Newer
Older
"id": "62c6c5c3-d2ba-4e79-b533-828c3083e8ea",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Num GPUs Available: 1\n",
"Physical devices cannot be modified after being initialized\n",
"Found 1529 images belonging to 6 classes.\n",
"Found 380 images belonging to 6 classes.\n",
"Epoch 1/10\n",
"24/24 [==============================] - 21s 851ms/step - loss: 1.1108 - accuracy: 0.5134 - val_loss: 0.8370 - val_accuracy: 0.6842\n",
"24/24 [==============================] - 21s 862ms/step - loss: 0.6156 - accuracy: 0.7613 - val_loss: 0.4063 - val_accuracy: 0.8447\n",
"24/24 [==============================] - 22s 915ms/step - loss: 0.2704 - accuracy: 0.9006 - val_loss: 0.2283 - val_accuracy: 0.9316\n",
"24/24 [==============================] - 22s 909ms/step - loss: 0.1532 - accuracy: 0.9477 - val_loss: 0.1145 - val_accuracy: 0.9579\n",
"24/24 [==============================] - 22s 905ms/step - loss: 0.1324 - accuracy: 0.9516 - val_loss: 0.2525 - val_accuracy: 0.9026\n",
"24/24 [==============================] - 22s 912ms/step - loss: 0.0737 - accuracy: 0.9719 - val_loss: 0.0519 - val_accuracy: 0.9816\n",
"24/24 [==============================] - 23s 952ms/step - loss: 0.0663 - accuracy: 0.9797 - val_loss: 0.0544 - val_accuracy: 0.9842\n",
"24/24 [==============================] - 22s 918ms/step - loss: 0.0482 - accuracy: 0.9817 - val_loss: 0.0489 - val_accuracy: 0.9711\n",
"24/24 [==============================] - 22s 928ms/step - loss: 0.0240 - accuracy: 0.9908 - val_loss: 0.0337 - val_accuracy: 0.9868\n",
"24/24 [==============================] - 22s 911ms/step - loss: 0.0224 - accuracy: 0.9922 - val_loss: 0.0411 - val_accuracy: 0.9895\n",
"6/6 [==============================] - 4s 717ms/step - loss: 0.0078 - accuracy: 1.0000\n",
"Test loss: 0.007844111882150173, Test accuracy: 1.0\n",
"Model: \"sequential_32\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" conv2d_128 (Conv2D) (None, 222, 222, 32) 896 \n",
" max_pooling2d_128 (MaxPooli (None, 111, 111, 32) 0 \n",
" ng2D) \n",
" conv2d_129 (Conv2D) (None, 109, 109, 64) 18496 \n",
" max_pooling2d_129 (MaxPooli (None, 54, 54, 64) 0 \n",
" ng2D) \n",
" conv2d_130 (Conv2D) (None, 52, 52, 128) 73856 \n",
" max_pooling2d_130 (MaxPooli (None, 26, 26, 128) 0 \n",
" ng2D) \n",
" conv2d_131 (Conv2D) (None, 24, 24, 256) 295168 \n",
" max_pooling2d_131 (MaxPooli (None, 12, 12, 256) 0 \n",
" ng2D) \n",
" \n",
"=================================================================\n",
"Total params: 9,859,526\n",
"Trainable params: 9,859,526\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\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",
"\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",
"physical_devices = tf.config.list_physical_devices('GPU')\n",
"print(\"Num GPUs Available: \", len(physical_devices))\n",
"if len(physical_devices) > 0:\n",
" try:\n",
" # Set memory growth to true\n",
" for device in physical_devices:\n",
" tf.config.experimental.set_memory_growth(device, True)\n",
" \n",
" # Optionally, you can set a memory limit if necessary\n",
" # tf.config.set_logical_device_configuration(\n",
" # physical_devices[0],\n",
" # [tf.config.LogicalDeviceConfiguration(memory_limit=4096)]) # Set to 4GB\n",
" except RuntimeError as e:\n",
" print(e)\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=(300, 300),\n",
" batch_size=64,\n",
" class_mode='categorical',\n",
" subset='training'\n",
")\n",
"\n",
"test_set = datagen.flow_from_directory(\n",
" path,\n",
" target_size=(300, 300),\n",
" batch_size=64,\n",
" class_mode='categorical',\n",
" subset='validation'\n",
")\n",
"\n",
"# draw Images\n",
"# images, labels = next(test_set)\n",
"\n",
"# class_indices = test_set.class_indices\n",
"# plot_images(images, labels, class_indices)\n",
"\n",
"\n",
"\n",
"# Step 4: Build the Model\n",
"model = Sequential()\n",
"model.add(Conv2D(32, (3, 3), input_shape=(300, 300, 3), activation='relu'))\n",
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
"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"
]
},
{
"cell_type": "code",
"execution_count": null,
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}