{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "__C28eZrvMVs"
      },
      "outputs": [],
      "source": [
        "# import packages\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "72UX8LnWJKyF"
      },
      "outputs": [],
      "source": [
        "# seeding \n",
        "import os\n",
        "os.environ['PYTHONHASHSEED'] = '0'\n",
        "import random as rn\n",
        "np.random.seed(42)\n",
        "rn.seed = 42\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "from keras import backend as K\n",
        "session_conf = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)\n",
        "sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=session_conf)\n",
        "tf.compat.v1.keras.backend.set_session(sess)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "-HFVNC3kd8Py"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install huggingface-hub\n",
        "!pip install gradio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "FZbdOwXgd_zu"
      },
      "outputs": [],
      "source": [
        "# Load the TensorBoard notebook extension\n",
        "%load_ext tensorboard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "P7qzX-MleAoA"
      },
      "outputs": [],
      "source": [
        "log_dir = \"logs/fit/\"\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AIVsff80vbMu",
        "outputId": "46152df0-846b-4d2e-935b-59c76732e578"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(50000, 32, 32, 3)\n",
            "(50000, 10)\n",
            "(10000, 32, 32, 3)\n",
            "(10000, 10)\n"
          ]
        }
      ],
      "source": [
        "# load dataset CIFAR 10\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
        "y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)\n",
        "y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)\n",
        "\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)\n",
        "\n",
        "class_names = [\n",
        "    \"Airplane\",\n",
        "    \"Automobile\",\n",
        "    \"Bird\",\n",
        "    \"Cat\",\n",
        "    \"Deer\",\n",
        "    \"Dog\",\n",
        "    \"Frog\",\n",
        "    \"Horse\",\n",
        "    \"Ship\",\n",
        "    \"Truck\",\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "wAUbWIxFv9H7"
      },
      "outputs": [],
      "source": [
        "# hyperParams\n",
        "AUTO = tf.data.AUTOTUNE\n",
        "BATCH_SIZE = 32\n",
        "IMG_SIZE = 32"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "eHiQq1ZnwCki"
      },
      "outputs": [],
      "source": [
        "# resize the image and it to a float between 0,1\n",
        "def preprocess_image(image, label):\n",
        "    image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))\n",
        "    image = tf.image.convert_image_dtype(image, tf.float32) / 255.0\n",
        "    return image, label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "Ryp4k2N7wPgq"
      },
      "outputs": [],
      "source": [
        "# package it into tensorflow dataset\n",
        "train_ds_one = (\n",
        "    tf.data.Dataset.from_tensor_slices((x_train, y_train))\n",
        "    .shuffle(1024)\n",
        "    .map(preprocess_image, num_parallel_calls=AUTO)\n",
        ")\n",
        "train_ds_two = (\n",
        "    tf.data.Dataset.from_tensor_slices((x_train, y_train))\n",
        "    .shuffle(1024)\n",
        "    .map(preprocess_image, num_parallel_calls=AUTO)\n",
        ")\n",
        "\n",
        "train_ds_simple = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n",
        "\n",
        "test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test))\n",
        "\n",
        "train_ds_simple = (\n",
        "    train_ds_simple.map(preprocess_image, num_parallel_calls=AUTO)\n",
        "    .batch(BATCH_SIZE)\n",
        "    .prefetch(AUTO)\n",
        ")\n",
        "\n",
        "# Combine two shuffled datasets from the same training data.\n",
        "train_ds = tf.data.Dataset.zip((train_ds_one, train_ds_two))\n",
        "\n",
        "test_ds = (\n",
        "    test_ds.map(preprocess_image, num_parallel_calls=AUTO)\n",
        "    .batch(BATCH_SIZE)\n",
        "    .prefetch(AUTO)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "oShMoYkuxScm"
      },
      "outputs": [],
      "source": [
        "# the cutmix procedure\n",
        "def sample_beta_distribution(size, concentration_0=0.2, concentration_1=0.2):\n",
        "    gamma_1_sample = tf.random.gamma(shape=[size], alpha=concentration_1)\n",
        "    gamma_2_sample = tf.random.gamma(shape=[size], alpha=concentration_0)\n",
        "    return gamma_1_sample / (gamma_1_sample + gamma_2_sample)\n",
        "\n",
        "\n",
        "@tf.function\n",
        "def get_box(lambda_value):\n",
        "    cut_rat = tf.math.sqrt(1.0 - lambda_value)\n",
        "\n",
        "    cut_w = IMG_SIZE * cut_rat  # rw\n",
        "    cut_w = tf.cast(cut_w, tf.int32)\n",
        "\n",
        "    cut_h = IMG_SIZE * cut_rat  # rh\n",
        "    cut_h = tf.cast(cut_h, tf.int32)\n",
        "\n",
        "    cut_x = tf.random.uniform((1,), minval=0, maxval=IMG_SIZE, dtype=tf.int32)  # rx\n",
        "    cut_y = tf.random.uniform((1,), minval=0, maxval=IMG_SIZE, dtype=tf.int32)  # ry\n",
        "\n",
        "    boundaryx1 = tf.clip_by_value(cut_x[0] - cut_w // 2, 0, IMG_SIZE)\n",
        "    boundaryy1 = tf.clip_by_value(cut_y[0] - cut_h // 2, 0, IMG_SIZE)\n",
        "    bbx2 = tf.clip_by_value(cut_x[0] + cut_w // 2, 0, IMG_SIZE)\n",
        "    bby2 = tf.clip_by_value(cut_y[0] + cut_h // 2, 0, IMG_SIZE)\n",
        "\n",
        "    target_h = bby2 - boundaryy1\n",
        "    if target_h == 0:\n",
        "        target_h += 1\n",
        "\n",
        "    target_w = bbx2 - boundaryx1\n",
        "    if target_w == 0:\n",
        "        target_w += 1\n",
        "\n",
        "    return boundaryx1, boundaryy1, target_h, target_w\n",
        "\n",
        "\n",
        "@tf.function\n",
        "def cutmix(train_ds_one, train_ds_two):\n",
        "    (image1, label1), (image2, label2) = train_ds_one, train_ds_two\n",
        "\n",
        "    alpha = [0.25]\n",
        "    beta = [0.25]\n",
        "\n",
        "    # Get a sample from the Beta distribution\n",
        "    lambda_value = sample_beta_distribution(1, alpha, beta)\n",
        "\n",
        "    # Define Lambda\n",
        "    lambda_value = lambda_value[0][0]\n",
        "\n",
        "    # Get the bounding box offsets, heights and widths\n",
        "    boundaryx1, boundaryy1, target_h, target_w = get_box(lambda_value)\n",
        "\n",
        "    # Get a patch from the second image (`image2`)\n",
        "    crop2 = tf.image.crop_to_bounding_box(\n",
        "        image2, boundaryy1, boundaryx1, target_h, target_w\n",
        "    )\n",
        "    # Pad the `image2` patch (`crop2`) with the same offset\n",
        "    image2 = tf.image.pad_to_bounding_box(\n",
        "        crop2, boundaryy1, boundaryx1, IMG_SIZE, IMG_SIZE\n",
        "    )\n",
        "    # Get a patch from the first image (`image1`)\n",
        "    crop1 = tf.image.crop_to_bounding_box(\n",
        "        image1, boundaryy1, boundaryx1, target_h, target_w\n",
        "    )\n",
        "    # Pad the `image1` patch (`crop1`) with the same offset\n",
        "    img1 = tf.image.pad_to_bounding_box(\n",
        "        crop1, boundaryy1, boundaryx1, IMG_SIZE, IMG_SIZE\n",
        "    )\n",
        "\n",
        "    # Modify the first image by subtracting the patch from `image1`\n",
        "    # (before applying the `image2` patch)\n",
        "    image1 = image1 - img1\n",
        "    # Add the modified `image1` and `image2`  together to get the CutMix image\n",
        "    image = image1 + image2\n",
        "\n",
        "    # Adjust Lambda in accordance to the pixel ration\n",
        "    lambda_value = 1 - (target_w * target_h) / (IMG_SIZE * IMG_SIZE)\n",
        "    lambda_value = tf.cast(lambda_value, tf.float32)\n",
        "\n",
        "    # Combine the labels of both images\n",
        "    label = lambda_value * label1 + (1 - lambda_value) * label2\n",
        "    return image, label\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "q2bxL-4ux9I7",
        "outputId": "65205568-275b-43e8-b25c-e77c916c66e1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Create the new dataset using our `cutmix` utility\n",
        "train_ds_cmu = (\n",
        "    train_ds.shuffle(1024)\n",
        "    .map(cutmix, num_parallel_calls=AUTO)\n",
        "    .batch(BATCH_SIZE)\n",
        "    .prefetch(AUTO)\n",
        ")\n",
        "\n",
        "# Let's preview 9 samples from the dataset\n",
        "image_batch, label_batch = next(iter(train_ds_cmu))\n",
        "plt.figure(figsize=(10, 10))\n",
        "for i in range(9):\n",
        "    ax = plt.subplot(3, 3, i + 1)\n",
        "    plt.title(class_names[np.argmax(label_batch[i])])\n",
        "    plt.imshow(image_batch[i])\n",
        "    plt.axis(\"off\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from keras.preprocessing.image import save_img\n",
        "\n",
        "# image_batch, label_batch = next(iter(train_ds_simple))\n",
        "\n",
        "# save_img('./content/examples/' + class_names[np.argmax(label_batch[0])] + '.jpeg', image_batch[0])\n",
        "# save_img('./content/examples/' + class_names[np.argmax(label_batch[1])] + '.jpeg', image_batch[1])"
      ],
      "metadata": {
        "id": "ixftvsjpvmsC"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "0LTczrrayAfm"
      },
      "outputs": [],
      "source": [
        "# resnet 20 model\n",
        "def resnet_layer(\n",
        "    inputs,\n",
        "    num_filters=16,\n",
        "    kernel_size=3,\n",
        "    strides=1,\n",
        "    activation=\"relu\",\n",
        "    batch_normalization=True,\n",
        "    conv_first=True,\n",
        "):\n",
        "    conv = keras.layers.Conv2D(\n",
        "        num_filters,\n",
        "        kernel_size=kernel_size,\n",
        "        strides=strides,\n",
        "        padding=\"same\",\n",
        "        kernel_initializer=\"he_normal\",\n",
        "        kernel_regularizer=keras.regularizers.l2(1e-4),\n",
        "    )\n",
        "    x = inputs\n",
        "    if conv_first:\n",
        "        x = conv(x)\n",
        "        if batch_normalization:\n",
        "            x = keras.layers.BatchNormalization()(x)\n",
        "        if activation is not None:\n",
        "            x = keras.layers.Activation(activation)(x)\n",
        "    else:\n",
        "        if batch_normalization:\n",
        "            x = keras.layers.BatchNormalization()(x)\n",
        "        if activation is not None:\n",
        "            x = keras.layers.Activation(activation)(x)\n",
        "        x = conv(x)\n",
        "    return x\n",
        "\n",
        "\n",
        "def resnet_v20(input_shape, depth, num_classes=10):\n",
        "    if (depth - 2) % 6 != 0:\n",
        "        raise ValueError(\"depth should be 6n+2 (eg 20, 32, 44 in [a])\")\n",
        "    # Start model definition.\n",
        "    num_filters = 16\n",
        "    num_res_blocks = int((depth - 2) / 6)\n",
        "\n",
        "    inputs = keras.layers.Input(shape=input_shape)\n",
        "    x = resnet_layer(inputs=inputs)\n",
        "    # Instantiate the stack of residual units\n",
        "    for stack in range(3):\n",
        "        for res_block in range(num_res_blocks):\n",
        "            strides = 1\n",
        "            if stack > 0 and res_block == 0:  # first layer but not first stack\n",
        "                strides = 2  # downsample\n",
        "            y = resnet_layer(inputs=x, num_filters=num_filters, strides=strides)\n",
        "            y = resnet_layer(inputs=y, num_filters=num_filters, activation=None)\n",
        "            if stack > 0 and res_block == 0:  # first layer but not first stack\n",
        "                # linear projection residual shortcut connection to match\n",
        "                # changed dims\n",
        "                x = resnet_layer(\n",
        "                    inputs=x,\n",
        "                    num_filters=num_filters,\n",
        "                    kernel_size=1,\n",
        "                    strides=strides,\n",
        "                    activation=None,\n",
        "                    batch_normalization=False,\n",
        "                )\n",
        "            x = keras.layers.add([x, y])\n",
        "            x = keras.layers.Activation(\"relu\")(x)\n",
        "        num_filters *= 2\n",
        "\n",
        "    # Add classifier on top.\n",
        "    # v1 does not use BN after last shortcut connection-ReLU\n",
        "    x = keras.layers.AveragePooling2D(pool_size=8)(x)\n",
        "    y = keras.layers.Flatten()(x)\n",
        "    outputs = keras.layers.Dense(\n",
        "        num_classes, activation=\"softmax\", kernel_initializer=\"he_normal\"\n",
        "    )(y)\n",
        "\n",
        "    # Instantiate model.\n",
        "    model = keras.models.Model(inputs=inputs, outputs=outputs)\n",
        "    return model\n",
        "\n",
        "\n",
        "def training_model():\n",
        "    return resnet_v20((32, 32, 3), 20)\n",
        "\n",
        "\n",
        "initial_model = training_model()\n",
        "initial_model.save_weights(\"initial_weights.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zfYYY5Y4zXv-",
        "outputId": "5b5b53f9-8103-48b9-8699-a956863ec8e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1563/1563 [==============================] - 60s 28ms/step - loss: 1.9318 - accuracy: 0.4017 - val_loss: 1.4540 - val_accuracy: 0.5320\n",
            "Epoch 2/20\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 1.6748 - accuracy: 0.5243 - val_loss: 1.3759 - val_accuracy: 0.5746\n",
            "Epoch 3/20\n",
            "1563/1563 [==============================] - 32s 21ms/step - loss: 1.5634 - accuracy: 0.5811 - val_loss: 1.3182 - val_accuracy: 0.5903\n",
            "Epoch 4/20\n",
            "1563/1563 [==============================] - 32s 20ms/step - loss: 1.4940 - accuracy: 0.6131 - val_loss: 0.9959 - val_accuracy: 0.7151\n",
            "Epoch 5/20\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 1.4484 - accuracy: 0.6349 - val_loss: 1.0150 - val_accuracy: 0.6995\n",
            "Epoch 6/20\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 1.4211 - accuracy: 0.6447 - val_loss: 1.0619 - val_accuracy: 0.6893\n",
            "Epoch 7/20\n",
            "1563/1563 [==============================] - 30s 19ms/step - loss: 1.3943 - accuracy: 0.6619 - val_loss: 1.0144 - val_accuracy: 0.7099\n",
            "Epoch 8/20\n",
            "1563/1563 [==============================] - 32s 20ms/step - loss: 1.3715 - accuracy: 0.6691 - val_loss: 0.9230 - val_accuracy: 0.7336\n",
            "Epoch 9/20\n",
            "1563/1563 [==============================] - 32s 20ms/step - loss: 1.3581 - accuracy: 0.6778 - val_loss: 0.8706 - val_accuracy: 0.7615\n",
            "Epoch 10/20\n",
            "1563/1563 [==============================] - 32s 21ms/step - loss: 1.3436 - accuracy: 0.6856 - val_loss: 0.8467 - val_accuracy: 0.7729\n",
            "Epoch 11/20\n",
            "1563/1563 [==============================] - 32s 20ms/step - loss: 1.3255 - accuracy: 0.6932 - val_loss: 0.9086 - val_accuracy: 0.7417\n",
            "Epoch 12/20\n",
            "1563/1563 [==============================] - 32s 20ms/step - loss: 1.3128 - accuracy: 0.6991 - val_loss: 0.7617 - val_accuracy: 0.8041\n",
            "Epoch 13/20\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 1.3102 - accuracy: 0.7024 - val_loss: 0.8892 - val_accuracy: 0.7581\n",
            "Epoch 14/20\n",
            "1563/1563 [==============================] - 32s 21ms/step - loss: 1.3025 - accuracy: 0.7052 - val_loss: 0.8239 - val_accuracy: 0.7781\n",
            "Epoch 15/20\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 1.2874 - accuracy: 0.7129 - val_loss: 0.9021 - val_accuracy: 0.7489\n",
            "Epoch 16/20\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 1.2795 - accuracy: 0.7171 - val_loss: 0.8257 - val_accuracy: 0.7876\n",
            "Epoch 17/20\n",
            "1563/1563 [==============================] - 32s 20ms/step - loss: 1.2779 - accuracy: 0.7171 - val_loss: 0.8330 - val_accuracy: 0.7820\n",
            "Epoch 18/20\n",
            "1563/1563 [==============================] - 32s 20ms/step - loss: 1.2699 - accuracy: 0.7209 - val_loss: 0.7723 - val_accuracy: 0.8005\n",
            "Epoch 19/20\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 1.2708 - accuracy: 0.7220 - val_loss: 0.8152 - val_accuracy: 0.7883\n",
            "Epoch 20/20\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 1.2588 - accuracy: 0.7268 - val_loss: 0.7931 - val_accuracy: 0.7961\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 0.7931 - accuracy: 0.7961\n",
            "Test accuracy: 79.61%\n"
          ]
        }
      ],
      "source": [
        "# train with cutmix data\n",
        "model = training_model()\n",
        "model.load_weights(\"initial_weights.h5\")\n",
        "\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "history = model.fit(train_ds_cmu, validation_data=test_ds, epochs=20, callbacks = [tensorboard_callback])\n",
        "\n",
        "test_loss, test_accuracy = model.evaluate(test_ds)\n",
        "print(\"Test accuracy: {:.2f}%\".format(test_accuracy * 100))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "igGj3xJlRZ0c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfb1c2b3-3b79-45a0-f1b6-241cccae2efd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1563/1563 [==============================] - 25s 15ms/step - loss: 1.4817 - accuracy: 0.5200 - val_loss: 1.4026 - val_accuracy: 0.5590\n",
            "Epoch 2/20\n",
            "1563/1563 [==============================] - 21s 14ms/step - loss: 1.0719 - accuracy: 0.6759 - val_loss: 1.1147 - val_accuracy: 0.6661\n",
            "Epoch 3/20\n",
            "1563/1563 [==============================] - 22s 14ms/step - loss: 0.9010 - accuracy: 0.7420 - val_loss: 1.0818 - val_accuracy: 0.6836\n",
            "Epoch 4/20\n",
            "1563/1563 [==============================] - 21s 14ms/step - loss: 0.7935 - accuracy: 0.7827 - val_loss: 1.0493 - val_accuracy: 0.7062\n",
            "Epoch 5/20\n",
            "1563/1563 [==============================] - 21s 14ms/step - loss: 0.7118 - accuracy: 0.8155 - val_loss: 1.0406 - val_accuracy: 0.7246\n",
            "Epoch 6/20\n",
            "1563/1563 [==============================] - 21s 14ms/step - loss: 0.6528 - accuracy: 0.8398 - val_loss: 0.9636 - val_accuracy: 0.7509\n",
            "Epoch 7/20\n",
            "1563/1563 [==============================] - 23s 15ms/step - loss: 0.6050 - accuracy: 0.8597 - val_loss: 0.9928 - val_accuracy: 0.7563\n",
            "Epoch 8/20\n",
            "1563/1563 [==============================] - 22s 14ms/step - loss: 0.5681 - accuracy: 0.8781 - val_loss: 1.2730 - val_accuracy: 0.7090\n",
            "Epoch 9/20\n",
            "1563/1563 [==============================] - 22s 14ms/step - loss: 0.5461 - accuracy: 0.8876 - val_loss: 1.5617 - val_accuracy: 0.6897\n",
            "Epoch 10/20\n",
            "1563/1563 [==============================] - 23s 14ms/step - loss: 0.5202 - accuracy: 0.9003 - val_loss: 1.2796 - val_accuracy: 0.7297\n",
            "Epoch 11/20\n",
            "1563/1563 [==============================] - 23s 14ms/step - loss: 0.5046 - accuracy: 0.9098 - val_loss: 1.1576 - val_accuracy: 0.7529\n",
            "Epoch 12/20\n",
            "1563/1563 [==============================] - 22s 14ms/step - loss: 0.4990 - accuracy: 0.9126 - val_loss: 1.7112 - val_accuracy: 0.6805\n",
            "Epoch 13/20\n",
            "1563/1563 [==============================] - 21s 14ms/step - loss: 0.4843 - accuracy: 0.9204 - val_loss: 1.2382 - val_accuracy: 0.7544\n",
            "Epoch 14/20\n",
            "1563/1563 [==============================] - 21s 14ms/step - loss: 0.4852 - accuracy: 0.9220 - val_loss: 1.4373 - val_accuracy: 0.7263\n",
            "Epoch 15/20\n",
            "1563/1563 [==============================] - 21s 14ms/step - loss: 0.4730 - accuracy: 0.9308 - val_loss: 1.5044 - val_accuracy: 0.7134\n",
            "Epoch 16/20\n",
            "1563/1563 [==============================] - 22s 14ms/step - loss: 0.4733 - accuracy: 0.9315 - val_loss: 1.7349 - val_accuracy: 0.6947\n",
            "Epoch 17/20\n",
            "1563/1563 [==============================] - 23s 14ms/step - loss: 0.4677 - accuracy: 0.9349 - val_loss: 1.8251 - val_accuracy: 0.6973\n",
            "Epoch 18/20\n",
            "1563/1563 [==============================] - 22s 14ms/step - loss: 0.4690 - accuracy: 0.9365 - val_loss: 2.0015 - val_accuracy: 0.6808\n",
            "Epoch 19/20\n",
            "1563/1563 [==============================] - 22s 14ms/step - loss: 0.4661 - accuracy: 0.9381 - val_loss: 1.7348 - val_accuracy: 0.7060\n",
            "Epoch 20/20\n",
            "1563/1563 [==============================] - 21s 14ms/step - loss: 0.4562 - accuracy: 0.9423 - val_loss: 1.4430 - val_accuracy: 0.7562\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 1.4430 - accuracy: 0.7562\n",
            "Test accuracy: 75.62%\n"
          ]
        }
      ],
      "source": [
        "# train with normal data\n",
        "model2 = training_model()\n",
        "model2.load_weights(\"initial_weights.h5\")\n",
        "model2.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "model2.fit(train_ds_simple, validation_data=test_ds, epochs=20)\n",
        "\n",
        "test_loss, test_accuracy = model2.evaluate(test_ds)\n",
        "print(\"Test accuracy: {:.2f}%\".format(test_accuracy * 100))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "tg6H-_nKduvW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387,
          "referenced_widgets": [
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        "outputId": "f3344f27-1bbf-4852-f2ea-a59c6be960be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "# login into Huggingface account\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "RYGTa_Dle1k2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477,
          "referenced_widgets": [
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        "outputId": "28e03357-af6a-4521-dc48-4585e8ee1d91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co./harsha163/CutMix_data_augmentation_for_image_classification into local empty directory.\n"
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            "INFO:tensorflow:Assets written to: harsha163/CutMix_data_augmentation_for_image_classification/assets\n"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file variables/variables.index:  45%|####4     | 3.34k/7.44k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f7a52f88049d44db9707136bf2bc89a4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file variables/variables.data-00000-of-00001:   0%|          | 3.34k/1.10M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2c850bd4067469ebeb0285af1f8f9bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "remote: Enforcing permissions...        \n",
            "remote: Allowed refs: all        \n",
            "To https://huggingface.co./harsha163/CutMix_data_augmentation_for_image_classification\n",
            "   235aba0..129f1e3  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co./harsha163/CutMix_data_augmentation_for_image_classification/commit/129f1e3bf84df96d60dfc5364a142f028eb2ecfe'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "# Push the Model to the Hugging face hub\n",
        "\n",
        "from huggingface_hub import push_to_hub_keras\n",
        "\n",
        "push_to_hub_keras(model,\n",
        "    \"harsha163/CutMix_data_augmentation_for_image_classification\",\n",
        "    log_dir = \"./logs/fit\",\n",
        "    tags = [\"data-augmentation\", \"image-classification\"]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VujtbTMpQ7S0"
      },
      "source": [
        "**Interface**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ka8PxCfwQ-Nm"
      },
      "outputs": [],
      "source": [
        "import numpy as np  \n",
        "import tensorflow as tf\n",
        "import gradio as gr\n",
        "from huggingface_hub import from_pretrained_keras\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "X94puQECRS1L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31d3b789-883b-4a06-fe95-c973fa60683f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "config.json not found in HuggingFace Hub\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        }
      ],
      "source": [
        "model = from_pretrained_keras(\"harsha163/CutMix_data_augmentation_for_image_classification\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "bEG9INtEWWrh"
      },
      "outputs": [],
      "source": [
        "# functions for inference                   \n",
        "IMG_SIZE = 32\n",
        "\n",
        "class_names = [\n",
        "    \"Airplane\",\n",
        "    \"Automobile\",\n",
        "    \"Bird\",\n",
        "    \"Cat\",\n",
        "    \"Deer\",\n",
        "    \"Dog\",\n",
        "    \"Frog\",\n",
        "    \"Horse\",\n",
        "    \"Ship\",\n",
        "    \"Truck\",\n",
        "]\n",
        "\n",
        "# resize the image and it to a float between 0,1\n",
        "def preprocess_image(image, label):\n",
        "    image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))\n",
        "    image = tf.image.convert_image_dtype(image, tf.float32) / 255.0\n",
        "    return image, label\n",
        "\n",
        "\n",
        "def read_image(image):\n",
        "    image = tf.convert_to_tensor(image)\n",
        "    image.set_shape([None, None, 3])\n",
        "    print('$$$$$$$$$$$$$$$$$$$$$ in read image $$$$$$$$$$$$$$$$$$$$$$')\n",
        "    print(image.shape)\n",
        "    plt.imshow(image)\n",
        "    plt.show()\n",
        "    # image = tf.image.resize(images=image, size=[IMG_SIZE, IMG_SIZE])\n",
        "    # image = image / 127.5 - 1\n",
        "    image, _ = preprocess_image(image, 1) #  1 here is a temporary label\n",
        "    return image\n",
        "\n",
        "def infer(input_image):\n",
        "    print('#$$$$$$$$$$$$$$$$$$$$$$$$$ IN INFER $$$$$$$$$$$$$$$$$$$$$$$')\n",
        "    image_tensor = read_image(input_image)\n",
        "    print(image_tensor.shape)\n",
        "    predictions = model.predict(np.expand_dims((image_tensor), axis=0))\n",
        "    predictions = np.squeeze(predictions)\n",
        "    predictions = np.argmax(predictions) # , axis=2\n",
        "    predicted_label = class_names[predictions.item()]\n",
        "    return str(predicted_label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "NRYNjjSsd8_C"
      },
      "outputs": [],
      "source": [
        "# %%capture\n",
        "# from keras.preprocessing.image import load_img\n",
        "# from PIL import Image\n",
        "# from torchvision.transforms import ToTensor\n",
        "\n",
        "# img_path=\"./content/examples/Truck.jpeg\"\n",
        "# image = Image.open(img_path)\n",
        "# # image = tf.convert_to_tensor()(image).unsqueeze(0) # unsqueeze to add artificial first dimension\n",
        "# # image = Variable(image)\n",
        "# lbl = infer(image)\n",
        "# print(type(gr.inputs.Image()))\n",
        "# print(type(input))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "qDraUCCJXOPc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbed3c5b-e369-4076-ef5b-c82c834dea19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.7/dist-packages/gradio/deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
            "  warnings.warn(value)\n"
          ]
        }
      ],
      "source": [
        "# get the inputs\n",
        "input = gr.inputs.Image(shape=(IMG_SIZE, IMG_SIZE))\n",
        "# the app outputs two segmented images\n",
        "output = [gr.outputs.Label()]\n",
        "# it's good practice to pass examples, description and a title to guide users\n",
        "examples = [[\"./content/examples/Frog.jpeg\"], [\"./content/examples/Truck.jpeg\"]] \n",
        "title = \"Image classification\"\n",
        "description = \"Upload an image or select from examples to classify it\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# gr.Interface(infer, input, output, examples=examples, allow_flagging=False, analytics_enabled=False,\n",
        "# title=title, description=description).launch(enable_queue=True, debug=True)\n",
        "gr.Interface(infer, input, output, examples=examples, allow_flagging=False, analytics_enabled=False, title=title, description=description).launch(enable_queue=True, debug=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YcQDyPHbPVN0",
        "outputId": "f4bf92fa-3f3f-4112-9f91-db52d72ddd16"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gradio/interface.py:360: UserWarning: The `allow_flagging` parameter in `Interface` nowtakes a string value ('auto', 'manual', or 'never'), not a boolean. Setting parameter to: 'never'.\n",
            "  \"The `allow_flagging` parameter in `Interface` now\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://16524.gradio.app\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co./spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://16524.gradio.app\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#$$$$$$$$$$$$$$$$$$$$$$$$$ IN INFER $$$$$$$$$$$$$$$$$$$$$$$\n",
            "$$$$$$$$$$$$$$$$$$$$$ in read image $$$$$$$$$$$$$$$$$$$$$$\n",
            "(32, 32, 3)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(32, 32, 3)\n",
            "#$$$$$$$$$$$$$$$$$$$$$$$$$ IN INFER $$$$$$$$$$$$$$$$$$$$$$$\n",
            "$$$$$$$$$$$$$$$$$$$$$ in read image $$$$$$$$$$$$$$$$$$$$$$\n",
            "(32, 32, 3)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(32, 32, 3)\n",
            "Keyboard interruption in main thread... closing server.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<gradio.routes.App at 0x7f617bfccf90>,\n",
              " 'http://127.0.0.1:7861/',\n",
              " 'https://16524.gradio.app')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "cutMix.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "4ed9d65177604e7580fccb1f04dacdf1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
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