diff --git a/submission.ipynb b/submission.ipynb
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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"
+  },
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+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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