[3]DAI_ASSIGNMENT_2_Part_B.ipynb

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  "metadata": {
    "colab": {
      "name": "DAI_ASSIGNMENT_2_Part_B_FGSM_CW_PGD.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lqEuJLSiVdAU",
        "outputId": "cdce7223-4a59-4c77-ec10-754411086388"
      },
      "source": [
        "!python --version"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python 2.7.17\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GPCdYYoLRqPc"
      },
      "source": [
        "You may use any external libraries or GitHub codes. However, the evaluation will test your\n",
        "knowledge of the algorithm and the choice of hyperparameters. Do cite the libraries/codes.\n",
        "\n",
        "AIM: ​Understand and Perform different types of Adversarial Attacks, Detection and Mitigation.\n",
        "Dataset: D-10 Dataset(​CLICK​ for Dataset) - 10 Class Animal Dataset\n",
        "Reference Paper: ​Adversarial Attacks and Defenses in Deep Learning\n",
        "\n",
        "b. Perform both ​targeted and ​untargeted attacks(on testing set) for any ​3 attacks ​among the following.​ [ 30 Marks]\n",
        "\n",
        "i. L-BFGS algorithm\n",
        "Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, et al. Intriguing properties of neural networks. 2013. arXiv:1312.6199.\n",
        "\n",
        "ii. Fast gradient sign method\n",
        "Goodfellow IJ, Shlens J, Szegedy C. Explaining and harnessing adversarial examples. 2014. arXiv:1412.6572.\n",
        "\n",
        "iii. BIM and PGD\n",
        "Kurakin A, Goodfellow I, Bengio S. Adversarial examples in the physical world. 2016. arXiv:1607.02533.\n",
        "\n",
        "iv. Momentum iterative attack\n",
        "Dong Y, Liao F, Pang T, Su H, Zhu J, Hu X, et al. Boosting adversarial attacks with momentum. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition; 2018 Jun 18–23; Salt Lake City, UT, USA; 2018. p. 9185–193.\n",
        "  \n",
        "v. Distributionally adversarial attack\n",
        "Zheng T, Chen C, Ren K. Distributionally adversarial attack. 2018. arXiv:1808.05537.\n",
        "\n",
        "vi. Carlini and Wagner attack\n",
        "Carlini N, Wagner D. Towards evaluating the robustness of neural networks. In: Proceedings of the 2017 IEEE Symposium on Security and Privacy; 2017 May 22–26; San Jose, CA, USA; 2017. p. 39–57.\n",
        "\n",
        "vii. Jacobian-based saliency map approach\n",
        "Papernot N, McDaniel P, Jha S, Fredrikson M, Celik ZB, Swami A. The limitations of deep learning in adversarial settings. In: Proceedings of the 2016 IEEE European Symposium on Security and Privacy; 2016 Mar 21–24; Saarbrucken, Germany; 2016. p. 372–87."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gXejwgqHQuyH",
        "outputId": "6aa8b5c0-8b06-4206-da6b-0242126d33c9"
      },
      "source": [
        "# FGSM - https://www.tensorflow.org/tutorials/generative/adversarial_fgsm\n",
        "# colab ref - https://colab.research.google.com/github/shreehari117/ML-2/blob/master/Sureshbabu_Shree_Hari_aml.ipynb\n",
        "# https://colab.research.google.com/github/andantillon/cleverhans/blob/master/tutorials/future/tf2/notebook_tutorials/mnist_fgsm_tutorial.ipynb\n",
        "\n",
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive') \n",
        "import os\n",
        "%cd '/content/gdrive/My Drive/DAI_ASSIGNMENT_2'\n",
        "!ls\n",
        "%cd '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images'\n",
        "!ls\n",
        "!find . -maxdepth 2 -type d\n",
        "!find . -maxdepth 1 -mindepth 1 -type d -exec sh -c 'echo \"{} : $(find \"{}\" -type f | wc -l)\" file\\(s\\)' \\;\n",
        "\n",
        "# Install bleeding edge version of cleverhans\n",
        "# !pip install cleverhans==2.1.0\n",
        "# !pip install cleverhans\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd \n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "import os\n",
        "import sys\n",
        "import time\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "from builtins import range, input\n",
        "\n",
        "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten, AveragePooling2D, Dropout, Conv2D, MaxPool2D, BatchNormalization, GlobalAveragePooling2D\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
        "from tensorflow.keras.applications import InceptionV3\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "from tensorflow.keras.applications.resnet50 import ResNet50\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from sklearn.metrics import confusion_matrix, roc_curve\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "import tensorflow as tf\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (8, 8)\n",
        "mpl.rcParams['axes.grid'] = False\n",
        "import cv2\n",
        "from glob import glob\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "#Install Cleverhans (version Cleverhans 2.1.0 is most compatable with Python 2.x)\n",
        "\n",
        "# import cleverhans\n",
        "\n",
        "print(\"\\nTensorflow Version: \" + tf.__version__)\n",
        "# print(\"Cleverhans Version: \" + cleverhans.__version__)\n",
        "print(\"GPU Available: \", tf.test.is_gpu_available())\n",
        "# from cleverhans.utils_keras import KerasModelWrapper\n",
        "# from cleverhans.attacks import FastGradientMethod, MadryEtAl, CarliniWagnerL2\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n",
            "/content/gdrive/My Drive/DAI_ASSIGNMENT_2\n",
            "Classes.py  Images  processed_images\n",
            "/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images\n",
            " cleverhans  'Copy of final_model.h5'   final_model.h5\t test   train\n",
            ".\n",
            "./train\n",
            "./train/cane\n",
            "./train/cavallo\n",
            "./train/elefante\n",
            "./train/farfalla\n",
            "./train/gallina\n",
            "./train/gatto\n",
            "./train/mucca\n",
            "./train/pecora\n",
            "./train/ragno\n",
            "./train/scoiattolo\n",
            "./test\n",
            "./test/cane\n",
            "./test/cavallo\n",
            "./test/elefante\n",
            "./test/farfalla\n",
            "./test/gallina\n",
            "./test/gatto\n",
            "./test/mucca\n",
            "./test/pecora\n",
            "./test/ragno\n",
            "./test/scoiattolo\n",
            "./cleverhans\n",
            "./cleverhans/.git\n",
            "./cleverhans/.github\n",
            "./cleverhans/assets\n",
            "./cleverhans/cleverhans\n",
            "./cleverhans/cleverhans_tutorials\n",
            "./cleverhans/defenses\n",
            "./cleverhans/docs\n",
            "./cleverhans/docsource\n",
            "./cleverhans/examples\n",
            "./cleverhans/scripts\n",
            "./cleverhans/tests_tf\n",
            "./cleverhans/tutorials\n",
            "./cleverhans/cleverhans.egg-info\n",
            "./train : 15702 file(s)\n",
            "./test : 10477 file(s)\n",
            "./cleverhans : 424 file(s)\n",
            "Collecting cleverhans\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4e/33/8968e53d7f81d4c89d2f17a3b730d3108ee0e55174f6c1d13a55cb7c9785/cleverhans-3.0.1.tar.gz (142kB)\n",
            "\u001b[K     |████████████████████████████████| 143kB 5.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: nose in /usr/local/lib/python2.7/dist-packages (from cleverhans) (1.3.7)\n",
            "Collecting pycodestyle\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/10/5b/88879fb861ab79aef45c7e199cae3ef7af487b5603dcb363517a50602dd7/pycodestyle-2.6.0-py2.py3-none-any.whl (41kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 5.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python2.7/dist-packages (from cleverhans) (1.2.2)\n",
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            "Collecting mnist~=0.2\n",
            "  Downloading https://files.pythonhosted.org/packages/c6/c4/5db3bfe009f8d71f1d532bbadbd0ec203764bba3a469e4703a889db8e5e0/mnist-0.2.2-py2.py3-none-any.whl\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python2.7/dist-packages (from cleverhans) (1.16.4)\n",
            "Requirement already satisfied: tensorflow-probability in /usr/local/lib/python2.7/dist-packages (from cleverhans) (0.9.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans) (0.10.0)\n",
            "Requirement already satisfied: backports.functools-lru-cache in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans) (1.5)\n",
            "Requirement already satisfied: subprocess32 in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans) (3.5.4)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans) (1.1.0)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans) (2018.9)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans) (1.15.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans) (2.5.3)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans) (2.4.0)\n",
            "Requirement already satisfied: mock; python_version == \"2.7\" in /usr/local/lib/python2.7/dist-packages (from mnist~=0.2->cleverhans) (2.0.0)\n",
            "Requirement already satisfied: gast>=0.2 in /usr/local/lib/python2.7/dist-packages (from tensorflow-probability->cleverhans) (0.2.2)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python2.7/dist-packages (from tensorflow-probability->cleverhans) (4.4.0)\n",
            "Requirement already satisfied: cloudpickle>=1.2.2 in /usr/local/lib/python2.7/dist-packages (from tensorflow-probability->cleverhans) (1.3.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python2.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->cleverhans) (44.1.1)\n",
            "Requirement already satisfied: funcsigs>=1; python_version < \"3.3\" in /usr/local/lib/python2.7/dist-packages (from mock; python_version == \"2.7\"->mnist~=0.2->cleverhans) (1.0.2)\n",
            "Requirement already satisfied: pbr>=0.11 in /usr/local/lib/python2.7/dist-packages (from mock; python_version == \"2.7\"->mnist~=0.2->cleverhans) (5.4.0)\n",
            "Building wheels for collected packages: cleverhans\n",
            "  Building wheel for cleverhans (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for cleverhans: filename=cleverhans-3.0.1-cp27-none-any.whl size=175561 sha256=9aa73af48dae96a5ea109717d789ae002e19f7ce2305494b23abdd1b0d350b48\n",
            "  Stored in directory: /root/.cache/pip/wheels/44/de/34/ba6eb751a29b7e111005a8ba4059fdea93275605c0531024f4\n",
            "Successfully built cleverhans\n",
            "Installing collected packages: pycodestyle, mnist, cleverhans\n",
            "Successfully installed cleverhans-3.0.1 mnist-0.2.2 pycodestyle-2.6.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0116 09:24:20.477720 139758441088896 deprecation.py:323] From <ipython-input-2-7adc8783e3a0>:64: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.config.list_physical_devices('GPU')` instead.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Tensorflow Version: 2.1.0\n",
            "Cleverhans Version: 3.0.1-9acbc88faf1d864c83e8d0ee0e71ba7f\n",
            "('GPU Available: ', True)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6CzCWrbWc2c",
        "outputId": "09df6a8c-f0ec-4880-979e-2a81602b9f48"
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd \n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "import os\n",
        "import sys\n",
        "import time\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "from builtins import range, input\n",
        "\n",
        "\n",
        "# ### for v1 ###\n",
        "# import tensorflow.compat.v1 as tf\n",
        "# from tensorflow.compat.v1 import keras\n",
        "# from tensorflow.compat.v1.keras.layers import Input, Lambda, Dense, Flatten, AveragePooling2D, Dropout, Conv2D, MaxPool2D, BatchNormalization, GlobalAveragePooling2D\n",
        "# from tensorflow.compat.v1.keras.models import Model, load_model\n",
        "# from tensorflow.compat.v1.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
        "# from tensorflow.compat.v1.keras.applications import InceptionV3\n",
        "# from tensorflow.compat.v1.keras.applications.resnet50 import preprocess_input\n",
        "# from tensorflow.compat.v1.keras.applications.resnet50 import ResNet50\n",
        "# from tensorflow.compat.v1.keras.preprocessing import image\n",
        "# from tensorflow.compat.v1.keras.preprocessing.image import ImageDataGenerator, load_img\n",
        "# from tensorflow.compat.v1.keras.models import Sequential, Model\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten, AveragePooling2D, Dropout, Conv2D, MaxPool2D, BatchNormalization, GlobalAveragePooling2D\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
        "from tensorflow.keras.applications import InceptionV3\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "from tensorflow.keras.applications.resnet50 import ResNet50\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from sklearn.metrics import confusion_matrix, roc_curve\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "import cv2\n",
        "from glob import glob\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "h,w = (224,224)\n",
        "batch = 32\n",
        "train = '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images/train'\n",
        "test = '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images/test'\n",
        "\n",
        "dataloader = ImageDataGenerator(\n",
        "                                  preprocessing_function = preprocess_input,\n",
        "                                  shear_range = 0.2,\n",
        "                                  zoom_range = 0.2,\n",
        "                                  horizontal_flip = True,\n",
        "                                  )\n",
        "train_loader = dataloader.flow_from_directory(\n",
        "    train, \n",
        "    target_size = (h,w),\n",
        "    batch_size = batch,\n",
        "    class_mode= 'categorical',\n",
        "    subset = 'training'\n",
        ")\n",
        "\n",
        "test_loader = dataloader.flow_from_directory(\n",
        "    test, \n",
        "    target_size = (h,w),\n",
        "    batch_size = 1,\n",
        "    class_mode= 'categorical',\n",
        "    subset = 'training'\n",
        ")\n",
        "\n",
        "print(\"testing example\")\n",
        "xtestex,ytestex = test_loader.next()\n",
        "print(xtestex.shape)\n",
        "print(ytestex.shape)\n",
        "print(\"training example\")\n",
        "xtrainex, ytrainex = train_loader.next()\n",
        "print(xtrainex.shape)\n",
        "print(ytrainex.shape)\n",
        "\n",
        "base_model = ResNet50(weights=None,input_shape=(224,224,3),include_top=False)\n",
        "base_model.summary()\n",
        "new_model = base_model.output\n",
        "new_model = AveragePooling2D(pool_size=(7, 7))(new_model)\n",
        "new_model = Flatten(name=\"flatten\")(new_model)\n",
        "new_model = Dense(256, activation=\"relu\")(new_model)\n",
        "# new_model = Dropout(0.5)(new_model)\n",
        "new_model = Dense(10, activation=\"softmax\")(new_model)\n",
        "model = Model(inputs=base_model.input, outputs=new_model)\n",
        "# import torch\n",
        "# device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "# model = model.to(device)\n",
        "\n",
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))\n",
        "with tf.device('/device:GPU:0'):\n",
        "  model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])\n",
        "  history = model.fit(train_loader,epochs=20)\n",
        "model.save('final_model_python2.h5')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 15702 images belonging to 10 classes.\n",
            "Found 10477 images belonging to 10 classes.\n",
            "testing example\n",
            "(1, 224, 224, 3)\n",
            "(1, 10)\n",
            "training example\n",
            "(32, 224, 224, 3)\n",
            "(32, 10)\n",
            "Model: \"resnet50\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          \n",
            "                                                                 conv2_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           \n",
            "                                                                 conv2_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          \n",
            "                                                                 conv3_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           \n",
            "                                                                 conv3_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          \n",
            "                                                                 conv4_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           \n",
            "                                                                 conv4_block6_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          \n",
            "                                                                 conv5_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           \n",
            "==================================================================================================\n",
            "Total params: 23,587,712\n",
            "Trainable params: 23,534,592\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n",
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "W0116 09:25:04.100039 139758441088896 data_adapter.py:1091] sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train for 491 steps\n",
            "Epoch 1/20\n",
            "491/491 [==============================] - 10399s 21s/step - loss: 2.1809 - accuracy: 0.2522\n",
            "Epoch 2/20\n",
            "491/491 [==============================] - 355s 723ms/step - loss: 1.7534 - accuracy: 0.3803\n",
            "Epoch 3/20\n",
            "491/491 [==============================] - 355s 724ms/step - loss: 1.4699 - accuracy: 0.4876\n",
            "Epoch 4/20\n",
            "491/491 [==============================] - 355s 722ms/step - loss: 1.2874 - accuracy: 0.5580\n",
            "Epoch 5/20\n",
            "491/491 [==============================] - 355s 723ms/step - loss: 1.1756 - accuracy: 0.5964\n",
            "Epoch 6/20\n",
            "491/491 [==============================] - 355s 723ms/step - loss: 1.0924 - accuracy: 0.6271\n",
            "Epoch 7/20\n",
            "491/491 [==============================] - 355s 723ms/step - loss: 1.0120 - accuracy: 0.6514\n",
            "Epoch 8/20\n",
            "491/491 [==============================] - 354s 722ms/step - loss: 0.9442 - accuracy: 0.6769\n",
            "Epoch 9/20\n",
            "491/491 [==============================] - 355s 723ms/step - loss: 0.8812 - accuracy: 0.7035\n",
            "Epoch 10/20\n",
            "491/491 [==============================] - 354s 722ms/step - loss: 0.8318 - accuracy: 0.7118\n",
            "Epoch 11/20\n",
            "491/491 [==============================] - 355s 722ms/step - loss: 0.7699 - accuracy: 0.7397\n",
            "Epoch 12/20\n",
            "491/491 [==============================] - 355s 723ms/step - loss: 0.7315 - accuracy: 0.7545\n",
            "Epoch 13/20\n",
            "491/491 [==============================] - 355s 724ms/step - loss: 0.6794 - accuracy: 0.7709\n",
            "Epoch 14/20\n",
            "491/491 [==============================] - 356s 725ms/step - loss: 0.6371 - accuracy: 0.7869\n",
            "Epoch 15/20\n",
            "491/491 [==============================] - 355s 723ms/step - loss: 0.5988 - accuracy: 0.7986\n",
            "Epoch 16/20\n",
            "491/491 [==============================] - 355s 723ms/step - loss: 0.5632 - accuracy: 0.8086\n",
            "Epoch 17/20\n",
            "491/491 [==============================] - 355s 722ms/step - loss: 0.5408 - accuracy: 0.8189\n",
            "Epoch 18/20\n",
            "491/491 [==============================] - 354s 722ms/step - loss: 0.5007 - accuracy: 0.8329\n",
            "Epoch 19/20\n",
            "491/491 [==============================] - 356s 725ms/step - loss: 0.4704 - accuracy: 0.8435\n",
            "Epoch 20/20\n",
            "491/491 [==============================] - 355s 722ms/step - loss: 0.4418 - accuracy: 0.8506\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "w1p9DUGcXPwy",
        "outputId": "86b22849-06b7-4b00-a2f6-961cb423db45"
      },
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.ylabel(\"accuracy\")\n",
        "plt.xlabel(\"EPOCH\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5,0,'EPOCH')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "SVFI06SJluFu",
        "outputId": "d0e42129-409f-4ccf-fcac-77f9b8be9066"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.xlabel(\"EPOCH\")\n",
        "plt.ylabel(\"error\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,'error')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rSyDfcKvlyvN"
      },
      "source": [
        "import cleverhans\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "# Install bleeding edge version of cleverhans\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WOeQuIWmmnhj",
        "outputId": "4782b6b0-5e98-4ae0-a04a-2b620e9a5d29"
      },
      "source": [
        "!pip install cleverhans==2.1.0"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: cleverhans==2.1.0 in /usr/local/lib/python2.7/dist-packages (2.1.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python2.7/dist-packages (from cleverhans==2.1.0) (1.16.4)\n",
            "Requirement already satisfied: pycodestyle in /usr/local/lib/python2.7/dist-packages (from cleverhans==2.1.0) (2.6.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python2.7/dist-packages (from cleverhans==2.1.0) (2.2.4)\n",
            "Requirement already satisfied: nose in /usr/local/lib/python2.7/dist-packages (from cleverhans==2.1.0) (1.3.7)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python2.7/dist-packages (from cleverhans==2.1.0) (1.2.2)\n",
            "Requirement already satisfied: mnist~=0.2 in /usr/local/lib/python2.7/dist-packages (from cleverhans==2.1.0) (0.2.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans==2.1.0) (0.10.0)\n",
            "Requirement already satisfied: backports.functools-lru-cache in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans==2.1.0) (1.5)\n",
            "Requirement already satisfied: subprocess32 in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans==2.1.0) (3.5.4)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans==2.1.0) (1.1.0)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans==2.1.0) (2018.9)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans==2.1.0) (1.15.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans==2.1.0) (2.5.3)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib->cleverhans==2.1.0) (2.4.0)\n",
            "Requirement already satisfied: mock; python_version == \"2.7\" in /usr/local/lib/python2.7/dist-packages (from mnist~=0.2->cleverhans==2.1.0) (2.0.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python2.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->cleverhans==2.1.0) (44.1.1)\n",
            "Requirement already satisfied: funcsigs>=1; python_version < \"3.3\" in /usr/local/lib/python2.7/dist-packages (from mock; python_version == \"2.7\"->mnist~=0.2->cleverhans==2.1.0) (1.0.2)\n",
            "Requirement already satisfied: pbr>=0.11 in /usr/local/lib/python2.7/dist-packages (from mock; python_version == \"2.7\"->mnist~=0.2->cleverhans==2.1.0) (5.4.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luPUxlZ7nr-_"
      },
      "source": [
        "from cleverhans.utils_keras import KerasModelWrapper\n",
        "from cleverhans.attacks import FastGradientMethod, MadryEtAl, DeepFool, CarliniWagnerL2\n",
        "#Import necessary packages\n",
        "from keras.datasets import mnist\n",
        "from keras import Sequential\n",
        "from keras.layers import Dense, BatchNormalization\n",
        "from keras import backend\n",
        "import keras\n",
        "import tensorflow.compat.v1 as tf\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.decomposition import PCA\n",
        "from cleverhans.utils_keras import KerasModelWrapper\n",
        "from cleverhans.attacks import FastGradientMethod, MadryEtAl, DeepFool, CarliniWagnerL2"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uomXGA81o5B-"
      },
      "source": [
        "# Edit the classifier name fed into KerasModel Wrapper with the name of the \n",
        "# classifier from above and then run this block\n",
        "\n",
        "#Get TensorFlow Session to pass into Cleverhans modules\n",
        "sess = tf.keras.backend.get_session()\n",
        "\n",
        "#Create wrapper for classifier model so that it can be passed into Cleverhans modules\n",
        "wrap = KerasModelWrapper(model)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "id": "30tn-UDspHVE",
        "outputId": "1cffbf6e-17ed-4bc7-9a51-45591d685404"
      },
      "source": [
        "#Implementing the FGSM attack\n",
        "#FGM Instance on trained classifier from Part 1\n",
        "fgsm = FastGradientMethod(wrap, sess=sess)\n",
        "\n",
        "#Attack parameters\n",
        "fgsm_params = {'eps':0.25, 'clip_min':0.0, 'clip_max':1.0}\n",
        "\n",
        "#Generate adversarial data\n",
        "fgsm_adv_data_test = fgsm.generate_np(test_loader, **fgsm_params)\n",
        "\n",
        "#Evaluate accuracy on target classifier \n",
        "fgsm_adv_score = model_classifier.evaluate(fgsm_adv_data_test, labels_test)\n",
        "print('FGSM Adversarial Attack Accuracy : %.2f%%' %(fgsm_adv_score[1]*100))\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0mTraceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-71230f0be5e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#FGM Instance on trained classifier from Part 1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mfgsm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFastGradientMethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m#Attack parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/cleverhans/attacks.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, back, sess, dtypestr)\u001b[0m\n\u001b[1;32m    254\u001b[0m             \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCallableModelWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'probs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFastGradientMethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtypestr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    257\u001b[0m         self.feedable_kwargs = {'eps': self.np_dtype,\n\u001b[1;32m    258\u001b[0m                                 \u001b[0;34m'y'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnp_dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/cleverhans/attacks.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, back, sess, dtypestr)\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnp_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtypestr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m         \u001b[0;32mimport\u001b[0m \u001b[0mcleverhans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattacks_tf\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mattacks_tf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m         \u001b[0mattacks_tf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnp_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnp_dtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0mattacks_tf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_dtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/cleverhans/attacks_tf.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils_tf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/cleverhans/utils_tf.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m def kl_with_logits(p_logits, q_logits, scope=None,\n\u001b[0;32m--> 368\u001b[0;31m                    loss_collection=tf.GraphKeys.REGULARIZATION_LOSSES):\n\u001b[0m\u001b[1;32m    369\u001b[0m     \"\"\"Helper function to compute kl-divergence KL(p || q)\n\u001b[1;32m    370\u001b[0m     \"\"\"\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'module' object has no attribute 'GraphKeys'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-sYK-Bxp1eH"
      },
      "source": [
        ""
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Khn5Pzgzs023"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}