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        "<a href=\"https://colab.research.google.com/github/ee17b031-iittp/Projected-Gradient-Descent-with-CIFAR10/blob/main/pgd_cifar10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from imageio import imwrite\n",
        "import cv2"
      ],
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    },
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        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
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        "(ds_train,ds_validation, ds_test),ds_info = tfds.load(\"cifar10\",\n",
        "                                       split=['train[:99%]','train[99%:]','test'],\n",
        "                                       shuffle_files=True,\n",
        "                                       as_supervised = True,\n",
        "                                       with_info = True)"
      ],
      "execution_count": 3,
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            "\u001b[1mDownloading and preparing dataset cifar10/3.0.2 (download: 162.17 MiB, generated: 132.40 MiB, total: 294.58 MiB) to /root/tensorflow_datasets/cifar10/3.0.2...\u001b[0m\n"
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            "\rShuffling and writing examples to /root/tensorflow_datasets/cifar10/3.0.2.incompleteCQ08QA/cifar10-train.tfrecord\n"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r\u001b[1mDataset cifar10 downloaded and prepared to /root/tensorflow_datasets/cifar10/3.0.2. Subsequent calls will reuse this data.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILTX8j4UR4cZ"
      },
      "source": [
        "img_height=128\n",
        "img_width=128\n",
        "batch_size=16"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtIZ_YbzR5Aa"
      },
      "source": [
        "def preprocess(img,label):\n",
        "  imag=tf.image.resize(img,size=[img_height,img_width])\n",
        "  return tf.divide(tf.cast(imag,tf.float32), tf.constant(255.0)),tf.one_hot(label,depth=10)\n",
        "\n",
        "ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)\n",
        "ds_train = ds_train.batch(batch_size)\n",
        "ds_train = ds_train.map(preprocess,\n",
        "                        num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "ds_train = ds_train.cache('./traindata')\n",
        "ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mmQyj8aSCZY"
      },
      "source": [
        "ds_validation = ds_validation.batch(batch_size)\n",
        "ds_validation = ds_validation.map(\n",
        "    preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "ds_validation = ds_validation.cache('./valdata')\n",
        "ds_validation = ds_validation.prefetch(tf.data.experimental.AUTOTUNE)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9GczjVfSI4S"
      },
      "source": [
        "ds_test = ds_test.batch(batch_size)\n",
        "ds_test = ds_test.map(\n",
        "    preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "ds_test = ds_test.cache('./testdata')\n",
        "ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11nrb7cYcBNk",
        "outputId": "008663c6-4888-4e89-e293-44b8faaf590e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "inputs = tf.keras.layers.Input(shape=(img_height, img_width, 3))\n",
        "res50 = tf.keras.applications.ResNet50(\n",
        "    include_top=True,\n",
        "    weights=\"imagenet\",\n",
        "    input_tensor=inputs)\n",
        "x = res50.layers[-20].output\n",
        "x = tf.keras.layers.Flatten()(x)\n",
        "x = tf.keras.layers.Dense(128)(x)\n",
        "x = tf.keras.layers.BatchNormalization()(x)\n",
        "output_layer = tf.keras.layers.Dense(10, activation='softmax')(x)\n",
        "model = tf.keras.Model(inputs, output_layer)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n",
            "102973440/102967424 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNMA5_HyVpkn"
      },
      "source": [
        "for layer in res50.layers:\n",
        "    if isinstance(layer,tf.keras.layers.BatchNormalization):\n",
        "      layer.trainable=True\n",
        "    else:\n",
        "      layer.trainable=False\n",
        "model.compile(\n",
        "        optimizer=tf.keras.optimizers.Adam(),\n",
        "        loss='categorical_crossentropy',\n",
        "        metrics=['accuracy'])"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYtKTJnUXsKZ",
        "outputId": "17b64542-90df-4939-851b-460b474c01fb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 128, 128, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, 134, 134, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, 64, 64, 64)   9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_bn (BatchNormalization)   (None, 64, 64, 64)   256         conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1_relu (Activation)         (None, 64, 64, 64)   0           conv1_bn[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, 66, 66, 64)   0           conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, 32, 32, 64)   0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, 32, 32, 64)   4160        pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, 32, 32, 64)   256         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, 32, 32, 64)   0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, 32, 32, 64)   36928       conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, 32, 32, 64)   256         conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, 32, 32, 64)   0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, 32, 32, 256)  16640       pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, 32, 32, 256)  16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_bn (BatchNormali (None, 32, 32, 256)  1024        conv2_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_bn (BatchNormali (None, 32, 32, 256)  1024        conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_add (Add)          (None, 32, 32, 256)  0           conv2_block1_0_bn[0][0]          \n",
            "                                                                 conv2_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Activation)   (None, 32, 32, 256)  0           conv2_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, 32, 32, 64)   16448       conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, 32, 32, 64)   256         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, 32, 32, 64)   0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, 32, 32, 64)   36928       conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, 32, 32, 64)   256         conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, 32, 32, 64)   0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, 32, 32, 256)  16640       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_bn (BatchNormali (None, 32, 32, 256)  1024        conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_add (Add)          (None, 32, 32, 256)  0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Activation)   (None, 32, 32, 256)  0           conv2_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, 32, 32, 64)   16448       conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, 32, 32, 64)   256         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, 32, 32, 64)   0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, 32, 32, 64)   36928       conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, 32, 32, 64)   256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, 32, 32, 64)   0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, 32, 32, 256)  16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_bn (BatchNormali (None, 32, 32, 256)  1024        conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_add (Add)          (None, 32, 32, 256)  0           conv2_block2_out[0][0]           \n",
            "                                                                 conv2_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Activation)   (None, 32, 32, 256)  0           conv2_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, 16, 16, 128)  32896       conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, 16, 16, 128)  512         conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, 16, 16, 128)  0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, 16, 16, 128)  147584      conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, 16, 16, 128)  512         conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, 16, 16, 128)  0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, 16, 16, 512)  131584      conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, 16, 16, 512)  66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_bn (BatchNormali (None, 16, 16, 512)  2048        conv3_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_bn (BatchNormali (None, 16, 16, 512)  2048        conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_add (Add)          (None, 16, 16, 512)  0           conv3_block1_0_bn[0][0]          \n",
            "                                                                 conv3_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Activation)   (None, 16, 16, 512)  0           conv3_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, 16, 16, 128)  65664       conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, 16, 16, 128)  512         conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, 16, 16, 128)  0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, 16, 16, 128)  147584      conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, 16, 16, 128)  512         conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, 16, 16, 128)  0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, 16, 16, 512)  66048       conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_bn (BatchNormali (None, 16, 16, 512)  2048        conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_add (Add)          (None, 16, 16, 512)  0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Activation)   (None, 16, 16, 512)  0           conv3_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, 16, 16, 128)  65664       conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, 16, 16, 128)  512         conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, 16, 16, 128)  0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, 16, 16, 128)  147584      conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, 16, 16, 128)  512         conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, 16, 16, 128)  0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, 16, 16, 512)  66048       conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_bn (BatchNormali (None, 16, 16, 512)  2048        conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_add (Add)          (None, 16, 16, 512)  0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Activation)   (None, 16, 16, 512)  0           conv3_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, 16, 16, 128)  65664       conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, 16, 16, 128)  512         conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, 16, 16, 128)  0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, 16, 16, 128)  147584      conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, 16, 16, 128)  512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, 16, 16, 128)  0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, 16, 16, 512)  66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_bn (BatchNormali (None, 16, 16, 512)  2048        conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_add (Add)          (None, 16, 16, 512)  0           conv3_block3_out[0][0]           \n",
            "                                                                 conv3_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Activation)   (None, 16, 16, 512)  0           conv3_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, 8, 8, 256)    131328      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, 8, 8, 256)    1024        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, 8, 8, 256)    0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, 8, 8, 256)    590080      conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, 8, 8, 256)    1024        conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, 8, 8, 256)    0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, 8, 8, 1024)   525312      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, 8, 8, 1024)   263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_bn (BatchNormali (None, 8, 8, 1024)   4096        conv4_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_bn (BatchNormali (None, 8, 8, 1024)   4096        conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_add (Add)          (None, 8, 8, 1024)   0           conv4_block1_0_bn[0][0]          \n",
            "                                                                 conv4_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Activation)   (None, 8, 8, 1024)   0           conv4_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, 8, 8, 256)    262400      conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, 8, 8, 256)    1024        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, 8, 8, 256)    0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, 8, 8, 256)    590080      conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, 8, 8, 256)    1024        conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, 8, 8, 256)    0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, 8, 8, 1024)   263168      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_bn (BatchNormali (None, 8, 8, 1024)   4096        conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_add (Add)          (None, 8, 8, 1024)   0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Activation)   (None, 8, 8, 1024)   0           conv4_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, 8, 8, 256)    262400      conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, 8, 8, 256)    1024        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, 8, 8, 256)    0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, 8, 8, 256)    590080      conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, 8, 8, 256)    1024        conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, 8, 8, 256)    0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, 8, 8, 1024)   263168      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_bn (BatchNormali (None, 8, 8, 1024)   4096        conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_add (Add)          (None, 8, 8, 1024)   0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Activation)   (None, 8, 8, 1024)   0           conv4_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, 8, 8, 256)    262400      conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, 8, 8, 256)    1024        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, 8, 8, 256)    0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, 8, 8, 256)    590080      conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, 8, 8, 256)    1024        conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, 8, 8, 256)    0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, 8, 8, 1024)   263168      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_bn (BatchNormali (None, 8, 8, 1024)   4096        conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_add (Add)          (None, 8, 8, 1024)   0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Activation)   (None, 8, 8, 1024)   0           conv4_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, 8, 8, 256)    262400      conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, 8, 8, 256)    1024        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, 8, 8, 256)    0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, 8, 8, 256)    590080      conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, 8, 8, 256)    1024        conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, 8, 8, 256)    0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, 8, 8, 1024)   263168      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_bn (BatchNormali (None, 8, 8, 1024)   4096        conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_add (Add)          (None, 8, 8, 1024)   0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Activation)   (None, 8, 8, 1024)   0           conv4_block5_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, 8, 8, 256)    262400      conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, 8, 8, 256)    1024        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, 8, 8, 256)    0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, 8, 8, 256)    590080      conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, 8, 8, 256)    1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, 8, 8, 256)    0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, 8, 8, 1024)   263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_bn (BatchNormali (None, 8, 8, 1024)   4096        conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_add (Add)          (None, 8, 8, 1024)   0           conv4_block5_out[0][0]           \n",
            "                                                                 conv4_block6_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Activation)   (None, 8, 8, 1024)   0           conv4_block6_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, 4, 4, 512)    524800      conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, 4, 4, 512)    2048        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, 4, 4, 512)    0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, 4, 4, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, 4, 4, 512)    2048        conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, 4, 4, 512)    0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, 4, 4, 2048)   2099200     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, 4, 4, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_bn (BatchNormali (None, 4, 4, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_bn (BatchNormali (None, 4, 4, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_add (Add)          (None, 4, 4, 2048)   0           conv5_block1_0_bn[0][0]          \n",
            "                                                                 conv5_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Activation)   (None, 4, 4, 2048)   0           conv5_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, 4, 4, 512)    1049088     conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, 4, 4, 512)    2048        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, 4, 4, 512)    0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 8192)         0           conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 128)          1048704     flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 128)          512         dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 10)           1290        batch_normalization[0][0]        \n",
            "==================================================================================================\n",
            "Total params: 16,745,738\n",
            "Trainable params: 1,092,106\n",
            "Non-trainable params: 15,653,632\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpM1paTgUCjE",
        "outputId": "f4194e2d-5cb5-4e8c-e38c-a061face3bed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "history = model.fit(ds_train, validation_data=ds_validation,verbose=1, epochs=20)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "3094/3094 [==============================] - 221s 71ms/step - loss: 0.5503 - accuracy: 0.8154 - val_loss: 0.2846 - val_accuracy: 0.8980\n",
            "Epoch 2/20\n",
            "3094/3094 [==============================] - 271s 88ms/step - loss: 0.2638 - accuracy: 0.9112 - val_loss: 0.2992 - val_accuracy: 0.8980\n",
            "Epoch 3/20\n",
            "3094/3094 [==============================] - 181s 58ms/step - loss: 0.1370 - accuracy: 0.9544 - val_loss: 0.3941 - val_accuracy: 0.8940\n",
            "Epoch 4/20\n",
            "3094/3094 [==============================] - 181s 58ms/step - loss: 0.0839 - accuracy: 0.9706 - val_loss: 0.4492 - val_accuracy: 0.8980\n",
            "Epoch 5/20\n",
            "3094/3094 [==============================] - 180s 58ms/step - loss: 0.0681 - accuracy: 0.9749 - val_loss: 0.4680 - val_accuracy: 0.9000\n",
            "Epoch 6/20\n",
            "3094/3094 [==============================] - 184s 59ms/step - loss: 0.0512 - accuracy: 0.9822 - val_loss: 0.5516 - val_accuracy: 0.9060\n",
            "Epoch 7/20\n",
            "3094/3094 [==============================] - 186s 60ms/step - loss: 0.0440 - accuracy: 0.9841 - val_loss: 0.5548 - val_accuracy: 0.9060\n",
            "Epoch 8/20\n",
            "3094/3094 [==============================] - 187s 60ms/step - loss: 0.0412 - accuracy: 0.9850 - val_loss: 0.5545 - val_accuracy: 0.9020\n",
            "Epoch 9/20\n",
            "3094/3094 [==============================] - 188s 61ms/step - loss: 0.0343 - accuracy: 0.9875 - val_loss: 0.5560 - val_accuracy: 0.9100\n",
            "Epoch 10/20\n",
            "3094/3094 [==============================] - 180s 58ms/step - loss: 0.0332 - accuracy: 0.9880 - val_loss: 0.6013 - val_accuracy: 0.8940\n",
            "Epoch 11/20\n",
            "3094/3094 [==============================] - 197s 64ms/step - loss: 0.0290 - accuracy: 0.9896 - val_loss: 0.6199 - val_accuracy: 0.9060\n",
            "Epoch 12/20\n",
            "3094/3094 [==============================] - 188s 61ms/step - loss: 0.0297 - accuracy: 0.9895 - val_loss: 0.5717 - val_accuracy: 0.9060\n",
            "Epoch 13/20\n",
            "3094/3094 [==============================] - 187s 60ms/step - loss: 0.0272 - accuracy: 0.9902 - val_loss: 0.5655 - val_accuracy: 0.9060\n",
            "Epoch 14/20\n",
            "3094/3094 [==============================] - 180s 58ms/step - loss: 0.0232 - accuracy: 0.9918 - val_loss: 0.6484 - val_accuracy: 0.8860\n",
            "Epoch 15/20\n",
            "3094/3094 [==============================] - 203s 66ms/step - loss: 0.0241 - accuracy: 0.9914 - val_loss: 0.6103 - val_accuracy: 0.9100\n",
            "Epoch 16/20\n",
            "3094/3094 [==============================] - 188s 61ms/step - loss: 0.0217 - accuracy: 0.9921 - val_loss: 0.6660 - val_accuracy: 0.8880\n",
            "Epoch 17/20\n",
            "3094/3094 [==============================] - 183s 59ms/step - loss: 0.0218 - accuracy: 0.9920 - val_loss: 0.6131 - val_accuracy: 0.9020\n",
            "Epoch 18/20\n",
            "3094/3094 [==============================] - 204s 66ms/step - loss: 0.0211 - accuracy: 0.9922 - val_loss: 0.5360 - val_accuracy: 0.9080\n",
            "Epoch 19/20\n",
            "3094/3094 [==============================] - 195s 63ms/step - loss: 0.0210 - accuracy: 0.9923 - val_loss: 0.5527 - val_accuracy: 0.9120\n",
            "Epoch 20/20\n",
            "3094/3094 [==============================] - 180s 58ms/step - loss: 0.0209 - accuracy: 0.9921 - val_loss: 0.5731 - val_accuracy: 0.9140\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHPlWDBfJWuJ"
      },
      "source": [
        "iterations = 7\n",
        "alpha = 2\n",
        "epsilon = 8/255"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCGCtS-mVoLQ"
      },
      "source": [
        "#pgd_generation\n",
        "image_count = 0\n",
        "flag = 0\n",
        "orig_label = []\n",
        "for x_train, labels in ds_train:\n",
        "  gen_img = tf.identity(x_train)\n",
        "  gen_img = gen_img + tf.random.uniform(gen_img.get_shape().as_list(), minval=-epsilon, maxval=epsilon, dtype=tf.dtypes.float32)\n",
        "  x_temp = x_train.numpy()\n",
        "  for iters in range(iterations):\n",
        "    imgv = tf.Variable(gen_img)\n",
        "    with tf.GradientTape() as tape:\n",
        "      tape.watch(imgv)\n",
        "      predictions = model(imgv)\n",
        "      loss = tf.keras.losses.CategoricalCrossentropy()(labels, predictions)\n",
        "      grads = tape.gradient(loss,imgv)\n",
        "    signed_grads = tf.sign(grads)\n",
        "    gen_img = gen_img + (alpha*signed_grads)\n",
        "    gen_img = tf.clip_by_value(gen_img, x_train-epsilon, x_train+epsilon)\n",
        "  for i in range(16):\n",
        "    image_count += 1\n",
        "    if image_count>200:\n",
        "      flag = 1\n",
        "      break\n",
        "    orig_label.append(labels[i])\n",
        "    gen_image = gen_img.numpy()[i]*255\n",
        "    orig_image = x_train.numpy()[i]*255\n",
        "    imwrite('/content/drive/My Drive/pgd_cifar10/'+str(image_count)+'_gen_img.png',np.clip(gen_image, 0, 255).astype('uint8'))\n",
        "    imwrite('/content/drive/My Drive/pgd_cifar10/'+str(image_count)+'_orig_img.png',np.clip(orig_image, 0, 255).astype('uint8'))\n",
        "  if flag==1:\n",
        "    break "
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VimSl3pTJhvW"
      },
      "source": [
        "generated_images = []\n",
        "for i in range(1, 201):\n",
        "  x = cv2.imread('/content/drive/My Drive/pgd_cifar10/'+str(i)+'_gen_img.png')\n",
        "  generated_images.append(x)\n",
        "generated_images = tf.convert_to_tensor(generated_images)\n",
        "orig_label = tf.convert_to_tensor(orig_label)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ib-Gz4h5GVFo"
      },
      "source": [
        "original_images = []\n",
        "for i in range(1, 201):\n",
        "  x = cv2.imread('/content/drive/My Drive/pgd_cifar10/'+str(i)+'_orig_img.png')\n",
        "  original_images.append(x)\n",
        "original_images = tf.convert_to_tensor(generated_images)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxAuLm9f1Pkp",
        "outputId": "5da972ad-700d-464d-9da6-1ec15f992191",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.evaluate(generated_images,orig_label, verbose = 1)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7/7 [==============================] - 0s 49ms/step - loss: 6225.9824 - accuracy: 0.0900\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6225.982421875, 0.09000000357627869]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmM2JyCp7eKz"
      },
      "source": [
        "def calculate_fid(images1, images2):\n",
        "  incp_model = InceptionV3(include_top=False, pooling='avg', input_shape=(299,299,3))\n",
        "  # calculate activations\n",
        "  act1 = incp_model.predict(images1)\n",
        "  act2 = incp_model.predict(images2)\n",
        "  # calculate mean and covariance statistics\n",
        "  mu1, sigma1 = act1.mean(axis=0), np.cov(act1, rowvar=False)\n",
        "  mu2, sigma2 = act2.mean(axis=0), np.cov(act2, rowvar=False)\n",
        "  # calculate sum squared difference between means\n",
        "  ssdiff = np.sum((mu1 - mu2)**2.0)\n",
        "  # calculate sqrt of product between cov\n",
        "  covmean = sqrtm(sigma1.dot(sigma2))\n",
        "  # check and correct imaginary numbers from sqrt\n",
        "  if np.iscomplexobj(covmean):\n",
        "    covmean = covmean.real\n",
        "  # calculate score\n",
        "  fid = ssdiff + np.trace(sigma1 + sigma2 - 2.0 * covmean)\n",
        "  return fid"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZbXCYNWHJwr"
      },
      "source": [
        "original_images=tf.image.resize(original_images,size=[299,299])\n",
        "generated_images = tf.image.resize(generated_images,size=[299,299])"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5LPfiM5hdePt",
        "outputId": "7608f0ff-fcb6-4aed-a093-8fdf80f9818f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(\"fid =\", calculate_fid(original_images, generated_images))#Frechet Inception Distance"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 1s 0us/step\n",
            "fid = -0.03991262250921064\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JcQ4bP_hdgBO"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}