stroke_not_stroke_classification.ipynb

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      "version": "3.10.12",
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        "name": "ipython",
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      "file_extension": ".py"
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    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "collapsed_sections": [
        "8nWKmlOhZoCQ"
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    "accelerator": "GPU"
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  "nbformat_minor": 0,
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## A Baseline Comparison for Image Classification between CNN, Resnet and Vision Transformers"
      ],
      "metadata": {
        "id": "OXTzLdqQ-EGN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Getting data from kaggle"
      ],
      "metadata": {
        "id": "jdfQU_Og9ywn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install kaggle\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ywv5Oqx2roX",
        "outputId": "ae9f8846-8454-4e31-b223-bba601f814ec"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.5.7)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.27.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.65.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.26.16)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.0.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4)\n",
            "cp: cannot stat 'kaggle.json': No such file or directory\n",
            "chmod: cannot access '/root/.kaggle/kaggle.json': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir ~/.kaggle\n",
        "!touch ~/.kaggle/kaggle.json\n",
        "\n",
        "### get your kaggle token from kaggle.com\n",
        "api_token = {\"username\":\"*****\",\"key\":\"***************\"}\n",
        "\n",
        "import json\n",
        "\n",
        "with open('/root/.kaggle/kaggle.json', 'w') as file:\n",
        "    json.dump(api_token, file)\n",
        "\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VaGUUinMydxf",
        "outputId": "109e330e-5640-4d78-a292-2956ba53f342"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! kaggle datasets download kaitavmehta/facial-droop-and-facial-paralysis-image"
      ],
      "metadata": {
        "id": "NnvQjt-i28wo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03419558-76b1-4e88-d3bb-3cf950d80063"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading facial-droop-and-facial-paralysis-image.zip to /content\n",
            " 79% 15.0M/19.0M [00:01<00:00, 24.7MB/s]\n",
            "100% 19.0M/19.0M [00:01<00:00, 18.1MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! kaggle datasets download danish003/face-images-of-acute-stroke-and-non-acute-stroke"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rdhUMwq5x9Hx",
        "outputId": "04ccef47-3848-4c11-f243-750e47073597"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading face-images-of-acute-stroke-and-non-acute-stroke.zip to /content\n",
            " 95% 35.0M/36.7M [00:01<00:00, 23.9MB/s]\n",
            "100% 36.7M/36.7M [00:01<00:00, 20.3MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "# Extract the face images dataset\n",
        "dataset_zip = \"/content/face-images-of-acute-stroke-and-non-acute-stroke.zip\"\n",
        "dataset_dir = \"/content/face-images-of-acute-stroke-and-non-acute-stroke\"\n",
        "\n",
        "with zipfile.ZipFile(dataset_zip, \"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"/content\")\n",
        "\n",
        "# Extract the additional stroke images dataset\n",
        "additional_zip = \"/content/facial-droop-and-facial-paralysis-image.zip\"\n",
        "additional_dir = \"/content/facial-droop-and-facial-paralysis-image/Strokefaces/droopy\"\n",
        "\n",
        "with zipfile.ZipFile(additional_zip, \"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"/content\")\n"
      ],
      "metadata": {
        "id": "jZW53QYY2AWg"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the path to the directories containing the face images\n",
        "stroke_dir = '/content/main/stroke_data'#os.path.join(dataset_dir, \"stroke_data\")\n",
        "new_stroke_dir = '/content/Strokefaces/droopy'#additional_dir\n",
        "non_stroke_dir = '/content/main/noStroke_data'#os.path.join(dataset_dir, \"noStroke_data\")"
      ],
      "metadata": {
        "id": "lvTpT38I2Dpr"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import cv2\n",
        "from tensorflow import keras\n",
        "from sklearn.model_selection import train_test_split\n",
        "import zipfile\n",
        "\n",
        "\n",
        "# Set the image dimensions\n",
        "img_width, img_height = 150, 150\n",
        "\n",
        "# Load and preprocess the stroke images\n",
        "stroke_images = []\n",
        "stroke_labels = []\n",
        "\n",
        "for filename in os.listdir(stroke_dir):\n",
        "    img = cv2.imread(os.path.join(stroke_dir, filename))\n",
        "    if img is not None:  # Check if the image file was successfully read\n",
        "        img = cv2.resize(img, (img_width, img_height))\n",
        "        stroke_images.append(img)\n",
        "        stroke_labels.append(1)  # Stroke label: 1\n",
        "\n",
        "# Load and preprocess the additional stroke images\n",
        "additional_stroke_images = []\n",
        "additional_stroke_labels = []\n",
        "\n",
        "for filename in os.listdir(new_stroke_dir):\n",
        "    img = cv2.imread(os.path.join(new_stroke_dir, filename))\n",
        "    if img is not None:  # Check if the image file was successfully read\n",
        "        img = cv2.resize(img, (img_width, img_height))\n",
        "        additional_stroke_images.append(img)\n",
        "        additional_stroke_labels.append(1)  # Stroke label: 1\n",
        "\n",
        "# Load and preprocess the non-stroke images\n",
        "non_stroke_images = []\n",
        "non_stroke_labels = []\n",
        "\n",
        "for filename in os.listdir(non_stroke_dir):\n",
        "    img = cv2.imread(os.path.join(non_stroke_dir, filename))\n",
        "    if img is not None:  # Check if the image file was successfully read\n",
        "        img = cv2.resize(img, (img_width, img_height))\n",
        "        non_stroke_images.append(img)\n",
        "        non_stroke_labels.append(0)"
      ],
      "metadata": {
        "id": "5UX3zbX41Q7_"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine the stroke, additional stroke, and non-stroke images and labels\n",
        "images = stroke_images + additional_stroke_images + non_stroke_images\n",
        "labels = stroke_labels + additional_stroke_labels + non_stroke_labels"
      ],
      "metadata": {
        "execution": {
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        "id": "Sf3bpZ2K2RD0"
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      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(labels)"
      ],
      "metadata": {
        "execution": {
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8104e57e-7c09-4681-dc5e-7387625cdb03"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4794"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "-- display images"
      ],
      "metadata": {
        "id": "NwnYJrc99te-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "def display_images_from_dir(image_dir, label):\n",
        "    images = []\n",
        "\n",
        "    # Load and preprocess the images\n",
        "    for filename in os.listdir(image_dir):\n",
        "        img = cv2.imread(os.path.join(image_dir, filename))\n",
        "        if img is not None:  # Check if the image file was successfully read\n",
        "            img = cv2.resize(img, (img_width, img_height))\n",
        "            images.append(img)\n",
        "\n",
        "    # Display a grid of 9 images\n",
        "    fig, axes = plt.subplots(3, 3, figsize=(10, 10))\n",
        "    fig.suptitle(label)\n",
        "    for i, ax in enumerate(axes.flatten()):\n",
        "        if i < len(images):\n",
        "            ax.imshow(cv2.cvtColor(images[i], cv2.COLOR_BGR2RGB))\n",
        "            ax.axis('off')\n",
        "        else:\n",
        "            ax.axis('off')\n",
        "    plt.show()\n",
        "\n",
        "# Display images from stroke directory\n",
        "#display_images_from_dir(stroke_dir, 'Stroke Images')\n",
        "\n",
        "# Display images from additional stroke directory\n",
        "#display_images_from_dir(new_stroke_dir, 'Additional Stroke Images')\n",
        "\n",
        "# Display images from non-stroke directory\n",
        "#display_images_from_dir(non_stroke_dir, 'Non-Stroke Images')\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-07-19T10:35:34.997345Z",
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        "trusted": true,
        "id": "TB4egXQ72RD9"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the images and labels to NumPy arrays\n",
        "images = np.array(images)\n",
        "labels = np.array(labels)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)\n",
        "\n",
        "# Normalize the pixel values\n",
        "X_train = X_train.astype('float32') / 255.0\n",
        "X_test = X_test.astype('float32') / 255.0"
      ],
      "metadata": {
        "id": "NJ3vdzwnBzDD"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CNN Model"
      ],
      "metadata": {
        "id": "XvvSIDXliYFD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model_cnn = keras.models.Sequential([\n",
        "# Note the input shape is the desired size of the image 200x200 with 3 bytes color\n",
        "# This is the first convolution\n",
        "keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)),\n",
        "keras.layers.MaxPooling2D(2, 2),\n",
        "# The second convolution\n",
        "keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
        "keras.layers.MaxPooling2D(2,2),\n",
        "# The third convolution\n",
        "keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "keras.layers.MaxPooling2D(2,2),\n",
        "# The fourth convolution\n",
        "keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "keras.layers.MaxPooling2D(2,2),\n",
        "# # The fifth convolution\n",
        "keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "keras.layers.MaxPooling2D(2,2),\n",
        "# Flatten the results to feed into a DNN\n",
        "keras.layers.Flatten(),\n",
        "# 512 neuron hidden layer\n",
        "keras.layers.Dense(512, activation='relu'),\n",
        "# Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('dandelions') and 1 for the other ('grass')\n",
        "keras.layers.Dense(1, activation='sigmoid')])\n",
        "# Compile the model\n",
        "model_cnn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history_cnn=model_cnn.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))\n",
        "\n",
        "# Evaluate the model\n",
        "test_loss_cnn, test_acc_cnn = model_cnn.evaluate(X_test, y_test)\n",
        "print(f\"Test loss model2: {test_loss_cnn}\")\n",
        "print(f\"Test accuracy model2: {test_acc_cnn}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIunXh53iURp",
        "outputId": "c4a48140-63d8-4921-dff8-29872c5b5af5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "120/120 [==============================] - 17s 29ms/step - loss: 0.6284 - accuracy: 0.6352 - val_loss: 0.5877 - val_accuracy: 0.7331\n",
            "Epoch 2/10\n",
            "120/120 [==============================] - 2s 18ms/step - loss: 0.5796 - accuracy: 0.7033 - val_loss: 0.5068 - val_accuracy: 0.7675\n",
            "Epoch 3/10\n",
            "120/120 [==============================] - 2s 18ms/step - loss: 0.4709 - accuracy: 0.7810 - val_loss: 0.4793 - val_accuracy: 0.7758\n",
            "Epoch 4/10\n",
            "120/120 [==============================] - 2s 17ms/step - loss: 0.4140 - accuracy: 0.8055 - val_loss: 0.4272 - val_accuracy: 0.8060\n",
            "Epoch 5/10\n",
            "120/120 [==============================] - 2s 19ms/step - loss: 0.3460 - accuracy: 0.8456 - val_loss: 0.3940 - val_accuracy: 0.8342\n",
            "Epoch 6/10\n",
            "120/120 [==============================] - 2s 18ms/step - loss: 0.2805 - accuracy: 0.8806 - val_loss: 0.4034 - val_accuracy: 0.8311\n",
            "Epoch 7/10\n",
            "120/120 [==============================] - 2s 17ms/step - loss: 0.2614 - accuracy: 0.8858 - val_loss: 0.3513 - val_accuracy: 0.8405\n",
            "Epoch 8/10\n",
            "120/120 [==============================] - 2s 18ms/step - loss: 0.1966 - accuracy: 0.9223 - val_loss: 0.3203 - val_accuracy: 0.8790\n",
            "Epoch 9/10\n",
            "120/120 [==============================] - 2s 17ms/step - loss: 0.1638 - accuracy: 0.9348 - val_loss: 0.3181 - val_accuracy: 0.8728\n",
            "Epoch 10/10\n",
            "120/120 [==============================] - 2s 17ms/step - loss: 0.1259 - accuracy: 0.9538 - val_loss: 0.2606 - val_accuracy: 0.9072\n",
            "30/30 [==============================] - 0s 9ms/step - loss: 0.2606 - accuracy: 0.9072\n",
            "Test loss model2: 0.2606442868709564\n",
            "Test accuracy model2: 0.907194972038269\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the training and validation losses for model2\n",
        "plt.plot(history_cnn.history['loss'], label='Training Loss (model2)')\n",
        "plt.plot(history_cnn.history['val_loss'], label='Validation Loss (model2)')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.title('Training and Validation Losses (model2)')\n",
        "plt.show()\n",
        "\n",
        "# Plot the training and validation accuracy for model2\n",
        "plt.plot(history_cnn.history['accuracy'], label='Training Accuracy (model2)')\n",
        "plt.plot(history_cnn.history['val_accuracy'], label='Validation Accuracy (model2)')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.title('Training and Validation Accuracy (model2)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "hbnv5zdfv4QF",
        "outputId": "eda0d9ef-88e9-49af-b361-5c0ab6947847"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_cnn.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5q-QqJ8biigc",
        "outputId": "6c1bce3d-625e-4924-fc60-dd0a75271953"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 148, 148, 16)      448       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 74, 74, 16)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 72, 72, 32)        4640      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 36, 36, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 34, 34, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 17, 17, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 15, 15, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 7, 7, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 5, 5, 64)          36928     \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 2, 2, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               131584    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 513       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 229,537\n",
            "Trainable params: 229,537\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Resnet"
      ],
      "metadata": {
        "id": "8nWKmlOhZoCQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.applications import ResNet50\n",
        "from keras.layers import Dense, Flatten\n",
        "from keras.models import Model\n",
        "\n",
        "# Create the ResNet model (ResNet50)\n",
        "base_model = ResNet50(weights=None, include_top=False, input_shape=(img_width, img_height, 3))\n",
        "\n",
        "# Add a few layers on top of the base ResNet model for classification\n",
        "x = base_model.output\n",
        "x = Flatten()(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "predictions = Dense(1, activation='sigmoid')(x)\n",
        "\n",
        "# Combine the base ResNet model and the added layers\n",
        "model_resnet = Model(inputs=base_model.input, outputs=predictions)\n",
        "\n",
        "# Compile the model\n",
        "model_resnet.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history_resnet = model_resnet.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))\n",
        "\n",
        "# Evaluate the model\n",
        "test_loss_resnet, test_acc_resnet = model_resnet.evaluate(X_test, y_test)\n",
        "print(f\"Test loss: {test_loss_resnet}\")\n",
        "print(f\"Test accuracy: {test_acc_resnet}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uGN4G27BZqDr",
        "outputId": "b0095bd8-7ddf-41e5-85e6-0432914acc42"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "120/120 [==============================] - 74s 253ms/step - loss: 5.0714 - accuracy: 0.5077 - val_loss: 0.6936 - val_accuracy: 0.5276\n",
            "Epoch 2/10\n",
            "120/120 [==============================] - 24s 203ms/step - loss: 0.7347 - accuracy: 0.5750 - val_loss: 0.6960 - val_accuracy: 0.4724\n",
            "Epoch 3/10\n",
            "120/120 [==============================] - 25s 209ms/step - loss: 0.5774 - accuracy: 0.7189 - val_loss: 0.8022 - val_accuracy: 0.6069\n",
            "Epoch 4/10\n",
            "120/120 [==============================] - 25s 204ms/step - loss: 0.4452 - accuracy: 0.8094 - val_loss: 0.5570 - val_accuracy: 0.7310\n",
            "Epoch 5/10\n",
            "120/120 [==============================] - 25s 213ms/step - loss: 0.3557 - accuracy: 0.8574 - val_loss: 0.7242 - val_accuracy: 0.7195\n",
            "Epoch 6/10\n",
            "120/120 [==============================] - 24s 202ms/step - loss: 0.3577 - accuracy: 0.8589 - val_loss: 0.5097 - val_accuracy: 0.8081\n",
            "Epoch 7/10\n",
            "120/120 [==============================] - 24s 204ms/step - loss: 0.2846 - accuracy: 0.8884 - val_loss: 0.3365 - val_accuracy: 0.8780\n",
            "Epoch 8/10\n",
            "120/120 [==============================] - 25s 212ms/step - loss: 0.2683 - accuracy: 0.8980 - val_loss: 0.9446 - val_accuracy: 0.6705\n",
            "Epoch 9/10\n",
            "120/120 [==============================] - 25s 204ms/step - loss: 0.2018 - accuracy: 0.9267 - val_loss: 3.0142 - val_accuracy: 0.6465\n",
            "Epoch 10/10\n",
            "120/120 [==============================] - 25s 211ms/step - loss: 0.2192 - accuracy: 0.9173 - val_loss: 0.2379 - val_accuracy: 0.9051\n",
            "30/30 [==============================] - 2s 57ms/step - loss: 0.2379 - accuracy: 0.9051\n",
            "Test loss: 0.2378641813993454\n",
            "Test accuracy: 0.9051094651222229\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the training and validation losses for model2\n",
        "plt.plot(history_resnet.history['loss'], label='Training Loss (model2)')\n",
        "plt.plot(history_resnet.history['val_loss'], label='Validation Loss (model2)')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.title('Training and Validation Losses (model2)')\n",
        "plt.show()\n",
        "\n",
        "# Plot the training and validation accuracy for model2\n",
        "plt.plot(history_resnet.history['accuracy'], label='Training Accuracy (model2)')\n",
        "plt.plot(history_resnet.history['val_accuracy'], label='Validation Accuracy (model2)')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.title('Training and Validation Accuracy (model2)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "gObA-v-cbwxm",
        "outputId": "321303d6-4bd8-41db-88d2-9f554cacbddc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_resnet.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R8IT4VZHaxPj",
        "outputId": "e530911e-a431-4313-a406-991a0f445840"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 150, 150, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv1_pad (ZeroPadding2D)      (None, 156, 156, 3)  0           ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " conv1_conv (Conv2D)            (None, 75, 75, 64)   9472        ['conv1_pad[0][0]']              \n",
            "                                                                                                  \n",
            " conv1_bn (BatchNormalization)  (None, 75, 75, 64)   256         ['conv1_conv[0][0]']             \n",
            "                                                                                                  \n",
            " conv1_relu (Activation)        (None, 75, 75, 64)   0           ['conv1_bn[0][0]']               \n",
            "                                                                                                  \n",
            " pool1_pad (ZeroPadding2D)      (None, 77, 77, 64)   0           ['conv1_relu[0][0]']             \n",
            "                                                                                                  \n",
            " pool1_pool (MaxPooling2D)      (None, 38, 38, 64)   0           ['pool1_pad[0][0]']              \n",
            "                                                                                                  \n",
            " conv2_block1_1_conv (Conv2D)   (None, 38, 38, 64)   4160        ['pool1_pool[0][0]']             \n",
            "                                                                                                  \n",
            " conv2_block1_1_bn (BatchNormal  (None, 38, 38, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_1_relu (Activatio  (None, 38, 38, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_2_conv (Conv2D)   (None, 38, 38, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block1_2_bn (BatchNormal  (None, 38, 38, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_2_relu (Activatio  (None, 38, 38, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_0_conv (Conv2D)   (None, 38, 38, 256)  16640       ['pool1_pool[0][0]']             \n",
            "                                                                                                  \n",
            " conv2_block1_3_conv (Conv2D)   (None, 38, 38, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block1_0_bn (BatchNormal  (None, 38, 38, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_3_bn (BatchNormal  (None, 38, 38, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_add (Add)         (None, 38, 38, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
            "                                                                  'conv2_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block1_out (Activation)  (None, 38, 38, 256)  0           ['conv2_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block2_1_conv (Conv2D)   (None, 38, 38, 64)   16448       ['conv2_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block2_1_bn (BatchNormal  (None, 38, 38, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_1_relu (Activatio  (None, 38, 38, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_2_conv (Conv2D)   (None, 38, 38, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_2_bn (BatchNormal  (None, 38, 38, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_2_relu (Activatio  (None, 38, 38, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_3_conv (Conv2D)   (None, 38, 38, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_3_bn (BatchNormal  (None, 38, 38, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_add (Add)         (None, 38, 38, 256)  0           ['conv2_block1_out[0][0]',       \n",
            "                                                                  'conv2_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block2_out (Activation)  (None, 38, 38, 256)  0           ['conv2_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block3_1_conv (Conv2D)   (None, 38, 38, 64)   16448       ['conv2_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block3_1_bn (BatchNormal  (None, 38, 38, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_1_relu (Activatio  (None, 38, 38, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_2_conv (Conv2D)   (None, 38, 38, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_2_bn (BatchNormal  (None, 38, 38, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_2_relu (Activatio  (None, 38, 38, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_3_conv (Conv2D)   (None, 38, 38, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_3_bn (BatchNormal  (None, 38, 38, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_add (Add)         (None, 38, 38, 256)  0           ['conv2_block2_out[0][0]',       \n",
            "                                                                  'conv2_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block3_out (Activation)  (None, 38, 38, 256)  0           ['conv2_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_1_conv (Conv2D)   (None, 19, 19, 128)  32896       ['conv2_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_1_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_1_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_2_conv (Conv2D)   (None, 19, 19, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block1_2_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_2_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_0_conv (Conv2D)   (None, 19, 19, 512)  131584      ['conv2_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_3_conv (Conv2D)   (None, 19, 19, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block1_0_bn (BatchNormal  (None, 19, 19, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_3_bn (BatchNormal  (None, 19, 19, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_add (Add)         (None, 19, 19, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
            "                                                                  'conv3_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block1_out (Activation)  (None, 19, 19, 512)  0           ['conv3_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block2_1_conv (Conv2D)   (None, 19, 19, 128)  65664       ['conv3_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block2_1_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_1_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_2_conv (Conv2D)   (None, 19, 19, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_2_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_2_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_3_conv (Conv2D)   (None, 19, 19, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_3_bn (BatchNormal  (None, 19, 19, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_add (Add)         (None, 19, 19, 512)  0           ['conv3_block1_out[0][0]',       \n",
            "                                                                  'conv3_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block2_out (Activation)  (None, 19, 19, 512)  0           ['conv3_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block3_1_conv (Conv2D)   (None, 19, 19, 128)  65664       ['conv3_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block3_1_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_1_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_2_conv (Conv2D)   (None, 19, 19, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_2_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_2_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_3_conv (Conv2D)   (None, 19, 19, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_3_bn (BatchNormal  (None, 19, 19, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_add (Add)         (None, 19, 19, 512)  0           ['conv3_block2_out[0][0]',       \n",
            "                                                                  'conv3_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block3_out (Activation)  (None, 19, 19, 512)  0           ['conv3_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block4_1_conv (Conv2D)   (None, 19, 19, 128)  65664       ['conv3_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block4_1_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_1_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_2_conv (Conv2D)   (None, 19, 19, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_2_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_2_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_3_conv (Conv2D)   (None, 19, 19, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_3_bn (BatchNormal  (None, 19, 19, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_add (Add)         (None, 19, 19, 512)  0           ['conv3_block3_out[0][0]',       \n",
            "                                                                  'conv3_block4_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block4_out (Activation)  (None, 19, 19, 512)  0           ['conv3_block4_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block1_1_conv (Conv2D)   (None, 10, 10, 256)  131328      ['conv3_block4_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block1_1_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_1_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_2_conv (Conv2D)   (None, 10, 10, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block1_2_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_2_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_0_conv (Conv2D)   (None, 10, 10, 1024  525312      ['conv3_block4_out[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_3_conv (Conv2D)   (None, 10, 10, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_0_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_3_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_add (Add)         (None, 10, 10, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
            "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block1_out (Activation)  (None, 10, 10, 1024  0           ['conv4_block1_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_1_conv (Conv2D)   (None, 10, 10, 256)  262400      ['conv4_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block2_1_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_1_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_2_conv (Conv2D)   (None, 10, 10, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block2_2_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_2_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_3_conv (Conv2D)   (None, 10, 10, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_3_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_add (Add)         (None, 10, 10, 1024  0           ['conv4_block1_out[0][0]',       \n",
            "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block2_out (Activation)  (None, 10, 10, 1024  0           ['conv4_block2_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_1_conv (Conv2D)   (None, 10, 10, 256)  262400      ['conv4_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block3_1_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_1_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_2_conv (Conv2D)   (None, 10, 10, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block3_2_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_2_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_3_conv (Conv2D)   (None, 10, 10, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_3_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_add (Add)         (None, 10, 10, 1024  0           ['conv4_block2_out[0][0]',       \n",
            "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block3_out (Activation)  (None, 10, 10, 1024  0           ['conv4_block3_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_1_conv (Conv2D)   (None, 10, 10, 256)  262400      ['conv4_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block4_1_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_1_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_2_conv (Conv2D)   (None, 10, 10, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block4_2_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_2_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_3_conv (Conv2D)   (None, 10, 10, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_3_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_add (Add)         (None, 10, 10, 1024  0           ['conv4_block3_out[0][0]',       \n",
            "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block4_out (Activation)  (None, 10, 10, 1024  0           ['conv4_block4_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_1_conv (Conv2D)   (None, 10, 10, 256)  262400      ['conv4_block4_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block5_1_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_1_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_2_conv (Conv2D)   (None, 10, 10, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block5_2_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_2_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_3_conv (Conv2D)   (None, 10, 10, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_3_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_add (Add)         (None, 10, 10, 1024  0           ['conv4_block4_out[0][0]',       \n",
            "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block5_out (Activation)  (None, 10, 10, 1024  0           ['conv4_block5_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_1_conv (Conv2D)   (None, 10, 10, 256)  262400      ['conv4_block5_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block6_1_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_1_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_2_conv (Conv2D)   (None, 10, 10, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block6_2_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_2_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_3_conv (Conv2D)   (None, 10, 10, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_3_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_add (Add)         (None, 10, 10, 1024  0           ['conv4_block5_out[0][0]',       \n",
            "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block6_out (Activation)  (None, 10, 10, 1024  0           ['conv4_block6_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv5_block1_1_conv (Conv2D)   (None, 5, 5, 512)    524800      ['conv4_block6_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block1_1_bn (BatchNormal  (None, 5, 5, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_1_relu (Activatio  (None, 5, 5, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_2_conv (Conv2D)   (None, 5, 5, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block1_2_bn (BatchNormal  (None, 5, 5, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_2_relu (Activatio  (None, 5, 5, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_0_conv (Conv2D)   (None, 5, 5, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block1_3_conv (Conv2D)   (None, 5, 5, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block1_0_bn (BatchNormal  (None, 5, 5, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_3_bn (BatchNormal  (None, 5, 5, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_add (Add)         (None, 5, 5, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
            "                                                                  'conv5_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block1_out (Activation)  (None, 5, 5, 2048)   0           ['conv5_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block2_1_conv (Conv2D)   (None, 5, 5, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block2_1_bn (BatchNormal  (None, 5, 5, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_1_relu (Activatio  (None, 5, 5, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_2_conv (Conv2D)   (None, 5, 5, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_2_bn (BatchNormal  (None, 5, 5, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_2_relu (Activatio  (None, 5, 5, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_3_conv (Conv2D)   (None, 5, 5, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_3_bn (BatchNormal  (None, 5, 5, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_add (Add)         (None, 5, 5, 2048)   0           ['conv5_block1_out[0][0]',       \n",
            "                                                                  'conv5_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block2_out (Activation)  (None, 5, 5, 2048)   0           ['conv5_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block3_1_conv (Conv2D)   (None, 5, 5, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block3_1_bn (BatchNormal  (None, 5, 5, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_1_relu (Activatio  (None, 5, 5, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_2_conv (Conv2D)   (None, 5, 5, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_2_bn (BatchNormal  (None, 5, 5, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_2_relu (Activatio  (None, 5, 5, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_3_conv (Conv2D)   (None, 5, 5, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_3_bn (BatchNormal  (None, 5, 5, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_add (Add)         (None, 5, 5, 2048)   0           ['conv5_block2_out[0][0]',       \n",
            "                                                                  'conv5_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block3_out (Activation)  (None, 5, 5, 2048)   0           ['conv5_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " flatten_1 (Flatten)            (None, 51200)        0           ['conv5_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 512)          26214912    ['flatten_1[0][0]']              \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 1)            513         ['dense_2[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 49,803,137\n",
            "Trainable params: 49,750,017\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Vision Transformers\n",
        "\n",
        "- https://colab.research.google.com/github/PytorchLightning/lightning-tutorials/blob/publication/.notebooks/course_UvA-DL/11-vision-transformer.ipynb#scrollTo=9ecbd461\n",
        "\n",
        "- https://keras.io/examples/vision/image_classification_with_vision_transformer/"
      ],
      "metadata": {
        "id": "N6bmP28qkznb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow_addons"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RfsSO4njmUWn",
        "outputId": "4c1a7d04-873b-4aae-ad67-efe098de2dfb"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow_addons\n",
            "  Downloading tensorflow_addons-0.21.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (612 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m612.1/612.1 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow_addons) (23.1)\n",
            "Collecting typeguard<3.0.0,>=2.7 (from tensorflow_addons)\n",
            "  Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)\n",
            "Installing collected packages: typeguard, tensorflow_addons\n",
            "Successfully installed tensorflow_addons-0.21.0 typeguard-2.13.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import tensorflow_addons as tfa"
      ],
      "metadata": {
        "id": "yd0XzLb4mQBJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25acfae9-182d-4145-fe0f-9c50dca1fdeb"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
            "\n",
            "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
            "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
            "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
            "\n",
            "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
            "\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = 2\n",
        "input_shape = (150, 150, 3)"
      ],
      "metadata": {
        "id": "lodKq0hIlC0z"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate = 0.01\n",
        "weight_decay = 0.0001\n",
        "batch_size = 256\n",
        "num_epochs = 100\n",
        "image_size = 72  # We'll resize input images to this size\n",
        "patch_size = 6  # Size of the patches to be extract from the input images\n",
        "num_patches = (image_size // patch_size) ** 2\n",
        "projection_dim = 64\n",
        "num_heads = 4\n",
        "transformer_units = [\n",
        "    projection_dim * 2,\n",
        "    projection_dim,\n",
        "]  # Size of the transformer layers\n",
        "transformer_layers = 8\n",
        "mlp_head_units = [2048, 1024]  # Size of the dense layers of the final classifier\n",
        "\n",
        "# Augmentation\n",
        "\n",
        "data_augmentation = keras.Sequential(\n",
        "    [\n",
        "        layers.Normalization(),\n",
        "        layers.Resizing(image_size, image_size),\n",
        "        layers.RandomFlip(\"horizontal\"),\n",
        "        layers.RandomRotation(factor=0.02),\n",
        "        layers.RandomZoom(\n",
        "            height_factor=0.2, width_factor=0.2\n",
        "        ),\n",
        "    ],\n",
        "    name=\"data_augmentation\",\n",
        ")\n",
        "# Compute the mean and the variance of the training data for normalization.\n",
        "data_augmentation.layers[0].adapt(X_train)\n",
        "\n",
        "#hidden layer\n",
        "def mlp(x, hidden_units, dropout_rate):\n",
        "    for units in hidden_units:\n",
        "        x = layers.Dense(units, activation=tf.nn.gelu)(x)\n",
        "        x = layers.Dropout(dropout_rate)(x)\n",
        "    return x\n",
        "\n",
        "#patch class\n",
        "\n",
        "class Patches(layers.Layer):\n",
        "    def __init__(self, patch_size):\n",
        "        super().__init__()\n",
        "        self.patch_size = patch_size\n",
        "\n",
        "    def call(self, images):\n",
        "        batch_size = tf.shape(images)[0]\n",
        "        patches = tf.image.extract_patches(\n",
        "            images=images,\n",
        "            sizes=[1, self.patch_size, self.patch_size, 1],\n",
        "            strides=[1, self.patch_size, self.patch_size, 1],\n",
        "            rates=[1, 1, 1, 1],\n",
        "            padding=\"VALID\",\n",
        "        )\n",
        "        patch_dims = patches.shape[-1]\n",
        "        patches = tf.reshape(patches, [batch_size, -1, patch_dims])\n",
        "        return patches\n",
        "\n",
        "\n",
        "class PatchEncoder(layers.Layer):\n",
        "    def __init__(self, num_patches, projection_dim):\n",
        "        super().__init__()\n",
        "        self.num_patches = num_patches\n",
        "        self.projection = layers.Dense(units=projection_dim)\n",
        "        self.position_embedding = layers.Embedding(\n",
        "            input_dim=num_patches, output_dim=projection_dim\n",
        "        )\n",
        "\n",
        "    def call(self, patch):\n",
        "        positions = tf.range(start=0, limit=self.num_patches, delta=1)\n",
        "        encoded = self.projection(patch) + self.position_embedding(positions)\n",
        "        return encoded\n"
      ],
      "metadata": {
        "id": "ZRvcrsbClCSk"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_vit_classifier():\n",
        "    inputs = layers.Input(shape=input_shape)\n",
        "    # Augment data.\n",
        "    augmented = data_augmentation(inputs)\n",
        "    # Create patches.\n",
        "    patches = Patches(patch_size)(augmented)\n",
        "    # Encode patches.\n",
        "    encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)\n",
        "\n",
        "    # Create multiple layers of the Transformer block.\n",
        "    for _ in range(transformer_layers):\n",
        "        # Layer normalization 1.\n",
        "        x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n",
        "        # Create a multi-head attention layer.\n",
        "        attention_output = layers.MultiHeadAttention(\n",
        "            num_heads=num_heads, key_dim=projection_dim, dropout=0.1\n",
        "        )(x1, x1)\n",
        "        # Skip connection 1.\n",
        "        x2 = layers.Add()([attention_output, encoded_patches])\n",
        "        # Layer normalization 2.\n",
        "        x3 = layers.LayerNormalization(epsilon=1e-6)(x2)\n",
        "        # MLP.\n",
        "        x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)\n",
        "        # Skip connection 2.\n",
        "        encoded_patches = layers.Add()([x3, x2])\n",
        "\n",
        "    # Create a [batch_size, projection_dim] tensor.\n",
        "    representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n",
        "    representation = layers.Flatten()(representation)\n",
        "    representation = layers.Dropout(0.5)(representation)\n",
        "    # Add MLP.\n",
        "    features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.5)\n",
        "    # Classify outputs.\n",
        "    logits = layers.Dense(num_classes)(features)\n",
        "    # Create the Keras model.\n",
        "    model = keras.Model(inputs=inputs, outputs=logits)\n",
        "    return model"
      ],
      "metadata": {
        "id": "y1B5CM7Jk2b8"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = create_vit_classifier()\n",
        "optimizer = tfa.optimizers.AdamW(\n",
        "        learning_rate=learning_rate, weight_decay=weight_decay\n",
        "    )\n",
        "\n",
        "model.compile(\n",
        "        optimizer=optimizer,\n",
        "        loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "        metrics=[\n",
        "            keras.metrics.SparseCategoricalAccuracy(name=\"accuracy\"),\n",
        "            keras.metrics.SparseTopKCategoricalAccuracy(5, name=\"top-5-accuracy\"),\n",
        "        ],\n",
        "    )\n",
        "\n",
        "checkpoint_filepath = \"/tmp/checkpoint\"\n",
        "checkpoint_callback = keras.callbacks.ModelCheckpoint(\n",
        "        checkpoint_filepath,\n",
        "        monitor=\"val_accuracy\",\n",
        "        save_best_only=True,\n",
        "        save_weights_only=True,\n",
        "    )\n",
        "\n",
        "history = model.fit(\n",
        "        x=X_train,\n",
        "        y=y_train,\n",
        "        batch_size=batch_size,\n",
        "        epochs=num_epochs,\n",
        "        validation_split=0.1,\n",
        "        callbacks=[checkpoint_callback],\n",
        "    )\n",
        "\n",
        "model.load_weights(checkpoint_filepath)\n",
        "_, accuracy, top_5_accuracy = model.evaluate(X_test, y_test)\n",
        "print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWy1cJVkm0Ft",
        "outputId": "1d0aea94-ad00-4d4b-ca02-5bc558f24bc4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "14/14 [==============================] - 34s 1s/step - loss: 136.7372 - accuracy: 0.5001 - top-5-accuracy: 1.0000 - val_loss: 1.0722 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 2/100\n",
            "14/14 [==============================] - 6s 423ms/step - loss: 0.9166 - accuracy: 0.4900 - top-5-accuracy: 1.0000 - val_loss: 0.6947 - val_accuracy: 0.4557 - val_top-5-accuracy: 1.0000\n",
            "Epoch 3/100\n",
            "14/14 [==============================] - 6s 429ms/step - loss: 0.7458 - accuracy: 0.4787 - top-5-accuracy: 1.0000 - val_loss: 0.6935 - val_accuracy: 0.4557 - val_top-5-accuracy: 1.0000\n",
            "Epoch 4/100\n",
            "14/14 [==============================] - 6s 405ms/step - loss: 0.7230 - accuracy: 0.4993 - top-5-accuracy: 1.0000 - val_loss: 0.6921 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 5/100\n",
            "14/14 [==============================] - 6s 407ms/step - loss: 0.6922 - accuracy: 0.5210 - top-5-accuracy: 1.0000 - val_loss: 0.6915 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 6/100\n",
            "14/14 [==============================] - 6s 401ms/step - loss: 0.7042 - accuracy: 0.5207 - top-5-accuracy: 1.0000 - val_loss: 0.6910 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 7/100\n",
            "14/14 [==============================] - 6s 418ms/step - loss: 0.6915 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 8/100\n",
            "14/14 [==============================] - 6s 394ms/step - loss: 0.6909 - accuracy: 0.5225 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 9/100\n",
            "14/14 [==============================] - 6s 397ms/step - loss: 0.6920 - accuracy: 0.5207 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 10/100\n",
            "14/14 [==============================] - 5s 390ms/step - loss: 0.6915 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 11/100\n",
            "14/14 [==============================] - 5s 389ms/step - loss: 0.6917 - accuracy: 0.5213 - top-5-accuracy: 1.0000 - val_loss: 0.6903 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 12/100\n",
            "14/14 [==============================] - 5s 392ms/step - loss: 0.6915 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6903 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 13/100\n",
            "14/14 [==============================] - 5s 384ms/step - loss: 0.6915 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6900 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 14/100\n",
            "14/14 [==============================] - 5s 393ms/step - loss: 0.6915 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 15/100\n",
            "14/14 [==============================] - 5s 386ms/step - loss: 0.6911 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6902 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 16/100\n",
            "14/14 [==============================] - 6s 396ms/step - loss: 0.6916 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 17/100\n",
            "14/14 [==============================] - 5s 393ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6903 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 18/100\n",
            "14/14 [==============================] - 6s 406ms/step - loss: 0.6911 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 19/100\n",
            "14/14 [==============================] - 5s 392ms/step - loss: 0.6917 - accuracy: 0.5213 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 20/100\n",
            "14/14 [==============================] - 6s 396ms/step - loss: 0.6915 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 21/100\n",
            "14/14 [==============================] - 6s 404ms/step - loss: 0.6911 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 22/100\n",
            "14/14 [==============================] - 5s 391ms/step - loss: 0.6915 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 23/100\n",
            "14/14 [==============================] - 6s 402ms/step - loss: 0.6915 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 24/100\n",
            "14/14 [==============================] - 5s 390ms/step - loss: 0.6917 - accuracy: 0.5213 - top-5-accuracy: 1.0000 - val_loss: 0.6903 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 25/100\n",
            "14/14 [==============================] - 6s 399ms/step - loss: 0.6918 - accuracy: 0.5213 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 26/100\n",
            "14/14 [==============================] - 5s 389ms/step - loss: 0.6914 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 27/100\n",
            "14/14 [==============================] - 6s 395ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6902 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 28/100\n",
            "14/14 [==============================] - 5s 393ms/step - loss: 0.6916 - accuracy: 0.5213 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 29/100\n",
            "14/14 [==============================] - 5s 390ms/step - loss: 0.6916 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 30/100\n",
            "14/14 [==============================] - 5s 392ms/step - loss: 0.6917 - accuracy: 0.5213 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 31/100\n",
            "14/14 [==============================] - 5s 388ms/step - loss: 0.6910 - accuracy: 0.5225 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 32/100\n",
            "14/14 [==============================] - 6s 399ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 33/100\n",
            "14/14 [==============================] - 6s 411ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 34/100\n",
            "14/14 [==============================] - 6s 440ms/step - loss: 0.6915 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6902 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 35/100\n",
            "14/14 [==============================] - 6s 396ms/step - loss: 0.6914 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 36/100\n",
            "14/14 [==============================] - 6s 399ms/step - loss: 0.6911 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 37/100\n",
            "14/14 [==============================] - 6s 396ms/step - loss: 0.6917 - accuracy: 0.5213 - top-5-accuracy: 1.0000 - val_loss: 0.6903 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 38/100\n",
            "14/14 [==============================] - 6s 452ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6902 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 39/100\n",
            "14/14 [==============================] - 6s 399ms/step - loss: 0.6914 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6900 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 40/100\n",
            "14/14 [==============================] - 6s 436ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 41/100\n",
            "14/14 [==============================] - 6s 441ms/step - loss: 0.6908 - accuracy: 0.5225 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 42/100\n",
            "14/14 [==============================] - 6s 441ms/step - loss: 0.6911 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6903 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 43/100\n",
            "14/14 [==============================] - 6s 464ms/step - loss: 0.6910 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6903 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 44/100\n",
            "14/14 [==============================] - 5s 386ms/step - loss: 0.6914 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 45/100\n",
            "14/14 [==============================] - 6s 400ms/step - loss: 0.6910 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 46/100\n",
            "14/14 [==============================] - 6s 408ms/step - loss: 0.6911 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 47/100\n",
            "14/14 [==============================] - 6s 408ms/step - loss: 0.6907 - accuracy: 0.5227 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 48/100\n",
            "14/14 [==============================] - 6s 443ms/step - loss: 0.6908 - accuracy: 0.5227 - top-5-accuracy: 1.0000 - val_loss: 0.6900 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 49/100\n",
            "14/14 [==============================] - 6s 451ms/step - loss: 0.6911 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6903 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 50/100\n",
            "14/14 [==============================] - 6s 398ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6902 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 51/100\n",
            "14/14 [==============================] - 6s 431ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6908 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 52/100\n",
            "14/14 [==============================] - 6s 413ms/step - loss: 0.6910 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6902 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 53/100\n",
            "14/14 [==============================] - 6s 452ms/step - loss: 0.6904 - accuracy: 0.5230 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 54/100\n",
            "14/14 [==============================] - 6s 417ms/step - loss: 0.6918 - accuracy: 0.5213 - top-5-accuracy: 1.0000 - val_loss: 0.6902 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 55/100\n",
            "14/14 [==============================] - 6s 413ms/step - loss: 0.6911 - accuracy: 0.5225 - top-5-accuracy: 1.0000 - val_loss: 0.6907 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 56/100\n",
            "14/14 [==============================] - 6s 397ms/step - loss: 0.6912 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6897 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 57/100\n",
            "14/14 [==============================] - 6s 402ms/step - loss: 0.6910 - accuracy: 0.5225 - top-5-accuracy: 1.0000 - val_loss: 0.6900 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 58/100\n",
            "14/14 [==============================] - 6s 448ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 59/100\n",
            "14/14 [==============================] - 6s 440ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 60/100\n",
            "14/14 [==============================] - 6s 439ms/step - loss: 0.6909 - accuracy: 0.5225 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 61/100\n",
            "14/14 [==============================] - 6s 460ms/step - loss: 0.6918 - accuracy: 0.5213 - top-5-accuracy: 1.0000 - val_loss: 0.6907 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 62/100\n",
            "14/14 [==============================] - 6s 412ms/step - loss: 0.6910 - accuracy: 0.5225 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 63/100\n",
            "14/14 [==============================] - 5s 392ms/step - loss: 0.6910 - accuracy: 0.5225 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 64/100\n",
            "14/14 [==============================] - 6s 401ms/step - loss: 0.6911 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6907 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 65/100\n",
            "14/14 [==============================] - 5s 392ms/step - loss: 0.6912 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6899 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 66/100\n",
            "14/14 [==============================] - 6s 422ms/step - loss: 0.6911 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 67/100\n",
            "14/14 [==============================] - 6s 399ms/step - loss: 0.6911 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 68/100\n",
            "14/14 [==============================] - 5s 390ms/step - loss: 0.6912 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6906 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 69/100\n",
            "14/14 [==============================] - 6s 429ms/step - loss: 0.6915 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 70/100\n",
            "14/14 [==============================] - 6s 425ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6903 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 71/100\n",
            "14/14 [==============================] - 6s 438ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 72/100\n",
            "14/14 [==============================] - 5s 393ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 73/100\n",
            "14/14 [==============================] - 6s 403ms/step - loss: 0.6916 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6903 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 74/100\n",
            "14/14 [==============================] - 5s 391ms/step - loss: 0.6915 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 75/100\n",
            "14/14 [==============================] - 6s 401ms/step - loss: 0.6919 - accuracy: 0.5210 - top-5-accuracy: 1.0000 - val_loss: 0.6903 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 76/100\n",
            "14/14 [==============================] - 5s 392ms/step - loss: 0.6901 - accuracy: 0.5236 - top-5-accuracy: 1.0000 - val_loss: 0.6902 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 77/100\n",
            "14/14 [==============================] - 6s 397ms/step - loss: 0.6917 - accuracy: 0.5213 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 78/100\n",
            "14/14 [==============================] - 6s 394ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6906 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 79/100\n",
            "14/14 [==============================] - 5s 394ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 80/100\n",
            "14/14 [==============================] - 6s 402ms/step - loss: 0.6912 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6900 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 81/100\n",
            "14/14 [==============================] - 6s 394ms/step - loss: 0.6915 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6907 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 82/100\n",
            "14/14 [==============================] - 6s 407ms/step - loss: 0.6913 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6903 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 83/100\n",
            "14/14 [==============================] - 6s 400ms/step - loss: 0.6911 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6902 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 84/100\n",
            "14/14 [==============================] - 6s 445ms/step - loss: 0.6909 - accuracy: 0.5225 - top-5-accuracy: 1.0000 - val_loss: 0.6907 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 85/100\n",
            "14/14 [==============================] - 6s 409ms/step - loss: 0.6912 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 86/100\n",
            "14/14 [==============================] - 6s 422ms/step - loss: 0.6908 - accuracy: 0.5227 - top-5-accuracy: 1.0000 - val_loss: 0.6900 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 87/100\n",
            "14/14 [==============================] - 6s 433ms/step - loss: 0.6909 - accuracy: 0.5227 - top-5-accuracy: 1.0000 - val_loss: 0.6908 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 88/100\n",
            "14/14 [==============================] - 6s 411ms/step - loss: 0.6903 - accuracy: 0.5233 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 89/100\n",
            "14/14 [==============================] - 6s 395ms/step - loss: 0.6909 - accuracy: 0.5227 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 90/100\n",
            "14/14 [==============================] - 6s 394ms/step - loss: 0.6912 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6902 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 91/100\n",
            "14/14 [==============================] - 6s 415ms/step - loss: 0.6950 - accuracy: 0.5230 - top-5-accuracy: 1.0000 - val_loss: 0.6902 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 92/100\n",
            "14/14 [==============================] - 5s 388ms/step - loss: 0.6916 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 93/100\n",
            "14/14 [==============================] - 6s 398ms/step - loss: 0.6911 - accuracy: 0.5222 - top-5-accuracy: 1.0000 - val_loss: 0.6902 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 94/100\n",
            "14/14 [==============================] - 5s 390ms/step - loss: 0.6917 - accuracy: 0.5213 - top-5-accuracy: 1.0000 - val_loss: 0.6905 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 95/100\n",
            "14/14 [==============================] - 6s 396ms/step - loss: 0.6914 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6902 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 96/100\n",
            "14/14 [==============================] - 6s 395ms/step - loss: 0.6915 - accuracy: 0.5216 - top-5-accuracy: 1.0000 - val_loss: 0.6903 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 97/100\n",
            "14/14 [==============================] - 6s 466ms/step - loss: 0.6916 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 98/100\n",
            "14/14 [==============================] - 6s 413ms/step - loss: 0.6916 - accuracy: 0.5219 - top-5-accuracy: 1.0000 - val_loss: 0.6911 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "Epoch 99/100\n",
            "14/14 [==============================] - 8s 583ms/step - loss: 0.6913 - accuracy: 0.5230 - top-5-accuracy: 1.0000 - val_loss: 0.6889 - val_accuracy: 0.5469 - val_top-5-accuracy: 1.0000\n",
            "Epoch 100/100\n",
            "14/14 [==============================] - 6s 401ms/step - loss: 0.6924 - accuracy: 0.5207 - top-5-accuracy: 1.0000 - val_loss: 0.6906 - val_accuracy: 0.5443 - val_top-5-accuracy: 1.0000\n",
            "30/30 [==============================] - 1s 37ms/step - loss: 0.6915 - accuracy: 0.5276 - top-5-accuracy: 1.0000\n",
            "Test accuracy: 52.76%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the training and validation losses\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.title('Training and Validation Losses')\n",
        "plt.show()\n",
        "\n",
        "# Plot the training and validation accuracy\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "-N8aQFyhvgkW",
        "outputId": "d79cbb05-8e0a-4ecd-e242-e4846fddfd7b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classify an image from URL\n",
        "import requests\n",
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def classify_image_from_url(image_url):\n",
        "    # Read the image from the URL\n",
        "    response = requests.get(image_url)\n",
        "    img_array = np.asarray(bytearray(response.content), dtype=np.uint8)\n",
        "    img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)\n",
        "\n",
        "    # Preprocess the image\n",
        "    img = cv2.resize(img, (img_width, img_height))\n",
        "    img = img.astype('float32') / 255.0\n",
        "    img = np.expand_dims(img, axis=0)\n",
        "\n",
        "    # Make predictions using the three models\n",
        "    predictions1 = model.predict(img)\n",
        "    predictions2 = model_resnet.predict(img)\n",
        "    predictions3 = model_cnn.predict(img)\n",
        "\n",
        "    # Get the predicted label for each model\n",
        "    label1 = \"Stroke\" if predictions1[0][0] > 0.5 else \"Non-Stroke\"\n",
        "    label2 = \"Stroke\" if predictions2[0][0] > 0.5 else \"Non-Stroke\"\n",
        "    label3 = \"Stroke\" if predictions3[0][0] > 0.5 else \"Non-Stroke\"\n",
        "\n",
        "    # Display the image and the predictions\n",
        "    plt.figure(figsize=(15, 5))\n",
        "    plt.subplot(1, 3, 1)\n",
        "    plt.imshow(img[0])\n",
        "    plt.title(f\"ViT Prediction: {label1}\")\n",
        "    plt.axis('off')\n",
        "\n",
        "    plt.subplot(1, 3, 2)\n",
        "    plt.imshow(img[0])\n",
        "    plt.title(f\"Resnet Prediction: {label2}\")\n",
        "    plt.axis('off')\n",
        "\n",
        "    plt.subplot(1, 3, 3)\n",
        "    plt.imshow(img[0])\n",
        "    plt.title(f\"CNN Prediction: {label3}\")\n",
        "    plt.axis('off')\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "iu2 = 'https://www.looper.com/img/gallery/did-dottie-from-bob-hearts-abishola-really-have-a-stroke/thats-not-medical-science-but-good-acting-at-work-1632270020.jpg'\n",
        "classify_image_from_url(iu2)\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-07-19T10:53:58.992309Z",
          "iopub.execute_input": "2023-07-19T10:53:58.992712Z",
          "iopub.status.idle": "2023-07-19T10:54:20.072280Z",
          "shell.execute_reply.started": "2023-07-19T10:53:58.992679Z",
          "shell.execute_reply": "2023-07-19T10:54:20.070642Z"
        },
        "trusted": true,
        "id": "Cs_c2FgY2REK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "147e02ff-a305-48cb-aae0-32a86f3a449e"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Randomly Testing Models on Train Data"
      ],
      "metadata": {
        "id": "uGWG0ImM9G7C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "def classify_image():\n",
        "    # Randomly select an image from the dataset\n",
        "    index = random.randint(0, len(images) - 1)\n",
        "    image = images[index]\n",
        "    label = labels[index]\n",
        "\n",
        "    # Display the image\n",
        "    plt.imshow(image)\n",
        "    plt.axis('off')\n",
        "    plt.show()\n",
        "\n",
        "    # Reshape and normalize the image\n",
        "    image = np.expand_dims(image, axis=0)\n",
        "    image = image.astype('float32') / 255.0\n",
        "\n",
        "    # Make predictions using the three models\n",
        "    prediction1 = model.predict(image)\n",
        "    prediction2 = model_resnet.predict(image)\n",
        "    prediction3 = model_cnn.predict(image)\n",
        "\n",
        "    # Determine the class based on the predictions\n",
        "    if prediction1[0][0] >= 0.5:\n",
        "        prediction_label1 = \"Stroke\"\n",
        "    else:\n",
        "        prediction_label1 = \"Not Stroke\"\n",
        "\n",
        "    if prediction2[0][0] >= 0.5:\n",
        "        prediction_label2 = \"Stroke\"\n",
        "    else:\n",
        "        prediction_label2 = \"Not Stroke\"\n",
        "\n",
        "    if prediction3[0][0] >= 0.5:\n",
        "        prediction_label3 = \"Stroke\"\n",
        "    else:\n",
        "        prediction_label3 = \"Not Stroke\"\n",
        "\n",
        "    # Print the label and predictions from all three models\n",
        "    print(\"Label:\", label)\n",
        "    print(\"ViT Prediction:\", prediction_label1)\n",
        "    print(\"Resnet Prediction:\", prediction_label2)\n",
        "    print(\"CNN Prediction:\", prediction_label3)\n",
        "classify_image()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "dTrLOkHn8Heb",
        "outputId": "72652080-566b-4e76-964c-d136aa471e32"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "Label: 0\n",
            "ViT Prediction: Not Stroke\n",
            "Resnet Prediction: Not Stroke\n",
            "CNN Prediction: Not Stroke\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Further Analysis\n",
        "- Understand reasons for overfitting and absurt result in losses.\n",
        "- Hyperparameter tuning.\n",
        "- Add more data.\n",
        "- Better pipeline for image data."
      ],
      "metadata": {
        "id": "oIJOdQK-9iEG"
      }
    }
  ]
}