vit_l16.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "T4",
      "cell_execution_strategy": "setup",
      "authorship_tag": "ABX9TyMmgS7y9FCXvctvbolIIPpi",
      "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_l16_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_116)**\n"
      ],
      "metadata": {
        "id": "BJOcpoBYkSnI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1) Pre-load module**"
      ],
      "metadata": {
        "id": "0HCrJhB4kc6G"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JMYBQt_qkLvr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a93e227f-e4ff-4979-d7f9-6307a07e8c52"
      },
      "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[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m612.3/612.3 kB\u001b[0m \u001b[31m8.7 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) (23.2)\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 03:43:49 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   44C    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": {
        "id": "KHWvSkp4sbpR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f47d54c6-9969-426b-a18e-20df8dcebb7e"
      },
      "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": {
        "id": "tIYkhmh1tRTc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e09c86d1-100b-4d03-9b83-69b61dfb1f6f"
      },
      "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": "7b16873c-9465-45c1-a143-09768b656444"
      },
      "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_l16(\n",
        "    image_size=image_size,\n",
        "    activation='relu',\n",
        "    pretrained=True,\n",
        "    include_top=False,\n",
        "    pretrained_top=False,\n",
        ")"
      ],
      "metadata": {
        "id": "zMTXKjvmtVcc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0dbb20e-3a25-4680-da38-f617820b468d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://github.com/faustomorales/vit-keras/releases/download/dl/ViT-L_16_imagenet21k+imagenet2012.npz\n",
            "1218991142/1218991142 [==============================] - 4s 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": {
        "id": "m4iMrTiVtX81",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e4773bc-0aa6-4b4c-847a-d5e08cc7476d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"vit-l16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 256, 256, 3)]     0         \n",
            "                                                                 \n",
            " embedding (Conv2D)          (None, 16, 16, 1024)      787456    \n",
            "                                                                 \n",
            " reshape (Reshape)           (None, 256, 1024)         0         \n",
            "                                                                 \n",
            " class_token (ClassToken)    (None, 257, 1024)         1024      \n",
            "                                                                 \n",
            " Transformer/posembed_input  (None, 257, 1024)         263168    \n",
            "  (AddPositionEmbs)                                              \n",
            "                                                                 \n",
            " Transformer/encoderblock_0  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_3  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_4  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_5  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_6  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_7  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_8  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_9  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 0 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 1 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 2 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 3 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 4 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 5 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 6 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 7 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 8 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 9 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            " 0 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            " 1 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            " 2 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            " 3 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoder_norm (  (None, 257, 1024)         2048      \n",
            " LayerNormalization)                                             \n",
            "                                                                 \n",
            " ExtractToken (Lambda)       (None, 1024)              0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 303363072 (1.13 GB)\n",
            "Trainable params: 303363072 (1.13 GB)\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": {
        "id": "5V8VwdtPtaJl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4dc0ae0-d522-4538-92cf-03b56b793bcb"
      },
      "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": {
        "id": "_6T8gi88tfwQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a04e933-6562-4be5-bbed-c5dc4423545a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"vit-l16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 256, 256, 3)]     0         \n",
            "                                                                 \n",
            " embedding (Conv2D)          (None, 16, 16, 1024)      787456    \n",
            "                                                                 \n",
            " reshape (Reshape)           (None, 256, 1024)         0         \n",
            "                                                                 \n",
            " class_token (ClassToken)    (None, 257, 1024)         1024      \n",
            "                                                                 \n",
            " Transformer/posembed_input  (None, 257, 1024)         263168    \n",
            "  (AddPositionEmbs)                                              \n",
            "                                                                 \n",
            " Transformer/encoderblock_0  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_3  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_4  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_5  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_6  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_7  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_8  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_9  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 0 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 1 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 2 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 3 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 4 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 5 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 6 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 7 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 8 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 9 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            " 0 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            " 1 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            " 2 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            " 3 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoder_norm (  (None, 257, 1024)         2048      \n",
            " LayerNormalization)                                             \n",
            "                                                                 \n",
            " ExtractToken (Lambda)       (None, 1024)              0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 303363072 (1.13 GB)\n",
            "Trainable params: 0 (0.00 Byte)\n",
            "Non-trainable params: 303363072 (1.13 GB)\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": {
        "id": "BoXtkAQZt2t0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b67947ee-7d24-444d-aceb-4f380be4a6f4"
      },
      "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, 1024)      787456    \n",
            "                                                                 \n",
            " reshape (Reshape)           (None, 256, 1024)         0         \n",
            "                                                                 \n",
            " class_token (ClassToken)    (None, 257, 1024)         1024      \n",
            "                                                                 \n",
            " Transformer/posembed_input  (None, 257, 1024)         263168    \n",
            "  (AddPositionEmbs)                                              \n",
            "                                                                 \n",
            " Transformer/encoderblock_0  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_3  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_4  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_5  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_6  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_7  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_8  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_9  ((None, 257, 1024),       12596224  \n",
            "  (TransformerBlock)          (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 0 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 1 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 2 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 3 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 4 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 5 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 6 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 7 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 8 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_1  ((None, 257, 1024),       12596224  \n",
            " 9 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            " 0 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            " 1 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            " 2 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoderblock_2  ((None, 257, 1024),       12596224  \n",
            " 3 (TransformerBlock)         (None, 16, None, None)             \n",
            "                             )                                   \n",
            "                                                                 \n",
            " Transformer/encoder_norm (  (None, 257, 1024)         2048      \n",
            " LayerNormalization)                                             \n",
            "                                                                 \n",
            " ExtractToken (Lambda)       (None, 1024)              0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1024)              0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1024)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 2)                 2050      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 303365122 (1.13 GB)\n",
            "Trainable params: 2050 (8.01 KB)\n",
            "Non-trainable params: 303363072 (1.13 GB)\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": {
        "id": "tUe3RvE8t_vw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f557bcb-3d57-410d-b3b0-b5359a6ec872"
      },
      "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": "5e150e9c-0b2c-4284-d84e-5031298a3524"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "313/313 [==============================] - 1474s 5s/step - loss: 0.0434 - accuracy: 0.9839 - val_loss: 0.0173 - val_accuracy: 0.9950\n",
            "Epoch 2/15\n",
            "313/313 [==============================] - 1480s 5s/step - loss: 0.0414 - accuracy: 0.9847 - val_loss: 0.0161 - val_accuracy: 0.9954\n",
            "Epoch 3/15\n",
            "313/313 [==============================] - 1481s 5s/step - loss: 0.0436 - accuracy: 0.9841 - val_loss: 0.0154 - val_accuracy: 0.9952\n",
            "Epoch 4/15\n",
            "313/313 [==============================] - 1481s 5s/step - loss: 0.0445 - accuracy: 0.9837 - val_loss: 0.0181 - val_accuracy: 0.9952\n",
            "Epoch 5/15\n",
            "313/313 [==============================] - 1481s 5s/step - loss: 0.0432 - accuracy: 0.9846 - val_loss: 0.0170 - val_accuracy: 0.9948\n",
            "Epoch 6/15\n",
            "313/313 [==============================] - 1480s 5s/step - loss: 0.0447 - accuracy: 0.9836 - val_loss: 0.0196 - val_accuracy: 0.9952\n",
            "Epoch 7/15\n",
            "313/313 [==============================] - 1481s 5s/step - loss: 0.0466 - accuracy: 0.9833 - val_loss: 0.0212 - val_accuracy: 0.9944\n",
            "Epoch 8/15\n",
            "313/313 [==============================] - 1481s 5s/step - loss: 0.0449 - accuracy: 0.9842 - val_loss: 0.0183 - val_accuracy: 0.9952\n",
            "Epoch 9/15\n",
            "313/313 [==============================] - 1481s 5s/step - loss: 0.0440 - accuracy: 0.9846 - val_loss: 0.0178 - val_accuracy: 0.9950\n",
            "Epoch 10/15\n",
            "313/313 [==============================] - 1482s 5s/step - loss: 0.0418 - accuracy: 0.9842 - val_loss: 0.0182 - val_accuracy: 0.9952\n",
            "Epoch 11/15\n",
            "313/313 [==============================] - 1483s 5s/step - loss: 0.0450 - accuracy: 0.9834 - val_loss: 0.0175 - val_accuracy: 0.9954\n",
            "Epoch 12/15\n",
            "313/313 [==============================] - 1483s 5s/step - loss: 0.0454 - accuracy: 0.9833 - val_loss: 0.0183 - val_accuracy: 0.9948\n",
            "Epoch 13/15\n",
            "313/313 [==============================] - 1484s 5s/step - loss: 0.0403 - accuracy: 0.9855 - val_loss: 0.0192 - val_accuracy: 0.9940\n",
            "Epoch 14/15\n",
            "313/313 [==============================] - 1483s 5s/step - loss: 0.0427 - accuracy: 0.9843 - val_loss: 0.0187 - val_accuracy: 0.9952\n",
            "Epoch 15/15\n",
            "313/313 [==============================] - 1483s 5s/step - loss: 0.0450 - accuracy: 0.9834 - val_loss: 0.0162 - val_accuracy: 0.9956\n",
            "Total time taken:  22217.95858502388\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": {
        "id": "9hsFC3DRuFdV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "0d6a3f13-4841-465a-a961-016cdff36068"
      },
      "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": {
        "id": "gKE-YMTvuIHP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "99cb3dca-1343-48dc-a001-14c2bebdec99"
      },
      "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_l16.h5')"
      ],
      "metadata": {
        "id": "_f5ezsjduJXY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8311f298-2d03-4d4f-91b4-2bfa4edecfca"
      },
      "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"
          ]
        }
      ]
    }
  ]
}