Face_Mask_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f66837cd-e573-42f5-9d87-2ff9f5db6e84",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow\n",
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      "Installing collected packages: werkzeug, ml-dtypes, markdown-it-py, h5py, cycler, contourpy, charset-normalizer, certifi, astunparse, absl-py, tensorboard, rich, requests, matplotlib, keras, tensorflow-intel, tensorflow\n",
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     ]
    },
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     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3.1 -> 24.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow matplotlib numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f6ca3e40-7480-471d-b615-cc71a7b4a3e9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3.1 -> 24.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4e9fae71-ea36-4c06-a012-e341ffcd07c9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
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      "Installing collected packages: pytz, tzdata, pandas\n",
      "Successfully installed pandas-2.2.3 pytz-2024.2 tzdata-2024.2\n"
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    },
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     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3.1 -> 24.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "482d05f2-0e57-4f7b-bdd9-c3d559c276f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4756 images belonging to 2 classes.\n",
      "Found 1188 images belonging to 2 classes.\n",
      "Found 3060 images belonging to 2 classes.\n",
      "Epoch 1/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 328ms/step - accuracy: 0.7336 - loss: 0.6233 - val_accuracy: 0.9122 - val_loss: 0.2270\n",
      "Epoch 2/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 37ms/step - accuracy: 0.9062 - loss: 0.2490 - val_accuracy: 0.9105 - val_loss: 0.2354\n",
      "Epoch 3/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 306ms/step - accuracy: 0.8832 - loss: 0.3034 - val_accuracy: 0.9155 - val_loss: 0.2717\n",
      "Epoch 4/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 36ms/step - accuracy: 0.8438 - loss: 0.4615 - val_accuracy: 0.9198 - val_loss: 0.2447\n",
      "Epoch 5/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 300ms/step - accuracy: 0.8958 - loss: 0.2833 - val_accuracy: 0.9307 - val_loss: 0.2079\n",
      "Epoch 6/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 35ms/step - accuracy: 0.9375 - loss: 0.2345 - val_accuracy: 0.9257 - val_loss: 0.2035\n",
      "Epoch 7/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 283ms/step - accuracy: 0.9052 - loss: 0.2570 - val_accuracy: 0.9299 - val_loss: 0.1900\n",
      "Epoch 8/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 40ms/step - accuracy: 0.9375 - loss: 0.2751 - val_accuracy: 0.9383 - val_loss: 0.1816\n",
      "Epoch 9/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 322ms/step - accuracy: 0.9087 - loss: 0.2504 - val_accuracy: 0.9181 - val_loss: 0.2104\n",
      "Epoch 10/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 34ms/step - accuracy: 0.9688 - loss: 0.1572 - val_accuracy: 0.9215 - val_loss: 0.1896\n",
      "Epoch 11/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 297ms/step - accuracy: 0.9162 - loss: 0.2280 - val_accuracy: 0.9375 - val_loss: 0.1863\n",
      "Epoch 12/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 35ms/step - accuracy: 0.9062 - loss: 0.2602 - val_accuracy: 0.9333 - val_loss: 0.1835\n",
      "Epoch 13/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 290ms/step - accuracy: 0.9175 - loss: 0.2176 - val_accuracy: 0.9383 - val_loss: 0.1594\n",
      "Epoch 14/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 35ms/step - accuracy: 0.9062 - loss: 0.2773 - val_accuracy: 0.9434 - val_loss: 0.1606\n",
      "Epoch 15/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 289ms/step - accuracy: 0.9199 - loss: 0.2235 - val_accuracy: 0.9476 - val_loss: 0.1640\n",
      "Epoch 16/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 35ms/step - accuracy: 0.8438 - loss: 0.4321 - val_accuracy: 0.9383 - val_loss: 0.1634\n",
      "Epoch 17/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 289ms/step - accuracy: 0.9174 - loss: 0.2129 - val_accuracy: 0.9375 - val_loss: 0.1685\n",
      "Epoch 18/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 35ms/step - accuracy: 0.9062 - loss: 0.1845 - val_accuracy: 0.9400 - val_loss: 0.1584\n",
      "Epoch 19/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 287ms/step - accuracy: 0.9244 - loss: 0.1879 - val_accuracy: 0.9434 - val_loss: 0.1498\n",
      "Epoch 20/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 35ms/step - accuracy: 0.8750 - loss: 0.5236 - val_accuracy: 0.9392 - val_loss: 0.1582\n",
      "Epoch 1/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 691ms/step - accuracy: 0.7564 - loss: 0.5617 - val_accuracy: 0.9012 - val_loss: 0.2655\n",
      "Epoch 2/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 51ms/step - accuracy: 0.7812 - loss: 0.4904 - val_accuracy: 0.9122 - val_loss: 0.2422\n",
      "Epoch 3/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 749ms/step - accuracy: 0.8780 - loss: 0.3150 - val_accuracy: 0.9113 - val_loss: 0.2370\n",
      "Epoch 4/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 55ms/step - accuracy: 0.8750 - loss: 0.3465 - val_accuracy: 0.9079 - val_loss: 0.2394\n",
      "Epoch 5/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m130s\u001b[0m 878ms/step - accuracy: 0.8969 - loss: 0.2794 - val_accuracy: 0.9172 - val_loss: 0.2099\n",
      "Epoch 6/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 63ms/step - accuracy: 0.8750 - loss: 0.2343 - val_accuracy: 0.9215 - val_loss: 0.2107\n",
      "Epoch 7/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 908ms/step - accuracy: 0.9019 - loss: 0.2603 - val_accuracy: 0.9257 - val_loss: 0.1991\n",
      "Epoch 8/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 62ms/step - accuracy: 0.9375 - loss: 0.1319 - val_accuracy: 0.9316 - val_loss: 0.1861\n",
      "Epoch 9/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 920ms/step - accuracy: 0.9039 - loss: 0.2598 - val_accuracy: 0.9367 - val_loss: 0.1901\n",
      "Epoch 10/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 50ms/step - accuracy: 0.8750 - loss: 0.2793 - val_accuracy: 0.9350 - val_loss: 0.1893\n",
      "Epoch 11/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 849ms/step - accuracy: 0.8995 - loss: 0.2651 - val_accuracy: 0.9400 - val_loss: 0.1842\n",
      "Epoch 12/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 60ms/step - accuracy: 0.9375 - loss: 0.1744 - val_accuracy: 0.9367 - val_loss: 0.1899\n",
      "Epoch 13/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 712ms/step - accuracy: 0.9038 - loss: 0.2658 - val_accuracy: 0.9299 - val_loss: 0.2051\n",
      "Epoch 14/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 51ms/step - accuracy: 0.9688 - loss: 0.1448 - val_accuracy: 0.9375 - val_loss: 0.1872\n",
      "Epoch 15/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 733ms/step - accuracy: 0.9037 - loss: 0.2707 - val_accuracy: 0.9291 - val_loss: 0.1898\n",
      "Epoch 16/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 57ms/step - accuracy: 0.8750 - loss: 0.2537 - val_accuracy: 0.9350 - val_loss: 0.1951\n",
      "Epoch 17/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 692ms/step - accuracy: 0.9114 - loss: 0.2450 - val_accuracy: 0.9434 - val_loss: 0.1583\n",
      "Epoch 18/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 56ms/step - accuracy: 0.9688 - loss: 0.1209 - val_accuracy: 0.9426 - val_loss: 0.1625\n",
      "Epoch 19/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 669ms/step - accuracy: 0.9172 - loss: 0.2261 - val_accuracy: 0.9316 - val_loss: 0.1873\n",
      "Epoch 20/20\n",
      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 51ms/step - accuracy: 1.0000 - loss: 0.1216 - val_accuracy: 0.9316 - val_loss: 0.1804\n",
      "\u001b[1m96/96\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 60ms/step - accuracy: 0.9483 - loss: 0.1566\n",
      "\u001b[1m96/96\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 48ms/step\n",
      "\u001b[1m96/96\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 144ms/step - accuracy: 0.9383 - loss: 0.2271\n",
      "\u001b[1m96/96\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 138ms/step\n",
      "Performance Summary:\n",
      "         Model  Accuracy  Precision    Recall  F1-Score\n",
      "0   Simple CNN  0.936601   0.890323  0.927954  0.908749\n",
      "1  Complex CNN  0.920261   0.874882  0.893372  0.884030\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Set correct data directories\n",
    "train_dir = 'FaceMaskDataset/train224'\n",
    "test_dir = 'FaceMaskDataset/test224'\n",
    "\n",
    "# Data Generators with Validation Split\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range=15,\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1,\n",
    "    horizontal_flip=True,\n",
    "    brightness_range=[0.2, 1.0],\n",
    "    validation_split=0.2  # 20% of the data will be used for validation\n",
    ")\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(128, 128),\n",
    "    batch_size=32,\n",
    "    class_mode='binary',\n",
    "    subset='training',\n",
    "    seed=42\n",
    ")\n",
    "\n",
    "validation_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(128, 128),\n",
    "    batch_size=32,\n",
    "    class_mode='binary',\n",
    "    subset='validation',\n",
    "    seed=42\n",
    ")\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    test_dir,\n",
    "    target_size=(128, 128),\n",
    "    batch_size=32,\n",
    "    class_mode='binary',\n",
    "    shuffle=False  # No need to shuffle the test data\n",
    ")\n",
    "\n",
    "# Model 1: Simple CNN\n",
    "model1 = Sequential()\n",
    "model1.add(Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)))\n",
    "model1.add(MaxPooling2D((2, 2)))\n",
    "model1.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model1.add(MaxPooling2D((2, 2)))\n",
    "model1.add(Flatten())\n",
    "model1.add(Dense(128, activation='relu'))\n",
    "model1.add(Dropout(0.5))\n",
    "model1.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Model 2: Complex CNN\n",
    "model2 = Sequential()\n",
    "model2.add(Conv2D(64, (3, 3), activation='relu', input_shape=(128, 128, 3)))\n",
    "model2.add(MaxPooling2D((2, 2)))\n",
    "model2.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "model2.add(MaxPooling2D((2, 2)))\n",
    "model2.add(Conv2D(256, (3, 3), activation='relu'))\n",
    "model2.add(MaxPooling2D((2, 2)))\n",
    "model2.add(Flatten())\n",
    "model2.add(Dense(256, activation='relu'))\n",
    "model2.add(Dropout(0.5))\n",
    "model2.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile Models with Adam Optimizer\n",
    "model1.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
    "    loss='binary_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "model2.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
    "    loss='binary_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "# Calculate steps_per_epoch and validation_steps\n",
    "steps_per_epoch = train_generator.samples // train_generator.batch_size\n",
    "validation_steps = validation_generator.samples // validation_generator.batch_size\n",
    "\n",
    "# Train Models\n",
    "history1 = model1.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=steps_per_epoch,\n",
    "    epochs=20,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_steps\n",
    ")\n",
    "\n",
    "history2 = model2.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=steps_per_epoch,\n",
    "    epochs=20,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_steps\n",
    ")\n",
    "\n",
    "# Evaluate Models\n",
    "# Evaluate Model 1\n",
    "loss1, accuracy1 = model1.evaluate(test_generator)\n",
    "test_generator.reset()\n",
    "y_pred1 = model1.predict(test_generator)\n",
    "y_pred1 = (y_pred1 > 0.5).astype(int).flatten()\n",
    "precision1 = precision_score(test_generator.classes, y_pred1)\n",
    "recall1 = recall_score(test_generator.classes, y_pred1)\n",
    "f1_1 = f1_score(test_generator.classes, y_pred1)\n",
    "\n",
    "# Evaluate Model 2\n",
    "loss2, accuracy2 = model2.evaluate(test_generator)\n",
    "test_generator.reset()\n",
    "y_pred2 = model2.predict(test_generator)\n",
    "y_pred2 = (y_pred2 > 0.5).astype(int).flatten()\n",
    "precision2 = precision_score(test_generator.classes, y_pred2)\n",
    "recall2 = recall_score(test_generator.classes, y_pred2)\n",
    "f1_2 = f1_score(test_generator.classes, y_pred2)\n",
    "\n",
    "# Performance Summary\n",
    "summary = {\n",
    "    'Model': ['Simple CNN', 'Complex CNN'],\n",
    "    'Accuracy': [accuracy1, accuracy2],\n",
    "    'Precision': [precision1, precision2],\n",
    "    'Recall': [recall1, recall2],\n",
    "    'F1-Score': [f1_1, f1_2]\n",
    "}\n",
    "\n",
    "print('Performance Summary:')\n",
    "print(pd.DataFrame(summary))\n",
    "\n",
    "# Plot Training History\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "# Plot accuracy\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history1.history['accuracy'], label='Simple CNN Training Accuracy')\n",
    "plt.plot(history1.history['val_accuracy'], label='Simple CNN Validation Accuracy')\n",
    "plt.plot(history2.history['accuracy'], label='Complex CNN Training Accuracy')\n",
    "plt.plot(history2.history['val_accuracy'], label='Complex CNN Validation Accuracy')\n",
    "plt.title('Model Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "# Plot loss\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history1.history['loss'], label='Simple CNN Training Loss')\n",
    "plt.plot(history1.history['val_loss'], label='Simple CNN Validation Loss')\n",
    "plt.plot(history2.history['loss'], label='Complex CNN Training Loss')\n",
    "plt.plot(history2.history['val_loss'], label='Complex CNN Validation Loss')\n",
    "plt.title('Model Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "781fe2de-5280-4eb0-a53b-37513905c179",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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