covid-19 classification by chest x-ray.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "51f1ab57",
   "metadata": {},
   "source": [
    "# Import the neccessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ae27a7ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import random\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad2db52",
   "metadata": {},
   "source": [
    "# Define preproccess data methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b59d855c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to preprocess image\n",
    "def preprocess_image(image_path, target_size=(256, 256)):\n",
    "    img = cv2.imread(image_path)\n",
    "    img_resized = cv2.resize(img, target_size)\n",
    "    img_normalized = img_resized.astype(\"float32\") / 255.0\n",
    "    return img_normalized\n",
    "\n",
    "def preprocess_data():\n",
    "    # Load and preprocess train and test data\n",
    "    train_dir = 'train'\n",
    "    test_dir = 'test'\n",
    "\n",
    "    train_filepaths = []\n",
    "    train_labels = []\n",
    "    for label in os.listdir(train_dir):\n",
    "        label_dir = os.path.join(train_dir, label)\n",
    "        for filename in os.listdir(label_dir):\n",
    "            filepath = os.path.join(label_dir, filename)\n",
    "            train_filepaths.append(filepath)\n",
    "            train_labels.append(label)\n",
    "\n",
    "    test_filepaths = []\n",
    "    test_labels = []\n",
    "    for label in os.listdir(test_dir):\n",
    "        label_dir = os.path.join(test_dir, label)\n",
    "        for filename in os.listdir(label_dir):\n",
    "            filepath = os.path.join(label_dir, filename)\n",
    "            test_filepaths.append(filepath)\n",
    "            test_labels.append(label)\n",
    "\n",
    "    x_train = np.array([preprocess_image(filepath) for filepath in train_filepaths])\n",
    "    y_train = np.array(train_labels)\n",
    "    x_test = np.array([preprocess_image(filepath) for filepath in test_filepaths])\n",
    "    y_test = np.array(test_labels)\n",
    "\n",
    "    # Convert labels to numerical values\n",
    "    label_to_index = {label: i for i, label in enumerate(np.unique(y_train))}\n",
    "    y_train = np.array([label_to_index[label] for label in y_train])\n",
    "    y_test = np.array([label_to_index[label] for label in y_test])\n",
    "\n",
    "    return x_train, y_train, x_test, y_test, train_filepaths, train_labels, test_filepaths, test_labels\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c84e65b6",
   "metadata": {},
   "source": [
    "# Create datasets and shuffle the train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a665570",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train, x_test, y_test, train_filepaths, train_labels, test_filepaths, test_labels = preprocess_data()\n",
    "\n",
    "# Shuffle indices\n",
    "shuffled_indices = np.random.permutation(len(x_train))\n",
    "\n",
    "# Shuffle x_train and y_train accordingly\n",
    "x_train = x_train[shuffled_indices]\n",
    "y_train = y_train[shuffled_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e55a9e6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>filepath</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train\\Covid\\01.jpeg</td>\n",
       "      <td>Covid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train\\Covid\\010.png</td>\n",
       "      <td>Covid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train\\Covid\\012.jpeg</td>\n",
       "      <td>Covid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train\\Covid\\015.jpg</td>\n",
       "      <td>Covid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train\\Covid\\019.png</td>\n",
       "      <td>Covid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               filepath  label\n",
       "0   train\\Covid\\01.jpeg  Covid\n",
       "1   train\\Covid\\010.png  Covid\n",
       "2  train\\Covid\\012.jpeg  Covid\n",
       "3   train\\Covid\\015.jpg  Covid\n",
       "4   train\\Covid\\019.png  Covid"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainData =pd.DataFrame({'filepath':train_filepaths,'label':train_labels}) \n",
    "trainData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe9b6b86",
   "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>filepath</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test\\Covid\\0100.jpeg</td>\n",
       "      <td>Covid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test\\Covid\\0102.jpeg</td>\n",
       "      <td>Covid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test\\Covid\\0105.png</td>\n",
       "      <td>Covid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>test\\Covid\\0106.jpeg</td>\n",
       "      <td>Covid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>test\\Covid\\0108.jpeg</td>\n",
       "      <td>Covid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               filepath  label\n",
       "0  test\\Covid\\0100.jpeg  Covid\n",
       "1  test\\Covid\\0102.jpeg  Covid\n",
       "2   test\\Covid\\0105.png  Covid\n",
       "3  test\\Covid\\0106.jpeg  Covid\n",
       "4  test\\Covid\\0108.jpeg  Covid"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testData =pd.DataFrame({'filepath':test_filepaths,'label':test_labels}) \n",
    "testData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72dab122",
   "metadata": {},
   "source": [
    "# Show some images from the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc682098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shuffle indices\n",
    "shuffled_indices = list(range(len(trainData)))\n",
    "random.shuffle(shuffled_indices)\n",
    "\n",
    "# Shuffle data based on shuffled indices\n",
    "shuffled_train_data = trainData.iloc[shuffled_indices].reset_index(drop=True)\n",
    "\n",
    "# Show some samples from the shuffled train dataset\n",
    "fig = plt.figure(figsize=(15, 15))\n",
    "for i in range(8):\n",
    "    fig.add_subplot(4, 4, i + 1).set_title(shuffled_train_data['label'][i])\n",
    "    img = cv2.imread(shuffled_train_data['filepath'][i])\n",
    "    plt.imshow(img)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac25d4e1",
   "metadata": {},
   "source": [
    "# Check dataset distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b4f3216b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Count occurrences of each label in the train set\n",
    "train_label_counts = trainData['label'].value_counts()\n",
    "\n",
    "# Count occurrences of each label in the test set\n",
    "test_label_counts = testData['label'].value_counts()\n",
    "\n",
    "# Plot histograms\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Plot histogram for train set\n",
    "axs[0].bar(train_label_counts.index, train_label_counts.values, color='blue')\n",
    "axs[0].set_title('Train Set Label Distribution')\n",
    "axs[0].set_xlabel('Label')\n",
    "axs[0].set_ylabel('Count')\n",
    "\n",
    "# Plot histogram for test set\n",
    "axs[1].bar(test_label_counts.index, test_label_counts.values, color='green')\n",
    "axs[1].set_title('Test Set Label Distribution')\n",
    "axs[1].set_xlabel('Label')\n",
    "axs[1].set_ylabel('Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e495a56e",
   "metadata": {},
   "source": [
    "### the data has more indices of covid, but got enough samples from each label.\n",
    "\n",
    "# Split the train set to train and validation set and print the amount of the samples in each set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a6135e83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of samples in train set:  200\n",
      "Num of samples in validation set:  51\n",
      "Num of samples in test set:  66\n"
     ]
    }
   ],
   "source": [
    "# first create validation set\n",
    "#split the train\n",
    "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.2, random_state=42)\n",
    "\n",
    "# Convert categorical labels to one-hot encoded format\n",
    "y_train_onehot = tf.keras.utils.to_categorical(y_train, 3)\n",
    "y_val_onehot = tf.keras.utils.to_categorical(y_val, 3)\n",
    "\n",
    "# Convert it also for the test set\n",
    "y_test_onehot = tf.keras.utils.to_categorical(y_test, 3)\n",
    "\n",
    "\n",
    "# Print the length of train, validation and test sets\n",
    "print(\"Num of samples in train set: \", len(y_train_onehot))\n",
    "print(\"Num of samples in validation set: \", len(y_val_onehot))\n",
    "print(\"Num of samples in test set: \", len(y_test_onehot))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b947faa6",
   "metadata": {},
   "source": [
    "# Simple softmax model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80811edd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "7/7 [==============================] - 1s 35ms/step - loss: 23.7864 - accuracy: 0.4450 - val_loss: 2.7173 - val_accuracy: 0.8235\n",
      "Epoch 2/50\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 6.5498 - accuracy: 0.6850 - val_loss: 2.1498 - val_accuracy: 0.8627\n",
      "Epoch 3/50\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 5.8151 - accuracy: 0.7900 - val_loss: 1.7222 - val_accuracy: 0.9216\n",
      "Epoch 4/50\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 6.1036 - accuracy: 0.7600 - val_loss: 2.8729 - val_accuracy: 0.8824\n",
      "Epoch 5/50\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 6.5122 - accuracy: 0.8000 - val_loss: 2.2584 - val_accuracy: 0.9608\n",
      "Epoch 6/50\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 3.3399 - accuracy: 0.8700 - val_loss: 3.7424 - val_accuracy: 0.8824\n",
      "Epoch 7/50\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 4.1818 - accuracy: 0.8900 - val_loss: 3.4724 - val_accuracy: 0.8627\n",
      "Epoch 8/50\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 2.3611 - accuracy: 0.8800 - val_loss: 4.3072 - val_accuracy: 0.8627\n",
      "Epoch 9/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 2.9152 - accuracy: 0.8700 - val_loss: 1.8073 - val_accuracy: 0.9804\n",
      "Epoch 10/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 1.3468 - accuracy: 0.9250 - val_loss: 2.2048 - val_accuracy: 0.9412\n",
      "Epoch 11/50\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 1.2957 - accuracy: 0.9350 - val_loss: 1.5564 - val_accuracy: 0.9412\n",
      "Epoch 12/50\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 1.1775 - accuracy: 0.9500 - val_loss: 2.1782 - val_accuracy: 0.9412\n",
      "Epoch 13/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 1.1083 - accuracy: 0.9200 - val_loss: 1.7922 - val_accuracy: 0.9216\n",
      "Epoch 14/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.4582 - accuracy: 0.9500 - val_loss: 2.9172 - val_accuracy: 0.9020\n",
      "Epoch 15/50\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.7002 - accuracy: 0.9300 - val_loss: 1.1136 - val_accuracy: 0.9412\n",
      "Epoch 16/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0177 - accuracy: 0.9950 - val_loss: 1.8168 - val_accuracy: 0.9412\n",
      "Epoch 17/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.1508 - accuracy: 0.9850 - val_loss: 1.2085 - val_accuracy: 0.9216\n",
      "Epoch 18/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.2099 - accuracy: 0.9700 - val_loss: 1.8230 - val_accuracy: 0.9216\n",
      "Epoch 19/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.1258 - accuracy: 0.9800 - val_loss: 1.2159 - val_accuracy: 0.9216\n",
      "Epoch 20/50\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.2695 - accuracy: 0.9750 - val_loss: 2.1011 - val_accuracy: 0.9216\n",
      "Epoch 21/50\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.2171 - accuracy: 0.9800 - val_loss: 1.0721 - val_accuracy: 0.9412\n",
      "Epoch 22/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.1679 - accuracy: 0.9800 - val_loss: 1.5031 - val_accuracy: 0.9412\n",
      "Epoch 23/50\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.2645 - accuracy: 0.9700 - val_loss: 1.7733 - val_accuracy: 0.8824\n",
      "Epoch 24/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.6449 - accuracy: 0.9250 - val_loss: 0.9622 - val_accuracy: 0.9608\n",
      "Epoch 25/50\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.3257 - accuracy: 0.9700 - val_loss: 1.3775 - val_accuracy: 0.9608\n",
      "Epoch 26/50\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.1798 - accuracy: 0.9700 - val_loss: 0.9915 - val_accuracy: 0.9804\n",
      "Epoch 27/50\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.1085 - accuracy: 0.9900 - val_loss: 0.7004 - val_accuracy: 0.9608\n",
      "Epoch 28/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0619 - accuracy: 0.9900 - val_loss: 1.9904 - val_accuracy: 0.9216\n",
      "Epoch 29/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0182 - accuracy: 0.9900 - val_loss: 1.2586 - val_accuracy: 0.9608\n",
      "Epoch 30/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0247 - accuracy: 0.9950 - val_loss: 0.5843 - val_accuracy: 0.9608\n",
      "Epoch 31/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0128 - accuracy: 0.9900 - val_loss: 1.3663 - val_accuracy: 0.9608\n",
      "Epoch 32/50\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 1.4553 - val_accuracy: 0.9608\n",
      "Epoch 33/50\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.1872 - val_accuracy: 0.9608\n",
      "Epoch 34/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 4.5486e-04 - accuracy: 1.0000 - val_loss: 1.0570 - val_accuracy: 0.9608\n",
      "Epoch 35/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 1.0084e-05 - accuracy: 1.0000 - val_loss: 0.9961 - val_accuracy: 0.9608\n",
      "Epoch 36/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 3.3904e-06 - accuracy: 1.0000 - val_loss: 0.9708 - val_accuracy: 0.9608\n",
      "Epoch 37/50\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 8.3559e-07 - accuracy: 1.0000 - val_loss: 0.9607 - val_accuracy: 0.9608\n",
      "Epoch 38/50\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 6.1032e-07 - accuracy: 1.0000 - val_loss: 0.9565 - val_accuracy: 0.9804\n",
      "Epoch 39/50\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 4.8814e-07 - accuracy: 1.0000 - val_loss: 0.9546 - val_accuracy: 0.9804\n",
      "Epoch 40/50\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 4.1602e-07 - accuracy: 1.0000 - val_loss: 0.9537 - val_accuracy: 0.9804\n",
      "Epoch 41/50\n",
      "7/7 [==============================] - 0s 20ms/step - loss: 3.9278e-07 - accuracy: 1.0000 - val_loss: 0.9533 - val_accuracy: 0.9804\n",
      "Epoch 42/50\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 3.7907e-07 - accuracy: 1.0000 - val_loss: 0.9532 - val_accuracy: 0.9804\n",
      "Epoch 43/50\n",
      "7/7 [==============================] - 0s 20ms/step - loss: 3.7430e-07 - accuracy: 1.0000 - val_loss: 0.9531 - val_accuracy: 0.9804\n",
      "Epoch 44/50\n",
      "7/7 [==============================] - 0s 22ms/step - loss: 3.6834e-07 - accuracy: 1.0000 - val_loss: 0.9531 - val_accuracy: 0.9804\n",
      "Epoch 45/50\n",
      "7/7 [==============================] - 0s 22ms/step - loss: 3.6536e-07 - accuracy: 1.0000 - val_loss: 0.9531 - val_accuracy: 0.9804\n",
      "Epoch 46/50\n",
      "7/7 [==============================] - 0s 22ms/step - loss: 3.6238e-07 - accuracy: 1.0000 - val_loss: 0.9531 - val_accuracy: 0.9804\n",
      "Epoch 47/50\n",
      "7/7 [==============================] - 0s 23ms/step - loss: 3.5881e-07 - accuracy: 1.0000 - val_loss: 0.9531 - val_accuracy: 0.9804\n",
      "Epoch 48/50\n",
      "7/7 [==============================] - 0s 22ms/step - loss: 3.5642e-07 - accuracy: 1.0000 - val_loss: 0.9532 - val_accuracy: 0.9804\n",
      "Epoch 49/50\n",
      "7/7 [==============================] - 0s 23ms/step - loss: 3.5225e-07 - accuracy: 1.0000 - val_loss: 0.9532 - val_accuracy: 0.9804\n",
      "Epoch 50/50\n",
      "7/7 [==============================] - 0s 22ms/step - loss: 3.5106e-07 - accuracy: 1.0000 - val_loss: 0.9532 - val_accuracy: 0.9804\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 2.5155 - accuracy: 0.8485\n",
      "Test Accuracy: 0.8484848737716675\n"
     ]
    }
   ],
   "source": [
    "# Define a simple neural network model with softmax activation\n",
    "class SoftmaxModel(tf.keras.Model):\n",
    "    def __init__(self, num_classes):\n",
    "        super(SoftmaxModel, self).__init__()\n",
    "        self.flatten = tf.keras.layers.Flatten(input_shape=(256, 256, 3))  # Flatten input images\n",
    "        self.dense = tf.keras.layers.Dense(num_classes, activation='softmax')  # Output layer with softmax activation\n",
    "\n",
    "    def call(self, x):\n",
    "        x = self.flatten(x)\n",
    "        return self.dense(x)\n",
    "\n",
    "# Create an instance of the softmax model\n",
    "num_classes = 3  # Number of classes\n",
    "model = SoftmaxModel(num_classes)\n",
    "learning_rate = 0.001\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate)\n",
    "\n",
    "# Compile the model with categorical cross-entropy loss\n",
    "model.compile(optimizer=optimizer,\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_train, y_train_onehot, epochs=50, batch_size=32, validation_data=(x_val, y_val_onehot))\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "test_loss, test_acc = model.evaluate(x_test, y_test_onehot)\n",
    "\n",
    "print(\"Test Accuracy:\", test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "739eb6c7",
   "metadata": {},
   "source": [
    "### got accuracy of 84%.\n",
    "\n",
    "# simple NN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "40fff24a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "7/7 [==============================] - 3s 342ms/step - loss: 48.1384 - accuracy: 0.4250 - val_loss: 12.8906 - val_accuracy: 0.5882\n",
      "Epoch 2/50\n",
      "7/7 [==============================] - 2s 334ms/step - loss: 9.2141 - accuracy: 0.6550 - val_loss: 1.5538 - val_accuracy: 0.8235\n",
      "Epoch 3/50\n",
      "7/7 [==============================] - 2s 333ms/step - loss: 1.7974 - accuracy: 0.8250 - val_loss: 3.5315 - val_accuracy: 0.7451\n",
      "Epoch 4/50\n",
      "7/7 [==============================] - 3s 366ms/step - loss: 2.7524 - accuracy: 0.7650 - val_loss: 0.6767 - val_accuracy: 0.9608\n",
      "Epoch 5/50\n",
      "7/7 [==============================] - 3s 392ms/step - loss: 3.0288 - accuracy: 0.7950 - val_loss: 5.7893 - val_accuracy: 0.7255\n",
      "Epoch 6/50\n",
      "7/7 [==============================] - 3s 370ms/step - loss: 4.1143 - accuracy: 0.7600 - val_loss: 4.3242 - val_accuracy: 0.7843\n",
      "Epoch 7/50\n",
      "7/7 [==============================] - 3s 367ms/step - loss: 4.5474 - accuracy: 0.8000 - val_loss: 4.3335 - val_accuracy: 0.7451\n",
      "Epoch 8/50\n",
      "7/7 [==============================] - 3s 366ms/step - loss: 3.0928 - accuracy: 0.8200 - val_loss: 2.0396 - val_accuracy: 0.8431\n",
      "Epoch 9/50\n",
      "7/7 [==============================] - 3s 363ms/step - loss: 1.5528 - accuracy: 0.8900 - val_loss: 2.6372 - val_accuracy: 0.8235\n",
      "Epoch 10/50\n",
      "7/7 [==============================] - 3s 360ms/step - loss: 1.3282 - accuracy: 0.9050 - val_loss: 1.1714 - val_accuracy: 0.8627\n",
      "Epoch 11/50\n",
      "7/7 [==============================] - 3s 359ms/step - loss: 0.7687 - accuracy: 0.9300 - val_loss: 1.5605 - val_accuracy: 0.8431\n",
      "Epoch 12/50\n",
      "7/7 [==============================] - 3s 363ms/step - loss: 0.8407 - accuracy: 0.9300 - val_loss: 1.1776 - val_accuracy: 0.9412\n",
      "Epoch 13/50\n",
      "7/7 [==============================] - 3s 359ms/step - loss: 0.9890 - accuracy: 0.9050 - val_loss: 1.9764 - val_accuracy: 0.7647\n",
      "Epoch 14/50\n",
      "7/7 [==============================] - 3s 361ms/step - loss: 0.7096 - accuracy: 0.9100 - val_loss: 0.9981 - val_accuracy: 0.9020\n",
      "Epoch 15/50\n",
      "7/7 [==============================] - 3s 366ms/step - loss: 1.0411 - accuracy: 0.9100 - val_loss: 2.8555 - val_accuracy: 0.7647\n",
      "Epoch 16/50\n",
      "7/7 [==============================] - 3s 363ms/step - loss: 0.8755 - accuracy: 0.8900 - val_loss: 0.9221 - val_accuracy: 0.9216\n",
      "Epoch 17/50\n",
      "7/7 [==============================] - 3s 361ms/step - loss: 0.5967 - accuracy: 0.9550 - val_loss: 1.6762 - val_accuracy: 0.8235\n",
      "Epoch 18/50\n",
      "7/7 [==============================] - 3s 373ms/step - loss: 0.6257 - accuracy: 0.9400 - val_loss: 2.5245 - val_accuracy: 0.8235\n",
      "Epoch 19/50\n",
      "7/7 [==============================] - 3s 362ms/step - loss: 1.2644 - accuracy: 0.8700 - val_loss: 0.4699 - val_accuracy: 0.9216\n",
      "Epoch 20/50\n",
      "7/7 [==============================] - 3s 364ms/step - loss: 0.2667 - accuracy: 0.9550 - val_loss: 2.5622 - val_accuracy: 0.8039\n",
      "Epoch 21/50\n",
      "7/7 [==============================] - 3s 365ms/step - loss: 0.5610 - accuracy: 0.9250 - val_loss: 0.9649 - val_accuracy: 0.9216\n",
      "Epoch 22/50\n",
      "7/7 [==============================] - 3s 366ms/step - loss: 0.2005 - accuracy: 0.9700 - val_loss: 1.1493 - val_accuracy: 0.8824\n",
      "Epoch 23/50\n",
      "7/7 [==============================] - 3s 369ms/step - loss: 0.1452 - accuracy: 0.9650 - val_loss: 0.7705 - val_accuracy: 0.9608\n",
      "Epoch 24/50\n",
      "7/7 [==============================] - 3s 370ms/step - loss: 0.4920 - accuracy: 0.9400 - val_loss: 4.1687 - val_accuracy: 0.7647\n",
      "Epoch 25/50\n",
      "7/7 [==============================] - 3s 381ms/step - loss: 0.8774 - accuracy: 0.8850 - val_loss: 1.3440 - val_accuracy: 0.9412\n",
      "Epoch 26/50\n",
      "7/7 [==============================] - 3s 368ms/step - loss: 0.0981 - accuracy: 0.9750 - val_loss: 0.9698 - val_accuracy: 0.9608\n",
      "Epoch 27/50\n",
      "7/7 [==============================] - 3s 373ms/step - loss: 0.5412 - accuracy: 0.9300 - val_loss: 0.5946 - val_accuracy: 0.9020\n",
      "Epoch 28/50\n",
      "7/7 [==============================] - 3s 373ms/step - loss: 0.2700 - accuracy: 0.9550 - val_loss: 1.2291 - val_accuracy: 0.9216\n",
      "Epoch 29/50\n",
      "7/7 [==============================] - 3s 379ms/step - loss: 0.4180 - accuracy: 0.9400 - val_loss: 1.1767 - val_accuracy: 0.9020\n",
      "Epoch 30/50\n",
      "7/7 [==============================] - 3s 374ms/step - loss: 0.5603 - accuracy: 0.9600 - val_loss: 1.7083 - val_accuracy: 0.8824\n",
      "Epoch 31/50\n",
      "7/7 [==============================] - 3s 374ms/step - loss: 0.4002 - accuracy: 0.9550 - val_loss: 0.8855 - val_accuracy: 0.9020\n",
      "Epoch 32/50\n",
      "7/7 [==============================] - 3s 379ms/step - loss: 0.9732 - accuracy: 0.8900 - val_loss: 2.4354 - val_accuracy: 0.8627\n",
      "Epoch 33/50\n",
      "7/7 [==============================] - 3s 382ms/step - loss: 1.9219 - accuracy: 0.8700 - val_loss: 3.7106 - val_accuracy: 0.7843\n",
      "Epoch 34/50\n",
      "7/7 [==============================] - 3s 384ms/step - loss: 1.4149 - accuracy: 0.8800 - val_loss: 1.4262 - val_accuracy: 0.9020\n",
      "Epoch 35/50\n",
      "7/7 [==============================] - 3s 387ms/step - loss: 0.9142 - accuracy: 0.9200 - val_loss: 1.4874 - val_accuracy: 0.9608\n",
      "Epoch 36/50\n",
      "7/7 [==============================] - 3s 402ms/step - loss: 0.1774 - accuracy: 0.9800 - val_loss: 0.9075 - val_accuracy: 0.9608\n",
      "Epoch 37/50\n",
      "7/7 [==============================] - 3s 387ms/step - loss: 0.1767 - accuracy: 0.9750 - val_loss: 0.9124 - val_accuracy: 0.9216\n",
      "Epoch 38/50\n",
      "7/7 [==============================] - 3s 378ms/step - loss: 0.1480 - accuracy: 0.9750 - val_loss: 2.2782 - val_accuracy: 0.9216\n",
      "Epoch 39/50\n",
      "7/7 [==============================] - 3s 381ms/step - loss: 0.0827 - accuracy: 0.9850 - val_loss: 0.7213 - val_accuracy: 0.9216\n",
      "Epoch 40/50\n",
      "7/7 [==============================] - 3s 384ms/step - loss: 0.2145 - accuracy: 0.9850 - val_loss: 1.6563 - val_accuracy: 0.9216\n",
      "Epoch 41/50\n",
      "7/7 [==============================] - 3s 383ms/step - loss: 0.1075 - accuracy: 0.9850 - val_loss: 1.0961 - val_accuracy: 0.9412\n",
      "Epoch 42/50\n",
      "7/7 [==============================] - 3s 385ms/step - loss: 0.0952 - accuracy: 0.9900 - val_loss: 0.9583 - val_accuracy: 0.9412\n",
      "Epoch 43/50\n",
      "7/7 [==============================] - 3s 387ms/step - loss: 5.6862e-07 - accuracy: 1.0000 - val_loss: 1.4063 - val_accuracy: 0.9608\n",
      "Epoch 44/50\n",
      "7/7 [==============================] - 3s 381ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 1.1879 - val_accuracy: 0.9608\n",
      "Epoch 45/50\n",
      "7/7 [==============================] - 3s 375ms/step - loss: 4.2403e-06 - accuracy: 1.0000 - val_loss: 1.0814 - val_accuracy: 0.9608\n",
      "Epoch 46/50\n",
      "7/7 [==============================] - 3s 396ms/step - loss: 2.2630e-06 - accuracy: 1.0000 - val_loss: 1.0301 - val_accuracy: 0.9608\n",
      "Epoch 47/50\n",
      "7/7 [==============================] - 3s 389ms/step - loss: 1.8917e-06 - accuracy: 1.0000 - val_loss: 1.0052 - val_accuracy: 0.9608\n",
      "Epoch 48/50\n",
      "7/7 [==============================] - 3s 380ms/step - loss: 1.6700e-06 - accuracy: 1.0000 - val_loss: 0.9931 - val_accuracy: 0.9608\n",
      "Epoch 49/50\n",
      "7/7 [==============================] - 3s 377ms/step - loss: 1.5735e-06 - accuracy: 1.0000 - val_loss: 0.9872 - val_accuracy: 0.9608\n",
      "Epoch 50/50\n",
      "7/7 [==============================] - 3s 376ms/step - loss: 1.4948e-06 - accuracy: 1.0000 - val_loss: 0.9843 - val_accuracy: 0.9608\n",
      "3/3 [==============================] - 0s 22ms/step - loss: 1.4562 - accuracy: 0.8636\n",
      "Test Accuracy: 0.8636363744735718\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Flatten, Dense\n",
    "\n",
    "# Define a Sequential model\n",
    "model = Sequential([\n",
    "    Flatten(input_shape=(256, 256, 3)),  # Flatten input images\n",
    "    Dense(256, activation='relu'),  # First dense layer with ReLU activation\n",
    "    Dense(128, activation='relu'),  # Second dense layer with ReLU activation\n",
    "    Dense(64, activation='relu'),  # third dense layer with ReLU activation\n",
    "    Dense(num_classes, activation='softmax')  # Output layer with softmax activation\n",
    "])\n",
    "\n",
    "# Define learning rate\n",
    "# learning_rate = 0.001\n",
    "\n",
    "# Create an optimizer with the specified learning rate\n",
    "# optimizer = tf.keras.optimizers.Adam(learning_rate)\n",
    "\n",
    "# Compile the model with categorical cross-entropy loss\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_train, y_train_onehot, epochs=50, batch_size=32, validation_data=(x_val, y_val_onehot))\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "test_loss, test_acc = model.evaluate(x_test, y_test_onehot)\n",
    "\n",
    "print(\"Test Accuracy:\", test_acc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9137c8",
   "metadata": {},
   "source": [
    "### got accuracy of 86%.\n",
    "\n",
    "\n",
    "# CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "74c20ef7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "13/13 [==============================] - 9s 671ms/step - loss: 4.7771 - accuracy: 0.5250 - val_loss: 0.8782 - val_accuracy: 0.5294\n",
      "Epoch 2/10\n",
      "13/13 [==============================] - 11s 880ms/step - loss: 0.5800 - accuracy: 0.8050 - val_loss: 0.4957 - val_accuracy: 0.8235\n",
      "Epoch 3/10\n",
      "13/13 [==============================] - 10s 795ms/step - loss: 0.2729 - accuracy: 0.9150 - val_loss: 0.3147 - val_accuracy: 0.9020\n",
      "Epoch 4/10\n",
      "13/13 [==============================] - 10s 772ms/step - loss: 0.2245 - accuracy: 0.9150 - val_loss: 0.3490 - val_accuracy: 0.9020\n",
      "Epoch 5/10\n",
      "13/13 [==============================] - 10s 755ms/step - loss: 0.1049 - accuracy: 0.9650 - val_loss: 0.1550 - val_accuracy: 0.9412\n",
      "Epoch 6/10\n",
      "13/13 [==============================] - 10s 760ms/step - loss: 0.0592 - accuracy: 0.9850 - val_loss: 0.1958 - val_accuracy: 0.9412\n",
      "Epoch 7/10\n",
      "13/13 [==============================] - 10s 761ms/step - loss: 0.0352 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9020\n",
      "Epoch 8/10\n",
      "13/13 [==============================] - 10s 780ms/step - loss: 0.0193 - accuracy: 0.9900 - val_loss: 0.1195 - val_accuracy: 0.9216\n",
      "Epoch 9/10\n",
      "13/13 [==============================] - 10s 791ms/step - loss: 0.0090 - accuracy: 1.0000 - val_loss: 0.1551 - val_accuracy: 0.9608\n",
      "Epoch 10/10\n",
      "13/13 [==============================] - 10s 809ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9608\n",
      "3/3 [==============================] - 1s 181ms/step - loss: 0.1515 - accuracy: 0.9697\n",
      "Test Accuracy: 0.9696969985961914\n"
     ]
    }
   ],
   "source": [
    "CNNModel = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)),\n",
    "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "    tf.keras.layers.Dropout(0.20),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "    tf.keras.layers.Dropout(0.10),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(64, activation='relu'),\n",
    "    tf.keras.layers.Dense(num_classes, activation='softmax')\n",
    "])\n",
    "\n",
    "# custom_optimizer = tf.keras.optimizers.Adam(learning_rate=0.00001)\n",
    "\n",
    "CNNModel.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = CNNModel.fit(x_train, y_train_onehot, epochs=10, batch_size=16, validation_data=(x_val, y_val_onehot))\n",
    "\n",
    "test_loss, test_acc = CNNModel.evaluate(x_test, y_test_onehot)\n",
    "\n",
    "print(\"Test Accuracy:\", test_acc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63eb4c34",
   "metadata": {},
   "source": [
    "## Got accuracy of 96%!\n",
    "\n",
    "### Lets plot the aacuracy and loss of train set and validation set through the training proccess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "788d7957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get training history\n",
    "train_loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "train_acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "epochs = range(1, len(train_loss) + 1)\n",
    "\n",
    "# Plotting loss and accuracy side by side\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plotting loss\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs, train_loss, 'b', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "# Plotting accuracy\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs, train_acc, 'b', label='Training accuracy')\n",
    "plt.plot(epochs, val_acc, 'r', label='Validation accuracy')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.tight_layout()  # Adjust spacing between subplots for better readability\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c665ebc1",
   "metadata": {},
   "source": [
    "### It looks good! the train and validation loss and accuracy goes together.\n",
    "\n",
    "### Show the confusion matrix and the precision, recall and F-1 score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9a24532a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 101ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Assuming CNNModel is already trained and x_test contains the test data\n",
    "\n",
    "# Define class labels\n",
    "labels = ['Covid-19', 'Normal', 'Viral Pneumonia']\n",
    "\n",
    "# Predict labels for test data\n",
    "y_pred = np.argmax(CNNModel.predict(x_test), axis=1)\n",
    "\n",
    "# Convert one-hot encoded labels back to categorical labels\n",
    "y_test_labels = np.argmax(y_test_onehot, axis=1)\n",
    "\n",
    "# Calculate confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test_labels, y_pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels)\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8cd3515a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covid-19:\n",
      "  Precision: 1.0\n",
      "  Recall: 1.0\n",
      "  F1 Score: 1.0\n",
      "\n",
      "Normal:\n",
      "  Precision: 0.95\n",
      "  Recall: 0.95\n",
      "  F1 Score: 0.9500000000000001\n",
      "\n",
      "Viral Pneumonia:\n",
      "  Precision: 0.95\n",
      "  Recall: 0.95\n",
      "  F1 Score: 0.9500000000000001\n",
      "\n",
      "Overall Accuracy: 0.9696969696969697\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score\n",
    "\n",
    "# Compute precision, recall, and accuracy\n",
    "precision = precision_score(y_test_labels, y_pred, average=None)\n",
    "recall = recall_score(y_test_labels, y_pred, average=None)\n",
    "accuracy = accuracy_score(y_test_labels, y_pred)\n",
    "f1 = f1_score(y_test_labels, y_pred, average=None)\n",
    "\n",
    "# Print precision, recall, and accuracy for each class\n",
    "for i, label in enumerate(labels):\n",
    "    print(f\"{label}:\")\n",
    "    print(f\"  Precision: {precision[i]}\")\n",
    "    print(f\"  Recall: {recall[i]}\")\n",
    "    print(f\"  F1 Score: {f1[i]}\")\n",
    "    print()\n",
    "\n",
    "# Print overall accuracy\n",
    "print(\"Overall Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08eb2f95",
   "metadata": {},
   "source": [
    "### 96% accuracy (almost 97%)!\n",
    "\n",
    "# Try with data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fec25d83",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 201 images belonging to 3 classes.\n",
      "Found 50 images belonging to 3 classes.\n",
      "Epoch 1/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 1.2320 - accuracy: 0.4438 - val_loss: 1.0914 - val_accuracy: 0.3125\n",
      "Epoch 2/50\n",
      "6/6 [==============================] - 12s 2s/step - loss: 1.0072 - accuracy: 0.5260 - val_loss: 0.8692 - val_accuracy: 0.5938\n",
      "Epoch 3/50\n",
      "6/6 [==============================] - 11s 2s/step - loss: 0.7913 - accuracy: 0.6391 - val_loss: 0.6171 - val_accuracy: 0.7500\n",
      "Epoch 4/50\n",
      "6/6 [==============================] - 12s 2s/step - loss: 0.5509 - accuracy: 0.7929 - val_loss: 0.6098 - val_accuracy: 0.7188\n",
      "Epoch 5/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.6482 - accuracy: 0.7456 - val_loss: 0.4931 - val_accuracy: 0.7500\n",
      "Epoch 6/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.4302 - accuracy: 0.8107 - val_loss: 0.6790 - val_accuracy: 0.6875\n",
      "Epoch 7/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.4411 - accuracy: 0.8047 - val_loss: 0.4504 - val_accuracy: 0.8750\n",
      "Epoch 8/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.4412 - accuracy: 0.7988 - val_loss: 0.3833 - val_accuracy: 0.8438\n",
      "Epoch 9/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.4406 - accuracy: 0.8107 - val_loss: 0.6730 - val_accuracy: 0.7812\n",
      "Epoch 10/50\n",
      "6/6 [==============================] - 13s 3s/step - loss: 0.4111 - accuracy: 0.8107 - val_loss: 0.5401 - val_accuracy: 0.8125\n",
      "Epoch 11/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.3394 - accuracy: 0.8580 - val_loss: 0.3714 - val_accuracy: 0.7812\n",
      "Epoch 12/50\n",
      "6/6 [==============================] - 14s 2s/step - loss: 0.3471 - accuracy: 0.8698 - val_loss: 0.3138 - val_accuracy: 0.8750\n",
      "Epoch 13/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2424 - accuracy: 0.8994 - val_loss: 0.4219 - val_accuracy: 0.8750\n",
      "Epoch 14/50\n",
      "6/6 [==============================] - 13s 3s/step - loss: 0.3259 - accuracy: 0.8876 - val_loss: 0.5104 - val_accuracy: 0.8750\n",
      "Epoch 15/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.3635 - accuracy: 0.8284 - val_loss: 0.7166 - val_accuracy: 0.7188\n",
      "Epoch 16/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2617 - accuracy: 0.8876 - val_loss: 0.2988 - val_accuracy: 0.9062\n",
      "Epoch 17/50\n",
      "6/6 [==============================] - 13s 3s/step - loss: 0.3486 - accuracy: 0.8757 - val_loss: 1.0401 - val_accuracy: 0.7812\n",
      "Epoch 18/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2526 - accuracy: 0.8876 - val_loss: 0.3846 - val_accuracy: 0.8750\n",
      "Epoch 19/50\n",
      "6/6 [==============================] - 14s 2s/step - loss: 0.2844 - accuracy: 0.8802 - val_loss: 0.4544 - val_accuracy: 0.8438\n",
      "Epoch 20/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.3313 - accuracy: 0.8698 - val_loss: 0.3905 - val_accuracy: 0.8750\n",
      "Epoch 21/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2596 - accuracy: 0.8994 - val_loss: 0.4594 - val_accuracy: 0.8438\n",
      "Epoch 22/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2847 - accuracy: 0.8994 - val_loss: 0.5239 - val_accuracy: 0.7812\n",
      "Epoch 23/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2864 - accuracy: 0.8935 - val_loss: 0.5605 - val_accuracy: 0.8125\n",
      "Epoch 24/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.3946 - accuracy: 0.8580 - val_loss: 0.4468 - val_accuracy: 0.8438\n",
      "Epoch 25/50\n",
      "6/6 [==============================] - 14s 3s/step - loss: 0.2756 - accuracy: 0.8817 - val_loss: 0.4643 - val_accuracy: 0.8438\n",
      "Epoch 26/50\n",
      "6/6 [==============================] - 15s 3s/step - loss: 0.2972 - accuracy: 0.9010 - val_loss: 0.6386 - val_accuracy: 0.8125\n",
      "Epoch 27/50\n",
      "6/6 [==============================] - 15s 3s/step - loss: 0.3100 - accuracy: 0.8594 - val_loss: 0.3450 - val_accuracy: 0.8438\n",
      "Epoch 28/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2738 - accuracy: 0.8698 - val_loss: 0.3677 - val_accuracy: 0.8438\n",
      "Epoch 29/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2585 - accuracy: 0.9053 - val_loss: 0.5170 - val_accuracy: 0.8438\n",
      "Epoch 30/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2687 - accuracy: 0.9112 - val_loss: 0.5326 - val_accuracy: 0.8438\n",
      "Epoch 31/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2393 - accuracy: 0.9112 - val_loss: 0.4025 - val_accuracy: 0.8750\n",
      "Epoch 32/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.3047 - accuracy: 0.8817 - val_loss: 0.2248 - val_accuracy: 0.9062\n",
      "Epoch 33/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2280 - accuracy: 0.9172 - val_loss: 0.3746 - val_accuracy: 0.7500\n",
      "Epoch 34/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2218 - accuracy: 0.9231 - val_loss: 0.7159 - val_accuracy: 0.7812\n",
      "Epoch 35/50\n",
      "6/6 [==============================] - 14s 2s/step - loss: 0.2273 - accuracy: 0.9062 - val_loss: 0.4298 - val_accuracy: 0.8750\n",
      "Epoch 36/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2397 - accuracy: 0.9112 - val_loss: 0.3572 - val_accuracy: 0.8750\n",
      "Epoch 37/50\n",
      "6/6 [==============================] - 14s 2s/step - loss: 0.2960 - accuracy: 0.8802 - val_loss: 0.2299 - val_accuracy: 0.8750\n",
      "Epoch 38/50\n",
      "6/6 [==============================] - 14s 2s/step - loss: 0.2097 - accuracy: 0.9172 - val_loss: 0.3040 - val_accuracy: 0.8750\n",
      "Epoch 39/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.1804 - accuracy: 0.9349 - val_loss: 0.4905 - val_accuracy: 0.8750\n",
      "Epoch 40/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2331 - accuracy: 0.9290 - val_loss: 0.3649 - val_accuracy: 0.8750\n",
      "Epoch 41/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.1815 - accuracy: 0.9231 - val_loss: 0.6505 - val_accuracy: 0.8438\n",
      "Epoch 42/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2139 - accuracy: 0.9290 - val_loss: 0.4584 - val_accuracy: 0.9062\n",
      "Epoch 43/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2083 - accuracy: 0.9349 - val_loss: 0.8816 - val_accuracy: 0.7812\n",
      "Epoch 44/50\n",
      "6/6 [==============================] - 14s 2s/step - loss: 0.1640 - accuracy: 0.9375 - val_loss: 0.6066 - val_accuracy: 0.8125\n",
      "Epoch 45/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.1944 - accuracy: 0.9408 - val_loss: 0.1126 - val_accuracy: 0.9375\n",
      "Epoch 46/50\n",
      "6/6 [==============================] - 13s 2s/step - loss: 0.2409 - accuracy: 0.8935 - val_loss: 0.2297 - val_accuracy: 0.9062\n",
      "Epoch 47/50\n",
      "6/6 [==============================] - 14s 2s/step - loss: 0.1562 - accuracy: 0.9467 - val_loss: 0.2911 - val_accuracy: 0.9062\n",
      "Epoch 48/50\n",
      "6/6 [==============================] - 15s 3s/step - loss: 0.1893 - accuracy: 0.9323 - val_loss: 0.4409 - val_accuracy: 0.9062\n",
      "Epoch 49/50\n",
      "6/6 [==============================] - 14s 2s/step - loss: 0.2277 - accuracy: 0.9172 - val_loss: 0.3769 - val_accuracy: 0.8750\n",
      "Epoch 50/50\n",
      "6/6 [==============================] - 14s 2s/step - loss: 0.1196 - accuracy: 0.9586 - val_loss: 0.5466 - val_accuracy: 0.8125\n",
      "3/3 [==============================] - 1s 162ms/step - loss: 0.1933 - accuracy: 0.9394\n",
      "Test Accuracy: 0.939393937587738\n"
     ]
    }
   ],
   "source": [
    "train_dir = 'train'\n",
    "test_dir = 'test'\n",
    "\n",
    "# Define data augmentation parameters\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range=20,\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    validation_split=0.2\n",
    ")\n",
    "\n",
    "# Flow images in batches from directory\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(256, 256),\n",
    "    batch_size=32,\n",
    "    class_mode='categorical',  # adjust for your problem\n",
    "    subset='training'           # specify subset as 'training' for training data\n",
    ")\n",
    "\n",
    "# Flow validation images in batches from directory\n",
    "val_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(256, 256),\n",
    "    batch_size=32,\n",
    "    class_mode='categorical',   # adjust for your problem\n",
    "    subset='validation'         # specify subset as 'validation' for validation data\n",
    ")\n",
    "\n",
    "# Define the CNN model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)),\n",
    "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(64, activation='relu'),\n",
    "    tf.keras.layers.Dense(num_classes, activation='softmax')\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // 32,\n",
    "    epochs=50,\n",
    "    validation_data=val_generator,\n",
    "    validation_steps=val_generator.samples // 32\n",
    ")\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "test_loss, test_acc = model.evaluate(x_test, y_test_onehot)\n",
    "print(\"Test Accuracy:\", test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "621893cc",
   "metadata": {},
   "source": [
    "## In conclusion we can see that the CNN (without the data augmentation) was the best model with 96% of accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0382cc19",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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