld_2, val_loss=0.550, train_loss=0.579]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 163.37it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 159.08it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 154.39it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 157.06it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 154.92it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 158.93it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 157.51it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 160.14it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 158.98it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 161.33it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 160.46it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 163.51it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 162.86it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 165.22it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 164.68it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 166.72it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 166.22it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 168.96it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 168.50it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 170.26it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 169.90it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 171.09it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 170.69it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 172.66it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 172.35it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 173.86it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 173.52it/s, v_num=ld_2, val_loss=0.550, train_loss=0.574]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 159.41it/s, v_num=ld_2, val_loss=0.540, train_loss=0.574]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 158.81it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 194.67it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 189.57it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 193.71it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 191.01it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 186.08it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 184.29it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 184.55it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 183.38it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 185.00it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 184.13it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 186.91it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 186.13it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 183.18it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 182.53it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 183.79it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 183.25it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 184.42it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 183.96it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 185.68it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 185.27it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 186.14it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 185.71it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 186.94it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 186.54it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 187.96it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 187.64it/s, v_num=ld_2, val_loss=0.540, train_loss=0.575]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 171.87it/s, v_num=ld_2, val_loss=0.545, train_loss=0.575]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 171.22it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 202.71it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 197.31it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 199.70it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 197.26it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 198.80it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 197.09it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 198.01it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 196.78it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 198.48it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 197.48it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 197.58it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 196.78it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 196.79it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 196.12it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 196.49it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 195.88it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 195.70it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 195.14it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 195.48it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 195.00it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 195.75it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 195.34it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 196.06it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 195.64it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 196.34it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 195.96it/s, v_num=ld_2, val_loss=0.545, train_loss=0.570]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 176.08it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 175.41it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 186.96it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 182.36it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 189.77it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 187.55it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 192.89it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 191.35it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 194.62it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 193.44it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 190.22it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 188.93it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 188.79it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 188.02it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 188.74it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 188.04it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 188.13it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 187.45it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 188.22it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 187.73it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 188.77it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 188.34it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 188.52it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 188.00it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 188.23it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 187.84it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 189.16it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 188.77it/s, v_num=ld_2, val_loss=0.541, train_loss=0.570]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 155.63it/s, v_num=ld_2, val_loss=0.540, train_loss=0.570]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 154.82it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 184.10it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 178.12it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 184.06it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 181.89it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 182.92it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 181.23it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 183.23it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 182.08it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 185.24it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 184.36it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 185.34it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 184.55it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 186.14it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 185.53it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 186.67it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 186.11it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 187.34it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 186.87it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 188.62it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 188.14it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 188.70it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 188.28it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 189.29it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 188.91it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 189.44it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 189.11it/s, v_num=ld_2, val_loss=0.540, train_loss=0.567]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 172.62it/s, v_num=ld_2, val_loss=0.549, train_loss=0.567]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 171.98it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 204.25it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 198.96it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 198.69it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 196.06it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 195.11it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 193.47it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 195.23it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 194.02it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 195.74it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 194.76it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 195.64it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 194.85it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 195.33it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 194.62it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 194.76it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 194.18it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 195.24it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 194.70it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 195.35it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 194.88it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 194.78it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 194.35it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 194.57it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 194.18it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 194.50it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 194.15it/s, v_num=ld_2, val_loss=0.549, train_loss=0.568]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 177.26it/s, v_num=ld_2, val_loss=0.548, train_loss=0.568]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 176.56it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 204.46it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 199.12it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 196.60it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 194.08it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 196.27it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 194.59it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 195.42it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 194.22it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 196.44it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 195.49it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 197.13it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 196.35it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 197.03it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 196.32it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 196.45it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 195.86it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 195.80it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 195.32it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 195.73it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 195.25it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 195.19it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 194.76it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 195.15it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 194.72it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 195.20it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 194.86it/s, v_num=ld_2, val_loss=0.548, train_loss=0.569]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 177.52it/s, v_num=ld_2, val_loss=0.552, train_loss=0.569]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 176.83it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 208.21it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 202.63it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 198.41it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 195.95it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 195.80it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 194.25it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 195.31it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 194.10it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 194.23it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 193.30it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 193.81it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 193.02it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 193.05it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 192.40it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 192.73it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 192.18it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 192.95it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 192.37it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 192.63it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 192.18it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 192.78it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 192.38it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 193.25it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 192.79it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 193.20it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 192.84it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 176.23it/s, v_num=ld_2, val_loss=0.552, train_loss=0.567]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 175.51it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 199.69it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 194.63it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 192.57it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 190.32it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 192.93it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 191.46it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 193.21it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 192.02it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 192.89it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 192.02it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 194.13it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 193.28it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 194.28it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 193.60it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 194.91it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 194.30it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 195.33it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 194.81it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 194.96it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 194.51it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 195.13it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 194.69it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 195.23it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 194.82it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 194.85it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 194.48it/s, v_num=ld_2, val_loss=0.552, train_loss=0.566]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 177.16it/s, v_num=ld_2, val_loss=0.564, train_loss=0.566]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 176.45it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 200.96it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 195.88it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 200.62it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 197.94it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 196.67it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 195.10it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 195.90it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 194.72it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 196.50it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 195.53it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 195.45it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 194.60it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 194.74it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 194.09it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 195.00it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 194.32it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 192.55it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 191.96it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 190.88it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 190.41it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 190.85it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 190.44it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 190.90it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 190.51it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 190.37it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 190.01it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 173.07it/s, v_num=ld_2, val_loss=0.558, train_loss=0.563]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 172.37it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 8%|▊ | 1/13 [00:00<00:00, 182.58it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 8%|▊ | 1/13 [00:00<00:00, 177.30it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 15%|█▌ | 2/13 [00:00<00:00, 185.15it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 15%|█▌ | 2/13 [00:00<00:00, 182.99it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 23%|██▎ | 3/13 [00:00<00:00, 184.06it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 23%|██▎ | 3/13 [00:00<00:00, 182.64it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 31%|███ | 4/13 [00:00<00:00, 184.76it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 31%|███ | 4/13 [00:00<00:00, 183.43it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 38%|███▊ | 5/13 [00:00<00:00, 184.28it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 38%|███▊ | 5/13 [00:00<00:00, 183.39it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 46%|████▌ | 6/13 [00:00<00:00, 185.52it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 46%|████▌ | 6/13 [00:00<00:00, 184.76it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 54%|█████▍ | 7/13 [00:00<00:00, 186.00it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 54%|█████▍ | 7/13 [00:00<00:00, 185.15it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 62%|██████▏ | 8/13 [00:00<00:00, 185.04it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 62%|██████▏ | 8/13 [00:00<00:00, 184.51it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 69%|██████▉ | 9/13 [00:00<00:00, 186.12it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 69%|██████▉ | 9/13 [00:00<00:00, 185.62it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 77%|███████▋ | 10/13 [00:00<00:00, 186.77it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 77%|███████▋ | 10/13 [00:00<00:00, 186.27it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 85%|████████▍ | 11/13 [00:00<00:00, 186.01it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 85%|████████▍ | 11/13 [00:00<00:00, 185.60it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 92%|█████████▏| 12/13 [00:00<00:00, 185.35it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 92%|█████████▏| 12/13 [00:00<00:00, 184.91it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 185.43it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 184.99it/s, v_num=ld_2, val_loss=0.558, train_loss=0.565]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 168.22it/s, v_num=ld_2, val_loss=0.564, train_loss=0.565]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 167.52it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 27: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 8%|▊ | 1/13 [00:00<00:00, 185.70it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 8%|▊ | 1/13 [00:00<00:00, 180.96it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 15%|█▌ | 2/13 [00:00<00:00, 186.55it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 15%|█▌ | 2/13 [00:00<00:00, 184.43it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 23%|██▎ | 3/13 [00:00<00:00, 186.70it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 23%|██▎ | 3/13 [00:00<00:00, 185.12it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 31%|███ | 4/13 [00:00<00:00, 186.38it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 31%|███ | 4/13 [00:00<00:00, 185.24it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 38%|███▊ | 5/13 [00:00<00:00, 187.19it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 38%|███▊ | 5/13 [00:00<00:00, 186.30it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 46%|████▌ | 6/13 [00:00<00:00, 187.07it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 46%|████▌ | 6/13 [00:00<00:00, 186.36it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 54%|█████▍ | 7/13 [00:00<00:00, 187.92it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 54%|█████▍ | 7/13 [00:00<00:00, 187.30it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 62%|██████▏ | 8/13 [00:00<00:00, 188.03it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 62%|██████▏ | 8/13 [00:00<00:00, 187.43it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 69%|██████▉ | 9/13 [00:00<00:00, 187.04it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 69%|██████▉ | 9/13 [00:00<00:00, 186.53it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 77%|███████▋ | 10/13 [00:00<00:00, 187.14it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 77%|███████▋ | 10/13 [00:00<00:00, 186.59it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 85%|████████▍ | 11/13 [00:00<00:00, 184.48it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 85%|████████▍ | 11/13 [00:00<00:00, 183.97it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 92%|█████████▏| 12/13 [00:00<00:00, 183.23it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 92%|█████████▏| 12/13 [00:00<00:00, 182.78it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 182.62it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 182.23it/s, v_num=ld_2, val_loss=0.564, train_loss=0.563]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 164.93it/s, v_num=ld_2, val_loss=0.559, train_loss=0.563]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 164.19it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 28: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 8%|▊ | 1/13 [00:00<00:00, 176.15it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 8%|▊ | 1/13 [00:00<00:00, 171.09it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 15%|█▌ | 2/13 [00:00<00:00, 170.77it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 15%|█▌ | 2/13 [00:00<00:00, 168.45it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 23%|██▎ | 3/13 [00:00<00:00, 170.86it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 23%|██▎ | 3/13 [00:00<00:00, 169.32it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 31%|███ | 4/13 [00:00<00:00, 171.37it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 31%|███ | 4/13 [00:00<00:00, 170.22it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 38%|███▊ | 5/13 [00:00<00:00, 171.95it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 38%|███▊ | 5/13 [00:00<00:00, 171.04it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 46%|████▌ | 6/13 [00:00<00:00, 171.14it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 46%|████▌ | 6/13 [00:00<00:00, 170.37it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 54%|█████▍ | 7/13 [00:00<00:00, 170.94it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 54%|█████▍ | 7/13 [00:00<00:00, 170.26it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 62%|██████▏ | 8/13 [00:00<00:00, 169.99it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 62%|██████▏ | 8/13 [00:00<00:00, 169.37it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 69%|██████▉ | 9/13 [00:00<00:00, 169.34it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 69%|██████▉ | 9/13 [00:00<00:00, 168.80it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 77%|███████▋ | 10/13 [00:00<00:00, 168.85it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 77%|███████▋ | 10/13 [00:00<00:00, 168.40it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 85%|████████▍ | 11/13 [00:00<00:00, 169.46it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 85%|████████▍ | 11/13 [00:00<00:00, 169.07it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 92%|█████████▏| 12/13 [00:00<00:00, 170.50it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 92%|█████████▏| 12/13 [00:00<00:00, 170.18it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 172.12it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 171.74it/s, v_num=ld_2, val_loss=0.559, train_loss=0.577]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 155.40it/s, v_num=ld_2, val_loss=0.552, train_loss=0.577]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 154.57it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 29: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 8%|▊ | 1/13 [00:00<00:00, 173.16it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 8%|▊ | 1/13 [00:00<00:00, 168.24it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 15%|█▌ | 2/13 [00:00<00:00, 171.63it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 15%|█▌ | 2/13 [00:00<00:00, 169.75it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 23%|██▎ | 3/13 [00:00<00:00, 177.65it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 23%|██▎ | 3/13 [00:00<00:00, 176.12it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 31%|███ | 4/13 [00:00<00:00, 179.04it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 31%|███ | 4/13 [00:00<00:00, 177.96it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 38%|███▊ | 5/13 [00:00<00:00, 179.38it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 38%|███▊ | 5/13 [00:00<00:00, 178.32it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 46%|████▌ | 6/13 [00:00<00:00, 179.55it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 46%|████▌ | 6/13 [00:00<00:00, 178.82it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 54%|█████▍ | 7/13 [00:00<00:00, 178.57it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 54%|█████▍ | 7/13 [00:00<00:00, 177.88it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 62%|██████▏ | 8/13 [00:00<00:00, 177.41it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 62%|██████▏ | 8/13 [00:00<00:00, 176.80it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 69%|██████▉ | 9/13 [00:00<00:00, 178.07it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 69%|██████▉ | 9/13 [00:00<00:00, 177.59it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 77%|███████▋ | 10/13 [00:00<00:00, 178.03it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 77%|███████▋ | 10/13 [00:00<00:00, 177.63it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 85%|████████▍ | 11/13 [00:00<00:00, 178.42it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 85%|████████▍ | 11/13 [00:00<00:00, 177.95it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 92%|█████████▏| 12/13 [00:00<00:00, 178.08it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 92%|█████████▏| 12/13 [00:00<00:00, 177.71it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 177.82it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 177.49it/s, v_num=ld_2, val_loss=0.552, train_loss=0.572]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 161.07it/s, v_num=ld_2, val_loss=0.554, train_loss=0.572]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 160.37it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 30: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 8%|▊ | 1/13 [00:00<00:00, 183.99it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 8%|▊ | 1/13 [00:00<00:00, 178.57it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 15%|█▌ | 2/13 [00:00<00:00, 175.78it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 15%|█▌ | 2/13 [00:00<00:00, 172.95it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 23%|██▎ | 3/13 [00:00<00:00, 174.04it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 23%|██▎ | 3/13 [00:00<00:00, 172.56it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 31%|███ | 4/13 [00:00<00:00, 175.49it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 31%|███ | 4/13 [00:00<00:00, 174.51it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 38%|███▊ | 5/13 [00:00<00:00, 178.35it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 38%|███▊ | 5/13 [00:00<00:00, 177.49it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 46%|████▌ | 6/13 [00:00<00:00, 180.59it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 46%|████▌ | 6/13 [00:00<00:00, 179.89it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 54%|█████▍ | 7/13 [00:00<00:00, 181.44it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 54%|█████▍ | 7/13 [00:00<00:00, 180.78it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 62%|██████▏ | 8/13 [00:00<00:00, 181.31it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 62%|██████▏ | 8/13 [00:00<00:00, 180.64it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 69%|██████▉ | 9/13 [00:00<00:00, 181.07it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 69%|██████▉ | 9/13 [00:00<00:00, 180.52it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 77%|███████▋ | 10/13 [00:00<00:00, 180.01it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 77%|███████▋ | 10/13 [00:00<00:00, 179.57it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 85%|████████▍ | 11/13 [00:00<00:00, 180.40it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 85%|████████▍ | 11/13 [00:00<00:00, 179.98it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 92%|█████████▏| 12/13 [00:00<00:00, 180.49it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 92%|█████████▏| 12/13 [00:00<00:00, 180.12it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 181.53it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 181.19it/s, v_num=ld_2, val_loss=0.554, train_loss=0.568]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 165.63it/s, v_num=ld_2, val_loss=0.555, train_loss=0.568]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 165.00it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 31: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 0%| | 0/13 [00:00, ?it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 8%|▊ | 1/13 [00:00<00:00, 194.02it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 8%|▊ | 1/13 [00:00<00:00, 189.39it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 15%|█▌ | 2/13 [00:00<00:00, 192.71it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 15%|█▌ | 2/13 [00:00<00:00, 190.26it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 23%|██▎ | 3/13 [00:00<00:00, 190.86it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 23%|██▎ | 3/13 [00:00<00:00, 189.19it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 31%|███ | 4/13 [00:00<00:00, 188.45it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 31%|███ | 4/13 [00:00<00:00, 187.22it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 38%|███▊ | 5/13 [00:00<00:00, 189.01it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 38%|███▊ | 5/13 [00:00<00:00, 188.13it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 46%|████▌ | 6/13 [00:00<00:00, 188.09it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 46%|████▌ | 6/13 [00:00<00:00, 187.34it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 54%|█████▍ | 7/13 [00:00<00:00, 187.96it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 54%|█████▍ | 7/13 [00:00<00:00, 187.37it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 62%|██████▏ | 8/13 [00:00<00:00, 188.96it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 62%|██████▏ | 8/13 [00:00<00:00, 188.34it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 69%|██████▉ | 9/13 [00:00<00:00, 188.69it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 69%|██████▉ | 9/13 [00:00<00:00, 188.20it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 77%|███████▋ | 10/13 [00:00<00:00, 188.43it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 77%|███████▋ | 10/13 [00:00<00:00, 187.99it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 85%|████████▍ | 11/13 [00:00<00:00, 188.98it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 85%|████████▍ | 11/13 [00:00<00:00, 188.56it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 92%|█████████▏| 12/13 [00:00<00:00, 188.71it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 92%|█████████▏| 12/13 [00:00<00:00, 188.30it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 188.20it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 187.86it/s, v_num=ld_2, val_loss=0.555, train_loss=0.565]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 171.77it/s, v_num=ld_2, val_loss=0.546, train_loss=0.565]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 171.08it/s, v_num=ld_2, val_loss=0.546, train_loss=0.565]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 168.69it/s, v_num=ld_2, val_loss=0.546, train_loss=0.565]
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Validate metric ┃ DataLoader 0 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
- │ binary_accuracy_val │ 0.8500000238418579 │
- │ binary_auroc_val │ 0.8892914056777954 │
- │ val_loss │ 0.5867059826850891 │
+ │ binary_accuracy_val │ 0.8999999761581421 │
+ │ binary_auroc_val │ 0.9490163922309875 │
+ │ val_loss │ 0.5460373163223267 │
└───────────────────────────┴───────────────────────────┘
-
Training: | | 0/? [00:00, ?it/s]
Training: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 151.52it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 148.63it/s, v_num=ld_3]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 177.18it/s, v_num=ld_3]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 175.02it/s, v_num=ld_3]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 183.18it/s, v_num=ld_3]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 181.76it/s, v_num=ld_3]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 192.36it/s, v_num=ld_3]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 191.08it/s, v_num=ld_3]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 199.22it/s, v_num=ld_3]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 198.25it/s, v_num=ld_3]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 200.78it/s, v_num=ld_3]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 199.74it/s, v_num=ld_3]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 202.40it/s, v_num=ld_3]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 201.70it/s, v_num=ld_3]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 205.36it/s, v_num=ld_3]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 204.74it/s, v_num=ld_3]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 207.78it/s, v_num=ld_3]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 207.22it/s, v_num=ld_3]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 208.39it/s, v_num=ld_3]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 207.74it/s, v_num=ld_3]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 208.78it/s, v_num=ld_3]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 208.29it/s, v_num=ld_3]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 209.76it/s, v_num=ld_3]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 209.25it/s, v_num=ld_3]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 209.60it/s, v_num=ld_3]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 209.20it/s, v_num=ld_3]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 189.21it/s, v_num=ld_3, val_loss=0.689]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 188.18it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 0: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 215.19it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 209.16it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 220.85it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 216.89it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 215.47it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 213.61it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 220.43it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 218.71it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 219.41it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 218.07it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 220.17it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 218.91it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 219.08it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 218.23it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 221.12it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 220.22it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 220.71it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 220.01it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 220.89it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 220.27it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 221.25it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 220.68it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 221.91it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 221.45it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 223.74it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 223.18it/s, v_num=ld_3, val_loss=0.689, train_loss=0.723]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 201.91it/s, v_num=ld_3, val_loss=0.693, train_loss=0.723]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 200.92it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 216.49it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 210.61it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 223.82it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 220.75it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 226.24it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 223.98it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 227.12it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 225.39it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 229.22it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 227.60it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 223.79it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 222.60it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 222.66it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 221.78it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 222.48it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 221.77it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 223.08it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 222.20it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 221.55it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 220.94it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 222.18it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 221.55it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 223.03it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 222.51it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 223.02it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 222.56it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 201.58it/s, v_num=ld_3, val_loss=0.693, train_loss=0.696]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 200.42it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 205.21it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 199.96it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 208.16it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 205.63it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 215.33it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 213.40it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 219.51it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 218.07it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 221.80it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 220.51it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 220.06it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 219.01it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 218.17it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 217.38it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 218.62it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 217.83it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 219.97it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 219.29it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 219.82it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 219.15it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 217.91it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 217.31it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 216.45it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 215.91it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 216.56it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 216.08it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 186.00it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 185.16it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 205.38it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 199.48it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 205.00it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 202.20it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 207.00it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 204.99it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 212.43it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 210.83it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 212.71it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 211.57it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 213.18it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 212.22it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 213.66it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 212.82it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 214.04it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 213.36it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 214.93it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 214.28it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 214.83it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 214.29it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 215.28it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 214.75it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 215.53it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 215.03it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 216.31it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 215.82it/s, v_num=ld_3, val_loss=0.693, train_loss=0.693]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 189.16it/s, v_num=ld_3, val_loss=0.687, train_loss=0.693]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 188.38it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 232.40it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 225.52it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 232.20it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 228.75it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 235.94it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 233.44it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 232.49it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 230.81it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 231.81it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 230.37it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 230.02it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 228.81it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 226.29it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 225.12it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 221.57it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 220.59it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 218.63it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 217.92it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 217.88it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 217.26it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 217.51it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 216.95it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 217.05it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 216.48it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 217.64it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 217.21it/s, v_num=ld_3, val_loss=0.687, train_loss=0.692]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 197.72it/s, v_num=ld_3, val_loss=0.654, train_loss=0.692]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 196.89it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 230.47it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 223.51it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 229.01it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 225.94it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 228.45it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 226.36it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 225.94it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 224.18it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 224.39it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 222.96it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 223.30it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 222.19it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 222.92it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 221.83it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 219.55it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 218.90it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 218.79it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 217.90it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 216.39it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 215.71it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 216.46it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 215.90it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 216.37it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 215.80it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 217.01it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 216.53it/s, v_num=ld_3, val_loss=0.654, train_loss=0.677]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 196.88it/s, v_num=ld_3, val_loss=0.609, train_loss=0.677]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 195.99it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 224.43it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 217.73it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 228.01it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 224.78it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 229.15it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 227.07it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 225.94it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 224.07it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 220.50it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 219.08it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 216.58it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 215.71it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 215.35it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 214.24it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 213.62it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 212.90it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 213.17it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 212.52it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 214.44it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 213.90it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 214.60it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 213.88it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 214.24it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 213.79it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 216.77it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 216.31it/s, v_num=ld_3, val_loss=0.609, train_loss=0.650]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 193.44it/s, v_num=ld_3, val_loss=0.566, train_loss=0.650]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 192.49it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 214.82it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 207.76it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 210.59it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 207.03it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 204.79it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 202.56it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 206.80it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 205.41it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 206.09it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 204.94it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 206.40it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 205.31it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 203.49it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 202.72it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 205.14it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 204.39it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 204.95it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 204.05it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 204.42it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 203.88it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 205.85it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 205.31it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 207.41it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 206.98it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 209.06it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 208.63it/s, v_num=ld_3, val_loss=0.566, train_loss=0.612]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 189.86it/s, v_num=ld_3, val_loss=0.599, train_loss=0.612]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 189.07it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 229.13it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 222.37it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 230.60it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 227.30it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 231.09it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 228.90it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 230.44it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 228.79it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 227.95it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 226.65it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 227.99it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 226.95it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 226.86it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 225.97it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 226.62it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 225.80it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 226.52it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 225.83it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 224.67it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 223.98it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 223.54it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 222.94it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 222.54it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 221.99it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 221.85it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 221.33it/s, v_num=ld_3, val_loss=0.599, train_loss=0.602]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 200.42it/s, v_num=ld_3, val_loss=0.560, train_loss=0.602]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 199.57it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 222.51it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 215.83it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 224.19it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 220.94it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 223.84it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 221.71it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 222.99it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 221.32it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 223.10it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 221.85it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 223.66it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 222.57it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 223.29it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 222.43it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 222.87it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 222.04it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 219.72it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 218.98it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 219.03it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 218.43it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 218.62it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 217.86it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 215.78it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 215.15it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 214.79it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 214.26it/s, v_num=ld_3, val_loss=0.560, train_loss=0.596]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 193.44it/s, v_num=ld_3, val_loss=0.584, train_loss=0.596]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 192.54it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 229.46it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 222.96it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 219.18it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 216.01it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 214.52it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 212.26it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 212.09it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 210.58it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 212.76it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 211.60it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 212.98it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 211.99it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 213.00it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 212.12it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 212.55it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 211.69it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 211.59it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 210.83it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 210.55it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 209.93it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 210.57it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 210.04it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 210.69it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 210.16it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 210.42it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 209.97it/s, v_num=ld_3, val_loss=0.584, train_loss=0.591]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 189.25it/s, v_num=ld_3, val_loss=0.556, train_loss=0.591]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 188.45it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 226.03it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 219.59it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 229.01it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 225.76it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 227.10it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 224.84it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 222.38it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 220.67it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 218.89it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 217.59it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 218.56it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 217.50it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 218.65it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 217.83it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 218.86it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 218.04it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 218.27it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 217.63it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 219.17it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 218.53it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 218.77it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 218.24it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 218.55it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 218.05it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 217.91it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 217.49it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 196.07it/s, v_num=ld_3, val_loss=0.556, train_loss=0.581]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 195.18it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 221.58it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 215.47it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 220.10it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 216.99it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 216.22it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 214.18it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 214.49it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 212.95it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 213.76it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 212.29it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 209.89it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 208.86it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 208.23it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 207.41it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 210.08it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 209.40it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 211.17it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 210.52it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 211.39it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 210.84it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 211.83it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 211.31it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 210.63it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 210.18it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 211.07it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 210.52it/s, v_num=ld_3, val_loss=0.556, train_loss=0.579]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 190.23it/s, v_num=ld_3, val_loss=0.563, train_loss=0.579]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 189.36it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 215.56it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 209.42it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 211.66it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 208.72it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 212.89it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 210.79it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 211.91it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 210.35it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 211.72it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 210.49it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 211.69it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 210.73it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 211.46it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 210.55it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 211.72it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 211.04it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 212.83it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 212.23it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 213.69it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 213.09it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 213.94it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 213.38it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 213.28it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 212.61it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 211.32it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 210.87it/s, v_num=ld_3, val_loss=0.563, train_loss=0.570]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 191.24it/s, v_num=ld_3, val_loss=0.577, train_loss=0.570]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 190.47it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 220.42it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 213.72it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 218.27it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 214.61it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 215.87it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 213.88it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 216.22it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 214.68it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 217.40it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 216.17it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 215.48it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 214.36it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 214.98it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 214.12it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 214.55it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 213.82it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 213.02it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 212.27it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 212.46it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 211.87it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 212.66it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 212.06it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 212.21it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 211.68it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 211.50it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 211.00it/s, v_num=ld_3, val_loss=0.577, train_loss=0.569]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 190.15it/s, v_num=ld_3, val_loss=0.564, train_loss=0.569]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 189.33it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 203.33it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 196.08it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 198.08it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 195.68it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 205.55it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 203.23it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 201.78it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 200.44it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 203.78it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 202.67it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 206.51it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 205.62it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 206.30it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 205.40it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 205.19it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 204.32it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 203.71it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 203.06it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 203.90it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 203.31it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 204.28it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 203.76it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 204.65it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 204.11it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 204.14it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 203.68it/s, v_num=ld_3, val_loss=0.564, train_loss=0.567]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 185.44it/s, v_num=ld_3, val_loss=0.565, train_loss=0.567]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 184.56it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 221.82it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 215.18it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 211.99it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 209.18it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 212.39it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 209.99it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 207.47it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 205.96it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 206.55it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 205.48it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 207.07it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 206.06it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 205.86it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 205.07it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 205.91it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 205.16it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 207.08it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 206.47it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 206.63it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 206.10it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 207.75it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 207.21it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 206.17it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 205.67it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 206.01it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 205.53it/s, v_num=ld_3, val_loss=0.565, train_loss=0.562]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 178.14it/s, v_num=ld_3, val_loss=0.563, train_loss=0.562]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 177.34it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 218.20it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 211.59it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 221.36it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 218.24it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 219.60it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 217.44it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 216.35it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 214.74it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 216.14it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 214.77it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 211.72it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 210.75it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 209.12it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 208.27it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 209.13it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 208.43it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 209.19it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 208.55it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 209.28it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 208.68it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 209.50it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 209.00it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 209.93it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 209.43it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 210.48it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 210.05it/s, v_num=ld_3, val_loss=0.563, train_loss=0.565]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 190.77it/s, v_num=ld_3, val_loss=0.568, train_loss=0.565]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 189.94it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 221.73it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 215.28it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 225.08it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 222.00it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 225.21it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 223.16it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 221.61it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 219.83it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 216.72it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 215.27it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 211.56it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 210.61it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 211.38it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 210.53it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 212.30it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 211.59it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 212.26it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 211.64it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 212.06it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 211.42it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 210.45it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 209.90it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 209.46it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 209.00it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 210.05it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 209.61it/s, v_num=ld_3, val_loss=0.568, train_loss=0.558]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 183.73it/s, v_num=ld_3, val_loss=0.557, train_loss=0.558]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 182.89it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 211.64it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 205.46it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 208.00it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 204.20it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 202.48it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 200.61it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 202.69it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 201.20it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 200.76it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 199.53it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 201.50it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 200.40it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 197.96it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 197.21it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 198.97it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 198.32it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 199.61it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 199.10it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 200.73it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 200.17it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 200.93it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 200.38it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 201.17it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 200.59it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 201.63it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 201.18it/s, v_num=ld_3, val_loss=0.557, train_loss=0.554]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 183.17it/s, v_num=ld_3, val_loss=0.560, train_loss=0.554]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 182.32it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 206.57it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 200.27it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 211.82it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 208.85it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 214.48it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 212.39it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 211.76it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 210.29it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 213.13it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 212.00it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 212.42it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 211.35it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 209.52it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 208.71it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 209.08it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 208.09it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 205.18it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 204.55it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 205.36it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 204.58it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 202.41it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 201.82it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 201.44it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 200.86it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 200.33it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 199.76it/s, v_num=ld_3, val_loss=0.560, train_loss=0.547]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 175.99it/s, v_num=ld_3, val_loss=0.562, train_loss=0.547]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 175.08it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 176.49it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 171.31it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 185.84it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 182.91it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 182.04it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 180.07it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 183.47it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 182.04it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 183.06it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 181.96it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 182.31it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 181.49it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 185.22it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 184.38it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 185.30it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 184.57it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 185.99it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 185.28it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 185.69it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 185.13it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 187.29it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 186.92it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 189.62it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 189.26it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 191.36it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 190.86it/s, v_num=ld_3, val_loss=0.562, train_loss=0.543]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 174.10it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 173.38it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 208.95it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 201.91it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 209.78it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 206.80it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 211.67it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 209.66it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 210.32it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 208.61it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 208.75it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 207.41it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 203.70it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 202.25it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 203.34it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 202.54it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 204.21it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 203.44it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 203.42it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 202.68it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 202.57it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 201.94it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 201.83it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 201.22it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 199.60it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 198.93it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 199.71it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 199.32it/s, v_num=ld_3, val_loss=0.564, train_loss=0.543]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 151.73it/s, v_num=ld_3, val_loss=0.561, train_loss=0.543]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 150.91it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 187.01it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 181.86it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 190.49it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 187.02it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 182.71it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 180.67it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 185.71it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 184.55it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 187.05it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 186.02it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 188.15it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 186.86it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 187.11it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 186.40it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 187.27it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 186.67it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 187.51it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 186.89it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 186.92it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 186.35it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 186.34it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 185.72it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 186.26it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 185.85it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 186.98it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 186.55it/s, v_num=ld_3, val_loss=0.561, train_loss=0.544]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 169.99it/s, v_num=ld_3, val_loss=0.570, train_loss=0.544]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 169.15it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 187.86it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 182.27it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 194.78it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 192.24it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 193.20it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 191.42it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 191.92it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 190.12it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 189.34it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 188.16it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 187.61it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 186.70it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 188.16it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 187.42it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 188.43it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 187.76it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 187.91it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 187.20it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 188.09it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 187.61it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 188.66it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 188.21it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 189.32it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 188.82it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 190.35it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 189.84it/s, v_num=ld_3, val_loss=0.570, train_loss=0.538]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 168.36it/s, v_num=ld_3, val_loss=0.572, train_loss=0.538]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 167.51it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 194.67it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 189.00it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 195.03it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 191.35it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 196.55it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 194.49it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 194.65it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 193.30it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 197.12it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 195.96it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 194.51it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 193.57it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 193.13it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 192.13it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 192.84it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 192.26it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 195.02it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 194.53it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 196.25it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 195.63it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 194.82it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 194.31it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 195.08it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 194.62it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 195.90it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 195.37it/s, v_num=ld_3, val_loss=0.572, train_loss=0.539]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 172.64it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 171.94it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 8%|▊ | 1/13 [00:00<00:00, 205.50it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 8%|▊ | 1/13 [00:00<00:00, 200.16it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 15%|█▌ | 2/13 [00:00<00:00, 203.00it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 15%|█▌ | 2/13 [00:00<00:00, 200.10it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 23%|██▎ | 3/13 [00:00<00:00, 206.47it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 23%|██▎ | 3/13 [00:00<00:00, 204.64it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 31%|███ | 4/13 [00:00<00:00, 203.75it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 31%|███ | 4/13 [00:00<00:00, 202.13it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 38%|███▊ | 5/13 [00:00<00:00, 202.17it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 38%|███▊ | 5/13 [00:00<00:00, 200.79it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 46%|████▌ | 6/13 [00:00<00:00, 199.51it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 46%|████▌ | 6/13 [00:00<00:00, 198.61it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 54%|█████▍ | 7/13 [00:00<00:00, 199.46it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 54%|█████▍ | 7/13 [00:00<00:00, 198.63it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 62%|██████▏ | 8/13 [00:00<00:00, 198.86it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 62%|██████▏ | 8/13 [00:00<00:00, 197.98it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 69%|██████▉ | 9/13 [00:00<00:00, 198.15it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 69%|██████▉ | 9/13 [00:00<00:00, 197.47it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 77%|███████▋ | 10/13 [00:00<00:00, 196.84it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 77%|███████▋ | 10/13 [00:00<00:00, 196.29it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 85%|████████▍ | 11/13 [00:00<00:00, 195.86it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 85%|████████▍ | 11/13 [00:00<00:00, 195.29it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 92%|█████████▏| 12/13 [00:00<00:00, 196.17it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 92%|█████████▏| 12/13 [00:00<00:00, 195.70it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 195.51it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 195.09it/s, v_num=ld_3, val_loss=0.561, train_loss=0.539]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 177.89it/s, v_num=ld_3, val_loss=0.569, train_loss=0.539]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 177.15it/s, v_num=ld_3, val_loss=0.569, train_loss=0.539]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 174.76it/s, v_num=ld_3, val_loss=0.569, train_loss=0.539]
+
Training: | | 0/? [00:00, ?it/s]
Training: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 159.44it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 156.16it/s, v_num=ld_3]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 172.11it/s, v_num=ld_3]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 170.34it/s, v_num=ld_3]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 173.87it/s, v_num=ld_3]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 172.40it/s, v_num=ld_3]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 180.47it/s, v_num=ld_3]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 179.43it/s, v_num=ld_3]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 184.39it/s, v_num=ld_3]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 183.58it/s, v_num=ld_3]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 186.49it/s, v_num=ld_3]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 185.68it/s, v_num=ld_3]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 189.21it/s, v_num=ld_3]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 188.61it/s, v_num=ld_3]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 189.81it/s, v_num=ld_3]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 189.27it/s, v_num=ld_3]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 190.31it/s, v_num=ld_3]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 189.74it/s, v_num=ld_3]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 191.20it/s, v_num=ld_3]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 190.79it/s, v_num=ld_3]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 192.09it/s, v_num=ld_3]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 191.63it/s, v_num=ld_3]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 190.60it/s, v_num=ld_3]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 190.12it/s, v_num=ld_3]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 190.47it/s, v_num=ld_3]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 190.11it/s, v_num=ld_3]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 170.54it/s, v_num=ld_3, val_loss=0.691]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 169.71it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 0: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 187.97it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 182.27it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 186.06it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 183.63it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 189.12it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 187.63it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 190.02it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 188.64it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 190.02it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 188.97it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 191.02it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 190.22it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 193.95it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 193.31it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 195.25it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 194.62it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 195.97it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 195.34it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 195.01it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 194.50it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 194.96it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 194.53it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 196.29it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 195.91it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 197.81it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 197.44it/s, v_num=ld_3, val_loss=0.691, train_loss=0.733]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 180.65it/s, v_num=ld_3, val_loss=0.676, train_loss=0.733]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 179.56it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 195.19it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 190.00it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 197.57it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 194.91it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 191.44it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 189.72it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 190.12it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 188.84it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 191.04it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 190.21it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 188.52it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 187.64it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 189.49it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 188.82it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 190.24it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 189.55it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 190.84it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 190.32it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 192.96it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 192.43it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 192.65it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 192.21it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 193.45it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 193.00it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 193.22it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 192.86it/s, v_num=ld_3, val_loss=0.676, train_loss=0.692]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 175.28it/s, v_num=ld_3, val_loss=0.625, train_loss=0.692]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 174.51it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 187.79it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 183.21it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 191.56it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 189.32it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 195.00it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 193.10it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 193.12it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 191.75it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 192.29it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 191.36it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 192.44it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 191.61it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 192.52it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 191.84it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 194.30it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 193.70it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 194.59it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 194.06it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 195.39it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 194.87it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 195.75it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 195.30it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 196.91it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 196.52it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 197.56it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 197.09it/s, v_num=ld_3, val_loss=0.625, train_loss=0.655]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 179.26it/s, v_num=ld_3, val_loss=0.584, train_loss=0.655]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 178.36it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 180.02it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 175.71it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 185.77it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 183.08it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 183.72it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 182.36it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 187.31it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 186.23it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 189.65it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 188.71it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 191.35it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 190.59it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 192.63it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 191.97it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 193.58it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 193.00it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 194.13it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 193.63it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 194.43it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 193.96it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 194.86it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 194.44it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 195.31it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 194.88it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 195.62it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 195.25it/s, v_num=ld_3, val_loss=0.584, train_loss=0.598]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 178.17it/s, v_num=ld_3, val_loss=0.579, train_loss=0.598]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 177.46it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 206.10it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 200.44it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 207.40it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 204.69it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 204.95it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 203.24it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 203.86it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 202.56it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 202.00it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 200.83it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 201.35it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 200.55it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 201.02it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 200.30it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 200.93it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 200.29it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 201.75it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 201.18it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 201.26it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 200.74it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 201.07it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 200.58it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 200.81it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 200.39it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 201.20it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 200.83it/s, v_num=ld_3, val_loss=0.579, train_loss=0.582]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 183.02it/s, v_num=ld_3, val_loss=0.573, train_loss=0.582]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 182.27it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 215.10it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 208.99it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 211.89it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 209.05it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 210.09it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 208.28it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 209.00it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 207.65it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 207.73it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 206.63it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 207.10it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 206.27it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 207.02it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 206.24it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 206.02it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 205.35it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 205.43it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 204.85it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 205.20it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 204.67it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 205.39it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 204.93it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 204.72it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 204.29it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 205.05it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 204.64it/s, v_num=ld_3, val_loss=0.573, train_loss=0.572]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 177.63it/s, v_num=ld_3, val_loss=0.586, train_loss=0.572]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 176.88it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 195.92it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 190.04it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 201.43it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 198.89it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 203.13it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 201.25it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 201.46it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 200.16it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 199.17it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 198.13it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 200.03it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 199.23it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 199.43it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 198.71it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 197.91it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 197.24it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 197.92it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 197.40it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 197.79it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 197.31it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 197.79it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 197.17it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 197.46it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 197.03it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 197.68it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 197.31it/s, v_num=ld_3, val_loss=0.586, train_loss=0.569]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 179.21it/s, v_num=ld_3, val_loss=0.611, train_loss=0.569]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 178.37it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 199.71it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 194.50it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 199.12it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 196.48it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 200.40it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 198.78it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 200.48it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 199.17it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 200.17it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 199.19it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 200.18it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 199.37it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 199.94it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 199.23it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 199.88it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 199.27it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 200.19it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 199.63it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 199.43it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 198.94it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 199.97it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 199.53it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 199.74it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 199.32it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 200.08it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 199.71it/s, v_num=ld_3, val_loss=0.611, train_loss=0.600]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 179.36it/s, v_num=ld_3, val_loss=0.575, train_loss=0.600]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 178.65it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 202.02it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 196.89it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 203.77it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 201.10it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 201.33it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 199.72it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 201.51it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 200.22it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 202.24it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 201.27it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 202.77it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 201.86it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 201.07it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 200.22it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 199.83it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 199.09it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 197.72it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 197.13it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 197.14it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 196.65it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 196.14it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 195.59it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 195.56it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 195.17it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 194.95it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 194.55it/s, v_num=ld_3, val_loss=0.575, train_loss=0.575]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 176.36it/s, v_num=ld_3, val_loss=0.573, train_loss=0.575]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 175.53it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 186.95it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 181.98it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 193.98it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 191.40it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 186.67it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 184.75it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 183.81it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 182.41it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 185.15it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 184.14it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 182.93it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 181.92it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 182.55it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 181.56it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 182.55it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 181.97it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 181.56it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 180.88it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 180.62it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 180.15it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 181.61it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 181.21it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 181.93it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 181.57it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 182.75it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 182.36it/s, v_num=ld_3, val_loss=0.573, train_loss=0.566]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 166.39it/s, v_num=ld_3, val_loss=0.574, train_loss=0.566]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 165.72it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 195.05it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 189.25it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 199.13it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 196.43it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 201.00it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 199.27it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 199.98it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 198.76it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 199.97it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 198.91it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 199.44it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 198.62it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 199.53it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 198.83it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 200.24it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 199.65it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 200.75it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 200.20it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 200.22it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 199.73it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 200.45it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 199.97it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 199.83it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 199.42it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 199.88it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 199.50it/s, v_num=ld_3, val_loss=0.574, train_loss=0.563]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 181.33it/s, v_num=ld_3, val_loss=0.565, train_loss=0.563]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 180.60it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 202.35it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 197.01it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 197.88it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 195.48it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 197.70it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 196.11it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 197.66it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 196.37it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 197.25it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 196.27it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 196.27it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 195.47it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 195.40it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 194.76it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 195.42it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 194.86it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 195.37it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 194.83it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 195.72it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 195.22it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 196.13it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 195.70it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 195.53it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 195.14it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 195.80it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 195.44it/s, v_num=ld_3, val_loss=0.565, train_loss=0.558]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 178.03it/s, v_num=ld_3, val_loss=0.581, train_loss=0.558]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 177.33it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 207.55it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 201.78it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 208.53it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 205.85it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 206.46it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 204.65it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 204.94it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 203.67it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 203.89it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 202.88it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 203.49it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 202.66it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 203.18it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 202.44it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 203.21it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 202.59it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 202.41it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 201.75it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 201.65it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 201.13it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 201.26it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 200.80it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 201.17it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 200.77it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 200.97it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 200.56it/s, v_num=ld_3, val_loss=0.581, train_loss=0.554]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 182.37it/s, v_num=ld_3, val_loss=0.572, train_loss=0.554]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 181.58it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 199.68it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 194.35it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 201.57it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 198.90it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 197.56it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 195.67it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 196.87it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 195.62it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 195.39it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 194.20it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 185.76it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 184.69it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 182.79it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 182.02it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 182.46it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 181.90it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 181.26it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 180.74it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 181.01it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 180.51it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 181.47it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 181.07it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 181.39it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 181.01it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 181.98it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 181.59it/s, v_num=ld_3, val_loss=0.572, train_loss=0.555]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 166.46it/s, v_num=ld_3, val_loss=0.568, train_loss=0.555]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 165.80it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 201.13it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 195.54it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 197.92it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 195.42it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 197.89it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 196.19it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 196.64it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 195.49it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 196.00it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 195.07it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 196.72it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 195.94it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 195.56it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 194.86it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 195.28it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 194.70it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 195.42it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 194.88it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 195.98it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 195.50it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 196.07it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 195.63it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 195.72it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 195.30it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 196.28it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 195.90it/s, v_num=ld_3, val_loss=0.568, train_loss=0.554]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 171.51it/s, v_num=ld_3, val_loss=0.577, train_loss=0.554]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 170.86it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 202.84it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 197.58it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 203.58it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 200.94it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 202.10it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 200.27it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 198.60it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 197.30it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 197.44it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 196.52it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 197.90it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 197.08it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 196.68it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 196.00it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 196.29it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 195.67it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 196.25it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 195.69it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 195.87it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 195.41it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 196.01it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 195.57it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 195.83it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 195.39it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 196.02it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 195.66it/s, v_num=ld_3, val_loss=0.577, train_loss=0.551]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 178.16it/s, v_num=ld_3, val_loss=0.566, train_loss=0.551]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 177.42it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 202.39it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 196.93it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 202.84it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 200.28it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 199.87it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 198.05it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 195.92it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 194.62it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 192.26it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 191.23it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 189.73it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 188.90it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 189.10it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 188.41it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 189.14it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 188.57it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 189.49it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 188.98it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 190.11it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 189.63it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 190.31it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 189.90it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 190.52it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 190.12it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 191.19it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 190.83it/s, v_num=ld_3, val_loss=0.566, train_loss=0.549]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 174.22it/s, v_num=ld_3, val_loss=0.585, train_loss=0.549]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 173.52it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 204.91it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 199.64it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 202.04it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 199.60it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 199.84it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 198.19it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 198.83it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 197.64it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 197.91it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 196.90it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 198.14it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 197.32it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 198.63it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 197.86it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 195.07it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 194.41it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 193.87it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 193.30it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 193.33it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 192.86it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 193.26it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 192.80it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 193.60it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 193.22it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 194.24it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 193.88it/s, v_num=ld_3, val_loss=0.585, train_loss=0.550]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 160.70it/s, v_num=ld_3, val_loss=0.567, train_loss=0.550]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 159.97it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 186.21it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 181.39it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 187.26it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 184.84it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 186.14it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 184.70it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 187.42it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 186.30it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 187.08it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 186.20it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 187.43it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 186.69it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 187.08it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 186.38it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 185.99it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 185.40it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 185.15it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 184.63it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 185.01it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 184.58it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 185.55it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 185.08it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 184.99it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 184.61it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 184.58it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 184.21it/s, v_num=ld_3, val_loss=0.567, train_loss=0.548]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 167.82it/s, v_num=ld_3, val_loss=0.575, train_loss=0.548]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 167.18it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 197.40it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 192.32it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 196.12it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 193.65it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 194.48it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 192.74it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 191.21it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 190.01it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 190.94it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 190.03it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 188.92it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 188.15it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 190.14it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 189.35it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 190.40it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 189.81it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 188.52it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 187.99it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 189.16it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 188.74it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 189.15it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 188.69it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 188.88it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 188.45it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 189.53it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 189.16it/s, v_num=ld_3, val_loss=0.575, train_loss=0.551]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 173.16it/s, v_num=ld_3, val_loss=0.594, train_loss=0.551]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 172.48it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 182.91it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 178.09it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 186.57it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 184.22it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 187.65it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 185.90it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 182.23it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 181.00it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 181.28it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 180.36it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 181.91it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 180.99it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 183.06it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 182.44it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 184.76it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 184.20it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 184.82it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 184.36it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 185.13it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 184.69it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 185.68it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 185.29it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 185.50it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 185.05it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 185.26it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 184.86it/s, v_num=ld_3, val_loss=0.594, train_loss=0.557]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 164.56it/s, v_num=ld_3, val_loss=0.575, train_loss=0.557]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 163.90it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 191.64it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 186.54it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 194.88it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 192.38it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 194.08it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 192.45it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 192.27it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 191.08it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 191.54it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 190.59it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 190.97it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 190.20it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 190.61it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 189.94it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 190.20it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 189.61it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 190.32it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 189.83it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 190.26it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 189.82it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 189.56it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 189.13it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 189.42it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 188.96it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 190.08it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 189.71it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 159.50it/s, v_num=ld_3, val_loss=0.575, train_loss=0.549]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 158.83it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 188.83it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 183.22it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 181.59it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 179.36it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 178.99it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 177.13it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 171.98it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 170.74it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 172.86it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 172.00it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 175.01it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 174.31it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 175.01it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 174.24it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 173.81it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 173.25it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 174.62it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 174.15it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 175.38it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 175.00it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 176.87it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 176.51it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 177.68it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 177.33it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 178.53it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 178.21it/s, v_num=ld_3, val_loss=0.575, train_loss=0.547]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 162.96it/s, v_num=ld_3, val_loss=0.571, train_loss=0.547]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 162.38it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 191.68it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 186.29it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 188.53it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 185.84it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 186.23it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 184.69it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 183.59it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 182.43it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 182.21it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 181.32it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 183.21it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 182.43it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 183.63it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 183.01it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 184.33it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 183.80it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 185.12it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 184.59it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 185.81it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 185.37it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 186.50it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 186.08it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 187.06it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 186.69it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 187.70it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 187.37it/s, v_num=ld_3, val_loss=0.571, train_loss=0.545]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 171.18it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 170.52it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 200.36it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 195.12it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 193.74it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 191.46it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 192.42it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 190.83it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 193.47it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 192.29it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 193.15it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 192.20it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 190.94it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 189.96it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 188.33it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 187.48it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 184.90it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 184.27it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 184.97it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 184.38it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 182.27it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 181.75it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 181.33it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 180.90it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 182.09it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 181.74it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 182.67it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 182.27it/s, v_num=ld_3, val_loss=0.579, train_loss=0.545]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 164.24it/s, v_num=ld_3, val_loss=0.576, train_loss=0.545]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 163.50it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 177.56it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 173.56it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 183.44it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 181.39it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 184.34it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 182.66it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 182.98it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 181.92it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 180.07it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 179.21it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 180.42it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 179.74it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 181.13it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 180.55it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 182.89it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 182.31it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 183.52it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 183.07it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 184.20it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 183.75it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 183.62it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 183.13it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 182.16it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 181.64it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 181.94it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 181.61it/s, v_num=ld_3, val_loss=0.576, train_loss=0.543]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 165.92it/s, v_num=ld_3, val_loss=0.575, train_loss=0.543]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 165.21it/s, v_num=ld_3, val_loss=0.575, train_loss=0.542]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 163.14it/s, v_num=ld_3, val_loss=0.575, train_loss=0.542]
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Validate metric ┃ DataLoader 0 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
- │ binary_accuracy_val │ 0.8199999928474426 │
- │ binary_auroc_val │ 0.900563657283783 │
- │ val_loss │ 0.5687531232833862 │
+ │ binary_accuracy_val │ 0.8399999737739563 │
+ │ binary_auroc_val │ 0.8928285241127014 │
+ │ val_loss │ 0.5749369263648987 │
└───────────────────────────┴───────────────────────────┘
-
Training: | | 0/? [00:00, ?it/s]
Training: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 150.51it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 147.28it/s, v_num=ld_4]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 174.17it/s, v_num=ld_4]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 172.58it/s, v_num=ld_4]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 184.93it/s, v_num=ld_4]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 183.57it/s, v_num=ld_4]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 195.52it/s, v_num=ld_4]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 194.38it/s, v_num=ld_4]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 199.67it/s, v_num=ld_4]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 198.59it/s, v_num=ld_4]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 202.53it/s, v_num=ld_4]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 201.61it/s, v_num=ld_4]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 204.46it/s, v_num=ld_4]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 203.76it/s, v_num=ld_4]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 206.18it/s, v_num=ld_4]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 205.43it/s, v_num=ld_4]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 206.34it/s, v_num=ld_4]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 205.74it/s, v_num=ld_4]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 207.26it/s, v_num=ld_4]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 206.74it/s, v_num=ld_4]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 207.37it/s, v_num=ld_4]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 206.82it/s, v_num=ld_4]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 208.47it/s, v_num=ld_4]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 208.11it/s, v_num=ld_4]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 211.40it/s, v_num=ld_4]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 210.90it/s, v_num=ld_4]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 188.36it/s, v_num=ld_4, val_loss=0.721]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 187.34it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 0: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 220.40it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 213.44it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 222.49it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 219.10it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 225.19it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 223.08it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 224.52it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 222.88it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 225.76it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 224.48it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 227.58it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 226.47it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 227.95it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 227.04it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 228.39it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 227.48it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 227.72it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 226.92it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 227.63it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 226.90it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 226.93it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 226.34it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 227.34it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 226.80it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 228.58it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 228.09it/s, v_num=ld_4, val_loss=0.721, train_loss=0.714]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 206.40it/s, v_num=ld_4, val_loss=0.692, train_loss=0.714]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 205.47it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 227.38it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 221.00it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 228.08it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 224.75it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 230.31it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 228.05it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 229.68it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 228.18it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 228.71it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 227.42it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 229.72it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 228.61it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 226.19it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 225.25it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 221.60it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 220.73it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 222.30it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 221.65it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 222.36it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 221.78it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 223.41it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 222.70it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 221.83it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 221.24it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 222.39it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 221.90it/s, v_num=ld_4, val_loss=0.692, train_loss=0.693]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 199.65it/s, v_num=ld_4, val_loss=0.677, train_loss=0.693]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 198.52it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 208.24it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 200.79it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 208.98it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 205.93it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 210.16it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 208.05it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 213.80it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 212.24it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 215.21it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 214.04it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 217.48it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 216.45it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 218.39it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 217.50it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 219.16it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 218.29it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 217.85it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 217.07it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 216.73it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 216.16it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 216.28it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 215.66it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 215.82it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 215.31it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 216.71it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 216.23it/s, v_num=ld_4, val_loss=0.677, train_loss=0.679]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 197.02it/s, v_num=ld_4, val_loss=0.651, train_loss=0.679]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 196.13it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 238.11it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 231.12it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 235.52it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 231.81it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 229.92it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 227.33it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 226.09it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 224.44it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 225.47it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 224.23it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 224.60it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 223.57it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 224.19it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 223.32it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 223.66it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 222.84it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 223.73it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 223.04it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 224.68it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 224.06it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 224.14it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 223.52it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 224.21it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 223.66it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 224.27it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 223.77it/s, v_num=ld_4, val_loss=0.651, train_loss=0.644]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 203.04it/s, v_num=ld_4, val_loss=0.631, train_loss=0.644]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 202.11it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 232.87it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 225.73it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 234.30it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 230.94it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 238.60it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 236.24it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 236.95it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 235.21it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 236.04it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 234.60it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 234.87it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 233.74it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 231.80it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 230.70it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 226.27it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 225.36it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 225.11it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 224.20it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 221.60it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 220.90it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 220.74it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 220.14it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 220.75it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 220.15it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 220.21it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 219.70it/s, v_num=ld_4, val_loss=0.631, train_loss=0.595]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 199.27it/s, v_num=ld_4, val_loss=0.617, train_loss=0.595]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 198.31it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 226.69it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 219.44it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 226.80it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 223.54it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 227.17it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 224.82it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 222.69it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 220.94it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 219.59it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 218.32it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 218.39it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 217.37it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 218.15it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 217.25it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 217.82it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 217.05it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 218.16it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 217.52it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 219.31it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 218.75it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 219.69it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 219.09it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 220.79it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 220.34it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 222.25it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 221.81it/s, v_num=ld_4, val_loss=0.617, train_loss=0.598]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 195.24it/s, v_num=ld_4, val_loss=0.604, train_loss=0.598]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 194.39it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 236.30it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 229.36it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 237.11it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 233.75it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 241.99it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 239.66it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 238.75it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 236.99it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 237.88it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 236.31it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 235.57it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 234.41it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 233.86it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 232.92it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 232.05it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 231.19it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 231.74it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 230.99it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 230.81it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 230.12it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 230.05it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 229.43it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 228.14it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 227.44it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 226.80it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 226.34it/s, v_num=ld_4, val_loss=0.604, train_loss=0.579]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 204.15it/s, v_num=ld_4, val_loss=0.606, train_loss=0.579]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 203.18it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 225.06it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 217.34it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 223.09it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 220.02it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 224.89it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 222.54it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 222.54it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 220.29it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 221.66it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 220.46it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 223.20it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 222.12it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 221.59it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 220.54it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 220.88it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 219.84it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 219.64it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 218.91it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 219.38it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 218.73it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 217.53it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 216.80it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 214.12it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 213.58it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 214.97it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 214.51it/s, v_num=ld_4, val_loss=0.606, train_loss=0.566]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 194.23it/s, v_num=ld_4, val_loss=0.620, train_loss=0.566]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 193.23it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 198.11it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 191.87it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 199.79it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 196.78it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 202.59it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 200.68it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 198.60it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 197.08it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 200.47it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 199.44it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 201.84it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 200.90it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 201.50it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 200.57it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 203.73it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 203.03it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 204.77it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 204.11it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 204.60it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 203.96it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 203.31it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 202.65it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 203.94it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 203.48it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 205.56it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 205.14it/s, v_num=ld_4, val_loss=0.620, train_loss=0.557]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 186.50it/s, v_num=ld_4, val_loss=0.616, train_loss=0.557]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 185.66it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 222.83it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 216.13it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 214.14it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 210.53it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 216.27it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 213.68it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 212.61it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 210.95it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 213.18it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 211.77it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 210.71it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 209.22it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 208.14it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 207.21it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 209.29it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 208.52it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 209.31it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 208.67it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 208.41it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 207.73it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 209.13it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 208.63it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 206.73it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 206.13it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 206.70it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 206.19it/s, v_num=ld_4, val_loss=0.616, train_loss=0.552]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 188.06it/s, v_num=ld_4, val_loss=0.611, train_loss=0.552]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 187.27it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 212.13it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 205.37it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 200.82it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 197.72it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 201.26it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 199.44it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 205.59it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 204.18it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 206.33it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 205.11it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 204.23it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 203.21it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 205.24it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 204.34it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 206.15it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 205.44it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 205.99it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 205.17it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 205.48it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 204.85it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 206.10it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 205.61it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 206.50it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 206.03it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 206.63it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 206.14it/s, v_num=ld_4, val_loss=0.611, train_loss=0.551]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 186.87it/s, v_num=ld_4, val_loss=0.613, train_loss=0.551]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 186.05it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 216.12it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 207.69it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 213.17it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 210.13it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 217.34it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 214.90it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 214.08it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 212.46it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 210.48it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 209.06it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 208.60it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 207.63it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 207.62it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 206.76it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 207.82it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 207.13it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 208.37it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 207.73it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 207.94it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 207.31it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 206.77it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 206.23it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 207.42it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 206.99it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 208.10it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 207.67it/s, v_num=ld_4, val_loss=0.613, train_loss=0.549]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 188.99it/s, v_num=ld_4, val_loss=0.606, train_loss=0.549]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 188.16it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 226.45it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 219.39it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 225.17it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 221.78it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 220.25it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 218.47it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 218.80it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 217.14it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 218.74it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 217.42it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 217.43it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 216.41it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 216.55it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 215.75it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 217.97it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 217.20it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 218.49it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 217.82it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 219.07it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 218.47it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 218.58it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 217.97it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 217.19it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 216.65it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 217.73it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 217.17it/s, v_num=ld_4, val_loss=0.606, train_loss=0.545]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 192.72it/s, v_num=ld_4, val_loss=0.604, train_loss=0.545]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 191.62it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 186.99it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 181.75it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 195.85it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 192.99it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 199.46it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 197.60it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 204.21it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 202.88it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 205.06it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 203.89it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 206.43it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 205.46it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 207.62it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 206.70it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 207.31it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 206.63it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 208.50it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 207.88it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 208.70it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 208.16it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 209.43it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 208.94it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 210.39it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 209.90it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 210.34it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 209.90it/s, v_num=ld_4, val_loss=0.604, train_loss=0.542]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 191.18it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 190.14it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 210.57it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 204.58it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 218.48it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 215.66it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 214.85it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 212.37it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 209.47it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 207.94it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 208.02it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 206.64it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 205.18it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 203.98it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 204.53it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 203.63it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 206.73it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 205.96it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 207.79it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 207.12it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 206.15it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 205.52it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 205.26it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 204.76it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 205.57it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 205.11it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 206.46it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 205.94it/s, v_num=ld_4, val_loss=0.613, train_loss=0.539]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 182.34it/s, v_num=ld_4, val_loss=0.595, train_loss=0.539]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 181.59it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 214.36it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 206.99it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 215.93it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 212.92it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 214.16it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 211.98it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 213.67it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 212.14it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 214.45it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 213.26it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 214.32it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 213.35it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 214.64it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 213.77it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 214.42it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 213.65it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 212.48it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 211.77it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 212.49it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 211.93it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 213.56it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 213.05it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 214.94it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 214.50it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 216.51it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 216.04it/s, v_num=ld_4, val_loss=0.595, train_loss=0.544]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 196.08it/s, v_num=ld_4, val_loss=0.613, train_loss=0.544]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 195.14it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 222.17it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 216.02it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 220.48it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 217.43it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 221.80it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 219.84it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 220.18it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 218.63it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 220.43it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 219.00it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 215.91it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 214.66it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 212.57it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 211.65it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 210.34it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 209.30it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 208.89it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 207.97it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 208.06it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 207.37it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 207.23it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 206.51it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 207.02it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 206.57it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 208.59it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 208.13it/s, v_num=ld_4, val_loss=0.613, train_loss=0.542]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 187.00it/s, v_num=ld_4, val_loss=0.609, train_loss=0.542]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 185.72it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 185.98it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 180.80it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 192.38it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 189.57it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 194.58it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 192.47it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 193.13it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 191.54it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 192.85it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 191.77it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 193.87it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 192.68it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 192.32it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 191.42it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 192.85it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 192.10it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 193.37it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 192.74it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 193.24it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 192.69it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 194.72it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 194.25it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 194.77it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 194.21it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 195.22it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 194.73it/s, v_num=ld_4, val_loss=0.609, train_loss=0.536]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 172.45it/s, v_num=ld_4, val_loss=0.615, train_loss=0.536]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 171.74it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 194.37it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 187.00it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 196.31it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 193.67it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 203.51it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 201.74it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 208.41it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 207.08it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 204.78it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 203.32it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 202.16it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 201.14it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 200.34it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 199.56it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 199.01it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 198.12it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 196.26it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 195.54it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 194.25it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 193.61it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 193.17it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 192.65it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 193.59it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 193.06it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 192.95it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 192.51it/s, v_num=ld_4, val_loss=0.615, train_loss=0.533]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 172.13it/s, v_num=ld_4, val_loss=0.602, train_loss=0.533]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 171.14it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 190.67it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 185.25it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 201.21it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 198.63it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 203.16it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 200.94it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 205.09it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 203.76it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 206.76it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 205.64it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 207.41it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 206.21it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 205.06it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 204.04it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 205.20it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 204.50it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 204.17it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 203.40it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 202.65it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 202.10it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 203.90it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 203.37it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 204.33it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 203.88it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 205.38it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 204.92it/s, v_num=ld_4, val_loss=0.602, train_loss=0.535]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 184.95it/s, v_num=ld_4, val_loss=0.615, train_loss=0.535]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 184.17it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 226.24it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 219.54it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 220.34it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 217.37it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 221.73it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 219.73it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 223.71it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 222.20it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 222.98it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 221.73it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 220.27it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 219.24it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 219.61it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 218.73it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 216.81it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 215.87it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 214.38it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 213.72it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 213.23it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 212.64it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 211.49it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 210.85it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 209.99it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 209.44it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 209.48it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 209.06it/s, v_num=ld_4, val_loss=0.615, train_loss=0.542]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 189.81it/s, v_num=ld_4, val_loss=0.612, train_loss=0.542]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 188.94it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 218.83it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 212.87it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 222.88it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 219.80it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 223.33it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 221.23it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 219.17it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 217.13it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 214.84it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 213.61it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 214.07it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 213.13it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 214.95it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 214.08it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 212.54it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 211.62it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 210.57it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 209.95it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 210.46it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 209.85it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 209.59it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 208.91it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 208.01it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 207.51it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 208.54it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 208.12it/s, v_num=ld_4, val_loss=0.612, train_loss=0.545]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 188.49it/s, v_num=ld_4, val_loss=0.615, train_loss=0.545]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 187.60it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 202.76it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 8%|▊ | 1/13 [00:00<00:00, 197.20it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 210.80it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 15%|█▌ | 2/13 [00:00<00:00, 207.94it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 214.50it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 23%|██▎ | 3/13 [00:00<00:00, 211.52it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 211.86it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 31%|███ | 4/13 [00:00<00:00, 210.45it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 212.74it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 38%|███▊ | 5/13 [00:00<00:00, 211.43it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 208.81it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 46%|████▌ | 6/13 [00:00<00:00, 207.81it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 206.73it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 54%|█████▍ | 7/13 [00:00<00:00, 205.76it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 206.77it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 62%|██████▏ | 8/13 [00:00<00:00, 206.07it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 206.84it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 69%|██████▉ | 9/13 [00:00<00:00, 206.21it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 206.46it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 77%|███████▋ | 10/13 [00:00<00:00, 205.94it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 206.56it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 85%|████████▍ | 11/13 [00:00<00:00, 206.05it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 207.31it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 92%|█████████▏| 12/13 [00:00<00:00, 206.85it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 207.23it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 206.80it/s, v_num=ld_4, val_loss=0.615, train_loss=0.539]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 187.63it/s, v_num=ld_4, val_loss=0.601, train_loss=0.539]
Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 186.86it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 23: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 193.90it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 8%|▊ | 1/13 [00:00<00:00, 188.32it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 196.11it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 15%|█▌ | 2/13 [00:00<00:00, 193.29it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 197.37it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 23%|██▎ | 3/13 [00:00<00:00, 195.81it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 200.76it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 31%|███ | 4/13 [00:00<00:00, 199.33it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 201.00it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 38%|███▊ | 5/13 [00:00<00:00, 199.82it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 201.00it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 46%|████▌ | 6/13 [00:00<00:00, 200.01it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 200.49it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 54%|█████▍ | 7/13 [00:00<00:00, 199.74it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 199.36it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 62%|██████▏ | 8/13 [00:00<00:00, 198.60it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 198.80it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 69%|██████▉ | 9/13 [00:00<00:00, 198.18it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 198.67it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 77%|███████▋ | 10/13 [00:00<00:00, 198.15it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 199.61it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 85%|████████▍ | 11/13 [00:00<00:00, 199.19it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 200.88it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 92%|█████████▏| 12/13 [00:00<00:00, 200.41it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 201.88it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 201.46it/s, v_num=ld_4, val_loss=0.601, train_loss=0.532]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 183.21it/s, v_num=ld_4, val_loss=0.591, train_loss=0.532]
Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 182.43it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 24: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 218.92it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 8%|▊ | 1/13 [00:00<00:00, 212.18it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 211.10it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 15%|█▌ | 2/13 [00:00<00:00, 208.25it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 206.36it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 23%|██▎ | 3/13 [00:00<00:00, 204.18it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 201.18it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 31%|███ | 4/13 [00:00<00:00, 199.83it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 203.40it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 38%|███▊ | 5/13 [00:00<00:00, 202.32it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 202.82it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 46%|████▌ | 6/13 [00:00<00:00, 201.80it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 202.58it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 54%|█████▍ | 7/13 [00:00<00:00, 201.80it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 202.02it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 62%|██████▏ | 8/13 [00:00<00:00, 201.26it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 201.19it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 69%|██████▉ | 9/13 [00:00<00:00, 200.51it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 200.88it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 77%|███████▋ | 10/13 [00:00<00:00, 200.35it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 201.51it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 85%|████████▍ | 11/13 [00:00<00:00, 200.97it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 201.60it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 92%|█████████▏| 12/13 [00:00<00:00, 200.99it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 201.64it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 201.17it/s, v_num=ld_4, val_loss=0.591, train_loss=0.534]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 173.08it/s, v_num=ld_4, val_loss=0.595, train_loss=0.534]
Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 172.16it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 25: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 190.77it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 8%|▊ | 1/13 [00:00<00:00, 185.66it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 186.58it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 15%|█▌ | 2/13 [00:00<00:00, 182.87it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 185.33it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 23%|██▎ | 3/13 [00:00<00:00, 183.25it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 186.79it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 31%|███ | 4/13 [00:00<00:00, 185.56it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 190.53it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 38%|███▊ | 5/13 [00:00<00:00, 189.37it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 190.92it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 46%|████▌ | 6/13 [00:00<00:00, 190.04it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 189.74it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 54%|█████▍ | 7/13 [00:00<00:00, 188.82it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 189.94it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 62%|██████▏ | 8/13 [00:00<00:00, 189.17it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 189.63it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 69%|██████▉ | 9/13 [00:00<00:00, 189.03it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 189.86it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 77%|███████▋ | 10/13 [00:00<00:00, 189.39it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 191.22it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 85%|████████▍ | 11/13 [00:00<00:00, 190.55it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 189.91it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 92%|█████████▏| 12/13 [00:00<00:00, 189.40it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 190.56it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 190.08it/s, v_num=ld_4, val_loss=0.595, train_loss=0.535]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 160.45it/s, v_num=ld_4, val_loss=0.610, train_loss=0.535]
Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 159.60it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 26: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 8%|▊ | 1/13 [00:00<00:00, 184.41it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 8%|▊ | 1/13 [00:00<00:00, 179.24it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 15%|█▌ | 2/13 [00:00<00:00, 196.92it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 15%|█▌ | 2/13 [00:00<00:00, 194.40it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 23%|██▎ | 3/13 [00:00<00:00, 196.27it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 23%|██▎ | 3/13 [00:00<00:00, 194.35it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 31%|███ | 4/13 [00:00<00:00, 192.14it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 31%|███ | 4/13 [00:00<00:00, 190.62it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 38%|███▊ | 5/13 [00:00<00:00, 194.68it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 38%|███▊ | 5/13 [00:00<00:00, 193.51it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 46%|████▌ | 6/13 [00:00<00:00, 197.28it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 46%|████▌ | 6/13 [00:00<00:00, 196.44it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 54%|█████▍ | 7/13 [00:00<00:00, 199.91it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 54%|█████▍ | 7/13 [00:00<00:00, 199.10it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 62%|██████▏ | 8/13 [00:00<00:00, 200.31it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 62%|██████▏ | 8/13 [00:00<00:00, 199.56it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 69%|██████▉ | 9/13 [00:00<00:00, 199.90it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 69%|██████▉ | 9/13 [00:00<00:00, 199.25it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 77%|███████▋ | 10/13 [00:00<00:00, 199.92it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 77%|███████▋ | 10/13 [00:00<00:00, 199.30it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 85%|████████▍ | 11/13 [00:00<00:00, 199.43it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 85%|████████▍ | 11/13 [00:00<00:00, 198.91it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 92%|█████████▏| 12/13 [00:00<00:00, 200.21it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 92%|█████████▏| 12/13 [00:00<00:00, 199.77it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 200.76it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 200.31it/s, v_num=ld_4, val_loss=0.610, train_loss=0.530]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 181.94it/s, v_num=ld_4, val_loss=0.615, train_loss=0.530]
Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 181.15it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 27: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 8%|▊ | 1/13 [00:00<00:00, 217.97it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 8%|▊ | 1/13 [00:00<00:00, 211.36it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 15%|█▌ | 2/13 [00:00<00:00, 218.03it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 15%|█▌ | 2/13 [00:00<00:00, 214.57it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 23%|██▎ | 3/13 [00:00<00:00, 209.37it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 23%|██▎ | 3/13 [00:00<00:00, 207.43it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 31%|███ | 4/13 [00:00<00:00, 208.01it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 31%|███ | 4/13 [00:00<00:00, 206.42it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 38%|███▊ | 5/13 [00:00<00:00, 207.41it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 38%|███▊ | 5/13 [00:00<00:00, 206.28it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 46%|████▌ | 6/13 [00:00<00:00, 205.08it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 46%|████▌ | 6/13 [00:00<00:00, 204.08it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 54%|█████▍ | 7/13 [00:00<00:00, 205.51it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 54%|█████▍ | 7/13 [00:00<00:00, 204.65it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 62%|██████▏ | 8/13 [00:00<00:00, 205.58it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 62%|██████▏ | 8/13 [00:00<00:00, 204.69it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 69%|██████▉ | 9/13 [00:00<00:00, 203.98it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 69%|██████▉ | 9/13 [00:00<00:00, 203.33it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 77%|███████▋ | 10/13 [00:00<00:00, 202.42it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 77%|███████▋ | 10/13 [00:00<00:00, 201.88it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 85%|████████▍ | 11/13 [00:00<00:00, 202.20it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 85%|████████▍ | 11/13 [00:00<00:00, 201.71it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 92%|█████████▏| 12/13 [00:00<00:00, 201.55it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 92%|█████████▏| 12/13 [00:00<00:00, 201.04it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 200.75it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 200.35it/s, v_num=ld_4, val_loss=0.615, train_loss=0.528]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 149.46it/s, v_num=ld_4, val_loss=0.603, train_loss=0.528]
Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 148.74it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 28: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 8%|▊ | 1/13 [00:00<00:00, 194.11it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 8%|▊ | 1/13 [00:00<00:00, 188.08it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 15%|█▌ | 2/13 [00:00<00:00, 203.15it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 15%|█▌ | 2/13 [00:00<00:00, 200.38it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 23%|██▎ | 3/13 [00:00<00:00, 201.82it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 23%|██▎ | 3/13 [00:00<00:00, 199.47it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 31%|███ | 4/13 [00:00<00:00, 194.37it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 31%|███ | 4/13 [00:00<00:00, 192.61it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 38%|███▊ | 5/13 [00:00<00:00, 194.52it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 38%|███▊ | 5/13 [00:00<00:00, 193.36it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 46%|████▌ | 6/13 [00:00<00:00, 195.80it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 46%|████▌ | 6/13 [00:00<00:00, 194.89it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 54%|█████▍ | 7/13 [00:00<00:00, 197.13it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 54%|█████▍ | 7/13 [00:00<00:00, 196.38it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 62%|██████▏ | 8/13 [00:00<00:00, 195.84it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 62%|██████▏ | 8/13 [00:00<00:00, 195.06it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 69%|██████▉ | 9/13 [00:00<00:00, 195.55it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 69%|██████▉ | 9/13 [00:00<00:00, 194.88it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 77%|███████▋ | 10/13 [00:00<00:00, 195.09it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 77%|███████▋ | 10/13 [00:00<00:00, 194.62it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 85%|████████▍ | 11/13 [00:00<00:00, 194.75it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 85%|████████▍ | 11/13 [00:00<00:00, 194.20it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 92%|█████████▏| 12/13 [00:00<00:00, 195.61it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 92%|█████████▏| 12/13 [00:00<00:00, 195.21it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 196.92it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 196.53it/s, v_num=ld_4, val_loss=0.603, train_loss=0.531]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 178.65it/s, v_num=ld_4, val_loss=0.616, train_loss=0.531]
Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 177.83it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 29: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 8%|▊ | 1/13 [00:00<00:00, 217.31it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 8%|▊ | 1/13 [00:00<00:00, 211.29it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 15%|█▌ | 2/13 [00:00<00:00, 217.75it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 15%|█▌ | 2/13 [00:00<00:00, 214.48it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 23%|██▎ | 3/13 [00:00<00:00, 218.39it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 23%|██▎ | 3/13 [00:00<00:00, 216.23it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 31%|███ | 4/13 [00:00<00:00, 218.61it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 31%|███ | 4/13 [00:00<00:00, 216.91it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 38%|███▊ | 5/13 [00:00<00:00, 216.05it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 38%|███▊ | 5/13 [00:00<00:00, 214.69it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 46%|████▌ | 6/13 [00:00<00:00, 211.76it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 46%|████▌ | 6/13 [00:00<00:00, 210.62it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 54%|█████▍ | 7/13 [00:00<00:00, 208.40it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 54%|█████▍ | 7/13 [00:00<00:00, 207.35it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 62%|██████▏ | 8/13 [00:00<00:00, 205.69it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 62%|██████▏ | 8/13 [00:00<00:00, 204.79it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 69%|██████▉ | 9/13 [00:00<00:00, 204.79it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 69%|██████▉ | 9/13 [00:00<00:00, 204.11it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 77%|███████▋ | 10/13 [00:00<00:00, 204.19it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 77%|███████▋ | 10/13 [00:00<00:00, 203.62it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 85%|████████▍ | 11/13 [00:00<00:00, 204.56it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 85%|████████▍ | 11/13 [00:00<00:00, 204.08it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 92%|█████████▏| 12/13 [00:00<00:00, 204.71it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 92%|█████████▏| 12/13 [00:00<00:00, 204.27it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 205.10it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 204.72it/s, v_num=ld_4, val_loss=0.616, train_loss=0.530]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 185.35it/s, v_num=ld_4, val_loss=0.606, train_loss=0.530]
Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 184.60it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 30: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 8%|▊ | 1/13 [00:00<00:00, 220.85it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 8%|▊ | 1/13 [00:00<00:00, 213.68it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 15%|█▌ | 2/13 [00:00<00:00, 216.45it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 15%|█▌ | 2/13 [00:00<00:00, 213.47it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 23%|██▎ | 3/13 [00:00<00:00, 215.36it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 23%|██▎ | 3/13 [00:00<00:00, 213.40it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 31%|███ | 4/13 [00:00<00:00, 214.79it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 31%|███ | 4/13 [00:00<00:00, 213.34it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 38%|███▊ | 5/13 [00:00<00:00, 212.84it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 38%|███▊ | 5/13 [00:00<00:00, 211.60it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 46%|████▌ | 6/13 [00:00<00:00, 210.51it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 46%|████▌ | 6/13 [00:00<00:00, 209.50it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 54%|█████▍ | 7/13 [00:00<00:00, 210.55it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 54%|█████▍ | 7/13 [00:00<00:00, 209.81it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 62%|██████▏ | 8/13 [00:00<00:00, 209.21it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 62%|██████▏ | 8/13 [00:00<00:00, 208.50it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 69%|██████▉ | 9/13 [00:00<00:00, 209.60it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 69%|██████▉ | 9/13 [00:00<00:00, 208.99it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 77%|███████▋ | 10/13 [00:00<00:00, 209.69it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 77%|███████▋ | 10/13 [00:00<00:00, 209.17it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 85%|████████▍ | 11/13 [00:00<00:00, 209.82it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 85%|████████▍ | 11/13 [00:00<00:00, 209.32it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 92%|█████████▏| 12/13 [00:00<00:00, 209.74it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 92%|█████████▏| 12/13 [00:00<00:00, 209.26it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 210.07it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 209.63it/s, v_num=ld_4, val_loss=0.606, train_loss=0.531]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 189.14it/s, v_num=ld_4, val_loss=0.612, train_loss=0.531]
Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 188.11it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 31: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 8%|▊ | 1/13 [00:00<00:00, 204.83it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 8%|▊ | 1/13 [00:00<00:00, 199.64it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 15%|█▌ | 2/13 [00:00<00:00, 208.64it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 15%|█▌ | 2/13 [00:00<00:00, 205.40it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 23%|██▎ | 3/13 [00:00<00:00, 207.64it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 23%|██▎ | 3/13 [00:00<00:00, 205.69it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 31%|███ | 4/13 [00:00<00:00, 199.38it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 31%|███ | 4/13 [00:00<00:00, 197.82it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 38%|███▊ | 5/13 [00:00<00:00, 196.55it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 38%|███▊ | 5/13 [00:00<00:00, 195.42it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 46%|████▌ | 6/13 [00:00<00:00, 196.18it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 46%|████▌ | 6/13 [00:00<00:00, 195.20it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 54%|█████▍ | 7/13 [00:00<00:00, 193.84it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 54%|█████▍ | 7/13 [00:00<00:00, 193.01it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 62%|██████▏ | 8/13 [00:00<00:00, 190.66it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 62%|██████▏ | 8/13 [00:00<00:00, 189.79it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 69%|██████▉ | 9/13 [00:00<00:00, 190.58it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 69%|██████▉ | 9/13 [00:00<00:00, 190.02it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 77%|███████▋ | 10/13 [00:00<00:00, 190.85it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 77%|███████▋ | 10/13 [00:00<00:00, 190.38it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 85%|████████▍ | 11/13 [00:00<00:00, 191.72it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 85%|████████▍ | 11/13 [00:00<00:00, 191.25it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 92%|█████████▏| 12/13 [00:00<00:00, 192.78it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 92%|█████████▏| 12/13 [00:00<00:00, 192.37it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 193.02it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 192.54it/s, v_num=ld_4, val_loss=0.612, train_loss=0.534]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 174.06it/s, v_num=ld_4, val_loss=0.609, train_loss=0.534]
Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 173.33it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 32: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 8%|▊ | 1/13 [00:00<00:00, 209.82it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 8%|▊ | 1/13 [00:00<00:00, 203.67it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 15%|█▌ | 2/13 [00:00<00:00, 207.99it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 15%|█▌ | 2/13 [00:00<00:00, 204.61it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 23%|██▎ | 3/13 [00:00<00:00, 200.35it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 23%|██▎ | 3/13 [00:00<00:00, 198.26it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 31%|███ | 4/13 [00:00<00:00, 201.26it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 31%|███ | 4/13 [00:00<00:00, 200.08it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 38%|███▊ | 5/13 [00:00<00:00, 199.35it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 38%|███▊ | 5/13 [00:00<00:00, 198.19it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 46%|████▌ | 6/13 [00:00<00:00, 195.82it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 46%|████▌ | 6/13 [00:00<00:00, 194.84it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 54%|█████▍ | 7/13 [00:00<00:00, 194.75it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 54%|█████▍ | 7/13 [00:00<00:00, 193.99it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 62%|██████▏ | 8/13 [00:00<00:00, 193.29it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 62%|██████▏ | 8/13 [00:00<00:00, 192.54it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 69%|██████▉ | 9/13 [00:00<00:00, 193.26it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 69%|██████▉ | 9/13 [00:00<00:00, 192.53it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 77%|███████▋ | 10/13 [00:00<00:00, 192.19it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 77%|███████▋ | 10/13 [00:00<00:00, 191.67it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 85%|████████▍ | 11/13 [00:00<00:00, 192.43it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 85%|████████▍ | 11/13 [00:00<00:00, 191.95it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 92%|█████████▏| 12/13 [00:00<00:00, 193.09it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 92%|█████████▏| 12/13 [00:00<00:00, 192.47it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 100%|██████████| 13/13 [00:00<00:00, 192.13it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 100%|██████████| 13/13 [00:00<00:00, 191.75it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 33: 100%|██████████| 13/13 [00:00<00:00, 167.30it/s, v_num=ld_4, val_loss=0.611, train_loss=0.528]
Epoch 33: 100%|██████████| 13/13 [00:00<00:00, 166.47it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 33: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 8%|▊ | 1/13 [00:00<00:00, 190.51it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 8%|▊ | 1/13 [00:00<00:00, 184.33it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 15%|█▌ | 2/13 [00:00<00:00, 191.52it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 15%|█▌ | 2/13 [00:00<00:00, 188.88it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 23%|██▎ | 3/13 [00:00<00:00, 192.56it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 23%|██▎ | 3/13 [00:00<00:00, 190.19it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 31%|███ | 4/13 [00:00<00:00, 193.86it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 31%|███ | 4/13 [00:00<00:00, 192.68it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 38%|███▊ | 5/13 [00:00<00:00, 193.41it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 38%|███▊ | 5/13 [00:00<00:00, 192.33it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 46%|████▌ | 6/13 [00:00<00:00, 194.02it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 46%|████▌ | 6/13 [00:00<00:00, 193.14it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 54%|█████▍ | 7/13 [00:00<00:00, 192.84it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 54%|█████▍ | 7/13 [00:00<00:00, 192.12it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 62%|██████▏ | 8/13 [00:00<00:00, 194.45it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 62%|██████▏ | 8/13 [00:00<00:00, 193.79it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 69%|██████▉ | 9/13 [00:00<00:00, 194.38it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 69%|██████▉ | 9/13 [00:00<00:00, 193.75it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 77%|███████▋ | 10/13 [00:00<00:00, 194.44it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 77%|███████▋ | 10/13 [00:00<00:00, 193.85it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 85%|████████▍ | 11/13 [00:00<00:00, 195.14it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 85%|████████▍ | 11/13 [00:00<00:00, 194.71it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 92%|█████████▏| 12/13 [00:00<00:00, 196.47it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 92%|█████████▏| 12/13 [00:00<00:00, 196.01it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 100%|██████████| 13/13 [00:00<00:00, 196.11it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 100%|██████████| 13/13 [00:00<00:00, 195.62it/s, v_num=ld_4, val_loss=0.611, train_loss=0.527]
Epoch 34: 100%|██████████| 13/13 [00:00<00:00, 175.52it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 34: 100%|██████████| 13/13 [00:00<00:00, 174.78it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 34: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 8%|▊ | 1/13 [00:00<00:00, 197.78it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 8%|▊ | 1/13 [00:00<00:00, 191.79it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 15%|█▌ | 2/13 [00:00<00:00, 198.29it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 15%|█▌ | 2/13 [00:00<00:00, 195.77it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 23%|██▎ | 3/13 [00:00<00:00, 203.46it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 23%|██▎ | 3/13 [00:00<00:00, 201.87it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 31%|███ | 4/13 [00:00<00:00, 204.99it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 31%|███ | 4/13 [00:00<00:00, 203.28it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 38%|███▊ | 5/13 [00:00<00:00, 201.43it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 38%|███▊ | 5/13 [00:00<00:00, 200.16it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 46%|████▌ | 6/13 [00:00<00:00, 202.16it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 46%|████▌ | 6/13 [00:00<00:00, 201.31it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 54%|█████▍ | 7/13 [00:00<00:00, 202.97it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 54%|█████▍ | 7/13 [00:00<00:00, 202.21it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 62%|██████▏ | 8/13 [00:00<00:00, 203.64it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 62%|██████▏ | 8/13 [00:00<00:00, 202.95it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 69%|██████▉ | 9/13 [00:00<00:00, 202.99it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 69%|██████▉ | 9/13 [00:00<00:00, 202.30it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 77%|███████▋ | 10/13 [00:00<00:00, 203.69it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 77%|███████▋ | 10/13 [00:00<00:00, 203.16it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 85%|████████▍ | 11/13 [00:00<00:00, 203.84it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 85%|████████▍ | 11/13 [00:00<00:00, 203.35it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 92%|█████████▏| 12/13 [00:00<00:00, 204.08it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 92%|█████████▏| 12/13 [00:00<00:00, 203.57it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 100%|██████████| 13/13 [00:00<00:00, 203.96it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 100%|██████████| 13/13 [00:00<00:00, 203.48it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 35: 100%|██████████| 13/13 [00:00<00:00, 184.29it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 35: 100%|██████████| 13/13 [00:00<00:00, 183.39it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 35: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 8%|▊ | 1/13 [00:00<00:00, 203.29it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 8%|▊ | 1/13 [00:00<00:00, 197.19it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 15%|█▌ | 2/13 [00:00<00:00, 207.96it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 15%|█▌ | 2/13 [00:00<00:00, 205.27it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 23%|██▎ | 3/13 [00:00<00:00, 205.86it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 23%|██▎ | 3/13 [00:00<00:00, 203.76it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 31%|███ | 4/13 [00:00<00:00, 203.61it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 31%|███ | 4/13 [00:00<00:00, 201.99it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 38%|███▊ | 5/13 [00:00<00:00, 202.44it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 38%|███▊ | 5/13 [00:00<00:00, 201.39it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 46%|████▌ | 6/13 [00:00<00:00, 204.03it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 46%|████▌ | 6/13 [00:00<00:00, 203.16it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 54%|█████▍ | 7/13 [00:00<00:00, 205.33it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 54%|█████▍ | 7/13 [00:00<00:00, 204.51it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 62%|██████▏ | 8/13 [00:00<00:00, 201.95it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 62%|██████▏ | 8/13 [00:00<00:00, 201.21it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 69%|██████▉ | 9/13 [00:00<00:00, 200.33it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 69%|██████▉ | 9/13 [00:00<00:00, 199.63it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 77%|███████▋ | 10/13 [00:00<00:00, 201.02it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 77%|███████▋ | 10/13 [00:00<00:00, 200.52it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 85%|████████▍ | 11/13 [00:00<00:00, 200.24it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 85%|████████▍ | 11/13 [00:00<00:00, 199.72it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 92%|█████████▏| 12/13 [00:00<00:00, 200.46it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 92%|█████████▏| 12/13 [00:00<00:00, 200.01it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 100%|██████████| 13/13 [00:00<00:00, 201.25it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 100%|██████████| 13/13 [00:00<00:00, 200.84it/s, v_num=ld_4, val_loss=0.614, train_loss=0.526]
Epoch 36: 100%|██████████| 13/13 [00:00<00:00, 178.48it/s, v_num=ld_4, val_loss=0.601, train_loss=0.526]
Epoch 36: 100%|██████████| 13/13 [00:00<00:00, 177.74it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 36: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 8%|▊ | 1/13 [00:00<00:00, 212.90it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 8%|▊ | 1/13 [00:00<00:00, 206.95it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 15%|█▌ | 2/13 [00:00<00:00, 212.61it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 15%|█▌ | 2/13 [00:00<00:00, 209.73it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 23%|██▎ | 3/13 [00:00<00:00, 213.75it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 23%|██▎ | 3/13 [00:00<00:00, 211.81it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 31%|███ | 4/13 [00:00<00:00, 213.95it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 31%|███ | 4/13 [00:00<00:00, 212.20it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 38%|███▊ | 5/13 [00:00<00:00, 205.84it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 38%|███▊ | 5/13 [00:00<00:00, 204.31it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 46%|████▌ | 6/13 [00:00<00:00, 199.34it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 46%|████▌ | 6/13 [00:00<00:00, 198.30it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 54%|█████▍ | 7/13 [00:00<00:00, 199.11it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 54%|█████▍ | 7/13 [00:00<00:00, 198.18it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 62%|██████▏ | 8/13 [00:00<00:00, 195.84it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 62%|██████▏ | 8/13 [00:00<00:00, 194.95it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 69%|██████▉ | 9/13 [00:00<00:00, 195.30it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 69%|██████▉ | 9/13 [00:00<00:00, 194.68it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 77%|███████▋ | 10/13 [00:00<00:00, 194.25it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 77%|███████▋ | 10/13 [00:00<00:00, 193.64it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 85%|████████▍ | 11/13 [00:00<00:00, 193.13it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 85%|████████▍ | 11/13 [00:00<00:00, 192.59it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 92%|█████████▏| 12/13 [00:00<00:00, 192.47it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 92%|█████████▏| 12/13 [00:00<00:00, 192.03it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 100%|██████████| 13/13 [00:00<00:00, 193.59it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 100%|██████████| 13/13 [00:00<00:00, 193.09it/s, v_num=ld_4, val_loss=0.601, train_loss=0.527]
Epoch 37: 100%|██████████| 13/13 [00:00<00:00, 174.58it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 37: 100%|██████████| 13/13 [00:00<00:00, 173.85it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 37: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 8%|▊ | 1/13 [00:00<00:00, 204.91it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 8%|▊ | 1/13 [00:00<00:00, 199.08it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 15%|█▌ | 2/13 [00:00<00:00, 203.31it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 15%|█▌ | 2/13 [00:00<00:00, 200.34it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 23%|██▎ | 3/13 [00:00<00:00, 200.32it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 23%|██▎ | 3/13 [00:00<00:00, 198.26it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 31%|███ | 4/13 [00:00<00:00, 194.89it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 31%|███ | 4/13 [00:00<00:00, 193.47it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 38%|███▊ | 5/13 [00:00<00:00, 194.30it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 38%|███▊ | 5/13 [00:00<00:00, 193.26it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 46%|████▌ | 6/13 [00:00<00:00, 194.23it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 46%|████▌ | 6/13 [00:00<00:00, 193.40it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 54%|█████▍ | 7/13 [00:00<00:00, 195.62it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 54%|█████▍ | 7/13 [00:00<00:00, 194.92it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 62%|██████▏ | 8/13 [00:00<00:00, 196.64it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 62%|██████▏ | 8/13 [00:00<00:00, 196.02it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 69%|██████▉ | 9/13 [00:00<00:00, 197.24it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 69%|██████▉ | 9/13 [00:00<00:00, 196.70it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 77%|███████▋ | 10/13 [00:00<00:00, 198.45it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 77%|███████▋ | 10/13 [00:00<00:00, 197.93it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 85%|████████▍ | 11/13 [00:00<00:00, 198.71it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 85%|████████▍ | 11/13 [00:00<00:00, 198.23it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 92%|█████████▏| 12/13 [00:00<00:00, 198.47it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 92%|█████████▏| 12/13 [00:00<00:00, 198.03it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 198.36it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 197.97it/s, v_num=ld_4, val_loss=0.616, train_loss=0.527]
Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 179.82it/s, v_num=ld_4, val_loss=0.609, train_loss=0.527]
Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 179.08it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 38: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 8%|▊ | 1/13 [00:00<00:00, 211.55it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 8%|▊ | 1/13 [00:00<00:00, 205.12it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 15%|█▌ | 2/13 [00:00<00:00, 210.46it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 15%|█▌ | 2/13 [00:00<00:00, 207.75it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 23%|██▎ | 3/13 [00:00<00:00, 208.45it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 23%|██▎ | 3/13 [00:00<00:00, 206.48it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 31%|███ | 4/13 [00:00<00:00, 206.13it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 31%|███ | 4/13 [00:00<00:00, 204.58it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 38%|███▊ | 5/13 [00:00<00:00, 205.53it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 38%|███▊ | 5/13 [00:00<00:00, 204.45it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 46%|████▌ | 6/13 [00:00<00:00, 206.87it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 46%|████▌ | 6/13 [00:00<00:00, 205.99it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 54%|█████▍ | 7/13 [00:00<00:00, 207.18it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 54%|█████▍ | 7/13 [00:00<00:00, 206.45it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 62%|██████▏ | 8/13 [00:00<00:00, 207.34it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 62%|██████▏ | 8/13 [00:00<00:00, 206.63it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 69%|██████▉ | 9/13 [00:00<00:00, 206.01it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 69%|██████▉ | 9/13 [00:00<00:00, 205.40it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 77%|███████▋ | 10/13 [00:00<00:00, 205.40it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 77%|███████▋ | 10/13 [00:00<00:00, 204.88it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 85%|████████▍ | 11/13 [00:00<00:00, 205.73it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 85%|████████▍ | 11/13 [00:00<00:00, 205.27it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 92%|█████████▏| 12/13 [00:00<00:00, 205.87it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 92%|█████████▏| 12/13 [00:00<00:00, 205.41it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 100%|██████████| 13/13 [00:00<00:00, 205.86it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 100%|██████████| 13/13 [00:00<00:00, 205.42it/s, v_num=ld_4, val_loss=0.609, train_loss=0.528]
Epoch 39: 100%|██████████| 13/13 [00:00<00:00, 183.66it/s, v_num=ld_4, val_loss=0.607, train_loss=0.528]
Epoch 39: 100%|██████████| 13/13 [00:00<00:00, 182.92it/s, v_num=ld_4, val_loss=0.607, train_loss=0.528]
Epoch 39: 100%|██████████| 13/13 [00:00<00:00, 179.55it/s, v_num=ld_4, val_loss=0.607, train_loss=0.528]
+
Training: | | 0/? [00:00, ?it/s]
Training: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 0%| | 0/13 [00:00, ?it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 135.10it/s]
Epoch 0: 8%|▊ | 1/13 [00:00<00:00, 132.63it/s, v_num=ld_4]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 151.98it/s, v_num=ld_4]
Epoch 0: 15%|█▌ | 2/13 [00:00<00:00, 150.50it/s, v_num=ld_4]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 165.19it/s, v_num=ld_4]
Epoch 0: 23%|██▎ | 3/13 [00:00<00:00, 163.96it/s, v_num=ld_4]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 172.92it/s, v_num=ld_4]
Epoch 0: 31%|███ | 4/13 [00:00<00:00, 172.02it/s, v_num=ld_4]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 176.85it/s, v_num=ld_4]
Epoch 0: 38%|███▊ | 5/13 [00:00<00:00, 175.94it/s, v_num=ld_4]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 180.61it/s, v_num=ld_4]
Epoch 0: 46%|████▌ | 6/13 [00:00<00:00, 179.95it/s, v_num=ld_4]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 182.05it/s, v_num=ld_4]
Epoch 0: 54%|█████▍ | 7/13 [00:00<00:00, 181.38it/s, v_num=ld_4]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 183.70it/s, v_num=ld_4]
Epoch 0: 62%|██████▏ | 8/13 [00:00<00:00, 182.97it/s, v_num=ld_4]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 184.50it/s, v_num=ld_4]
Epoch 0: 69%|██████▉ | 9/13 [00:00<00:00, 184.04it/s, v_num=ld_4]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 186.65it/s, v_num=ld_4]
Epoch 0: 77%|███████▋ | 10/13 [00:00<00:00, 186.14it/s, v_num=ld_4]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 186.47it/s, v_num=ld_4]
Epoch 0: 85%|████████▍ | 11/13 [00:00<00:00, 186.10it/s, v_num=ld_4]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 187.66it/s, v_num=ld_4]
Epoch 0: 92%|█████████▏| 12/13 [00:00<00:00, 187.21it/s, v_num=ld_4]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 188.39it/s, v_num=ld_4]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 188.05it/s, v_num=ld_4]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 170.66it/s, v_num=ld_4, val_loss=0.686]
Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 169.71it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 0: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 191.95it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 8%|▊ | 1/13 [00:00<00:00, 187.13it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 197.48it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 15%|█▌ | 2/13 [00:00<00:00, 195.07it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 201.86it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 23%|██▎ | 3/13 [00:00<00:00, 199.76it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 199.14it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 31%|███ | 4/13 [00:00<00:00, 197.79it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 198.75it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 38%|███▊ | 5/13 [00:00<00:00, 197.72it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 199.04it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 46%|████▌ | 6/13 [00:00<00:00, 197.94it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 197.70it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 54%|█████▍ | 7/13 [00:00<00:00, 197.00it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 199.73it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 62%|██████▏ | 8/13 [00:00<00:00, 199.05it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 200.03it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 69%|██████▉ | 9/13 [00:00<00:00, 199.46it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 200.57it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 77%|███████▋ | 10/13 [00:00<00:00, 200.09it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 200.87it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 85%|████████▍ | 11/13 [00:00<00:00, 200.38it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 199.58it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 92%|█████████▏| 12/13 [00:00<00:00, 199.11it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 199.30it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 198.88it/s, v_num=ld_4, val_loss=0.686, train_loss=0.722]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 180.69it/s, v_num=ld_4, val_loss=0.670, train_loss=0.722]
Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 179.90it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 1: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 200.60it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 8%|▊ | 1/13 [00:00<00:00, 195.77it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 187.51it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 15%|█▌ | 2/13 [00:00<00:00, 184.86it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 190.45it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 23%|██▎ | 3/13 [00:00<00:00, 188.87it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 191.51it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 31%|███ | 4/13 [00:00<00:00, 190.15it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 189.01it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 38%|███▊ | 5/13 [00:00<00:00, 188.02it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 189.67it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 46%|████▌ | 6/13 [00:00<00:00, 188.89it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 189.81it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 54%|█████▍ | 7/13 [00:00<00:00, 189.16it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 190.43it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 62%|██████▏ | 8/13 [00:00<00:00, 189.76it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 190.15it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 69%|██████▉ | 9/13 [00:00<00:00, 189.62it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 190.32it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 77%|███████▋ | 10/13 [00:00<00:00, 189.86it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 191.90it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 85%|████████▍ | 11/13 [00:00<00:00, 191.46it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 193.54it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 92%|█████████▏| 12/13 [00:00<00:00, 193.16it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 196.03it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 195.67it/s, v_num=ld_4, val_loss=0.670, train_loss=0.688]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 177.77it/s, v_num=ld_4, val_loss=0.623, train_loss=0.688]
Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 176.75it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 2: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 184.84it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 8%|▊ | 1/13 [00:00<00:00, 179.63it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 184.21it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 15%|█▌ | 2/13 [00:00<00:00, 181.21it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 185.54it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 23%|██▎ | 3/13 [00:00<00:00, 183.76it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 186.25it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 31%|███ | 4/13 [00:00<00:00, 184.97it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 186.70it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 38%|███▊ | 5/13 [00:00<00:00, 185.63it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 186.39it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 46%|████▌ | 6/13 [00:00<00:00, 185.54it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 185.97it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 54%|█████▍ | 7/13 [00:00<00:00, 185.26it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 185.03it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 62%|██████▏ | 8/13 [00:00<00:00, 184.33it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 184.02it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 69%|██████▉ | 9/13 [00:00<00:00, 183.46it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 184.85it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 77%|███████▋ | 10/13 [00:00<00:00, 184.40it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 186.03it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 85%|████████▍ | 11/13 [00:00<00:00, 185.63it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 186.83it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 92%|█████████▏| 12/13 [00:00<00:00, 186.47it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 187.89it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 187.54it/s, v_num=ld_4, val_loss=0.623, train_loss=0.656]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 171.95it/s, v_num=ld_4, val_loss=0.566, train_loss=0.656]
Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 171.28it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 3: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 214.96it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 8%|▊ | 1/13 [00:00<00:00, 208.94it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 211.37it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 15%|█▌ | 2/13 [00:00<00:00, 208.09it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 201.41it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 23%|██▎ | 3/13 [00:00<00:00, 199.59it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 198.94it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 31%|███ | 4/13 [00:00<00:00, 197.55it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 196.99it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 38%|███▊ | 5/13 [00:00<00:00, 195.95it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 197.03it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 46%|████▌ | 6/13 [00:00<00:00, 196.19it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 196.73it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 54%|█████▍ | 7/13 [00:00<00:00, 196.00it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 196.36it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 62%|██████▏ | 8/13 [00:00<00:00, 195.75it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 197.30it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 69%|██████▉ | 9/13 [00:00<00:00, 196.76it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 198.23it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 77%|███████▋ | 10/13 [00:00<00:00, 197.78it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 199.10it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 85%|████████▍ | 11/13 [00:00<00:00, 198.65it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 199.37it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 92%|█████████▏| 12/13 [00:00<00:00, 198.92it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 200.47it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 200.06it/s, v_num=ld_4, val_loss=0.566, train_loss=0.612]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 182.49it/s, v_num=ld_4, val_loss=0.589, train_loss=0.612]
Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 181.73it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 4: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 215.38it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 8%|▊ | 1/13 [00:00<00:00, 209.46it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 214.56it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 15%|█▌ | 2/13 [00:00<00:00, 211.68it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 213.81it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 23%|██▎ | 3/13 [00:00<00:00, 211.82it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 208.20it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 31%|███ | 4/13 [00:00<00:00, 206.85it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 206.16it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 38%|███▊ | 5/13 [00:00<00:00, 205.09it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 204.60it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 46%|████▌ | 6/13 [00:00<00:00, 203.71it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 202.05it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 54%|█████▍ | 7/13 [00:00<00:00, 201.23it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 201.30it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 62%|██████▏ | 8/13 [00:00<00:00, 200.48it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 198.42it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 69%|██████▉ | 9/13 [00:00<00:00, 197.76it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 195.58it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 77%|███████▋ | 10/13 [00:00<00:00, 194.98it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 194.88it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 85%|████████▍ | 11/13 [00:00<00:00, 194.40it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 194.25it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 92%|█████████▏| 12/13 [00:00<00:00, 193.78it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 194.94it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 194.52it/s, v_num=ld_4, val_loss=0.589, train_loss=0.592]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 177.10it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 176.10it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 5: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 193.80it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 8%|▊ | 1/13 [00:00<00:00, 188.53it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 197.05it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 15%|█▌ | 2/13 [00:00<00:00, 194.57it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 199.56it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 23%|██▎ | 3/13 [00:00<00:00, 197.79it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 198.75it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 31%|███ | 4/13 [00:00<00:00, 197.44it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 197.33it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 38%|███▊ | 5/13 [00:00<00:00, 196.34it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 197.75it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 46%|████▌ | 6/13 [00:00<00:00, 196.93it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 199.28it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 54%|█████▍ | 7/13 [00:00<00:00, 198.61it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 199.32it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 62%|██████▏ | 8/13 [00:00<00:00, 198.72it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 199.52it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 69%|██████▉ | 9/13 [00:00<00:00, 198.98it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 200.16it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 77%|███████▋ | 10/13 [00:00<00:00, 199.65it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 200.20it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 85%|████████▍ | 11/13 [00:00<00:00, 199.74it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 199.93it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 92%|█████████▏| 12/13 [00:00<00:00, 199.49it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 200.35it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 199.98it/s, v_num=ld_4, val_loss=0.566, train_loss=0.592]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 167.93it/s, v_num=ld_4, val_loss=0.563, train_loss=0.592]
Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 167.06it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 6: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 192.80it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 8%|▊ | 1/13 [00:00<00:00, 187.90it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 199.26it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 15%|█▌ | 2/13 [00:00<00:00, 196.50it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 194.15it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 23%|██▎ | 3/13 [00:00<00:00, 192.56it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 193.59it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 31%|███ | 4/13 [00:00<00:00, 192.30it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 192.55it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 38%|███▊ | 5/13 [00:00<00:00, 191.59it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 191.61it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 46%|████▌ | 6/13 [00:00<00:00, 190.80it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 192.94it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 54%|█████▍ | 7/13 [00:00<00:00, 192.28it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 192.68it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 62%|██████▏ | 8/13 [00:00<00:00, 192.00it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 190.99it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 69%|██████▉ | 9/13 [00:00<00:00, 190.38it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 191.01it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 77%|███████▋ | 10/13 [00:00<00:00, 190.47it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 189.92it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 85%|████████▍ | 11/13 [00:00<00:00, 189.44it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 189.13it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 92%|█████████▏| 12/13 [00:00<00:00, 188.67it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 189.77it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 189.42it/s, v_num=ld_4, val_loss=0.563, train_loss=0.575]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 173.16it/s, v_num=ld_4, val_loss=0.557, train_loss=0.575]
Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 172.49it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 7: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 191.71it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 8%|▊ | 1/13 [00:00<00:00, 186.87it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 189.00it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 15%|█▌ | 2/13 [00:00<00:00, 186.58it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 191.46it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 23%|██▎ | 3/13 [00:00<00:00, 189.93it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 190.17it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 31%|███ | 4/13 [00:00<00:00, 188.88it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 189.59it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 38%|███▊ | 5/13 [00:00<00:00, 188.61it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 190.41it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 46%|████▌ | 6/13 [00:00<00:00, 189.52it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 190.48it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 54%|█████▍ | 7/13 [00:00<00:00, 189.68it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 191.54it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 62%|██████▏ | 8/13 [00:00<00:00, 190.95it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 192.20it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 69%|██████▉ | 9/13 [00:00<00:00, 191.61it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 190.96it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 77%|███████▋ | 10/13 [00:00<00:00, 190.39it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 190.38it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 85%|████████▍ | 11/13 [00:00<00:00, 189.86it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 189.32it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 92%|█████████▏| 12/13 [00:00<00:00, 188.87it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 187.64it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 187.26it/s, v_num=ld_4, val_loss=0.557, train_loss=0.572]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 171.43it/s, v_num=ld_4, val_loss=0.559, train_loss=0.572]
Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 170.56it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 8: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 179.05it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 8%|▊ | 1/13 [00:00<00:00, 174.89it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 186.12it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 15%|█▌ | 2/13 [00:00<00:00, 183.86it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 189.14it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 23%|██▎ | 3/13 [00:00<00:00, 187.57it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 191.71it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 31%|███ | 4/13 [00:00<00:00, 190.44it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 192.80it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 38%|███▊ | 5/13 [00:00<00:00, 191.73it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 193.21it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 46%|████▌ | 6/13 [00:00<00:00, 192.31it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 191.69it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 54%|█████▍ | 7/13 [00:00<00:00, 190.91it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 190.13it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 62%|██████▏ | 8/13 [00:00<00:00, 189.52it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 190.80it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 69%|██████▉ | 9/13 [00:00<00:00, 190.27it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 191.64it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 77%|███████▋ | 10/13 [00:00<00:00, 191.20it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 192.10it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 85%|████████▍ | 11/13 [00:00<00:00, 191.67it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 192.62it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 92%|█████████▏| 12/13 [00:00<00:00, 192.20it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 193.25it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 192.92it/s, v_num=ld_4, val_loss=0.559, train_loss=0.565]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 176.57it/s, v_num=ld_4, val_loss=0.562, train_loss=0.565]
Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 175.88it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 9: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 209.42it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 8%|▊ | 1/13 [00:00<00:00, 203.54it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 198.89it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 15%|█▌ | 2/13 [00:00<00:00, 196.37it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 197.20it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 23%|██▎ | 3/13 [00:00<00:00, 195.56it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 197.32it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 31%|███ | 4/13 [00:00<00:00, 196.06it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 197.71it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 38%|███▊ | 5/13 [00:00<00:00, 196.60it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 197.62it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 46%|████▌ | 6/13 [00:00<00:00, 196.80it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 198.05it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 54%|█████▍ | 7/13 [00:00<00:00, 197.36it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 198.52it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 62%|██████▏ | 8/13 [00:00<00:00, 197.92it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 198.72it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 69%|██████▉ | 9/13 [00:00<00:00, 198.16it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 198.70it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 77%|███████▋ | 10/13 [00:00<00:00, 198.19it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 198.73it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 85%|████████▍ | 11/13 [00:00<00:00, 198.24it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 197.72it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 92%|█████████▏| 12/13 [00:00<00:00, 197.21it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 196.17it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 195.68it/s, v_num=ld_4, val_loss=0.562, train_loss=0.567]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 176.60it/s, v_num=ld_4, val_loss=0.569, train_loss=0.567]
Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 175.73it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 10: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 176.80it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 8%|▊ | 1/13 [00:00<00:00, 172.26it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 179.71it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 15%|█▌ | 2/13 [00:00<00:00, 177.56it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 181.49it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 23%|██▎ | 3/13 [00:00<00:00, 180.04it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 185.61it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 31%|███ | 4/13 [00:00<00:00, 184.27it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 184.62it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 38%|███▊ | 5/13 [00:00<00:00, 183.69it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 186.72it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 46%|████▌ | 6/13 [00:00<00:00, 185.92it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 187.96it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 54%|█████▍ | 7/13 [00:00<00:00, 187.23it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 188.53it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 62%|██████▏ | 8/13 [00:00<00:00, 187.96it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 188.93it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 69%|██████▉ | 9/13 [00:00<00:00, 188.43it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 190.64it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 77%|███████▋ | 10/13 [00:00<00:00, 190.20it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 191.83it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 85%|████████▍ | 11/13 [00:00<00:00, 191.38it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 192.03it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 92%|█████████▏| 12/13 [00:00<00:00, 191.63it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 193.21it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 192.85it/s, v_num=ld_4, val_loss=0.569, train_loss=0.563]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 175.65it/s, v_num=ld_4, val_loss=0.563, train_loss=0.563]
Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 174.98it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 11: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 207.85it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 8%|▊ | 1/13 [00:00<00:00, 202.35it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 199.94it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 15%|█▌ | 2/13 [00:00<00:00, 197.36it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 198.41it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 23%|██▎ | 3/13 [00:00<00:00, 196.71it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 198.69it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 31%|███ | 4/13 [00:00<00:00, 197.41it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 199.69it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 38%|███▊ | 5/13 [00:00<00:00, 198.70it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 200.19it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 46%|████▌ | 6/13 [00:00<00:00, 199.38it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 200.41it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 54%|█████▍ | 7/13 [00:00<00:00, 199.62it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 198.79it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 62%|██████▏ | 8/13 [00:00<00:00, 198.11it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 196.78it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 69%|██████▉ | 9/13 [00:00<00:00, 196.22it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 196.79it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 77%|███████▋ | 10/13 [00:00<00:00, 196.27it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 196.61it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 85%|████████▍ | 11/13 [00:00<00:00, 196.15it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 196.68it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 92%|█████████▏| 12/13 [00:00<00:00, 196.28it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 197.60it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 197.20it/s, v_num=ld_4, val_loss=0.563, train_loss=0.565]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 165.33it/s, v_num=ld_4, val_loss=0.569, train_loss=0.565]
Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 164.67it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 12: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 188.92it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 8%|▊ | 1/13 [00:00<00:00, 184.07it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 194.10it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 15%|█▌ | 2/13 [00:00<00:00, 191.61it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 194.73it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 23%|██▎ | 3/13 [00:00<00:00, 193.15it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 193.06it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 31%|███ | 4/13 [00:00<00:00, 191.77it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 192.21it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 38%|███▊ | 5/13 [00:00<00:00, 191.29it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 192.78it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 46%|████▌ | 6/13 [00:00<00:00, 191.98it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 194.03it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 54%|█████▍ | 7/13 [00:00<00:00, 193.35it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 194.42it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 62%|██████▏ | 8/13 [00:00<00:00, 193.76it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 188.83it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 69%|██████▉ | 9/13 [00:00<00:00, 188.15it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 188.79it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 77%|███████▋ | 10/13 [00:00<00:00, 188.30it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 189.07it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 85%|████████▍ | 11/13 [00:00<00:00, 188.50it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 186.36it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 92%|█████████▏| 12/13 [00:00<00:00, 185.82it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 186.07it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 185.60it/s, v_num=ld_4, val_loss=0.569, train_loss=0.566]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 168.66it/s, v_num=ld_4, val_loss=0.566, train_loss=0.566]
Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 168.00it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 13: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 177.75it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 8%|▊ | 1/13 [00:00<00:00, 173.31it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 175.46it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 15%|█▌ | 2/13 [00:00<00:00, 173.09it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 175.46it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 23%|██▎ | 3/13 [00:00<00:00, 173.55it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 175.48it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 31%|███ | 4/13 [00:00<00:00, 174.26it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 177.06it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 38%|███▊ | 5/13 [00:00<00:00, 176.03it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 173.41it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 46%|████▌ | 6/13 [00:00<00:00, 172.53it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 171.92it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 54%|█████▍ | 7/13 [00:00<00:00, 171.20it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 171.41it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 62%|██████▏ | 8/13 [00:00<00:00, 170.85it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 172.88it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 69%|██████▉ | 9/13 [00:00<00:00, 172.35it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 174.16it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 77%|███████▋ | 10/13 [00:00<00:00, 173.71it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 175.39it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 85%|████████▍ | 11/13 [00:00<00:00, 175.02it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 176.87it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 92%|█████████▏| 12/13 [00:00<00:00, 176.54it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 178.35it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 177.99it/s, v_num=ld_4, val_loss=0.566, train_loss=0.561]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 160.84it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 160.08it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 14: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 176.36it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 8%|▊ | 1/13 [00:00<00:00, 171.14it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 173.00it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 15%|█▌ | 2/13 [00:00<00:00, 170.53it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 172.58it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 23%|██▎ | 3/13 [00:00<00:00, 171.06it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 176.35it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 31%|███ | 4/13 [00:00<00:00, 175.28it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 178.98it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 38%|███▊ | 5/13 [00:00<00:00, 178.07it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 178.49it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 46%|████▌ | 6/13 [00:00<00:00, 177.70it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 179.41it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 54%|█████▍ | 7/13 [00:00<00:00, 178.80it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 180.15it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 62%|██████▏ | 8/13 [00:00<00:00, 179.54it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 179.99it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 69%|██████▉ | 9/13 [00:00<00:00, 179.40it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 179.28it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 77%|███████▋ | 10/13 [00:00<00:00, 178.81it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 179.72it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 85%|████████▍ | 11/13 [00:00<00:00, 179.31it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 179.96it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 92%|█████████▏| 12/13 [00:00<00:00, 179.52it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 180.03it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 179.64it/s, v_num=ld_4, val_loss=0.562, train_loss=0.561]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 164.57it/s, v_num=ld_4, val_loss=0.559, train_loss=0.561]
Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 163.92it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 15: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 197.43it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 8%|▊ | 1/13 [00:00<00:00, 191.85it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 195.08it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 15%|█▌ | 2/13 [00:00<00:00, 192.55it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 192.41it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 23%|██▎ | 3/13 [00:00<00:00, 190.79it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 192.99it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 31%|███ | 4/13 [00:00<00:00, 191.80it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 193.09it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 38%|███▊ | 5/13 [00:00<00:00, 191.98it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 190.59it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 46%|████▌ | 6/13 [00:00<00:00, 189.80it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 188.52it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 54%|█████▍ | 7/13 [00:00<00:00, 187.82it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 188.68it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 62%|██████▏ | 8/13 [00:00<00:00, 188.14it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 189.02it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 69%|██████▉ | 9/13 [00:00<00:00, 188.50it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 189.11it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 77%|███████▋ | 10/13 [00:00<00:00, 188.61it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 189.52it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 85%|████████▍ | 11/13 [00:00<00:00, 189.07it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 189.44it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 92%|█████████▏| 12/13 [00:00<00:00, 189.08it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 190.15it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 189.81it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 173.60it/s, v_num=ld_4, val_loss=0.559, train_loss=0.558]
Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 172.92it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 16: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 198.06it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 8%|▊ | 1/13 [00:00<00:00, 192.57it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 197.26it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 15%|█▌ | 2/13 [00:00<00:00, 194.82it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 195.20it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 23%|██▎ | 3/13 [00:00<00:00, 193.43it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 191.42it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 31%|███ | 4/13 [00:00<00:00, 190.10it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 192.03it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 38%|███▊ | 5/13 [00:00<00:00, 191.02it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 192.62it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 46%|████▌ | 6/13 [00:00<00:00, 191.75it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 190.94it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 54%|█████▍ | 7/13 [00:00<00:00, 190.20it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 189.30it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 62%|██████▏ | 8/13 [00:00<00:00, 188.74it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 189.62it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 69%|██████▉ | 9/13 [00:00<00:00, 189.12it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 191.00it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 77%|███████▋ | 10/13 [00:00<00:00, 190.50it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 191.44it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 85%|████████▍ | 11/13 [00:00<00:00, 191.01it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 191.47it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 92%|█████████▏| 12/13 [00:00<00:00, 191.07it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 192.04it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 191.68it/s, v_num=ld_4, val_loss=0.559, train_loss=0.557]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 174.25it/s, v_num=ld_4, val_loss=0.560, train_loss=0.557]
Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 173.57it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 17: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 201.98it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 8%|▊ | 1/13 [00:00<00:00, 196.62it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 201.47it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 15%|█▌ | 2/13 [00:00<00:00, 198.87it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 199.35it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 23%|██▎ | 3/13 [00:00<00:00, 197.75it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 198.42it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 31%|███ | 4/13 [00:00<00:00, 197.09it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 197.82it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 38%|███▊ | 5/13 [00:00<00:00, 196.91it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 198.35it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 46%|████▌ | 6/13 [00:00<00:00, 197.50it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 198.19it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 54%|█████▍ | 7/13 [00:00<00:00, 197.48it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 198.32it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 62%|██████▏ | 8/13 [00:00<00:00, 197.72it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 198.31it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 69%|██████▉ | 9/13 [00:00<00:00, 197.81it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 198.24it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 77%|███████▋ | 10/13 [00:00<00:00, 197.75it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 198.26it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 85%|████████▍ | 11/13 [00:00<00:00, 197.77it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 198.25it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 92%|█████████▏| 12/13 [00:00<00:00, 197.86it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 198.56it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 198.19it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 179.05it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 178.37it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 18: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 205.05it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 8%|▊ | 1/13 [00:00<00:00, 199.13it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 200.68it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 15%|█▌ | 2/13 [00:00<00:00, 198.10it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 201.84it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 23%|██▎ | 3/13 [00:00<00:00, 200.15it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 200.74it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 31%|███ | 4/13 [00:00<00:00, 199.49it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 199.78it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 38%|███▊ | 5/13 [00:00<00:00, 198.83it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 199.04it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 46%|████▌ | 6/13 [00:00<00:00, 198.26it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 198.64it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 54%|█████▍ | 7/13 [00:00<00:00, 197.97it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 198.50it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 62%|██████▏ | 8/13 [00:00<00:00, 197.88it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 198.18it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 69%|██████▉ | 9/13 [00:00<00:00, 197.62it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 198.08it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 77%|███████▋ | 10/13 [00:00<00:00, 197.57it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 197.20it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 85%|████████▍ | 11/13 [00:00<00:00, 196.80it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 197.47it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 92%|█████████▏| 12/13 [00:00<00:00, 197.09it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 197.97it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 197.56it/s, v_num=ld_4, val_loss=0.562, train_loss=0.555]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 174.23it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 173.55it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 19: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 189.97it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 8%|▊ | 1/13 [00:00<00:00, 184.50it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 191.41it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 15%|█▌ | 2/13 [00:00<00:00, 189.05it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 192.69it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 23%|██▎ | 3/13 [00:00<00:00, 191.09it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 191.71it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 31%|███ | 4/13 [00:00<00:00, 190.54it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 192.39it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 38%|███▊ | 5/13 [00:00<00:00, 191.41it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 192.85it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 46%|████▌ | 6/13 [00:00<00:00, 192.09it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 191.88it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 54%|█████▍ | 7/13 [00:00<00:00, 191.07it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 192.21it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 62%|██████▏ | 8/13 [00:00<00:00, 191.63it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 192.70it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 69%|██████▉ | 9/13 [00:00<00:00, 192.12it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 191.64it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 77%|███████▋ | 10/13 [00:00<00:00, 191.05it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 191.28it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 85%|████████▍ | 11/13 [00:00<00:00, 190.84it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 191.30it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 92%|█████████▏| 12/13 [00:00<00:00, 190.89it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 191.21it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 190.76it/s, v_num=ld_4, val_loss=0.570, train_loss=0.555]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 155.37it/s, v_num=ld_4, val_loss=0.560, train_loss=0.555]
Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 154.63it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 20: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 182.65it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 8%|▊ | 1/13 [00:00<00:00, 178.19it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 187.69it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 15%|█▌ | 2/13 [00:00<00:00, 185.36it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 189.91it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 23%|██▎ | 3/13 [00:00<00:00, 187.75it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 187.14it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 31%|███ | 4/13 [00:00<00:00, 185.73it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 185.13it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 38%|███▊ | 5/13 [00:00<00:00, 184.09it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 181.77it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 46%|████▌ | 6/13 [00:00<00:00, 180.76it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 180.43it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 54%|█████▍ | 7/13 [00:00<00:00, 179.75it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 180.88it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 62%|██████▏ | 8/13 [00:00<00:00, 180.32it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 181.73it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 69%|██████▉ | 9/13 [00:00<00:00, 181.29it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 182.96it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 77%|███████▋ | 10/13 [00:00<00:00, 182.52it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 184.32it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 85%|████████▍ | 11/13 [00:00<00:00, 183.91it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 185.13it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 92%|█████████▏| 12/13 [00:00<00:00, 184.74it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 186.01it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 185.63it/s, v_num=ld_4, val_loss=0.560, train_loss=0.554]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 169.88it/s, v_num=ld_4, val_loss=0.567, train_loss=0.554]
Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 169.23it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 21: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 0%| | 0/13 [00:00, ?it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 196.70it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 8%|▊ | 1/13 [00:00<00:00, 191.79it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 187.78it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 15%|█▌ | 2/13 [00:00<00:00, 185.31it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 186.63it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 23%|██▎ | 3/13 [00:00<00:00, 184.84it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 182.16it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 31%|███ | 4/13 [00:00<00:00, 181.08it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 182.31it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 38%|███▊ | 5/13 [00:00<00:00, 181.38it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 181.33it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 46%|████▌ | 6/13 [00:00<00:00, 180.46it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 179.71it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 54%|█████▍ | 7/13 [00:00<00:00, 178.95it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 179.13it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 62%|██████▏ | 8/13 [00:00<00:00, 178.54it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 180.07it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 69%|██████▉ | 9/13 [00:00<00:00, 179.62it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 181.61it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 77%|███████▋ | 10/13 [00:00<00:00, 181.21it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 182.50it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 85%|████████▍ | 11/13 [00:00<00:00, 182.01it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 181.81it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 92%|█████████▏| 12/13 [00:00<00:00, 181.38it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 181.82it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 181.44it/s, v_num=ld_4, val_loss=0.567, train_loss=0.556]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 147.63it/s, v_num=ld_4, val_loss=0.563, train_loss=0.556]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 146.90it/s, v_num=ld_4, val_loss=0.563, train_loss=0.557]
Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 144.27it/s, v_num=ld_4, val_loss=0.563, train_loss=0.557]
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Validate metric ┃ DataLoader 0 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
- │ binary_accuracy_val │ 0.8299999833106995 │
- │ binary_auroc_val │ 0.8937374353408813 │
- │ val_loss │ 0.6073752641677856 │
+ │ binary_accuracy_val │ 0.8700000047683716 │
+ │ binary_auroc_val │ 0.9372000694274902 │
+ │ val_loss │ 0.5630534887313843 │
└───────────────────────────┴───────────────────────────┘
-.. GENERATED FROM PYTHON SOURCE LINES 118-123
+.. GENERATED FROM PYTHON SOURCE LINES 120-125
6. Plotting the results 📊
----------------------------
@@ -290,7 +292,7 @@ Now we're ready to plot the results of our model.
We're using the :class:`~.ConfusionMatrix` class to plot the confusion matrix.
We're seeing each fold's confusion matrices separately on the right, and the confusion matrix created from the concatenated validation sets from each fold on the left.
-.. GENERATED FROM PYTHON SOURCE LINES 123-129
+.. GENERATED FROM PYTHON SOURCE LINES 125-130
.. code-block:: Python
@@ -298,13 +300,12 @@ We're seeing each fold's confusion matrices separately on the right, and the con
confusion_matrix_fig = ConfusionMatrix.from_final_val_data(
single_model_list
)
-
plt.show()
.. image-sg:: /auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_006.png
- :alt: TabularCrossmodalMultiheadAttention: confusion matrix, Tabular Crossmodal multi-head attention: binary_auroc=0.775, Fold 1: binary_auroc=0.825, Fold 2: binary_auroc=0.715, Fold 3: binary_auroc=0.889, Fold 4: binary_auroc=0.901, Fold 5: binary_auroc=0.894
+ :alt: TabularCrossmodalMultiheadAttention: confusion matrix, Tabular Crossmodal multi-head attention: binary_auroc=0.914, Fold 1: binary_auroc=0.889, Fold 2: binary_auroc=0.910, Fold 3: binary_auroc=0.949, Fold 4: binary_auroc=0.893, Fold 5: binary_auroc=0.937
:srcset: /auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_006.png
:class: sphx-glr-single-img
@@ -315,7 +316,7 @@ We're seeing each fold's confusion matrices separately on the right, and the con
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 13.653 seconds)
+ **Total running time of the script:** (0 minutes 12.125 seconds)
.. _sphx_glr_download_auto_examples_training_and_testing_plot_one_model_binary_kfold.py:
diff --git a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold_codeobj.pickle b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold_codeobj.pickle
index 80b0f9f7..dcd69c32 100644
Binary files a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold_codeobj.pickle and b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold_codeobj.pickle differ
diff --git a/docs/auto_examples/training_and_testing/plot_two_models_traintest.ipynb b/docs/auto_examples/training_and_testing/plot_two_models_traintest.ipynb
index a3a6b63c..7a5a1ab5 100644
--- a/docs/auto_examples/training_and_testing/plot_two_models_traintest.ipynb
+++ b/docs/auto_examples/training_and_testing/plot_two_models_traintest.ipynb
@@ -15,7 +15,7 @@
},
"outputs": [],
"source": [
- "import importlib\n\nimport matplotlib.pyplot as plt\nfrom tqdm.auto import tqdm\nimport os\n\nfrom docs.examples import generate_sklearn_simulated_data\nfrom fusilli.data import get_data_module\nfrom fusilli.eval import RealsVsPreds, ModelComparison\nfrom fusilli.train import train_and_save_models\nfrom fusilli.utils.model_chooser import import_chosen_fusion_models"
+ "import importlib\n\nimport matplotlib.pyplot as plt\nfrom tqdm.auto import tqdm\nimport os\n\nfrom docs.examples import generate_sklearn_simulated_data\nfrom fusilli.data import get_data_module\nfrom fusilli.eval import RealsVsPreds, ModelComparison\nfrom fusilli.train import train_and_save_models\nfrom fusilli.utils.model_chooser import import_chosen_fusion_models\n\n# sphinx_gallery_thumbnail_number = 5"
]
},
{
diff --git a/docs/auto_examples/training_and_testing/plot_two_models_traintest.py b/docs/auto_examples/training_and_testing/plot_two_models_traintest.py
index 7833343e..2949c431 100644
--- a/docs/auto_examples/training_and_testing/plot_two_models_traintest.py
+++ b/docs/auto_examples/training_and_testing/plot_two_models_traintest.py
@@ -27,6 +27,8 @@
from fusilli.train import train_and_save_models
from fusilli.utils.model_chooser import import_chosen_fusion_models
+# sphinx_gallery_thumbnail_number = 5
+
# %%
# 1. Import fusion models 🔍
# --------------------------------
diff --git a/docs/auto_examples/training_and_testing/plot_two_models_traintest.rst b/docs/auto_examples/training_and_testing/plot_two_models_traintest.rst
index 338659d8..fbc00dc1 100644
--- a/docs/auto_examples/training_and_testing/plot_two_models_traintest.rst
+++ b/docs/auto_examples/training_and_testing/plot_two_models_traintest.rst
@@ -32,7 +32,7 @@ Regression: Comparing Two Tabular Models Trained on Simulated Data
- 📈 Plotting the results of multiple models as a bar chart.
- 💾 Saving the results of multiple models as a CSV file.
-.. GENERATED FROM PYTHON SOURCE LINES 17-30
+.. GENERATED FROM PYTHON SOURCE LINES 17-32
.. code-block:: Python
@@ -49,6 +49,7 @@ Regression: Comparing Two Tabular Models Trained on Simulated Data
from fusilli.train import train_and_save_models
from fusilli.utils.model_chooser import import_chosen_fusion_models
+ # sphinx_gallery_thumbnail_number = 5
@@ -56,7 +57,8 @@ Regression: Comparing Two Tabular Models Trained on Simulated Data
-.. GENERATED FROM PYTHON SOURCE LINES 31-40
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-42
1. Import fusion models 🔍
--------------------------------
@@ -68,7 +70,7 @@ The function returns list of class objects that match the conditions. If no cond
We're importing ConcatTabularData and TabularChannelWiseMultiAttention models for this example. Both are multimodal tabular models.
-.. GENERATED FROM PYTHON SOURCE LINES 40-47
+.. GENERATED FROM PYTHON SOURCE LINES 42-49
.. code-block:: Python
@@ -86,7 +88,7 @@ We're importing ConcatTabularData and TabularChannelWiseMultiAttention models fo
.. code-block:: pytb
Traceback (most recent call last):
- File "/Users/florencetownend/Library/CloudStorage/OneDrive-UniversityCollegeLondon/Projects/fusilli/docs/examples/training_and_testing/plot_two_models_traintest.py", line 45, in
+ File "/Users/florencetownend/Library/CloudStorage/OneDrive-UniversityCollegeLondon/Projects/fusilli/docs/examples/training_and_testing/plot_two_models_traintest.py", line 47, in
fusion_models = import_chosen_fusion_models(model_conditions)
File "/Users/florencetownend/Library/CloudStorage/OneDrive-UniversityCollegeLondon/Projects/fusilli/fusilli/utils/model_chooser.py", line 323, in import_chosen_fusion_models
imported_models = get_models(model_conditions, skip_models)
@@ -109,7 +111,7 @@ We're importing ConcatTabularData and TabularChannelWiseMultiAttention models fo
-.. GENERATED FROM PYTHON SOURCE LINES 48-59
+.. GENERATED FROM PYTHON SOURCE LINES 50-61
2. Set the training parameters 🎯
-----------------------------------
@@ -123,7 +125,7 @@ For training and testing, the necessary parameters are:
- ``pred_type``: the type of prediction to be performed. This is either ``regression``, ``binary``, or ``classification``. For this example we're using regression.
- ``loss_log_dir``: the directory to save the loss logs to. This is used for plotting the loss curves.
-.. GENERATED FROM PYTHON SOURCE LINES 59-75
+.. GENERATED FROM PYTHON SOURCE LINES 61-77
.. code-block:: Python
@@ -144,14 +146,14 @@ For training and testing, the necessary parameters are:
os.rmdir(os.path.join(params["loss_log_dir"], dir))
-.. GENERATED FROM PYTHON SOURCE LINES 76-80
+.. GENERATED FROM PYTHON SOURCE LINES 78-82
3. Generating simulated data 🔮
--------------------------------
Time to create some simulated data for our models to work their wonders on.
This function also simulated image data which we aren't using here.
-.. GENERATED FROM PYTHON SOURCE LINES 80-89
+.. GENERATED FROM PYTHON SOURCE LINES 82-91
.. code-block:: Python
@@ -165,7 +167,7 @@ This function also simulated image data which we aren't using here.
)
-.. GENERATED FROM PYTHON SOURCE LINES 90-101
+.. GENERATED FROM PYTHON SOURCE LINES 92-103
4. Training the first fusion model 🏁
--------------------------------------
@@ -179,14 +181,14 @@ This function takes the following inputs:
First we'll create a dictionary to store both the trained models so we can compare them later.
-.. GENERATED FROM PYTHON SOURCE LINES 101-103
+.. GENERATED FROM PYTHON SOURCE LINES 103-105
.. code-block:: Python
all_trained_models = {} # create dictionary to store trained models
-.. GENERATED FROM PYTHON SOURCE LINES 104-111
+.. GENERATED FROM PYTHON SOURCE LINES 106-113
To train the first model we need to:
@@ -196,7 +198,7 @@ To train the first model we need to:
4. *Train and test the model*: This is done with the :func:`~fusilli.train.train_and_save_models` function. This function takes the datamodule, the parameters, the fusion model, and the initialised model as inputs. It returns a list of the trained models (in this case, only one model).
5. *Add the trained model to the ``all_trained_models`` dictionary*: This is so we can compare the results of the two models later.
-.. GENERATED FROM PYTHON SOURCE LINES 111-133
+.. GENERATED FROM PYTHON SOURCE LINES 113-135
.. code-block:: Python
@@ -223,7 +225,7 @@ To train the first model we need to:
all_trained_models[fusion_model.__name__] = model_1_list
-.. GENERATED FROM PYTHON SOURCE LINES 134-139
+.. GENERATED FROM PYTHON SOURCE LINES 136-141
5. Plotting the results of the first model 📊
-----------------------------------------------
@@ -231,7 +233,7 @@ Let's unveil the results of our first model's hard work. We're using the :class:
This class takes the trained model as an input and returns a plot of the real values vs the predicted values from the final validation data (when using from_final_val_data).
If you want to plot the results from the test data, you can use from_new_data instead. See the example notebook on plotting with new data for more detail.
-.. GENERATED FROM PYTHON SOURCE LINES 139-144
+.. GENERATED FROM PYTHON SOURCE LINES 141-146
.. code-block:: Python
@@ -241,17 +243,17 @@ If you want to plot the results from the test data, you can use from_new_data in
plt.show()
-.. GENERATED FROM PYTHON SOURCE LINES 145-148
+.. GENERATED FROM PYTHON SOURCE LINES 147-150
6. Training the second fusion model 🏁
---------------------------------------
It's time for our second fusion model to shine! Here we train the second fusion model: TabularChannelWiseMultiAttention. We're using the same steps as before, but this time we're using the second model in the ``fusion_models`` list.
-.. GENERATED FROM PYTHON SOURCE LINES 151-152
+.. GENERATED FROM PYTHON SOURCE LINES 153-154
Choose the model
-.. GENERATED FROM PYTHON SOURCE LINES 152-173
+.. GENERATED FROM PYTHON SOURCE LINES 154-175
.. code-block:: Python
@@ -277,12 +279,12 @@ Choose the model
all_trained_models[fusion_model.__name__] = model_2_list
-.. GENERATED FROM PYTHON SOURCE LINES 174-176
+.. GENERATED FROM PYTHON SOURCE LINES 176-178
7. Plotting the results of the second model 📊
-----------------------------------------------
-.. GENERATED FROM PYTHON SOURCE LINES 176-181
+.. GENERATED FROM PYTHON SOURCE LINES 178-183
.. code-block:: Python
@@ -292,7 +294,7 @@ Choose the model
plt.show()
-.. GENERATED FROM PYTHON SOURCE LINES 182-187
+.. GENERATED FROM PYTHON SOURCE LINES 184-189
8. Comparing the results of the two models 📈
----------------------------------------------
@@ -300,7 +302,7 @@ Let the ultimate showdown begin! We're comparing the results of our two models.
We're using the :class:`~fusilli.eval.ModelComparison` class to compare the results of the two models.
This class takes the trained models as an input and returns a plot of the results of the two models and a Pandas DataFrame of the metrics of the two models.
-.. GENERATED FROM PYTHON SOURCE LINES 187-194
+.. GENERATED FROM PYTHON SOURCE LINES 189-196
.. code-block:: Python
@@ -312,13 +314,13 @@ This class takes the trained models as an input and returns a plot of the result
plt.show()
-.. GENERATED FROM PYTHON SOURCE LINES 195-198
+.. GENERATED FROM PYTHON SOURCE LINES 197-200
9. Saving the metrics of the two models 💾
-------------------------------------------
Time to archive our models' achievements. We're using the :class:`~fusilli.eval.ModelComparison` class to save the metrics of the two models.
-.. GENERATED FROM PYTHON SOURCE LINES 198-200
+.. GENERATED FROM PYTHON SOURCE LINES 200-202
.. code-block:: Python
@@ -328,7 +330,7 @@ Time to archive our models' achievements. We're using the :class:`~fusilli.eval.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.002 seconds)
+ **Total running time of the script:** (0 minutes 0.003 seconds)
.. _sphx_glr_download_auto_examples_training_and_testing_plot_two_models_traintest.py:
diff --git a/docs/auto_examples/training_and_testing/plot_two_models_traintest_codeobj.pickle b/docs/auto_examples/training_and_testing/plot_two_models_traintest_codeobj.pickle
index 0111840c..e794df78 100644
Binary files a/docs/auto_examples/training_and_testing/plot_two_models_traintest_codeobj.pickle and b/docs/auto_examples/training_and_testing/plot_two_models_traintest_codeobj.pickle differ
diff --git a/docs/auto_examples/training_and_testing/sg_execution_times.rst b/docs/auto_examples/training_and_testing/sg_execution_times.rst
index c0eb2b2f..0077a9cb 100644
--- a/docs/auto_examples/training_and_testing/sg_execution_times.rst
+++ b/docs/auto_examples/training_and_testing/sg_execution_times.rst
@@ -6,7 +6,7 @@
Computation times
=================
-**00:00.008** total execution time for 3 files **from auto_examples/training_and_testing**:
+**00:12.208** total execution time for 3 files **from auto_examples/training_and_testing**:
.. container::
@@ -32,12 +32,12 @@ Computation times
* - Example
- Time
- Mem (MB)
+ * - :ref:`sphx_glr_auto_examples_training_and_testing_plot_one_model_binary_kfold.py` (``plot_one_model_binary_kfold.py``)
+ - 00:12.125
+ - 0.0
* - :ref:`sphx_glr_auto_examples_training_and_testing_plot_model_comparison_loop_kfold.py` (``plot_model_comparison_loop_kfold.py``)
- - 00:00.006
+ - 00:00.080
- 0.0
* - :ref:`sphx_glr_auto_examples_training_and_testing_plot_two_models_traintest.py` (``plot_two_models_traintest.py``)
- - 00:00.002
- - 0.0
- * - :ref:`sphx_glr_auto_examples_training_and_testing_plot_one_model_binary_kfold.py` (``plot_one_model_binary_kfold.py``)
- - 00:00.000
+ - 00:00.003
- 0.0
diff --git a/docs/conf.py b/docs/conf.py
index bd79ee73..0fee62cc 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -115,6 +115,7 @@
],
),
"within_subsection_order": FileNameSortKey,
+ 'default_thumb_file': '_static/pink_pasta_logo.png',
}
diff --git a/docs/contributing_examples/A_template_other_fusion_codeobj.pickle b/docs/contributing_examples/A_template_other_fusion_codeobj.pickle
index 7ec59e8d..0c11f908 100644
Binary files a/docs/contributing_examples/A_template_other_fusion_codeobj.pickle and b/docs/contributing_examples/A_template_other_fusion_codeobj.pickle differ
diff --git a/docs/contributing_examples/template_graph_fusion_codeobj.pickle b/docs/contributing_examples/template_graph_fusion_codeobj.pickle
index 1dad9f1a..db1eb3b3 100644
Binary files a/docs/contributing_examples/template_graph_fusion_codeobj.pickle and b/docs/contributing_examples/template_graph_fusion_codeobj.pickle differ
diff --git a/docs/data_loading.rst b/docs/data_loading.rst
index 470b44d9..c4bd7189 100644
--- a/docs/data_loading.rst
+++ b/docs/data_loading.rst
@@ -58,9 +58,9 @@ Tabular and Image Data
Tabular data should follow the format specified above. Image data should be in a ``.pt`` file format with dimensions
``(num_samples, num_channels, height, width)``.
-For example, for 100 2D 28x28 grey-scale images, my images.pt file would have the dimensions ``(100, 1, 28, 28)``.
+For example, for 100 2D 28x28 grey-scale images, my images.pt file would have the dimensions ``(100, 1, 28, 28)`` when I use ``torch.load()``.
-For 100 3D 32x32x32 RGB images, my images.pt file would have the dimensions ``(100, 3, 32, 32, 32)``.
+For 100 3D 32x32x32 RGB images, my images.pt file would have the dimensions ``(100, 3, 32, 32, 32)`` when I use ``torch.load()``.
**Example of loading tabular and image data:**
@@ -122,12 +122,12 @@ If you use a different suffix than the default "_test", you must pass the suffix
}
# Using the training data (params["tabular1_source"], params["tabular2_source"], and params["img_source"])
- data_module = get_data_module(params)
+ data_module = get_data_module(fusion_model=some_example_model, params=params)
# Train the model on params["tabular1_source"], params["tabular2_source"], and params["img_source"]
- trained_model_dict = train_and_save_models(data_module, params, some_example_model)
+ trained_model= train_and_save_models(data_module, params, some_example_model)
# Evaluate the model on the external test data:
# params["tabular1_source_testing"], params["tabular2_source_testing"], and params["img_source_testing"]
- RealsVsPreds.from_new_data(model, params, data_file_suffix="_testing")
+ RealsVsPreds.from_new_data(trained_model, params, data_file_suffix="_testing")
diff --git a/docs/examples/customising_behaviour/customising_training_parameters.py b/docs/examples/customising_behaviour/customising_training_parameters.py
index 517e2737..d11bd9f2 100644
--- a/docs/examples/customising_behaviour/customising_training_parameters.py
+++ b/docs/examples/customising_behaviour/customising_training_parameters.py
@@ -153,3 +153,4 @@
The ``extra_log_string_dict`` argument is also used to modify the logging behaviour of the model. For more information, see :ref:`wandb`.
"""
+# sphinx_gallery_thumbnail_path = '_static/pink_pasta_logo.png'
diff --git a/docs/examples/customising_behaviour/loss_figures/AttentionWeightedGNN.png b/docs/examples/customising_behaviour/loss_figures/AttentionWeightedGNN.png
index 01c47a6e..4728d2b9 100644
Binary files a/docs/examples/customising_behaviour/loss_figures/AttentionWeightedGNN.png and b/docs/examples/customising_behaviour/loss_figures/AttentionWeightedGNN.png differ
diff --git a/docs/examples/customising_behaviour/loss_figures/ConcatTabularFeatureMaps.png b/docs/examples/customising_behaviour/loss_figures/ConcatTabularFeatureMaps.png
index afcdb6d3..961a5545 100644
Binary files a/docs/examples/customising_behaviour/loss_figures/ConcatTabularFeatureMaps.png and b/docs/examples/customising_behaviour/loss_figures/ConcatTabularFeatureMaps.png differ
diff --git a/docs/examples/customising_behaviour/loss_logs/modify_layers/AttentionWeightedGNN/metrics.csv b/docs/examples/customising_behaviour/loss_logs/modify_layers/AttentionWeightedGNN/metrics.csv
index c24c2a30..6bf58b09 100644
--- a/docs/examples/customising_behaviour/loss_logs/modify_layers/AttentionWeightedGNN/metrics.csv
+++ b/docs/examples/customising_behaviour/loss_logs/modify_layers/AttentionWeightedGNN/metrics.csv
@@ -1,12 +1,12 @@
-val_loss,step,R2_val,MAE_val,epoch,R2_train,train_loss,MAE_train
-63.55864334106445,0,-0.06568336486816406,6.7931671142578125,0,,,
-,0,,,0,-0.01151895523071289,74.97073364257812,6.880713939666748
-63.6014289855957,1,-0.06640076637268066,6.7944135665893555,1,,,
-,1,,,1,-0.009949326515197754,74.8543930053711,6.875021457672119
-63.642791748046875,2,-0.06709432601928711,6.7956414222717285,2,,,
-,2,,,2,-0.011220455169677734,74.94860076904297,6.879720687866211
-63.685707092285156,3,-0.06781387329101562,6.79683780670166,3,,,
-,3,,,3,-0.0103987455368042,74.88771057128906,6.879384994506836
-63.727577209472656,4,-0.06851589679718018,6.798135280609131,4,,,
-,4,,,4,-0.01016998291015625,74.8707504272461,6.8800506591796875
-63.727577209472656,5,-0.06851589679718018,6.798135280609131,5,,,
+val_loss,epoch,MAE_val,step,R2_val,train_loss,R2_train,MAE_train
+167.5082550048828,0,10.717531204223633,0,-0.004385232925415039,,,
+,0,,0,,105.55506896972656,-0.003792405128479004,8.279817581176758
+167.48513793945312,1,10.717973709106445,1,-0.00424647331237793,,,
+,1,,1,,105.5949478149414,-0.004171609878540039,8.286035537719727
+167.4565887451172,2,10.718071937561035,2,-0.004075288772583008,,,
+,2,,2,,105.68045806884766,-0.004984736442565918,8.289670944213867
+167.42849731445312,3,10.718225479125977,3,-0.003906846046447754,,,
+,3,,3,,105.51983642578125,-0.003457307815551758,8.283498764038086
+167.40155029296875,4,10.718470573425293,4,-0.0037453174591064453,,,
+,4,,4,,105.49263000488281,-0.0031986236572265625,8.279643058776855
+167.40155029296875,5,10.718470573425293,5,-0.0037453174591064453,,,
diff --git a/docs/examples/customising_behaviour/loss_logs/modify_layers/ConcatTabularFeatureMaps/metrics.csv b/docs/examples/customising_behaviour/loss_logs/modify_layers/ConcatTabularFeatureMaps/metrics.csv
index 1cd6205a..52f822ee 100644
--- a/docs/examples/customising_behaviour/loss_logs/modify_layers/ConcatTabularFeatureMaps/metrics.csv
+++ b/docs/examples/customising_behaviour/loss_logs/modify_layers/ConcatTabularFeatureMaps/metrics.csv
@@ -1,12 +1,12 @@
-val_loss,step,R2_val,MAE_val,epoch,R2_train,train_loss,MAE_train
-63.1016845703125,9,-0.0239332914352417,6.413779258728027,0,,,
-,9,,,0,-0.28295496106147766,75.14381408691406,6.9738664627075195
-63.12262725830078,19,-0.024272918701171875,6.4155426025390625,1,,,
-,19,,,1,-0.10543932020664215,75.0732650756836,6.972143650054932
-63.1646728515625,29,-0.024955391883850098,6.419016361236572,2,,,
-,29,,,2,-0.5992171168327332,75.05265045166016,6.971622467041016
-63.20314407348633,39,-0.025579452514648438,6.422268867492676,3,,,
-,39,,,3,-0.07159312069416046,75.01472473144531,6.969532012939453
-63.29044723510742,49,-0.026996254920959473,6.4294538497924805,4,,,
-,49,,,4,-0.20253725349903107,74.99994659423828,6.9703569412231445
-63.29044723510742,50,-0.026996254920959473,6.4294538497924805,5,,,
+val_loss,epoch,MAE_val,step,R2_val,train_loss,R2_train,MAE_train
+115.2126693725586,0,8.956426620483398,9,-0.14734911918640137,,,
+,0,,9,,118.92069244384766,-0.1175713986158371,8.718819618225098
+115.06486511230469,1,8.952523231506348,19,-0.14587712287902832,,,
+,1,,19,,118.92134857177734,-0.11324342340230942,8.719310760498047
+115.07295227050781,2,8.95265007019043,29,-0.14595770835876465,,,
+,2,,29,,118.88328552246094,-0.13166308403015137,8.718779563903809
+115.05082702636719,3,8.951873779296875,39,-0.14573729038238525,,,
+,3,,39,,118.8876953125,-0.2592984735965729,8.718381881713867
+114.9400634765625,4,8.948206901550293,49,-0.14463436603546143,,,
+,4,,49,,118.8436050415039,-0.20507344603538513,8.717238426208496
+114.9400634765625,5,8.948206901550293,50,-0.14463436603546143,,,
diff --git a/docs/examples/customising_behaviour/plot_modify_layer_sizes.py b/docs/examples/customising_behaviour/plot_modify_layer_sizes.py
index 06465246..1b8daf8a 100644
--- a/docs/examples/customising_behaviour/plot_modify_layer_sizes.py
+++ b/docs/examples/customising_behaviour/plot_modify_layer_sizes.py
@@ -20,9 +20,10 @@
#
# First, we will set up the experiment by importing the necessary packages, creating the simulated data, and setting the parameters for the experiment.
#
-# For a more detailed explanation of this process, please see the :ref:`train_test_examples` tutorials.
+# For a more detailed explanation of this process, please see the example tutorials.
#
+# sphinx_gallery_thumbnail_path = '_static/modify_thumbnail.png'
import matplotlib.pyplot as plt
import os
import torch.nn as nn
@@ -101,7 +102,7 @@
# * - Attribute
# - Guidance
# * - :attr:`~.AttentionWeightedGNN.graph_conv_layers`
-# - ``nn.Sequential`` of ``torch_geometric.nn` Layers.
+# - ``nn.Sequential`` of ``torch_geometric.nn`` Layers.
# * - :attr:`~.AttentionWeightedGNN.dropout_prob`
# - Float between (not including) 0 and 1.
#
@@ -164,6 +165,8 @@
print("Fusion model:\n", trained_model_list[0].model)
# %%
+# You can see that the input features to the ``final_prediction`` layer changed to fit with our modification to the ``graph_conv_layers`` output features!
+#
# What happens when the modifications are incorrect?
# ----------------------------------------------------
#
@@ -226,7 +229,7 @@
# %%
# What about modifying multiple attributes with the **conflicting modifications**?
# -------------------------------------------------------------------------------------
-
+#
#
# For this, let's switch to looking at the :class:`~fusilli.fusionmodels.tabularfusion.concat_feature_maps.ConcatTabularFeatureMaps` model.
# This model concatenates the feature maps of the two modalities and then passes them through a prediction layer.
diff --git a/docs/examples/training_and_testing/README.rst b/docs/examples/training_and_testing/README.rst
index f91b4609..0170c6ed 100644
--- a/docs/examples/training_and_testing/README.rst
+++ b/docs/examples/training_and_testing/README.rst
@@ -3,4 +3,6 @@
Running Fusilli on your own data
==========================================
-These are examples of how to train and validate fusion models with Fusilli.
\ No newline at end of file
+These are examples of how to train and validate fusion models with Fusilli.
+
+
diff --git a/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_0/metrics.csv b/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_0/metrics.csv
index e719134d..ab4e8f54 100644
--- a/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_0/metrics.csv
+++ b/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_0/metrics.csv
@@ -1,90 +1,56 @@
-epoch,binary_auroc_val,step,val_loss,binary_accuracy_val,train_loss,binary_auroc_train,binary_accuracy_train
-0,0.6422275304794312,12,0.7012183666229248,0.5199999809265137,,,
-0,,12,,,0.7143906950950623,0.6695048809051514,0.4975000023841858
-1,0.7087339758872986,25,0.6870812177658081,0.5199999809265137,,,
-1,,25,,,0.6884341239929199,0.7467377185821533,0.4975000023841858
-2,0.7367788553237915,38,0.6741982102394104,0.5199999809265137,,,
-2,,38,,,0.6693103909492493,0.8109018206596375,0.4975000023841858
-3,0.7932692766189575,51,0.6504029035568237,0.75,,,
-3,,51,,,0.6390573978424072,0.8407748341560364,0.6675000190734863
-4,0.8381410241127014,64,0.6215775012969971,0.7599999904632568,,,
-4,,64,,,0.5966801643371582,0.9005104899406433,0.8100000023841858
-5,0.8313301801681519,77,0.6141452193260193,0.800000011920929,,,
-5,,77,,,0.5765408873558044,0.9290128350257874,0.8349999785423279
-6,0.8501603007316589,90,0.620589017868042,0.7300000190734863,,,
-6,,90,,,0.5526933073997498,0.9225618243217468,0.8899999856948853
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diff --git a/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_1/metrics.csv b/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_1/metrics.csv
index 5f5b88d1..bf28682a 100644
--- a/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_1/metrics.csv
+++ b/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_1/metrics.csv
@@ -1,34 +1,62 @@
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diff --git a/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_2/metrics.csv b/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_2/metrics.csv
index bcf47a0c..16663cfa 100644
--- a/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_2/metrics.csv
+++ b/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_2/metrics.csv
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diff --git a/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_3/metrics.csv b/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_3/metrics.csv
index 8fcd4103..e66f62ba 100644
--- a/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_3/metrics.csv
+++ b/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_3/metrics.csv
@@ -1,58 +1,56 @@
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-26,,350,,,0.5387552380561829,0.9422136545181274,0.9275000095367432
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-27,,363,,,0.539226233959198,0.9438387155532837,0.9300000071525574
-28,0.900563657283783,364,0.5687531232833862,0.8199999928474426,,,
+val_loss,binary_accuracy_val,step,epoch,binary_auroc_val,train_loss,binary_accuracy_train,binary_auroc_train
+0.6909504532814026,0.47999998927116394,12,0,0.7824519276618958,,,
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+,,233,17,,0.5500975251197815,0.9350000023841858,0.9633530378341675
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+,,246,18,,0.5475243926048279,0.9399999976158142,0.9617783427238464
+0.575404942035675,0.8600000143051147,259,19,0.8836137652397156,,,
+,,259,19,,0.5512694120407104,0.9225000143051147,0.9601110816001892
+0.5941245555877686,0.7599999904632568,272,20,0.8920272588729858,,,
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+0.5747604370117188,0.8500000238418579,285,21,0.8934295177459717,,,
+,,285,21,,0.5485545992851257,0.9375,0.9561912417411804
+0.5751659274101257,0.8500000238418579,298,22,0.8954326510429382,,,
+,,298,22,,0.5472170114517212,0.9350000023841858,0.9662303328514099
+0.5714908242225647,0.8500000238418579,311,23,0.8934295177459717,,,
+,,311,23,,0.5453129410743713,0.9399999976158142,0.9658167362213135
+0.5789478421211243,0.8299999833106995,324,24,0.8882211446762085,,,
+,,324,24,,0.5445793867111206,0.9399999976158142,0.9635571241378784
+0.5763249397277832,0.8500000238418579,337,25,0.8930287957191467,,,
+,,337,25,,0.5428630709648132,0.9449999928474426,0.9659469723701477
+0.5749369263648987,0.8399999737739563,350,26,0.8928285241127014,,,
+,,350,26,,0.5421866774559021,0.9449999928474426,0.9682925343513489
+0.5749369263648987,0.8399999737739563,351,27,0.8928285241127014,,,
diff --git a/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_4/metrics.csv b/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_4/metrics.csv
index 20b3d3c5..cb4578c6 100644
--- a/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_4/metrics.csv
+++ b/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_4/metrics.csv
@@ -1,82 +1,48 @@
-epoch,binary_auroc_val,step,val_loss,binary_accuracy_val,train_loss,binary_auroc_train,binary_accuracy_train
-0,0.6270707249641418,12,0.7205723524093628,0.550000011920929,,,
-0,,12,,,0.7142954468727112,0.6083438396453857,0.49000000953674316
-1,0.7701009511947632,25,0.6923736333847046,0.550000011920929,,,
-1,,25,,,0.692568838596344,0.7299098968505859,0.49000000953674316
-2,0.8222222328186035,38,0.6768297553062439,0.550000011920929,,,
-2,,38,,,0.6786987781524658,0.8516606092453003,0.49000000953674316
-3,0.842424213886261,51,0.6510421633720398,0.7300000190734863,,,
-3,,51,,,0.6435143351554871,0.8469188213348389,0.612500011920929
-4,0.8468687534332275,64,0.6314790844917297,0.7799999713897705,,,
-4,,64,,,0.5952818989753723,0.889496386051178,0.7975000143051147
-5,0.8226262927055359,77,0.6169912219047546,0.7900000214576721,,,
-5,,77,,,0.5982217192649841,0.9030898809432983,0.7599999904632568
-6,0.8840404748916626,90,0.6043382883071899,0.8299999833106995,,,
-6,,90,,,0.5794934630393982,0.8999670147895813,0.8224999904632568
-7,0.8751515746116638,103,0.6060068011283875,0.8399999737739563,,,
-7,,103,,,0.5661002397537231,0.907722532749176,0.8424999713897705
-8,0.8739393353462219,116,0.6201812028884888,0.7900000214576721,,,
-8,,116,,,0.5574885606765747,0.91168212890625,0.8650000095367432
-9,0.8808081746101379,129,0.6159329414367676,0.800000011920929,,,
-9,,129,,,0.5516414046287537,0.9179368019104004,0.8899999856948853
-10,0.8775758147239685,142,0.6107947826385498,0.8199999928474426,,,
-10,,142,,,0.5508862137794495,0.9261478185653687,0.8899999856948853
-11,0.8913131952285767,155,0.6132524609565735,0.800000011920929,,,
-11,,155,,,0.5486111640930176,0.9279526472091675,0.8899999856948853
-12,0.8690908551216125,168,0.6058670282363892,0.8299999833106995,,,
-12,,168,,,0.5446937680244446,0.924893856048584,0.8949999809265137
-13,0.8761616349220276,181,0.6038558483123779,0.8299999833106995,,,
-13,,181,,,0.5421572923660278,0.9231563806533813,0.9049999713897705
-14,0.8705050945281982,194,0.612667441368103,0.8199999928474426,,,
-14,,194,,,0.539376437664032,0.9237447381019592,0.9125000238418579
-15,0.8905050158500671,207,0.5954322218894958,0.8399999737739563,,,
-15,,207,,,0.544013500213623,0.9280430674552917,0.9075000286102295
-16,0.8765656352043152,220,0.6133000254631042,0.8100000023841858,,,
-16,,220,,,0.5424632430076599,0.9325312972068787,0.8999999761581421
-17,0.8791919946670532,233,0.608955442905426,0.8199999928474426,,,
-17,,233,,,0.536297619342804,0.9295540452003479,0.9150000214576721
-18,0.8757575750350952,246,0.6152135729789734,0.8100000023841858,,,
-18,,246,,,0.5334329605102539,0.9270873069763184,0.9225000143051147
-19,0.8834344744682312,259,0.6024828553199768,0.8199999928474426,,,
-19,,259,,,0.5351195931434631,0.9240635633468628,0.9200000166893005
-20,0.9012120962142944,272,0.6153839230537415,0.7900000214576721,,,
-20,,272,,,0.5419520139694214,0.934303343296051,0.8949999809265137
-21,0.8975756764411926,285,0.6120014190673828,0.800000011920929,,,
-21,,285,,,0.544977068901062,0.932090163230896,0.8974999785423279
-22,0.9032323360443115,298,0.6149550080299377,0.8100000023841858,,,
-22,,298,,,0.5388228297233582,0.9382585287094116,0.9100000262260437
-23,0.8957576155662537,311,0.600746750831604,0.8399999737739563,,,
-23,,311,,,0.531538724899292,0.9316678047180176,0.9275000095367432
-24,0.8892929553985596,324,0.5912111401557922,0.8500000238418579,,,
-24,,324,,,0.5342770218849182,0.9291132092475891,0.9175000190734863
-25,0.8896969556808472,337,0.5946808457374573,0.8500000238418579,,,
-25,,337,,,0.5348299741744995,0.9246534705162048,0.9225000143051147
-26,0.87494957447052,350,0.6095221042633057,0.8199999928474426,,,
-26,,350,,,0.529801070690155,0.9315860271453857,0.9300000071525574
-27,0.8696969747543335,363,0.615399956703186,0.8199999928474426,,,
-27,,363,,,0.5283816456794739,0.934262216091156,0.9375
-28,0.8814141750335693,376,0.6031569242477417,0.8299999833106995,,,
-28,,376,,,0.5312106609344482,0.9335075616836548,0.9275000095367432
-29,0.8765656352043152,389,0.6156668663024902,0.8100000023841858,,,
-29,,389,,,0.5304827094078064,0.9339916706085205,0.925000011920929
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-31,0.8797979950904846,415,0.6123383045196533,0.8199999928474426,,,
-31,,415,,,0.5336142182350159,0.9280501008033752,0.9200000166893005
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-33,0.8864647150039673,441,0.6106898188591003,0.8299999833106995,,,
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-35,,467,,,0.5256887674331665,0.9434303045272827,0.9399999976158142
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-36,,480,,,0.5274398922920227,0.9383721351623535,0.9375
-37,0.8971717357635498,493,0.6158947944641113,0.8100000023841858,,,
-37,,493,,,0.5271857976913452,0.9302121996879578,0.9375
-38,0.8955556750297546,506,0.6089950799942017,0.8299999833106995,,,
-38,,506,,,0.5278543829917908,0.928286075592041,0.9375
-39,0.8937374353408813,519,0.6073752641677856,0.8299999833106995,,,
-39,,519,,,0.5277900099754333,0.9326534271240234,0.9375
-40,0.8937374353408813,520,0.6073752641677856,0.8299999833106995,,,
+val_loss,binary_accuracy_val,step,epoch,binary_auroc_val,train_loss,binary_accuracy_train,binary_auroc_train
+0.686049222946167,0.5,12,0,0.8436000347137451,,,
+,,12,0,,0.7216865420341492,0.5299999713897705,0.7406684756278992
+0.6703799962997437,0.5,25,1,0.884399950504303,,,
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+0.5659933090209961,0.8799999952316284,51,3,0.9214000701904297,,,
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+,,103,7,,0.571550726890564,0.8899999856948853,0.928240180015564
+0.5589205026626587,0.8799999952316284,116,8,0.9374001026153564,,,
+,,116,8,,0.5647629499435425,0.9049999713897705,0.9246901869773865
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+,,129,9,,0.5674784779548645,0.8924999833106995,0.9269760251045227
+0.5694619417190552,0.8500000238418579,142,10,0.928600013256073,,,
+,,142,10,,0.5634103417396545,0.9024999737739563,0.9210529923439026
+0.5633764863014221,0.8600000143051147,155,11,0.926800012588501,,,
+,,155,11,,0.5646228790283203,0.9075000286102295,0.922433614730835
+0.5693679451942444,0.8500000238418579,168,12,0.9287998676300049,,,
+,,168,12,,0.5664188861846924,0.8899999856948853,0.9246780872344971
+0.5662060379981995,0.8600000143051147,181,13,0.9254001379013062,,,
+,,181,13,,0.5612626671791077,0.9100000262260437,0.9328082799911499
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+,,194,14,,0.5605978965759277,0.9125000238418579,0.9292641282081604
+0.559342086315155,0.8799999952316284,207,15,0.9328000545501709,,,
+,,207,15,,0.5583831667900085,0.9175000190734863,0.9261724948883057
+0.5585382580757141,0.8700000047683716,220,16,0.9339999556541443,,,
+,,220,16,,0.5565151572227478,0.9200000166893005,0.9342958927154541
+0.5603170394897461,0.8799999952316284,233,17,0.9328000545501709,,,
+,,233,17,,0.5553870797157288,0.9225000143051147,0.9355185627937317
+0.5615874528884888,0.8700000047683716,246,18,0.9323999881744385,,,
+,,246,18,,0.5550589561462402,0.9200000166893005,0.9303997159004211
+0.5695570707321167,0.8500000238418579,259,19,0.9328000545501709,,,
+,,259,19,,0.5546401739120483,0.9225000143051147,0.9299198389053345
+0.5603280067443848,0.8799999952316284,272,20,0.9264000654220581,,,
+,,272,20,,0.5535286068916321,0.925000011920929,0.9332154989242554
+0.5666249394416809,0.8399999737739563,285,21,0.9308000802993774,,,
+,,285,21,,0.5561432838439941,0.9175000190734863,0.9329145550727844
+0.5630534887313843,0.8700000047683716,298,22,0.9372000694274902,,,
+,,298,22,,0.5566124320030212,0.9100000262260437,0.9306011199951172
+0.5630534887313843,0.8700000047683716,299,23,0.9372000694274902,,,
diff --git a/docs/examples/training_and_testing/plot_model_comparison_loop_kfold.py b/docs/examples/training_and_testing/plot_model_comparison_loop_kfold.py
index a6dc1f95..96a39e96 100644
--- a/docs/examples/training_and_testing/plot_model_comparison_loop_kfold.py
+++ b/docs/examples/training_and_testing/plot_model_comparison_loop_kfold.py
@@ -54,6 +54,8 @@
from fusilli.train import train_and_save_models
from fusilli.utils.model_chooser import import_chosen_fusion_models
+# sphinx_gallery_thumbnail_number = -1
+
# from IPython.utils import io # for hiding the tqdm progress bar
# %%
@@ -127,6 +129,8 @@
# In this section, we train all the fusion models using the generated data and specified parameters.
# We store the results of each model for later analysis.
+# Using %%capture to hide the progress bar and plots (there are a lot of them!)
+
all_trained_models = {}
for i, fusion_model in enumerate(fusion_models):
@@ -148,6 +152,8 @@
# Save to all_trained_models
all_trained_models[fusion_model_name] = single_model_list
+ plt.close("all")
+
# %%
# 5. Plotting the results of the individual models
# -------------------------------------------------
diff --git a/docs/examples/training_and_testing/plot_one_model_binary_kfold.py b/docs/examples/training_and_testing/plot_one_model_binary_kfold.py
index 995ebb84..fd75925a 100644
--- a/docs/examples/training_and_testing/plot_one_model_binary_kfold.py
+++ b/docs/examples/training_and_testing/plot_one_model_binary_kfold.py
@@ -24,6 +24,8 @@
from fusilli.eval import ConfusionMatrix
from fusilli.train import train_and_save_models
+# sphinx_gallery_thumbnail_number = -1
+
# %%
# 1. Import the fusion model 🔍
# --------------------------------
@@ -124,5 +126,4 @@
confusion_matrix_fig = ConfusionMatrix.from_final_val_data(
single_model_list
)
-
plt.show()
diff --git a/docs/examples/training_and_testing/plot_two_models_traintest.py b/docs/examples/training_and_testing/plot_two_models_traintest.py
index 7833343e..5a4b7869 100644
--- a/docs/examples/training_and_testing/plot_two_models_traintest.py
+++ b/docs/examples/training_and_testing/plot_two_models_traintest.py
@@ -27,6 +27,8 @@
from fusilli.train import train_and_save_models
from fusilli.utils.model_chooser import import_chosen_fusion_models
+# sphinx_gallery_thumbnail_number = -1
+
# %%
# 1. Import fusion models 🔍
# --------------------------------
diff --git a/docs/experiment_setup.rst b/docs/experiment_setup.rst
index c3748c46..038ab5d8 100644
--- a/docs/experiment_setup.rst
+++ b/docs/experiment_setup.rst
@@ -25,7 +25,7 @@ Here is the dictionary structure for defining necessary directories:
.. warning::
- Fusilli utilizes predetermined file names to save files in these directories. Overwriting may occur if files with the same names exist. It's highly recommended to maintain separate directories for each Fusilli run to manage files belonging to each run effectively.
+ Fusilli utilises predetermined file names to save files in these directories. Overwriting may occur if files with the same names exist. **It's highly recommended to maintain separate directories for each Fusilli experiment** to manage files belonging to each run effectively.
Example for Creating Directory Structure
@@ -36,7 +36,6 @@ Here's an example block to set up the necessary directory structure:
.. code-block:: python
import os
- from datetime import datetime
# Create a timestamp for the run
run_name = "Run1"
diff --git a/docs/fusion_model_explanations.rst b/docs/fusion_model_explanations.rst
index c6f42415..eea6a908 100644
--- a/docs/fusion_model_explanations.rst
+++ b/docs/fusion_model_explanations.rst
@@ -250,7 +250,7 @@ Incoming!
Graph-based
-----------
.. warning::
- It is not possible to use graph-based models with any evaluation with completely unseen data, such as in the method :meth:`.RealsVsPreds.from_new_data`.
+ ⚠️ It is not possible to use external test set data with graph-based fusion models. Trying to use a "from new data" method such as :meth:`.RealsVsPreds.from_new_data` will result in an error.
:class:`.EdgeCorrGNN`
~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/docs/index.rst b/docs/index.rst
index 9123603e..5cb112d1 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -10,6 +10,9 @@ Don't be silly, use fusilli! 🍝
Have you got multimodal data but not sure how to combine them for your machine learning task? Look no further!
This library provides a way to compare different fusion methods for your multimodal data.
+.. image:: _static/fusilli_pipeline_diagram.png
+ :align: left
+
-----
Why would you want to use fusilli?
@@ -33,7 +36,7 @@ Why would you want to use fusilli?
.. toctree::
:maxdepth: 1
- :caption: 🌸 Table of Contents 🌸
+ :caption: 🌸 Getting Started 🌸
introduction
fusion_model_explanations
@@ -41,10 +44,16 @@ Why would you want to use fusilli?
data_loading
experiment_setup
quick_start
+
+-----
+
+.. toctree::
+ :maxdepth: 1
+ :caption: 🌸 Further Guidance 🌸
+
choosing_model
modifying_models
logging_with_wandb
- developers_guide
glossary
-----
@@ -60,8 +69,9 @@ Why would you want to use fusilli?
.. toctree::
:maxdepth: 2
- :caption: 🌸 How to contribute 🌸
+ :caption: 🌸 Contributing 🌸
+ developers_guide
contributing_examples/index
-----
diff --git a/docs/logging_with_wandb.rst b/docs/logging_with_wandb.rst
index e00f4975..03161165 100644
--- a/docs/logging_with_wandb.rst
+++ b/docs/logging_with_wandb.rst
@@ -49,4 +49,4 @@ Upon training and inspecting Weights and Biases, the run will be labeled as ``Ed
**What if you're not using Weights and Biases?**
-When not utilizing Weights and Biases, Fusilli plots loss curves and saves them locally as PNG files. In this scenario, the WandB project name is replaced by user-specified tags in the PNG file name. For instance, running the same fusion model without using Weights and Biases will produce a PNG file named ``EdgeCorrGNN_dropout_prob_0.2.png``, saved in ``params["loss_fig_path"]``.
\ No newline at end of file
+When not using Weights and Biases, Fusilli plots loss curves and saves them locally as PNG files. In this scenario, the WandB project name is replaced by user-specified tags in the PNG file name. For instance, running the same fusion model without using Weights and Biases will produce a PNG file named ``EdgeCorrGNN_dropout_prob_0.2.png``, saved in ``params["loss_fig_path"]``.
\ No newline at end of file
diff --git a/docs/modifying_models.rst b/docs/modifying_models.rst
index 2ee539bd..cbb966fc 100644
--- a/docs/modifying_models.rst
+++ b/docs/modifying_models.rst
@@ -109,7 +109,7 @@ Modifiable Attributes
* - Attribute
- Guidance
* - :attr:`~.AttentionWeightedGNN.graph_conv_layers`
- - ``nn.Sequential`` of ``torch_geometric.nn` Layers.
+ - ``nn.Sequential`` of ``torch_geometric.nn`` Layers.
* - :attr:`~.AttentionWeightedGNN.dropout_prob`
- Float between (not including) 0 and 1.
diff --git a/docs/quick_start.rst b/docs/quick_start.rst
index 86e1e6b3..9975fd93 100644
--- a/docs/quick_start.rst
+++ b/docs/quick_start.rst
@@ -5,7 +5,7 @@ This script provides a simple setup to train a model using ``fusilli`` on a sing
.. note::
- For a more detailed guide on using Fusilli, refer to the :ref:`example_notebooks`.
+ For a more detailed guide on using Fusilli, refer to the :ref:`train_test_examples`.
This code showcases the necessary steps to execute Fusilli on a single dataset.
@@ -27,7 +27,7 @@ Ensure the elements in the ``params`` dictionary contain specific keys; you can
# Import the example fusion model
from fusilli.fusionmodels.tabularfusion.example_model import ExampleModel
- # Set paths to your tabular datasets (CSV files with study_id and pred_label columns)
+ # Set paths to the data
tabular_1_path = "path/to/tabular_1.csv"
tabular_2_path = "path/to/tabular_2.csv"
image_path = "path/to/image_file.pt"
diff --git a/docs/sg_execution_times.rst b/docs/sg_execution_times.rst
index 82865512..84f05b94 100644
--- a/docs/sg_execution_times.rst
+++ b/docs/sg_execution_times.rst
@@ -6,7 +6,7 @@
Computation times
=================
-**00:07.276** total execution time for 8 files **from all galleries**:
+**00:12.208** total execution time for 8 files **from all galleries**:
.. container::
@@ -32,19 +32,19 @@ Computation times
* - Example
- Time
- Mem (MB)
- * - :ref:`sphx_glr_auto_examples_customising_behaviour_plot_modify_layer_sizes.py` (``examples/customising_behaviour/plot_modify_layer_sizes.py``)
- - 00:07.268
+ * - :ref:`sphx_glr_auto_examples_training_and_testing_plot_one_model_binary_kfold.py` (``examples/training_and_testing/plot_one_model_binary_kfold.py``)
+ - 00:12.125
- 0.0
* - :ref:`sphx_glr_auto_examples_training_and_testing_plot_model_comparison_loop_kfold.py` (``examples/training_and_testing/plot_model_comparison_loop_kfold.py``)
- - 00:00.006
+ - 00:00.080
- 0.0
* - :ref:`sphx_glr_auto_examples_training_and_testing_plot_two_models_traintest.py` (``examples/training_and_testing/plot_two_models_traintest.py``)
- - 00:00.002
+ - 00:00.003
- 0.0
* - :ref:`sphx_glr_auto_examples_customising_behaviour_customising_training_parameters.py` (``examples/customising_behaviour/customising_training_parameters.py``)
- 00:00.000
- 0.0
- * - :ref:`sphx_glr_auto_examples_training_and_testing_plot_one_model_binary_kfold.py` (``examples/training_and_testing/plot_one_model_binary_kfold.py``)
+ * - :ref:`sphx_glr_auto_examples_customising_behaviour_plot_modify_layer_sizes.py` (``examples/customising_behaviour/plot_modify_layer_sizes.py``)
- 00:00.000
- 0.0
* - :ref:`sphx_glr_contributing_examples_A_template_other_fusion.py` (``how_to_contribute/A_template_other_fusion.py``)
diff --git a/fusilli/data.py b/fusilli/data.py
index f3374d5d..e7ebf557 100644
--- a/fusilli/data.py
+++ b/fusilli/data.py
@@ -316,7 +316,7 @@ def load_img(self):
return dataset, [None, None, img_dim]
- def load_tabular_tabularular(self):
+ def load_tabular_tabular(self):
"""
Loads the tabular1 and tabular2 multimodal dataset
@@ -473,7 +473,7 @@ def __init__(
"img": LoadDatasets(self.sources, image_downsample_size).load_img,
"tabular_tabular": LoadDatasets(
self.sources, image_downsample_size
- ).load_tabular_tabularular,
+ ).load_tabular_tabular,
"tabular_image": LoadDatasets(
self.sources, image_downsample_size
).load_tab_and_img,
@@ -736,7 +736,7 @@ def __init__(
"img": LoadDatasets(self.sources, self.image_downsample_size).load_img,
"tabular_tabular": LoadDatasets(
self.sources, self.image_downsample_size
- ).load_tabular_tabularular,
+ ).load_tabular_tabular,
"tabular_image": LoadDatasets(
self.sources, self.image_downsample_size
).load_tab_and_img,
@@ -1050,7 +1050,7 @@ def __init__(
"img": LoadDatasets(self.sources, self.image_downsample_size).load_img,
"tabular_tabular": LoadDatasets(
self.sources, self.image_downsample_size
- ).load_tabular_tabularular,
+ ).load_tabular_tabular,
"tabular_image": LoadDatasets(
self.sources, self.image_downsample_size
).load_tab_and_img,
@@ -1206,7 +1206,7 @@ def __init__(
"img": LoadDatasets(self.sources, self.image_downsample_size).load_img,
"tabular_tabular": LoadDatasets(
self.sources, self.image_downsample_size
- ).load_tabular_tabularular,
+ ).load_tabular_tabular,
"tabular_image": LoadDatasets(
self.sources, self.image_downsample_size
).load_tab_and_img,
diff --git a/fusilli/eval.py b/fusilli/eval.py
index 0c78e034..dc2276e1 100644
--- a/fusilli/eval.py
+++ b/fusilli/eval.py
@@ -1,7 +1,7 @@
"""
This module contains classes and functions for evaluating trained models (i.e. plotting results from training).
The setup for this module has been inspired by the scikit-learn API for plotting results, which involves each plot
-being a class with a ``from_model`` method that takes in a trained model and returns a plot with the validation data,
+being a class with a ``from_final_val_data`` method that takes in a trained model and returns a plot with the validation data,
and a ``from_new_data`` method that takes in a trained model and new data and returns a plot.
"""
@@ -643,7 +643,7 @@ def from_new_data(
overall_kfold_metrics,
)
- figure.suptitle("From new data")
+ figure.suptitle("Evaluation: External Test Data")
elif len(model_list) == 1:
# isinstance(model, nn.Module): # train/test model
@@ -675,7 +675,7 @@ def from_new_data(
metric_values,
)
- figure.suptitle("From new data")
+ figure.suptitle("Evaluation: External Test Data")
else:
raise ValueError("Argument 'model_list' is an empty list. ")
@@ -746,7 +746,7 @@ def from_final_val_data(cls, model_list):
overall_kfold_metrics,
)
- figure.suptitle("From final val data")
+ figure.suptitle("Evaluation: Validation Data")
elif len(model_list) == 1:
# isinstance(model[0], nn.Module): # train/test model
@@ -777,7 +777,7 @@ def from_final_val_data(cls, model_list):
metric_values,
)
- figure.suptitle("From final val data")
+ figure.suptitle("Evaluation: Validation Data")
else:
raise ValueError(("Argument 'model_list' is an empty list. "))
diff --git a/fusilli/fusionmodels/tabularfusion/activation.py b/fusilli/fusionmodels/tabularfusion/activation.py
index 33208c7f..4890d5a0 100644
--- a/fusilli/fusionmodels/tabularfusion/activation.py
+++ b/fusilli/fusionmodels/tabularfusion/activation.py
@@ -1,3 +1,7 @@
+"""
+Activation-function fusion model for tabular data.
+"""
+
import torch.nn as nn
from fusilli.fusionmodels.base_model import ParentFusionModel
import torch
@@ -15,10 +19,10 @@ class ActivationFusion(ParentFusionModel, nn.Module):
----------
pred_type : str
Type of prediction to be performed.
- mod1_layers : dict
+ mod1_layers : nn.ModuleDict
Dictionary containing the layers of the first modality. Calculated in the
:meth:`~ParentFusionModel.set_mod1_layers` method.
- mod2_layers : dict
+ mod2_layers : nn.ModuleDict
Dictionary containing the layers of the second modality. Calculated in the
:meth:`~ParentFusionModel.set_mod2_layers` method.
fused_dim : int
@@ -33,11 +37,11 @@ class ActivationFusion(ParentFusionModel, nn.Module):
:meth:`~ActivationFusion.calc_fused_layers` method.
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Activation function map fusion"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_tabular"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "operation"
def __init__(self, pred_type, data_dims, params):
@@ -49,7 +53,7 @@ def __init__(self, pred_type, data_dims, params):
data_dims : list
Dictionary containing the dimensions of the data.
params : dict
- Dictionary containing the parameters of the model.
+ Dictionary containing the user-specified parameters.
"""
ParentFusionModel.__init__(self, pred_type, data_dims, params)
diff --git a/fusilli/fusionmodels/tabularfusion/attention_and_activation.py b/fusilli/fusionmodels/tabularfusion/attention_and_activation.py
index 55653417..a3f22f86 100644
--- a/fusilli/fusionmodels/tabularfusion/attention_and_activation.py
+++ b/fusilli/fusionmodels/tabularfusion/attention_and_activation.py
@@ -1,3 +1,7 @@
+"""
+Using activation functions to fuse tabular data, with self-attention on the second tabular modality.
+"""
+
import torch.nn as nn
from fusilli.fusionmodels.base_model import ParentFusionModel
@@ -17,10 +21,10 @@ class AttentionAndSelfActivation(ParentFusionModel, nn.Module):
----------
pred_type : str
Type of prediction to be performed.
- mod1_layers : dict
+ mod1_layers : nn.ModuleDict
Dictionary containing the layers of the first modality. Calculated in the
:meth:`~ParentFusionModel.set_mod1_layers` method.
- mod2_layers : dict
+ mod2_layers : nn.ModuleDict
Dictionary containing the layers of the second modality. Calculated in the
:meth:`~ParentFusionModel.set_mod2_layers` method.
fused_dim : int
@@ -37,11 +41,11 @@ class AttentionAndSelfActivation(ParentFusionModel, nn.Module):
Reduction ratio of the channel attention module.
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Activation function and tabular self-attention"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_tabular"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "operation"
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/fusionmodels/tabularfusion/attention_weighted_GNN.py b/fusilli/fusionmodels/tabularfusion/attention_weighted_GNN.py
index 69d458f9..a66955b3 100644
--- a/fusilli/fusionmodels/tabularfusion/attention_weighted_GNN.py
+++ b/fusilli/fusionmodels/tabularfusion/attention_weighted_GNN.py
@@ -1,10 +1,5 @@
"""
-Inspired by the multi-modal brain age paper:
-- Pretraining a model on concatenated tabular modalities
-- Getting out the attention weights from the model: what does this mean?
-- Make the graph structure:
-- node features are second modality,
-- edges are based on the attention weights: get weighted phenotypes by multiplying the attention weights by the multiple-
+Attention weighted GNN model: the edge weights are the attention weights from a pre-trained MLP and the node features are the second modality.
"""
import torch.nn as nn
from fusilli.fusionmodels.base_model import ParentFusionModel
@@ -434,6 +429,9 @@ class AttentionWeightedGNN(ParentFusionModel, nn.Module):
"""
Graph neural network with the edge weighting as the distances between each nodes' weighted phenotypes and the node features as the second tabular modality features.
+ This is a model inspired by method in `Bintsi et al. (2023) `_ : *Multimodal brain age estimation using interpretable adaptive population-graph learning*.
+
+
Attributes
----------
pred_type : str
@@ -447,13 +445,13 @@ class AttentionWeightedGNN(ParentFusionModel, nn.Module):
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Attention-weighted GNN"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_tabular"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "graph"
# class: Graph maker class.
diff --git a/fusilli/fusionmodels/tabularfusion/channelwise_att.py b/fusilli/fusionmodels/tabularfusion/channelwise_att.py
index 43a968c7..8d0ace3d 100644
--- a/fusilli/fusionmodels/tabularfusion/channelwise_att.py
+++ b/fusilli/fusionmodels/tabularfusion/channelwise_att.py
@@ -34,15 +34,14 @@ class TabularChannelWiseMultiAttention(ParentFusionModel, nn.Module):
Springer International Publishing. https://doi.org/10.1007/978-3-030-59713-9_24
Accompanying code: (our model is inspired by the work of Duanmu et al. (2020) [1])
- https://github.com/HongyiDuanmu26/Prediction-of-pCR-with-Integrative-Deep-Learning/
- blob/main/CustomNet.py
+ https://github.com/HongyiDuanmu26/Prediction-of-pCR-with-Integrative-Deep-Learning/blob/main/CustomNet.py
Attributes
----------
- mod1_layers : dict
+ mod1_layers : nn.ModuleDict
Dictionary containing the layers of the 1st type of tabular data.
- mod2_layers : dict
+ mod2_layers : nn.ModuleDict
Dictionary containing the layers of the 2nd type of tabular data.
match_dim_layers : nn.ModuleDict
Module dictionary containing the linear layers to make the dimensions of the two types of
@@ -59,11 +58,11 @@ class TabularChannelWiseMultiAttention(ParentFusionModel, nn.Module):
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Channel-wise multiplication net (tabular)"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_tabular"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "attention"
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/fusionmodels/tabularfusion/concat_data.py b/fusilli/fusionmodels/tabularfusion/concat_data.py
index 5366ed3f..72853bd4 100644
--- a/fusilli/fusionmodels/tabularfusion/concat_data.py
+++ b/fusilli/fusionmodels/tabularfusion/concat_data.py
@@ -29,11 +29,11 @@ class ConcatTabularData(ParentFusionModel, nn.Module):
Calculated in the :meth:`~ParentFusionModel.set_final_pred_layers` method.
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Concatenating tabular data"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_tabular"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "operation"
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/fusionmodels/tabularfusion/concat_feature_maps.py b/fusilli/fusionmodels/tabularfusion/concat_feature_maps.py
index 8145b130..0a032dd5 100644
--- a/fusilli/fusionmodels/tabularfusion/concat_feature_maps.py
+++ b/fusilli/fusionmodels/tabularfusion/concat_feature_maps.py
@@ -17,10 +17,10 @@ class ConcatTabularFeatureMaps(ParentFusionModel, nn.Module):
----------
pred_type : str
Type of prediction to be performed.
- mod1_layers : dict
+ mod1_layers : nn.ModuleDict
Dictionary containing the layers of the first modality. Calculated in the
:meth:`~ParentFusionModel.set_mod1_layers` method.
- mod2_layers : dict
+ mod2_layers : nn.ModuleDict
Dictionary containing the layers of the second modality. Calculated in the
:meth:`~ParentFusionModel.set_mod2_layers` method.
fused_dim : int
@@ -35,11 +35,11 @@ class ConcatTabularFeatureMaps(ParentFusionModel, nn.Module):
:meth:`~ConcatTabularFeatureMaps.calc_fused_layers` method.
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Concatenating tabular feature maps"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_tabular"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "operation"
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/fusionmodels/tabularfusion/crossmodal_att.py b/fusilli/fusionmodels/tabularfusion/crossmodal_att.py
index 7a662db4..2a2754b0 100644
--- a/fusilli/fusionmodels/tabularfusion/crossmodal_att.py
+++ b/fusilli/fusionmodels/tabularfusion/crossmodal_att.py
@@ -33,9 +33,9 @@ class TabularCrossmodalMultiheadAttention(ParentFusionModel, nn.Module):
Type of prediction to be performed.
attention_embed_dim : int
Number of features of the multihead attention layer.
- mod1_layers : dict
+ mod1_layers : nn.ModuleDict
Dictionary containing the layers of the first modality.
- mod2_layers : dict
+ mod2_layers : nn.ModuleDict
Dictionary containing the layers of the second modality.
fused_dim : int
Number of features of the fused layers. This is the flattened output size of the
@@ -55,11 +55,11 @@ class TabularCrossmodalMultiheadAttention(ParentFusionModel, nn.Module):
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Tabular Crossmodal multi-head attention"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_tabular"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "attention"
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/fusionmodels/tabularfusion/decision.py b/fusilli/fusionmodels/tabularfusion/decision.py
index 40687bf6..e3b076e9 100644
--- a/fusilli/fusionmodels/tabularfusion/decision.py
+++ b/fusilli/fusionmodels/tabularfusion/decision.py
@@ -16,9 +16,9 @@ class TabularDecision(ParentFusionModel, nn.Module):
Attributes
----------
- mod1_layers : dict
+ mod1_layers : nn.ModuleDict
Dictionary containing the layers of the 1st type of tabular data.
- mod2_layers : dict
+ mod2_layers : nn.ModuleDict
Dictionary containing the layers of the 2nd type of tabular data.
fused_layers : nn.Sequential
Sequential layer containing the fused layers.
@@ -31,11 +31,11 @@ class TabularDecision(ParentFusionModel, nn.Module):
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Tabular decision"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_tabular"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "operation"
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/fusionmodels/tabularfusion/edge_corr_gnn.py b/fusilli/fusionmodels/tabularfusion/edge_corr_gnn.py
index c3d0ef3a..49e79f51 100644
--- a/fusilli/fusionmodels/tabularfusion/edge_corr_gnn.py
+++ b/fusilli/fusionmodels/tabularfusion/edge_corr_gnn.py
@@ -1,5 +1,5 @@
"""
-Edge correlation GNN model.
+Edge correlation GNN model: edges are weighted by the correlation between the nodes' first tabular modality features.
"""
import torch.nn as nn
@@ -108,11 +108,11 @@ class EdgeCorrGNN(ParentFusionModel, nn.Module):
take in 256 features.
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Edge Correlation GNN"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_tabular"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "graph"
# class: Graph maker class.
graph_maker = EdgeCorrGraphMaker
diff --git a/fusilli/fusionmodels/tabularfusion/mcvae_model.py b/fusilli/fusionmodels/tabularfusion/mcvae_model.py
index 7eff1d7c..bba5e2e6 100644
--- a/fusilli/fusionmodels/tabularfusion/mcvae_model.py
+++ b/fusilli/fusionmodels/tabularfusion/mcvae_model.py
@@ -355,11 +355,11 @@ class MCVAE_tab(ParentFusionModel, nn.Module):
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "MCVAE Tabular"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_tabular"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "subspace"
# class: Subspace method class.
subspace_method = MCVAESubspaceMethod
diff --git a/fusilli/fusionmodels/tabularimagefusion/channelwise_att.py b/fusilli/fusionmodels/tabularimagefusion/channelwise_att.py
index 1b47a919..9a423d1c 100644
--- a/fusilli/fusionmodels/tabularimagefusion/channelwise_att.py
+++ b/fusilli/fusionmodels/tabularimagefusion/channelwise_att.py
@@ -33,15 +33,14 @@ class ImageChannelWiseMultiAttention(ParentFusionModel, nn.Module):
Springer International Publishing. https://doi.org/10.1007/978-3-030-59713-9_24
Accompanying code: (our model is inspired by the work of Duanmu et al. (2020) [1])
- https://github.com/HongyiDuanmu26/Prediction-of-pCR-with-Integrative-Deep-Learning/
- blob/main/CustomNet.py
+ https://github.com/HongyiDuanmu26/Prediction-of-pCR-with-Integrative-Deep-Learning/blob/main/CustomNet.py
Attributes
----------
- mod1_layers : dict
+ mod1_layers : nn.ModuleDict
Dictionary containing the layers of the 1st type of tabular data.
- img_layers : dict
+ img_layers : nn.ModuleDict
Dictionary containing the layers of the image data.
match_dim_layers : nn.ModuleDict
Module dictionary containing the linear layers to make the dimensions of the two types of
@@ -56,11 +55,11 @@ class ImageChannelWiseMultiAttention(ParentFusionModel, nn.Module):
Sequential layer containing the final prediction layers.
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Channel-wise Image attention"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_image"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "attention"
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubleloss.py b/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubleloss.py
index 8cb188c2..ac0018fe 100644
--- a/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubleloss.py
+++ b/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubleloss.py
@@ -68,13 +68,13 @@ class ConcatImgLatentTabDoubleLoss(ParentFusionModel, nn.Module):
Size of the fused layers: latent dimension size + tabular data dimension size.
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = (
"Trained Together Latent Image + Tabular Data"
)
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_image"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "subspace"
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubletrain.py b/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubletrain.py
index ee0b8be0..a527c1af 100644
--- a/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubletrain.py
+++ b/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubletrain.py
@@ -441,13 +441,13 @@ class ConcatImgLatentTabDoubleTrain(ParentFusionModel, nn.Module):
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Pretrained Latent Image + Tabular Data"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_image"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "subspace"
- # class: Class containing the method to train the latent image space.
+ #: class: Class containing the method to train the latent image space.
subspace_method = concat_img_latent_tab_subspace_method
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_data.py b/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_data.py
index 673b358b..c9c16d17 100644
--- a/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_data.py
+++ b/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_data.py
@@ -20,7 +20,7 @@ class ConcatImageMapsTabularData(ParentFusionModel, nn.Module):
----------
pred_type : str
Type of prediction to be performed.
- img_layers : dict
+ img_layers : nn.ModuleDict
Dictionary containing the layers of the image data.
fused_layers : nn.Sequential
Sequential layer containing the fused layers. Calculated in the
diff --git a/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_maps.py b/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_maps.py
index 0abe4682..07369791 100644
--- a/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_maps.py
+++ b/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_maps.py
@@ -19,10 +19,10 @@ class ConcatImageMapsTabularMaps(ParentFusionModel, nn.Module):
----------
pred_type : str
Type of prediction to be performed.
- mod1_layers : dict
+ mod1_layers : nn.ModuleDict
Dictionary containing the layers of the first modality.
Calculated in the :meth:`~ParentFusionModel.set_mod1_layers` method.
- img_layers : dict
+ img_layers : nn.ModuleDict
Dictionary containing the layers of the image data.
Calculated in the :meth:`~ParentFusionModel.set_img_layers` method.
fused_dim : int
@@ -37,11 +37,11 @@ class ConcatImageMapsTabularMaps(ParentFusionModel, nn.Module):
Calculated in the :meth:`~ConcatImageMapsTabularMaps.calc_fused_layers` method.
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Concatenating tabular and image feature maps"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_image"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "operation"
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/fusionmodels/tabularimagefusion/crossmodal_att.py b/fusilli/fusionmodels/tabularimagefusion/crossmodal_att.py
index 478c560f..026bbd2d 100644
--- a/fusilli/fusionmodels/tabularimagefusion/crossmodal_att.py
+++ b/fusilli/fusionmodels/tabularimagefusion/crossmodal_att.py
@@ -32,9 +32,9 @@ class CrossmodalMultiheadAttention(ParentFusionModel, nn.Module):
Type of prediction to be performed.
attention_embed_dim : int
Number of features of the multihead attention layer.
- mod1_layers : dict
+ mod1_layers : nn.ModuleDict
Dictionary containing the layers of the first modality.
- img_layers : dict
+ img_layers : nn.ModuleDict
Dictionary containing the layers of the image data.
fused_dim : int
Number of features of the fused layers. This is the flattened output size of the
@@ -57,11 +57,11 @@ class CrossmodalMultiheadAttention(ParentFusionModel, nn.Module):
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Crossmodal multi-head attention"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_image"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "attention"
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/fusionmodels/tabularimagefusion/decision.py b/fusilli/fusionmodels/tabularimagefusion/decision.py
index bf65b97d..73e9f88e 100644
--- a/fusilli/fusionmodels/tabularimagefusion/decision.py
+++ b/fusilli/fusionmodels/tabularimagefusion/decision.py
@@ -18,9 +18,9 @@ class ImageDecision(ParentFusionModel, nn.Module):
Attributes
----------
- mod1_layers : dict
+ mod1_layers : nn.ModuleDict
Dictionary containing the layers of the 1st type of tabular data.
- img_layers : dict
+ img_layers : nn.ModuleDict
Dictionary containing the layers of the image data.
fused_layers : nn.Sequential
Sequential layer containing the fused layers.
@@ -40,11 +40,11 @@ class ImageDecision(ParentFusionModel, nn.Module):
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Image decision fusion"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_image"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "operation"
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/fusionmodels/tabularimagefusion/denoise_tab_img_maps.py b/fusilli/fusionmodels/tabularimagefusion/denoise_tab_img_maps.py
index 8ddc4874..736cbc29 100644
--- a/fusilli/fusionmodels/tabularimagefusion/denoise_tab_img_maps.py
+++ b/fusilli/fusionmodels/tabularimagefusion/denoise_tab_img_maps.py
@@ -711,11 +711,11 @@ class DAETabImgMaps(ParentFusionModel, nn.Module):
Final prediction layers.
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Denoising tabular autoencoder with image maps"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "tabular_image"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "subspace"
# class: Subspace method.
subspace_method = denoising_autoencoder_subspace_method
diff --git a/fusilli/fusionmodels/unimodal/image.py b/fusilli/fusionmodels/unimodal/image.py
index 3b1f95b8..407a185f 100644
--- a/fusilli/fusionmodels/unimodal/image.py
+++ b/fusilli/fusionmodels/unimodal/image.py
@@ -1,5 +1,5 @@
"""
-unimodal model using only the image data.
+Unimodal model using only the image data.
"""
import torch.nn as nn
@@ -11,11 +11,11 @@
class ImgUnimodal(ParentFusionModel, nn.Module):
"""
- This class implements a uni-modal model using only the image data.
+ A uni-modal model using only the image data.
Attributes
----------
- img_layers : dict
+ img_layers : nn.ModuleDict
Dictionary containing the layers of the image data.
fused_dim : int
Number of features of the fused layers. This is the flattened output size of the
@@ -26,11 +26,11 @@ class ImgUnimodal(ParentFusionModel, nn.Module):
Sequential layer containing the final prediction layers.
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Image unimodal"
- # str: Type of modality.
+ #: str: Type of modality.
modality_type = "img"
- # str: Type of fusion.
+ #: str: Type of fusion.
fusion_type = "unimodal"
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/fusionmodels/unimodal/tabular1.py b/fusilli/fusionmodels/unimodal/tabular1.py
index 3b426631..790534bc 100644
--- a/fusilli/fusionmodels/unimodal/tabular1.py
+++ b/fusilli/fusionmodels/unimodal/tabular1.py
@@ -14,7 +14,7 @@ class Tabular1Unimodal(ParentFusionModel, nn.Module):
Attributes
----------
- mod1_layers : dict
+ mod1_layers : nn.ModuleDict
Dictionary containing the layers of the 1st type of tabular data.
fused_layers : nn.Sequential
Sequential layer containing the fused layers.
diff --git a/fusilli/fusionmodels/unimodal/tabular2.py b/fusilli/fusionmodels/unimodal/tabular2.py
index e73fc085..8ff65be8 100644
--- a/fusilli/fusionmodels/unimodal/tabular2.py
+++ b/fusilli/fusionmodels/unimodal/tabular2.py
@@ -14,7 +14,7 @@ class Tabular2Unimodal(ParentFusionModel, nn.Module):
Attributes
----------
- mod2_layers : dict
+ mod2_layers : nn.ModuleDict
Dictionary containing the layers of the 2nd type of tabular data.
fused_dim : int
Dimension of the fused layer.
@@ -27,11 +27,11 @@ class Tabular2Unimodal(ParentFusionModel, nn.Module):
"""
- # str: Name of the method.
+ #: str: Name of the method.
method_name = "Tabular2 uni-modal"
- # str: Modality type.
+ #: str: Modality type.
modality_type = "tabular2"
- # str: Fusion type.
+ #: str: Fusion type.
fusion_type = "unimodal"
def __init__(self, pred_type, data_dims, params):
diff --git a/fusilli/utils/simulated_data/tabular1data.csv b/fusilli/utils/simulated_data/tabular1data.csv
index bf5b6f9c..6269bbb8 100644
--- a/fusilli/utils/simulated_data/tabular1data.csv
+++ b/fusilli/utils/simulated_data/tabular1data.csv
@@ -1,101 +1,501 @@
study_id,feature1,feature2,feature3,feature4,feature5,feature6,feature7,feature8,feature9,feature10,pred_label
-0,0.08159315829762914,0.02246919080183284,-0.005159745762685981,-0.004957608139752365,0.03737679033947965,0.0006521170988175482,-0.057827845717823845,-0.03229771752159221,0.09824172462371694,-0.06501844414557813,-9.386501915581157
-1,-0.1262433903294737,0.02602828396926107,-0.019238580535962304,-0.058506990430689694,-0.06925439883989286,0.03182122786346663,-0.050048127547209165,-0.08728928998927518,0.07056490687076378,-0.05811630800020113,-1.7468407776207453
-2,0.13000024036424696,0.005400793987473151,0.006261102010361295,0.055035373519369386,-0.09735584942045881,-0.012650736612037887,-0.032052917493646624,0.061212038169855584,-0.10416698516573175,0.015779934702012426,11.42337826512294
-3,-0.04255190982124984,0.01813707035345594,-0.005875197458733732,-0.07740351249968323,-0.0840280795019577,-0.023151852591026093,-0.05244266053889351,-0.058333099377864434,0.044741033010231294,-0.06605247748294685,-8.960424867675506
-4,-0.0038399913014329832,0.037421694444201174,-0.10088260105023562,-0.03964042228046778,-0.026315588284689002,-0.015406944632969303,0.026890486220676014,0.013981160635245814,-0.011126124387698425,0.03713366587571829,-10.549998717151903
-5,-0.07616645857396359,0.05826163762450585,-0.00923068515883973,-0.0015463431143725626,-0.03951515973271376,0.0018176735637221786,-0.06262553584299495,-0.06774927328117569,0.06126128157149735,-0.003149317633706167,-8.035734875145458
-6,0.035179389204659677,0.013829617919256578,0.04212292495880592,0.018875632364696575,0.017722815287436425,0.030967467976504667,0.05278314755172025,0.024301781203359615,-0.05065616627521664,0.08899440243814973,10.266721864453325
-7,0.061999335449191326,0.05330647442754529,0.026708546042501127,0.02949180015257474,0.051889948617753937,0.02856808310965284,0.07256786302285999,0.06392203816558793,-0.0549898347343045,0.021877461559219446,8.239865282641073
-8,-0.023076403221848472,0.028753922862946583,-0.010692721101189871,0.03318387339053408,0.0060779698370528,0.0009459060698822433,-0.029605830606456312,-0.012576056744089478,-0.0008649567079881861,-0.0005433128911842275,-3.2578711445649855
-9,0.05385294594794113,-0.026301794106871425,-0.04224705103845617,-0.031474922698909114,0.007316378446616212,0.004096141743637711,-0.014762637497378406,-0.08354176923158442,0.010107978069296648,-0.06741322103239578,-5.290820723069421
-10,0.09154820159086238,-0.038944428933548284,-0.03387735903180146,0.028890207000764844,0.062208661459360826,0.030607501210674862,-0.02559448918675375,0.07172805552037267,-0.028318859466505412,-0.0879108617210766,2.7079871868814758
-11,-0.06168283010567295,0.10240974590412923,0.049294731647136714,-0.04747374205258075,0.015311493655316727,-0.011441934541140026,0.005239423965477844,0.010708489558852154,-0.04791031669260296,0.03550485073015664,-6.103849258750555
-12,0.09274801207023413,-0.053201851825759344,0.04990946959465308,0.03535393757936806,0.0672271592141766,-0.042377798008663606,-0.02671733732133201,0.02065454753803106,0.08244028517136921,-0.024745726122547176,13.28732628932773
-13,-0.05371560489406412,0.001227852165816356,-0.019874883887056754,-0.05032126699078499,-0.022688963702692497,0.05168815474950034,-0.03214536455528765,0.022502819061524303,0.030850847585475273,0.021567449575399196,-6.231781841297989
-14,0.08950426653901783,-0.0018599311235876967,-0.01601334397402197,0.014970902770881166,-0.005687918539269931,-0.038545648361143846,-0.037595470446231724,0.1091873654887757,-0.08164485437829092,0.05325547641812374,-1.9720660856175272
-15,-0.05885769342231169,-0.1085279806420845,-0.014559506999756425,-0.006186168015160208,-0.06405431166314916,-0.007436392430884717,-0.07079775540785137,-0.03229898310687918,-0.06583759179265583,-0.019168742996502914,-9.098360025404926
-16,0.0883438205171994,-0.0532241059833079,0.0009806021863979168,0.0066915867779576465,0.010384354956975995,-0.13330462999782788,-0.04426796719478455,-0.04534065722127617,-0.050622973615452226,-0.07385543101991542,-8.237257883042794
-17,0.06497788576392204,-0.07003148190502217,0.040694279346543426,0.0366004780193434,-0.050678638466406106,-0.0438069565877061,0.007442973937072228,-0.050686124267551896,-0.04687241910599981,0.01917891948211256,8.20925913415061
-18,0.014287915243098692,-0.0006943528520836004,0.1205652402947724,-0.010229101643508152,0.09728531032299323,0.08743946154804676,0.10223942881724693,0.01802584721530927,-0.03494056074538256,0.061706356278148834,23.018362850690895
-19,-0.04649016318453624,-0.052690219718414526,-0.017674002991158174,0.060018232224569694,-0.010670285425096558,0.00893962266336448,-0.007177963878880913,-0.09435345569007558,0.007332405696706269,-0.03939132224669865,-14.290925287099572
-20,-0.011024344718729215,0.016628260011180007,-0.05249760902627256,0.033823131142938376,-0.016992900503619235,-0.02638636610246912,0.015612410612247933,-0.05211669992693923,0.013784509395284373,-0.08999947059929819,-3.870533454322295
-21,0.057136949176246975,-0.10179230149389286,0.04397036894917175,-0.01911813000907851,-0.07280272261201937,0.026885008964991457,0.019987048706885044,-0.03464894399550192,-0.011778261812468776,-0.037372512734885156,1.3455718801684244
-22,0.003335895397407895,0.025104758847336737,0.008464733495722658,-0.044979026591015527,0.010248030459219062,0.028530160381419874,-0.0005096371377265559,-0.035741165445118656,-0.02749438065114126,-0.018038387013265763,-4.627309227802801
-23,0.02182354864947108,0.0815036204862636,-0.03289032556574552,0.034230072895081634,0.06324636128032826,-0.046765572514237776,0.035694389948954845,-0.026985242676094624,0.0239356672004137,-0.010645304684121833,1.4585604593839765
-24,0.015890689367775664,0.02797120043524521,0.0019976037053078045,-0.058031838878763294,0.07286453949645681,0.0010691096926081045,0.03863665117518108,-0.03722328557805983,0.03159517352863098,-0.032665027432260685,7.5655125271543575
-25,0.003093385674283326,-0.08127472452466368,-0.0345699789385955,-0.0157966654584476,-0.05390177035957872,0.0056346760188645305,-0.02731412029562563,-0.020743762902495518,0.004082579086461495,-0.022816398378703296,-13.837327864463767
-26,0.0217051715624496,-0.047950656753775925,0.0025246183041621718,-0.0031196458118670505,-0.015701010515384153,0.023529764970595658,0.02215030949779874,0.012469606895830576,0.05348757534271306,-0.05704216911590501,-2.1155340245730194
-27,0.01856153977110838,-0.01695275432917592,-0.012522673568381576,-0.046130561304892986,0.0025256859585112623,-0.0644609325731707,-0.014540240655003738,-0.019758787413464676,0.06775992358412647,-0.05478741271547062,-8.48372277782832
-28,0.11695303522624614,-0.014484389794455606,-0.0002936696791112759,0.024521179638128787,0.01849103812897191,-0.0074568711920034135,0.047346923588467205,0.06998474583864274,-0.022176783618085293,-0.02807279447422792,2.7312586700945305
-29,0.023760955523445256,-0.030829126125099655,0.05180320151428505,0.029195170863437567,0.07351391861324175,0.0790587872217917,-0.019685638279456984,-0.033064585723664296,0.037683996620824396,-0.020667564775851358,13.85121257322645
-30,-0.033064053181551756,0.04829169606337679,-0.007361712280779094,0.04499644796571652,0.04144419593181973,-0.01452166148295831,0.031862068080561884,-0.009801424285644955,0.04741647016857491,-0.019480099833722456,1.1854456953502606
-31,-0.05726914516702313,0.053750296615612266,-0.04574690060838864,-0.05152371623101522,0.05183228206582604,-0.00452993797676592,0.048890173588601676,0.05452869003095138,0.041548655965371856,8.622004746637753e-05,4.360278015394725
-32,0.02069812032198973,-0.0331280778156792,0.045346842784613726,0.02466791536312213,-0.04333797020667102,-0.015804063282979414,0.04293647628602867,-0.013841624461221263,-0.006542369888131356,0.033724509946305575,-2.756013035250208
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diff --git a/fusilli/utils/simulated_data/tabular2data.csv b/fusilli/utils/simulated_data/tabular2data.csv
index e8b9bcbe..94a49218 100644
--- a/fusilli/utils/simulated_data/tabular2data.csv
+++ b/fusilli/utils/simulated_data/tabular2data.csv
@@ -1,101 +1,501 @@
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