autoencoder_v1.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import BatchNormalization\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers import Conv2DTranspose\n",
        "from keras.layers import LeakyReLU\n",
        "from keras.layers import Activation\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Reshape\n",
        "from keras.layers import Input\n",
        "from keras.layers import Dropout\n",
        "from keras.models import Model\n",
        "from tensorflow.keras import backend as K\n",
        "from keras.layers import Rescaling, Resizing\n",
        "from keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, LambdaCallback, TensorBoard, BackupAndRestore\n",
        "from keras import regularizers\n",
        "import numpy as np\n",
        "import os\n",
        "import datetime\n",
        "import gc #garbage collector\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "from tqdm import tqdm\n",
        "import cv2\n",
        "import psutil\n",
        "from random import shuffle, sample"
      ],
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        {
          "name": "stderr",
          "text": "2024-05-23 22:39:20.316163: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-05-23 22:39:20.316298: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-05-23 22:39:20.436776: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
          "output_type": "stream"
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    {
      "cell_type": "code",
      "source": [
        "physical_devices = tf.config.experimental.list_physical_devices('GPU')\n",
        "if len(physical_devices) > 0:\n",
        "    tf.config.experimental.set_memory_growth(physical_devices[0], True)"
      ],
      "metadata": {
        "execution": {
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      "source": [
        "latentDim = 2048\n",
        "inputShape = (64, 64, 3)\n",
        "chanDim = 3\n",
        "depth = 1\n",
        "\n",
        "inputs = Input(shape=inputShape)\n",
        "x = inputs\n",
        "\n",
        "\n",
        "x = Conv2D(256, (3, 3), strides=2, padding=\"same\")(x)\n",
        "x = LeakyReLU(alpha=0.2)(x)\n",
        "x = BatchNormalization(axis=chanDim)(x)\n",
        "\n",
        "x = Conv2D(128, (3, 3), strides=2, padding=\"same\")(x)\n",
        "x = LeakyReLU(alpha=0.2)(x)\n",
        "x = BatchNormalization(axis=chanDim)(x)\n",
        "\n",
        "x = Conv2D(64, (3, 3), strides=2, padding=\"same\")(x)\n",
        "x = LeakyReLU(alpha=0.2)(x)\n",
        "x = BatchNormalization(axis=chanDim)(x)\n",
        "\n",
        "volumeSize = K.int_shape(x)\n",
        "x = Flatten()(x)\n",
        "latent = Dense(latentDim)(x)\n",
        "encoder = Model(inputs, latent, name=\"encoder\")\n",
        "\n",
        "latentInputs = Input(shape=(latentDim,))\n",
        "x = Dense(np.prod(volumeSize[1:]))(latentInputs)\n",
        "x = Reshape((volumeSize[1], volumeSize[2], volumeSize[3]))(x)\n",
        "\n",
        "\n",
        "x = Conv2DTranspose(64, (3, 3), strides=2, padding=\"same\")(x)\n",
        "x = LeakyReLU(alpha=0.2)(x)\n",
        "x = BatchNormalization(axis=chanDim)(x)\n",
        "\n",
        "x = Conv2DTranspose(128, (3, 3), strides=2, padding=\"same\")(x)\n",
        "x = LeakyReLU(alpha=0.2)(x)\n",
        "x = BatchNormalization(axis=chanDim)(x)\n",
        "\n",
        "x = Conv2DTranspose(256, (3, 3), strides=2, padding=\"same\")(x)\n",
        "x = LeakyReLU(alpha=0.2)(x)\n",
        "x = BatchNormalization(axis=chanDim)(x)\n",
        "\n",
        "x = Conv2DTranspose(3, (3, 3), padding=\"same\")(x)\n",
        "outputs = Activation(\"sigmoid\")(x)\n",
        "\n",
        "decoder = Model(latentInputs, outputs, name=\"decoder\")\n",
        "\n",
        "autoencoder = Model(inputs, decoder(encoder(inputs)), name=\"autoencoder\")\n",
        "\n",
        "autoencoder.compile(loss=\"mse\", optimizer=Adam(learning_rate=1e-2))"
      ],
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        {
          "name": "stderr",
          "text": "/opt/conda/lib/python3.10/site-packages/keras/src/layers/activations/leaky_relu.py:41: UserWarning: Argument `alpha` is deprecated. Use `negative_slope` instead.\n  warnings.warn(\n",
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        "!pip install gdown"
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        {
          "name": "stdout",
          "text": "Collecting gdown\n  Downloading gdown-5.2.0-py3-none-any.whl.metadata (5.8 kB)\nRequirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.10/site-packages (from gdown) (4.12.2)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from gdown) (3.13.1)\nRequirement already satisfied: requests[socks] in /opt/conda/lib/python3.10/site-packages (from gdown) (2.31.0)\nRequirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from gdown) (4.66.1)\nRequirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.10/site-packages (from beautifulsoup4->gdown) (2.5)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (3.3.2)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (3.6)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (1.26.18)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (2024.2.2)\nRequirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (1.7.1)\nDownloading gdown-5.2.0-py3-none-any.whl (18 kB)\nInstalling collected packages: gdown\nSuccessfully installed gdown-5.2.0\n",
          "output_type": "stream"
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    {
      "cell_type": "code",
      "source": [
        "!gdown 'https://drive.google.com/uc?id=1UYRfDxHJVdxvLXnyRtcvt4MlUbWMM5ry'"
      ],
      "metadata": {
        "execution": {
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      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Downloading...\nFrom: https://drive.google.com/uc?id=1UYRfDxHJVdxvLXnyRtcvt4MlUbWMM5ry\nTo: /kaggle/working/real_face_best_model_64.h5\n100%|██████████████████████████████████████| 70.3M/70.3M [00:02<00:00, 31.7MB/s]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "autoencoder = load_model('real_face_best_model_64.h5')"
      ],
      "metadata": {
        "trusted": true,
        "id": "mLLiKtKyezbv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import os\n",
        "from tensorflow.keras.models import load_model, Model\n",
        "from tensorflow.keras.preprocessing.image import img_to_array, load_img\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from tqdm import tqdm"
      ],
      "metadata": {
        "execution": {
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "# Define the paths to your folders\n",
        "real_faces_path = \"/kaggle/input/real-fake-faces/face_fake/face_fake\"\n",
        "fake_faces_path = \"/kaggle/input/real-fake-faces/face_real/face_real\""
      ],
      "metadata": {
        "execution": {
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      "metadata": {
        "id": "4X2YV84mfQRl"
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    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the encoder part from the autoencoder\n",
        "encoder = Model(inputs=autoencoder.input, outputs=autoencoder.get_layer(\"encoder\").output)\n",
        "encoder.trainable = False  # Freeze the encoder"
      ],
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    {
      "cell_type": "code",
      "source": [
        "!rm -r real_vs_fake"
      ],
      "metadata": {
        "execution": {
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      "source": [
        "import os\n",
        "import shutil\n",
        "import random\n",
        "\n",
        "# Define paths\n",
        "source_folder = \"/kaggle/input/real-fake-faces/face_real/face_real\"\n",
        "destination_folder = \"real_vs_fake\"\n",
        "\n",
        "# Create destination folder if it doesn't exist\n",
        "if not os.path.exists(destination_folder):\n",
        "    os.makedirs(destination_folder)\n",
        "\n",
        "# Create test, train, and valid folders within destination folder\n",
        "folders = ['test', 'train', 'valid']\n",
        "for folder in folders:\n",
        "    os.makedirs(os.path.join(destination_folder, folder, 'real'), exist_ok=True)\n",
        "\n",
        "# List all images in source folder\n",
        "images = os.listdir(source_folder)\n",
        "random.shuffle(images)\n",
        "\n",
        "# Calculate the number of images for each category\n",
        "total_images = len(images)\n",
        "test_count = 5000\n",
        "train_count = int(0.8 * (total_images - test_count))\n",
        "valid_count = total_images - test_count - train_count\n",
        "\n",
        "# Move images to respective folders\n",
        "for folder, count in [('test', test_count), ('train', train_count), ('valid', valid_count)]:\n",
        "    for i in range(count):\n",
        "        image = images.pop()\n",
        "        shutil.copy(os.path.join(source_folder, image), os.path.join(destination_folder, folder, 'real'))\n",
        "\n",
        "# Copy train, valid, and test folders to real_vs_fake\n",
        "# for folder in folders:\n",
        "#     shutil.copytree(os.path.join(destination_folder, folder), os.path.join(destination_folder, 'real_vs_fake', folder))"
      ],
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      "cell_type": "code",
      "source": [
        "# Define paths\n",
        "source_folder = \"/kaggle/input/real-fake-faces/face_fake/face_fake\"\n",
        "destination_folder = \"real_vs_fake\"\n",
        "\n",
        "# Create destination folder if it doesn't exist\n",
        "if not os.path.exists(destination_folder):\n",
        "    os.makedirs(destination_folder)\n",
        "\n",
        "# Create test, train, and valid folders within destination folder\n",
        "folders = ['test', 'train', 'valid']\n",
        "for folder in folders:\n",
        "    os.makedirs(os.path.join(destination_folder, folder, 'fake'), exist_ok=True)\n",
        "\n",
        "# List all images in source folder\n",
        "images = os.listdir(source_folder)\n",
        "random.shuffle(images)\n",
        "\n",
        "# Calculate the number of images for each category\n",
        "total_images = len(images)\n",
        "test_count = 5000\n",
        "train_count = int(0.8 * (total_images - test_count))\n",
        "valid_count = total_images - test_count - train_count\n",
        "\n",
        "# Move images to respective folders\n",
        "for folder, count in [('test', test_count), ('train', train_count), ('valid', valid_count)]:\n",
        "    for i in range(count):\n",
        "        image = images.pop()\n",
        "        shutil.copy(os.path.join(source_folder, image), os.path.join(destination_folder, folder, 'fake'))\n",
        "\n",
        "# Copy train, valid, and test folders to real_vs_fake\n",
        "# for folder in folders:\n",
        "#     shutil.copytree(os.path.join(destination_folder, folder), os.path.join(destination_folder, 'real_vs_fake', folder))\n"
      ],
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      "source": [
        "def count_files(folder_path):\n",
        "    # Initialize count\n",
        "    file_count = 0\n",
        "\n",
        "    # Walk through the folder and count files\n",
        "    for _, _, files in os.walk(folder_path):\n",
        "        file_count += len(files)\n",
        "\n",
        "    return file_count"
      ],
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      "source": [
        "# Specify the folder path\n",
        "folders = ['test', 'train', 'valid']\n",
        "folder_path = 'real_vs_fake/'\n",
        "\n",
        "for i in folders:\n",
        "  print(\"Number of files in fake/\"+str(i)+\" :\", count_files(folder_path+i+'/fake'))"
      ],
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        "outputId": "8a73ae2a-57fd-4a7a-b66f-a7455b7e448f"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Number of files in fake/test : 5000\nNumber of files in fake/train : 23070\nNumber of files in fake/valid : 5768\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the folder path\n",
        "folders = ['test', 'train', 'valid']\n",
        "folder_path = 'real_vs_fake/'\n",
        "\n",
        "for i in folders:\n",
        "  print(\"Number of files in real/\"+str(i)+\" :\", count_files(folder_path+i+'/real'))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-23T22:45:16.866133Z",
          "iopub.execute_input": "2024-05-23T22:45:16.866403Z",
          "iopub.status.idle": "2024-05-23T22:45:16.920044Z",
          "shell.execute_reply.started": "2024-05-23T22:45:16.866380Z",
          "shell.execute_reply": "2024-05-23T22:45:16.919065Z"
        },
        "trusted": true,
        "id": "aKsPLQk1ezbw",
        "outputId": "919603db-9e72-4860-f54d-3b00b74f52fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Number of files in real/test : 5000\nNumber of files in real/train : 23712\nNumber of files in real/valid : 5929\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_path = 'real_vs_fake/'\n",
        "image_gen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=40,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest'\n",
        ")\n",
        "\n",
        "# Create the data generator with augmentation\n",
        "train_flow = image_gen.flow_from_directory(\n",
        "    base_path + 'train/',\n",
        "    target_size=(64, 64),\n",
        "    batch_size=64,\n",
        "    class_mode='binary'\n",
        ")\n",
        "\n",
        "image_gen1 = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "valid_flow = image_gen1.flow_from_directory(\n",
        "    base_path + 'valid/',\n",
        "    target_size=(64, 64),\n",
        "    batch_size=64,\n",
        "    class_mode='binary'\n",
        ")\n",
        "\n",
        "image_gen1 = ImageDataGenerator(rescale=1./255.)\n",
        "test_flow = image_gen.flow_from_directory(\n",
        "    base_path + 'test/',\n",
        "    target_size=(64, 64),\n",
        "    batch_size=64,\n",
        "    class_mode='binary',\n",
        "    shuffle=False\n",
        ")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-23T22:45:16.921328Z",
          "iopub.execute_input": "2024-05-23T22:45:16.921702Z",
          "iopub.status.idle": "2024-05-23T22:45:19.959640Z",
          "shell.execute_reply.started": "2024-05-23T22:45:16.921672Z",
          "shell.execute_reply": "2024-05-23T22:45:19.958809Z"
        },
        "trusted": true,
        "id": "3kGE9qQrezbw",
        "outputId": "e60a52e8-c3c0-4b86-968b-91154cddfb22"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Found 46782 images belonging to 2 classes.\nFound 11697 images belonging to 2 classes.\nFound 10000 images belonging to 2 classes.\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Input, Dense, Dropout\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# Freeze the encoder layers\n",
        "for layer in encoder.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "# Using the existing encoder model\n",
        "encoded_input = encoder.output\n",
        "\n",
        "# Adding new layers for binary classification\n",
        "x = Dense(1024, activation=\"relu\")(encoded_input)\n",
        "# x = Dropout(0.3)(x)\n",
        "# x = Dense(512, activation=\"relu\")(x)\n",
        "# x = Dropout(0.3)(x)\n",
        "x = Dense(64, activation=\"relu\")(x)\n",
        "# x = Dropout(0.3)(x)\n",
        "outputs = Dense(1, activation=\"sigmoid\")(x)\n",
        "\n",
        "# Creating the binary classification model\n",
        "binary_classifier = Model(encoder.input, outputs, name=\"binary_classifier\")\n",
        "\n",
        "# Compiling the model\n",
        "binary_classifier.compile(loss=\"binary_crossentropy\", optimizer=Adam(learning_rate=5e-4), metrics=[\"accuracy\"])\n",
        "\n",
        "# Summary of the new model\n",
        "binary_classifier.summary()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-23T22:45:19.960646Z",
          "iopub.execute_input": "2024-05-23T22:45:19.960898Z",
          "iopub.status.idle": "2024-05-23T22:45:20.042415Z",
          "shell.execute_reply.started": "2024-05-23T22:45:19.960876Z",
          "shell.execute_reply": "2024-05-23T22:45:20.041418Z"
        },
        "trusted": true,
        "id": "BN2q4G33ezbw",
        "outputId": "0a76837f-5e5a-49bf-db8e-b9d84fb2512f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\u001b[1mModel: \"binary_classifier\"\u001b[0m\n",
            "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"binary_classifier\"</span>\n</pre>\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │         \u001b[38;5;34m7,168\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ leaky_re_lu (\u001b[38;5;33mLeakyReLU\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │         \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │       \u001b[38;5;34m295,040\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ leaky_re_lu_1 (\u001b[38;5;33mLeakyReLU\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │           \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │        \u001b[38;5;34m73,792\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ leaky_re_lu_2 (\u001b[38;5;33mLeakyReLU\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │           \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │     \u001b[38;5;34m8,390,656\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │     \u001b[38;5;34m2,098,176\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │        \u001b[38;5;34m65,600\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m65\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
            "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">7,168</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ leaky_re_lu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,040</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ leaky_re_lu_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,792</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ leaky_re_lu_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">8,390,656</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,098,176</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,600</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m10,932,289\u001b[0m (41.70 MB)\n",
            "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">10,932,289</span> (41.70 MB)\n</pre>\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,163,841\u001b[0m (8.25 MB)\n",
            "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,163,841</span> (8.25 MB)\n</pre>\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m8,768,448\u001b[0m (33.45 MB)\n",
            "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,768,448</span> (33.45 MB)\n</pre>\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=5)  # Stop training if no improvement after 3 epochs\n",
        "checkpoint = ModelCheckpoint('DenseNetImagenet.model.keras', monitor='val_loss', save_best_only=True)  # Save the best model\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-23T22:45:20.043653Z",
          "iopub.execute_input": "2024-05-23T22:45:20.044116Z",
          "iopub.status.idle": "2024-05-23T22:45:20.049618Z",
          "shell.execute_reply.started": "2024-05-23T22:45:20.044076Z",
          "shell.execute_reply": "2024-05-23T22:45:20.048461Z"
        },
        "trusted": true,
        "id": "JvlzvwY7ezbw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = binary_classifier.fit(\n",
        "    train_flow,\n",
        "    epochs = 50,\n",
        "    validation_data =valid_flow,\n",
        "    callbacks=[checkpoint]\n",
        ")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-23T22:45:20.050702Z",
          "iopub.execute_input": "2024-05-23T22:45:20.051412Z",
          "iopub.status.idle": "2024-05-24T00:11:59.804483Z",
          "shell.execute_reply.started": "2024-05-23T22:45:20.051357Z",
          "shell.execute_reply": "2024-05-24T00:11:59.803384Z"
        },
        "trusted": true,
        "id": "EQ7aqxsLezbx",
        "outputId": "7af53759-5438-4f8b-a580-2b75e4417b03"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Epoch 1/50\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/opt/conda/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n  self._warn_if_super_not_called()\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "\u001b[1m  3/731\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m51s\u001b[0m 71ms/step - accuracy: 0.5174 - loss: 0.6925  ",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1716504325.603506      85 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 147ms/step - accuracy: 0.5259 - loss: 0.6914 - val_accuracy: 0.5339 - val_loss: 0.6868\nEpoch 2/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 143ms/step - accuracy: 0.5602 - loss: 0.6829 - val_accuracy: 0.5573 - val_loss: 0.6794\nEpoch 3/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 142ms/step - accuracy: 0.5704 - loss: 0.6785 - val_accuracy: 0.5850 - val_loss: 0.6748\nEpoch 4/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 142ms/step - accuracy: 0.5840 - loss: 0.6743 - val_accuracy: 0.6076 - val_loss: 0.6627\nEpoch 5/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 141ms/step - accuracy: 0.5856 - loss: 0.6722 - val_accuracy: 0.6031 - val_loss: 0.6657\nEpoch 6/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 142ms/step - accuracy: 0.5853 - loss: 0.6732 - val_accuracy: 0.5914 - val_loss: 0.6656\nEpoch 7/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 140ms/step - accuracy: 0.5917 - loss: 0.6694 - val_accuracy: 0.6133 - val_loss: 0.6589\nEpoch 8/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 139ms/step - accuracy: 0.5914 - loss: 0.6693 - val_accuracy: 0.6087 - val_loss: 0.6576\nEpoch 9/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 141ms/step - accuracy: 0.5900 - loss: 0.6693 - val_accuracy: 0.6111 - val_loss: 0.6569\nEpoch 10/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 142ms/step - accuracy: 0.5953 - loss: 0.6684 - val_accuracy: 0.6088 - val_loss: 0.6573\nEpoch 11/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 141ms/step - accuracy: 0.5954 - loss: 0.6660 - val_accuracy: 0.6126 - val_loss: 0.6652\nEpoch 12/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 140ms/step - accuracy: 0.5951 - loss: 0.6676 - val_accuracy: 0.5961 - val_loss: 0.6611\nEpoch 13/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 138ms/step - accuracy: 0.5953 - loss: 0.6668 - val_accuracy: 0.6161 - val_loss: 0.6539\nEpoch 14/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 143ms/step - accuracy: 0.5934 - loss: 0.6660 - val_accuracy: 0.6133 - val_loss: 0.6545\nEpoch 15/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 142ms/step - accuracy: 0.5965 - loss: 0.6650 - val_accuracy: 0.6151 - val_loss: 0.6519\nEpoch 16/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 142ms/step - accuracy: 0.6004 - loss: 0.6626 - val_accuracy: 0.6148 - val_loss: 0.6517\nEpoch 17/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 137ms/step - accuracy: 0.5957 - loss: 0.6666 - val_accuracy: 0.6096 - val_loss: 0.6546\nEpoch 18/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 138ms/step - accuracy: 0.5968 - loss: 0.6646 - val_accuracy: 0.6064 - val_loss: 0.6593\nEpoch 19/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 138ms/step - accuracy: 0.5984 - loss: 0.6648 - val_accuracy: 0.6176 - val_loss: 0.6500\nEpoch 20/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 138ms/step - accuracy: 0.5961 - loss: 0.6626 - val_accuracy: 0.6177 - val_loss: 0.6513\nEpoch 21/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 138ms/step - accuracy: 0.5991 - loss: 0.6633 - val_accuracy: 0.6150 - val_loss: 0.6501\nEpoch 22/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 137ms/step - accuracy: 0.6011 - loss: 0.6618 - val_accuracy: 0.6081 - val_loss: 0.6580\nEpoch 23/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 138ms/step - accuracy: 0.6033 - loss: 0.6616 - val_accuracy: 0.6009 - val_loss: 0.6568\nEpoch 24/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 140ms/step - accuracy: 0.5996 - loss: 0.6633 - val_accuracy: 0.6228 - val_loss: 0.6491\nEpoch 25/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 140ms/step - accuracy: 0.5962 - loss: 0.6643 - val_accuracy: 0.6096 - val_loss: 0.6513\nEpoch 26/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 140ms/step - accuracy: 0.6043 - loss: 0.6623 - val_accuracy: 0.6184 - val_loss: 0.6480\nEpoch 27/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 142ms/step - accuracy: 0.6045 - loss: 0.6592 - val_accuracy: 0.6184 - val_loss: 0.6498\nEpoch 28/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 142ms/step - accuracy: 0.6006 - loss: 0.6620 - val_accuracy: 0.6265 - val_loss: 0.6444\nEpoch 29/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 137ms/step - accuracy: 0.6018 - loss: 0.6612 - val_accuracy: 0.6234 - val_loss: 0.6447\nEpoch 30/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 141ms/step - accuracy: 0.6018 - loss: 0.6608 - val_accuracy: 0.6179 - val_loss: 0.6476\nEpoch 31/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 141ms/step - accuracy: 0.6006 - loss: 0.6614 - val_accuracy: 0.6191 - val_loss: 0.6488\nEpoch 32/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 142ms/step - accuracy: 0.6002 - loss: 0.6620 - val_accuracy: 0.6190 - val_loss: 0.6506\nEpoch 33/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 143ms/step - accuracy: 0.6056 - loss: 0.6589 - val_accuracy: 0.6279 - val_loss: 0.6437\nEpoch 34/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 142ms/step - accuracy: 0.6066 - loss: 0.6599 - val_accuracy: 0.6137 - val_loss: 0.6467\nEpoch 35/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 141ms/step - accuracy: 0.6050 - loss: 0.6585 - val_accuracy: 0.6239 - val_loss: 0.6439\nEpoch 36/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 142ms/step - accuracy: 0.6016 - loss: 0.6607 - val_accuracy: 0.6183 - val_loss: 0.6506\nEpoch 37/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 140ms/step - accuracy: 0.6075 - loss: 0.6581 - val_accuracy: 0.6123 - val_loss: 0.6481\nEpoch 38/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 141ms/step - accuracy: 0.6093 - loss: 0.6572 - val_accuracy: 0.6273 - val_loss: 0.6427\nEpoch 39/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 142ms/step - accuracy: 0.6090 - loss: 0.6591 - val_accuracy: 0.5990 - val_loss: 0.6535\nEpoch 40/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 141ms/step - accuracy: 0.6049 - loss: 0.6605 - val_accuracy: 0.6303 - val_loss: 0.6416\nEpoch 41/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 141ms/step - accuracy: 0.6095 - loss: 0.6581 - val_accuracy: 0.6211 - val_loss: 0.6464\nEpoch 42/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 141ms/step - accuracy: 0.6057 - loss: 0.6589 - val_accuracy: 0.6216 - val_loss: 0.6449\nEpoch 43/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 141ms/step - accuracy: 0.6098 - loss: 0.6559 - val_accuracy: 0.6283 - val_loss: 0.6432\nEpoch 44/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 143ms/step - accuracy: 0.6081 - loss: 0.6578 - val_accuracy: 0.6226 - val_loss: 0.6426\nEpoch 45/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 142ms/step - accuracy: 0.6099 - loss: 0.6562 - val_accuracy: 0.6255 - val_loss: 0.6447\nEpoch 46/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 142ms/step - accuracy: 0.6085 - loss: 0.6560 - val_accuracy: 0.6283 - val_loss: 0.6394\nEpoch 47/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 143ms/step - accuracy: 0.6063 - loss: 0.6589 - val_accuracy: 0.6240 - val_loss: 0.6462\nEpoch 48/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 141ms/step - accuracy: 0.6088 - loss: 0.6568 - val_accuracy: 0.6262 - val_loss: 0.6426\nEpoch 49/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 140ms/step - accuracy: 0.6118 - loss: 0.6564 - val_accuracy: 0.6297 - val_loss: 0.6436\nEpoch 50/50\n\u001b[1m731/731\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 140ms/step - accuracy: 0.6084 - loss: 0.6570 - val_accuracy: 0.6218 - val_loss: 0.6433\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix, roc_auc_score, f1_score, mean_absolute_error, mean_squared_error, accuracy_score\n",
        "\n",
        "# Accuracy and loss history\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot training & validation accuracy values\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-24T00:26:25.162900Z",
          "iopub.execute_input": "2024-05-24T00:26:25.165244Z",
          "iopub.status.idle": "2024-05-24T00:26:25.989532Z",
          "shell.execute_reply.started": "2024-05-24T00:26:25.165210Z",
          "shell.execute_reply": "2024-05-24T00:26:25.988468Z"
        },
        "trusted": true,
        "id": "8HlwllhBezbx",
        "outputId": "3f316eca-eb6d-4c87-f95a-bbbc68a2ee9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1200x600 with 2 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = binary_classifier.evaluate(test_flow)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-24T00:27:18.383971Z",
          "iopub.execute_input": "2024-05-24T00:27:18.384349Z",
          "iopub.status.idle": "2024-05-24T00:27:40.446119Z",
          "shell.execute_reply.started": "2024-05-24T00:27:18.384320Z",
          "shell.execute_reply": "2024-05-24T00:27:40.445222Z"
        },
        "trusted": true,
        "id": "GOCBD7eJezbx",
        "outputId": "db7996bc-05c3-4bdd-b858-cfea338c5f55"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "\u001b[1m  1/157\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m24s\u001b[0m 160ms/step - accuracy: 0.6875 - loss: 0.5641",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/opt/conda/lib/python3.10/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n  self._warn_if_super_not_called()\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 138ms/step - accuracy: 0.6292 - loss: 0.6264\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "W0000 00:00:1716510460.433756      85 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n",
          "output_type": "stream"
        }
      ]
    }
  ]
}