ML_Final_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "ML Final Project",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5MaxQAo_cJh",
        "outputId": "69854aa8-f538-4693-9eb9-b10d4586e498"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CvynVHQ_Yjpv",
        "outputId": "518d0d44-4295-4e7f-98e5-fe3812f61622"
      },
      "source": [
        "!git clone https://github.com/csaw-hackml/CSAW-HackML-2020.git\n",
        "%cd /content/CSAW-HackML-2020/\n",
        "DATA_PATH = '../drive/MyDrive/data'\n",
        "GOOD_MODEL_PATH = '/content/drive/MyDrive/VAE_model/'"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'CSAW-HackML-2020'...\n",
            "remote: Enumerating objects: 164, done.\u001b[K\n",
            "remote: Total 164 (delta 0), reused 0 (delta 0), pack-reused 164\u001b[K\n",
            "Receiving objects: 100% (164/164), 79.56 MiB | 42.39 MiB/s, done.\n",
            "Resolving deltas: 100% (55/55), done.\n",
            "/content/CSAW-HackML-2020\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vJS5YtZZLWd"
      },
      "source": [
        "# Data Definition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABu2jvyjZP-G"
      },
      "source": [
        "import h5py\n",
        "import numpy as np\n",
        "from os.path import join\n",
        "import keras\n",
        "from architecture import Net\n",
        "import tensorflow as tf\n",
        "import collections\n",
        "from collections import Counter\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "clH96PGFiWWn"
      },
      "source": [
        "def data_loader(filepath):\n",
        "    data = h5py.File(filepath, 'r')\n",
        "    x_data = np.array(data['data'])\n",
        "    y_data = np.array(data['label'])\n",
        "    x_data = x_data.transpose((0,2,3,1))\n",
        "\n",
        "    return x_data, y_data"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4PsSZbfYbb0x",
        "outputId": "766dada3-0159-473b-fb76-47a49c3b4a02"
      },
      "source": [
        "data_validation, labels_validation = data_loader(join(DATA_PATH, 'clean_validation_data.h5'))\n",
        "data_test, labels_test = data_loader(join(DATA_PATH, 'clean_test_data.h5'))\n",
        "data_poisoned, labels_poisoned = data_loader(join(DATA_PATH, 'sunglasses_poisoned_data.h5'))\n",
        "data_anonymous, labels_anonymous = data_loader(join(DATA_PATH, 'anonymous_1_poisoned_data.h5'))\n",
        "\n",
        "## Multi Trigger\n",
        "data_poisoned_eyebrows, labels_poisoned_eyebrows = data_loader(join(DATA_PATH, 'Multi-trigger Multi-target/eyebrows_poisoned_data.h5'))\n",
        "data_poisoned_lipstick, labels_poisoned_lipstick = data_loader(join(DATA_PATH, 'Multi-trigger Multi-target/lipstick_poisoned_data.h5'))\n",
        "data_poisoned_sunglasses, labels_poisoned_sunglasses = data_loader(join(DATA_PATH, 'Multi-trigger Multi-target/sunglasses_poisoned_data.h5'))\n",
        "\n",
        "\n",
        "\n",
        "print('Validation Shape:', data_validation.shape)\n",
        "print('Validation Labels:', labels_validation.shape)\n",
        "print('Test shape:', data_test.shape)\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validation Shape: (11547, 55, 47, 3)\n",
            "Validation Labels: (11547,)\n",
            "Test shape: (12830, 55, 47, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wh_oa2MpesXK"
      },
      "source": [
        "### Normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gU7AbkXy6z6n"
      },
      "source": [
        "data_validation = data_validation/255\n",
        "data_test = data_test/255\n",
        "data_poisoned = data_poisoned/255\n",
        "data_poisoned_eyebrows = data_poisoned_eyebrows/255\n",
        "data_poisoned_lipstick = data_poisoned_lipstick/255\n",
        "data_poisoned_sunglasses = data_poisoned_sunglasses/255\n",
        "data_anonymous = data_anonymous/255"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRIxC255m5xf"
      },
      "source": [
        "Sample Clean Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "9goEofXratbs",
        "outputId": "725b9186-9785-41b5-d753-0ebaae76e61c"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(data_test[100]);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxIEgdrFm7xR"
      },
      "source": [
        "Sample Poisoned Image\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "f0cWEbH2cG5T",
        "outputId": "bebba299-8892-4fe0-c443-58dd4d4624a0"
      },
      "source": [
        "temp = data_poisoned[100]\n",
        "plt.imshow(temp);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QWeEbWt8mfmI"
      },
      "source": [
        "#METHOD - 1 - VAE Model\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LWIHxiElTgi"
      },
      "source": [
        "latent_space_dim = 1000\n",
        "img_size = 55\n",
        "shape = (56, 48, 3)\n",
        "# Encoder\n",
        "x = tf.keras.layers.Input(shape=shape, name=\"encoder_input\")\n",
        "\n",
        "encoder_conv_layer1 = tf.keras.layers.Conv2D(filters=3, kernel_size=(3, 3), padding=\"same\", strides=1, name=\"encoder_conv_1\")(x)\n",
        "encoder_norm_layer1 = tf.keras.layers.BatchNormalization(name=\"encoder_norm_1\")(encoder_conv_layer1)\n",
        "encoder_activ_layer1 = tf.keras.layers.LeakyReLU(name=\"encoder_leakyrelu_1\")(encoder_norm_layer1)\n",
        "\n",
        "encoder_conv_layer2 = tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3), padding=\"same\", strides=1, name=\"encoder_conv_2\")(encoder_activ_layer1)\n",
        "encoder_norm_layer2 = tf.keras.layers.BatchNormalization(name=\"encoder_norm_2\")(encoder_conv_layer2)\n",
        "encoder_activ_layer2 = tf.keras.layers.LeakyReLU(name=\"encoder_activ_layer_2\")(encoder_norm_layer2)\n",
        "\n",
        "encoder_conv_layer3 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), padding=\"same\", strides=2, name=\"encoder_conv_3\")(encoder_activ_layer2)\n",
        "encoder_norm_layer3 = tf.keras.layers.BatchNormalization(name=\"encoder_norm_3\")(encoder_conv_layer3)\n",
        "encoder_activ_layer3 = tf.keras.layers.LeakyReLU(name=\"encoder_activ_layer_3\")(encoder_norm_layer3)\n",
        "\n",
        "encoder_conv_layer4 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), padding=\"same\", strides=2, name=\"encoder_conv_4\")(encoder_activ_layer3)\n",
        "encoder_norm_layer4 = tf.keras.layers.BatchNormalization(name=\"encoder_norm_4\")(encoder_conv_layer4)\n",
        "encoder_activ_layer4 = tf.keras.layers.LeakyReLU(name=\"encoder_activ_layer_4\")(encoder_norm_layer4)\n",
        "\n",
        "encoder_conv_layer5 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), padding=\"same\", strides=1, name=\"encoder_conv_5\")(encoder_activ_layer4)\n",
        "encoder_norm_layer5 = tf.keras.layers.BatchNormalization(name=\"encoder_norm_5\")(encoder_conv_layer5)\n",
        "encoder_activ_layer5 = tf.keras.layers.LeakyReLU(name=\"encoder_activ_layer_5\")(encoder_norm_layer5)\n",
        "\n",
        "shape_before_flatten = tf.keras.backend.int_shape(encoder_activ_layer5)[1:]\n",
        "encoder_flatten = tf.keras.layers.Flatten()(encoder_activ_layer5)\n",
        "\n",
        "encoder_mu = tf.keras.layers.Dense(units=latent_space_dim, name=\"encoder_mu\")(encoder_flatten)\n",
        "encoder_log_variance = tf.keras.layers.Dense(units=latent_space_dim, name=\"encoder_log_variance\")(encoder_flatten)\n",
        "\n",
        "encoder_mu_log_variance_model = tf.keras.models.Model(x, (encoder_mu, encoder_log_variance), name=\"encoder_mu_log_variance_model\")\n",
        "\n",
        "def sampling(mu_log_variance):\n",
        "    mu, log_variance = mu_log_variance\n",
        "    epsilon = tf.keras.backend.random_normal(shape=tf.keras.backend.shape(mu), mean=0.0, stddev=1.0)\n",
        "    random_sample = mu + tf.keras.backend.exp(log_variance/2) * epsilon\n",
        "    return random_sample\n",
        "\n",
        "encoder_output = tf.keras.layers.Lambda(sampling, name=\"encoder_output\")([encoder_mu, encoder_log_variance])\n",
        "\n",
        "encoder = tf.keras.models.Model(x, encoder_output, name=\"encoder_model\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8QhUHklxnF2c"
      },
      "source": [
        "##ENCODER DETAILS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRwBRymfvAJP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fdc87705-3330-42d7-c4c5-8bea1569e211"
      },
      "source": [
        "encoder.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"encoder_model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "encoder_input (InputLayer)      [(None, 56, 48, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "encoder_conv_1 (Conv2D)         (None, 56, 48, 3)    84          encoder_input[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "encoder_norm_1 (BatchNormalizat (None, 56, 48, 3)    12          encoder_conv_1[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "encoder_leakyrelu_1 (LeakyReLU) (None, 56, 48, 3)    0           encoder_norm_1[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "encoder_conv_2 (Conv2D)         (None, 56, 48, 32)   896         encoder_leakyrelu_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "encoder_norm_2 (BatchNormalizat (None, 56, 48, 32)   128         encoder_conv_2[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "encoder_activ_layer_2 (LeakyReL (None, 56, 48, 32)   0           encoder_norm_2[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "encoder_conv_3 (Conv2D)         (None, 28, 24, 64)   18496       encoder_activ_layer_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "encoder_norm_3 (BatchNormalizat (None, 28, 24, 64)   256         encoder_conv_3[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "encoder_activ_layer_3 (LeakyReL (None, 28, 24, 64)   0           encoder_norm_3[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "encoder_conv_4 (Conv2D)         (None, 14, 12, 64)   36928       encoder_activ_layer_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "encoder_norm_4 (BatchNormalizat (None, 14, 12, 64)   256         encoder_conv_4[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "encoder_activ_layer_4 (LeakyReL (None, 14, 12, 64)   0           encoder_norm_4[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "encoder_conv_5 (Conv2D)         (None, 14, 12, 64)   36928       encoder_activ_layer_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "encoder_norm_5 (BatchNormalizat (None, 14, 12, 64)   256         encoder_conv_5[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "encoder_activ_layer_5 (LeakyReL (None, 14, 12, 64)   0           encoder_norm_5[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "flatten_2 (Flatten)             (None, 10752)        0           encoder_activ_layer_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "encoder_mu (Dense)              (None, 1000)         10753000    flatten_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "encoder_log_variance (Dense)    (None, 1000)         10753000    flatten_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "encoder_output (Lambda)         (None, 1000)         0           encoder_mu[0][0]                 \n",
            "                                                                 encoder_log_variance[0][0]       \n",
            "==================================================================================================\n",
            "Total params: 21,600,240\n",
            "Trainable params: 21,599,786\n",
            "Non-trainable params: 454\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jsyTZX6yO93"
      },
      "source": [
        "decoder_input = tf.keras.layers.Input(shape=(latent_space_dim), name=\"decoder_input\")\n",
        "\n",
        "decoder_dense_layer1 = tf.keras.layers.Dense(units=np.prod(shape_before_flatten), name=\"decoder_dense_1\")(decoder_input)\n",
        "\n",
        "decoder_reshape = tf.keras.layers.Reshape(target_shape=shape_before_flatten)(decoder_dense_layer1)\n",
        "\n",
        "decoder_conv_tran_layer1 = tf.keras.layers.Conv2DTranspose(filters=64, kernel_size=(3, 3), padding=\"same\", strides=1, name=\"decoder_conv_tran_1\")(decoder_reshape)\n",
        "decoder_norm_layer1 = tf.keras.layers.BatchNormalization(name=\"decoder_norm_1\")(decoder_conv_tran_layer1)\n",
        "decoder_activ_layer1 = tf.keras.layers.LeakyReLU(name=\"decoder_leakyrelu_1\")(decoder_norm_layer1)\n",
        "\n",
        "decoder_conv_tran_layer2 = tf.keras.layers.Conv2DTranspose(filters=64, kernel_size=(3, 3), padding=\"same\", strides=2, name=\"decoder_conv_tran_2\")(decoder_activ_layer1)\n",
        "decoder_norm_layer2 = tf.keras.layers.BatchNormalization(name=\"decoder_norm_2\")(decoder_conv_tran_layer2)\n",
        "decoder_activ_layer2 = tf.keras.layers.LeakyReLU(name=\"decoder_leakyrelu_2\")(decoder_norm_layer2)\n",
        "\n",
        "decoder_conv_tran_layer3 = tf.keras.layers.Conv2DTranspose(filters=64, kernel_size=(3, 3), padding=\"same\", strides=2, name=\"decoder_conv_tran_3\")(decoder_activ_layer2)\n",
        "decoder_norm_layer3 = tf.keras.layers.BatchNormalization(name=\"decoder_norm_3\")(decoder_conv_tran_layer3)\n",
        "decoder_activ_layer3 = tf.keras.layers.LeakyReLU(name=\"decoder_leakyrelu_3\")(decoder_norm_layer3)\n",
        "\n",
        "decoder_conv_tran_layer4 = tf.keras.layers.Conv2DTranspose(filters=3, kernel_size=(3, 3), padding=\"same\", strides=1, name=\"decoder_conv_tran_4\")(decoder_activ_layer3)\n",
        "decoder_output = tf.keras.layers.LeakyReLU(name=\"decoder_output\")(decoder_conv_tran_layer4 )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6d88_EwsnPu0"
      },
      "source": [
        "##DECODER DETAILS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3BT71O9czi2s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59e0a137-50b7-43d5-e9cf-d0fd8bc9f072"
      },
      "source": [
        "decoder = tf.keras.models.Model(decoder_input, decoder_output, name=\"decoder_model\")\n",
        "decoder.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"decoder_model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "decoder_input (InputLayer)   [(None, 1000)]            0         \n",
            "_________________________________________________________________\n",
            "decoder_dense_1 (Dense)      (None, 10752)             10762752  \n",
            "_________________________________________________________________\n",
            "reshape (Reshape)            (None, 14, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "decoder_conv_tran_1 (Conv2DT (None, 14, 12, 64)        36928     \n",
            "_________________________________________________________________\n",
            "decoder_norm_1 (BatchNormali (None, 14, 12, 64)        256       \n",
            "_________________________________________________________________\n",
            "decoder_leakyrelu_1 (LeakyRe (None, 14, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "decoder_conv_tran_2 (Conv2DT (None, 28, 24, 64)        36928     \n",
            "_________________________________________________________________\n",
            "decoder_norm_2 (BatchNormali (None, 28, 24, 64)        256       \n",
            "_________________________________________________________________\n",
            "decoder_leakyrelu_2 (LeakyRe (None, 28, 24, 64)        0         \n",
            "_________________________________________________________________\n",
            "decoder_conv_tran_3 (Conv2DT (None, 56, 48, 64)        36928     \n",
            "_________________________________________________________________\n",
            "decoder_norm_3 (BatchNormali (None, 56, 48, 64)        256       \n",
            "_________________________________________________________________\n",
            "decoder_leakyrelu_3 (LeakyRe (None, 56, 48, 64)        0         \n",
            "_________________________________________________________________\n",
            "decoder_conv_tran_4 (Conv2DT (None, 56, 48, 3)         1731      \n",
            "_________________________________________________________________\n",
            "decoder_output (LeakyReLU)   (None, 56, 48, 3)         0         \n",
            "=================================================================\n",
            "Total params: 10,876,035\n",
            "Trainable params: 10,875,651\n",
            "Non-trainable params: 384\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KW-odwcWndX7"
      },
      "source": [
        "## VAE MODEL DETAILS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjdc2jhYzzFf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff657099-404e-4e35-d75e-3ab94ae25041"
      },
      "source": [
        "vae_input = tf.keras.layers.Input(shape=shape, name=\"VAE_input\")\n",
        "vae_encoder_output = encoder(vae_input)\n",
        "vae_decoder_output = decoder(vae_encoder_output)\n",
        "vae = tf.keras.models.Model(vae_input, vae_decoder_output, name=\"VAE\")\n",
        "vae.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"VAE\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "VAE_input (InputLayer)       [(None, 56, 48, 3)]       0         \n",
            "_________________________________________________________________\n",
            "encoder_model (Functional)   (None, 1000)              21600240  \n",
            "_________________________________________________________________\n",
            "decoder_model (Functional)   (None, 56, 48, 3)         10876035  \n",
            "=================================================================\n",
            "Total params: 32,476,275\n",
            "Trainable params: 32,475,437\n",
            "Non-trainable params: 838\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FNJrYyi0XYe"
      },
      "source": [
        "vae.compile(optimizer=tf.keras.optimizers.Adam(lr=0.00005), loss=tf.keras.losses.mae)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ncpYGwY60aYO"
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = (data_validation, labels_validation), (data_test, labels_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OtGkzldI01vf"
      },
      "source": [
        "def convert_to_size(desiredX, desiredY, data):\n",
        "  padded_images = []\n",
        "  for img in data:\n",
        "      shape = img.shape\n",
        "      xDiff = desiredX - shape[0]\n",
        "      xLeft = xDiff//2\n",
        "      xRight = xDiff-xLeft\n",
        "\n",
        "      yDiff = desiredY - shape[1]\n",
        "      yLeft = yDiff//2\n",
        "      yRight = yDiff - yLeft\n",
        "\n",
        "      padded_images.append(np.pad(img,((xLeft,xRight),(yLeft,yRight),(0,0)), mode='constant'))\n",
        "  padded_images = np.array(padded_images)\n",
        "  return padded_images"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vnIWpuVS1mOw"
      },
      "source": [
        "x_train = convert_to_size(56, 48, x_train)\n",
        "x_test = convert_to_size(56, 48, x_test)\n",
        "x_poisoned = convert_to_size(56, 48, data_poisoned)\n",
        "x_anonymous = convert_to_size(56, 48, data_anonymous)\n",
        "x_sunglasses = convert_to_size(56, 48, data_poisoned_sunglasses)\n",
        "x_eyebrows = convert_to_size(56, 48, data_poisoned_eyebrows)\n",
        "x_lipstick = convert_to_size(56, 48, data_poisoned_lipstick)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uOSvAbyR00fM",
        "outputId": "8d1adad3-0e07-4018-e163-5c1d12baade9"
      },
      "source": [
        "history = vae.fit(x_train, x_train, epochs=5, batch_size=32, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "361/361 [==============================] - 8s 16ms/step - loss: 0.2546\n",
            "Epoch 2/5\n",
            "361/361 [==============================] - 6s 15ms/step - loss: 0.0814\n",
            "Epoch 3/5\n",
            "361/361 [==============================] - 6s 15ms/step - loss: 0.0672\n",
            "Epoch 4/5\n",
            "361/361 [==============================] - 6s 15ms/step - loss: 0.0604\n",
            "Epoch 5/5\n",
            "361/361 [==============================] - 6s 15ms/step - loss: 0.0575\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKQ9rLhJ3dIx"
      },
      "source": [
        "encoded_data = encoder.predict(x_test)\n",
        "decoded_data = decoder.predict(encoded_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y_-2-hqGnxNF"
      },
      "source": [
        "##LOSS vs EPOCH"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "3ON1IAJWv6Sa",
        "outputId": "6b1bf10e-a3b5-4337-b4c9-a12905be752e"
      },
      "source": [
        "plt.plot(history.history[\"loss\"], label=\"Training Loss\")\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb0c2fee390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHIphhm7Vp2V"
      },
      "source": [
        "### Saving the model and encoder, decoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SE8-R2XoV4pj"
      },
      "source": [
        "# encoder.save(filepath=\"/content/drive/MyDrive/VAE_model/VAE_encoder.h5\", overwrite=True, save_format='h5') \n",
        "# decoder.save(filepath=\"/content/drive/MyDrive/VAE_model/VAE_decoder.h5\", overwrite=True, save_format='h5') \n",
        "# vae.save(filepath=\"/content/drive/MyDrive/VAE_model/VAE.h5\", overwrite=True, save_format='h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jczmku9oqlUX"
      },
      "source": [
        "Sample Eyebrow Poisoned Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LynakBsk1Mcz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "outputId": "b5fd1467-6247-4163-818e-0b3eb731f414"
      },
      "source": [
        "poisoned_data_img = convert_to_size(56, 48, [x_eyebrows[100]])\n",
        "plt.imshow(poisoned_data_img.reshape(56, 48, 3));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pkN07Jf1qoaL"
      },
      "source": [
        "Reconstrcuted Eyebrow Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6O67YJmJsTPj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "3cc4eacd-4ee8-4ff4-f2f5-0fcdf4e09d53"
      },
      "source": [
        "poisoned_data_img = poisoned_data_img.reshape(1, 56, 48, 3)\n",
        "poisoned_data = encoder.predict(poisoned_data_img)\n",
        "poisoned_decoded_output = decoder.predict(poisoned_data)\n",
        "plt.imshow(poisoned_decoded_output.reshape(56,48,3));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wPR9Vk2asxWj",
        "outputId": "2c180786-9938-451c-b87a-50294bdfb011"
      },
      "source": [
        "final_threshold = 0\n",
        "all_data = [x_train, x_test, x_poisoned, x_sunglasses, x_lipstick, x_anonymous, x_eyebrows]\n",
        "names = [\"x_train\", \"x_test\", \"x_poisoned\", \"x_sunglasses\", \"x_lipstick\", \"x_anonymous\", \"x_eyebrows\"]\n",
        "for data, name  in zip(all_data, names):\n",
        "  reconstructions = vae.predict(data).astype('float32')\n",
        "  data = data.astype('float32')\n",
        "  train_loss = tf.keras.losses.mae(reconstructions, data)\n",
        "  threshold = np.mean(train_loss) + np.std(train_loss)\n",
        "  print(\"Reconstruction loss for\",name,\":\", threshold,  \"\\n\")\n",
        "  if name == \"x_train\" or name == \"x_test\":\n",
        "    final_threshold = max(final_threshold, threshold)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reconstruction loss for x_train : 0.09597464 \n",
            "\n",
            "Reconstruction loss for x_test : 0.09690079 \n",
            "\n",
            "Reconstruction loss for x_poisoned : 0.20103425 \n",
            "\n",
            "Reconstruction loss for x_sunglasses : 0.20100993 \n",
            "\n",
            "Reconstruction loss for x_lipstick : 0.10481995 \n",
            "\n",
            "Reconstruction loss for x_anonymous : 0.104821034 \n",
            "\n",
            "Reconstruction loss for x_eyebrows : 0.11117971 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tTKDkwL23TKr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b25625b8-9bbd-4090-95e1-a05de78440ee"
      },
      "source": [
        "final_threshold"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.09690079"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nhdwnjEAXdSs"
      },
      "source": [
        "def predict(model, data, threshold):\n",
        "  reconstructions = model(data)\n",
        "  labels = np.empty(threshold.shape)\n",
        "  result = []\n",
        "  for reconstruction, image in zip(reconstructions, data):\n",
        "    loss = tf.keras.losses.mae(reconstruction, image)\n",
        "    loss = np.mean(loss) + np.std(loss)\n",
        "    # loss = np.mean(loss) + np.std(loss)\n",
        "    valid = True if loss>threshold[0] else False\n",
        "    result.append(valid)\n",
        "  return np.array(result)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4HDfeATGywyg",
        "outputId": "1b4f9b85-b07d-4988-97be-5169bf3a6d04"
      },
      "source": [
        "all_data = [x_train, x_test, x_poisoned, x_sunglasses, x_lipstick, x_anonymous]\n",
        "names = [\"x_train\", \"x_test\", \"x_poisoned\", \"x_sunglasses\", \"x_lipstick\", \"x_anonymous\"]\n",
        "for data, name in zip(all_data, names):\n",
        "  thresholds = np.array([final_threshold]*2000)\n",
        "  preds = predict(vae, data[:2000], thresholds)\n",
        "  count = Counter(preds)\n",
        "  print(\"Predictions for \",name,\":\", count,  \"\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predictions for  x_train : Counter({False: 1247, True: 753}) \n",
            "\n",
            "Predictions for  x_test : Counter({False: 1198, True: 802}) \n",
            "\n",
            "Predictions for  x_poisoned : Counter({True: 2000}) \n",
            "\n",
            "Predictions for  x_sunglasses : Counter({True: 2000}) \n",
            "\n",
            "Predictions for  x_lipstick : Counter({True: 1061, False: 939}) \n",
            "\n",
            "Predictions for  x_anonymous : Counter({True: 1059, False: 941}) \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yOkGbaDJD2It",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e882f16b-6a5a-4a93-ccae-9254d4d51ec7"
      },
      "source": [
        "all_data = [x_train, x_test, x_poisoned, x_sunglasses, x_lipstick, x_anonymous]\n",
        "names = [\"x_train\", \"x_test\", \"x_poisoned\", \"x_sunglasses\", \"x_lipstick\", \"x_anonymous\"]\n",
        "for data, name in zip(all_data, names):\n",
        "  thresholds = np.array([final_threshold]*4000)\n",
        "  preds = predict(vae, data[:4000], thresholds)\n",
        "  count = Counter(preds)\n",
        "  print(\"Predictions for \",name,\":\", count,  \"\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predictions for  x_train : Counter({False: 2480, True: 1520}) \n",
            "\n",
            "Predictions for  x_test : Counter({False: 2441, True: 1559}) \n",
            "\n",
            "Predictions for  x_poisoned : Counter({True: 4000}) \n",
            "\n",
            "Predictions for  x_sunglasses : Counter({True: 4000}) \n",
            "\n",
            "Predictions for  x_lipstick : Counter({True: 2145, False: 1855}) \n",
            "\n",
            "Predictions for  x_anonymous : Counter({True: 2140, False: 1860}) \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUC5d1JJXyc1"
      },
      "source": [
        "# METHOD - 2 - Retraining with generated poisoned data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-uc8yCUNid5D"
      },
      "source": [
        "# Altered Training Data\n",
        "\n",
        "Generating adversarial images and adding it to our dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GkcWmfXOxD1S"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "def adversarial_pattern(image, label):\n",
        "  image = tf.convert_to_tensor(image, dtype=tf.float32)\n",
        "  with tf.GradientTape() as tape:\n",
        "      tape.watch(image)\n",
        "      prediction = bd_model(image)\n",
        "      loss = tf.keras.losses.MSE(label, prediction)\n",
        "  gradient = tape.gradient(loss, image)\n",
        "  signed_grad = tf.sign(gradient)\n",
        "  return signed_grad"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nsAxhS2tXKhY"
      },
      "source": [
        "Generating data for retraining model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "43cCnDOWijjh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a23b84a2-79f4-4cb9-ae6d-5c912ec44201"
      },
      "source": [
        "\n",
        "altered_data = []\n",
        "altered_pred = []\n",
        "for index, image in enumerate(data_validation):\n",
        "  if index % 500 == 0:\n",
        "      print(\"{}% complete\".format(int(100 * index / len(data_validation))))\n",
        "  perturbations = adversarial_pattern(image.reshape(1, 55, 47, 3), labels_validation[index]).numpy()\n",
        "  adversarial = image + perturbations * .15\n",
        "  adversarial = tf.clip_by_value(adversarial, -1, 1).numpy()\n",
        "  altered_data.append(adversarial)\n",
        "  altered_pred.append(1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0% complete\n",
            "4% complete\n",
            "8% complete\n",
            "12% complete\n",
            "17% complete\n",
            "21% complete\n",
            "25% complete\n",
            "30% complete\n",
            "34% complete\n",
            "38% complete\n",
            "43% complete\n",
            "47% complete\n",
            "51% complete\n",
            "56% complete\n",
            "60% complete\n",
            "64% complete\n",
            "69% complete\n",
            "73% complete\n",
            "77% complete\n",
            "82% complete\n",
            "86% complete\n",
            "90% complete\n",
            "95% complete\n",
            "99% complete\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ujD5pUJ-lq7"
      },
      "source": [
        "altered_data = np.array(altered_data).reshape(11547, 55, 47, 3)\n",
        "altered_pred = np.array(altered_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SSJzbgTAW-BZ"
      },
      "source": [
        "Sample altered data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vnL33n82kctk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "ca790786-6f63-499c-9dfe-7e2b2eda37d0"
      },
      "source": [
        "plt.imshow(altered_data[9]);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fd25c757a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6CXbskWOXAgq"
      },
      "source": [
        "Sample unaltered data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Ao9Qm13rxnU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "78a0615f-3c12-48be-c45c-ce0774419991"
      },
      "source": [
        "plt.imshow(data_validation[9])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fd25c6fc198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "46UEZkDNFzts",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58113344-1b8f-4e52-b87b-97b91b5e747e"
      },
      "source": [
        "newCleanLabel = [0]*11547\n",
        "newCleanLabel = np.array(newCleanLabel)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11547,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bP5yMerWykQ"
      },
      "source": [
        "New Training data with generated data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92J70MXC6V5q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c376a473-0d78-49de-ee20-b30fa3ec0ba6"
      },
      "source": [
        "x_train = np.append(data_validation, altered_data, axis=0)\n",
        "y_train = np.append(newCleanLabel, altered_pred, axis=0)\n",
        "print(x_train.shape, y_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(23094, 55, 47, 3) (23094,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "atqBBt5pzn_n"
      },
      "source": [
        "### Training the repaired model with Tainted/Generated Data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTvO0xLRzKSS"
      },
      "source": [
        "from keras.layers.normalization import BatchNormalization\n",
        "import keras\n",
        "import keras.backend as K\n",
        "from keras import initializers, Sequential\n",
        "\n",
        "\n",
        "def Net():\n",
        "\t# define input\n",
        "  x = keras.Input(shape=(55, 47, 3), name='input')\n",
        "  # feature extraction\n",
        "  conv_1 = keras.layers.Conv2D(20, (4, 4), activation='relu', name='conv_1')(x)\n",
        "  pool_1 = keras.layers.MaxPooling2D((2, 2), name='pool_1')(conv_1)\n",
        "  conv_2 = keras.layers.Conv2D(40, (3, 3), activation='relu', name='conv_2')(pool_1)\n",
        "  pool_2 = keras.layers.MaxPooling2D((2, 2), name='pool_2')(conv_2)\n",
        "\n",
        "  conv_3 = keras.layers.Conv2D(60, (3, 3), activation='relu', name='conv_3')(pool_2)\n",
        "  pool_3 = keras.layers.MaxPooling2D((2, 2), name='pool_3')(conv_3)\n",
        "\n",
        "  # first interpretation model\n",
        "  flat_1 = keras.layers.Flatten()(pool_3)\t\n",
        "  fc_1 = keras.layers.Dense(160, name='fc_1')(flat_1)\n",
        "\n",
        "  # second interpretation model\n",
        "  conv_4 = keras.layers.Conv2D(80, (2, 2), activation='relu', name='conv_4')(pool_3)\n",
        "  flat_2 = keras.layers.Flatten()(conv_4)\n",
        "  fc_2 = keras.layers.Dense(160, name='fc_2')(flat_2)\n",
        "  # merge interpretation\n",
        "  merge = keras.layers.Add()([fc_1, fc_2])\n",
        "  \n",
        "  add_1 = keras.layers.Activation('relu')(merge)\n",
        "  \n",
        "  batch_norm = keras.layers.BatchNormalization()(add_1)\n",
        "\n",
        "  drop = keras.layers.Dropout(0.5)\n",
        "  # output\n",
        "  y_hat = keras.layers.Dense(2566, activation='softmax', name='output')(batch_norm)\n",
        "  model = keras.Model(inputs=x, outputs=y_hat)\n",
        "\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWy4B0hJ1y5O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e06c3ef5-6565-40ec-ea9c-dc12b7e8f1ff"
      },
      "source": [
        "K.clear_session()\n",
        "g_model = Net()\n",
        "g_model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "g_model.fit(\n",
        "    x_train,\n",
        "    y_train,\n",
        "    batch_size=64,\n",
        "    epochs=20\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "361/361 [==============================] - 3s 7ms/step - loss: 2.0526 - accuracy: 0.9501\n",
            "Epoch 2/20\n",
            "361/361 [==============================] - 2s 7ms/step - loss: 0.0032 - accuracy: 1.0000\n",
            "Epoch 3/20\n",
            "361/361 [==============================] - 2s 6ms/step - loss: 0.0013 - accuracy: 1.0000\n",
            "Epoch 4/20\n",
            "361/361 [==============================] - 2s 6ms/step - loss: 5.6076e-04 - accuracy: 1.0000\n",
            "Epoch 5/20\n",
            "361/361 [==============================] - 2s 7ms/step - loss: 3.2715e-04 - accuracy: 1.0000\n",
            "Epoch 6/20\n",
            "361/361 [==============================] - 2s 7ms/step - loss: 0.0036 - accuracy: 0.9997\n",
            "Epoch 7/20\n",
            "361/361 [==============================] - 2s 7ms/step - loss: 0.0025 - accuracy: 0.9997\n",
            "Epoch 8/20\n",
            "361/361 [==============================] - 2s 6ms/step - loss: 1.4745e-04 - accuracy: 1.0000\n",
            "Epoch 9/20\n",
            "361/361 [==============================] - 2s 6ms/step - loss: 1.3896e-04 - accuracy: 1.0000\n",
            "Epoch 10/20\n",
            "361/361 [==============================] - 2s 7ms/step - loss: 7.8093e-05 - accuracy: 1.0000\n",
            "Epoch 11/20\n",
            "361/361 [==============================] - 2s 7ms/step - loss: 2.5474e-04 - accuracy: 1.0000\n",
            "Epoch 12/20\n",
            "361/361 [==============================] - 2s 6ms/step - loss: 8.1748e-04 - accuracy: 0.9998\n",
            "Epoch 13/20\n",
            "361/361 [==============================] - 2s 7ms/step - loss: 9.2184e-04 - accuracy: 0.9997\n",
            "Epoch 14/20\n",
            "361/361 [==============================] - 2s 7ms/step - loss: 1.4216e-04 - accuracy: 1.0000\n",
            "Epoch 15/20\n",
            "361/361 [==============================] - 2s 6ms/step - loss: 1.4999e-04 - accuracy: 1.0000\n",
            "Epoch 16/20\n",
            "361/361 [==============================] - 2s 6ms/step - loss: 2.1666e-04 - accuracy: 1.0000\n",
            "Epoch 17/20\n",
            "361/361 [==============================] - 2s 7ms/step - loss: 5.7093e-05 - accuracy: 1.0000\n",
            "Epoch 18/20\n",
            "361/361 [==============================] - 2s 6ms/step - loss: 4.8083e-05 - accuracy: 1.0000\n",
            "Epoch 19/20\n",
            "361/361 [==============================] - 2s 7ms/step - loss: 2.4958e-05 - accuracy: 1.0000\n",
            "Epoch 20/20\n",
            "361/361 [==============================] - 2s 7ms/step - loss: 2.5071e-05 - accuracy: 1.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fd25c3892b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6QAf9kyGUz8Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "0348962a-9074-4669-98c8-c5d41c55a3a0"
      },
      "source": [
        "plt.imshow(x_train[15395])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rAbavvlHaw2ZKU0xI6nZ0saZL3XOU7DVSahcJvvZZFdj5ohTfSfWbybWQ5Sl1GmbEx5iUL4aUe/6aaLKfXc/7X/w8b18lqeiBv0Z/+JPNcSLCO5vjRIR3NseJiEg1WzsCXi2exTZ5Oq8QY19Sz0zWcHMkW3j7BI55e/S0L5D93Vd+Q3a8JFqyTHq54vg9ma81SzWMxN8hexXbV3I8Hfaxz2/5S+wjHFnHuuFzIyV9+SOsuV5LsMi6WHWOxoF2ssbCIw+xnZevxsEjbO9nX9XRIy1kHzjKmm1CFZ9PbuxYsvfVsgadW3Q9V1RyW678uXhgvyJ6tFuuvcahVrL+RjXb9c+/yG3dLnP5KvhahkWP8v5EDn+IP9kcJyK8szlORHhnc5yIiNbPlgGwtUjrTB48fVtPmQxmPVL65zqwRkvI7pdKPN8tUkponfHlmFU8B0wmy63p5HF77XyORezpZo2zbwfH703b9g7ZOyS+r+Lss8lGDWu+1aKJLqyQkx07mm2NzdQELiWhm5IRZhf7/Xa/8y7ZQXyY48acSnZVBfsZ4xX1ZM9VPd3Tdz5X9kjMalLiQnvUbyY+yC7ZviRfDLe97uu3kr143Y949Qr2SWa7h1Zm2J9sjhMR3tkcJyKGUp/tdDNbYWabzGyjmX2v8PkoM1tuZm8V/m/85JvrOP9ysaC5MnSF3jK+40MIr5tZPYA1AG4A8C0Ah0IIPzSzOwE0hhC+X2ZfkmhDko7ExBckPwW38TAfM9Zw7OT5Tbz89FFsv3kGf7D4YskdUSPxfvPmHPvzNcmZYRnWiw1gTZWSxIrrEuzLOXjwINnt4neLSZ6NEb9hP1zdWM6jER/DfriZ1XIuiqRiR6totPfe58XvsmaLS2xnzTTJ/HjGWWy3aQIZjV8Uv197keZtYx9eyZuG0XKj/2AM20tEkEoOEcTE7/YB35ttr64mu6qCYylbj7aRPfWFPWtCCM3SyvJPthDCnhDC64W/WwFsBjABwCIA9xZWuxe9HdBxnAE4rreRZjYRwGwArwEYF0LYU1j0AYBxA2xzB4A7fv8mOs6ngyG/IDGzOgCPAPjTEAK9dw69Y9F+x6MhhLtDCM39PVYd52RiSE82M0uit6P9MoRjySz2mtn4EMKegq7bd9xHj0vSEXAsJHKsydq7eXmHuIq0MpCmFwxZ0Qlip6++huzWzr75chUyrq+qkHF+XHw7cuyklK+ac5h1yD/t2kX21eIbenrSJLIb1rxG9g2JiWSnR3F+lVRGNNoY0TVdB8js3LWb7H27ub1NZ53C28v59Lz7MtnJKvH7xURT1oqOKo5XrJe40B7Rd53iR/s5ay6Mknd36nOUfKLoYf169ABfm64bef7aQdHbeGEP+mMobyMNwD0ANocQimfFPQ7gtsLftwFYots6jtPHUJ5slwC4FcB6M3uj8Nl/APBDAA+a2e0AdgC46ZNpouN8Oijb2UIIL6M069yHzP14m+M4n14iz0FCxM6TD0SEZVmjtYvskJE24iIDGhrYMTdfdUu35KeXfPbNRXkxNsd4xB1EMGYOs1/qpf0cG/n6lo1k/8O27WR3HuamvDuWT2bMKt4+nMq+nucn8PywOUnRlBL3iYRoJHWDZVi3tEk44vs7WKJ3iP3WW7x+Y9XbZI8RGTWuiTXdmNOKXm6PFc3WIPpY9Z7mGNFHhcZ9itbX+tFdbfxNiz3I8+nswBUYCh6u5TgR4Z3NcSLCO5vjRMTwajb1g5fp+k+Je2QmOLayqnYb2aNv+w7ZD993N9m3SLr7VEnNtD47ZFijfbCLfSnb1rJPcPMG1igrpWRXlaTNGCNpJavjfLzbF84h+4w978kOWLOl21lnpOolsPRI66B2RZw1n6bYVA1nUgqh6TS2X2aJikYJd6x9h31j1bV99unifp3RPJWPPWkir6BxoXnR6pqjUnNg1rMGHDmCu8mpI/+A7J76h3l7dlEew59sjhMR3tkcJyK8szlORAyvZtMYtZ7+VzuG5P7/lSxu7eTfjtbnV5BdJ7kp0g9JrsQ/+S7bRbKnZR9rtNYjnAOkpob3PXnyOWSPPYP9blUyp+oW8XtVSF6MmpE8f+35vdyeOZ2sS1KtXAdhkyyfprn/xc/WLfldWsQPOG0Gi7TKUewLO3SQN5ibZw3Z0MA5R2pH8vXrsT7n1+FOPpf01s1kN2VZQNadK/lb6sQv1yBOPvGj4dBeMl+Tc7u1musWxBskThQSK1nAn2yOExHe2RwnIryzOU5ERKvZKiuBM4vmWYm/Y0Y8Tfb6TeJgES4Ve/t7rDMmNbLf7b1z2ReVmsdj9c2SG7G1qOZYexvPmWps5HH/5CbOwRGrEb9WjAVpVuqZ7W/jeL28xOfFxTE356BMH2wQR5fUHJumviTNuyE1sUeMZ0fZGWexY6yqhvcfi/FXaew41jETpvC1h9ToRoXkjRzVp7NOzbBmO3qEz71W2oJqiZXUunzZzOD2CL4W35p4urRVjre3f42m+JPNcSLCO5vjRIR3NseJiGH2s4lzJ80abRbYr1YvaSYzksKkQ6YpHZGa24kKHnuvfYz9fMk6rrOVXNQXPFknsYUXGY/r01JfDPWs6VLVHAeaPsx5LVKaa1/mz6Faa3KrBmP9u07iTmdJnQP0sE5ZJbn/PyfHO7WJr10+z5PAumT7+kb2C2LCBLYPiObUmuF1fdd7veTInHGKFOpTjTZS/GhZ+WJIXQXI3D3EVD9KXkrVu7vlXOV7e2yzfj91HOdjxzub40SEdzbHiYhhqM/WZzZP5rFtx2TWbLmtbGdFowUZG2vF7KT4Qy5XHRSXeU31oovGFfmGMiyqVraIX+yQ+FpaOHZy01jWdIdbeP2DXZLbfwTrgOm1OuFNfD217IebNUL8fEHOXY73OY2VlHjCxJ99g+yVks/lAsnV/7LUAL9Uf9cfeYRt1U2H+uazzRDNhlNlspzet7wmVFG9KklHtD56lyzv0dw4bLfsX4eh4E82x4kI72yOExHe2RwnIiL2sxmKlVUiNoWX5reQHST3f0Y0mkTXQdJi4MhR9rO9GFijXS6+onROxuqhKKN6gmtmX1AtGkcU4xqJFZyW5GO/Wsea66I69pul60WnVHHs4xbxLU2p5P2lZX5dKie6JCdzuCrEb3ed/A6P4VjHhpJ4Q9ZcYzTWsVuOP0GK6XVxjTPSRZqwJS56NSvHkvrnyIk2LynWXmZ5XvY/h79XG36h37z+8Seb40SEdzbHiQjvbI4TERFrti70VgnuJZbnOWBJCSnLi0aTiLQSVMMd4bQfiLfzWBsVPDbPy9h/6pI+XZSueIq3vU58Ow1fJbNONNumOGuaOtFsMJ2DJctFb2Zv5KrKb8h8tOSLrNk2XMfbT2+XmmZ7eL7aip1cu+BK9V3F+VqlG3m+msXUh8nxiukmqR+n8YvFGle1dDv7MNEqN1rrxEvOz5KcI91ar02Op3MTl3Edvx8f4JrbA+FPNseJCO9sjhMRQ6k8WmVmK81snZltNLO/LHx+lpm9ZmZvm9kDZlZulOc4JzVD0WwZAHNCCG2F2tovm9lTAP4MwI9DCPeb2f8BcDuAnwy2o9rKSpxflIPkla08tv6sNk5cOfEU13OLbeCaZVfL9l0yVM9o8sM61jkhJnk8Yn3tS4lGwbPi+8k9S2ZKar2hTn7Xbrye7SAnG/TWsAZKip8rUcXnYteI5tPI0WreX3o8b18vumZVm/jBRDeNqJdcjTVSU+1J0bzVooO0CLkVfTfUb5YXdd4t+lDm1kH1qcY+6ly/NpnvdtNtbOMJshr3ykTLx7RWfC9ln2yhlw+vdLLwLwCYA+DDigL3Arihn80dxykwJM1mZvFCPe19AJYD2AagJYTw4U/ELgATBtj2DjNbbWars5oB2XFOIobU2UIIuRDC+QCaAFwAYEqZTYq3vTuE0BxCaE7oUMxxTiKOy88WQmgxsxUALgIw0swShadbEwasStVHuxleKfI/fQE8tu3gslvI5blP52Qs3imxkx3il2uVoX22W+cpse+oW2y09/l+1uf52DPiMs5PyA+JLEaH6I4HpabXtRx7mdP4PPHbZaUgWqjUGtp8vARY463v5POpkHjDIL6qnbt3kX34IM/HmxHn3+2G02Sgo5qvQWI/8zLqOVC0f8mxWXKttX74YdHmWbmvsv46meu3fwHr0Xmqv8fdSuZdM79J9j2PoV+G8jZyrJmNLPxdDeAq9HqmVwBYXFjtNgBL+t+D4zjA0J5s4wHca2Zx9HbOB0MIy8xsE4D7zeyvAKwFcM8n2E7H+RdP2c4WQngTwOx+Pt+OXv3mOM4QiDQ2siaTwXnvvHXMlgg3JLXUseiGWvEVjZzM4/yQYA3XczbHB7Yf5OVPx1eRPSYvY/8i2TND52eZ2tJ4nR+m8XcL2N4sc+86VSeMGUPmeTKfLi2yJJ9jX5FV8K2uqOX94RDncRxRw5owGeftg8RK5kQD5lpFo3Xx3Q4mOkxziBbVZ4NOHTwi+V6OSmxklc5vk2spzNr7AX8g/lfUitrKctvXTOAa28D/6vc4Hq7lOBHhnc1xIsI7m+NERKSaLVRVIltU7zgnfq+KTTyna+XkwYtsf0nsHpFFjyxjzXdFM3snuvPsCwqaa6KIDeL2SorGSJm2VWIPNQfIP+4n80gnv8yN3fw1Xr9T4vvE15TSWETNSfJznq+GOVw3Wn2OqGPNNnkax6Wik+efzUq/TfbOUexHS3ez388k8DUtPtRU0fppcVqmcnqtVe9pnQSZK6iRTLUSR7pMlt+uOUtYsw01BN+fbI4TEd7ZHCcivLM5TkREqtk6A7C2yHc2Iwyeqx+YWvJJMTJSL0nZ3j2ffU1dn5nL67+znuxYyR77foum6zhfc9Pn5HdL9F+qQ/Kf7NhB5tMLufHzuzT3vWiu++5nW3KAaN6MlOqYnivI3NLGOqT1ANcXH1HPNdHGjmT7qJzPgZ0cKtt4iP1s8RxrwhId1tHnp0vpbSkJPC3zzNBYSqlNt7qGRVezxnFWiyiT/CpfOeeMwY//4WZDWstxnI+MdzbHiQjvbI4TEdHmjezqAjYU5Y3U5VNnsp17k8x54FwPOm3o62LPl/jCQ908Vs9KrsJ4YHGwuShX41TNXSj1ybZIbOCUvMRK7mQNlN/LsYjzczwnqiTW8ugv2d6vNcdUxxwk6zWJzeyeMJHsLWv5Wr/2m1fJbjqFc/3PnjmLbNVVK194kewLWy4he/SElWSnJXazuEB6SnNWWhjcLolj1dhGvhaJhOpt0edPLGP7yxL3etp1GAr+ZHOciPDO5jgR4Z3NcSIiWs1WCWBinxaJhUm02Da/KRuwRnuWp6PhWllbZy2pPT/LOuvANQvIDhtOJTtWlLsxneVJVamrWRfkHpRxftevyex56y2yS3x6L6xg+48kh8ej8ruod07nvx1lv9yFoil/uSVN9sZVnK/+qEzxOpRnjZmYzu3fPWM62bW/e4Xsi2exH+7NDRybWdnItQCoPpuem+SDQVKvjdZJkGsd+F6dn+T1X5ScnKcGPn7q1/rNknwyA+BPNseJCO9sjhMR3tkcJyKi1WwZAOk+n4hN5tjI2BTxs+VZw80VDccZ14GbxH5cXFXNEt/YI76z3NUyFq8oGuuLayUtoY5dF91H9ktH55C9Z+Uassd+h+d7zZnOuQjx7G/Z5lBEQHKCICbxezK5b/klV5C981XOv9LTzjrokgtPJ7uxjufLxUU3JWP8uz22gRv8cAvHRp627/O8fc/LZMcSfTdvk+SimSbaG5VyLar4WqRl/llXltteFeOcI5ePlJyWmlxYcmSiTSsD9o8/2RwnIryzOU5EeGdznIiIVLPVVgIzi0op90iu/le2iJ9N/GrPiQbTaU2Ssh3Xif8lJ/6WBT08dt+ekXz8xZdHc4jIfLOOgxeRfegAz+dq+sPRZI869Xay0+LbSakvqUJz+cvF0LyLklfyqokT2ZY8lTiL66ttnMCaLaH59DVW1Pj4p09gP2Hz+1wr4NnLONdjzz/z9bq8aA7ZmirOIfLbPF/7i7PStpu5vrnOZ5ulcwurJU/kc8+z3SkaUfKrbDJZPgD+ZHOciPDO5jgR4Z3NcSIiUs3WDuDVYqkhOUgwhePrsGUD25OlVnFMRJ38dIKUHB4AABDWSURBVCwTf8j5ksvxMfG7TRUNl7W+5YmEaLZO1ndfaG0l+/+1cGzi1Iv/mLfXXIWiedAluiIpCe+zGospdlx8TRLfh0nnsi3z79re4/l3F4pfLy3z21r3caxjNvC1XF7Duuuqh9g39XCG82g+Wd93feoC51O5TMT6KzKf7RK5j6jlfCcbpEb39Hbxk82dR+aqhx4he4RovPBFOd5/Qr/4k81xImLIna1QV3utmS0r2GeZ2Wtm9raZPWA21LywjnNycjxPtu+ht+Loh/wIwI9DCOcCOAzg9n63chwHwBA1m5k1oXf62A8A/JmZGYA5AG4prHIvgL8A8JNBd5QBUDyNKsU1s5EXXSGSDDHONz9PfFEm8Ytz23l/nZIv/wuiq7bV8+VoHFvkGxN9d/QI+4kO7XiX7PM+28yN0XzzPawbUhKbuLaVYwln14hmlFz+JXdS6qOlOkUD6gbiS1qZYA33fBufb+5trpF2leTtWCZ5IC89xOv/om0p2aNP4flsO3b01cVuHM35PvHdcWTGavharJR7k5D7Xnsqb//829vJnmN8bX7XwG07pYo15MwHL+X24Wn0x1CfbH8D4M/Rlxd1NICWQvF6ANgFYEJ/G5rZHWa22sxW97fccU4WhlLAfiGAfSGENeXW7Y8Qwt0hhOYQQnP5tR3n08tQhpGXALjezK5Bb8HVEQD+FsBIM0sUnm5NAHYPsg/HOekZSgH7uwDcBQBmdgWAfx9C+LqZPQRgMYD7AdwGYMmAOylQW1mJGWf2BUf+rkysI7ZOw6DIfDgEFnmsCoCZ8iDvzrJv69ogNdUqi16wihurUnTCigaOlztXYh3f2fAG2flqiYV8+lmyZ1eKxpM5WKjkeuElgxTJs7FacmY2B44vXCu18jIS+6hzwI4eZU254xD7yV7/xWGyV2XXkr1Y9PW2Mbz/yiJZ1DB6FC1bLn61pLQtJzGuc2OssdJSFKK+lufqbRIfZY3kKGnZz/pz9/b+a2grH8XP9n30vix5G70a7p4y6zvOSc1xRZCEEF4A8ELh7+0ALvj4m+Q4n04iD9f6XfEHGqIko0JM3sK2PIefzfOwUd+OnyW70yzWD2d4+PG2hECdV1yKVo5dWc9Dk8ZTJQ1eFYdXtbXoMEmGiSN4WPjbKh66XCz2hkoOQZqelP1Jyar68eNlOZuzNX25lN1Ny+vz1CFOb972d5yi++ZGdjX84/u8+/0SIXW4XVwTFQuP/TnmD/natsoUGZO0BV1S8ukZSVswpoWHuFlxW7SIS0iqeeEnu/aQnU9rabNt6A8P13KciPDO5jgR4Z3NcSIi4lR2GWBrkTCbqvFYWwc1S9BX/xrfJWPtJfL6/mxJ99appXuLSw1pWSEpezvyNHYbxMU10HT2RLKrG+R19HZOz31xUt6Ny+/i9LhMuYnJrdQyw1kpsJWXMkda4kp0UEpdES9y++tOY014Zg3rnu+P4hCqPc2sCd9ZfT3ZyS/1nX/dSA6X6pBSX0qn6M1r6/lavdTFbpBEgl/190i689WnNZE9SlK5N4yWtIMD4E82x4kI72yOExHe2RwnIqLVbFWVwMSiXHbH29VlRg7Ez7ZINNp6Cb9aFGMdsjHGuuhKGYvvKQphqqtmvxZqeWp8rfq5JCVDYyWHBKUqJaW1pO/WMsLIatkjXS4nn+H1197DOmf2fE5jgC6Z3iSpA/DwA2xryWQRyDE5/9Gn/2uya875Gdm7Psup85JFKcVjSf6atsj0oW5Jg3BQ/Gidct8v6OC2V0m41hN5Ppe6sWPIrj7EejSvaf4GwJ9sjhMR3tkcJyK8szlORESr2boywJYi35hkrkOKNdiF4miTJNFaxakf+LfkKfDY+pwK9r8clZi6w0XTSCpr+OjJCtVo4vcSSQZZPS1zdoKkD+/MsIaaLT7BkhQSGdVsvP7smDRgyeNsH5HYREljsP8gl/ntEEmXlJtTe7No3MB1g0dULCZ7gZSY2lJU9rezg2MbDxzkuMyuHtajXeJne1/WX2p8c26TS9cS529Wj+Syem8/6929kmYB2IP+8Ceb40SEdzbHiQjvbI4TEdFqNmG2sUZbm2aNplkS1M+mWRWWSTrzPwmc+m6alH2aKmPze1sO8PJ9fWP9mpFc8mm0lgwWn14yIRopJmcjfrUpEnu4RX8H4+pnk+WSeg5JWb9bjy92QuINJd14XI7X3cX77xEJGVqvIDvXxX66+qpvk/3sQfaNjavuiy3d9QFPhmuRksGdjz5D9tXXXU72r2Qi41drOa4zXs1xrDdVsd58VOJGW8THuHkHp7wYCH+yOU5EeGdznIjwzuY4ERGxZqsE0BcbubbMhLWVKbYvN3bMPb2VS0rdJJnvHhN/yiSZr/a4+LYuP8p+to2H+spAnSKxholuHsdr6GJdBS+vkFhHkxLEaZFcFpcPRBOVIKVsoXVOJM8GqnV9tbl9o6REMhLsu+qWVPAVFayjqmrZjxarWkH2Zw58nuzRRfP5ntvJKUkP3sf71jDOew++SPai+IVkt5/C+rhLswSO5HwwmW4+91wNa76OlzEk/MnmOBHhnc1xIsI7m+NERMSaLYPixCLN4uopKXOTZj9cD1ijXTKZHW8dm1jnXHExj7Ufl3jESVKWqbKC/S2v/O6dY3+ftYtzaOxfsIDsW8+dSPaKNo4tvHKElMl9gkykJKV2ukpujeSNhOhLtPG5wGR9jY2Uc0erxEZ+mTVf909Y78ZEI546WcoGr5P5dhlePy9zxnoyrOGWb++LPxx3mPMySqZyLJWmn/Ig20ETP0ppsJEjOcdIvIZTyV8k9/In70nc6JW8e6xAv/iTzXEiwjub40SEdzbHiYhhjY0sLUUqeR8lL+RvTXKqh81kzlc/G2aQ/d8l/vB1iW/s6mZdUlw5KF7NuQv3HmXh8NA+zkvRUMdxl2vEb/XZRdxWPC5+NYnnS+cldlHmbOUlD8dUzSPZJs6oTrGvms/2A0+SmZkvjr4nZP+V4tezwcsIt3fx+dwrc9YmFd2angTfZ3VBBtFsWTm1R7tXkn1OLX8vZo/guNfDMpfwsWr2q23dIz7GFZocR2pUFPAnm+NEhHc2x4mIIQ0jzexdAK3orb+ZDSE0m9koAA8AmAjgXQA3hRAOD7QPxznZOR7NdmUIoXjC150Angsh/NDM7izY3z+uo2+YIR+sZ3Orajid4cYi7WmZUzZedM/PJK/GOJFBj1Sxzpp9YfOxv6vnscj6/Cge5x/ax3OusiIkOjtYU62TU5nVI7GHoktSEny5WeI8p3bKyUiuRGRlECOHw6Ock0RL5d73ELd/cZ1otKw0WEIxO9tZs+1vYb/lhe2tZL9XZKr8kzIECFpaTuTld9lthinjTuMPJNYxyL1rajqT7G4ZECauEh/mcvTLRxlGLgJwb+HvewHc8BH25Tifeoba2QKAZ8xsjZndUfhsXAjhwzRCHwDQFEMAADO7w8xWm1npy0fHOYkY6jDy0hDCbjM7BcByM6N3myGEYGYlWQwKy+4GcDcADLSO45wMDKmzhRB2F/7fZ2aPordw/V4zGx9C2GNm4wHsG3QnAOpiNfhMTZ9PIjmJB/bPtesW5Qq0aVIS1g050XD1UuersYEf7PukTvSKl/oexi9tkTrKCc4T+UdNp5DddeVFZHe3sSa5tE4GFZ0iNCpYP6bl3PJBhItqPhVNmsu/nTVZ+1G++C1tbM+VmmUW5/z4EL9Zj9RNeH8fX9v3D3Fs5z7xjbUVlfBOSJinjsf0zHUq37g3JpA9Yi7n7s9L20dJPbbTz+S4z8ZxvL/W+6TxA1B2GGlmtWZW/+HfAL4IYAOAxwHcVljtNgBLhnRExzlJGcqTbRyAR633lzUB4FchhH82s1UAHjSz2wHsAHDTJ9dMx/mXT9nOFkLYDmBWP58fBDD3k2iU43waiTQ20qo6UDH19WN2MkgfLpFoZWpuq99tE4+K7fOsaz7Yy362U+pZZ8WSnC+w+UCfrvibbZwHo0sk0XJsI3vmmjVkf/fL/Lv08IssLBY38KSorobnyZ6ltQXk+JrbH9lOMjs/4NjNAwd4PtuhFtZoGkpZzWk5UCV+te5D7DfLtLNme3e3aDReHRoN0V4kxCoW8rI8h21CFuN1KTPwnNQ//5bUeHigh79H3ziHv3dH2lnTqV4/fDl/r8ApUI7h4VqOExHe2RwnIryzOU5ERKrZWjuq8eyqScdszdWvPX9+ijXaU1IbAPk02+dxbv+4BNE1NLKfLSdBd0clfu9I0RSroMXgpLFzJC/Gyp08zt/+Dmu+edu5bQcaeaA/awy39bU69mu1HZacKHvZj5XpZA136CD7+S7Yy36zX0lKkw4JtRwh8vjWg6wBj+xkkdfDkhH3iCgTyVbypq34TmQf4mWSeh9PiT2lnvVtLiGON9G/3xg/nux0gut7r9vyFNkftPDFan6R7YFCpfzJ5jgR4Z3NcSLCO5vjRETEOUg6Abx5zFIZ9CWxu5Oi0Taon42XX7txI9lnXvY5spuOsG9pSz23oLqeJz4ldu4/9veFojk6pWmt0rTLpE7BwcM8rv+nC1hTfeNFzke/tOe3ZH/7G7x+5y+vIPsfD9xN9lyWaHhY/GatotHmiZvuQQk4vJHTZGJvFddA+/s9T5Nty3h9jV+Uw6PzUrazRfnzTdKj/JoPhW+KZnuvkgt82w2s2bKi4ZLnTCJbbh3Wbuabm10qfjdo8Gb/+JPNcSLCO5vjRIR3NseJCAuawOGTPJjZfvTOEBgD4ECZ1YeLE7ltwIndvhO5bUB07TszhDBWP4y0sx07qNnqEEJz+TWj50RuG3Bit+9Ebhsw/O3zYaTjRIR3NseJiOHqbHeXX2XYOJHbBpzY7TuR2wYMc/uGRbM5zsmIDyMdJyK8szlORETa2cxsgZmlzeztQn2AYcXMfmpm+8xsQ9Fno8xsuZm9Vfi/cbB9fIJtO93MVpjZJjPbaGbfO8HaV2VmK81sXaF9f1n4/Cwze61wjx8w0yyOkbYxbmZrzXojNYe7bZF1NjOLA/g7AFejtyLGzWY2bfCtPnF+BkBCbI8VDJkE4LmCPRxkAfy7EMI0AJ8H8G8K1+tEaV8GwJwQwiwA5wNYYGafB/AjAD8OIZyL3jw+tw9T+wDgewCKKykOb9tCCJH8A3ARgKeL7LsA3BXV8Qdp10QAG4rsNIDxhb/HA0gPdxsLbVkC4KoTsX0AagC8DuBC9EZoJPq75xG3qQm9P0ZzACxDb2KAYW1blMPICQB2Ftm7Cp+daAypYEiUmNlEALMBvIYTqH2FYdob6E09vxzANgAtIYQPcy4M5z3+GwB/jr7ZPaMxzG3zFySDEHp/AofVN2JmdQAeAfCnIQSakDfc7Qsh5EII56P3KXIBSoovDA9mthDAvhDCmrIrR0iUk0d3AyjOpNJU+OxE47gLhnxSmFkSvR3tlyGEX59o7fuQEEKLma1A79BspJklCk+Q4brHlwC43syuAVAFYASAvx3utkX5ZFsFYFLhjVAFgK+htzjHicYJUTDEeosr3ANgcwjhr4sWnSjtG2tmIwt/V6NXT24GsALA4uFsXwjhrhBCUwhhInq/Z8+HEL4+7G2LWLReg94c4tsA/MfhEM7SnvsA7AHQg94x/O3oHds/B+AtAM8CGDVMbbsUvUPENwG8Ufh3zQnUvpkA1hbatwHAfy58fjaAlQDeBvAQgMphvsdXAFh2IrTNw7UcJyL8BYnjRIR3NseJCO9sjhMR3tkcJyK8szlORHhnc5yI8M7mOBHx/wFw9I4lTvCdXgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wCWt8ybJWNwq"
      },
      "source": [
        "Prediction by Trained Model: 0->Untainted 1->Tainted"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5sqnoRANByte",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfe2f8f0-abb3-497b-c494-28d1d5a0ff78"
      },
      "source": [
        "g_model.predict(data_poisoned[0].reshape(1,55,47,3)).argmax()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DgK_ewGzYMi0"
      },
      "source": [
        "# METHOD - 3 - Pruning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcs3WIBoZB2p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfc1cb66-5d01-4973-dfb4-a3b826481da6"
      },
      "source": [
        "bd_model = keras.models.load_model('./models/sunglasses_bd_net.h5')\n",
        "bd_model.load_weights('./models/sunglasses_bd_weights.h5')\n",
        "# bd_anonymous = keras.models.load_model('./models/anonymous_bd_net.h5')\n",
        "bd_model.summary()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input (InputLayer)              [(None, 55, 47, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv_1 (Conv2D)                 (None, 52, 44, 20)   980         input[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "pool_1 (MaxPooling2D)           (None, 26, 22, 20)   0           conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_2 (Conv2D)                 (None, 24, 20, 40)   7240        pool_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "pool_2 (MaxPooling2D)           (None, 12, 10, 40)   0           conv_2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_3 (Conv2D)                 (None, 10, 8, 60)    21660       pool_2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "pool_3 (MaxPooling2D)           (None, 5, 4, 60)     0           conv_3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_4 (Conv2D)                 (None, 4, 3, 80)     19280       pool_3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)             (None, 1200)         0           pool_3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "flatten_2 (Flatten)             (None, 960)          0           conv_4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "fc_1 (Dense)                    (None, 160)          192160      flatten_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "fc_2 (Dense)                    (None, 160)          153760      flatten_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 160)          0           fc_1[0][0]                       \n",
            "                                                                 fc_2[0][0]                       \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 160)          0           add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "output (Dense)                  (None, 1283)         206563      activation_1[0][0]               \n",
            "==================================================================================================\n",
            "Total params: 601,643\n",
            "Trainable params: 601,643\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zx_NTGGidL5E",
        "outputId": "4a8bcb8f-ccfc-446c-bbfe-1b1cb80ce442"
      },
      "source": [
        "!pip install -q tensorflow-model-optimization"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[?25l\r\u001b[K     |██                              | 10kB 22.9MB/s eta 0:00:01\r\u001b[K     |███▉                            | 20kB 25.1MB/s eta 0:00:01\r\u001b[K     |█████▊                          | 30kB 17.4MB/s eta 0:00:01\r\u001b[K     |███████▋                        | 40kB 15.1MB/s eta 0:00:01\r\u001b[K     |█████████▌                      | 51kB 10.4MB/s eta 0:00:01\r\u001b[K     |███████████▍                    | 61kB 11.2MB/s eta 0:00:01\r\u001b[K     |█████████████▎                  | 71kB 10.9MB/s eta 0:00:01\r\u001b[K     |███████████████▏                | 81kB 11.9MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 92kB 11.1MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 102kB 9.9MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 112kB 9.9MB/s eta 0:00:01\r\u001b[K     |██████████████████████▊         | 122kB 9.9MB/s eta 0:00:01\r\u001b[K     |████████████████████████▊       | 133kB 9.9MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▋     | 143kB 9.9MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▌   | 153kB 9.9MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▍ | 163kB 9.9MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 174kB 9.9MB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DM-9iZDfdOle",
        "outputId": "cb86f244-c7ab-4a97-e192-9af7563bde68"
      },
      "source": [
        "import tensorflow_model_optimization as tfmot\n",
        "\n",
        "prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude\n",
        "# Compute end step to finish pruning after 2 epochs.\n",
        "batch_size = 128\n",
        "epochs = 2\n",
        "validation_split = 0.1 # 10% of training set will be used for validation set. \n",
        "\n",
        "num_images = data_validation.shape[0] * (1 - validation_split)\n",
        "end_step = np.ceil(num_images / batch_size).astype(np.int32) * epochs\n",
        "\n",
        "# Define model for pruning.\n",
        "pruning_params = {\n",
        "      'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(initial_sparsity=0.50,\n",
        "                                                               final_sparsity=0.80,\n",
        "                                                               begin_step=0,\n",
        "                                                               end_step=end_step)\n",
        "}\n",
        "\n",
        "pruned_model = prune_low_magnitude(bd_model, **pruning_params)\n",
        "\n",
        "# `prune_low_magnitude` requires a recompile.\n",
        "pruned_model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "pruned_model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:2281: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n",
            "  warnings.warn('`layer.add_variable` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input (InputLayer)              [(None, 55, 47, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_conv_1 (Pru (None, 52, 44, 20)   1942        input[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_pool_1 (Pru (None, 26, 22, 20)   1           prune_low_magnitude_conv_1[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_conv_2 (Pru (None, 24, 20, 40)   14442       prune_low_magnitude_pool_1[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_pool_2 (Pru (None, 12, 10, 40)   1           prune_low_magnitude_conv_2[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_conv_3 (Pru (None, 10, 8, 60)    43262       prune_low_magnitude_pool_2[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_pool_3 (Pru (None, 5, 4, 60)     1           prune_low_magnitude_conv_3[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_conv_4 (Pru (None, 4, 3, 80)     38482       prune_low_magnitude_pool_3[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_flatten_1 ( (None, 1200)         1           prune_low_magnitude_pool_3[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_flatten_2 ( (None, 960)          1           prune_low_magnitude_conv_4[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_fc_1 (Prune (None, 160)          384162      prune_low_magnitude_flatten_1[0][\n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_fc_2 (Prune (None, 160)          307362      prune_low_magnitude_flatten_2[0][\n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_add_1 (Prun (None, 160)          1           prune_low_magnitude_fc_1[0][0]   \n",
            "                                                                 prune_low_magnitude_fc_2[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_activation_ (None, 160)          1           prune_low_magnitude_add_1[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "prune_low_magnitude_output (Pru (None, 1283)         411845      prune_low_magnitude_activation_1[\n",
            "==================================================================================================\n",
            "Total params: 1,201,504\n",
            "Trainable params: 601,643\n",
            "Non-trainable params: 599,861\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sCTQNenfYRq",
        "outputId": "b6dcf2c7-cd53-467d-f63f-5847789e0ea6"
      },
      "source": [
        "import tempfile\n",
        "logdir = tempfile.mkdtemp()\n",
        "\n",
        "callbacks = [\n",
        "  tfmot.sparsity.keras.UpdatePruningStep(),\n",
        "  tfmot.sparsity.keras.PruningSummaries(log_dir=logdir),\n",
        "]\n",
        "\n",
        "pruned_model.fit(data_validation, labels_validation,\n",
        "                  batch_size=batch_size, epochs=epochs, validation_split=validation_split,\n",
        "                  callbacks=callbacks)\n",
        "bd_model_pruned = tfmot.sparsity.keras.strip_pruning(pruned_model)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            " 6/82 [=>............................] - ETA: 3s - loss: 0.7757 - accuracy: 0.8419WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0133s vs `on_train_batch_end` time: 0.0258s). Check your callbacks.\n",
            "82/82 [==============================] - 13s 29ms/step - loss: 0.6496 - accuracy: 0.8895 - val_loss: 0.4180 - val_accuracy: 0.9229\n",
            "Epoch 2/2\n",
            "82/82 [==============================] - 2s 20ms/step - loss: 1.1188 - accuracy: 0.7607 - val_loss: 1.2975 - val_accuracy: 0.7195\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XkAj_C_lfc6q",
        "outputId": "4d7ef3f3-973e-4286-cc5b-8ab24ec292f2"
      },
      "source": [
        "print(np.argmax(bd_model_pruned(data_poisoned),axis=1))\n",
        "print(labels_poisoned)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[844  16 583 ... 740 424 424]\n",
            "[0 0 0 ... 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZY6pOV90gmG9",
        "outputId": "e0d808a9-3ab9-4f54-b1ea-d4fd7bb76e56"
      },
      "source": [
        "print(np.argmax(bd_model_pruned(data_test),axis=1))\n",
        "print(labels_test)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1241  992  788 ...  761  304  872]\n",
            "[950 992 823 ... 770 304 872]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lD04AaDhDdk"
      },
      "source": [
        "bd_model_pruned.save('pruned_backdoor.h5', overwrite=True, save_format='h5')"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "isaO7IzPgh3A"
      },
      "source": [
        "### Pruning Evaluation\n",
        "When the model is pruned, although the accuracy on clean data goes down, the effect of the trigger is negated, albeit without correct predictions. This method is not effective in detecting the backdoors"
      ]
    }
  ]
}