File size: 22,545 Bytes
2413a8b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 |
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "AV-1n4EQ4zoM"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"import pandas as pd\n",
"import os"
]
},
{
"cell_type": "code",
"source": [
"#Create an instance of ImageDataGenerator\n",
"train_datagen = ImageDataGenerator(rescale=1./255)\n",
"val_datagen = ImageDataGenerator(rescale=1./255)\n",
"class_labels = {'cocci': 0, 'bacilli': 1, 'spirilla': 2}\n",
"\n",
"# Load training data from the 'train' folder\n",
"# Each subfolder (bacilli, cocci, spirilla) represents a class\n",
"train_data = train_datagen.flow_from_directory(\n",
" '/content/drive/MyDrive/Bacterial Classification/train', # Path to the train folder\n",
" target_size=(224, 224), # Resize all images to 224x224\n",
" batch_size=32, # Number of images per batch\n",
" class_mode='categorical', # Multi-class classification\n",
" classes=class_labels # Explicit class mapping\n",
"\n",
")\n",
"\n",
"# Load validation data from the 'validation' folder\n",
"# Each subfolder (bacilli, cocci, spirilla) represents a class\n",
"val_data = val_datagen.flow_from_directory(\n",
" '/content/drive/MyDrive/Bacterial Classification/validation',# Path to the validation folder\n",
" target_size=(224, 224),\n",
" batch_size=32,\n",
" class_mode='categorical',\n",
" classes=class_labels\n",
"\n",
")\n",
"\n",
"# Check class mappings\n",
"print(\"Training Class Indices:\", train_data.class_indices)\n",
"print(\"Validation Class Indices:\", val_data.class_indices)\n"
],
"metadata": {
"id": "JoFVIVmJTVPX"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from tensorflow.keras.applications import MobileNetV2\n",
"from tensorflow.keras.layers import GlobalAveragePooling2D\n",
"from tensorflow.keras.optimizers import Adam\n",
"from tensorflow.keras.callbacks import EarlyStopping\n",
"\n",
"base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
"base_model.trainable = False # Freeze the base model\n",
"\n",
"model = tf.keras.Sequential([\n",
" base_model,\n",
" GlobalAveragePooling2D(),\n",
" tf.keras.layers.Dense(128, activation='relu'),\n",
" tf.keras.layers.Dropout(0.5),\n",
" tf.keras.layers.Dense(3, activation='softmax')\n",
"])\n",
"\n",
"model.compile(\n",
" optimizer=Adam(learning_rate=0.0001), # Lower learning rate\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy']\n",
")\n",
"early_stopping = EarlyStopping(\n",
" monitor='val_loss',\n",
" patience=3,\n",
" restore_best_weights=True\n",
")\n",
"# Train the model\n",
"history = model.fit(\n",
" train_data,\n",
" validation_data=val_data,\n",
" epochs=50, # Allow more epochs but stop early if needed\n",
" callbacks=[early_stopping]\n",
")\n",
"\n",
"\n",
"# Evaluate the model on the validation dataset\n",
"val_loss, val_accuracy = model.evaluate(val_data)\n",
"print(f\"Validation Loss: {val_loss}\")\n",
"print(f\"Validation Accuracy: {val_accuracy}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2PYZtsrhVGjZ",
"outputId": "9d6cef82-2302-48f3-dab6-c7406711c331"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/50\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-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"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 7s/step - accuracy: 0.3350 - loss: 1.6972 - val_accuracy: 0.3417 - val_loss: 1.3020\n",
"Epoch 2/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - accuracy: 0.3816 - loss: 1.3227 - val_accuracy: 0.4750 - val_loss: 1.1209\n",
"Epoch 3/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 223ms/step - accuracy: 0.5357 - loss: 0.9564 - val_accuracy: 0.5583 - val_loss: 1.0034\n",
"Epoch 4/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - accuracy: 0.5961 - loss: 0.8981 - val_accuracy: 0.5667 - val_loss: 0.9151\n",
"Epoch 5/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 226ms/step - accuracy: 0.5730 - loss: 0.9111 - val_accuracy: 0.5833 - val_loss: 0.8556\n",
"Epoch 6/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - accuracy: 0.7188 - loss: 0.6853 - val_accuracy: 0.6333 - val_loss: 0.8078\n",
"Epoch 7/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 218ms/step - accuracy: 0.7019 - loss: 0.6919 - val_accuracy: 0.6750 - val_loss: 0.7685\n",
"Epoch 8/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 236ms/step - accuracy: 0.7730 - loss: 0.5996 - val_accuracy: 0.6833 - val_loss: 0.7381\n",
"Epoch 9/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 203ms/step - accuracy: 0.7472 - loss: 0.5987 - val_accuracy: 0.6500 - val_loss: 0.7141\n",
"Epoch 10/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 196ms/step - accuracy: 0.7470 - loss: 0.6248 - val_accuracy: 0.6833 - val_loss: 0.6917\n",
"Epoch 11/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 223ms/step - accuracy: 0.7687 - loss: 0.5358 - val_accuracy: 0.6833 - val_loss: 0.6693\n",
"Epoch 12/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - accuracy: 0.8054 - loss: 0.4860 - val_accuracy: 0.6917 - val_loss: 0.6535\n",
"Epoch 13/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 196ms/step - accuracy: 0.8217 - loss: 0.4857 - val_accuracy: 0.6833 - val_loss: 0.6379\n",
"Epoch 14/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 245ms/step - accuracy: 0.8586 - loss: 0.4347 - val_accuracy: 0.7000 - val_loss: 0.6292\n",
"Epoch 15/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 204ms/step - accuracy: 0.8516 - loss: 0.3888 - val_accuracy: 0.7083 - val_loss: 0.6151\n",
"Epoch 16/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 191ms/step - accuracy: 0.8199 - loss: 0.4157 - val_accuracy: 0.7333 - val_loss: 0.6084\n",
"Epoch 17/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 228ms/step - accuracy: 0.8377 - loss: 0.4106 - val_accuracy: 0.7250 - val_loss: 0.5958\n",
"Epoch 18/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 221ms/step - accuracy: 0.9195 - loss: 0.3326 - val_accuracy: 0.7250 - val_loss: 0.5859\n",
"Epoch 19/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 234ms/step - accuracy: 0.8840 - loss: 0.3327 - val_accuracy: 0.7083 - val_loss: 0.5821\n",
"Epoch 20/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 207ms/step - accuracy: 0.8947 - loss: 0.3532 - val_accuracy: 0.7333 - val_loss: 0.5776\n",
"Epoch 21/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 201ms/step - accuracy: 0.9053 - loss: 0.2998 - val_accuracy: 0.7417 - val_loss: 0.5665\n",
"Epoch 22/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 209ms/step - accuracy: 0.9031 - loss: 0.3000 - val_accuracy: 0.7417 - val_loss: 0.5620\n",
"Epoch 23/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 185ms/step - accuracy: 0.8956 - loss: 0.2904 - val_accuracy: 0.7333 - val_loss: 0.5560\n",
"Epoch 24/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 186ms/step - accuracy: 0.9194 - loss: 0.2869 - val_accuracy: 0.7417 - val_loss: 0.5498\n",
"Epoch 25/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 250ms/step - accuracy: 0.9128 - loss: 0.2674 - val_accuracy: 0.7333 - val_loss: 0.5458\n",
"Epoch 26/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 201ms/step - accuracy: 0.9213 - loss: 0.2319 - val_accuracy: 0.7333 - val_loss: 0.5432\n",
"Epoch 27/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 189ms/step - accuracy: 0.9412 - loss: 0.2338 - val_accuracy: 0.7500 - val_loss: 0.5397\n",
"Epoch 28/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 259ms/step - accuracy: 0.9427 - loss: 0.2247 - val_accuracy: 0.7500 - val_loss: 0.5345\n",
"Epoch 29/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 195ms/step - accuracy: 0.9304 - loss: 0.2206 - val_accuracy: 0.7500 - val_loss: 0.5316\n",
"Epoch 30/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 201ms/step - accuracy: 0.9419 - loss: 0.2098 - val_accuracy: 0.7500 - val_loss: 0.5289\n",
"Epoch 31/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 248ms/step - accuracy: 0.9420 - loss: 0.1824 - val_accuracy: 0.7500 - val_loss: 0.5273\n",
"Epoch 32/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 198ms/step - accuracy: 0.9590 - loss: 0.1871 - val_accuracy: 0.7500 - val_loss: 0.5244\n",
"Epoch 33/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 223ms/step - accuracy: 0.9613 - loss: 0.1816 - val_accuracy: 0.7417 - val_loss: 0.5233\n",
"Epoch 34/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 258ms/step - accuracy: 0.9629 - loss: 0.1428 - val_accuracy: 0.7417 - val_loss: 0.5217\n",
"Epoch 35/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 167ms/step - accuracy: 0.9606 - loss: 0.1835 - val_accuracy: 0.7583 - val_loss: 0.5231\n",
"Epoch 36/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 234ms/step - accuracy: 0.9366 - loss: 0.1920 - val_accuracy: 0.7500 - val_loss: 0.5246\n",
"Epoch 37/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 238ms/step - accuracy: 0.9464 - loss: 0.1747 - val_accuracy: 0.7583 - val_loss: 0.5184\n",
"Epoch 38/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 187ms/step - accuracy: 0.9601 - loss: 0.1621 - val_accuracy: 0.7583 - val_loss: 0.5132\n",
"Epoch 39/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - accuracy: 0.9691 - loss: 0.1530 - val_accuracy: 0.7583 - val_loss: 0.5097\n",
"Epoch 40/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 207ms/step - accuracy: 0.9655 - loss: 0.1480 - val_accuracy: 0.7667 - val_loss: 0.5113\n",
"Epoch 41/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 184ms/step - accuracy: 0.9671 - loss: 0.1483 - val_accuracy: 0.7583 - val_loss: 0.5122\n",
"Epoch 42/50\n",
"\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 207ms/step - accuracy: 0.9775 - loss: 0.1268 - val_accuracy: 0.7583 - val_loss: 0.5124\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 284ms/step - accuracy: 0.7763 - loss: 0.4887\n",
"Validation Loss: 0.509696900844574\n",
"Validation Accuracy: 0.7583333253860474\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
"from tensorflow.keras.utils import load_img, img_to_array\n",
"\n",
"# Load the file containing test image names\n",
"test_images = pd.read_csv('/content/drive/MyDrive/Bacterial Classification/test_filenames.txt', header=None)\n",
"test_images.columns = ['Image Name']\n",
"\n",
"# Path to the test folder containing the images\n",
"test_dir = '/content/drive/MyDrive/Bacterial Classification/test'\n",
"\n",
"# Placeholder for predictions\n",
"predictions = []\n",
"\n",
"# Process each image and predict\n",
"for img_name in test_images['Image Name']:\n",
" # Construct the full path to the image\n",
" img_path = os.path.join(test_dir, img_name)\n",
"\n",
" # Load and preprocess the image\n",
" img = load_img(img_path, target_size=(224, 224)) # Resize image to match the model's input size\n",
" img_array = img_to_array(img) / 255.0 # Normalize pixel values\n",
" img_array = np.expand_dims(img_array, axis=0) # Add batch dimension\n",
"\n",
" # Make a prediction using the trained model\n",
" prediction = model.predict(img_array, verbose=0) # Suppress verbose output\n",
" predictions.append(prediction.argmax()) # Append the predicted class index (0, 1, 2)\n",
"\n",
"# Add predictions to the DataFrame\n",
"test_images['Predicted Class'] = predictions"
],
"metadata": {
"id": "Wy-i6rizMrt9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"predictions"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Pt1zSfXsTIlt",
"outputId": "145ef195-25ca-450b-bd05-bae27efe6fc5"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 1,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 1,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 2,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 0,\n",
" 1,\n",
" 1,\n",
" 2,\n",
" 0,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 2,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 0,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 0,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 1,\n",
" 2,\n",
" 2,\n",
" 1,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 1,\n",
" 1,\n",
" 2,\n",
" 1,\n",
" 2,\n",
" 1,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 1,\n",
" 1,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2]"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"model.save('/content/drive/MyDrive/Bacterial Classification/saved_model.keras')"
],
"metadata": {
"id": "RfyBMOReTZfR"
},
"execution_count": null,
"outputs": []
}
]
} |