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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import json\n",
"import os\n",
"import shutil\n",
"import tensorflow as tf\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.utils import image_dataset_from_directory\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
"from tensorflow.keras.callbacks import Callback"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def create_dataframe(annotations_path):\n",
" with open(annotations_path, 'r') as file:\n",
" data = json.load(file)\n",
"\n",
" images = pd.DataFrame(data['images']).rename(columns={'id': 'image_id'})[['image_id', 'file_name']]\n",
"\n",
" categories = pd.DataFrame(data['categories'])[['id', 'name']]\n",
" categories.rename(columns={'id': 'category_id'}, inplace=True)\n",
"\n",
" usecols = ['image_id', 'category_id']\n",
" annotations = pd.DataFrame(data['annotations'])[usecols]\n",
"\n",
" dataframe = annotations.merge(categories, on='category_id').merge(images, on='image_id')[['file_name', 'name']]\n",
" \n",
" return dataframe"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def copy_images_to_destination(base_dir, dataframe, split):\n",
" images_dir = os.path.join(base_dir, 'images')\n",
"\n",
" for index, row in dataframe.iterrows():\n",
" file_name = row['file_name']\n",
" file_class = row['name']\n",
"\n",
" dest_dir = os.path.join(split, file_class)\n",
" os.makedirs(dest_dir, exist_ok=True)\n",
"\n",
" source_path = os.path.join(images_dir, file_name)\n",
" destination_path = os.path.join(dest_dir, file_name)\n",
"\n",
" shutil.copyfile(source_path, destination_path)\n",
"\n",
" print(\"Done copying images.\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file_name</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>131094.jpg</td>\n",
" <td>soft-cheese</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>131094.jpg</td>\n",
" <td>ham-raw</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>131094.jpg</td>\n",
" <td>hard-cheese</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>131094.jpg</td>\n",
" <td>bread-wholemeal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>131094.jpg</td>\n",
" <td>cottage-cheese</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76486</th>\n",
" <td>117029.jpg</td>\n",
" <td>damson-plum</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76487</th>\n",
" <td>117524.jpg</td>\n",
" <td>damson-plum</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76488</th>\n",
" <td>117849.jpg</td>\n",
" <td>damson-plum</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76489</th>\n",
" <td>123468.jpg</td>\n",
" <td>damson-plum</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76490</th>\n",
" <td>095795.jpg</td>\n",
" <td>bean-seeds</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>76491 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" file_name name\n",
"0 131094.jpg soft-cheese\n",
"1 131094.jpg ham-raw\n",
"2 131094.jpg hard-cheese\n",
"3 131094.jpg bread-wholemeal\n",
"4 131094.jpg cottage-cheese\n",
"... ... ...\n",
"76486 117029.jpg damson-plum\n",
"76487 117524.jpg damson-plum\n",
"76488 117849.jpg damson-plum\n",
"76489 123468.jpg damson-plum\n",
"76490 095795.jpg bean-seeds\n",
"\n",
"[76491 rows x 2 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df = create_dataframe('train/annotations.json')\n",
"train_df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"splits = ['train', 'val']\n",
"\n",
"for split in splits:\n",
" root = f'{split}'\n",
"\n",
" for index, row in train_df.iterrows():\n",
" directory_name = row['name']\n",
" directory_path = os.path.join(root, directory_name)\n",
"\n",
" if not os.path.exists(directory_path):\n",
" os.makedirs(directory_path)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file_name</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>149022.jpg</td>\n",
" <td>espresso-with-caffeine</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>149022.jpg</td>\n",
" <td>dark-chocolate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>167905.jpg</td>\n",
" <td>espresso-with-caffeine</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>121313.jpg</td>\n",
" <td>espresso-with-caffeine</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>153429.jpg</td>\n",
" <td>espresso-with-caffeine</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1825</th>\n",
" <td>144675.jpg</td>\n",
" <td>oat-milk</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1826</th>\n",
" <td>103273.jpg</td>\n",
" <td>soup-potato</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1827</th>\n",
" <td>159922.jpg</td>\n",
" <td>red-cabbage</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1828</th>\n",
" <td>011275.jpg</td>\n",
" <td>pasta-in-conch-form</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1829</th>\n",
" <td>166537.jpg</td>\n",
" <td>chocolate</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1830 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" file_name name\n",
"0 149022.jpg espresso-with-caffeine\n",
"1 149022.jpg dark-chocolate\n",
"2 167905.jpg espresso-with-caffeine\n",
"3 121313.jpg espresso-with-caffeine\n",
"4 153429.jpg espresso-with-caffeine\n",
"... ... ...\n",
"1825 144675.jpg oat-milk\n",
"1826 103273.jpg soup-potato\n",
"1827 159922.jpg red-cabbage\n",
"1828 011275.jpg pasta-in-conch-form\n",
"1829 166537.jpg chocolate\n",
"\n",
"[1830 rows x 2 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val_df = create_dataframe('val/annotations.json')\n",
"val_df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done copying images.\n"
]
}
],
"source": [
"base_dir = 'train'\n",
"dataframe = train_df\n",
"copy_images_to_destination(base_dir, dataframe, 'train')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done copying images.\n"
]
}
],
"source": [
"base_dir = 'val'\n",
"dataframe = val_df\n",
"copy_images_to_destination(base_dir, dataframe, 'val')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 70397 files belonging to 498 classes.\n",
"Found 1799 files belonging to 498 classes.\n"
]
}
],
"source": [
"train = image_dataset_from_directory(\n",
" directory='train',\n",
" label_mode='categorical',\n",
" batch_size=32,\n",
" image_size=(299, 299)\n",
")\n",
"\n",
"val = image_dataset_from_directory(\n",
" directory='val',\n",
" label_mode='categorical',\n",
" batch_size=32,\n",
" image_size=(299, 299)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"train_datagen = ImageDataGenerator(\n",
" rescale=1./255,\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True\n",
")\n",
"\n",
"val_datagen = ImageDataGenerator(rescale=1./255)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"class MyCallback(Callback):\n",
" def on_epoch_end(self, epoch, logs={}):\n",
" if logs.get('val_categorical_accuracy') >= 0.81:\n",
" print('Validation accuracy reached 81%. Stopping training.')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" conv2d (Conv2D) (None, 297, 297, 32) 896 \n",
" \n",
" max_pooling2d (MaxPooling2D (None, 148, 148, 32) 0 \n",
" ) \n",
" \n",
" conv2d_1 (Conv2D) (None, 146, 146, 64) 18496 \n",
" \n",
" max_pooling2d_1 (MaxPooling (None, 73, 73, 64) 0 \n",
" 2D) \n",
" \n",
" conv2d_2 (Conv2D) (None, 71, 71, 128) 73856 \n",
" \n",
" max_pooling2d_2 (MaxPooling (None, 35, 35, 128) 0 \n",
" 2D) \n",
" \n",
" flatten (Flatten) (None, 156800) 0 \n",
" \n",
" dense (Dense) (None, 128) 20070528 \n",
" \n",
" dense_1 (Dense) (None, 498) 64242 \n",
" \n",
"=================================================================\n",
"Total params: 20,228,018\n",
"Trainable params: 20,228,018\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = Sequential()\n",
"model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(299, 299, 3)))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"model.add(Conv2D(128, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"model.add(Flatten())\n",
"model.add(Dense(128, activation='relu'))\n",
"model.add(Dense(498, activation='softmax'))\n",
"\n",
"model.summary()\n",
"\n",
"model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
" loss=tf.keras.losses.CategoricalCrossentropy(),\n",
" metrics=[tf.keras.metrics.CategoricalAccuracy()])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/32\n",
" 6/2200 [..............................] - ETA: 6:25 - loss: 504.5968 - categorical_accuracy: 0.0052 WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0608s vs `on_train_batch_end` time: 0.0957s). Check your callbacks.\n",
"2200/2200 [==============================] - 291s 130ms/step - loss: 6.9090 - categorical_accuracy: 0.0398 - val_loss: 5.5961 - val_categorical_accuracy: 0.0411\n",
"Epoch 2/32\n",
"2200/2200 [==============================] - 279s 127ms/step - loss: 5.4654 - categorical_accuracy: 0.0420 - val_loss: 5.5951 - val_categorical_accuracy: 0.0417\n",
"Epoch 3/32\n",
"2200/2200 [==============================] - 276s 125ms/step - loss: 5.4428 - categorical_accuracy: 0.0449 - val_loss: 5.6058 - val_categorical_accuracy: 0.0417\n",
"Epoch 4/32\n",
"2200/2200 [==============================] - 285s 130ms/step - loss: 5.3952 - categorical_accuracy: 0.0528 - val_loss: 5.6658 - val_categorical_accuracy: 0.0411\n",
"Epoch 5/32\n",
"2200/2200 [==============================] - 282s 128ms/step - loss: 5.3362 - categorical_accuracy: 0.0630 - val_loss: 5.7703 - val_categorical_accuracy: 0.0406\n",
"Epoch 6/32\n",
"2200/2200 [==============================] - 326s 148ms/step - loss: 5.2673 - categorical_accuracy: 0.0755 - val_loss: 5.7254 - val_categorical_accuracy: 0.0411\n",
"Epoch 7/32\n",
"2200/2200 [==============================] - 300s 136ms/step - loss: 5.2040 - categorical_accuracy: 0.0875 - val_loss: 5.8228 - val_categorical_accuracy: 0.0411\n",
"Epoch 8/32\n",
"2200/2200 [==============================] - 382s 174ms/step - loss: 5.1794 - categorical_accuracy: 0.0927 - val_loss: 6.0131 - val_categorical_accuracy: 0.0411\n",
"Epoch 9/32\n",
"2200/2200 [==============================] - 372s 169ms/step - loss: 5.1426 - categorical_accuracy: 0.0984 - val_loss: 6.0550 - val_categorical_accuracy: 0.0406\n",
"Epoch 10/32\n",
"2200/2200 [==============================] - 335s 152ms/step - loss: 5.0958 - categorical_accuracy: 0.1058 - val_loss: 6.3628 - val_categorical_accuracy: 0.0389\n",
"Epoch 11/32\n",
"2200/2200 [==============================] - 354s 161ms/step - loss: 5.0727 - categorical_accuracy: 0.1111 - val_loss: 6.4603 - val_categorical_accuracy: 0.0378\n",
"Epoch 12/32\n",
"2200/2200 [==============================] - 356s 162ms/step - loss: 5.0326 - categorical_accuracy: 0.1166 - val_loss: 6.7461 - val_categorical_accuracy: 0.0417\n",
"Epoch 13/32\n",
"2200/2200 [==============================] - 354s 161ms/step - loss: 5.0137 - categorical_accuracy: 0.1208 - val_loss: 6.9263 - val_categorical_accuracy: 0.0395\n",
"Epoch 14/32\n",
"2200/2200 [==============================] - 349s 159ms/step - loss: 4.9708 - categorical_accuracy: 0.1281 - val_loss: 6.9836 - val_categorical_accuracy: 0.0378\n",
"Epoch 15/32\n",
"2200/2200 [==============================] - 368s 167ms/step - loss: 4.9531 - categorical_accuracy: 0.1318 - val_loss: 6.6221 - val_categorical_accuracy: 0.0384\n",
"Epoch 16/32\n",
"2200/2200 [==============================] - 360s 164ms/step - loss: 4.9288 - categorical_accuracy: 0.1357 - val_loss: 6.6952 - val_categorical_accuracy: 0.0378\n",
"Epoch 17/32\n",
"2200/2200 [==============================] - 359s 163ms/step - loss: 4.8955 - categorical_accuracy: 0.1403 - val_loss: 6.6760 - val_categorical_accuracy: 0.0400\n",
"Epoch 18/32\n",
"2200/2200 [==============================] - 354s 161ms/step - loss: 4.8613 - categorical_accuracy: 0.1455 - val_loss: 7.7695 - val_categorical_accuracy: 0.0384\n",
"Epoch 19/32\n",
"2200/2200 [==============================] - 327s 148ms/step - loss: 4.8498 - categorical_accuracy: 0.1494 - val_loss: 7.5958 - val_categorical_accuracy: 0.0361\n",
"Epoch 20/32\n",
"2200/2200 [==============================] - 362s 165ms/step - loss: 4.7999 - categorical_accuracy: 0.1556 - val_loss: 7.8458 - val_categorical_accuracy: 0.0372\n",
"Epoch 21/32\n",
"2200/2200 [==============================] - 361s 164ms/step - loss: 4.7786 - categorical_accuracy: 0.1594 - val_loss: 8.5637 - val_categorical_accuracy: 0.0389\n",
"Epoch 22/32\n",
"2200/2200 [==============================] - 360s 164ms/step - loss: 4.7561 - categorical_accuracy: 0.1645 - val_loss: 8.0804 - val_categorical_accuracy: 0.0384\n",
"Epoch 23/32\n",
"2200/2200 [==============================] - 301s 137ms/step - loss: 4.7279 - categorical_accuracy: 0.1694 - val_loss: 8.9041 - val_categorical_accuracy: 0.0372\n",
"Epoch 24/32\n",
"2200/2200 [==============================] - 310s 140ms/step - loss: 4.6962 - categorical_accuracy: 0.1732 - val_loss: 9.0381 - val_categorical_accuracy: 0.0361\n",
"Epoch 25/32\n",
"2200/2200 [==============================] - 314s 142ms/step - loss: 4.6756 - categorical_accuracy: 0.1769 - val_loss: 8.6350 - val_categorical_accuracy: 0.0378\n",
"Epoch 26/32\n",
"2200/2200 [==============================] - 296s 134ms/step - loss: 4.6531 - categorical_accuracy: 0.1820 - val_loss: 9.3287 - val_categorical_accuracy: 0.0367\n",
"Epoch 27/32\n",
"2200/2200 [==============================] - 282s 128ms/step - loss: 4.6207 - categorical_accuracy: 0.1875 - val_loss: 9.8095 - val_categorical_accuracy: 0.0361\n",
"Epoch 28/32\n",
"2200/2200 [==============================] - 349s 158ms/step - loss: 4.6045 - categorical_accuracy: 0.1904 - val_loss: 9.4419 - val_categorical_accuracy: 0.0378\n",
"Epoch 29/32\n",
"2200/2200 [==============================] - 326s 148ms/step - loss: 4.5832 - categorical_accuracy: 0.1945 - val_loss: 9.4719 - val_categorical_accuracy: 0.0361\n",
"Epoch 30/32\n",
"2200/2200 [==============================] - 361s 164ms/step - loss: 4.5393 - categorical_accuracy: 0.2010 - val_loss: 9.8935 - val_categorical_accuracy: 0.0395\n",
"Epoch 31/32\n",
"2200/2200 [==============================] - 334s 152ms/step - loss: 4.5176 - categorical_accuracy: 0.2052 - val_loss: 9.9011 - val_categorical_accuracy: 0.0378\n",
"Epoch 32/32\n",
"2200/2200 [==============================] - 344s 156ms/step - loss: 4.4989 - categorical_accuracy: 0.2082 - val_loss: 10.2300 - val_categorical_accuracy: 0.0378\n"
]
}
],
"source": [
"callback = MyCallback()\n",
"history = model.fit(train, epochs=32, validation_data=val, callbacks=[callback])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "gpu",
"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": "3.9.18"
}
},
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}
|