原文：https://tensorflow.google.cn/tutorials/estimator/keras_model_to_estimator
TensorFlow 完全支持 TensorFlow Estimator，可以从新的和现有的 tf.keras 模型创建 Estimator。本教程包含了该过程完整且最为简短的示例。
import tensorflow as tf
import numpy as np
import tensorflow_datasets as tfds 在 Keras 中，需要通过组装层来构建模型。模型（通常）是由层构成的计算图。最常见的模型类型是一种叠加层：tf.keras.Sequential 模型。
构建一个简单的全连接网络（即多层感知器）：
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(16, activation='relu', input_shape=(4,)),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(3)
]) 编译模型并获取摘要。
model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer='adam')
model.summary() Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 16) 80
_________________________________________________________________
dropout (Dropout) (None, 16) 0
_________________________________________________________________
dense_1 (Dense) (None, 3) 51
=================================================================
Total params: 131
Trainable params: 131
Non-trainable params: 0
_________________________________________________________________使用 Datasets API 可以扩展到大型数据集或多设备训练。
Estimator 需要控制构建输入流水线的时间和方式。为此，它们需要一个“输入函数”或 input_fn。Estimator 将不使用任何参数调用此函数。input_fn 必须返回 tf.data.Dataset。
def input_fn():
split = tfds.Split.TRAIN
dataset = tfds.load('iris', split=split, as_supervised=True)
dataset = dataset.map(lambda features, labels: ({'dense_input':features}, labels))
dataset = dataset.batch(32).repeat()
return dataset 测试您的 input_fn
for features_batch, labels_batch in input_fn().take(1):
print(features_batch)
print(labels_batch) Downloading and preparing dataset iris/2.0.0 (download: 4.44 KiB, generated: Unknown size, total: 4.44 KiB) to /home/kbuilder/tensorflow_datasets/iris/2.0.0...
Shuffling and writing examples to /home/kbuilder/tensorflow_datasets/iris/2.0.0.incompleteQ29ZWS/iris-train.tfrecord
Dataset iris downloaded and prepared to /home/kbuilder/tensorflow_datasets/iris/2.0.0\. Subsequent calls will reuse this data.
{'dense_input': <tf.Tensor: shape=(32, 4), dtype=float32, numpy=
array([[5.1, 3.4, 1.5, 0.2],
[7.7, 3\. , 6.1, 2.3],
[5.7, 2.8, 4.5, 1.3],
[6.8, 3.2, 5.9, 2.3],
[5.2, 3.4, 1.4, 0.2],
[5.6, 2.9, 3.6, 1.3],
[5.5, 2.6, 4.4, 1.2],
[5.5, 2.4, 3.7, 1\. ],
[4.6, 3.4, 1.4, 0.3],
[7.7, 2.8, 6.7, 2\. ],
[7\. , 3.2, 4.7, 1.4],
[4.6, 3.2, 1.4, 0.2],
[6.5, 3\. , 5.2, 2\. ],
[5.5, 4.2, 1.4, 0.2],
[5.4, 3.9, 1.3, 0.4],
[5\. , 3.5, 1.3, 0.3],
[5.1, 3.8, 1.5, 0.3],
[4.8, 3\. , 1.4, 0.1],
[6.5, 3\. , 5.8, 2.2],
[7.6, 3\. , 6.6, 2.1],
[6.7, 3.3, 5.7, 2.1],
[7.9, 3.8, 6.4, 2\. ],
[6.7, 3\. , 5.2, 2.3],
[5.8, 4\. , 1.2, 0.2],
[6.3, 2.5, 5\. , 1.9],
[5\. , 3\. , 1.6, 0.2],
[6.9, 3.1, 5.1, 2.3],
[6.1, 3\. , 4.6, 1.4],
[5.8, 2.7, 4.1, 1\. ],
[5.2, 2.7, 3.9, 1.4],
[6.7, 3\. , 5\. , 1.7],
[5.7, 2.6, 3.5, 1\. ]], dtype=float32)>}
tf.Tensor([0 2 1 2 0 1 1 1 0 2 1 0 2 0 0 0 0 0 2 2 2 2 2 0 2 0 2 1 1 1 1 1], shape=(32,), dtype=int64)可以使用 tf.estimator API 来训练 tf.keras.Model，方法是使用 tf.keras.estimator.model_to_estimator 将模型转换为 tf.estimator.Estimator 对象。
import tempfile
model_dir = tempfile.mkdtemp()
keras_estimator = tf.keras.estimator.model_to_estimator(
keras_model=model, model_dir=model_dir) INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using the Keras model provided.
INFO:tensorflow:Using the Keras model provided.
Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/keras.py:220: set_learning_phase (from tensorflow.python.keras.backend) is deprecated and will be removed after 2020-10-11.
Instructions for updating:
Simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/keras.py:220: set_learning_phase (from tensorflow.python.keras.backend) is deprecated and will be removed after 2020-10-11.
Instructions for updating:
Simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp13998n2j', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp13998n2j', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}训练和评估 Estimator。
keras_estimator.train(input_fn=input_fn, steps=500)
eval_result = keras_estimator.evaluate(input_fn=input_fn, steps=10)
print('Eval result: {}'.format(eval_result)) WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmp/tmp13998n2j/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmp/tmp13998n2j/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting from: /tmp/tmp13998n2j/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting from: /tmp/tmp13998n2j/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-started 4 variables.
INFO:tensorflow:Warm-started 4 variables.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp13998n2j/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp13998n2j/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 1.5731332, step = 0
INFO:tensorflow:loss = 1.5731332, step = 0
INFO:tensorflow:global_step/sec: 444.326
INFO:tensorflow:global_step/sec: 444.326
INFO:tensorflow:loss = 0.79164267, step = 100 (0.227 sec)
INFO:tensorflow:loss = 0.79164267, step = 100 (0.227 sec)
INFO:tensorflow:global_step/sec: 515.459
INFO:tensorflow:global_step/sec: 515.459
INFO:tensorflow:loss = 0.5765847, step = 200 (0.193 sec)
INFO:tensorflow:loss = 0.5765847, step = 200 (0.193 sec)
INFO:tensorflow:global_step/sec: 518.855
INFO:tensorflow:global_step/sec: 518.855
INFO:tensorflow:loss = 0.48571444, step = 300 (0.193 sec)
INFO:tensorflow:loss = 0.48571444, step = 300 (0.193 sec)
INFO:tensorflow:global_step/sec: 527.318
INFO:tensorflow:global_step/sec: 527.318
INFO:tensorflow:loss = 0.3836534, step = 400 (0.190 sec)
INFO:tensorflow:loss = 0.3836534, step = 400 (0.190 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 500...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 500...
INFO:tensorflow:Saving checkpoints for 500 into /tmp/tmp13998n2j/model.ckpt.
INFO:tensorflow:Saving checkpoints for 500 into /tmp/tmp13998n2j/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 500...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 500...
INFO:tensorflow:Loss for final step: 0.46023262.
INFO:tensorflow:Loss for final step: 0.46023262.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_v1.py:2048: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_v1.py:2048: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-09-22T19:57:20Z
INFO:tensorflow:Starting evaluation at 2020-09-22T19:57:20Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp13998n2j/model.ckpt-500
INFO:tensorflow:Restoring parameters from /tmp/tmp13998n2j/model.ckpt-500
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.16498s
INFO:tensorflow:Inference Time : 0.16498s
INFO:tensorflow:Finished evaluation at 2020-09-22-19:57:20
INFO:tensorflow:Finished evaluation at 2020-09-22-19:57:20
INFO:tensorflow:Saving dict for global step 500: global_step = 500, loss = 0.33660004
INFO:tensorflow:Saving dict for global step 500: global_step = 500, loss = 0.33660004
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 500: /tmp/tmp13998n2j/model.ckpt-500
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 500: /tmp/tmp13998n2j/model.ckpt-500
Eval result: {'loss': 0.33660004, 'global_step': 500}