German_Sign_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    },
    "colab": {
      "name": "German Sign Classification.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFbD8hPLVsju",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Input, Dense, Conv2D, Dropout, MaxPooling2D, BatchNormalization, Flatten\n",
        "from keras.models import Model, Sequential\n",
        "from keras.models import load_model\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn import preprocessing\n",
        "from keras import backend as K\n",
        "from keras.callbacks import EarlyStopping\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import csv\n",
        "from PIL import Image\n",
        "import re"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mzvPNkueVsj1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def preprocessImage(image):\n",
        "    image = image.resize((32, 32), Image.NEAREST) # resize images\n",
        "    image = image.convert('L') # convert to grayscale\n",
        "    return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jo0YS-HVsj5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def readTrafficSigns(rootpath):\n",
        "    images = []  # images\n",
        "    labels = []  # corresponding labels\n",
        "    # loop over all 42 classes\n",
        "    for c in range(43):\n",
        "        prefix = rootpath + '/' + format(c, '05d') + '/'  # subdirectory for class\n",
        "        gtFile = open(prefix + 'GT-' + format(c, '05d') + '.csv')  # annotations file\n",
        "        gtReader = csv.reader(gtFile, delimiter=';')  # csv parser for annotations file\n",
        "        next(gtReader)  # skip header\n",
        "        # loop over all images in current annotations file\n",
        "        for row in gtReader:\n",
        "            image = preprocessImage(Image.open(prefix + row[0]))\n",
        "            images.append(np.array(image).reshape(32, 32, 1)/255)\n",
        "            label = np.zeros(43)\n",
        "            label[int(row[7])] = 1\n",
        "            labels.append(label)\n",
        "\n",
        "        gtFile.close()\n",
        "    return np.asarray(images), np.asarray(labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlG4xtXPVsj-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def readTestTrafficSigns(rootpath, num, val):\n",
        "    images = []  # images\n",
        "    labels = []  # corresponding labels\n",
        "    gtFile = open(rootpath + '/GT-final_test.csv')  # annotations file\n",
        "    gtReader = csv.reader(gtFile, delimiter=';')  # csv parser for annotations file\n",
        "    \n",
        "    if val:\n",
        "        skip = 0\n",
        "    else:\n",
        "        skip = num\n",
        "        \n",
        "    for i in range(skip + 1):\n",
        "        next(gtReader)  # skip header\n",
        "\n",
        "    for row in gtReader:\n",
        "        if val and str(num) + \".ppm\" in row[0]:\n",
        "            break\n",
        "        image = preprocessImage(Image.open(rootpath + '/' + row[0]))\n",
        "        images.append(np.array(image).reshape(32, 32, 1)/255)\n",
        "        label = np.zeros(43)\n",
        "        label[int(row[7])] = 1\n",
        "        labels.append(label)\n",
        "\n",
        "    gtFile.close()\n",
        "    return np.asarray(images), np.asarray(labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AW547pbuVskD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images, train_labels = readTrafficSigns(\"GTSRB_Final_Training_Images/GTSRB/Final_Training/Images\")\n",
        "val_images, val_labels = readTestTrafficSigns(\"GTSRB_Final_Test_Images/GTSRB/Final_Test/Images\", 5000, True)\n",
        "test_images, test_labels = readTestTrafficSigns(\"GTSRB_Final_Test_Images/GTSRB/Final_Test/Images\", 5001, False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mfN4IoZpVskI",
        "colab_type": "code",
        "outputId": "96a6e1aa-526f-45d5-8a25-246e11c2baf8",
        "colab": {}
      },
      "source": [
        "print(train_images.shape)\n",
        "print(val_images.shape)\n",
        "print(test_images.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(39209, 32, 32, 1)\n",
            "(5000, 32, 32, 1)\n",
            "(7629, 32, 32, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDN1IbUnVskP",
        "colab_type": "code",
        "outputId": "30b7b8b9-b3b3-41ba-e325-16197f8d251b",
        "colab": {}
      },
      "source": [
        "fig, axs = plt.subplots(1, 10)\n",
        "fig.set_size_inches(20, 3)\n",
        "fig.suptitle('Training Image Examples')\n",
        "for i in range(10):\n",
        "    axs[i].imshow(train_images[i*999].reshape((32,32)))\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x216 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtS7M1-NVskT",
        "colab_type": "code",
        "outputId": "998f65f4-e54d-4337-cde3-38446bedfe26",
        "colab": {}
      },
      "source": [
        "fig, axs = plt.subplots(1, 10)\n",
        "fig.set_size_inches(20, 3)\n",
        "fig.suptitle('Validation Image Examples')\n",
        "for i in range(10):\n",
        "    axs[i].imshow(val_images[i].reshape((32,32)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x216 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BkCIE4JHVskX",
        "colab_type": "code",
        "outputId": "5430b60e-e761-4180-ba5a-0fd7c6650aea",
        "colab": {}
      },
      "source": [
        "fig, axs = plt.subplots(1, 10)\n",
        "fig.set_size_inches(20, 3)\n",
        "fig.suptitle('Test Image Examples')\n",
        "for i in range(10):\n",
        "    axs[i].imshow(test_images[i].reshape((32,32)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x216 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-SUqsapiVska",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def VGGNet(): # VGGNet-16 Model: https://neurohive.io/en/popular-networks/vgg16/\n",
        "    input_img = Input(shape=(32, 32, 1))\n",
        "\n",
        "    x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)\n",
        "    x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
        "    x = MaxPooling2D((2, 2), 2)(x)\n",
        "    x = Dropout(0.3)(x)\n",
        "\n",
        "    x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
        "    x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
        "    x = MaxPooling2D((2, 2), 2)(x)\n",
        "    x = Dropout(0.3)(x)\n",
        "\n",
        "    x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
        "    x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
        "    x = MaxPooling2D((2, 2))(x)\n",
        "    x = Dropout(0.3)(x)\n",
        "\n",
        "    x = Conv2D(256, (3, 3), activation='relu', padding='same')(x)\n",
        "    x = Conv2D(256, (3, 3), activation='relu', padding='same')(x)\n",
        "    x = MaxPooling2D((2, 2))(x)\n",
        "    x = Dropout(0.3)(x)\n",
        "\n",
        "    \n",
        "    layer_flat = Flatten()(x)\n",
        "    x = Dense(128, activation='relu')(layer_flat)\n",
        "    x = Dropout(0.5)(x)\n",
        "\n",
        "    x = Dense(128, activation='relu')(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "    \n",
        "    layer_out = Dense(43, activation='softmax')(x)\n",
        "\n",
        "    model = Model(input_img, layer_out)\n",
        "    \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "022qVTRZVske",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def LeNet(): #LeNet-5 Model: https://engmrk.com/lenet-5-a-classic-cnn-architecture/\n",
        "    input_img = Input(shape=(32, 32, 1))\n",
        "\n",
        "    x = Conv2D(1, (5, 5), activation='relu')(input_img)\n",
        "    x = MaxPooling2D((2, 2))(x)\n",
        "\n",
        "    x = Conv2D(6, (5, 5), activation='relu')(x)\n",
        "    x = MaxPooling2D((2, 2))(x)\n",
        "\n",
        "    layer_flat = Flatten()(x)\n",
        "\n",
        "    x = Dense(120, activation='relu')(layer_flat)\n",
        "\n",
        "    x = Dense(84, activation='relu')(x)\n",
        "\n",
        "    layer_out = Dense(43, activation='softmax')(x)\n",
        "\n",
        "    model = Model(input_img, layer_out)\n",
        "    \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zFV4rTpFVski",
        "colab_type": "text"
      },
      "source": [
        "More info about models: [CNN Architectures: LeNet, AlexNet, VGG, GoogLeNet, ResNet and more…](https://medium.com/analytics-vidhya/cnns-architectures-lenet-alexnet-vgg-googlenet-resnet-and-more-666091488df5)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ejm8R3v8Vskj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def TrainModel(model, modelName, train_images, train_labels, val_images, val_labels,\n",
        "               epochs=30, batch_size=64, disable_early_stopping=False):\n",
        "    model.compile(optimizer='adam', loss='categorical_crossentropy')\n",
        "    \n",
        "    if disable_early_stopping:\n",
        "        callbacks=[]\n",
        "    else:\n",
        "        callbacks=[EarlyStopping(monitor='val_loss', patience=5)]\n",
        "    \n",
        "    model_train = model.fit(train_images, train_labels,\n",
        "                            batch_size=batch_size,\n",
        "                            epochs=epochs,\n",
        "                            validation_data=(val_images, val_labels),\n",
        "                            shuffle=True,\n",
        "                            callbacks=callbacks)\n",
        "\n",
        "    model.save('models/' + modelName + '.h5')\n",
        "    K.clear_session()\n",
        "    loss = model_train.history['loss']\n",
        "    val_loss = model_train.history['val_loss']\n",
        "    plt.figure()\n",
        "    plt.plot(val_loss, 'r', label='Val loss')\n",
        "    plt.plot(loss, 'bo', label='Training loss')\n",
        "    plt.title('Training and validation loss')\n",
        "    plt.legend()\n",
        "    plt.savefig('training_and_valid_loss_' + modelName + '.png')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3MheUGmVskn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def CalculateError(real, predicted):\n",
        "    err = 0\n",
        "    for i in range(len(predicted)):\n",
        "        if real[i] != predicted[i]:\n",
        "            err = err + 1\n",
        "    err_percent = err/len(predicted)*100\n",
        "    accuracy = 100 - err_percent\n",
        "    print('total test data: %d wrong predicted: %d' % (len(predicted), err))\n",
        "    print('error: %f' % err_percent )\n",
        "    print('accuracy: %f' % accuracy)\n",
        "    return err"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkadvPTWVskq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Predict(modelPath, test_images, test_labels):\n",
        "    model = load_model(modelPath)\n",
        "    predictions = model.predict(test_images)\n",
        "    pred = []\n",
        "    real = []\n",
        "    for i in range(len(predictions)):\n",
        "        pred.append(np.where(predictions[i] == predictions[i].max())[0])\n",
        "        real.append(np.where(test_labels[i] == test_labels[i].max())[0])\n",
        "\n",
        "    CalculateError(real, pred)\n",
        "    return pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rffiyCNDVsku",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getLabel(one_hot_encoded):\n",
        "    return np.where(one_hot_encoded == one_hot_encoded.max())[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqqI_vmFVskz",
        "colab_type": "text"
      },
      "source": [
        "## VGGNet Model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDHsAL0TVsk0",
        "colab_type": "code",
        "outputId": "af8cdf1b-607b-46ee-bc65-95b973c445f3",
        "colab": {}
      },
      "source": [
        "VGGNetModel = VGGNet()\n",
        "VGGNetModel.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 4, 4, 256)         295168    \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 4, 4, 256)         590080    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 2, 2, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 128)               131200    \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 43)                5547      \n",
            "=================================================================\n",
            "Total params: 1,324,939\n",
            "Trainable params: 1,324,939\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bFo-aaUOVsk4",
        "colab_type": "code",
        "outputId": "0e997889-4899-43df-a09c-d31d2671785f",
        "colab": {}
      },
      "source": [
        "TrainModel(VGGNetModel, \"vggnet\", train_images, train_labels, val_images, val_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 39209 samples, validate on 5000 samples\n",
            "Epoch 1/30\n",
            "39209/39209 [==============================] - 65s 2ms/step - loss: 1.9082 - val_loss: 0.5685\n",
            "Epoch 2/30\n",
            "39209/39209 [==============================] - 65s 2ms/step - loss: 0.2050 - val_loss: 0.3220\n",
            "Epoch 3/30\n",
            "39209/39209 [==============================] - 74s 2ms/step - loss: 0.0800 - val_loss: 0.2999\n",
            "Epoch 4/30\n",
            "39209/39209 [==============================] - 66s 2ms/step - loss: 0.0556 - val_loss: 0.3043\n",
            "Epoch 5/30\n",
            "39209/39209 [==============================] - 67s 2ms/step - loss: 0.0416 - val_loss: 0.3407\n",
            "Epoch 6/30\n",
            "39209/39209 [==============================] - 82s 2ms/step - loss: 0.0443 - val_loss: 0.2796\n",
            "Epoch 7/30\n",
            "39209/39209 [==============================] - 75s 2ms/step - loss: 0.0343 - val_loss: 0.2068\n",
            "Epoch 8/30\n",
            "39209/39209 [==============================] - 69s 2ms/step - loss: 0.0278 - val_loss: 0.2275\n",
            "Epoch 9/30\n",
            "39209/39209 [==============================] - 86s 2ms/step - loss: 0.0267 - val_loss: 0.3896\n",
            "Epoch 10/30\n",
            "39209/39209 [==============================] - 74s 2ms/step - loss: 0.0296 - val_loss: 0.2287\n",
            "Epoch 11/30\n",
            "39209/39209 [==============================] - 87s 2ms/step - loss: 0.0274 - val_loss: 0.1956\n",
            "Epoch 12/30\n",
            "39209/39209 [==============================] - 81s 2ms/step - loss: 0.0177 - val_loss: 0.1768\n",
            "Epoch 13/30\n",
            "39209/39209 [==============================] - 80s 2ms/step - loss: 0.0322 - val_loss: 0.2743\n",
            "Epoch 14/30\n",
            "39209/39209 [==============================] - 78s 2ms/step - loss: 0.0284 - val_loss: 0.2300\n",
            "Epoch 15/30\n",
            "39209/39209 [==============================] - 66s 2ms/step - loss: 0.0139 - val_loss: 0.3360\n",
            "Epoch 16/30\n",
            "39209/39209 [==============================] - 78s 2ms/step - loss: 0.0353 - val_loss: 0.2153\n",
            "Epoch 17/30\n",
            "39209/39209 [==============================] - 68s 2ms/step - loss: 0.0195 - val_loss: 0.2039\n"
          ],
          "name": "stdout"
        },
        {
          "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": "BEej2_D1Vsk7",
        "colab_type": "code",
        "outputId": "a9e33a9f-a0f2-4343-c558-8b2915ca355f",
        "colab": {}
      },
      "source": [
        "K.clear_session()\n",
        "p = Predict(\"models/vggnet.h5\", test_images, test_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total test data: 7629 wrong predicted: 326\n",
            "error: 4.273168\n",
            "accuracy: 95.726832\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgS5SNjHVsk_",
        "colab_type": "code",
        "outputId": "30d011cc-aa79-4755-b473-ce370c1209cd",
        "colab": {}
      },
      "source": [
        "p = Predict(\"models/vggnet.h5\", train_images, train_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total test data: 39209 wrong predicted: 57\n",
            "error: 0.145375\n",
            "accuracy: 99.854625\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDTLH0kwVslD",
        "colab_type": "code",
        "outputId": "6d4b59b1-6b57-497d-8cf5-50429dee04c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        }
      },
      "source": [
        "p = Predict(\"models/vggnet.h5\", val_i0.01mages, val_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-f61c1d907b4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"models/vggnet.h5\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'Predict' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DAVefBy_VslJ",
        "colab_type": "text"
      },
      "source": [
        "## LeNet-5 Model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5me1HSeVslK",
        "colab_type": "code",
        "outputId": "f6958f19-df56-4d30-f494-ae243ba7974e",
        "colab": {}
      },
      "source": [
        "LeNetModel = LeNet()\n",
        "LeNetModel.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 28, 28, 1)         26        \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 10, 10, 6)         156       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 6)           0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 150)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 120)               18120     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 84)                10164     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 43)                3655      \n",
            "=================================================================\n",
            "Total params: 32,121\n",
            "Trainable params: 32,121\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HV5GypwuVslP",
        "colab_type": "code",
        "outputId": "b44514cb-9764-469b-aef7-a6ec01a583ca",
        "colab": {}
      },
      "source": [
        "TrainModel(LeNetModel, \"lenet5\", train_images, train_labels, val_images, val_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 39209 samples, validate on 5000 samples\n",
            "Epoch 1/30\n",
            "39209/39209 [==============================] - 6s 151us/step - loss: 1.7384 - val_loss: 1.2122\n",
            "Epoch 2/30\n",
            "39209/39209 [==============================] - 4s 115us/step - loss: 0.7062 - val_loss: 0.9532\n",
            "Epoch 3/30\n",
            "39209/39209 [==============================] - 4s 111us/step - loss: 0.4970 - val_loss: 0.8731\n",
            "Epoch 4/30\n",
            "39209/39209 [==============================] - 5s 129us/step - loss: 0.3816 - val_loss: 0.7728\n",
            "Epoch 5/30\n",
            "39209/39209 [==============================] - 4s 107us/step - loss: 0.3043 - val_loss: 0.7217\n",
            "Epoch 6/30\n",
            "39209/39209 [==============================] - 4s 95us/step - loss: 0.2552 - val_loss: 0.7633\n",
            "Epoch 7/30\n",
            "39209/39209 [==============================] - 5s 124us/step - loss: 0.2148 - val_loss: 0.7351\n",
            "Epoch 8/30\n",
            "39209/39209 [==============================] - 3s 81us/step - loss: 0.1871 - val_loss: 0.7082\n",
            "Epoch 9/30\n",
            "39209/39209 [==============================] - 4s 112us/step - loss: 0.1665 - val_loss: 0.7307\n",
            "Epoch 10/30\n",
            "39209/39209 [==============================] - 5s 124us/step - loss: 0.1438 - val_loss: 0.7346\n",
            "Epoch 11/30\n",
            "39209/39209 [==============================] - 4s 105us/step - loss: 0.1297 - val_loss: 0.7520\n",
            "Epoch 12/30\n",
            "39209/39209 [==============================] - 4s 94us/step - loss: 0.1179 - val_loss: 0.7558\n",
            "Epoch 13/30\n",
            "39209/39209 [==============================] - 4s 93us/step - loss: 0.1060 - val_loss: 0.7813\n"
          ],
          "name": "stdout"
        },
        {
          "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": "M3n7TkAkVslU",
        "colab_type": "code",
        "outputId": "a328c909-7039-4a2c-8851-ba3fb9442808",
        "colab": {}
      },
      "source": [
        "K.clear_session()\n",
        "p = Predict(\"models/lenet5.h5\", test_images, test_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total test data: 7629 wrong predicted: 1077\n",
            "error: 14.117184\n",
            "accuracy: 85.882816\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwrL0rvjVslX",
        "colab_type": "code",
        "outputId": "115ae2c1-228b-4483-b18d-56f2aab081fb",
        "colab": {}
      },
      "source": [
        "p = Predict(\"models/lenet5.h5\", train_images, train_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total test data: 39209 wrong predicted: 1149\n",
            "error: 2.930450\n",
            "accuracy: 97.069550\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bPPcLMMVslb",
        "colab_type": "code",
        "outputId": "79c39b75-57c4-4152-8a2b-d16ed4fecfb6",
        "colab": {}
      },
      "source": [
        "p = Predict(\"models/lenet5.h5\", val_images, val_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total test data: 5000 wrong predicted: 689\n",
            "error: 13.780000\n",
            "accuracy: 86.220000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0GRk1lCVsle",
        "colab_type": "text"
      },
      "source": [
        "# Adversarial Training\n",
        "\n",
        "## LeNet-5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1nM_MqcuVslf",
        "colab_type": "text"
      },
      "source": [
        "First create the adversarial training and test sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3an8_4KTVslg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def readAdversarialData(path, num, val):\n",
        "    images = []  # images\n",
        "    labels = []  # corresponding labels\n",
        "    gtFile = open(path + '/info.csv')  # annotations file\n",
        "    gtReader = csv.reader(gtFile, delimiter=',')  # csv parser for annotations file\n",
        "    \n",
        "    if val:\n",
        "        skip = 0\n",
        "    else:\n",
        "        skip = num\n",
        "        \n",
        "    for i in range(skip + 1):\n",
        "        next(gtReader)  # skip header\n",
        "\n",
        "    for row in gtReader:\n",
        "        if val and str(num) + \".jpg\" in row[0]:\n",
        "            break\n",
        "        image = preprocessImage(Image.open(path + '/' + row[0]))\n",
        "        images.append(np.array(image).reshape(32, 32, 1)/255)\n",
        "        label = np.zeros(43)\n",
        "        label_ind = int(re.search(r'\\d+', row[1]).group())\n",
        "        label[label_ind] = 1\n",
        "        labels.append(label)\n",
        "\n",
        "    gtFile.close()\n",
        "    return images, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txmmZM0OVslj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "adv_ep1_images, adv_ep1_labels = readAdversarialData(\"adversarial/LeNet/epsilon_1\", 1000, True) # epsilon = 0.01\n",
        "adv_ep10_images, adv_ep10_labels = readAdversarialData(\"adversarial/LeNet/epsilon_10\", 1000, True) # epsilon = 0.10\n",
        "adv_ep15_images, adv_ep15_labels = readAdversarialData(\"adversarial/LeNet/epsilon_15\", 1000, True) # epsilon = 0.15"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7y-oF5KGVslo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "adv_training_images = []\n",
        "adv_training_labels = []\n",
        "adv_training_images.extend(adv_ep1_images[:500])\n",
        "adv_training_labels.extend(adv_ep1_labels[:500])\n",
        "adv_training_images.extend(adv_ep10_images[:500])\n",
        "adv_training_labels.extend(adv_ep10_labels[:500])\n",
        "adv_training_images.extend(adv_ep15_images[:500])\n",
        "adv_training_labels.extend(adv_ep15_labels[:500])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "25ZklGjfVsls",
        "colab_type": "code",
        "outputId": "acee8a3b-109f-44b4-ccac-5a5cb4c6fb67",
        "colab": {}
      },
      "source": [
        "np.asarray(adv_training_images).shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1500, 32, 32, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQyDxjMWVslv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "adv_test_images = []\n",
        "adv_test_labels = []\n",
        "adv_test_images.extend(adv_ep1_images[500:])\n",
        "adv_test_labels.extend(adv_ep1_labels[500:])\n",
        "adv_test_images.extend(adv_ep10_images[500:])\n",
        "adv_test_labels.extend(adv_ep10_labels[500:])\n",
        "adv_test_images.extend(adv_ep15_images[500:])\n",
        "adv_test_labels.extend(adv_ep15_labels[500:])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AFgehMVVslz",
        "colab_type": "code",
        "outputId": "4731972c-9d79-4d06-b71e-993153c5a994",
        "colab": {}
      },
      "source": [
        "np.asarray(adv_test_images).shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(629, 32, 32, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "piSf1QWTVsl5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_training_images = []\n",
        "new_training_labels = []\n",
        "new_test_images = []\n",
        "new_test_labels = []\n",
        "\n",
        "new_training_images.extend(train_images)\n",
        "new_training_images.extend(adv_training_images)\n",
        "new_training_labels.extend(train_labels)\n",
        "new_training_labels.extend(adv_training_labels)\n",
        "\n",
        "new_test_images.extend(test_images)\n",
        "new_test_images.extend(adv_test_images)\n",
        "new_test_labels.extend(test_labels)\n",
        "new_test_labels.extend(adv_test_labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBT5BdOZVsl8",
        "colab_type": "code",
        "outputId": "77760000-ec75-4439-91f0-67eea6d51230",
        "colab": {}
      },
      "source": [
        "np.asarray(new_training_images).shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40709, 32, 32, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kSZxIqtcVsmA",
        "colab_type": "code",
        "outputId": "1798fc51-5af9-4377-842c-a26426595915",
        "colab": {}
      },
      "source": [
        "np.asarray(new_test_images).shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8258, 32, 32, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ni2Wv_bTVsmC",
        "colab_type": "text"
      },
      "source": [
        "#### New model that will be trained with adversarial dataset. We use the same structure."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mX14PxgOVsmD",
        "colab_type": "code",
        "outputId": "ad3acf72-5f53-4719-b970-7762f761fc67",
        "colab": {}
      },
      "source": [
        "LeNetAdvModel = LeNet()\n",
        "TrainModel(LeNetAdvModel, \"lenet5_adv\", np.asarray(new_training_images),\n",
        "           np.asarray(new_training_labels), val_images, val_labels, epochs=20, disable_early_stopping=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 40709 samples, validate on 5000 samples\n",
            "Epoch 1/20\n",
            "40709/40709 [==============================] - 6s 137us/step - loss: 1.8142 - val_loss: 1.1696\n",
            "Epoch 2/20\n",
            "40709/40709 [==============================] - 5s 116us/step - loss: 0.8149 - val_loss: 0.9385\n",
            "Epoch 3/20\n",
            "40709/40709 [==============================] - 4s 110us/step - loss: 0.6261 - val_loss: 0.7565\n",
            "Epoch 4/20\n",
            "40709/40709 [==============================] - 5s 128us/step - loss: 0.5111 - val_loss: 0.7786\n",
            "Epoch 5/20\n",
            "40709/40709 [==============================] - 6s 136us/step - loss: 0.4354 - val_loss: 0.6865\n",
            "Epoch 6/20\n",
            "40709/40709 [==============================] - 5s 116us/step - loss: 0.3687 - val_loss: 0.6807\n",
            "Epoch 7/20\n",
            "40709/40709 [==============================] - 5s 119us/step - loss: 0.3242 - val_loss: 0.6175\n",
            "Epoch 8/20\n",
            "40709/40709 [==============================] - 5s 118us/step - loss: 0.2963 - val_loss: 0.6694\n",
            "Epoch 9/20\n",
            "40709/40709 [==============================] - 5s 125us/step - loss: 0.2577 - val_loss: 0.6058\n",
            "Epoch 10/20\n",
            "40709/40709 [==============================] - 5s 113us/step - loss: 0.2342 - val_loss: 0.6386\n",
            "Epoch 11/20\n",
            "40709/40709 [==============================] - 5s 112us/step - loss: 0.2133 - val_loss: 0.6250\n",
            "Epoch 12/20\n",
            "40709/40709 [==============================] - 5s 113us/step - loss: 0.2028 - val_loss: 0.6491\n",
            "Epoch 13/20\n",
            "40709/40709 [==============================] - 5s 112us/step - loss: 0.1716 - val_loss: 0.5629\n",
            "Epoch 14/20\n",
            "40709/40709 [==============================] - 5s 112us/step - loss: 0.1574 - val_loss: 0.5746\n",
            "Epoch 15/20\n",
            "40709/40709 [==============================] - 5s 117us/step - loss: 0.1499 - val_loss: 0.6187\n",
            "Epoch 16/20\n",
            "40709/40709 [==============================] - 5s 116us/step - loss: 0.1368 - val_loss: 0.5896\n",
            "Epoch 17/20\n",
            "40709/40709 [==============================] - 5s 119us/step - loss: 0.1288 - val_loss: 0.5998\n",
            "Epoch 18/20\n",
            "40709/40709 [==============================] - 5s 115us/step - loss: 0.1228 - val_loss: 0.5724\n",
            "Epoch 19/20\n",
            "40709/40709 [==============================] - 5s 114us/step - loss: 0.1056 - val_loss: 0.7162\n",
            "Epoch 20/20\n",
            "40709/40709 [==============================] - 5s 115us/step - loss: 0.1075 - val_loss: 0.5930\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pqubNmzNv3jxOPPFEHnroIa6++moApk+fzjXXXMOMGTPo0aMHRUVFSW1HiV5EUi6dt/fctGkTTZs2Za+99mLlypW73VA8FU444QSeeeYZAObPn1/mGUOsY489lrfeeot169ZRWFjI5MmT6d27N2vWrMHdOe+88xg5ciSzZ8+mqKiIgoICTj75ZEaPHs3atWvZWrocrJJURi8iKZfO23t269aNTp06cfjhh5OXl8cJJ5yQ8m1cf/31XHrppXTq1Om7oaTYpSxt2rThzjvv5KSTTsLd6d+/P2eeeSazZ8/myiuvxN0xM379619TWFjIRRddxLfffktxcTG33HILTZs2TSrehLoprmn5+fk+c+bMdIchIjHUTfFOhYWFFBYWkpuby+LFi+nbty+LFy9mjz1q5ti5st0U64heRKSSNm/ezCmnnEJhYSHuztixY2ssyVdF5kYmIpKhmjVrxqxZs9IdRsJUGSsiCcvEot66piqfgRK9iCQkNzeXdevWKdmnkbuzbt06cnNzK7Wcim5EJCFt2rShoKCANWvWpDuUOi03N5c2bdpUahklehFJSE5ODu3bt093GFIFKroREclySvQiIllOiV5EJMtVmOjNbLyZrTazj+NMP8nMNprZnGj4Zcy0fma2yMyWmNnu/X+KiEi1S+SI/jGgXwXzvOPuXaNhJICZ1QceAk4HOgGDzaxTeSsREZHUqzDRu/vbwPoqrLsHsMTdl7r7dmAyMLAK6xERkSSkqoz+eDOba2avmllJD/ytgS9j5imIxomISA1KRTv62UCeu282szOAvwKHVnYlZjYUGArQtrruTiAiUgclfUTv7pvcfXP0/BUgx8xaACuAg2JmbRONi7eece6e7+75LVu2TDYsERGJJJ3ozayVmVn0vEe0znXADOBQM2tvZg2AC4GpyW5PREQqp8KiGzObBJwEtDCzAuAOIAfA3R8GzgV+bGaFwH+ACz30elRoZtcB04H6wHh3X1At70JEROLSHaZERLJAeXeY0pWxIiJZToleRCTLKdGLiGQ5JXoRkSynRC8ikuWU6EVEspwSvYhIllOiFxHJckr0IiJZToleRCTLKdGLiGQ5JXoRkSynRC8ikuWU6EVEspwSvYhIllOiFxHJckr0IiJZToleRCTLKdGLiGQ5JXoRkSxXYaI3s/FmttrMPo4z/WIzm2dm883sPTPrEjNtWTR+jpnpbt8iImmQyBH9Y0C/cqb/G+jt7p2BO4Fxpab3cfeu8e5OLiIi1WuPimZw97fNrF0509+LefkB0Cb5sEREJFVSXUZ/JfBqzGsHXjOzWWY2NMXbEhGRBFR4RJ8oM+tDSPQ9Y0b3dPcVZrYf8LqZferub8dZfigwFKBt27apCktEpM5LyRG9mR0FPAIMdPd1JePdfUX0uBqYAvSItw53H+fu+e6e37Jly1SEJSIipCDRm1lb4Hngh+7+Wcz4Pc2saclzoC9QZssdERGpPhUW3ZjZJOAkoIWZFQB3ADkA7v4w8EugOfAHMwMojFrY7A9MicbtATzl7tOq4T2IiEg5Eml1M7iC6VcBV5UxfinQZfclRESkJunKWBGRLKdELyKS5bIr0f/3v7B9e7qjEBHJKNmT6DdsgM6d4be/TXckIiIZJXsSfbNmIdHfeSd88UW6oxERyRjZk+gBxowJjzfdlN44REQySHYl+rZt4fbb4S9/gddeS3c0IiIZIbsSPcDNN8Ohh8L114fKWRGROi77En3DhvDAA/DZZzuLckRE6rDsS/QA/frBoEGhYvbLL9MdjYhIWmVnoodwNO+uilkRqfOyN9Hn5cFtt8Fzz8Hf/pbuaERE0iZ7Ez3ALbfAwQfDddfpilkRqbOyO9Hn5sKDD8KiRaqYFZE6K7sTPcDpp8PAgaqYFZE6K/sTPcB990FRUWhjLyJSx9SNRN+uXaiYffZZVcyKSJ1TNxI9wK23horZ669XxayI1Cl1J9Hn5sL998Onn4aiHBGROqLuJHqAM8+EAQNg5EgoKEh3NCIiNSKhRG9m481stZl9HGe6mdkDZrbEzOaZWbeYaZeZ2eJouCxVgVdZScXsLbekOxIRkRqR6BH9Y0C/cqafDhwaDUOBPwKY2b7AHcCxQA/gDjPbp6rBpkT79jBsGDz9NLzxRlpDERGpCQklend/G1hfziwDgSc8+ABoZmYHAKcBr7v7enf/Bnid8v8wasbPfw4dOqhiVkTqhFSV0bcGYq9GKojGxRu/GzMbamYzzWzmmjVrUhRWHI0ahYrZhQtDl8YiIlksYypj3X2cu+e7e37Lli2rf4NnnRWGESNgxYrq356ISJqkKtGvAA6Ked0mGhdvfGa4/34oLFTFrIhktVQl+qnApVHrm+OAje6+EpgO9DWzfaJK2L7RuMzQoUOomJ08Gd56K93RiIhUi0SbV04C3gcOM7MCM7vSzK4xs2uiWV4BlgJLgD8BPwFw9/XAncCMaBgZjcsc//u/oSXOddfBjh3pjkZEJOXM3dMdw27y8/N95syZNbfBF18MF1L95jfq+ExEaiUzm+Xu+WVNy5jK2LTq3z9cNTtiBHz1VbqjERFJKSX6EvffH4puhg6F4uJ0RyMikjJK9CUOPjgU3bz8Mowene5oRERSRok+1rXXwvnnw/Dh8I9/pDsaEZGUUKKPZQaPPAKHHAKDB8OqVemOSEQkaUr0pTVtCs89Bxs2wEUXhZ4uRURqMSX6snTuDH/4A7z5ZmiJIyJSiynRxzNkCFx+Odx1F0yblu5oRESqTIm+PL//fTi6v+QS+PLLiucXEclASvTladw4lNdv3w4XXKAuEkSkVlKir8j3vhda4rz/fugXR0SkllGiT8T554dOz8aMgeefT3c0IiKVokSfqN/8Bo45JlTQLlmS7mhERBKmRB+ZOBHatYN69cLjxImlZmjYEJ59FurXh/POg23b0hCliEjlKdETkvrQobB8ObiHx6FDy0j2eXnw5JMwZw789KdpiVVEpLKU6Ald22zduuu4rVvD+N2ceWa4K9W4cTBhQo3EJyKSDCV64IsvKjeeO++EXr3g6qthwYJqi0tEJBWU6IG2bSs3nj32CPeZbdIklNdv3lxtsYmIJEuJHrj77nBtVKzGjcP4uA44ACZNgkWL4JprQuG+iEgGUqIHLr44FLnn5YWeivPywuuLL65gwZNPhl/9KtTajhtX+Q3/978wfz4880xYzyWXwNixusOViKRUQjcHN7N+wP1AfeARdx9VavoYoE/0sjGwn7s3i6YVAfOjaV+4+4CKtlfjNwdPRnExnHEGvPVWuHq2W7fd59m0CRYu3H1YunRnUjeDli1h9Wo46ST485+hQ4cafSsiUnuVd3PwChO9mdUHPgP+BygAZgCD3f2TOPNfDxzt7ldErze7e5PKBFyrEj3A2rVw9NGQkxOOyBcv3jWhx95wPCcndKvQseOuw/e+B40awfjxcNNNUFgIv/41/OQnoXG/iEg5kk30xwMj3P206PX/Abj7PXHmfw+4w91fj15nf6KHcDTfq1dI0BBuYHL44bsn9A4dQmVueb78MjTknzYNevcOR/cHH1z970FEaq3yEn0FGQeA1kBsH70FwLFxNpQHtAfejBmda2YzgUJglLv/Nc6yQ4GhAG3jNnfJYMcfD//6F3z9dUjoBx4YimOq4qCD4JVX4LHH4MYb4aijYNSocE9bHd2LSCWlOmtcCDzn7rH338uL/mUuAu4zszIPTd19nLvnu3t+y5YtUxxWDTnySDj1VGjduupJvoRZ6FdnwYJwVH/DDdCnD3z+eWpiFZE6I5FEvwI4KOZ1m2hcWS4EJsWOcPcV0eNS4O/A0ZWOsi5r0wZefjmU3c+dG47uH3hALXNEJGGJJPoZwKFm1t7MGhCS+dTSM5nZ4cA+wPsx4/Yxs4bR8xbACUCZlbhSjpKj+48/Di1yfvrT8KheNEUyxyefhIOyDFRhonf3QuA6YDqwEHjG3ReY2Ugzi20qeSEw2Xet3e0IzDSzucBbhDJ6JfqqatMGXnoplN3PmxeO7u+/X0f3Iun2r3/BCSfAWWfBL36RcRdQJtSOvqbVylY3NW3FitDXzssvQ8+eoWjn0EPTHZVI3bNgQTjDbtQoPD75JAwZEi6izMmpsTDKa3WjJhy1VevW8OKL8PjjoUinSxe47z7d11akJi1eHBpg5OTAm2+G3+OvfhXOuvv3z5h+sHREnw2++ioc3b/0UnidkwN77hmGxo13Pk/k9fHHQ6dO6X0/IrXBsmVw4omhK5N//CM0qy7x5z+H32SXLuGsu1Wrag8n2Xb0kukOPBCmTg3D/PmwZUsYtm7d+XzLltAVw8qVu47bsmX38sQzzoBbbw3NOpNtJloZJXHU5DZFqqKgIPR1tWVL6P4kNskDXHll6PjwvPPg+98PFz9+73vpiRUd0Yt7OCLZsgU2boSnnoIHHwx97uTnh4R/9tkVX82bzPZnzQqnuk89BfvsAzffHMo4S3cpKpIJVq0KV8GvXAlvvBHuJR3PjBnhZkXFxeGM+7jjqi0sldFLfGaQmwvNm4fuGW6/PZySjh0bzgAuuCAciTz4YPgzSJWVK8MN1zt3Dj+UP/8Z+vaF/fYLVwDn5YWyzrVrU7dNkWStXRvK5AsKwtXr5SV5CNPffx+aNQtnAFN3a5leM9w944bu3bu7ZICiIvcpU9y//313cN93X/fbb3f/+uuqrW/bNvdnn3U/80z3+vXDOo8/3n3sWPdvvgnzFBe7v/OOe//+YXqjRu7XXuv++eepe1+ZorjYffly95dech81yv2SS9x/8Qv3zZvTHZmU5Ztv3Lt1c2/Y0P2NNyq37KpV7scc416vnvvDD1dLeMBMj5NT057UyxpqY6KfMME9L8/dLDxOmJDuiFLsn/90/8EPwhts2ND9Rz9y//TTipcrLnb/6CP3n/zEfZ99wleudWv3//u/ipdfsMD9iivcc3LCD+SCC9xnzkzN+4lVVOQ+b577H/7gfu+94cN74w33hQvdN2wI7yFZ69a5//3v7r//vfvVV4c/z732CvujZDjwwPCYl+f+4ovJb1NSZ9Mm9+OOC9/FV16p2jo2b3Y/44zwGd9+e2q+VzGU6KvZhAnujRvv+ptt3DgLk727+6JFIVE1bBiS/sCB7u++u/t8X33lPnq0e6dOYYfk5roPHuw+fbp7YWHltllQ4P7zn+9MjCef7D5tWtV/KNu2hZhHjQpnF82a7frhlR4aNXLv0MG9Z0/3885zv+EG93vucX/8cffXXnOfP9997doQz9at4c/o0Ufdb77ZvW/fnQm8ZGjWzP3EE8Of3x//GM5gSs5o3nln5z4755zw3iW9tmxx7907nIU+/3xy69qxw/2qq8Lne/nl7tu3pyRE9/ITvSpjU6BdO1i+fPfxeXmhuDsrrV4Nv/89PPQQrF8fmmXecgsUFYWK1WnTQgXU8ceHitXzzw/llMnYuBH+9CcYMyY0KT3qqFBZfMEF5V+YsmlTKCd9550wfPQRbNsWph12WGgiVzI0bx7qD776KjzGe/7tt7tvp0GD0E11yZXKDRuGpqqdO4fhyCPDY0U9m27fDr/9LYwcGd7X3XeH+xLUr1/1fVcT1q0L7cbbts2ellPbtsHAgfD66+FOcoMHJ79O9/DZjhgB/frBs8+G+08nKan+6NOhtiX6evXKvuLZrA70TrBlCzz6KPzud/Dvf4dxrVvDpZfCZZeFRJpq27eHFjr33hv6FznoIPjZz+Cqq8J9AL7+Gt59d2dinzs3fBD164c7gPXsGZJ6z57hrl5VsWVL2X8CjRrtTOwHH5xca6XPPw8V09OnhxZQY8eWfQezdPryS/jrX2HKlNCWvLg47NMePXYOxxwT/kBrmx074JxzwoWJ48eH/qZSqaStfdeuoa39/vsntbryEn3ai2nKGmpb0U1eXtln/Hl56Y6sBu3Y4T51aijKqGzRTFUVFYWKzF69dhaJHHLIrkUuffq4//KX7q+/7v7ttzUTVyoVF7tPmuS+//6hnuLGG0N5cTotWhSKro45Zue+7tjR/bbb3B96yH3IkFD8ZLZz+sEHh6K7MWNCfc/Wrel9DxXZsSMU00F4T9XlpZdCOW/79mG/JgEV3VSviRPDDaG2bt05rnHjBG8wLuwnxd4AAA0OSURBVKnx4Yehg7ctW3YWw3TrVqN9jVSrDRvgttvg4YfDGdODD8IPflAz23YPnXZNmQLPPx/OoiCcZZx9NgwaFO6mVtqmTeEaiY8+2jkUFIRp9euHoreSI/4ePUIxV0nxVFERfPNNKA4qa1i/vuzxDRuGz71bN+jePTweemjlbthTXByKG598MhSh3XRTUruvQh99FNrauyfV1l5FNzVg4kQYPhy++CIUUd59t5K8VIMPPgin+/PmwYABIeFXxx3ZiorgvfdCYp8yJVRC1asXLhQaNCj8yVRlu199FS4iKkn8M2aEuhcIXXC0ahUS9oYN8ddRvz7su28oDio9fPstzJ4d9s9//xvmb9o03NM5NvkfdljZdR7ucM014SjtzjvDdSU1YcmSUF6/YUOo2KtCmb0SvUg22bEjnL3ccUeoCBo5MtyBLJn6APeQJEuS+wsvhAr3Bg3ChWyDBoVOulJ997fi4tAxWEniX7duZ9KOl8z32qviyt4dO8KZx6xZIfHPmhXqav7znzB9zz1D2Xhs8u/YMVyV/cAD4ezp7rtT+14rsnp16KDw5JOrtLgSvUg2Wr48VNa+/HJIWmPHhiKQElu3huSxZk14rOh5yRFwkyahKOHss+H008MRcTYoLIRPP901+c+Zs/OK7wYNQkX/jTeGxgW1rOWQEr1ItnIPR+A33BBa/hx9dCi/XrMmfpcVjRqFFh4tW4YuJ/bbb+fzjh3DEWVubs2+j3QpKoLPPtuZ+A86KCT6WpbkQYleJPtt2hTKlOfN25m8YxN47PM990x3tFIN1E2xSLbba69wXYFIGdR7pYhIllOizxATJ4auFOrVC48TJ6Y7IhHJFgklejPrZ2aLzGyJmQ0rY/oQM1tjZnOi4aqYaZeZ2eJouCyVwWeLkguuli8PdWvLl4fXSvYikgoVVsaaWX3gM+B/gAJgBjDY3T+JmWcIkO/u15Vadl9gJpAPODAL6O7u35S3zbpWGVsnO0UTkZRK9g5TPYAl7r7U3bcDk4GBCW77NOB1d18fJffXgX4JLltnfPFF5caLiFRGIom+NfBlzOuCaFxp55jZPDN7zswOquSymNlQM5tpZjPXrFmTQFjZI96V5NVxZbuI1D2pqox9EWjn7kcRjtofr+wK3H2cu+e7e37LVF9mneHuvnv3+2A3blzzV2CLSHZKJNGvAA6Ked0mGvcdd1/n7tH10zwCdE90WQmdn40bF8rkzcKjer4UkVRJJNHPAA41s/Zm1gC4ENjlVuZmdkDMywHAwuj5dKCvme1jZvsAfaNxUsrFF4eK1+Li8KgkLyKpUuGVse5eaGbXERJ0fWC8uy8ws5GEju6nAjeY2QCgEFgPDImWXW9mdxL+LABGuvv6angfIiISh/q6yRLqD1+kblNfN1mu9B2uSi64AiV7EVEXCFlh+PBdb2MI4fXw4emJR0QyixJ9FtAFVyJSHiX6LKALrkSkPEr0WUAXXIlIeZTos4AuuBKR8ijRZ4lkL7hSf/gi2UvNK0XNM0WynI7oRc0zRbKcEr2oeaZIllOiFzXPFMlySvSi5pkiWU6JXlLSPFOtdkQyl1rdCBCSelVb2KjVjkhm0xG9JE2tdkQymxK9JC0VrXZU9CNSfZToJWnJttopKfpZvhzcdxb9KNmLpIYSvSQt2VY7KvoRqV5K9JK0ZFvt6IItkeqlRC8pkUynaqm4YEtl/CLxJZTozayfmS0ysyVmNqyM6TeZ2SdmNs/M3jCzvJhpRWY2JxqmpjJ4yQ7JFv2ojF+kfBUmejOrDzwEnA50AgabWadSs/0LyHf3o4DngNEx0/7j7l2jYUCK4pYskmzRj8r4RcqXyBF9D2CJuy919+3AZGBg7Azu/pa7l/zUPgDapDZMyXbJFP2oeadI+RJJ9K2BL2NeF0Tj4rkSeDXmda6ZzTSzD8zsB/EWMrOh0Xwz16xZk0BYIoGad4qUL6WVsWZ2CZAP3BszOs/d84GLgPvM7OCylnX3ce6e7+75LVu2TGVYkuUyoXmnzggkkyWS6FcAB8W8bhON24WZnQoMBwa4+39Lxrv7iuhxKfB34Ogk4hXZTbqbd+qMQDJdIol+BnCombU3swbAhcAurWfM7GhgLCHJr44Zv4+ZNYyetwBOAD5JVfAiJdLZvFNnBJLpKkz07l4IXAdMBxYCz7j7AjMbaWYlrWjuBZoAz5ZqRtkRmGlmc4G3gFHurkQvGSXZoh+dEUimM3dPdwy7yc/P95kzZ6Y7DKlDJk4MR+BffBGO5O++O/GzgnbtQnIuLS8vnF1U9/IiAGY2K6oP3Y2ujBUhuaKfdJ8RgIp+pHxK9CJJSrYyWM1Dpbop0YukQDrPCFQZLBVRohdJs2xoHqo/isymRC+SAWpz81D9UWQ+JXqRWi7dlcH6o8h8SvQitVy6K4P1R5H51I5epI4rSZSxybZx48T/LJK9DqBevZCgSzMLRVnVvf1k33+mUDt6EYkr2TOCZIuOavsZBSR/RlDtZxTunnFD9+7dXURqjwkT3PPy3M3C44QJlVu2cWP3cFwfhsaNE19HXt6uy5YMeXmJLW9W9vJmNRN/ssuXAGZ6nJya9qRe1qBEL1K31OY/inQvX6K8RK+iGxFJu2Sal6a76CnZoqNUdIFRESV6Ean10vlHkWwdQ7LLJ0KJXkTqvHR2YZHs8olQohcRSUKyZwTJLp8ItaMXEckCakcvIlKHKdGLiGQ5JXoRkSynRC8ikuWU6EVEslxGtroxszVAGf3RJaQFsDaF4aSa4kuO4kuO4ktOJseX5+4ty5qQkYk+GWY2M14To0yg+JKj+JKj+JKT6fHFo6IbEZEsp0QvIpLlsjHRj0t3ABVQfMlRfMlRfMnJ9PjKlHVl9CIisqtsPKIXEZEYSvQiIlmu1iZ6M+tnZovMbImZDStjekMzezqa/qGZtavB2A4ys7fM7BMzW2BmPy1jnpPMbKOZzYmGX9ZUfNH2l5nZ/Gjbu3UVasED0f6bZ2bdajC2w2L2yxwz22RmN5aap0b3n5mNN7PVZvZxzLh9zex1M1scPe4TZ9nLonkWm9llNRjfvWb2afT5TTGzZnGWLfe7UI3xjTCzFTGf4Rlxli33t16N8T0dE9syM5sTZ9lq339Ji3ePwUwegPrA50AHoAEwF+hUap6fAA9Hzy8Enq7B+A4AukXPmwKflRHfScBLadyHy4AW5Uw/A3gVMOA44MM0ftZfEy4GSdv+A3oB3YCPY8aNBoZFz4cBvy5juX2BpdHjPtHzfWoovr7AHtHzX5cVXyLfhWqMbwRwSwKff7m/9eqKr9T03wK/TNf+S3aorUf0PYAl7r7U3bcDk4GBpeYZCDwePX8OOMXMrCaCc/eV7j47ev4tsBBoXRPbTqGBwBMefAA0M7MD0hDHKcDn7l7VK6VTwt3fBtaXGh37HXsc+EEZi54GvO7u6939G+B1oF9NxOfur7l7YfTyA6BNqrebqDj7LxGJ/NaTVl58Ud44H5iU6u3WlNqa6FsDX8a8LmD3RPrdPNGXfSPQvEaiixEVGR0NfFjG5OPNbK6ZvWpmR9RoYODAa2Y2y8yGljE9kX1cEy4k/g8snfsPYH93Xxk9/xrYv4x5MmU/XkE4QytLRd+F6nRdVLQ0Pk7RVybsvxOBVe6+OM70dO6/hNTWRF8rmFkT4C/Aje6+qdTk2YTiiC7Ag8Bfazi8nu7eDTgduNbMetXw9itkZg2AAcCzZUxO9/7bhYdz+Ixsq2xmw4FCYGKcWdL1XfgjcDDQFVhJKB7JRIMp/2g+439LtTXRrwAOinndJhpX5jxmtgewN7CuRqIL28whJPmJ7v586enuvsndN0fPXwFyzKxFTcXn7iuix9XAFMIpcqxE9nF1Ox2Y7e6rSk9I9/6LrCopzooeV5cxT1r3o5kNAc4CLo7+jHaTwHehWrj7Kncvcvdi4E9xtpvu/bcHcDbwdLx50rX/KqO2JvoZwKFm1j466rsQmFpqnqlASQuHc4E3433RUy0q0/szsNDdfxdnnlYldQZm1oPwWdTIH5GZ7WlmTUueEyrtPi4121Tg0qj1zXHAxphiipoS90gqnfsvRux37DLghTLmmQ70NbN9oqKJvtG4amdm/YCfAwPcfWuceRL5LlRXfLF1PoPibDeR33p1OhX41N0LypqYzv1XKemuDa7qQGgV8hmhRn54NG4k4UsNkEs45V8CfAR0qMHYehJO4+cBc6LhDOAa4JponuuABYRWBB8A36/B+DpE250bxVCy/2LjM+ChaP/OB/Jr+PPdk5C4944Zl7b9R/jDWQnsIJQTX0mo83kDWAz8Ddg3mjcfeCRm2Sui7+ES4PIajG8JoXy75DtY0grtQOCV8r4LNRTfk9F3ax4heR9QOr7o9W6/9ZqILxr/WMl3LmbeGt9/yQ7qAkFEJMvV1qIbERFJkBK9iEiWU6IXEclySvQiIllOiV5EJMsp0YuIZDklehGRLPf/Jh4fLyZ4CP0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFOqIXtZVsmG",
        "colab_type": "code",
        "outputId": "d55bca9b-330d-4d96-d216-5b6209f68eb2",
        "colab": {}
      },
      "source": [
        "K.clear_session()\n",
        "p = Predict(\"models/lenet5_adv.h5\", np.asarray(new_test_images), np.asarray(new_test_labels))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total test data: 8258 wrong predicted: 1183\n",
            "error: 14.325503\n",
            "accuracy: 85.674497\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6dDOoTuVsmK",
        "colab_type": "code",
        "outputId": "ef502528-8584-4b4a-ee7a-20ea7354f088",
        "colab": {}
      },
      "source": [
        "p = Predict(\"models/lenet5_adv.h5\", np.asarray(new_training_images), np.asarray(new_training_labels))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total test data: 40709 wrong predicted: 896\n",
            "error: 2.200987\n",
            "accuracy: 97.799013\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "850nj6nLVsmN",
        "colab_type": "text"
      },
      "source": [
        "#### Let's compare the accuracy of the original model and adversarial trained model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtRELMDvVsmP",
        "colab_type": "code",
        "outputId": "745c1382-0c7e-4bc9-fb67-f3de0fa89005",
        "colab": {}
      },
      "source": [
        "p_adv_original = Predict(\"models/lenet5.h5\", np.asarray(adv_test_images), np.asarray(adv_test_labels))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total test data: 629 wrong predicted: 514\n",
            "error: 81.717011\n",
            "accuracy: 18.282989\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qzZSzljVsmS",
        "colab_type": "code",
        "outputId": "7d982b5e-2d46-48c2-fa62-2b7c38732476",
        "colab": {}
      },
      "source": [
        "p_adv_trained = Predict(\"models/lenet5_adv.h5\", np.asarray(adv_test_images), np.asarray(adv_test_labels))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total test data: 629 wrong predicted: 249\n",
            "error: 39.586645\n",
            "accuracy: 60.413355\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l55sG63-VsmW",
        "colab_type": "text"
      },
      "source": [
        "An example of wrongly predicted adversarial sample"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51wSmFA4VsmW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def displayExampleImage(img_ind, orig_model_path, adv_model_path, adv_test_images, adv_test_labels, eps_range=[210,420]):\n",
        "    eps=0\n",
        "    if img_ind < eps_range[0]:\n",
        "        eps = 0.01\n",
        "    elif img_ind < eps_range[1]:\n",
        "        eps = 0.1\n",
        "    else:\n",
        "        eps = 0.15\n",
        "    fig = plt.figure()\n",
        "    fig.set_size_inches(20, 3)\n",
        "    fig.suptitle('Adversarial Sample - epsilon = %.2f' % eps)\n",
        "    plt.imshow(adv_test_images[img_ind].reshape((32,32)))\n",
        "    plt.show()\n",
        "\n",
        "    advModel = load_model(adv_model_path)\n",
        "    adv_pred = advModel.predict(np.asarray([adv_test_images[img_ind]]))[0]\n",
        "\n",
        "    origModel = load_model(orig_model_path)\n",
        "    orig_pred = origModel.predict(np.asarray([adv_test_images[img_ind]]))[0]\n",
        "\n",
        "    print('Real label: %d' % getLabel(adv_test_labels[img_ind]))\n",
        "    print('Predicted label by original model: %d' % getLabel(orig_pred))\n",
        "    print('Predicted label by adversarially trained model: %d' % getLabel(adv_pred))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CKvJdHGBVsmZ",
        "colab_type": "code",
        "outputId": "d1f0133e-c321-48cc-fde9-89b8354315bd",
        "colab": {}
      },
      "source": [
        "img_ind = 89\n",
        "displayExampleImage(img_ind, 'models/lenet5.h5', 'models/lenet5_adv.h5', adv_test_images, adv_test_labels, eps_range=[210,420])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Real label: 5\n",
            "Predicted label by original model: 3\n",
            "Predicted label by adversarially trained model: 5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jKqNcG-FVsmb",
        "colab_type": "text"
      },
      "source": [
        "## VGGNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0_6iBYvVsme",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "adv_ep1_images2, adv_ep1_labels2 = readAdversarialData(\"adversarial/VGGNet/epsilon_1\", 1000, True) # epsilon = 0.01\n",
        "adv_ep10_images2, adv_ep10_labels2 = readAdversarialData(\"adversarial/VGGNet/epsilon_10\", 1000, True) # epsilon = 0.10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uIrTwmGJVsmg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "adv_training_images2 = []\n",
        "adv_training_labels2 = []\n",
        "adv_training_images2.extend(adv_ep1_images2[:350])\n",
        "adv_training_labels2.extend(adv_ep1_labels2[:350])\n",
        "adv_training_images2.extend(adv_ep10_images2[:350])\n",
        "adv_training_labels2.extend(adv_ep10_labels2[:350])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wa9zD5FDVsml",
        "colab_type": "code",
        "outputId": "0bc7884e-0633-4650-a906-e258d5fefca4",
        "colab": {}
      },
      "source": [
        "np.asarray(adv_training_images2).shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(700, 32, 32, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9vaLFtTtVsmn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "adv_test_images2 = []\n",
        "adv_test_labels2 = []\n",
        "adv_test_images2.extend(adv_ep1_images2[350:])\n",
        "adv_test_labels2.extend(adv_ep1_labels2[350:])\n",
        "adv_test_images2.extend(adv_ep10_images2[350:])\n",
        "adv_test_labels2.extend(adv_ep10_labels2[350:])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zxnmqc_XVsmr",
        "colab_type": "code",
        "outputId": "a5fdb340-ab58-47cf-f934-0a1746aa652e",
        "colab": {}
      },
      "source": [
        "np.asarray(adv_test_images2).shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 32, 32, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yR6oRsydVsmu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_training_images2 = []\n",
        "new_training_labels2 = []\n",
        "new_test_images2 = []\n",
        "new_test_labels2 = []\n",
        "\n",
        "new_training_images2.extend(train_images)\n",
        "new_training_images2.extend(adv_training_images2)\n",
        "new_training_labels2.extend(train_labels)\n",
        "new_training_labels2.extend(adv_training_labels2)\n",
        "\n",
        "new_test_images2.extend(test_images)\n",
        "new_test_images2.extend(adv_test_images2)\n",
        "new_test_labels2.extend(test_labels)\n",
        "new_test_labels2.extend(adv_test_labels2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvBq0MfnVsmw",
        "colab_type": "code",
        "outputId": "4e9f9f9a-1035-44c0-af42-769f2f2c0d74",
        "colab": {}
      },
      "source": [
        "np.asarray(new_training_images2).shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39909, 32, 32, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gE9SE1amVsmy",
        "colab_type": "code",
        "outputId": "d28c3b3b-b414-4090-ec6f-28aa301d29f4",
        "colab": {}
      },
      "source": [
        "np.asarray(new_test_images2).shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7932, 32, 32, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "2fPNOqRUVsm2",
        "colab_type": "code",
        "outputId": "3e909a15-d61a-4ffe-dbf0-11177fe679c4",
        "colab": {}
      },
      "source": [
        "VGGNetAdvModel = VGGNet()\n",
        "TrainModel(VGGNetAdvModel, \"vggnet_adv\", np.asarray(new_training_images2),\n",
        "           np.asarray(new_training_labels2), val_images, val_labels, epochs=30, disable_early_stopping=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 39909 samples, validate on 5000 samples\n",
            "Epoch 1/30\n",
            "39909/39909 [==============================] - 83s 2ms/step - loss: 3.5023 - val_loss: 2.9158\n",
            "Epoch 2/30\n",
            "39909/39909 [==============================] - 75s 2ms/step - loss: 2.0524 - val_loss: 1.1353\n",
            "Epoch 3/30\n",
            "39909/39909 [==============================] - 79s 2ms/step - loss: 1.1171 - val_loss: 0.6134\n",
            "Epoch 4/30\n",
            "39909/39909 [==============================] - 81s 2ms/step - loss: 0.6864 - val_loss: 0.4135\n",
            "Epoch 5/30\n",
            "39909/39909 [==============================] - 73s 2ms/step - loss: 0.5065 - val_loss: 0.3366\n",
            "Epoch 6/30\n",
            "39909/39909 [==============================] - 75s 2ms/step - loss: 0.4126 - val_loss: 0.2606\n",
            "Epoch 7/30\n",
            "39909/39909 [==============================] - 74s 2ms/step - loss: 0.3504 - val_loss: 0.2358\n",
            "Epoch 8/30\n",
            "39909/39909 [==============================] - 68s 2ms/step - loss: 0.2978 - val_loss: 0.2556\n",
            "Epoch 9/30\n",
            "39909/39909 [==============================] - 68s 2ms/step - loss: 0.2665 - val_loss: 0.2300\n",
            "Epoch 10/30\n",
            "39909/39909 [==============================] - 68s 2ms/step - loss: 0.2384 - val_loss: 0.1771\n",
            "Epoch 11/30\n",
            "39909/39909 [==============================] - 68s 2ms/step - loss: 0.2259 - val_loss: 0.1715\n",
            "Epoch 12/30\n",
            "39909/39909 [==============================] - 68s 2ms/step - loss: 0.2028 - val_loss: 0.1618\n",
            "Epoch 13/30\n",
            "39909/39909 [==============================] - 68s 2ms/step - loss: 0.1967 - val_loss: 0.1898\n",
            "Epoch 14/30\n",
            "39909/39909 [==============================] - 68s 2ms/step - loss: 0.1817 - val_loss: 0.1676\n",
            "Epoch 15/30\n",
            "39909/39909 [==============================] - 68s 2ms/step - loss: 0.1859 - val_loss: 0.1656\n",
            "Epoch 16/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1807 - val_loss: 0.1419\n",
            "Epoch 17/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1713 - val_loss: 0.1466\n",
            "Epoch 18/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1611 - val_loss: 0.1966\n",
            "Epoch 19/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1594 - val_loss: 0.1910\n",
            "Epoch 20/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1570 - val_loss: 0.1505\n",
            "Epoch 21/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1574 - val_loss: 0.1451\n",
            "Epoch 22/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1607 - val_loss: 0.1369\n",
            "Epoch 23/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1484 - val_loss: 0.1407\n",
            "Epoch 24/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1491 - val_loss: 0.1502\n",
            "Epoch 25/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1637 - val_loss: 0.1714\n",
            "Epoch 26/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1588 - val_loss: 0.1342\n",
            "Epoch 27/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1457 - val_loss: 0.1674\n",
            "Epoch 28/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1429 - val_loss: 0.1584\n",
            "Epoch 29/30\n",
            "39909/39909 [==============================] - 69s 2ms/step - loss: 0.1531 - val_loss: 0.1558\n",
            "Epoch 30/30\n",
            "39909/39909 [==============================] - 70s 2ms/step - loss: 0.1473 - val_loss: 0.1564\n"
          ],
          "name": "stdout"
        },
        {
          "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": "w4NLSWyvVsm6",
        "colab_type": "code",
        "outputId": "2661a1ba-e021-46bd-bfaa-652f9ef1c0cc",
        "colab": {}
      },
      "source": [
        "K.clear_session()\n",
        "p = Predict(\"models/vggnet_adv.h5\", np.asarray(new_test_images2), np.asarray(new_test_labels2))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total test data: 7932 wrong predicted: 349\n",
            "error: 4.399899\n",
            "accuracy: 95.600101\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXizoGUPVsm8",
        "colab_type": "code",
        "outputId": "250bf7b4-62a5-41ed-d616-b318928c7b63",
        "colab": {}
      },
      "source": [
        "p = Predict(\"models/vggnet_adv.h5\", np.asarray(new_training_images2), np.asarray(new_training_labels2))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total test data: 39909 wrong predicted: 293\n",
            "error: 0.734170\n",
            "accuracy: 99.265830\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FfsKI5f6Vsm-",
        "colab_type": "text"
      },
      "source": [
        "#### Let's compare the accuracy of the original model and adversarial trained model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RGsjRmeIVsm_",
        "colab_type": "code",
        "outputId": "bfc89221-a26c-43fc-a3f6-1bb2eded4fb5",
        "colab": {}
      },
      "source": [
        "p_adv_original = Predict(\"models/vggnet.h5\", np.asarray(adv_test_images2), np.asarray(adv_test_labels2))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total test data: 303 wrong predicted: 139\n",
            "error: 45.874587\n",
            "accuracy: 54.125413\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "BAGsB1rAVsnC",
        "colab_type": "code",
        "outputId": "18ffc4be-1c1a-49b7-892b-71d185e4e488",
        "colab": {}
      },
      "source": [
        "p_adv_trained = Predict(\"models/vggnet_adv.h5\", np.asarray(adv_test_images2), np.asarray(adv_test_labels2))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total test data: 303 wrong predicted: 101\n",
            "error: 33.333333\n",
            "accuracy: 66.666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pdyH6KdVVsnE",
        "colab_type": "text"
      },
      "source": [
        "An example of wrongly predicted adversarial sample for VGGNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_jTr4XCVsnF",
        "colab_type": "code",
        "outputId": "e40aca58-65ce-4c53-d438-24defd303008",
        "colab": {}
      },
      "source": [
        "img_ind = 283 # 283\n",
        "displayExampleImage(img_ind, 'models/vggnet.h5', 'models/vggnet_adv.h5', adv_test_images2, adv_test_labels2, eps_range=[152,304])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Real label: 33\n",
            "Predicted label by original model: 14\n",
            "Predicted label by adversarially trained model: 33\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}