{
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"id": "62c6c5c3-d2ba-4e79-b533-828c3083e8ea",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1529 images belonging to 6 classes.\n",
"Found 380 images belonging to 6 classes.\n",
"Epoch 1/10\n",
"48/48 [==============================] - 29s 470ms/step - loss: 1.0985 - accuracy: 0.5284 - val_loss: 0.7411 - val_accuracy: 0.6868\n",
"Epoch 2/10\n",
"48/48 [==============================] - 20s 417ms/step - loss: 0.5369 - accuracy: 0.7757 - val_loss: 0.5103 - val_accuracy: 0.7921\n",
"Epoch 3/10\n",
"48/48 [==============================] - 20s 417ms/step - loss: 0.2837 - accuracy: 0.8875 - val_loss: 0.8421 - val_accuracy: 0.7105\n",
"Epoch 4/10\n",
"48/48 [==============================] - 20s 417ms/step - loss: 0.1952 - accuracy: 0.9228 - val_loss: 0.2502 - val_accuracy: 0.8947\n",
"Epoch 5/10\n",
"48/48 [==============================] - 20s 419ms/step - loss: 0.1007 - accuracy: 0.9647 - val_loss: 0.0922 - val_accuracy: 0.9632\n",
"Epoch 6/10\n",
"48/48 [==============================] - 19s 404ms/step - loss: 0.0690 - accuracy: 0.9719 - val_loss: 0.1608 - val_accuracy: 0.9316\n",
"Epoch 7/10\n",
"48/48 [==============================] - 19s 406ms/step - loss: 0.0707 - accuracy: 0.9745 - val_loss: 0.1129 - val_accuracy: 0.9500\n",
"Epoch 8/10\n",
"48/48 [==============================] - 19s 401ms/step - loss: 0.0674 - accuracy: 0.9791 - val_loss: 0.0428 - val_accuracy: 0.9868\n",
"Epoch 9/10\n",
"48/48 [==============================] - 19s 400ms/step - loss: 0.0260 - accuracy: 0.9922 - val_loss: 0.0619 - val_accuracy: 0.9711\n",
"Epoch 10/10\n",
"48/48 [==============================] - 20s 404ms/step - loss: 0.0142 - accuracy: 0.9961 - val_loss: 0.0146 - val_accuracy: 0.9947\n",
"12/12 [==============================] - 4s 329ms/step - loss: 0.0094 - accuracy: 0.9974\n",
"Test loss: 0.009386303834617138, Test accuracy: 0.9973683953285217\n",
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" conv2d (Conv2D) (None, 222, 222, 32) 896 \n",
" \n",
" max_pooling2d (MaxPooling2D (None, 111, 111, 32) 0 \n",
" ) \n",
" \n",
" conv2d_1 (Conv2D) (None, 109, 109, 64) 18496 \n",
" \n",
" max_pooling2d_1 (MaxPooling (None, 54, 54, 64) 0 \n",
" 2D) \n",
" \n",
" conv2d_2 (Conv2D) (None, 52, 52, 128) 73856 \n",
" \n",
" max_pooling2d_2 (MaxPooling (None, 26, 26, 128) 0 \n",
" 2D) \n",
" \n",
" conv2d_3 (Conv2D) (None, 24, 24, 256) 295168 \n",
" \n",
" max_pooling2d_3 (MaxPooling (None, 12, 12, 256) 0 \n",
" 2D) \n",
" \n",
" flatten (Flatten) (None, 36864) 0 \n",
" \n",
" dense (Dense) (None, 256) 9437440 \n",
" \n",
" dense_1 (Dense) (None, 128) 32896 \n",
" \n",
" dense_2 (Dense) (None, 6) 774 \n",
" \n",
"=================================================================\n",
"Total params: 9,859,526\n",
"Trainable params: 9,859,526\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"It took: 3.55 minutes\n"
]
}
],
"source": [
"import time\n",
"from keras.models import Sequential\n",
"from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
"from keras.preprocessing.image import ImageDataGenerator\n",
"from keras.losses import CategoricalCrossentropy\n",
"from tensorflow.keras.callbacks import EarlyStopping\n",
"from tensorflow.keras.optimizers import Adam\n",
"import tensorflow as tf\n",
"from keras import layers, models\n",
"\n",
"from Func.getSubFolders import count_sub_folders\n",
"\n",
"path = 'Data'\n",
"output = 'Model/keras_model.h5'\n",
"start_time = time.time()\n",
"\n",
"# runtime error\n",
"# physical_devices = tf.config.experimental.list_physical_devices('GPU')\n",
"# print(\"Num GPUs Available: \", len(physical_devices))\n",
"# if len(physical_devices) > 0:\n",
"# tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
"# else:\n",
"# print(\"No GPU available. Using CPU.\")\n",
"\n",
"\n",
"# Step 1: Load and Preprocess Images\n",
"datagen = ImageDataGenerator(\n",
" rescale=1. / 255,\n",
" validation_split=0.2,\n",
" width_shift_range=0.2,\n",
" height_shift_range=0.2,\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
")\n",
"\n",
"test_datagen = ImageDataGenerator(rescale=1. / 255)\n",
"\n",
"# Step 2: Label the Data\n",
"train_set = datagen.flow_from_directory(\n",
" path,\n",
" target_size=(224, 224),\n",
" batch_size=32,\n",
" class_mode='categorical',\n",
" subset='training'\n",
")\n",
"\n",
"test_set = datagen.flow_from_directory(\n",
" path,\n",
" target_size=(224, 224),\n",
" batch_size=32,\n",
" class_mode='categorical',\n",
" subset='validation'\n",
")\n",
"\n",
"# Step 4: Build the Model\n",
"model = Sequential()\n",
"model.add(Conv2D(32, (3, 3), input_shape=(224, 224, 3), activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"\n",
"model.add(Conv2D(128, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"\n",
"model.add(Conv2D(256, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"\n",
"model.add(Flatten())\n",
"model.add(Dense(units=256, activation='relu'))\n",
"model.add(Dense(units=128, activation='relu'))\n",
"model.add(Dense(units=count_sub_folders(path), activation='softmax'))\n",
"# def createLayers(input_shape=(224, 224, 3)):\n",
"# inputs = tf.keras.Input(shape=input_shape)\n",
"#\n",
"# x = layers.Conv2D(48, (1, 1), padding='same', use_bias=False, name='block_1_expand')(inputs)\n",
"# x = layers.BatchNormalization(name='block_1_expand_BN')(x)\n",
"# x = layers.ReLU(6., name='block_1_expand_relu')(x)\n",
"#\n",
"# x = layers.DepthwiseConv2D((3, 3), padding='same', use_bias=False, name='block_1_depthwise')(x)\n",
"# x = layers.BatchNormalization(name='block_1_depthwise_BN')(x)\n",
"# x = layers.ReLU(6., name='block_1_depthwise_relu')(x)\n",
"#\n",
"# x = layers.Conv2D(8, (1, 1), padding='same', use_bias=False, name='block_1_project')(x)\n",
"# x = layers.BatchNormalization(name='block_1_project_BN')(x)\n",
"#\n",
"# for i in range(2,5):\n",
"# x1 = layers.Conv2D(48, (1, 1), padding='same', use_bias=False, name=f'block_{i}_expand')(x)\n",
"# x1 = layers.BatchNormalization(name=f'block_{i}_expand_BN')(x1)\n",
"# x1 = layers.ReLU(6., name=f'block_{i}_expand_relu')(x1)\n",
"#\n",
"# x1 = layers.DepthwiseConv2D((3, 3), padding='same', use_bias=False, name=f'block_{i}_depthwise')(x1)\n",
"# x1 = layers.BatchNormalization(name=f'block_{i}_depthwise_BN')(x1)\n",
"# x1 = layers.ReLU(6., name=f'block_{i}_depthwise_relu')(x1)\n",
"#\n",
"# x1 = layers.Conv2D(8, (1, 1), padding='same', use_bias=False, name=f'block_{i}_project')(x1)\n",
"# x1 = layers.BatchNormalization(name=f'block_{i}_project_BN')(x1)\n",
"#\n",
"# x = layers.Add(name=f'block_{i}_add')([x, x1])\n",
"#\n",
"# x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
"# outputs = tf.keras.layers.Dense(count_sub_folders(path), activation='softmax')(x)\n",
"# model = models.Model(inputs, outputs, name='testModel')\n",
"#\n",
"# return model\n",
"#\n",
"#\n",
"# model = createLayers()\n",
"\n",
"# Compile the Model after pruning\n",
"model.compile(optimizer=Adam(learning_rate=0.001),\n",
" loss=CategoricalCrossentropy(from_logits=False),\n",
" metrics=['accuracy'])\n",
"\n",
"# Step 6: Train the Model\n",
"early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
"\n",
"model.fit(train_set, validation_data=test_set, epochs=10, callbacks=early_stopping)\n",
"\n",
"# Step 7: Evaluate the Model\n",
"loss, accuracy = model.evaluate(test_set)\n",
"print(f'Test loss: {loss}, Test accuracy: {accuracy}')\n",
"\n",
"# Save the trained model\n",
"model.save(output)\n",
"end_time = time.time()\n",
"\n",
"execute_time = (end_time - start_time) / 60\n",
"\n",
"model.summary()\n",
"\n",
"# Print the result\n",
"print(f\"It took: {execute_time:0.2f} minutes\")\n"
]
},
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