vit_b16.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "T4",
      "cell_execution_strategy": "setup",
      "authorship_tag": "ABX9TyNLllkQaSIdH86uAtrxGXdo",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Crepopcorn/cat-vs-dog-vit-project/blob/main/vit_b16_scratch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Dogs-vs-Cats classifier (vit_b16)**"
      ],
      "metadata": {
        "id": "BJOcpoBYkSnI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1) Pre-load module**"
      ],
      "metadata": {
        "id": "0HCrJhB4kc6G"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JMYBQt_qkLvr",
        "outputId": "c5783761-7f18-4c35-d9e8-be33bf057a71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow-addons\n",
            "  Downloading tensorflow_addons-0.22.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (612 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/612.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.4/612.3 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m612.3/612.3 kB\u001b[0m \u001b[31m10.8 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) (23.2)\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-0.22.0 typeguard-2.13.3\n",
            "Collecting vit-keras\n",
            "  Downloading vit_keras-0.1.2-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from vit-keras) (1.11.3)\n",
            "Collecting validators (from vit-keras)\n",
            "  Downloading validators-0.22.0-py3-none-any.whl (26 kB)\n",
            "Requirement already satisfied: numpy<1.28.0,>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from scipy->vit-keras) (1.23.5)\n",
            "Installing collected packages: validators, vit-keras\n",
            "Successfully installed validators-0.22.0 vit-keras-0.1.2\n",
            "Tue Oct 31 22:56:33 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   39C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow-addons\n",
        "!pip install vit-keras\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive/\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHWvSkp4sbpR",
        "outputId": "8a6bf39d-a4b5-4b00-f6a0-01844ca465b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_size=[256,256]\n",
        "train_dir='/content/drive/My Drive/data/train'\n",
        "test_dir='/content/drive/My Drive/data/val'\n",
        "!ls '/content/drive/My Drive/data/train'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tIYkhmh1tRTc",
        "outputId": "66df8b9c-4ad1-4a07-c1e1-877a41a53097"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cat  dog\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from vit_keras import vit, utils\n",
        "from keras.models import Model\n",
        "from keras.layers import Dense, Dropout, Flatten\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential,Model\n",
        "#from keras.optimizers import Adam\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers.legacy import Adam"
      ],
      "metadata": {
        "id": "RAl_MMaztTpG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d8ddfe9-a457-440a-e924-a6f816196d71"
      },
      "execution_count": null,
      "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": "markdown",
      "source": [
        "**2) Preprocessing**"
      ],
      "metadata": {
        "id": "X_tWBXbdvPQU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image_size = 256\n",
        "base_model = vit.vit_b16(\n",
        "    image_size=image_size,\n",
        "    activation='relu',\n",
        "    pretrained=True,\n",
        "    include_top=False,\n",
        "    pretrained_top=False,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zMTXKjvmtVcc",
        "outputId": "3375acb4-ef51-49fd-a2a4-460b3274923e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://github.com/faustomorales/vit-keras/releases/download/dl/ViT-B_16_imagenet21k+imagenet2012.npz\n",
            "347502902/347502902 [==============================] - 2s 0us/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/vit_keras/utils.py:81: UserWarning: Resizing position embeddings from 24, 24 to 16, 16\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(base_model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m4iMrTiVtX81",
        "outputId": "0409e862-9e1c-4ba3-c039-6c00105fe90d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"vit-b16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 256, 256, 3)]     0         \n",
            "                                                                 \n",
            " embedding (Conv2D)          (None, 16, 16, 768)       590592    \n",
            "                                                                 \n",
            " reshape (Reshape)           (None, 256, 768)          0         \n",
            "                                                                 \n",
            " class_token (ClassToken)    (None, 257, 768)          768       \n",
            "                                                                 \n",
            " Transformer/posembed_input  (None, 257, 768)          197376    \n",
            "  (AddPositionEmbs)                                              \n",
            "                                                                 \n",
            " Transformer/encoderblock_0  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_3  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_4  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_5  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_6  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_7  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_8  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_9  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 768),        7087872   \n",
            " 0 (TransformerBlock)         (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 768),        7087872   \n",
            " 1 (TransformerBlock)         (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoder_norm (  (None, 257, 768)          1536      \n",
            " LayerNormalization)                                             \n",
            "                                                                 \n",
            " ExtractToken (Lambda)       (None, 768)               0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 85844736 (327.47 MB)\n",
            "Trainable params: 85844736 (327.47 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in base_model.layers:\n",
        "    layer.trainable=False\n",
        "\n",
        "print(\"Base model layers are freezed!!!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5V8VwdtPtaJl",
        "outputId": "f1762d8d-f3f2-4b72-f976-d648a5fe5e02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Base model layers are freezed!!!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(base_model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6T8gi88tfwQ",
        "outputId": "3874aeef-304c-4ee0-e2be-1bd7a9a122bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"vit-b16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 256, 256, 3)]     0         \n",
            "                                                                 \n",
            " embedding (Conv2D)          (None, 16, 16, 768)       590592    \n",
            "                                                                 \n",
            " reshape (Reshape)           (None, 256, 768)          0         \n",
            "                                                                 \n",
            " class_token (ClassToken)    (None, 257, 768)          768       \n",
            "                                                                 \n",
            " Transformer/posembed_input  (None, 257, 768)          197376    \n",
            "  (AddPositionEmbs)                                              \n",
            "                                                                 \n",
            " Transformer/encoderblock_0  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_3  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_4  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_5  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_6  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_7  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_8  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_9  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 768),        7087872   \n",
            " 0 (TransformerBlock)         (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 768),        7087872   \n",
            " 1 (TransformerBlock)         (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoder_norm (  (None, 257, 768)          1536      \n",
            " LayerNormalization)                                             \n",
            "                                                                 \n",
            " ExtractToken (Lambda)       (None, 768)               0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 85844736 (327.47 MB)\n",
            "Trainable params: 0 (0.00 Byte)\n",
            "Non-trainable params: 85844736 (327.47 MB)\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = Dropout(0.2)(base_model.output)\n",
        "x = Flatten()(x)\n",
        "x = Dense(2,'softmax')(x)\n",
        "\n",
        "model = Model(inputs=base_model.input,outputs=x)"
      ],
      "metadata": {
        "id": "B15m6TNcthaQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Final Model is:\\n\",model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BoXtkAQZt2t0",
        "outputId": "28ac9594-284c-4980-b2c8-e675fd4b08f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 256, 256, 3)]     0         \n",
            "                                                                 \n",
            " embedding (Conv2D)          (None, 16, 16, 768)       590592    \n",
            "                                                                 \n",
            " reshape (Reshape)           (None, 256, 768)          0         \n",
            "                                                                 \n",
            " class_token (ClassToken)    (None, 257, 768)          768       \n",
            "                                                                 \n",
            " Transformer/posembed_input  (None, 257, 768)          197376    \n",
            "  (AddPositionEmbs)                                              \n",
            "                                                                 \n",
            " Transformer/encoderblock_0  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_3  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_4  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_5  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_6  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_7  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_8  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_9  ((None, 257, 768),        7087872   \n",
            "  (TransformerBlock)          (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 768),        7087872   \n",
            " 0 (TransformerBlock)         (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 768),        7087872   \n",
            " 1 (TransformerBlock)         (None, 12, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoder_norm (  (None, 257, 768)          1536      \n",
            " LayerNormalization)                                             \n",
            "                                                                 \n",
            " ExtractToken (Lambda)       (None, 768)               0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 768)               0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 768)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 2)                 1538      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 85846274 (327.48 MB)\n",
            "Trainable params: 1538 (6.01 KB)\n",
            "Non-trainable params: 85844736 (327.47 MB)\n",
            "_________________________________________________________________\n",
            "Final Model is:\n",
            " None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "opt=Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, decay=0.0, amsgrad=False)"
      ],
      "metadata": {
        "id": "urIYHcjmt4I9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='categorical_crossentropy',optimizer=opt,metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "sFjmGzb5t7Ba"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RANDOM_SEED=123\n",
        "train_datagen = ImageDataGenerator(\n",
        "    width_shift_range=0.4,\n",
        "    height_shift_range=0.4,\n",
        "    rescale=1.0/255,\n",
        "    horizontal_flip=True\n",
        ")\n",
        "\n",
        "test_datagen = ImageDataGenerator(\n",
        "    rescale=1.0/255,\n",
        ")\n",
        "\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    color_mode='rgb',\n",
        "    target_size=img_size,\n",
        "    batch_size=64,\n",
        "    class_mode='categorical',\n",
        "    seed=RANDOM_SEED\n",
        ")\n",
        "\n",
        "\n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    color_mode='rgb',\n",
        "    target_size=img_size,\n",
        "    batch_size=64,\n",
        "    class_mode='categorical',\n",
        "    seed=RANDOM_SEED\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUe3RvE8t_vw",
        "outputId": "9468fb2b-6144-4ade-ea95-58315be79323"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 20000 images belonging to 2 classes.\n",
            "Found 5000 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3) Train Model**"
      ],
      "metadata": {
        "id": "fElJaSQbvkeE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "start = time.time()\n",
        "hist = model.fit(\n",
        "    train_generator,\n",
        "    epochs=15,\n",
        "    validation_data = validation_generator\n",
        ")\n",
        "\n",
        "end = time.time()\n",
        "print(\"Total time taken: \",end-start)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wt1Jer8UuBKV",
        "outputId": "28b7f0b9-6624-4666-b3a6-9e0217ae3a8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "313/313 [==============================] - 997s 3s/step - loss: 0.0569 - accuracy: 0.9798 - val_loss: 0.0161 - val_accuracy: 0.9944\n",
            "Epoch 2/15\n",
            "313/313 [==============================] - 477s 2s/step - loss: 0.0532 - accuracy: 0.9808 - val_loss: 0.0165 - val_accuracy: 0.9942\n",
            "Epoch 3/15\n",
            "313/313 [==============================] - 477s 2s/step - loss: 0.0522 - accuracy: 0.9808 - val_loss: 0.0193 - val_accuracy: 0.9934\n",
            "Epoch 4/15\n",
            "313/313 [==============================] - 477s 2s/step - loss: 0.0506 - accuracy: 0.9814 - val_loss: 0.0166 - val_accuracy: 0.9944\n",
            "Epoch 5/15\n",
            "313/313 [==============================] - 477s 2s/step - loss: 0.0474 - accuracy: 0.9822 - val_loss: 0.0154 - val_accuracy: 0.9942\n",
            "Epoch 6/15\n",
            "313/313 [==============================] - 477s 2s/step - loss: 0.0516 - accuracy: 0.9806 - val_loss: 0.0155 - val_accuracy: 0.9946\n",
            "Epoch 7/15\n",
            "313/313 [==============================] - 478s 2s/step - loss: 0.0491 - accuracy: 0.9819 - val_loss: 0.0161 - val_accuracy: 0.9952\n",
            "Epoch 8/15\n",
            "313/313 [==============================] - 477s 2s/step - loss: 0.0517 - accuracy: 0.9808 - val_loss: 0.0181 - val_accuracy: 0.9934\n",
            "Epoch 9/15\n",
            "313/313 [==============================] - 477s 2s/step - loss: 0.0494 - accuracy: 0.9812 - val_loss: 0.0158 - val_accuracy: 0.9948\n",
            "Epoch 10/15\n",
            "313/313 [==============================] - 477s 2s/step - loss: 0.0437 - accuracy: 0.9843 - val_loss: 0.0174 - val_accuracy: 0.9952\n",
            "Epoch 11/15\n",
            "313/313 [==============================] - 477s 2s/step - loss: 0.0495 - accuracy: 0.9808 - val_loss: 0.0167 - val_accuracy: 0.9948\n",
            "Epoch 12/15\n",
            "313/313 [==============================] - 477s 2s/step - loss: 0.0517 - accuracy: 0.9798 - val_loss: 0.0167 - val_accuracy: 0.9942\n",
            "Epoch 13/15\n",
            "313/313 [==============================] - 477s 2s/step - loss: 0.0506 - accuracy: 0.9820 - val_loss: 0.0171 - val_accuracy: 0.9944\n",
            "Epoch 14/15\n",
            "313/313 [==============================] - 477s 2s/step - loss: 0.0510 - accuracy: 0.9801 - val_loss: 0.0159 - val_accuracy: 0.9944\n",
            "Epoch 15/15\n",
            "313/313 [==============================] - 477s 2s/step - loss: 0.0458 - accuracy: 0.9820 - val_loss: 0.0205 - val_accuracy: 0.9922\n",
            "Total time taken:  7680.6104345321655\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4) Plot Graph**"
      ],
      "metadata": {
        "id": "CS5nRG55vq0X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting the figure for accuracy\n",
        "plt.figure(figsize=(10,7))\n",
        "plt.plot(hist.history['accuracy'])\n",
        "plt.plot(hist.history['val_accuracy'])\n",
        "plt.title('Model Accuracy Vision Transformer')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['train', 'val'], loc='lower right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "9hsFC3DRuFdV",
        "outputId": "d1af2cfc-c1e2-4788-9d93-1d84804bd2f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting the figure for loss\n",
        "plt.figure(figsize=(10,7))\n",
        "plt.plot(hist.history['loss'])\n",
        "plt.plot(hist.history['val_loss'])\n",
        "plt.title('Model Loss Vision Transformer')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['train', 'val'], loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "gKE-YMTvuIHP",
        "outputId": "7018c5c9-f9f9-44f7-c95c-49a406bc0abe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('dogCat_vit_b16.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_f5ezsjduJXY",
        "outputId": "56b7a77d-b42d-4815-9af1-3f0bd8ce71d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    }
  ]
}