Image_classification_with_Vision_Transformer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "6sdmDYoMefUW"
      },
      "outputs": [],
      "source": [
        "#Setup\n",
        "\n",
        "import os\n",
        "\n",
        "os.environ[\"KERAS_BACKEND\"] = \"jax\"  # @param [\"tensorflow\", \"jax\", \"torch\"]\n",
        "\n",
        "import keras\n",
        "from keras import layers\n",
        "from keras import ops\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Prepare the data\n",
        "\n",
        "num_classes = 100\n",
        "input_shape = (32, 32, 3)\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()\n",
        "\n",
        "print(f\"x_train shape: {x_train.shape} - y_train shape: {y_train.shape}\")\n",
        "print(f\"x_test shape: {x_test.shape} - y_test shape: {y_test.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FOD0_i2keq-u",
        "outputId": "20aee5e8-bb79-400f-f175-b554cab4009e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\n",
            "\u001b[1m169001437/169001437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 0us/step\n",
            "x_train shape: (50000, 32, 32, 3) - y_train shape: (50000, 1)\n",
            "x_test shape: (10000, 32, 32, 3) - y_test shape: (10000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Configure the hyperparameters\n",
        "\n",
        "learning_rate = 0.001\n",
        "weight_decay = 0.0001\n",
        "batch_size = 256\n",
        "num_epochs = 10  # For real training, use num_epochs=100. 10 is a test value\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 = [\n",
        "    2048,\n",
        "    1024,\n",
        "]  # Size of the dense layers of the final classifier"
      ],
      "metadata": {
        "id": "iJsCFVebet9a"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Use data 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(height_factor=0.2, width_factor=0.2),\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)"
      ],
      "metadata": {
        "id": "t1_ZQPT3exea"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Implement multilayer perceptron (MLP)\n",
        "\n",
        "def mlp(x, hidden_units, dropout_rate):\n",
        "    for units in hidden_units:\n",
        "        x = layers.Dense(units, activation=keras.activations.gelu)(x)\n",
        "        x = layers.Dropout(dropout_rate)(x)\n",
        "    return x"
      ],
      "metadata": {
        "id": "mECkWpSie0S0"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Implement patch creation as a layer\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",
        "        input_shape = ops.shape(images)\n",
        "        batch_size = input_shape[0]\n",
        "        height = input_shape[1]\n",
        "        width = input_shape[2]\n",
        "        channels = input_shape[3]\n",
        "        num_patches_h = height // self.patch_size\n",
        "        num_patches_w = width // self.patch_size\n",
        "        patches = keras.ops.image.extract_patches(images, size=self.patch_size)\n",
        "        patches = ops.reshape(\n",
        "            patches,\n",
        "            (\n",
        "                batch_size,\n",
        "                num_patches_h * num_patches_w,\n",
        "                self.patch_size * self.patch_size * channels,\n",
        "            ),\n",
        "        )\n",
        "        return patches\n",
        "\n",
        "    def get_config(self):\n",
        "        config = super().get_config()\n",
        "        config.update({\"patch_size\": self.patch_size})\n",
        "        return config"
      ],
      "metadata": {
        "id": "vPnDv_U_e3S2"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Let's display patches for a sample image\n",
        "\n",
        "plt.figure(figsize=(4, 4))\n",
        "image = x_train[np.random.choice(range(x_train.shape[0]))]\n",
        "plt.imshow(image.astype(\"uint8\"))\n",
        "plt.axis(\"off\")\n",
        "\n",
        "resized_image = ops.image.resize(\n",
        "    ops.convert_to_tensor([image]), size=(image_size, image_size)\n",
        ")\n",
        "patches = Patches(patch_size)(resized_image)\n",
        "print(f\"Image size: {image_size} X {image_size}\")\n",
        "print(f\"Patch size: {patch_size} X {patch_size}\")\n",
        "print(f\"Patches per image: {patches.shape[1]}\")\n",
        "print(f\"Elements per patch: {patches.shape[-1]}\")\n",
        "\n",
        "n = int(np.sqrt(patches.shape[1]))\n",
        "plt.figure(figsize=(4, 4))\n",
        "for i, patch in enumerate(patches[0]):\n",
        "    ax = plt.subplot(n, n, i + 1)\n",
        "    patch_img = ops.reshape(patch, (patch_size, patch_size, 3))\n",
        "    plt.imshow(ops.convert_to_numpy(patch_img).astype(\"uint8\"))\n",
        "    plt.axis(\"off\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "5zgvnc27e644",
        "outputId": "3c89d3d8-e2bc-4486-a76a-50312204b61d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image size: 72 X 72\n",
            "Patch size: 6 X 6\n",
            "Patches per image: 144\n",
            "Elements per patch: 108\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 144 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Implement the patch encoding layer\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 = ops.expand_dims(\n",
        "            ops.arange(start=0, stop=self.num_patches, step=1), axis=0\n",
        "        )\n",
        "        projected_patches = self.projection(patch)\n",
        "        encoded = projected_patches + self.position_embedding(positions)\n",
        "        return encoded\n",
        "\n",
        "    def get_config(self):\n",
        "        config = super().get_config()\n",
        "        config.update({\"num_patches\": self.num_patches})\n",
        "        return config"
      ],
      "metadata": {
        "id": "4xeWL-nUe_8Z"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Build the ViT model\n",
        "\n",
        "def create_vit_classifier():\n",
        "    inputs = keras.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": "HWfJbHpLfEDp"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Compile, train, and evaluate the mode\n",
        "\n",
        "def run_experiment(model):\n",
        "    optimizer = keras.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.weights.h5\"\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)}%\")\n",
        "    print(f\"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%\")\n",
        "\n",
        "    return history\n",
        "\n",
        "\n",
        "vit_classifier = create_vit_classifier()\n",
        "history = run_experiment(vit_classifier)\n",
        "\n",
        "\n",
        "def plot_history(item):\n",
        "    plt.plot(history.history[item], label=item)\n",
        "    plt.plot(history.history[\"val_\" + item], label=\"val_\" + item)\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(item)\n",
        "    plt.title(\"Train and Validation {} Over Epochs\".format(item), fontsize=14)\n",
        "    plt.legend()\n",
        "    plt.grid()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "plot_history(\"loss\")\n",
        "plot_history(\"top-5-accuracy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aNyeHidVfIQS",
        "outputId": "b170dd7b-2c2e-46c1-bbb3-a2f630d5011c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m346s\u001b[0m 2s/step - accuracy: 0.0313 - loss: 4.8642 - top-5-accuracy: 0.1209 - val_accuracy: 0.1078 - val_loss: 3.9121 - val_top-5-accuracy: 0.3232\n",
            "Epoch 2/10\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m311s\u001b[0m 2s/step - accuracy: 0.0905 - loss: 3.9728 - top-5-accuracy: 0.2832 - val_accuracy: 0.1584 - val_loss: 3.5657 - val_top-5-accuracy: 0.4094\n",
            "Epoch 3/10\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m311s\u001b[0m 2s/step - accuracy: 0.1266 - loss: 3.6946 - top-5-accuracy: 0.3619 - val_accuracy: 0.2026 - val_loss: 3.3057 - val_top-5-accuracy: 0.4806\n",
            "Epoch 4/10\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m315s\u001b[0m 2s/step - accuracy: 0.1665 - loss: 3.4853 - top-5-accuracy: 0.4246 - val_accuracy: 0.2336 - val_loss: 3.1775 - val_top-5-accuracy: 0.5140\n",
            "Epoch 5/10\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m327s\u001b[0m 2s/step - accuracy: 0.1932 - loss: 3.3120 - top-5-accuracy: 0.4743 - val_accuracy: 0.2600 - val_loss: 3.0345 - val_top-5-accuracy: 0.5478\n",
            "Epoch 6/10\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m317s\u001b[0m 2s/step - accuracy: 0.2163 - loss: 3.1942 - top-5-accuracy: 0.5070 - val_accuracy: 0.2778 - val_loss: 2.8931 - val_top-5-accuracy: 0.5760\n",
            "Epoch 7/10\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m317s\u001b[0m 2s/step - accuracy: 0.2449 - loss: 3.0346 - top-5-accuracy: 0.5478 - val_accuracy: 0.3078 - val_loss: 2.7386 - val_top-5-accuracy: 0.6156\n",
            "Epoch 8/10\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m317s\u001b[0m 2s/step - accuracy: 0.2720 - loss: 2.9067 - top-5-accuracy: 0.5811 - val_accuracy: 0.3178 - val_loss: 2.7031 - val_top-5-accuracy: 0.6186\n",
            "Epoch 9/10\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m314s\u001b[0m 2s/step - accuracy: 0.2854 - loss: 2.8193 - top-5-accuracy: 0.5987 - val_accuracy: 0.3404 - val_loss: 2.5944 - val_top-5-accuracy: 0.6460\n",
            "Epoch 10/10\n",
            "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m312s\u001b[0m 2s/step - accuracy: 0.3118 - loss: 2.6855 - top-5-accuracy: 0.6285 - val_accuracy: 0.3560 - val_loss: 2.4851 - val_top-5-accuracy: 0.6672\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 170ms/step - accuracy: 0.3655 - loss: 2.4485 - top-5-accuracy: 0.6772\n",
            "Test accuracy: 36.9%\n",
            "Test top 5 accuracy: 67.67%\n"
          ]
        },
        {
          "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": {}
        }
      ]
    }
  ]
}