densenet.ipynb

{
 "cells": [
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     "text": [
      "2024-10-09 19:53:37.647433: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-10-09 19:53:37.655565: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-10-09 19:53:37.665323: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-10-09 19:53:37.668183: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-10-09 19:53:37.675577: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-10-09 19:53:38.102835: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1232 images belonging to 7 classes.\n",
      "Found 310 images belonging to 7 classes.\n",
      "Found 522 images belonging to 7 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1728483818.706974    8817 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728483818.728820    8817 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728483818.728948    8817 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728483818.729674    8817 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728483818.729765    8817 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728483818.729821    8817 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728483818.778059    8817 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728483818.778170    8817 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728483818.778240    8817 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-10-09 19:53:38.778300: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1192 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3050 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "\u001b[1m29084464/29084464\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 0us/step\n",
      "Epoch 1/40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shashank-jangde/Data/major/.venv/lib/python3.12/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1728483829.388543   11262 service.cc:146] XLA service 0x71153c08a1b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1728483829.388562   11262 service.cc:154]   StreamExecutor device (0): NVIDIA GeForce RTX 3050 Ti Laptop GPU, Compute Capability 8.6\n",
      "2024-10-09 19:53:49.561642: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2024-10-09 19:53:50.723773: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8907\n",
      "2024-10-09 19:53:50.974146: W external/local_xla/xla/service/gpu/nvptx_compiler.cc:762] The NVIDIA driver's CUDA version is 12.2 which is older than the ptxas CUDA version (12.3.107). Because the driver is older than the ptxas version, XLA is disabling parallel compilation, which may slow down compilation. You should update your NVIDIA driver or use the NVIDIA-provided CUDA forward compatibility packages.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m  4/154\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m7s\u001b[0m 48ms/step - accuracy: 0.3307 - loss: 5.6387 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1728483841.207737   11262 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 88ms/step - accuracy: 0.4848 - loss: 3.6630 - val_accuracy: 0.7303 - val_loss: 0.8278\n",
      "Epoch 2/40\n"
     ]
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-09 19:54:14.636371: I tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n",
      "2024-10-09 19:54:14.636497: I tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n",
      "\t [[IteratorGetNext/_4]]\n",
      "/home/shashank-jangde/anaconda3/lib/python3.12/contextlib.py:158: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
      "  self.gen.throw(value)\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 68ms/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.6667 - val_loss: 0.7628\n",
      "Epoch 3/40\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-09 19:54:25.027889: I tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n",
      "\t [[IteratorGetNext/_2]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 60ms/step - accuracy: 0.6619 - loss: 1.0738 - val_accuracy: 0.7237 - val_loss: 0.7344\n",
      "Epoch 4/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 187us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.8333 - val_loss: 0.7026\n",
      "Epoch 5/40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-09 19:54:34.723051: I tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n",
      "\t [[IteratorGetNext/_2]]\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 60ms/step - accuracy: 0.6590 - loss: 1.0570 - val_accuracy: 0.7566 - val_loss: 0.6999\n",
      "Epoch 6/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.6667 - val_loss: 0.8817\n",
      "Epoch 7/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 61ms/step - accuracy: 0.6639 - loss: 0.9357 - val_accuracy: 0.7763 - val_loss: 0.6442\n",
      "Epoch 8/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.6667 - val_loss: 0.9215\n",
      "Epoch 9/40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-09 19:54:54.199474: I tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n",
      "\t [[IteratorGetNext/_2]]\n"
     ]
    },
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     "name": "stdout",
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     "text": [
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 61ms/step - accuracy: 0.6782 - loss: 0.9192 - val_accuracy: 0.7993 - val_loss: 0.6260\n",
      "Epoch 10/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.5000 - val_loss: 1.1561\n",
      "Epoch 11/40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-09 19:55:03.984545: I tensorflow/core/framework/local_rendezvous.cc:423] Local rendezvous recv item cancelled. Key hash: 18219901301422828555\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 61ms/step - accuracy: 0.6935 - loss: 0.8831 - val_accuracy: 0.7895 - val_loss: 0.5861\n",
      "Epoch 12/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.8333 - val_loss: 0.8567\n",
      "Epoch 13/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 62ms/step - accuracy: 0.6935 - loss: 0.8815 - val_accuracy: 0.7829 - val_loss: 0.6087\n",
      "Epoch 14/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.5832\n",
      "Epoch 15/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 62ms/step - accuracy: 0.7028 - loss: 0.8842 - val_accuracy: 0.7730 - val_loss: 0.6041\n",
      "Epoch 16/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 186us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.8333 - val_loss: 0.3382\n",
      "Epoch 17/40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-09 19:55:34.035751: I tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n",
      "\t [[IteratorGetNext/_2]]\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 62ms/step - accuracy: 0.7141 - loss: 0.8079 - val_accuracy: 0.7730 - val_loss: 0.6595\n",
      "Epoch 18/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.8333 - val_loss: 0.3041\n",
      "Epoch 19/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 62ms/step - accuracy: 0.7200 - loss: 0.7910 - val_accuracy: 0.8224 - val_loss: 0.5438\n",
      "Epoch 20/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.8333 - val_loss: 0.2810\n",
      "Epoch 21/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 61ms/step - accuracy: 0.7406 - loss: 0.7188 - val_accuracy: 0.8026 - val_loss: 0.5253\n",
      "Epoch 22/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.3225\n",
      "Epoch 23/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 61ms/step - accuracy: 0.7388 - loss: 0.7421 - val_accuracy: 0.8191 - val_loss: 0.5737\n",
      "Epoch 24/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 184us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.0869\n",
      "Epoch 25/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 62ms/step - accuracy: 0.7455 - loss: 0.7276 - val_accuracy: 0.7336 - val_loss: 0.7060\n",
      "Epoch 26/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.5000 - val_loss: 1.3413\n",
      "Epoch 27/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 62ms/step - accuracy: 0.7472 - loss: 0.7205 - val_accuracy: 0.7993 - val_loss: 0.5625\n",
      "Epoch 28/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.2044\n",
      "Epoch 29/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 63ms/step - accuracy: 0.6835 - loss: 0.7924 - val_accuracy: 0.8421 - val_loss: 0.5004\n",
      "Epoch 30/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 182us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.5000 - val_loss: 1.1554\n",
      "Epoch 31/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 62ms/step - accuracy: 0.7236 - loss: 0.7817 - val_accuracy: 0.8618 - val_loss: 0.4877\n",
      "Epoch 32/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.6667 - val_loss: 0.6073\n",
      "Epoch 33/40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-09 19:56:53.902295: I tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n",
      "\t [[IteratorGetNext/_2]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 61ms/step - accuracy: 0.7469 - loss: 0.7379 - val_accuracy: 0.8553 - val_loss: 0.4550\n",
      "Epoch 34/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.3671\n",
      "Epoch 35/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 61ms/step - accuracy: 0.7395 - loss: 0.7416 - val_accuracy: 0.8125 - val_loss: 0.5388\n",
      "Epoch 36/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 195us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.8333 - val_loss: 0.3548\n",
      "Epoch 37/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 62ms/step - accuracy: 0.7497 - loss: 0.6992 - val_accuracy: 0.8717 - val_loss: 0.4395\n",
      "Epoch 38/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.8333 - val_loss: 0.9252\n",
      "Epoch 39/40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-09 19:57:23.641198: I tensorflow/core/framework/local_rendezvous.cc:423] Local rendezvous recv item cancelled. Key hash: 18219901301422828555\n",
      "2024-10-09 19:57:23.641264: I tensorflow/core/framework/local_rendezvous.cc:423] Local rendezvous recv item cancelled. Key hash: 4172252715613517361\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 62ms/step - accuracy: 0.7716 - loss: 0.6695 - val_accuracy: 0.8651 - val_loss: 0.4591\n",
      "Epoch 40/40\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.8333 - val_loss: 0.3882\n",
      "\u001b[1m 1/65\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 60ms/step - accuracy: 0.6250 - loss: 1.2430"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-09 19:57:33.583304: I tensorflow/core/framework/local_rendezvous.cc:423] Local rendezvous recv item cancelled. Key hash: 4172252715613517361\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m65/65\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - accuracy: 0.7691 - loss: 0.7097\n",
      "Test accuracy: 0.807692289352417\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications import DenseNet121\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Dense, Flatten, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "# Set the paths\n",
    "train_dir = './dataset/train'\n",
    "val_dir = './dataset/val'\n",
    "test_dir = './dataset/test'\n",
    "\n",
    "# ImageDataGenerator for data augmentation\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range=40,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='nearest'\n",
    ")\n",
    "\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# Flow images from the directories\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=8,\n",
    "    class_mode='categorical'\n",
    ")\n",
    "\n",
    "val_generator = val_datagen.flow_from_directory(\n",
    "    val_dir,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=8,\n",
    "    class_mode='categorical'\n",
    ")\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    test_dir,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=8,\n",
    "    class_mode='categorical'\n",
    ")\n",
    "\n",
    "# Load the DenseNet121 model without the top classification layers\n",
    "base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
    "\n",
    "# Freeze the convolutional base\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "# Add custom layers on top\n",
    "x = Flatten()(base_model.output)\n",
    "x = Dense(512, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(7, activation='softmax')(x)  # Adjust for your number of categories\n",
    "\n",
    "# Create the model\n",
    "model = Model(inputs=base_model.input, outputs=x)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(\n",
    "    optimizer=Adam(learning_rate=0.0001),\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    epochs=40,\n",
    "    validation_data=val_generator,\n",
    "    validation_steps=val_generator.samples // val_generator.batch_size\n",
    ")\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "test_loss, test_acc = model.evaluate(test_generator, steps=test_generator.samples // test_generator.batch_size)\n",
    "print(f'Test accuracy: {test_acc}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 82ms/step - accuracy: 0.7180 - loss: 0.7991 - val_accuracy: 0.8651 - val_loss: 0.4464\n",
      "Epoch 2/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.0563\n",
      "Epoch 3/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 59ms/step - accuracy: 0.7598 - loss: 0.6627 - val_accuracy: 0.8750 - val_loss: 0.4244\n",
      "Epoch 4/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.6667 - val_loss: 0.9468\n",
      "Epoch 5/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 60ms/step - accuracy: 0.7704 - loss: 0.6524 - val_accuracy: 0.8717 - val_loss: 0.4374\n",
      "Epoch 6/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 198us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.1412\n",
      "Epoch 7/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 60ms/step - accuracy: 0.7734 - loss: 0.6577 - val_accuracy: 0.8750 - val_loss: 0.4397\n",
      "Epoch 8/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.2238\n",
      "Epoch 9/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 61ms/step - accuracy: 0.7770 - loss: 0.6373 - val_accuracy: 0.8750 - val_loss: 0.4278\n",
      "Epoch 10/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.8333 - val_loss: 0.5965\n",
      "Epoch 11/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 61ms/step - accuracy: 0.7881 - loss: 0.6233 - val_accuracy: 0.8684 - val_loss: 0.4340\n",
      "Epoch 12/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.3552\n",
      "Epoch 13/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 60ms/step - accuracy: 0.7670 - loss: 0.6692 - val_accuracy: 0.8750 - val_loss: 0.4269\n",
      "Epoch 14/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.0404\n",
      "Epoch 15/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 61ms/step - accuracy: 0.7728 - loss: 0.6755 - val_accuracy: 0.8487 - val_loss: 0.4725\n",
      "Epoch 16/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.1574\n",
      "Epoch 17/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 61ms/step - accuracy: 0.7711 - loss: 0.6572 - val_accuracy: 0.8553 - val_loss: 0.4719\n",
      "Epoch 18/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.1400\n",
      "Epoch 19/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 61ms/step - accuracy: 0.7990 - loss: 0.6179 - val_accuracy: 0.8618 - val_loss: 0.4380\n",
      "Epoch 20/20\n",
      "\u001b[1m154/154\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 207us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.8333 - val_loss: 0.5565\n"
     ]
    }
   ],
   "source": [
    "# Unfreeze the top layers of the model for fine-tuning\n",
    "for layer in base_model.layers[-4:]:  # Unfreeze the last 4 layers, adjust as needed\n",
    "    layer.trainable = True\n",
    "\n",
    "# Re-compile the model with a lower learning rate\n",
    "model.compile(\n",
    "    optimizer=Adam(learning_rate=1e-5),\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "# Fine-tune the model\n",
    "history_finetune = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    epochs=20,\n",
    "    validation_data=val_generator,\n",
    "    validation_steps=val_generator.samples // val_generator.batch_size\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [],
   "source": [
    "model.save(\"densenet_model.keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming you have the history object for DenseNet training\n",
    "# Replace 'history' with the appropriate variable name if different\n",
    "densenet_history = history_finetune  # If you fine-tuned DenseNet, use that history\n",
    "\n",
    "# Plot accuracy and loss over epochs\n",
    "acc = densenet_history.history['accuracy']\n",
    "val_acc = densenet_history.history['val_accuracy']\n",
    "loss = densenet_history.history['loss']\n",
    "val_loss = densenet_history.history['val_loss']\n",
    "\n",
    "epochs_range = range(len(acc))\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# Plotting Accuracy\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs_range, acc, label='Training Accuracy', color='blue')\n",
    "plt.plot(epochs_range, val_acc, label='Validation Accuracy', color='orange')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('DenseNet: Training and Validation Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "\n",
    "# Plotting Loss\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, loss, label='Training Loss', color='green')\n",
    "plt.plot(epochs_range, val_loss, label='Validation Loss', color='red')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('DenseNet: Training and Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "major_kernel",
   "language": "python",
   "name": "major_kernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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