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   "cell_type": "code",
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   "metadata": {
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   "outputs": [],
   "source": [
    "import csv\n",
    "import os\n",
    "import re\n",
    "import time\n",
    "import math, re, os\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from kaggle_datasets import KaggleDatasets\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix\n",
    "\n",
    "def create_model(image_size):\n",
    "    model = tf.keras.Sequential()\n",
    "    \n",
    "    if image_size <= 10:\n",
    "        model.add(tf.keras.layers.Conv2D(32, (2, 2), activation='relu', input_shape=(image_size, image_size, 3)))\n",
    "        model.add(tf.keras.layers.MaxPooling2D((2, 2), padding='same')) \n",
    "    else:\n",
    "        model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(image_size, image_size, 3)))\n",
    "        model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "    if image_size > 10:\n",
    "        model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "        model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
    "    \n",
    "    if image_size > 20:\n",
    "        model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "        model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "    model.add(tf.keras.layers.GlobalAveragePooling2D())\n",
    "    \n",
    "    model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
    "    model.add(tf.keras.layers.Dense(7, activation='softmax')) \n",
    "\n",
    "    return model\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    dataset_path = os.path.abspath(\"/kaggle/input\")\n",
    "    evaluation_csv = os.path.abspath(\"/kaggle/working/evaluation_ready.csv\")\n",
    "\n",
    "    with open(evaluation_csv, \"w\") as csv_file:\n",
    "        csv_writer = csv.writer(csv_file, delimiter=\";\")\n",
    "        csv_writer.writerow([\"size\",\"errors\", \"duration_100_tests\"])\n",
    "\n",
    "    datasets = os.listdir(dataset_path)\n",
    "    for dataset_name in datasets:\n",
    "        folders = os.listdir(os.path.abspath(os.path.join(dataset_path, dataset_name)))\n",
    "        single_dataset_path = os.path.abspath(os.path.join(dataset_path, dataset_name, folders[0]))\n",
    "        \n",
    "        image_size = int(re.search(r'(\\d+)$', dataset_name)[0])\n",
    "\n",
    "        test_amount = 100\n",
    "        errors = []\n",
    "\n",
    "        start = time.perf_counter()\n",
    "        for test_number in range(test_amount):\n",
    "            print(f\"Image: {image_size} starting test {test_number}\\n\\n\")\n",
    "            \n",
    "            ds = tf.data.Dataset.load(single_dataset_path)\n",
    "        \n",
    "            ds = ds.shuffle(buffer_size=43349)\n",
    "             \n",
    "                # Split into training and testing\n",
    "            train_ds, test_ds = tf.keras.utils.split_dataset(ds, left_size=0.9)\n",
    "            \n",
    "                # Batch and prefetch\n",
    "            train_data = train_ds.batch(5).prefetch(tf.data.AUTOTUNE)\n",
    "            test_data = test_ds.batch(5).prefetch(tf.data.AUTOTUNE)\n",
    "                    \n",
    "            model = create_model(image_size)\n",
    "\n",
    "            tf.keras.utils.plot_model(model, to_file='/kaggle/working/model.png', show_shapes=True)\n",
    "        \n",
    "            model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
    "                              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "                              metrics=['accuracy'])\n",
    "        \n",
    "            \n",
    "            model.fit(train_data, epochs=7)\n",
    "        \n",
    "            test_loss, test_acc = model.evaluate(test_data, verbose=2)\n",
    "            errors.append(1 - test_acc)\n",
    "\n",
    "        end = time.perf_counter()\n",
    "        with open(evaluation_csv, \"a\") as csv_file:\n",
    "            csv_writer = csv.writer(csv_file, delimiter=\";\")\n",
    "            csv_writer.writerow([image_size, errors, round(end-start, 4)])"
   ]
  }
 ],
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