car-damage-detection-first-stage-damage-or-not.ipynb

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    "colab": {
      "name": "damage detection.ipynb",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nadiaaaa95/car-damage/blob/main/damage_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3ftYmwBvd6R"
      },
      "source": [
        "data into data1a - Whole(train and validation) or Damaged(train and validation)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OAlt2TW_vd6R"
      },
      "source": [
        "import urllib.request\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import display,Image,clear_output\n",
        "import numpy as np\n",
        "import os\n",
        "import h5py\n",
        "import json\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "from collections import Counter,defaultdict"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bhsDGeMzvd6R",
        "outputId": "ad612c87-87a6-48d5-824a-84f4179e0739"
      },
      "source": [
        "import keras\n",
        "from keras.preprocessing.image import ImageDataGenerator, img_to_array, array_to_img, load_img\n",
        "from keras.models import Sequential, load_model,Model\n",
        "from keras.layers import Dense, Flatten, Dropout, Activation, add\n",
        "from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
        "from keras.applications.vgg16 import VGG16,preprocess_input\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.utils import get_file\n",
        "from keras import optimizers\n",
        "from keras.regularizers import l1, l2\n",
        "from keras.callbacks import ModelCheckpoint, History"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "/home/akash/anaconda3/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
            "  return f(*args, **kwds)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "478vzfWjvd6S",
        "outputId": "c7d336df-bbc4-4054-b6a9-38c9c1ce9f2e"
      },
      "source": [
        "from keras import backend as K\n",
        "K.backend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'tensorflow'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Or_hFiQZvd6S"
      },
      "source": [
        "WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
        "weights_path = get_file('vgg16_weights.h5', WEIGHTS_PATH_NO_TOP)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "992iDnNPvd6S"
      },
      "source": [
        "### Defining input data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ew31Y92Tvd6S"
      },
      "source": [
        "# path to the model weights file\n",
        "location = './car-damage-dataset/data1a'\n",
        "top_model_weights_path=location+'/top_model_weights.h5' # will be saved into when we create our model\n",
        "fine_tuned_model_path = location+'/ft_model.h5'\n",
        "\n",
        "# dimensions of our images\n",
        "img_width, img_height = 256, 256\n",
        "\n",
        "train_data_dir = location+'/training'\n",
        "val_data_dir = location+'/validation'\n",
        "\n",
        "train_samples = [len(os.listdir(os.path.join(train_data_dir,i))) for i in os.listdir(train_data_dir)]\n",
        "nb_train_samples = sum(train_samples)\n",
        "val_samples = [len(os.listdir(os.path.join(val_data_dir,i))) for i in os.listdir(val_data_dir)]\n",
        "nb_val_samples = sum(val_samples)\n",
        "\n",
        "nb_epoch = 50"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNrmiTefvd6S",
        "outputId": "34de1de4-ea87-4075-ec86-802cf375e098"
      },
      "source": [
        "train_samples"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[920, 920]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfD9q3USvd6T",
        "outputId": "c1c84e82-a0da-4d8e-b214-f664f7887e21"
      },
      "source": [
        "K.image_data_format()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'channels_last'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0hqR6cNvd6T"
      },
      "source": [
        "if K.image_data_format() == 'channels_first':\n",
        "    input_shape = (3, img_width, img_height)\n",
        "else:\n",
        "    input_shape = (img_width, img_height, 3)    # img_width first or img_height"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "piEvsc3lvd6T",
        "outputId": "0a2bb2e2-8865-4a96-f539-400008a2c6ca"
      },
      "source": [
        "input_shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(256, 256, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nv_At9jYvd6T"
      },
      "source": [
        "# def load_vgg16(weights_path='../vgg16_weights.h5'):\n",
        "#     model = Sequential()\n",
        "#     model.add(ZeroPadding2D(padding=(1,1),input_shape=(224,224,3)))   # for best performance you should set\n",
        "#                                                                       # `image_data_format='channels_last'` \n",
        "#                                                                       # in your Keras config\n",
        "#                                                                       # at ~/.keras/keras.json.\n",
        "#     model.add(Convolution2D(filters=64, kernel_size=3, strides=3, activation='relu'))\n",
        "#     model.add(ZeroPadding2D(padding=(1,1)))\n",
        "#     model.add(Convolution2D(filters=64, kernel_size=3, strides=3, activation='relu'))\n",
        "#     model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "\n",
        "#     model.add(ZeroPadding2D(padding=(1,1)))\n",
        "#     model.add(Convolution2D(filters=128, kernel_size=3, strides=3, activation='relu'))\n",
        "#     model.add(ZeroPadding2D(padding=(1,1)))\n",
        "#     model.add(Convolution2D(filters=128, kernel_size=3, strides=3, activation='relu'))\n",
        "#     model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "\n",
        "#     model.add(ZeroPadding2D(padding=(1,1)))\n",
        "#     model.add(Convolution2D(filters=256, kernel_size=3, strides=3, activation='relu'))\n",
        "\n",
        "#     model.add(ZeroPadding2D(padding=(1,1)))\n",
        "#     model.add(Convolution2D(filters=256, kernel_size=3, strides=3, activation='relu'))\n",
        "#     model.add(ZeroPadding2D(padding=(1,1)))\n",
        "#     model.add(Convolution2D(filters=256, kernel_size=3, strides=3, activation='relu'))\n",
        "#     model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "\n",
        "#     model.add(ZeroPadding2D(padding=(1,1)))\n",
        "#     model.add(Convolution2D(filters=512, kernel_size=3, strides=3, activation='relu'))\n",
        "#     model.add(ZeroPadding2D(padding=(1,1)))\n",
        "#     model.add(Convolution2D(filters=512, kernel_size=3, strides=3, activation='relu'))\n",
        "#     model.add(ZeroPadding2D(padding=(1,1)))\n",
        "#     model.add(Convolution2D(filters=512, kernel_size=3, strides=3, activation='relu'))\n",
        "#     model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "\n",
        "#     model.add(ZeroPadding2D(padding=(1,1)))\n",
        "#     model.add(Convolution2D(filters=512, kernel_size=3, strides=3, activation='relu'))\n",
        "#     model.add(ZeroPadding2D(padding=(1,1)))\n",
        "#     model.add(Convolution2D(filters=512, kernel_size=3, strides=3, activation='relu'))\n",
        "#     model.add(ZeroPadding2D(padding=(1,1)))\n",
        "#     model.add(Convolution2D(filters=512, kernel_size=3, strides=3, activation='relu'))\n",
        "#     model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "    \n",
        "#     # assert os.path.exists(weights_path), 'Model weights not found (see \"weights_path\")'\n",
        "    \n",
        "#     # Use below code to freeze the conv model i.e. its learning\n",
        "#     '''\n",
        "#     for layer in model.layers:\n",
        "#         layer.trainable = False\n",
        "#     '''\n",
        "    \n",
        "#     if weights_path:\n",
        "#     # note: this chops off the last layers of VGG16 \n",
        "\n",
        "#     # loads the weights of the VGG16 networks\n",
        "#     # NOTE: when there is a complete match between model definition\n",
        "#     # and your weights savefile, you can simply call model.load_weights(filename)    \n",
        "#         f = h5py.File(weights_path)\n",
        "\n",
        "#         # LEFT CURRENTLY"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPR7hLTnvd6U",
        "outputId": "19c88a65-9219-49db-def8-ade9471cfbe9"
      },
      "source": [
        "# if(os.path.exists(location+'vgg16.h5')):\n",
        "#     base_model = load_model('vgg16.h5')\n",
        "# else:\n",
        "#     base_model = VGG16(weights='imagenet')\n",
        "#     base_model.save(location)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-103-f4a4f2254f52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mbase_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'vgg16.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mbase_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVGG16\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'imagenet'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/applications/vgg16.py\u001b[0m in \u001b[0;36mVGG16\u001b[0;34m(include_top, weights, input_tensor, input_shape, pooling, classes)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minput_tensor\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m         \u001b[0mimg_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mInput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_keras_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/topology.py\u001b[0m in \u001b[0;36mInput\u001b[0;34m(shape, batch_shape, name, dtype, sparse, tensor)\u001b[0m\n\u001b[1;32m   1437\u001b[0m                              \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1438\u001b[0m                              \u001b[0msparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m                              input_tensor=tensor)\n\u001b[0m\u001b[1;32m   1440\u001b[0m     \u001b[0;31m# Return tensor including _keras_shape and _keras_history.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m     \u001b[0;31m# Note that in this case train_output and test_output are the same pointer.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     85\u001b[0m                 warnings.warn('Update your `' + object_name +\n\u001b[1;32m     86\u001b[0m                               '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 87\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/topology.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input_shape, batch_size, batch_input_shape, dtype, input_tensor, sparse, name)\u001b[0m\n\u001b[1;32m   1346\u001b[0m                                          \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1347\u001b[0m                                          \u001b[0msparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1348\u001b[0;31m                                          name=self.name)\n\u001b[0m\u001b[1;32m   1349\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1350\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_placeholder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36mplaceholder\u001b[0;34m(shape, ndim, dtype, sparse, name)\u001b[0m\n\u001b[1;32m    486\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse_placeholder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    487\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 488\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    489\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_keras_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_uses_learning_phase\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36mplaceholder\u001b[0;34m(dtype, shape, name)\u001b[0m\n\u001b[1;32m   1597\u001b[0m     \u001b[0mevaluated\u001b[0m \u001b[0mdirectly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1598\u001b[0m   \"\"\"\n\u001b[0;32m-> 1599\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_placeholder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py\u001b[0m in \u001b[0;36m_placeholder\u001b[0;34m(dtype, shape, name)\u001b[0m\n\u001b[1;32m   3089\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_graph_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3090\u001b[0m     _, _, _op = _op_def_lib._apply_op_helper(\n\u001b[0;32m-> 3091\u001b[0;31m         \"Placeholder\", dtype=dtype, shape=shape, name=name)\n\u001b[0m\u001b[1;32m   3092\u001b[0m     \u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3093\u001b[0m     \u001b[0m_inputs_flat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[0;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[1;32m    785\u001b[0m         op = g.create_op(op_type_name, inputs, output_types, name=scope,\n\u001b[1;32m    786\u001b[0m                          \u001b[0minput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattr_protos\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m                          op_def=op_def)\n\u001b[0m\u001b[1;32m    788\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0moutput_structure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_def\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_stateful\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    789\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mcreate_op\u001b[0;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)\u001b[0m\n\u001b[1;32m   2956\u001b[0m         op_def=op_def)\n\u001b[1;32m   2957\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcompute_shapes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2958\u001b[0;31m       \u001b[0mset_shapes_for_outputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2959\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2960\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_record_op_seen_by_control_dependencies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mset_shapes_for_outputs\u001b[0;34m(op)\u001b[0m\n\u001b[1;32m   2226\u001b[0m         (op, len(shapes), len(op.outputs), shape_func.__name__, str(shapes)))\n\u001b[1;32m   2227\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshapes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2228\u001b[0;31m     \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mset_shape\u001b[0;34m(self, shape)\u001b[0m\n\u001b[1;32m    405\u001b[0m         \u001b[0mthis\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m     \"\"\"\n\u001b[0;32m--> 407\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36mmerge_with\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    569\u001b[0m       \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIf\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mself\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mother\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mare\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcompatible\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m     \"\"\"\n\u001b[0;32m--> 571\u001b[0;31m     \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    572\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dims\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36mas_shape\u001b[0;34m(shape)\u001b[0m\n\u001b[1;32m    844\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mas_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m   \u001b[0;34m\"\"\"Converts the given object to a TensorShape.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 846\u001b[0;31m   \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTensorShape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    847\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    848\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWY0UtQ2vd6V"
      },
      "source": [
        "base_model = VGG16(weights='imagenet',include_top=False,input_shape=input_shape ,pooling=max)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "su_79KS4vd6V",
        "outputId": "ecfddc52-2935-4ff3-a771-cc99597e0847"
      },
      "source": [
        "base_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 256, 256, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 256, 256, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 256, 256, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 128, 128, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 128, 128, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 128, 128, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 64, 64, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 64, 64, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 64, 64, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 64, 64, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 32, 32, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 32, 32, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 16, 16, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 8, 8, 512)         0         \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 14,714,688\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kM3ZikAtvd6V"
      },
      "source": [
        "# Set base_model layers to non-trainable\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable=False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qf1O2xJyvd6W",
        "outputId": "bea5909e-6c65-479d-d8b2-d4af544dca8e"
      },
      "source": [
        "base_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 256, 256, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 256, 256, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 256, 256, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 128, 128, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 128, 128, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 128, 128, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 64, 64, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 64, 64, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 64, 64, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 64, 64, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 32, 32, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 32, 32, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 16, 16, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 8, 8, 512)         0         \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 0\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itTrZlSVvd6W"
      },
      "source": [
        "def preprocess_input_vgg(x):\n",
        "    \"\"\"Wrapper around keras.applications.vgg16.preprocess_input()\n",
        "    to make it compatible for use with keras.preprocessing.image.ImageDataGenerator's\n",
        "    `preprocessing_function` argument.\n",
        "    \n",
        "    Parameters\n",
        "    ----------\n",
        "    x : a numpy 3darray (a single image to be preprocessed)\n",
        "    \n",
        "    Note we cannot pass keras.applications.vgg16.preprocess_input()\n",
        "    directly to to keras.preprocessing.image.ImageDataGenerator's\n",
        "    `preprocessing_function` argument because the former expects a\n",
        "    4D tensor whereas the latter expects a 3D tensor. Hence the\n",
        "    existence of this wrapper.\n",
        "    \n",
        "    Returns a numpy 3darray (the preprocessed image).\n",
        "    \n",
        "    \"\"\"\n",
        "    X = np.expand_dims(x, axis=0)\n",
        "    X = preprocess_input(X)\n",
        "    return X[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ej22Zsetvd6W"
      },
      "source": [
        "def save_bottleneck_features(location):    # SAMAJANA\n",
        "    datagen = ImageDataGenerator(preprocessing_function=preprocess_input_vgg)\n",
        "    \n",
        "    if(os.path.exists(location+'/bottleneck_features_train.npy')):\n",
        "        print('Already exists',location+'/bottleneck_features_train.npy')\n",
        "    else:\n",
        "        train_generator = datagen.flow_from_directory(train_data_dir,\n",
        "                                                      target_size=(img_height,img_width),\n",
        "                                                      batch_size=16,\n",
        "                                                      class_mode=None,         # only data, no labels\n",
        "                                                      shuffle=False)           # keep data in same order as labels\n",
        "    \n",
        "        bottleneck_features_train = base_model.predict_generator(train_generator,\n",
        "                                                                 nb_train_samples // 16,\n",
        "                                                                 verbose=1)\n",
        "        \n",
        "        print('Saving',location+'/bottleneck_features_train.npy')\n",
        "        np.save(open(location+'/bottleneck_features_train.npy', 'wb'), bottleneck_features_train)\n",
        "        #np.save(location+'/bottleneck_features_train.npy',bottleneck_features_train)\n",
        "    \n",
        "    if(os.path.exists(location+'/bottleneck_features_val.npy')):\n",
        "        print('Already exists',location+'/bottleneck_features_val.npy')\n",
        "    else:\n",
        "        # Repeat it with validation data\n",
        "        val_generator = datagen.flow_from_directory(val_data_dir,\n",
        "                                                    target_size=(img_height,img_width),\n",
        "                                                    batch_size=16,\n",
        "                                                    class_mode=None,\n",
        "                                                    shuffle=False)\n",
        "\n",
        "        bottleneck_features_val = base_model.predict_generator(val_generator,\n",
        "                                                               nb_val_samples / 16,\n",
        "                                                               verbose=1)\n",
        "        print('Saving',location+'/bottleneck_features_val.npy')\n",
        "        np.save(open(location+'/bottleneck_features_val.npy','wb'),bottleneck_features_val)\n",
        "        #np.save(location+'/bottleneck_features_val.npy',bottleneck_features_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7rBB0DLvd6W"
      },
      "source": [
        "val_labels = np.array([0]*val_samples[0] +\n",
        "                          [1]*val_samples[1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTu-keKvvd6W"
      },
      "source": [
        "train_data = np.load(open(location+'/bottleneck_features_train.npy', 'rb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8I36yppLvd6Y",
        "outputId": "fc7c4f86-73ba-491b-cc7b-1299b975b353"
      },
      "source": [
        "train_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1840, 8, 8, 512)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWfPno02vd6Y",
        "outputId": "b67b10a3-6819-43df-c2ce-a7e7461b31f0"
      },
      "source": [
        "val_data = np.load(open(location+'/bottleneck_features_val.npy', 'rb'))\n",
        "val_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(460, 8, 8, 512)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ml9AGnBovd6Y"
      },
      "source": [
        "def train_binary_model(location):\n",
        "    train_data = np.load(open(location+'/bottleneck_features_train.npy', 'rb'))\n",
        "    print(train_data.shape[1:])\n",
        "    train_labels = np.array([0]*train_samples[0]+ \n",
        "                            [1]*train_samples[1])\n",
        "\n",
        "    val_data = np.load(open(location+'/bottleneck_features_val.npy', 'rb'))\n",
        "    val_labels = np.array([0]*val_samples[0] +\n",
        "                          [1]*val_samples[1])\n",
        "\n",
        "    model = Sequential()\n",
        "    model.add(Flatten(input_shape=(train_data.shape[1:])))     # 8, 8, 512\n",
        "    model.add(Dense(units=256, activation='relu',\n",
        "                    kernel_regularizer=l2(l=0.01)))\n",
        "    model.add(Dropout(rate=0.5))\n",
        "    model.add(Dense(units=1, activation='sigmoid'))\n",
        "\n",
        "    model.compile(optimizer=optimizers.SGD(lr=0.0001, momentum=0.9),    # check the lr\n",
        "                  loss='binary_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "\n",
        "    checkpoint = ModelCheckpoint(top_model_weights_path, \n",
        "                                 monitor='val_acc', \n",
        "                                 verbose=1,\n",
        "                                 save_best_only=True,\n",
        "                                 save_weights_only=True, \n",
        "                                 mode='auto')     # ?read documentation\n",
        "\n",
        "    fit = model.fit(train_data, train_labels, epochs=nb_epoch, batch_size=16,\n",
        "                    validation_data=(val_data, val_labels), callbacks=[checkpoint])\n",
        "\n",
        "    with open(location+'/top_history.txt', 'w') as f:\n",
        "        json.dump(fit.history, f)\n",
        "\n",
        "    return model, fit.history"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6MSAOxQEvd6Y"
      },
      "source": [
        "# # either bottleneck or this\n",
        "\n",
        "# top_model = Sequential()\n",
        "# top_model.add(Conv2D(32, (3, 3), input_shape=input_shape))\n",
        "# top_model.add(Activation('relu'))\n",
        "# top_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "# top_model.add(Conv2D(32, (3, 3)))\n",
        "# top_model.add(Activation('relu'))\n",
        "# top_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "# top_model.add(Conv2D(64, (3, 3)))\n",
        "# top_model.add(Activation('relu'))\n",
        "# top_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "# top_model = Sequential()\n",
        "# top_model.add(Flatten(input_shape=base_model.output_shape[1:]))\n",
        "# top_model.add(Dense(units=256,activation='relu',kernel_regularizer=l2(l=0.01)))\n",
        "# top_model.add(Dropout(rate=0.5))\n",
        "# top_model.add(Dense(units=1,activation='sigmoid'))\n",
        "\n",
        "# top_model.compile(optimizer=optimizers.SGD(lr=0.0001,momentum=0.9),    # check the lr\n",
        "#                   loss='binary_crossentropy',\n",
        "#                  metrics = ['accuracy'])\n",
        "\n",
        "# # prepare data augmentation configuration\n",
        "# train_datagen = ImageDataGenerator(rescale=1./255,\n",
        "#                                    rotation_range=40,\n",
        "#                                    width_shift_range=0.2,\n",
        "#                                    height_shift_range=0.2,\n",
        "#                                    shear_range=0.2,\n",
        "#                                    zoom_range=0.2,\n",
        "#                                    horizontal_flip=True,\n",
        "#                                    fill_mode='nearest')\n",
        "\n",
        "# # this is the augmentation configuration we will use for testing:\n",
        "# # only rescaling\n",
        "# test_datagen = ImageDataGenerator(rescale=1. / 255)\n",
        "\n",
        "# train_generator = train_datagen.flow_from_directory(train_data_dir,\n",
        "#                                                     target_size=(img_height, img_width),\n",
        "#                                                     batch_size=8,\n",
        "#                                                     class_mode='binary')   # categorical for multiclass\n",
        "    \n",
        "# test_generator = test_datagen.flow_from_directory(val_data_dir,\n",
        "#                                                   target_size=(img_height, img_width),\n",
        "#                                                   batch_size=8,\n",
        "#                                                   class_mode='binary',     # categorical for multiclass\n",
        "#                                                   shuffle=False)\n",
        "\n",
        "# model.fit_generator(\n",
        "#     train_generator,\n",
        "#     steps_per_epoch=nb_train_samples // 8,\n",
        "#     epochs=nb_epoch,\n",
        "#     validation_data=validation_generator,\n",
        "#     validation_steps=nb_validation_samples // 8)\n",
        "\n",
        "# model.save_weights(top_model_weights_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "VKLBV7h2vd6Z",
        "outputId": "6b8dc034-5d62-4614-8c8e-4603983220ec"
      },
      "source": [
        "top_model = Sequential()\n",
        "top_model.add(Flatten(input_shape=base_model.output_shape[1:]))\n",
        "top_model.add(Dense(units=256,activation='relu',kernel_regularizer=l2(l=0.01)))\n",
        "top_model.add(Dropout(rate=0.5))\n",
        "top_model.add(Dense(units=1,activation='sigmoid'))\n",
        "    \n",
        "top_model.load_weights(top_model_weights_path) # load weights_path\n",
        "    \n",
        "#base_model.add(top_model)\n",
        "    \n",
        "model = Model(inputs=base_model.input, outputs=top_model(base_model.output))\n",
        "print(model.layers[-1].get_weights())\n",
        "print(top_model.get_weights())\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([[-0.01012065, -0.01048303,  0.0032335 , ...,  0.00972222,\n",
            "        -0.01335548, -0.01087625],\n",
            "       [ 0.00905526, -0.00504446, -0.00211019, ...,  0.00015012,\n",
            "        -0.00304197,  0.00717667],\n",
            "       [ 0.01050402, -0.00598194,  0.01128646, ...,  0.00608847,\n",
            "         0.00694825, -0.00326765],\n",
            "       ..., \n",
            "       [ 0.00228057,  0.00289785, -0.00409706, ..., -0.0096005 ,\n",
            "        -0.01046735,  0.00817893],\n",
            "       [-0.00052284, -0.00416786, -0.00747126, ..., -0.00335509,\n",
            "         0.01101705, -0.00097601],\n",
            "       [-0.00150657,  0.00663762, -0.00871113, ..., -0.00802929,\n",
            "        -0.00821638, -0.00677615]], dtype=float32), array([ -6.63930041e-05,  -6.27243571e-05,  -4.21339973e-05,\n",
            "        -3.36487865e-05,  -1.31908173e-05,   1.40916454e-04,\n",
            "         3.21922744e-05,  -9.27699875e-05,  -1.64583562e-05,\n",
            "        -7.52625201e-05,  -6.59315119e-05,   6.05078058e-05,\n",
            "        -4.62493745e-06,   2.27251494e-05,  -1.93112064e-05,\n",
            "        -1.38478526e-05,  -2.43670002e-05,   2.52712562e-05,\n",
            "         1.22849873e-04,  -5.67450406e-05,  -1.40729553e-05,\n",
            "        -4.66946585e-05,  -1.11470254e-04,   7.71815830e-05,\n",
            "         3.60833510e-05,  -4.10605899e-05,   3.99478413e-05,\n",
            "         3.03622819e-05,  -2.34833601e-06,  -7.29053136e-06,\n",
            "        -3.12944394e-05,   5.80759524e-06,   1.14288327e-04,\n",
            "        -1.31211418e-05,  -3.33256248e-05,  -3.77881188e-05,\n",
            "        -3.59795777e-05,  -6.23083952e-06,  -3.26455847e-05,\n",
            "        -2.62920785e-05,  -3.30111361e-05,  -9.48238812e-06,\n",
            "        -1.71164593e-05,   1.30050930e-05,   2.60636771e-05,\n",
            "         3.72646196e-06,  -8.99121223e-05,  -4.32252891e-05,\n",
            "        -2.93856756e-05,  -6.68446319e-06,   1.09819921e-05,\n",
            "         2.47536318e-05,   4.38133065e-06,  -6.98478689e-05,\n",
            "         7.87173412e-05,  -4.11947804e-06,  -6.30899467e-06,\n",
            "         1.18504095e-05,  -1.13525501e-04,   6.25687826e-05,\n",
            "        -1.60942349e-04,  -2.04196840e-06,   1.71990414e-05,\n",
            "        -1.93313881e-05,   2.87854873e-05,  -1.54491412e-04,\n",
            "        -6.66494525e-05,  -2.90935350e-05,  -1.94044842e-05,\n",
            "         6.04557936e-05,  -1.22534126e-04,  -3.53062278e-05,\n",
            "        -4.24191239e-05,  -7.25215432e-05,   2.82166930e-05,\n",
            "         1.06065045e-06,   6.16623875e-05,  -1.21720323e-05,\n",
            "        -2.13952171e-05,  -1.38145206e-05,  -6.55098411e-05,\n",
            "         1.63364730e-05,   5.78114959e-05,  -6.33383897e-05,\n",
            "        -4.20465676e-06,  -4.60839765e-05,  -5.24475436e-05,\n",
            "        -3.99866403e-05,  -3.56980308e-05,  -5.86093338e-05,\n",
            "        -7.00711462e-06,  -1.32935293e-05,  -2.72292309e-05,\n",
            "        -7.70976112e-06,  -8.86831403e-05,  -4.33842615e-05,\n",
            "        -3.13491400e-05,   9.13490203e-06,   3.05104595e-06,\n",
            "        -5.71447199e-05,  -6.51073715e-05,  -1.63680288e-05,\n",
            "        -2.06388740e-05,  -2.22892832e-05,  -2.14506344e-05,\n",
            "        -7.85489010e-06,  -8.96075016e-06,   4.94603119e-05,\n",
            "        -1.08531485e-05,   3.82959151e-05,   4.27981904e-05,\n",
            "        -4.81870775e-05,   4.31619083e-05,   5.74621627e-05,\n",
            "         1.93899450e-06,  -5.96948921e-05,  -2.36500782e-05,\n",
            "        -2.95604641e-05,   2.09809259e-05,  -2.67456362e-05,\n",
            "        -3.62759565e-05,   6.54221458e-06,   9.48833404e-05,\n",
            "        -6.22585139e-05,  -7.17541843e-05,  -1.02063364e-04,\n",
            "        -7.28657324e-06,  -5.02967559e-05,  -4.17754673e-05,\n",
            "         4.73773180e-05,  -2.06259356e-05,  -1.08028282e-04,\n",
            "        -8.27373806e-05,   9.39414531e-06,  -1.40759308e-04,\n",
            "         2.98674076e-05,   4.55700319e-05,   6.34226353e-06,\n",
            "        -4.63029064e-05,   3.49121319e-06,  -1.39495151e-05,\n",
            "         2.68146050e-05,  -2.39128913e-05,  -6.05593632e-05,\n",
            "        -7.47093291e-05,  -4.70974155e-05,   4.09420172e-05,\n",
            "         3.60877630e-06,  -4.78593029e-05,   1.02271406e-05,\n",
            "        -9.98944743e-05,  -1.70191342e-05,  -2.19754475e-05,\n",
            "        -4.51710075e-05,  -3.01961263e-05,  -3.90370842e-05,\n",
            "         3.05752037e-05,  -1.45479265e-04,  -2.34514264e-05,\n",
            "         3.04704663e-05,  -5.35693543e-05,   6.37201156e-05,\n",
            "        -1.80762909e-05,  -3.31148913e-05,  -7.35691865e-05,\n",
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            "        -7.00891324e-05,  -3.91223184e-05,   1.03989652e-04,\n",
            "         1.33884914e-05,   1.37241150e-05,  -7.72540443e-05,\n",
            "        -8.85863701e-05,  -6.53749157e-05,   6.32144656e-05,\n",
            "        -7.35388812e-06,  -1.10624796e-05,  -5.30547777e-06,\n",
            "        -4.71020721e-05,  -5.25768774e-05,  -4.88433543e-05,\n",
            "        -5.80305168e-05,  -2.77842064e-05,  -6.00681888e-05,\n",
            "         6.06362737e-05,  -5.00387578e-05,   1.02118967e-04,\n",
            "        -2.54744173e-05,  -6.72085753e-06,  -4.20239485e-05,\n",
            "        -2.02619540e-05,  -7.14055495e-05,  -6.22713924e-05,\n",
            "         6.34105672e-05,  -6.73857721e-05,  -5.99654632e-05,\n",
            "        -3.46375382e-05,  -4.36097471e-05,  -1.72141124e-04,\n",
            "        -3.46757843e-05], dtype=float32), array([[ 0.12124304],\n",
            "       [-0.0685415 ],\n",
            "       [ 0.08793068],\n",
            "       [ 0.10112542],\n",
            "       [ 0.02003647],\n",
            "       [-0.09571368],\n",
            "       [-0.07856353],\n",
            "       [ 0.10306241],\n",
            "       [ 0.04205618],\n",
            "       [ 0.12411634],\n",
            "       [ 0.04212575],\n",
            "       [-0.07728707],\n",
            "       [-0.14832675],\n",
            "       [-0.05081724],\n",
            "       [-0.00855604],\n",
            "       [-0.06572163],\n",
            "       [ 0.04207853],\n",
            "       [-0.12049444],\n",
            "       [-0.13690935],\n",
            "       [ 0.02434364],\n",
            "       [ 0.08194617],\n",
            "       [ 0.03715052],\n",
            "       [ 0.13027504],\n",
            "       [-0.09557465],\n",
            "       [-0.09170835],\n",
            "       [-0.04112567],\n",
            "       [-0.12997003],\n",
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            "       [ 0.05421448],\n",
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            "       [ 0.06342798],\n",
            "       [-0.05946992],\n",
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            "       [ 0.02292988],\n",
            "       [-0.12970236],\n",
            "       [ 0.10566287],\n",
            "       [-0.04877049],\n",
            "       [ 0.00083501],\n",
            "       [ 0.03101758],\n",
            "       [ 0.05239249],\n",
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            "       [ 0.00218051],\n",
            "       [ 0.10247836],\n",
            "       [-0.07443643],\n",
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            "       [ 0.07421674],\n",
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            "       [-0.07898359],\n",
            "       [ 0.10029709],\n",
            "       [-0.07721163],\n",
            "       [-0.05822454],\n",
            "       [-0.12567149],\n",
            "       [-0.04606552],\n",
            "       [ 0.09727198],\n",
            "       [-0.08810759],\n",
            "       [-0.10366042],\n",
            "       [-0.06516092],\n",
            "       [-0.02990677],\n",
            "       [ 0.12092585],\n",
            "       [-0.11780827],\n",
            "       [ 0.13633348],\n",
            "       [ 0.04990653],\n",
            "       [-0.05601411],\n",
            "       [-0.0810312 ],\n",
            "       [-0.10200635],\n",
            "       [ 0.0914215 ],\n",
            "       [-0.1249425 ],\n",
            "       [-0.00280975],\n",
            "       [-0.05155902],\n",
            "       [ 0.06943927],\n",
            "       [ 0.13694784],\n",
            "       [-0.06976711],\n",
            "       [ 0.09436301],\n",
            "       [-0.13423137],\n",
            "       [-0.0839032 ],\n",
            "       [-0.06352707],\n",
            "       [-0.13394238],\n",
            "       [-0.13714279],\n",
            "       [ 0.04855333],\n",
            "       [-0.02738726],\n",
            "       [ 0.03922187],\n",
            "       [ 0.09008253],\n",
            "       [-0.0876732 ],\n",
            "       [-0.14010377],\n",
            "       [ 0.06901252],\n",
            "       [-0.07550932],\n",
            "       [ 0.10935163],\n",
            "       [ 0.03021149],\n",
            "       [ 0.07162279],\n",
            "       [-0.14793549],\n",
            "       [ 0.02653366],\n",
            "       [-0.05302908],\n",
            "       [ 0.04865133],\n",
            "       [ 0.03904669],\n",
            "       [-0.11424024],\n",
            "       [ 0.1006542 ],\n",
            "       [-0.13602293],\n",
            "       [ 0.03407434],\n",
            "       [ 0.04001173],\n",
            "       [ 0.08635794],\n",
            "       [ 0.0506657 ],\n",
            "       [-0.01846587],\n",
            "       [-0.13205092],\n",
            "       [ 0.06734935],\n",
            "       [-0.02321076],\n",
            "       [-0.0134723 ],\n",
            "       [ 0.02335557],\n",
            "       [-0.06181452],\n",
            "       [-0.04020237],\n",
            "       [ 0.03662939],\n",
            "       [ 0.12018163],\n",
            "       [-0.0791977 ],\n",
            "       [-0.05622166],\n",
            "       [-0.07080186],\n",
            "       [-0.0275593 ],\n",
            "       [ 0.12254007],\n",
            "       [ 0.04617819],\n",
            "       [ 0.08941662],\n",
            "       [-0.01511961],\n",
            "       [-0.16531567],\n",
            "       [-0.02655232],\n",
            "       [ 0.06183282],\n",
            "       [-0.12531835],\n",
            "       [-0.12135493],\n",
            "       [ 0.10796561],\n",
            "       [ 0.09157646],\n",
            "       [-0.04181038],\n",
            "       [ 0.04112628],\n",
            "       [ 0.02319223],\n",
            "       [-0.09940879],\n",
            "       [ 0.02562439],\n",
            "       [ 0.1017007 ],\n",
            "       [ 0.12261383],\n",
            "       [-0.01948871],\n",
            "       [ 0.09936224],\n",
            "       [-0.05929748],\n",
            "       [ 0.02696436],\n",
            "       [ 0.0196094 ],\n",
            "       [ 0.07218593],\n",
            "       [ 0.05022663],\n",
            "       [ 0.00135238],\n",
            "       [-0.06026578],\n",
            "       [-0.05636784],\n",
            "       [ 0.0901245 ],\n",
            "       [ 0.07751135],\n",
            "       [ 0.01276771],\n",
            "       [-0.07364471],\n",
            "       [-0.06387722],\n",
            "       [-0.12323207],\n",
            "       [-0.05077241],\n",
            "       [ 0.13558112],\n",
            "       [-0.12071223],\n",
            "       [ 0.06318224],\n",
            "       [-0.12879305],\n",
            "       [-0.10619131],\n",
            "       [ 0.11066172],\n",
            "       [-0.03673645],\n",
            "       [ 0.10982454],\n",
            "       [ 0.06073479],\n",
            "       [ 0.03201565],\n",
            "       [-0.11250038],\n",
            "       [-0.1557404 ],\n",
            "       [ 0.08940601],\n",
            "       [ 0.04775262],\n",
            "       [ 0.11671195],\n",
            "       [ 0.02310331],\n",
            "       [ 0.07629927],\n",
            "       [-0.16682336],\n",
            "       [ 0.10758672],\n",
            "       [-0.09783013],\n",
            "       [-0.10837113],\n",
            "       [-0.03514739],\n",
            "       [ 0.03650935],\n",
            "       [-0.03000464],\n",
            "       [-0.10076618],\n",
            "       [ 0.08775338],\n",
            "       [-0.04964183],\n",
            "       [ 0.02362729],\n",
            "       [ 0.16362149],\n",
            "       [ 0.12163698],\n",
            "       [-0.07519589],\n",
            "       [ 0.04674587],\n",
            "       [ 0.10549577],\n",
            "       [-0.08334521],\n",
            "       [-0.06120995],\n",
            "       [ 0.12282745],\n",
            "       [ 0.05725805],\n",
            "       [-0.02434438],\n",
            "       [ 0.04825849],\n",
            "       [ 0.07647444],\n",
            "       [-0.05304407],\n",
            "       [-0.0323233 ],\n",
            "       [-0.07268901],\n",
            "       [ 0.03067316],\n",
            "       [-0.10883604],\n",
            "       [-0.05065865],\n",
            "       [ 0.04483581],\n",
            "       [-0.07894646],\n",
            "       [ 0.13194965],\n",
            "       [-0.08992589],\n",
            "       [ 0.10762957],\n",
            "       [-0.12205317],\n",
            "       [-0.00702157],\n",
            "       [ 0.08006062],\n",
            "       [ 0.12223145],\n",
            "       [-0.01931478],\n",
            "       [ 0.10382156],\n",
            "       [-0.11158417],\n",
            "       [-0.06152328],\n",
            "       [-0.11998478],\n",
            "       [-0.13431723],\n",
            "       [-0.0896455 ],\n",
            "       [ 0.14430308],\n",
            "       [ 0.00643983],\n",
            "       [ 0.06616772],\n",
            "       [ 0.0901897 ],\n",
            "       [-0.061427  ],\n",
            "       [ 0.09033413],\n",
            "       [-0.03717859],\n",
            "       [ 0.1204911 ],\n",
            "       [ 0.0673934 ],\n",
            "       [ 0.03329101],\n",
            "       [-0.11965302],\n",
            "       [-0.13618621],\n",
            "       [-0.08934002],\n",
            "       [-0.0824893 ],\n",
            "       [ 0.10913774],\n",
            "       [ 0.07903811],\n",
            "       [ 0.14401788],\n",
            "       [ 0.13817118],\n",
            "       [-0.09800476],\n",
            "       [ 0.09437063],\n",
            "       [ 0.00959632],\n",
            "       [-0.13082927],\n",
            "       [ 0.08756316],\n",
            "       [ 0.07589345],\n",
            "       [ 0.1248664 ],\n",
            "       [ 0.07018562],\n",
            "       [-0.02011305],\n",
            "       [ 0.13105294],\n",
            "       [-0.05985064],\n",
            "       [-0.0301464 ],\n",
            "       [-0.13553558],\n",
            "       [ 0.03718253],\n",
            "       [-0.01084033],\n",
            "       [-0.05165053],\n",
            "       [ 0.03620179],\n",
            "       [ 0.0412594 ],\n",
            "       [ 0.10842907],\n",
            "       [-0.04829417],\n",
            "       [ 0.13529982],\n",
            "       [ 0.10075952],\n",
            "       [-0.06418979],\n",
            "       [ 0.02081472],\n",
            "       [ 0.12059508],\n",
            "       [ 0.13636257]], dtype=float32), array([-0.00040775], dtype=float32)]\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 256, 256, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 256, 256, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 256, 256, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 128, 128, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 128, 128, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 128, 128, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 64, 64, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 64, 64, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 64, 64, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 64, 64, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 32, 32, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 32, 32, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 16, 16, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 8, 8, 512)         0         \n",
            "_________________________________________________________________\n",
            "sequential_1 (Sequential)    (None, 1)                 8389121   \n",
            "=================================================================\n",
            "Total params: 23,103,809\n",
            "Trainable params: 8,389,121\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G1KTSZHyvd6Z"
      },
      "source": [
        "def finetune_binary_model(location):\n",
        "    # build a classifier model to put on top of the convolutional model\n",
        "    top_model = Sequential()\n",
        "    top_model.add(Flatten(input_shape=base_model.output_shape[1:]))\n",
        "    top_model.add(Dense(units=256,activation='relu',kernel_regularizer=l2(l=0.01)))\n",
        "    top_model.add(Dropout(rate=0.5))\n",
        "    top_model.add(Dense(units=1,activation='sigmoid'))\n",
        "    \n",
        "    top_model.load_weights(top_model_weights_path) # load weights_path\n",
        "    \n",
        "    #base_model.add(top_model)\n",
        "    \n",
        "    model = Model(inputs=base_model.input, outputs=top_model(base_model.output))\n",
        "    #model.add(top_model)\n",
        "    \n",
        "#     input1 = keras.layers.Input(shape=(16,))\n",
        "#     x1 = keras.layers.Dense(8, activation='relu')(input1)\n",
        "#     input2 = keras.layers.Input(shape=(32,))\n",
        "#     x2 = keras.layers.Dense(8, activation='relu')(input2)\n",
        "#     added = keras.layers.add([x1, x2])\n",
        "\n",
        "#     out = keras.layers.Dense(4)(added)\n",
        "#     model = keras.models.Model(inputs=[input1, input2], outputs=out)\n",
        "\n",
        "#     # finetune\n",
        "#     top_model = Sequential()\n",
        "#     top_model.add(Flatten(input_shape=base_model.output_shape[1:]))\n",
        "#     top_model.add(Dense(256, activation='relu'))\n",
        "#     top_model.add(Dropout(0.5))\n",
        "#     top_model.add(Dense(6, activation='sigmoid'))\n",
        "#     top_model.load_weights(top_model_weights_path)\n",
        "\n",
        "#     model = Model(input= base_model.input, output= top_model(model.output))\n",
        "#     model.add(top_model)\n",
        "\n",
        "    \n",
        "#     # set the first 25 layers (up to the last conv block)\n",
        "#     # to non-trainable - weights will not be updated\n",
        "#     for layer in model.layers[:25]:\n",
        "#         layer.trainable=False\n",
        "\n",
        "    # compile the model with a SGD/momentum optimizer \n",
        "    # and a very slow learning rate\n",
        "    model.compile(optimizer=optimizers.SGD(lr=0.00001,momentum=0.9),    # reduced learning rate by 1/10\n",
        "                       loss='binary_crossentropy',\n",
        "                       metrics=['accuracy'])\n",
        "    \n",
        "    # prepare data augmentation configuration\n",
        "    train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input_vgg,\n",
        "                                       rotation_range=40,\n",
        "                                       width_shift_range=0.2,\n",
        "                                       height_shift_range=0.2,\n",
        "                                       shear_range=0.2,\n",
        "                                       zoom_range=0.2,\n",
        "                                       horizontal_flip=True,\n",
        "                                       fill_mode='nearest')\n",
        "    \n",
        "    test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input_vgg)\n",
        "    \n",
        "    train_generator = train_datagen.flow_from_directory(train_data_dir,\n",
        "                                                        target_size=(img_height, img_width),\n",
        "                                                        batch_size=8,\n",
        "                                                        class_mode='binary')   # categorical for multiclass\n",
        "    \n",
        "    test_generator = test_datagen.flow_from_directory(val_data_dir,\n",
        "                                                      target_size=(img_height, img_width),\n",
        "                                                      batch_size=8,\n",
        "                                                      class_mode='binary',     # categorical for multiclass\n",
        "                                                      shuffle=False)\n",
        "    \n",
        "    checkpoint = ModelCheckpoint(filepath=fine_tuned_model_path,monitor='val_acc',verbose=1,save_best_only=True,\n",
        "                                 save_weights_only=False,mode='auto')\n",
        "    \n",
        "    # fine-tune the model\n",
        "    fit = model.fit_generator(train_generator,\n",
        "                              steps_per_epoch=nb_train_samples/8,\n",
        "                              #epochs=nb_epoch,\n",
        "                              epochs=10,\n",
        "                              validation_data=test_generator,\n",
        "                              validation_steps=nb_val_samples/8,\n",
        "                              verbose=1,\n",
        "                              callbacks=[checkpoint])\n",
        "\n",
        "#     model.fit_generator(\n",
        "#     train_generator,\n",
        "#     steps_per_epoch=nb_train_samples // batch_size,\n",
        "#     epochs=epochs,\n",
        "#     validation_data=validation_generator,\n",
        "#     validation_steps=nb_validation_samples // batch_size,\n",
        "#     verbose=2)\n",
        "    \n",
        "    with open(location+'/ft_history.txt', 'w') as f:\n",
        "        json.dump(fit.history, f)\n",
        "    \n",
        "    return model, fit.history"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v61lI2Fovd6Z"
      },
      "source": [
        "def evaluate_binary_model(model,directory,labels):\n",
        "    test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input_vgg)\n",
        "    test_generator = test_datagen.flow_from_directory(directory,\n",
        "                                                     target_size=(img_height,img_width),\n",
        "                                                     batch_size=8,\n",
        "                                                     class_mode='binary',     # categorical for multiclass\n",
        "                                                     shuffle=False)\n",
        "    \n",
        "    predictions = model.predict_generator(test_generator,\n",
        "                                          steps=len(labels)/8,\n",
        "                                          verbose=1)   \n",
        "    \n",
        "    # use for multiclass\n",
        "    # pred_labels = np.argmax(predictions, axis=1)\n",
        "    \n",
        "    pred_labels = [0 if i <0.5 else 1 for i in predictions]\n",
        "\n",
        "    print()\n",
        "    print(classification_report(labels, pred_labels))\n",
        "    print()\n",
        "    cm = confusion_matrix(labels, pred_labels)\n",
        "#     sns.heatmap(cm, annot=True, fmt='g');\n",
        "    return cm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZ01eME3vd6Z"
      },
      "source": [
        "def view_images(img_dir,img_list):\n",
        "    for img in img_list:\n",
        "        clear_output()\n",
        "        display(Image(img_dir+img,width=200))\n",
        "        num = input(\"c to continue, q to quit\")\n",
        "        if num == 'c':\n",
        "            pass\n",
        "        else:\n",
        "            return 'Finished for now.'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v8XbAhrnvd6Z"
      },
      "source": [
        "def print_best_model_results(model_hist):\n",
        "    best_epoch = np.argmax(model_hist['val_acc'])\n",
        "    print('epoch:', best_epoch+1,', val_acc:', model_hist['val_acc'][best_epoch],', val_loss:', \n",
        "          model_hist['val_loss'][best_epoch])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_AyXiCSvd6a"
      },
      "source": [
        "def plot_metrics(hist, stop=50):  # stop -> no of data pts in plot\n",
        "                                # hist(history) -> dict\n",
        "    fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n",
        "    # axes = axes.flatten()   # flatten -> numpy flatten\n",
        "\n",
        "    ax0.plot(range(stop), hist['acc'], label='Training', color='#FF533D')\n",
        "    ax0.plot(range(stop), hist['val_acc'], label='Validation', color='#03507E')\n",
        "    ax0.set_title('Accuracy')\n",
        "    ax0.set_ylabel('Accuracy')\n",
        "    ax0.set_xlabel('Epoch')\n",
        "    ax0.legend(loc='lower right')\n",
        "\n",
        "    ax1.plot(range(stop), hist['loss'], label='Training', color='#FF533D')\n",
        "    ax1.plot(range(stop), hist['val_loss'],\n",
        "             label='Validation', color='#03507E')\n",
        "    ax1.set_title('Loss')\n",
        "    ax1.set_ylabel('Loss')\n",
        "    ax1.set_xlabel('Epoch')\n",
        "    ax1.legend(loc='upper right')\n",
        "\n",
        "    plt.tight_layout()\n",
        "\n",
        "    print(\"Best Model: \")\n",
        "    print_best_model_results(hist)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5GPE1YGvd6a"
      },
      "source": [
        "### Testing Image Generation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFxKljbAvd6a"
      },
      "source": [
        "datagen = ImageDataGenerator(rotation_range=40,\n",
        "                             width_shift_range=0.2,\n",
        "                             height_shift_range=0.2,\n",
        "                             shear_range=0.2,\n",
        "                             zoom_range=0.2,\n",
        "                             horizontal_flip=True,\n",
        "                             fill_mode='nearest')\n",
        "\n",
        "img = load_img(path='./car-damage-dataset/data1a/training/00-damage/0354.JPEG')  # this is a PIL image\n",
        "x = img_to_array(img)   # x shape (height,width,3)\n",
        "x = x.reshape((1,)+x.shape)  # this is a numpy array of shape (1,height,width,3)\n",
        "\n",
        "# the .flow() command below generates batches of randomly transformed images\n",
        "# and saves the results to the 'preview/' directory\n",
        "if not os.path.exists('./car-damage-dataset/data1a_preview'):\n",
        "    os.makedirs('./car-damage-dataset/data1a_preview')\n",
        "i = 0 \n",
        "for batch in datagen.flow(x, batch_size=1,\n",
        "                         save_to_dir='./car-damage-dataset/data1a_preview', save_prefix='damage_car',\n",
        "                         save_format='jpeg'):\n",
        "    i +=1\n",
        "    if i > 30:\n",
        "        break # otherwise the generator would loop indefinitely"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "alHANL36vd6a"
      },
      "source": [
        "view_images('./car-damage-dataset/data1a_preview/',os.listdir('./car-damage-dataset/data1a_preview'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gYb2QOp4vd6a"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6Tr_biLvd6a",
        "outputId": "1eb7e42a-a83e-440c-818e-ab6709b122e6"
      },
      "source": [
        "# do not rerun!!\n",
        "save_bottleneck_features(location)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Already exists ./car-damage-dataset/data1a/bottleneck_features_train.npy\n",
            "Already exists ./car-damage-dataset/data1a/bottleneck_features_val.npy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2uG6LBivd6a"
      },
      "source": [
        "sns.set_style('whitegrid')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "641LIlx9vd6a"
      },
      "source": [
        "d1a_model1,d1a_history1 = train_binary_model(location)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYgG8yyFvd6a"
      },
      "source": [
        "# Winner\n",
        "plot_metrics(d1a_history1)  # sgd with lr = 0.0001, sigmoid, with l2 = 0.001"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "racbUUENvd6b"
      },
      "source": [
        "### Fine Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Mga7Zo0evd6b"
      },
      "source": [
        "ft_model,ft_history = finetune_binary_model(location)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9btEwo3vd6b"
      },
      "source": [
        "# Winner\n",
        "plot_metrics(ft_history,stop=10)   # sgd with lr = 0.0001, sigmoid, with l2 = 0.001"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hG2RlWvevd6b"
      },
      "source": [
        "### Load Modal Point"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3TMgTbfvd6b"
      },
      "source": [
        "ft_model = load_model(location+'/ft_model.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGV3NTiPvd6b"
      },
      "source": [
        "with open(location+'/top_history.txt') as f:\n",
        "    top_history = json.load(f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F7UOrroDvd6b",
        "outputId": "20168e51-0db4-4a57-b898-72322a1fcbb4"
      },
      "source": [
        "plot_metrics(top_history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Model: \n",
            "epoch: 23 , val_acc: 0.95 , val_loss: 5.388070894324261\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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n/Uo821sWjI2NtZ/HxsaydOlSVqxYQdWqVZk0aRLp6XmXouk/sFlZV1eX7OzsfO99b/Ofjo6Oto3m3juF5UACZCEqk5wc+GsRrFqqBJ7VreH0CSVgBiUQ1NdXZoaNjKF5K/BoDy1agVm1J9NHA0MYLRXZnnUZFtUhJrJo73QIISqcW7duYWJigqmpKUlJSezevZsOHTqU6jPc3NzYvHkz7u7unDp1ipiYfDZKlxEJkIWoLFLvwNefKpvKOvvDiDeV1GigbDi7t3zg1g0lMG7c4v55IZ6wTAsrZePjtWRlo6UQ4qni4uKCg4MD3bp1w87ODldX11J/xuDBg3nnnXcICAjAxcUFR0dHTE1NS/05+ZEAWYjKIPECzJ4C58/B8PHQtXfuWblqFuDaRvkQogLItLi7jCbpogTIQlRQb7zxhvbzunXratO/AahUKj7//PN8r1u2bJn28wfXHPv7++Pv7w/Am2++mW/7GjVqsG3bNkApdz937lwMDQ2JjY1l+PDh2NraPuaoikYCZCGeZunpcHAPBM4FNPD+59DUvbx7JUShMu4FxUkXlRR8QgjxkDt37jBs2DCysrLQaDRMnz4dvZIWnComCZCFKC85OXAxQUkndi+/771iF9dTlPRiDk73c/SaVVWuu3ENDu5VllIcDVNSsNW2h3dmStEF8dTINLdSPpGNekKIAlStWpXVq1eXy7MlQBbiSUm6eDff792Ps6fhzu3753V0lKUQFlZQ1UKpMnZg1/3z6prY6xvChTgluLasoexubtleSZkmZY3FU0RjYKCkDJRiIUKICqhMA+SdO3cyY8YMcnJy6NevX57E0OfPn+e9994jOTkZc3NzPv/8c2xsbNi3b1+uMoVnz57lyy+/xNvbm3fffZcDBw5gZmYGwOzZs59IRRUhSuzMSSWzxJEDyms9faWSXAcfZWbYzl4Jds0tQPeh/yVv34SY09qgWnMhAXoPUYLi+g1l9794ulnbygyyEKJCKrMAOTs7m+nTp7N48WLUajV9+/bFy8srV5LnOXPm0LNnT3r16sXevXuZO3cun3/+Oa1bt9YuBL927RpdunShXbt22usmT56Mn1/FyQsoRL7OnlYC44N7lBRqA0dCc4/7pYyLwsQMmropH0BcZKT8QSgqD2tbOH28vHshhBB55FO/tXRERERQt25d7OzsMDAwwN/fn5CQkFxtYmJiaNNG2VXfunXrPOcBgoOD6dChQ67k1EJUaAmx8MUHMHkEREXAwBHw3V/QZ4iynliWQgihUNvClcuQnVXePRFCiFzKLEBOTEzExsZG+1qtVpOYmJirjZOTE8HBwQBs27aN27dvk5KSkqtNUFAQ3bp1y3Xsyy+/JCAggJkzZ5KRkVFGIxCiBK4mwbujlc1z/YbdDYxfyr+8sRDPOmtbpYT5laTy7okQ4q4hQ4awa9euXMeWLFnCRx99VOA1LVq0AJTYb9y4cQXe99ixY4989pIlS0hNTdW+HjlyJDdu3Chiz0tXmS2xyK88oOqh9ZKTJ0/mk08+Yc2aNbi7u6NWq3Ol70hKSuL06dO0b99ee2zixInUqFGDzMxMPvjgAwIDA3n99dfzPCsyMrLYfU5LSyvRdU+Tyj7G8h5fzRU/Y5aVydk3PiLTsgacSyjV+5f3+J6Eyj7Gyj6+YlHXVP6bePH+50KIcuXv78+mTZtyVcXbtGkTkydPLvRatVrN119/XeJnL126lO7du2tXDSxcuLDE93pcZRYg29jYcOnSJe3rxMRErK2tc7VRq9UsWLAAgNu3b7N161bt5juAzZs34+Pjk6uO9717GBgY0Lt3bxYtWpTv80uyTjPyGVjfWdnHWOTxpd5Rds/bNyi8bVGdPgERB6DPEBq061h6931AZf/+QeUfY2HjO3jw4BPsTTmzvpvwXzbqCVFh+Pr68tVXX5GRkYGBgQEJCQkkJSXh5OTE0KFDuXHjBllZWYwfPx5vb+9c1yYkJDBmzBg2btxIWloaU6ZMITo6GgcHB9LS0rTtpk2bxrFjx0hPT8fX15dx48axdOlSkpKSGDp0KObm5vz66694eXmxcuVKLC0tWbx4MatWrQKgb9++DBs2jISEBEaOHImbmxuHDx9GrVbz3XffYWRk9NhfhzILkJs0aUJsbCzx8fGo1WqCgoKYO3durjb3slfo6OgQGBhInz59cp0PCgpi4sSJuY4lJSVhbW2NRqNh+/btODo6ltUQRGX23RzY+zf49YIhr4Gh4ePdLycHFn2tVATrOah0+ihEGTp79myuSlbx8fGMGzeOYcOGaY/t37+f1157jdq1lfzaPj4++b5jV2LVa4COrgTIQhTg17+PsDjkcKne8+XOLRjSqXmB5y0sLGjatCk7d+7E29ubTZs20bVrV4yMjPj2228xNTUlOTmZAQMG0Llz5zyrA+5ZtmwZRkZGbNiwgaioKHr37q099+abb2Jubk52djbDhg0jKiqKl156iSVLlvDLL79gaWmZ617Hjx9n9erVLF++HI1GQ//+/fHw8KBq1arExcUxb948Pv30U8aPH09wcDA9evR47K9TmQXIenp6fPjhh4wYMYLs7Gz69OmDo6Mj8+fPp3HjxnTu3JkDBw4wb948VCoV7u7uTJs2TXt9QkICFy9exMPDI9d9J02aREpKChqNBicnJz7++OOyGoKorP47owTH9o6wZQ2cOAITPoS6DiW/565tEB0Jr78n643FU6F+/frabEHZ2dl07NgRHx+fPO3c3d358ccfy6YTunpgZS0BshAVzL1lFt7e3gQFBTFz5kw0Gg3z5s0jLCwMHR0dEhMTuXLlCjVq1Mj3HmFhYQwZMgRQ9pw1atRIe27z5s0sX76crKwsLl++TExMDE5OTgX25+DBg3h7e1OlivL71cfHh/DwcLy8vKhdu7b2XTkXFxfOnz9fKl+DMs2D7OnpiaenZ65j48eP137u5+dXYLq22rVr51kkDsr6FCEey1+LwMQUPv4KzkTCgpnKxrrBY+CFPsXPLZx6B37/ERo4Q8cuZdNnIcrQ3r17sbOzo1atWk/+4da2UixEiAIM6dT8kbO9ZcXb25vZs2dz4sQJ0tLScHFxYfXq1SQnJ7N69Wr09fXx8vIiPT39kffJb3Y5Pj6eRYsWsXLlSqpVq8a7775b6H3y29d2j4GBgfZzXV3dQu9VVFJJTzxbzpyE8H+V1GsmZkpe4rmL4dvZsPhrOLJfKeCRcvWB0s9XIS0V/Psqlese/h9+7R9K27emK9XwhHjK5Jct6J4jR47QvXt3rK2teeeddwpc1lbcjYf3NivaGFbB7L8znKmEGxcr+4bMyj4+qPxjfNT4nJycmDhxIq1atSIyMpLo6GhUKhXR0dEcO3aM8+fPEx0dzc2bN8nJySEyMpLExETS09OJjIykTp06/Prrr1SrVo24uDiioqKIjY1FV1cXHR0dEhISOHnyJKGhodSuXZvIyEh0dXWJiIhArVYDkJGRwZkzZ7CysuKPP/7A09MTjUZDUFAQEyZMIDo6Wvs8UJbhpqamal8/zvdPAmTxbPnzZ6Voxwt97x+rZgFTZivLLX79Dg7vV44bGYOllVL6GeDbWRAaBKPeArt6yrHLl2DDn9DeGxo1frJjEaIUZGRkEBoayltvvZXnnIuLC6GhoZiYmLBjxw7Gjh3L1q1b871PcTdWajcrnnSGg7txrmev/D9XiTzrG04rg8o+xkeN73//+x+vv/463333HQ4ODqjVal599VWmTp2Ks7Mz9evXp0GDBtSuXRsdHR2cnZ0xMzPD0NAQZ2dn6tWrx5QpU5g8eTLOzs40a9YMe3t7mjRpws6dO5k0aRJ2dnZ4eHhQs2ZNnJ2deemll5g9ezY1atTg119/xcDAAEdHR1q1asXly5d5//33ARg0aBD+/v4kJCRonwewZ88e7ty5o31dlO9fQRujJUAWz46TR5X8xENezbtOWKWCrr2htSfcuaUExVVM7p/PyYG/N8GvP8Ck4dCtv5Ln+NfvARUMHv0kRyJEqdm5cycuLi5YWVnlOWdqaqr93NPTk48//pjk5OQ8G2gei/puJovLl+7/4SmEKHc+Pj6cOnVK+9rS0pK//vor37aHDysbCWvXrs3GjRsBMDIy4ssvv8y3/ezZs/M9PmTIEO26ZYDQ0FDt5y+//DIvv/xyrvYPPg/glVdeedSQikUCZPFs0GiU2WNzSyVzRUEsqisfD9PRgc7doGV7JUhetwx2blWWYvR/GazUZdd3IcpQUFAQ/v7++Z67fPkyVlZWqFQqIiIiyMnJwcLConQ7cC/VW+IFCZCFEBWGBMii8riWjM6d2/mfO3YITh6B4ePB8DHyI1Y1h7HvgtcLEDhXuVePgSW/nxDlKDU1lT179jB9+nTtsWXLlgEwcOBAgoODWbZsGbq6uhgZGWmzDpWqB4uFCCFEBSEBsnj6XU6EVUvh70046ugqmSh6/k9Zawx3Z49/UtJJ+QSUzjOdm8K8JZCVBQ8UshHiaWJsbMz+/ftzHRs48P4ffIMHD2bw4MFl24mq5sofmpLqTQhRgUiALJ5eKVdg9W+wbYPy2juAm5cuUm39nxC8VlknHNAfoo4rVe5GTwJ9g0ffszhUKgmOhXhcKpWyzEICZCFEBSIBsnj63LwOq39VguDsbOj0AvR5CWqouRAZSbVhY2H5Ylj5C2xapWy2U9dU2gkhKh4JkIUQFYwEyOLpkZMDf2+G336A2zeVohz9ht1fw3iPnT289THEDlGKgoTthvEfgJ78cxeiQlLbwonDynKo0l7jLIQQJSARg3g6nDsLgfMgKgKcmsLINwsvDW3fAN6ZqQTTJmZPpp9CiOKztlWK8dy8rqxJFkKIciYBsqjY0lJhxRLYuByqmMKr70CnrsWrWCfBsRAV271Ub0kXJUAWQlQIEiCLiulaMoQEKeuMky9DZ38YPOZ+ZgohROVhfXeZVNJFaFB5q5YJIZ4eEiCLikOjgRNHYOtaOLBLSaHW2BUmfgROTcq7d0KIsqJ+oFiIEEJUABIgi8cXGQFXL0P7ziW/x55QJfNEQhyYmoFfb/DpDrXqlF4/hRAVk3EVqFpNMlkIISoMCZDF47kYDzPeVtYKG1cBtzbFv8e6ZfDr92DvCGOnQFsvMDQs/b4KISoua1uppieEqDCKsdNJiIdkpMPcaaCnD3XqwTcz4Epi0a/XaJTA+NfvlaB41g/KBjwJjoV49kguZCFEBSIBsii5xd9AbDS8MRXe/hSys2DeR8ra4cJkZ8EPnymzx116KHmKpSqdEM8ua1vlD+zs7PLuiRBCSIAsSmj3dti2HnoMVJZV2NrBmLeVks7LFj762nszzyFB0HcojJwIurpPpt9CiIpJXVP54zr5Snn3RAghyjZA3rlzJ76+vvj4+BAYGJjn/Pnz5xk6dCgBAQEMGTKES5cuac85OzvTo0cPevTowZgxY7TH4+Pj6devH126dGHChAlkZGSU5RBEfs6fgx8+VzJLDBx5/3i7zuDbU5kVDt+T/7VXL8PMd5QsFS+/AS++IpWzhBC5cyELIUQ5K7MAOTs7m+nTp/PTTz8RFBTExo0biY6OztVmzpw59OzZkw0bNvDaa68xd+5c7TkjIyPWrVvHunXr+OGHH7THv/jiC4YNG8bWrVupWrUqK1euLKshiPykp8O8aaBvABM+zFu+eehYqOcIC2bA5bvrkXNy4GgYfDYVXu0PkUdh3Pvg3+/J918IUTFJgCyEqEDKLECOiIigbt262NnZYWBggL+/PyEhIbnaxMTE0KaNkvWgdevWec4/TKPRsG/fPnx9fQHo1atXodeIUrbka4iLUdYdW6nznjcwhIkfK+sIv/wI1v8J4wbBJ28pZaID+sPXv0PHLk+86/dcv53Gt5v2M2/dv+XWByHEQ6zUyrtJEiALISqAMkvzlpiYiI2Njfa1Wq0mIiIiVxsnJyeCg4MZOnQo27Zt4/bt26SkpGBhYUF6ejq9e/dGT0+PUaNG4e3tTUpKClWrVkXv7qyljY0NiYn5Z02IjIwsdp/T0tJKdN3TpKRj1L19E+vgVZgf3suVDn5cNq4Gj7iPWcAgai9fCKdPcKeOAyl9h3PTxRWNnj4kX1c+ysCjxhd5IYW/9p9h05E4UjOVjUCOZSgvAAAgAElEQVQd6phhavT0bA6Uf6NPv8o+vhLT14fq1lIsRAhRIZRZgKzRaPIcUz201nTy5Ml88sknrFmzBnd3d9RqtTb4/fvvv1Gr1cTHxzN06FAaNmyIqalpofe8x9m5+OVKIyMjS3Td06TYY8zJgdBN8Nv3kHoHeg3C6sVXsNIt5J+OszM0aACW1lSxd6DK43W7yPIb31+7jjF/4z4OnE7A2ECfgZ7NqG9jwfu/hZBlbIGzk90T6t3jk3+jFU9UwmWO/ncpz3ELM2N8mjnk+RlV2PgOHjxY6n18aljbSoAshKgQyixAtrGxybXpLjExEWtr61xt1Go1CxYsAOD27dts3boVMzMz7TkAOzs7PDw8OHnyJL6+vty4cYOsrCz09PS4dOlSnnuKUhQbA4FfKJkpnJvCqLfArl7Rr3ctQdGQUrbzRCyD5q3EqZYVX77SlSGdmmFuYkxsUgrv/xbC8XOJtHmKAmRRscQmpdDyrR9JzcjMc05XR4fT34+nrrV5OfTsKVW7LuwOUf4w15EkS0KI8lNmAXKTJk2IjY0lPj4etVpNUFBQrk14AMnJyZibm6Ojo0NgYCB9+vQB4Pr16xgbG2NgYEBycjKHDh1ixIgRqFQqWrVqRXBwMP7+/qxZswYvL6+yGsKzK/UOLF8EQavA1FSpbve831OZbWLZzghMjAw4MHc0VQwNtMfrWFXD1MiA43HFKGzyBGVlZ5OZlZPP8bzHRNGkZWSSzxtbGBuWfInNhJ82o1LBrlkjsDA1ynWuahVDalpWLfG9n0kOjWDrOkg8r6SOFEKIclJmAbKenh4ffvghI0aMIDs7mz59+uDo6Mj8+fNp3LgxnTt35sCBA8ybNw+VSoW7uzvTpk0DlM1706ZNQ6VSodFoGDlyJA0aNADg7bff5s033+Srr77C2dmZfv0kE0Kp0Whg/05Y/LWSjs07AAaNArNq5d2zXBZtP8hHy/7m6PyxWJgaF9guIzOLlXtO0sPDKVdwDKCjo4NLHWuOn0sq6+4W2+aDZxj61SqSb6XmOWekr8uADqcZ7duSlo618rx9r9Fo2BMVz49bwthy6Ayv+Ljx6aDO6Oo+m7NxaRmZrNxzkh+Dw9gbFZ9vm6Fezfn5jV7FvveGA1FsDDvF7Jd85F2I0uLgpPw35pQEyEKIclVmATKAp6cnnp6euY6NHz9e+7mfnx9+fn55rnN1dWXDhg353tPOzk5Su5WFxAvw81dwaB/UdVAyUTRqXN69yiMtI5OPlv3NheSb/PbPUd7o1rrAtluPxJByK5UBHZrke75JXTVr9kWi0WgKXMv+JGk0Guau/Zcpv26nmb2aSb3a5WlzKOo/Vu05yS+hR3Ctb8tov5a82KEJ2Tk5/L4jgsDgcI7FJWJmbIibQ00+X7ObE+eS+PXNPlQzMcrnqZVT9MWrBAaH80voEa7evEPDmtWZ2s8Tk4c2ZB6PS+KX0CP0buOCv3vDIt//dloGE37ajIudNeMDyn8pUUmcPXuWN998U/s6Pj6ecePGMWzYMO0xjUbDjBkz2LFjB0ZGRsyePRsXF5ey65SdPRgYQEwUtPcuu+cIIUQhyjRAFk+BzEwlFduqX0BHF4a+Di/0hsI24ZWTJaGHuZB8kxpVTQgMDud1/1YFBrd/7T6GpakxPs3q53vepY41P207yKWUW9hampVK/zQaDRN+2sTpC1fznKtiqE//do3p1doZA/3cX9+0jExGf7ee33dE0K+dCz+/0TPPrDdAZKQVgRP688fOCH7cEs7o79bz9pJgcnI03ErLoEV9W354NYAXOzTB1NiQH7YcYMJPm2n3zkLWvPc/HGtWz9PfA2fOsyTkEG0a1eElr+YlGnd2dg6Tf9mKmbEBr/i4YWdV/Hcdrt64w9TfttPY2rjQTXrZ2Tl8tmY3O0/E5jl3MzWDfafi0dPVoYeHE6P9WtKpSb18/51kZGZx+OxFxi8MolMT+3y/5vmZsWIHcZev8feM4ejrPZ1VIOvXr8+6desAJW99x44d8fHxydVm586dxMbGsnXrVo4ePcpHH33EihUryq5Tunpg7wjRUWX3DCGEKIKKGQWJJ+fPn5TKd609lcp21SvupseMzCw+W72bVg1rM8LHjZHfrmPXyTg6utjnaXs7LYN1+6MY5Nk0TzB6T+O6ykbQ4+eSSi1A3nTwDN9uOkCTumqqPLS2NSrhCuv2R2FdzYSXvV0Z2cUNe2sLLiTfoM/sPwk7c57p//NiSt+Oj5zRrlrFiDF+Hoz2bcmeqHgWbT+Iro4OI3zc8iy7GOPngVPtGgz4bDltJgeybFJ/fJo7cCs1nWW7jhEYHM7hsxdRqVT8vP0QNaub4d3Modjjfu+37czfsBeVSsXMlTvp5t6IUb7udGnugE4RNludOJdEr5l/cDYxBYCrWXp80N8z32tv3EljyJerCAo/TTN7G4wMcn9/dXV0+GhgJ17xdiv0+2qgr8c3o/3x/mAJs1ftYvr/Ohfa15PxScxbt4eXOjWnw3N1C23/NNi7dy92dnbUqlUr1/GQkBB69uyJSqWiefPm3Lhxg6SkpLLdHO3gBH9vUnKpSwl6IUQ5kQD5WabRwL+h4NYWJn1S4ttEX7zKtiMxRW5f3awK/dq5FHtZw287jnLu8nUWjO7G843tmbQ4mMDg8HwD5I1hp7iTnsmLHZoWeL/GdZRf8sfjEvFpXvyg8GEajYYZK3ZQt4Y5B74YnWdmMScnh61HYvhxSxifr9nNZ6t349uiAUf/u8TN1HRWvzuQ7q2civw8lUpFO+c6tHOu88h2zzeux77PR9F71jL8P/mVXq2c2XY0hht30mlqr+bb0d3o7uFE14+XMvCLFez9bCQNbKs/8p4P+v2fo8xd+y+v+rVkYs92/LTtIIu3H2L9gSjqqy0Y6evOMK8W1Khmku/1Gw5EMeTLVZgaGxDyycvMX/M3n/z1D8fjElk8rhemxobattEXr9Jr5jJOX7jK1yNf4NWuHo+9POb5xvUY/HwzPl/zL4M8m9GollWBbTUaDW8EBmFmbMicoeVX7Ka0BQUF0a1btzzHH85nfy/3fH4BcnFzOxeUD7qasRk101KJ2f0PGdY1i3XPiqay57yu7OODyj9GGV/BJEB+lsXFwJVE6Df0sW7z2g8bCY04W6xrqhjq061loyK3z8rOZvbKXbg51KSrqyMqlYqXOjXjh+Bw5l27hbV57hzZf+46Rq3qVenwXMHBY41qJqjNTTl+rnQyWWw/epYDpxP4dnS3fN9219HRwc/VET9XR85dvsZP2w6yaPshqhobsmnaEJrUzacyYSmpp7Zg16xXeOWbtWw6eIbebZwZ7deSNo3stAHm6ikDaf12IL1nLePfOSMxeyAwLUh49HlGfbeeji72zHulK/p6uswY7M20Ac+zZl8kP2wJY8rSbUz7I5Q+bZ9jtG9L2jnX0W7Anb1qFx/+EYqbgy2r3h1IrepVqaHTio7NGjH5l610fO9nVk8ZiL21BduPxjDwixWogM3ThuDVNP+lMyUxZ2gXNhw4xRuBQQR/9FKBQfeyncfYcTyW78Z0KzDgf9pkZGQQGhrKW2+9ledcUfLZ31Pc3NUF5oM2NYbVS3DQZCr51J9iT1tO7+Kq7OODyj9GGV/BueclQK6MNq+CGrbg3vbR7cL+VVK3uRXS7hGu3U5l54lYxgW05t3eHQptn52jocOUn5ixYgf+7g2LPPv3567jnE1MYfXLftprRvm25Jug/SwJPczkB56dfPMOWw5HM/YFj0Lf3m9c15oTpZTJYsaKHdSqXpVhnVsU2rZODXOm/68zH73YqUhLEEqDqbEhf00eQE5OTr7PrG9jybK3+/PCx78y9KvVrHxnwCP7dinlJn1m/4mNuSl/vd0/1x8FBvp6DOjQhAEdmnDiXBKBwWH8+s9Rlu08RpO6akb5urPrZBzLdx9nYMcmBL7WQ5tuTaVSMaF7W5ztrBk0dwWt3w5kSKfmfL1hH061rFjz3kDq21iW6tdGbW7Kp4M780ZgEMt3H893Y+e126lMWryFlo61GOHjVqrPL087d+7ExcUFK6u8M+cP57N/Irnna9qBkbGSyeL5vJu4hRDiSXg2cz9VZilXYfECWPIN+SZ9fVD4v+D4HJiXPNjYciiarOwc+rVtjLW5aaEftpZmTO7dnrAz59l2tGjLMrKzc5i1cidN7dUEeNyfdXa2q0FHF3sWbj1ITs79/MBr9kWSmZXNwI4FL6+4p3EdNSfOXSb7MfML7zgey+6TcUzq2Q7DAtY85+dJBcdFfWbnpvX54mVf1h+I4uM//ymwXXpmFn3n/EXKrVRWTxn4yNlUlzrWzB/pz7mf3uKHVwPQ09XhjcAgVvx7glkv+bB0Qp98cxH7tmjAnjkjqW5WhS/X7cHfvSG754wo9eD4nlFd3HFzqMlbi7dw/Xaa9njStVvMWbUL94k/cOXmHb4d3a1cvm9lJSgoCH9//3zPeXl5sXbtWjQaDUeOHMHMzKzsA2RdXajnqGSyEEKIciIzyJXNP1sgJxsunYfICHiuWf7trl5WfgENGvVYj9twQNl05uFYq/DGdw31asHMFTuZsXxHvqV4H7Zq70lOnb/Cn5P652k72tedQfNWsu3oWXxbKLmy/9x1DEfb6rjWty20L43rWJOakcnZxJQ8GR6KY8aKHajNTSvFzOLr/q2IiL3EjBU7aGqvpk/b3Gm97q3D3Xcqnj8n9adZPZsC7pSbqbEhI7q484qPGwdjLqCvq1votQ1rWbFnzkh2nYzjBTfHMg1MdXV1+HZMN9pMXsi0ZaH0bvMcgcHhrNp7ksysbDq62PP1KH9cHZ7udbEPSk1NZc+ePUyfPl17bNmyZQAMHDgQT09PduzYgY+PD8bGxsycOfPJdMzBCbauheysCptRRwhRuclPnspEo4GQjdDACRLi4J/N2gA54cp1xv64kdefb4gzQPge5Rr39iV+XEZmFlsORdO7jXOxClEY6usxqVc7Jvy0mR0nYnm+ccHlq3Nycpi5YgfOtWvQu03edUQ9WzvfTfkWhm+LBly+kco/x2OZ2u/RmSDuuZfJ4sS5pBIHyHuj4gmNOMtnw7o8VlW2ikKlUrFgdDciE64wdP5qPlu9O9f5jKxsjsUl8l6/jvRtV/ycuCqVCvcGRf+DqpqJUbHWqz8O9wa1GOPrzoKg/SwI2k+1KkaM8XVnlG9LnO1qPJE+PEnGxsbs378/17GBAwdqP1epVNoCTk+UQyPIyID4OLB//A20QghRXBIgVyYnjyozx29MhROHYc/fMHw8GBkzcdEWgsJPU01fg0+7lhC+G2xqQe2Sp6naFXmO63fSCGhZ9MwL97zi7caslbuYsXzHIwPk9QdOcfxcEksn9Ml39tBQX49hnVswb90eEq5cZ8uxc2g0Gl4soDjIw56zq4FKpeJYXCI9W5dso8KMFTuwqlqF0b4tS3R9RWSor8fKdwbwzi9bSbmVlue8v3tDPnqxUzn0rOx9MrgzaZlZtHGyY0D7JpgYFS03sihF2op6URIgCyHKhQTIT7ELyTfYGxVP7zbPKbOlIRuhiqmS07iGDYRugn072GzmwOq9J7E0NWbD4Vhup1zD5Ngh8OulbNLLx4awUzjVsnrkrOrGA1EYGejh3bz42QSMDfV5q2dbJi/Zyp6oc7R1ypttIjMrmxkrdtDA1pL+7QueqRzZxY0v1v7Lz9sPsfloHC3q2+JUu2izfSZGBtRXW3A8rmSZLMKjz7Pl0Bk+Hdy50gVSNhZm/DKhT3l344kzNzFm4es9y7sbzzabWlDFBM6egs75r48WQoiyVHl2mjxjcnJyePHzFQz4fDnfbT4At27Cvn+ggzcYGoFzU7CpRWroZsYvDMKplhV/vt2fm2mZLF+xGbIyoWX+yyvOXLhKn9nLePnrNfmmeQJlHeqGsFN4N3MocvWxh432bYlV1SrMWL4jz7krN27j9/FSDp+9yLQXO6H3iIIB9W0s6dLcgQVB+zmWkMyA9sUrkf04mSxmrtiJhakxr3X1KNH1Qoh86OhA/UayUU8IUW4kQC5FqemZ3LiT9+3osvDrP0fZE3WO+moLJv68hb//Wq2s2et8N9m/SgXP+zHneApnE1P4epQ/nZrUo4F1NX7cfQpMzcAp/0By9qqd5ORo2HcqntBj/+Xb5vi5JGKTrhHwGGtDTYwMeLN7W4IPRxN25rz2+LG4RNpMXsi+UwksHterSNkoRvu2JOVWKgD9ixsg11Fz5mIyaRmZxbouIvYS6w9E8Ua3VlStYlSsa4UQhXBoBLExkFm8/y+FEKI0SIBcSm6nZdDxvZ/pMm1pmT/r6o07vPPLVto42RE2dwwNa1ry4qYz/GfnDPUbatuddmrNZ5q6DKxrglfT+qhUKvp51Cf8pobwBq3y3R3+X2IKv/0TwWhfd2wtzPKd3QVYf0CZ2fF3b5jv+aJ67QUPLEyNmblCec66/ZG0f/cn0jKyCP30ZYZ0al6k+7zg7oidVTXc7GtQp4Z5sfrQuK412Tk5RCZcKfI1GZlZjFu4CTNjQ97wb12s5wkhisDBSXmnK754RYiEEKI0SIBcCjQaDa98s5bDZy8SHn2eSyk3y/R57/++nZRbaXw7uhvVTIxY/b/WZOfk0PuGPbdS07V9emPlPox0VHyeegTu5gnub6WhCtkszMw/l+lnq3ehq6PivX6eTOrVjp0nYtl1Mi5Puw0HTuHRsDY2FmaPNRYzY0PGdWvNhrBTvPr9evrM/hPn2jXY/8UoWjWsXeT76OnqEvrpy3wxsPhFTxrXuZ/JoqgmLtrC7pNxfDemGxamxsV+phCiEA53352KOVW+/RBCPJMkQC4Fs1ftYuWeEwzyVJYChBwtfMZjT9Q5ukz7hTvpGcV61r5T8fy07RBvdGtFU3slh6xjxA7+MDjNiZR0hn+zlpycHFb8e4KQo2f5xNMBm6sJSk5koObZ47yoc5llp67kKoYAEH/lOktCjzDc25Va1asysosb1tVM8swiX0i+QXj0+cdaXvEgZYmCIQu3HmSQZ1P+/vRlalpWLfZ96qktsDIrfrDqWNMSAz1djhVxo15gcBg/bAljUq92RVr+IYQoAWtbMK0qAbIQolxIgPyYNoSd4sM/QhnYsQmLx/XCqmqVIlWICwwOJzTiLNuOFK2aHEBWdjZvBAZha2HKtHspttJSYfd2urRvwZyhXVi99yTv/badtxZtoUV9W8aM7AfGVZScyIBpVASjG1blTnomv+84muv+X6zZjUaj4e1eyua9KoYGvNmjLduPxrD/dIK2XVD4aYBSC5DNTYxZMq43gWO7s2R87yeeS1hPVxfn2jWKlMli18k4xi3chJ+rIzMGeT+B3gnxjFKplFnkaNmoJ4R48iRAfgwn45N46ctVuNa3JfC1Hujo6NC5aX22H40pMPsDKIHuprtB5oYDRZ8d+X5zGIfPXmTe8K6YGRsqB/f+A6l3oHM3JnRvw5Dnm/HFmn+5dO0W347uhq5xFWjrpbSLOYXh1UTcPNvi3qAmP24J1/bzYvJNftp2iJc6Naeu9f01vGP8WmJpapxrFnnDgVPUV1vgUqf0Ss52b+XEcG+3IhX3KAtFyWRx7vI1+s/5i/pqC36b2KdYxVGEECXg4KSsQU5PL++eCCGeMWX6G37nzp34+vri4+NDYGBgnvPnz59n6NChBAQEMGTIEC5dugRAZGQkAwYMwN/fn4CAADZt2qS95t1338XLy4sePXrQo0cPIiMjy3IIBUq5lUrvWcuoYqjPqndf1M56+jRvwKWUWxx/RLC1Jyqe5FupWFczISj8NNnZOYU+72LyTT78I5QuLRrQp+1z90+EbISaduDcFJVKxfevBvCCW0Pe69sRj3treDt1VWaav/9Mee3ellG+7pyIT+LfyHMAfLH2X7Kyc3ind4dczzUzNmR89zZsOniaQzEXuJ2WQUjEWbp5NCq3YLYsNK6rJuHqDW0mjIfdSc+gz+w/Sc/KYvWUgZibyLpjIcqcQyPIzoa4or/TJoQQpaHMCoVkZ2czffp0Fi9ejFqtpm/fvnh5edGgQQNtmzlz5tCzZ0969erF3r17mTt3Lp9//jlGRkbMmTMHe3t7EhMT6dOnD+3bt6dqVWVd6uTJk/Hz8yurrhcqKzub/81dQdzl62yfPozaVtW057ybKUUzth+JocndMsYP2xB2CgM9XT4d3JlR365n/5mEvIUyMjOUXwoxpyA6irf3J5ORZsTX+v+h+u0HsKgO+oYQdQyGvKot+GFkoM/69wflvlejxmBbG2LPkGZrh5GVmgHtLZi0OJjA4HAa1qxOYHA4Azs2wcHWMk9/X3+hFfPW7mHmip0M6dSM9MysElXPq8juzYYfP5dEh+dyVxfUaDSMWLCOI/9dYt3U/xW5CIkQ4jFpN+pFQcPnHt1WCCFKUZkFyBEREdStWxc7OzsA/P39CQkJyRUgx8TE8N577wHQunVrxo4dC0C9evdLD6vVaiwtLUlOTtYGyOXtp20H2XYkhh9f604759yBbW2rajjXrsG2ozG82SNvRgWNRsOGA1F0alKPPm1cGPtjEOsPRN0PkEOCIHgtnIuBrCwADlex4c+bzrxf4w4N4s9BxFUlgAbQNwBP30d3WKWC57vCsoXcdGqGEUoO4iHPN2Ph1oMYGeiRlpnFu3065Ht5NRMjXvdvxYwVO7h07RbmJka0d85b+e5pdu+PmRPnEvMEyJ+t3s3y3ceZMdibF9weL62dEKIYqltDNQulop4QQjxBZbbEIjExERsbG+1rtVpNYmLuTVBOTk4EBwcDsG3bNm7fvk1KSkquNhEREWRmZlKnzv2A7MsvvyQgIICZM2eSkVG8LBCPKyMzizmrdtPGyY7h3q75tvFuVp9dJ+LyLTwRlXCF6IvJBHg0opqJEZ4u9my8tw5Zo4G/foY7tyBgAEyaDt8vJ7D1YIwN9Bk/bzp89xf8sQ0Wb4S5i2HeYjDPO+ubR2d/aO7B9RZttIdG+bYkIyubRdsP0a+dyyNnRscHtMbUyIB9p+Lp6uaIvl7Ble2eRrWrV6VaFSOOx+VeGrMx7BTv/x7Cix2aMLl3/pUHhRBl5N5GPamoJ4R4wspsBjm/TWoPr1mdPHkyn3zyCWvWrMHd3R21Wo2e3v0uJSUl8fbbbzNnzhx0dJRYfuLEidSoUYPMzEw++OADAgMDef311/M8qyRrk9PS0gq9blVYDPFXrvNet+ZEReX/Q7tRdQNSMzJZFryb1g1scp37eYdy/0bmukRGRtKyTjW2H41h8679OKrSaJB8hYsB/+Oaa0cAbiUk8duOo/g2seNSfCyXHn7YnQy4VsSx9nkl1xh1AFf7GhyKvcwA19qFjr2/hwOLdkbiWtOs3NZ+F6Yo38OC1K9hyoHI/7TXn026zqDvt+Fka87Ezo0K/H4/SY8zvqdFZR9jZR9fqXNwgiMHlH0URrL2XwjxZJRZgGxjY6PddAfKjLK1de6sB2q1mgULFgBw+/Zttm7dipmZUnji1q1bjB49mgkTJtC8+f1qavfuYWBgQO/evVm0aFG+z3d2di52nyMjIx95XVZ2NkvnB+PeoCYjuncqcJOanX193vxjD6dTsnj5ofvtX/ovrvVt6dTaDYAR1W2ZteEQkclZdDW9BYBtZz9sa9sD8P3mA6RmZPHOAB+cHWsVe0yFjfGn8RZExCbS4/lmhV77Rd16NLSvzWvdWmOoX2b/dB5LYd/DR/Fwiuav3cdxcnLi+p00en+zEBMjQ4I+Glbs6nxl5XHG97So7GMsbHwHDx58gr15Cjg4KYWOYqPBqUl590YI8YwosyUWTZo0ITY2lvj4eDIyMggKCsLLyytXm+TkZHLuVngLDAykT58+AGRkZDB27Fh69OhB165dc12TlKS8Ba7RaNi+fTuOjo5lNYQ8lu08xtnEFKb283xkBgdTY0NaN6zN9ofyISddu8W+UwkEeNzf4FbX2pxm9jZKureTR6CqOdRS1sBqNBoCg8NxrW+Le4OaZTKmpvY2DC5CcAxQtYoRk3q1r7DB8eNqXFfNtdtpnLt8nUFzVxJ7+RrLJw+oMMGxEM+kexv1joaVbz+EEM+UMguQ9fT0+PDDDxkxYgQvvPACXbt2xdHRkfnz5xMSEgLAgQMH8PPzw9fXlytXrvDqq68CsHnzZsLDw1mzZk2edG6TJk0iICCAgIAAUlJStNeUtezsHGat3Ekzexu6FaFAhk9zBw6fvcjl67e1xzYdPINGoyHAI/f13Vo24t+oc1w9cRyea6bNSLH3VDzH4hIZ5eteqVKqVVT3MlkMmreS4MPRfDPyBdo/tGFPCPGEWVSHVh1h/Z9wOc8iMyGEKBNlOhXo6emJp6dnrmPjx4/Xfu7n55dvurZ7QXF+li5dWrqdLKIVe05w+sJV/nq7f5GCVe9mDnz4RyghEWd5sYPytuCGsCjsrKrRzD73uuTurZyYsWIHm5KyGBJwfzY3MDgcM2ND7fWibN3LZLHvVDyv+rVkRBf3cu6REAKAl9+A8UNg8TcweUZ590YI8QyQUmBFkJOTw6wVO3nOrga9WhdtbaSbQ00sTI3ZfreUdGp6JtuOxBDQMm+BDdf6ttQ0MWCDxgpclPXWV2/cYcW/JxjyfDNM71XNE2XKwtSYhjWr49nYnnmvdC38AiHEk2Glhr5D4cAuOLS3vHsjhHgGSIBcBGv3R3EiPokpfTtqs2kURldXB68m9dh2t+x0SMRZ7qRn0s0j7/IMlUpFgEUOwVQnTa3kjf7l78OkZ2Yx0ldmMZ+kPZ+NZMu0lypdGjsh8nPjxg3GjRuHn58fXbt25fDhw7nO79+/Hzc3N+27evc2VZeLbv2hdl34eb6UnhZClDkJkAuh0WiYuWIHjrbV6d+ucbGu9WnuwPmrN4hKuMLGsFOYGRvyvIt9vm27pcZxG13+iTxHTk4OC4MP0tapToHV+ETZMDcxluBYPDNmzJhBhw4d2B/sq/4AACAASURBVLJlC+vWrcPBwSFPG3d3d9atW8e6devyTan5xOjrw4g3IfECrP29/PohhHgmSIBciI3hpzny3yWm9O2Irm7xvlzezZVfNluPRLMx/BR+rg0wyC8DRMoVOqVEY6KnYmPYKf4+HsuZi1cZLbPHQogycuvWLcLCwujbty+gpM6sKNVKC9TYFdp7w9o/4GJCefdGCFGJSYD8CPdmj+upLRjYsfgb5eytLWhga8n8DXu5lHKLgJZO+Tc8eRQjVQ5dnGqxIewUP24Jo7pZFfq0fe4xRyCEEPmLj4/H0tKSKVOm0LNnT6ZOncqdO3fytDty5Ajdu3dnxIgRnDlzphx6+pChr4GenrLUIp+CVEIIURoqZ0LbUnLgzHnCzpznm1H+JX7b3aeZA99vCUNXRwc/1wb5Nzp5FIyMCXjelTUL1rN670km9miLkYH+Y/ReCCEKlpWVxcmTJ/nggw9o1qwZn376KYGBgUyYMEHbxsXFhdDQUExMTNixYwdjx45l69at+d6vuNUBH6eioEWnbthsWk7Cqj+46eJaons8CZW9amJlHx9U/jHK+AomAfIjBAaHYWpkUORCGvnxbq4EyO2fq4OlWZX8G508Ck5NeKGlEzo6G8jJ0TBSUowJIcqQjY0NNjY2NGum/Hzz8/MjMDAwVxtTU1Pt556ennz88cckJydjaWmZ537FrX74WBUTGzrCyUPU3roK6tYFt/+zd99hUR1dAId/y1IFpKmLBQuCFVRU7IrBoCKo2HvsJWg0fjGW2LsmamKJJsYeDcbeiL1hwdjFgjViDWCjKm3h+2MTlABSpHve59mH5d65c2dsHO+emdMA0rmAOid97FUhC4KCPkeZX+rVS/Pevyh5xKuIN/x+8hrdm1TD+AO2WWtqVw4zIwN6OFVLuUFYCDy6D1VqUKSwIc1r2NDasSK2JSwyfU8hhEhL0aJFsbS05K+//gLA19c32SK9Z8+ekfBPGoOfnx/x8fGYmZl98L3vB72i7y+HufYgKHMdKLXBc4wm1WLuN/C/PnB0L8TGfvDYhBAC0vEEef369bRu3RoTE5OcGE+ese7oZaJi4hjc0vGD+jEx1OfJqlGpp2j4+2m+VtE8xdnxTbcPup8QQqTXxIkTGTVqFLGxsVhZWTF79my8vLwA6NatG/v378fLywulUom+vj4LFizIkqqehQ30ePA8nNYzNnBq7gBKmGdicaB1RVj8G5w+Cju94MfZsHGFZju4T1uDQSqf2AkhRDqkGSA/e/aMjh07UqVKFTp06EDjxo0LfNnjhIQElu8/T90KpahezjLtC9KQ4s4V/7pxBXR1obxmAZ+2UrYYE0LkjMqVK7Nt27Ykx7p1e/uf9J49e9KzZ88sv69F4UL82NuJviuO0nbmbxyZ0Tdzn9QptaGxi2Zni8tnNbtbrP0Rdv+u2RKuTuMsH7sQ4uOQZorFyJEjOXDgAB07dmT79u00b96cBQsW8PDhw5wYX644fj2AW0+eM7jFhz09Tpcbl6FCVc0en0II8ZGoXMKMjaM64xcQRNfvNhGnVme+M4UCHOrC1IUw40cobArfjod5E+HV86wbtBDio5GuHGSFQkHRokUpUqQISqWS0NBQhg8fzrfffpvd48sVy/efx8zIgE4Nq2bvjSIjIOAuVKmRvfcRQog8yLWWLUsGu7H/0l2+WO6dmO/8QSrZw5zl0GMQXPCFEZ/Bwd0QH//hfQshPhppplisW7eOHTt2YGZmRseOHRk9ejQ6OjrEx8fTvHlzRo8enRPjzDFBIRFsP+OPp2sdDPSy+anuzauafTyrZH6XDCGEyM8GNq9NQHAIc7eeoGwxM8Z0yIK0CG1taNcT6jnBT/Pg5+/gxEEYNU3zdFkIIdKQZoD86tUrFi9eTMmSJZMc19LS4ueff862geWWNYcvERunZlBOVLG7cVnzD7ltNj+pFkKIPGx6d2ceBIcwfv0hyhQzpWvjjBdmSlFxK5jyAxz2huXzYO826NIva/oWQhRoaaZYODk5JdnBIiIigitXrgAk2xIov1PHx/PLgfM0tStHxZJFsv+GN66ATWXQy/w2ckIIkd9paWmx8gsP6leyYsQvfxAdG5d1nSsU8Kk7VLSDc6eyrl8hRIGWZoA8ZcoUDA0NE78vVKgQU6ZMyc4x5ZrTdwIJCA5hcMssfnocGqJ5vZsD9+Y1/HVL8o+FEALQ09FmfCcnXoS/ZvfZW1l/A8dGEHAHgv/O+r6FEAVOmikWCQkJSbZ109LSIi4uC/93n4dsOnsPlakRbetUypoO37yG31fBH1shXq1JpzC1APMioKMLarXkHwshxD9cqpfHqogJqw5fpGNWL5J2bATrlsK5k+DWKWv7FkIUOGk+QbaysmLdunXExsYSGxvL2rVrsbKyyomx5ahHz0PxufmUvs0c3r9vcXokJIDvMRjRC7w3g3Mr6DcCWneBqjVA3wBCX0JZG82KayGEECiVWvR2rsHBy/d4EByStZ0XLwWly2kCZCGESEOaAfLUqVO5dOkSTZo0wcnJCT8/P6ZPn56uzn18fGjRogUuLi4sX7482fknT57Qu3dvWrduTa9evQgMDEw89++ey82bN2f79u2Jx69du0br1q1xcXFhxowZWbMtEODl40cCCQxoXuvDOgp6CrNGw/xJUNgEZi6FIV9Dqw7QYzB8MR4mLYAffoV5qzTBshBCCAD6NHMAYM2RS1nfuWMjuOEH4aEABL4KZ8cZ/6y/jxAi30szQLawsOD777/H19eX06dPM3/+fCwsLNLsWK1WM23aNFasWIG3tzd79uzh7t27SdrMnTsXDw8Pdu/ejaenJ/PnzwcgJCSEJUuWsGnTJjZv3sySJUsIDdX8gzZlyhSmTZvGgQMHCAgIwMfHJzPzTsbJrhwT29ambDGzzHdybB+M/ExTPrrPMJi7XFMERAghRLqULWbGp9WtWXvkEmp1Fu9d7NhYk+52wReAyb8doePcjfg/epa19xFC5HtpBsjR0dFs2LCBKVOmMG7cuMRXWvz8/ChTpgxWVlbo6uri5ubG4cOHk7S5d+8e9evXB6BevXqJ50+ePEnDhg0xNTXFxMSEhg0bcuLECYKDg4mIiMDBwQGFQoGHh0eyPjOrboVSdKpjk/kOIiNg1SIoVwEWrgf3zpoyqEIIkc0ePnxITEwMAH/++Sfr1q0jLCwsl0eVef0+rcnDZ6Ec8vsrazsuXxHMi8LZE0THxrHV9wYAa49mw9NqIUS+lmYE9/XXX2Ntbc3JkycZOnQou3fvxtraOs2Og4KCsLS0TPxepVLh5+eXpE2lSpXYv38/vXv35uDBg0RGRvLq1asUrw0KCkp23NLSkqCgoBTv7++f8Y/NoqKiMnUdgMUxb4q9juC+c1uigp9DcN4sb/ohc8wPZH75X0GfY3bM74svvmDr1q08ePCA8ePH4+zszFdffcUvv/ySpffJKW3qVMLCuBCrDl6ghUPGHlwMX+6Nvq423/ZpkfykQgF1GsHRvew/609IZBTFzYzZcMyPGT2aoa1UZtEMhBD5XZoB8sOHD1m0aBGHDx+mXbt2uLu7079//zQ7Tik3+N3dMABGjx7N9OnT2b59O7Vr10alUqGtrZ3qtenp81+VK1dOc4z/5e/vn6nrePMa/jwGNetTrlkK/yjnIZmeYz4h88v/Cvoc05rfhQsXMtynlpYW2traHDx4kN69e9OrVy88PDw+ZJi5Sk9Hm55Nq7N071mehUZS1MQw7YuAC/eesnTvWbSVWoxs04Di5sbJGzk2gn3b8fI+QdHChizo35Ju8zZz4PI9WtWqkMUzEULkV2mmWGhra2LowoULc/v2bcLDw3ny5EmaHVtaWiZZdBcUFESxYsWStFGpVCxZsoQdO3YwcuRIAIyNjVO99r/HAwMDk/WZK/bvgIgw6Ng7t0cihPgIaWtrs2fPHnbs2EHTpk0B8v12nP0+rUlsnJr1x66k+5opXkcoXEiPOHU8vxw8n3KjKjUINyjM7ltBdGpYlbb/PK1ee1jSLIQQb6UZIHfp0oXQ0FC+/PJLPv/8c9zc3Bg4cGCaHdvb2xMQEMCjR4+IiYnB29sbZ2fnJG1evnxJ/D/FM5YvX06HDh0AaNSoESdPniQ0NJTQ0FBOnjxJo0aNKFasGIaGhly+fJmEhAR27NhBs2bNMjPvrBMdBbt/h2q1oUKV3B2LEOKjNHv2bC5fvsyQIUOwsrLi0aNHtGnTJreH9UGqli5G3QqlWHXoYrp2Kzp98yF7L9xhXMcmtHCw4Zf9F4iNUydvqKPDzlK1iYqHrg2roqujTfcm9uw+d4sXYa+zYSZCiPzovQFyfHw8hoaGmJiY4OjoyOHDh/H19aVr165pdqytrc2kSZMYMGAArVq1wtXVFVtbWxYuXJi4sO7s2bO0bNmSFi1a8Pz5cz7//HMATE1N8fT0pGPHjnTs2JGhQ4diamoKaHaxmDBhAi4uLpQuXZomTZp86K/Bhzm0G0JfydNjIUSusbGxYcKECbi7uxMaGkpkZCSDBg3K7WF9sP4uNfF//Iwztx6n2Xbyb0coZmKIp2sdPnetw9+vwtn5580U23pFm1KWN9RXaHZH6t3MgZg4NRtPXs3S8Qsh8q/35iBraWmxYcMGWrVqlanOnZyccHJySnJsxIgRie9btmxJy5YtU7z23+D4v+zt7dmzZ0+mxpPlYqJhp5emXLRUxBNC5JJevXqxbNky4uLi8PDwwNzcHEdHx3TtOJSXdW5ox/9W7mPVoQvUr5R6gaqjV+9z9Op9FvRriaG+Lq41bSlbzJSle88mq8j3LDSSQw9CGKV8juLcSahSnRrlilOjnCVrD19iaKu62T0tIUQ+kGaKRYMGDVi5ciV///03ISEhiS8BHN0LL5/L02MhRK4KDw/HyMiIgwcP0r59e7Zt28bp06dze1gfzMhAj86N7Nh06jrhb6JTbJOQkMDk345Q0qIwg1rUBjQV+Qa3dMTnegDXHiTd6WjL6euo4xPoVqmIpqreP+kbn31Sg4t//Y1fQGCyewghPj5pBshbt25lw4YN9OjRg/bt29O+ffvEXOGPWmwsbN+gKQRiXzO3RyOE+Iip1WqCg4PZu3dv4iK9gqLfpzWJjIph9Jr9RMcmX3i4/9JdTt98yDcdm6Cvq5N4vG8zB/R0tPlp37kk7b18rmJfRoWdUyMIfAKPAwDo7lQNHW0la49cztb5CCHyhzQD5CNHjiR7ZVVxjnzNZz88D9I8PU5lqzkhhMgJnp6e9O/fHysrK6pVq8ajR48oW7Zsbg8rS9StUIrh7vX45cAFGoz+hesPgxPPJSQkMMXrCGWLmdL3nxLV/ypS2JAujez49dgVQiOjAAgIfsXpmw/p2tgeajfQNDx7IrG9e+0K/HbcL+XFfUKIj0qa+yDv2LEjxeP5eY/ND6aOg23roXwlcJB8NSFE7nJ1dcXV1TXxeysrKxYvXpzmdWFhYUyYMIHbt2+jUCiYNWsWDg5vA82EhARmzpzJ8ePH0dfXZ86cOVStWvU9PWY9hULBgv6uOFezZuCSndT9+mfm9m6Op2sddp+9xfm7T1kxzANdneQ/zj53rcO6o5f59dhlhrnV4/cT1wDo0tgOzMw0nwAe3w8oIOoNn6lD2R4Wyd5J02hTOBri1BAXq/k3X60GLS3wHAslUs+HFkIUDGkGyFevvl3VGx0dja+vL1WrVv24A+SLZyDoKfQeKk+PhRC5LjAwkOnTp3Px4kUUCgW1atVi/PjxSSqPpmTmzJk0btyYRYsWERMTQ1RUVJLzPj4+BAQEcODAAa5cucKUKVPYvHlzdk4lVe6OFbm80JP+i3cw4pc/2HvhDo+fh2Jb3IKeTauleI2jbUlq25Tkp73nGNqqLhtPXKVBpdKULWamadD4U1i5ELx+AaUSVz0DVFo1WBMQQ5sSr0BbCdo6oNQGHV24eRU2r4ERE3Nu4kKIXJFmgDxxYtJ/CMLDw/n666+zbUD5wu3roK0tT4+FEHnCuHHjcHd3Z+HChQDs2rWLcePGsXr16lSviYiI4Ny5c8yZMwcAXV1ddHV1k7Q5fPgwHh4eKBQKatSoQVhYGMHBwblWoEllasTuCT1YuvcsY9YeIComjl9HdnhviWhP1zr0W7ydxXvOcPVBEIsHub096doBmrQAXT3Q0UEb6LFmP4v2nCF4whSKmRol7Wztj+C9Gbr0A8uS2TNJIUSekGaA/F/6+vo8ePAgO8aSf9y7BaXLa54oCCFELnv58mWSxdPt27dn7dq1773m0aNHmJubM27cOG7evEnVqlUZP348hQoVSmwTFBSU5Cm0paVlilVRQVNCOyOioqIyfM2/nMsVZuPnLpy5F4S9hfK9/VQroo1pIV1Gr9mPUktBtSI6723fuKwJC9TxzN90kD6NKyU5p125FuX/2EromqUEtu2Z5jj/O8d7QaGM23yGhT0bUdw0feWz87IP+T3MLwr6HGV+qUszQB4yZEji+4SEBO7evZsk1+2jk5AA925CA+e02wohRA4wMzNj586duLu7A7Bnz57E4kqpiYuL48aNG0ycOJHq1aszY8YMli9fzpdffpnYJqUKdopU0soqV66coTH7+/tn+Jqk9wP3pulrO6DFU+ZtP0XLmrY0dHR4b9vKlcHpsD9LD1/Ho0nt5PsvN3PD7MgfmA38EiyKpt6ROg7/W7eTzHHxsd34P32F7+NIxtevnb7B52Ef+nuYHxT0Ocr84MKFCykeT3MXi379+iW+Bg0axIYNGxg1alTmRloQBD2FyAjNAj0hhMgDZs2axd69e2nYsCGNGjVi//79zJ49+73XWFpaYmlpSfXqmiJHLVu25MaNG8naBAa+3Rc4MDAw19IrPsSQlo4Y6esysHmtdLX3+qoTJcyNaTtzA/6PniU92bYbxMfD7t9T7+BFMAztSpEjuxMPvY6OSazUt+mfxYJCiLwrzQC5ePHiVK9enTp16lCrVi1MTU15/Djtsp8F1t1/SpeWr5i74xBCiH+UKFGCn376iTNnzuDr68vSpUs5cODAe68pWrQolpaW/PXXXwD4+vpSvnz5JG2cnZ3ZsWMHCQkJXL58GWNj43wZIJctZsaL9eNoWzd9T8qKmRrxx6ReaCu1aDXtV568CHt7UlUCGn0KB3dBWApFs2Ki4buJ8DwY0wunNME0sPX0DcJeR9O5kR3XHwUnK2AihMhb0gyQR4wYkeQjNS0trSTloj86925qco+tyuX2SIQQIlVr1qxJs83EiRMZNWoUrVu3xt/fnyFDhuDl5YWXlxcATk5OWFlZ4eLiwsSJE5k8eXI2jzr7KJVp/rhLonxxc7wn9SIkMgq3ab/yKuLN25Pte0BMNDG7NrP19HXWH7uiOZ6QACt/gLv+0NgFnfAQuKV5Wrzy0EVsi1vwfX9XtLQU/H5SniILkZelmYOsVquTrGzW1dUlNjY2WweVp927BWVtNLtYCCFEHpVS/vB/Va5cmW3btiU51q1bt8T3CoUiXwfFH8rBujhbx3bFbfp62s32Yu+kXhjo6XBPWZgVqias3Xaf4ATNJ6q2JSyoG3ABDntDh17g0YN436No+R7lduESnLzxgJk9P0VlaoSzvTWbTl5jWnfnVHO6hRC5K83/UpubmyepnHfo0CHMzMyydVB5Vnw8/HVL0iuEEHmeBF5Zw7maNWtHtOeU/0PazfaixZS1VPRcyIK/ldRPCGFr0+KoTI0YvWw7CSsXQo260LkfGBQi0tYOzhxn9aGLKLW0+OyTGgB0aWTHvcCXXLj3NJdnJ4RITZqPQadOncqoUaOYPn06oFm0MXfu3GwfWJ709yOIeiML9IQQeYKDg0OKgXBCQgLR0dG5MKKCqXMjO4JCIhi5ci+li5owtbszfZwdKLlsGlz5g6BWw/H87QS7zKxp++Uk+Gdf5jC7WujfuMKvhy/QqpYtxc2NAfCoVxnPn/ew8cRVatvIfspC5EVpBsilS5dm06ZNREZGkpCQgJGRUVqXFFz3bmm+yhNkIUQecOnSpdwewkfjC/d6uNWuQJmipm/zmdv3hEnD6bfnexYrqjBWuxKt9Auh8881ERWrcUGpIjA8in6f1kzsy8zIgBYONmw+dZ1vezdHSytj+dFCiOyX5t/KBQsWEBYWhqGhIUZGRoSGhvL999/nxNjynns3QU8fSpbO7ZEIIYTIYdaW5kkX+1WpAZWqoR0ZzhwPR+48C2f5gfOJp+P19FllVBFLrVhcHWyS9NWlkR1PXoRx6uajnBq+ECID0gyQfXx8KFy4cOL3JiYm+Pj4ZOug8qx7t6CcLShlgZ4QQghgxAQYO5tWvTrR1K4c038/RmhkFADBYW/YG6JFr4SnaN9LWs2rtWNFDHR12HTiaordhkS+oet3mzh4+V62T0EIkVyaAbJarSYmJibx+6ioqCTffzTUcXD/jqRXCCGEeKuoJdRuiEKh4Ns+zXke9pq5204AsOvifdQJ0E/nOZw+kuQyIwM93B0rsOX0DeLU6iTnomJiaT97I1tOX2f0mv3p2pFECJG10gyQ27RpQ+/evdm8eTObN2+mb9++eHh4pKtzHx8fWrRogYuLC8uXL092/unTp/Tq1QsPDw9at27N8ePHAdi1axdt27ZNfFWqVCmxlnavXr1o0aJF4rkXL15kZL6Z9/gBREfJAj0hhBApqlm+BD2cqrFw9xkeBIew/cJfNK5SBtua1eDM8cSiIf/q0sieZ2GRHL0akHgsPj6ePgu343M9gHb1KnP1QRBHrt7P4ZkIIdLMFRg4cCAVK1bE19eXhIQEGjduzNOnaW9No1armTZtGqtXr0alUtGxY0ecnZ2xsXmbh7Vs2TJcXV3p3r07d+/eZdCgQRw5coQ2bdrQpk0bAG7duoWnp2eSWtrz5s3D3t4+M/PNvMQFehIgCyGESNn0Hs3YcvoG7Wb/xsMXEUzt4QLKUnDuJNy+DpXe/uxqWdMGYwM9Np28ikuN8iQkJDBq9X62nL7O3N7NGdqqDtaDvmfhLl+aVbPOxVkJ8fFJ19LZokWLolAoOHDgQIrlSFPi5+dHmTJlsLKyQldXFzc3tyT7KYNmn86IiAgAwsPDUyxh6u3tjbu7e3qGmb3u3QSDQlC8VG6PRAghRB5VuqgpI1rXwy8gCCM9HTo0qAK1G2oqsPoeTdJWX1cHj7qV2ObrT3RsHPN3nGLRnjMMb12P/7VtgL6uDoNbOvLHhdvcevI8l2YkxMcp1SfI9+/fx9vbG29vb0xNTWnVqhUJCQn8+uuv6eo4KCgIS0vLxO9VKhV+fn5J2gwbNoz+/fuzfv163rx5w+rVq5P188cff7B06dIkx7755hu0tLRo3rw5np6eKe4D+m9KRkZERUWlel3Z65eJtyzFw1u3MtxvXvK+ORYEMr/8r6DPsaDPT8CY9o359egVmlUuTiE9XUAXatQB3+PQexi8s61b50b2/HrsCkOW7uLXY1fo3MiOeX1aJP5cG9LSkblbT7B4zxmWDM4DD4uE+EikGiC7urpSu3ZtfvrpJ8qUKQPAmjVr0t1xSosK/hvIent7065dO/r168elS5cYPXo0e/bsSdwT8sqVKxgYGFChQoXEa+bNm4dKpSIiIoLhw4ezc+fOFHOi303JSC9/f/+Ur4uNhaCn4No+U/3mJanOsYCQ+eV/BX2Oac3vwoULOTgakR1MDPXx//EL7t+7+/Zg/aYppll8Wt0acyMDfj12BSe7sqwe3i7JvsgqUyO6N6nGuqOXmdbdGXPjQlkyxtfRMbwIf8OL8Ne8DH/Dq4g3vIx4Q4NKpalaOvmnudnhiN9f+AUE8mWbBjlyPyEyItUAefHixXh7e/PZZ5/RuHFj3NzcMrSS1tLSksDAwMTvg4KCkqVQbNmyhRUrVgCailDR0dG8evUKCwsLQBNAu7m5JblGpVIBYGRkhLu7O35+fuleNJhpj+5DbIzsYCGEECJdjAz00H53z+R30yzeCZB1tJUMb12Po1fvs21sN/R0kv9YHtGmPmuOXOKXAxcY06HxB49twc5TjFl7MMWf6VWtinF5YcqfzGa1yV5H8L35iKb25ahRrni230+IjEg1B9nFxYUffviBvXv3UqdOHdasWcOLFy+YPHkyJ0+eTLNje3t7AgICePToETExMXh7e+Ps7JykTfHixfH19QXg3r17REdHY25uDmhW8u7bty9JgBwXF8fLly8BiI2N5dixY9ja2mZ81hklC/SEEEJ8iEKGUN1Rk2bxn90sJnRuyuHpfTEx1E/xUvsyKpyrWbN071li49QptkmvPy7cZszag7SsacNPn7dm8+guHJreh0s/eDKvbwuuPwrmlP/DD7pHegSFRHDm1mMAZm/+SGsriDwtzV0sChUqlLirREhICPv27WP58uU0atTo/R1razNp0iQGDBiAWq2mQ4cO2NrasnDhQuzs7GjWrBljx45lwoQJrFmzBoVCwZw5cxL/13ru3DksLS2xsrJK7DMmJoYBAwYQGxtLfHw89evXp3Pnzh/4S5AOf90EQyNQlcj+ewkhhCiYGjrD+VPw7Xj4zBNKWKV9zT9GtK5P25kb2HL6Ot2aVMvU7e88fUGvBVupXlbF7193/ic/+i1rlRnTfz/O8v3naVSlTKbukV7e52+RkJCAu2NFtp3x58ajYKpY5UxqhxDpkaGScKampnTt2pWuXbumq72TkxNOTk5Jjo0YMSLxvY2NDRs3bkzx2rp167Jp06YkxwoVKsS2bdsyMuSsce+W5ulxDnzkJIQQooBq2AyeBcG2X2HkZ9DcAzr3AWOTNC91rWlDxZJFWLjbl66N7TOcAhH+JpoOc7zQVmqxZWzXZMExgKG+Lr0+qc7y/eeZ368lRU0MM3SPjNh19hZlipqyYlhbrAd9z5wtJ1g3skO23U+IjErXNm8ftZhoeHBP8o+FEEJ8GC0taN8TlniBsxvs3w5Du8FOL806l/deqsUX7nU5f/dphlMgEhIS6LdoiIJ58QAAIABJREFUOzefPOe3UZ0oW8ws1bYDm9cmJk7N2iOXMnSPjIiMiuHQlXu0qVORIoUNGdzSkY0nr3L37xwq/CVEOkiAnJYH90CtlgBZCCFE1jA1h8GjYP5qqFgVfl0GvVxhcAcYPRBmfg0/zoYNP2t2UPpHr6Y1MDMyYOFu3wzdbvYWH7af8WfuZ83TLDhStXQxGlUpwy8HLhD/n1zprHLoyj2iYuJoXUezruertg3QUSr5dlva65uEyCkSIKdFFugJIYTIDlblYPx3MOl7cOsI1WqDiSmEhsDVC7BrI8wYBZHhgCYFYmDzWmw/40+dr37if6v2st33BsEhEanewvv8bSZ7HaV7k2p82aZ+uoY1uEVt7gW+5JDfX1kyzf/adfYmpob6NP4nz9nSzJgBLjVZd/QyD4JDsuWeQmRUhnKQP0r3bkFhUyiiyu2RCCGEKIiq1dK8/uvGFZj6JSyaCWNmgZYW4zs5YaCrw7Fr91m+/zyLdp8BoFLJIpQuZkr4m2jCX0cT/iaG8DfRhLyOonpZFT95tk533nL7+lUYuXIvy/edp3kNm6ycKWp1PN7nb9OqVgV0tJWJx0e1a8TyAxeYt/0ki6UgisgDJEBOy72bmvQKWaAnhBAiJ1WpDn2GwcqFsGUtdO6Lob4uE7s0ZWKXpsTExnH+3lNO3njAyRsPeRYWibGBHsVMDDE20MNYXxczYwOGtHRMcVFeavR0tOnTzIHvd/ry5EUYJS0KZ9mUTt96xPOw17Spk/RTWasiJvT+pAarDl9iXKcmlDDPunsKkRkSIL9PfDw8eQA16+X2SIQQIss5OztjaGiIlpYWSqUy2S5Bf/75J56enpQqVQrQ7I8/bNiw3Bjqx6tle7h7EzatBuuKUPtt1TldHW0aVCpNg0qlGd0+a2870KU287afYuWhC0zq8kmW9bvr7E10tZW0qJn8yfTo9o1YffgS83ecZn6/lll2TyEyQwLk94mM0CzQMzXP7ZEIIUS2WLt2bWKBppTUrl2bn3/+OQdHJJJQKGDQKE1F10UzYO7PUDz9+ydnVvni5rjUKM/Kgxf5pmMTtJVv0yHi1GpWHrzIxZv3+KlSpXSnbiQkJLD77E0+sS+HsYFesvPWluZ0b2LP8v3nGduhcbZuMydEWmSR3vuEh2q+FjbN3XEIIYT4eOnpwdczQKmEuePhzeu359RqePoIfI/BmePw6nmW3XZIS0eevAjjj/N3gH8C3HO3qDFiKUN/3sPK4/5sPX0j3f35P37G3b9fJkuveNfYjk2Iio2j0dgVzNh0jPtBrz54HkJkhjxBfp+wf1bTSoAshCig+vfvj0KhoEuXLnTp0iXZ+cuXL9OmTRuKFSvGmDFjsLW1TbEff3//DN03Kioqw9fkN1k9x0Id+lJ67UJeTx5BrGkR9IIeoxf8FK3Y2CTtYsyK8MbKmtdlbHhTujzRqpKZWkdjbRhPscIGzN92jJjwF8z/4zLn7gdTtogxP/RoxJKDfoxa5Y1t4QR031lwl5qVxzTBdCUz5Xt/Xb7v0ZANp24zxesoU7yO4lCmCO41ytKiWmlMDNKfS50VCvqfU5lf6iRAfp/EADntKkdCCJHfeHl5oVKpePHiBX379sXa2hpHR8fE81WrVuXIkSMYGhpy/Phxhg4dyoEDB1Lsq3Llyhm6t7+/f4avyW+yfI6VKwNxGK5bBsbBUKY8ONTVfC1tDXGxcPs6ureuoXvzKiZ+ZzXX1WkMnmPByDjDtxzsGsz0349x6vbfFClciMWD3BjgUgsdbSUGukoGrz7O0fthfNmmQZp9+a45SW2bkjjVrZnGNCszrIMLD5+F8JuPHxuO+TF953kW7Pfj4NTe1KlQKsPzyKyC/udU5gcXLlxI8bgEyO8T9k+KhbE8QRZCFDwqlWb7SgsLC1xcXPDz80sSIBsZGSW+d3JyYurUqbx8+fK9Ocsim7l3huZtQTd5Di8AFe2gdRdISIDgv+HUEfh9JYzuD/+bBjYZ29N/UPPa7D57kxY1bRndrhEmhvqJ5xrYFqe5gw0zN/vw2Sc1MDculGo/f78M5+ztx0zr7pzue5cuasrYDk0Y074xF//6my7f/k63eZs5N3/we+8lRFaQHOT3kRQLIUQB9fr1ayIiIhLfnzp1Kln6xLNnz0hISADAz8+P+Ph4zMxSL1MsckhqwfG7FApQldCUtp6+BOITYIIn/LFVEzynU3FzY84v+JyZPT9NEhz/a27v5oS+jmLWFp/39rPnvKbo1vvyj1OjUCioVb4EXqM68/RVOP0W70j8cylEdpEnyO8TFgL6BpoFEkIIUYC8ePGCoUOHAqBWq3F3d6dJkyZ4eXkB0K1bN/bv34+XlxdKpRJ9fX0WLFiQ7h0LRB5SoSp8txKWzIJVCzUFSD4fDYZGaV+bBvsyKvo4O/DjH2fxdK2DtWXKny7sOnsTa5UZVUsXy/S9HG1L8m3v5oxcuZcFO0/zlUfDTPclRFokQH6fsFAwlvxjIUTBY2Vlxa5du5Id79atW+L7nj170rNnz5wclsguxoU11fh2/w4blkPAHRg1A8qW/+Cup3T7hI0nrjJ+/SG8RnVOdv5l+GuO+N1nSEvHD/4P1jC3upy48YBvfj1E/UpWNKhU+oP6EyI1kmLxPmEhkl4hhBCiYNDSgrbdYNoiiI6C8Z/DycMf3G0J88J85dGQzaeu43vzUeLxiDfRzN16gspDFxMTp6ZLI7sPvpdCoeCXoW0pU9SE7vM28zws8oP7FCIlEiC/T1iI7GAhhBCiYKlkD9/+AuVs4YepsHYJqOM+qMuv2jbA0syI0Wv3ExkVw/wdp7AZ8gPj1x/C0bYkp+cOzLLdJ0wM9dn4dWeCQyPps3A78fHxWdKvEO+SAPl9wkNlBwshhBAFj1kRmPwDuLaH3Ztg2lcQmvmiHEYGekzt5ozvzUeU7j+fMWsP4GBdnJNzBrBnYk8cbUtm4eChZvkSLOjfkn0X7/Dt9pNZ2rcQkM0Bso+PDy1atMDFxYXly5cnO//06VN69eqFh4cHrVu35vjx4wA8fvyYatWq0bZtW9q2bcukSZMSr7l27RqtW7fGxcWFGTNmZO9K1rAQMJEnyEIIIQogHR3o/yUMGwd3rsPoAXD6aKar8fVxdqCpXTlq25bg2Mx+7J38GfUqZl9Z7MEtHOna2J6JG46w59ytbLuP+Dhl2yI9tVrNtGnTWL16NSqVio4dO+Ls7IyNjU1im2XLluHq6kr37t25e/cugwYN4siRIwCULl2anTt3Jut3ypQpTJs2jRo1ajBw4EB8fHxwcnLK+glER2lekoMshBCiIGvqqik08t1EWDBZc8yiKNhU1rwqVIUq1dOsxqdUanFoep9sH+6/FAoFy4e24c7TF/RcsIUTcwZgX0aVY/cXBVu2PUH28/OjTJkyWFlZoauri5ubG4cPJ10MoFAoEvfhDA8Pp1ix92//EhwcTEREBA4ODigUCjw8PJL1mWWkSIgQQoiPhXVFWLQeZvwIfYZB5erw4B5s+BkmD4ef50EezPUtpKfLtnHdKFxID4+ZvxEcEpHbQxIFRLY9QQ4KCsLS0jLxe5VKhZ+fX5I2w4YNo3///qxfv543b96wevXqxHOPHz/Gw8MDIyMjvvzyS2rXrp2sT0tLS4KCglK8f2Zqb79bs1v/yQPKAY/CwokoQHXKpe56/lbQ5wcFf44FfX4iH9PR1Szgq2T/9lh4KOz4DXZ6gVIJA0am+SQ5p5W0KMy2cd34ZPxqOszdyKFpfdDTkV1sxYfJtj9BKeUG/3f/Q29vb9q1a0e/fv24dOkSo0ePZs+ePRQrVoyjR49iZmbGtWvXGDp0KN7e3unq81+ZqS2epGZ3VBgAVlXsoFLBqVMuddfzt4I+Pyj4c0xrfhcuXMjB0QiRBmMT6DlE8/7fILnv8DwXJNe2Kcmq4R50m7eZIUt3sWp4uxTjg/j4eLS0ZH8CkbZsC5AtLS0JDAxM/D4oKChZCsWWLVtYsWIFAA4ODkRHR/Pq1SssLCzQ1dUFwM7OjtKlS3P//v1kfQYGBqaZlpFp4f+kWEgOshBCiI+ZQqEJkuPiwHszKLXhM8/cHlUynRracfPxc6ZuPEplq6J82bo+l+4H8uetR5y5/Zg/bz0mKDSC7k2qMcytLtXKWqbdqfhoZVuAbG9vT0BAAI8ePUKlUuHt7c38+fOTtClevDi+vr60b9+ee/fuER0djbm5OS9fvsTExASlUsmjR48ICAjAysoKU1NTDA0NuXz5MtWrV2fHjh306tUreyYQGqL5KgGyEEKIj51CoclNjldrqvFpa4NDY825hAR4EQx/3YYHf0H12pqFfblgQmcnbj5+xvj1h5m68RjRsZr9nUtZFKZeRSsM9XXw8rnKqkMXcbIryxdu9WjtWBGlUp4qi6SyLUDW1tZm0qRJDBgwALVaTYcOHbC1tWXhwoXY2dnRrFkzxo4dy4QJE1izZg0KhYI5c+agUCg4d+4cixYtQqlUolQqmTp1KqammkB1ypQpjBs3jqioKJo0aUKTJk2yZwJhIaClzJJa9UIIIUS+p1BAvxGaJ8nbN1Dylj9oa8H9O28/dQXYvx0WrMmVB0wKhYIVwzwwKaSPoYEu9SqUom6FUpQq8nbL1u/6tGDloYss23uWjnM3UraYKT2bVse5mjV1K5SS/GUBZGOADODk5JRsC7YRI0YkvrexsWHjxo3JrmvRogUtWrRIsU97e3v27NmTtQNNSXiopopeHsuzEkIIIXKNQgED/wdaWhgd8YZSZaFOIyhrC9YVNG0mD4dfvof/TcmVn6EGejos/bx1qufNjQvxdbtGjGxTn11nb7HE+09mbfFhxqbjGOjq0KCyFU3tyvGJfTmMcmjnjsioGHos2EILBxs+d62TI/cU7yf/TUqNlJkWQgghktPSgoH/41bDllSuUiX5+U59wesXONUEGjXL+fGlk7ZSSfv6VWhfvwohkW84cf0BR6/e5+jV+0zcoNlCtqixPt2cHtKpoR31KpZKdWOADxEfH0/fRdvZc+4W+y7eoV5FKxysi2f5fUTGSICcmrAQyT8WQgghUpNasOjRDc6fghULoGp1TVnrPM7U0IDWdSrRuk4lAJ6FRnLY7y9W7zvDz/vPs2jPGcoUNaVjw6q0qlWBYiaGmBrqY2qoj76u9gcFztN+P8Y23xtM6OzEqkMX6fPDNv6cNwh9XZ2smp7IBAmQUxMWCmXK5/YohBBCiPxFqQ3DvoGv+8Gyb2Hc3HyXrljUxJCuje2pXkSbkmXKsevsLX4/eZWFu32Zv+NUkra62kpMCulTt2Iphraqy6fVrdMdMG8+dY0Zm47Tx9mByV0/oV5FK9ynr2fSb0f4tk/KqaYiZ0iAnBpJsRBCCCEyp2Rp6DEYVi+GI97QzD23R5RphQvp07NpdXo2rc7L8NecvfOE0MgoQiKjCH2t+foi7DW7z91iz7lbVCpZhKFudenVtDpGBnqp9nvh3lP6LdpBg0ql+XGIOwqFgpY1bRncojbf7/LF3bEiTaqWzbmJiiQkQE6JOg4iwiTFQgghhMgs1w5w9qQmSLavBcXyf16tuXEhWta0TfHcotg4Np+6zhLvM3yx3Jvx6w/Rx9kBd8eK1K9ohYHe25SJv1+G0362F0VNCrFlTJckO2fM7d2cQ1f+ov/iHVz8/nOM3xNki+wjAXJKIsI1X+UJshBCCJE5WlowdCx81RcWz4SGzSD477evoKegpw8ubaF5GzAxy+0RfxA9HW16Nq1OD6dq/Hn7MUu8/2Tp3rMs2nMGXW0ldSuUwsmuLI2rlmXihsO8injDidkDKGaadDtZIwM9Vg1vxycTVvH16v385Nkml2b0cZMAOSVhUiRECCGE+GDFikOfL2DZXPD3Ax1dKGqpOW5bRRMk/74Stv2q2fGiVUco988T2thYzR7LN/3g5lV4FggjJmq2lsvDFAoF9SpaUa+iFT8OdufUzYccvxbA8WsBzNriQ/ym4wBsGdOV6uVSrubXsHJpvmrbkO+2n6R1nUq41a6Qk1MQSICcMqmiJ4T4CDg7O2NoaIiWlhZKpZJt27YlOZ+QkMDMmTM5fvw4+vr6zJkzh6pVc6dCmsjHmrlBhSpgaAym5pony+968gD+2ArH9sHRvVC5mqZQ150bEBOtaWNZUvPp7uyxMPunfPPz2cRQn1a1KtCqlibADY2M4tTNh2j9k2/8PlO6fcK+i3cY9ONO1o3sgLN9uWzZZk6kTALklPxbEcg4f/wFFEKIzFq7di3m5uYpnvPx8SEgIIADBw5w5coVpkyZwubNm3N4hKJAsCqX+rmSZTTFR7oPhMPecHgP6BcClzZQ2R4q2oOZBdy+DpNHwLcTYPICzdPofObfgDk99HS0WTeyA62m/kqLyWtxtC3JuA5NcHesgNZ//5Mhspz8Cqfk3xQLE8lBFkJ8vA4fPoyHhwcKhYIaNWoQFhZGcHBwbg9LFFSGxtCmKyxcD3OXQ98voF5TTXAMUKEqDB2nSbn46TtISMjV4eYE+zIq7vw0gqVD3Hke9pr2c7xw+HIZG45dIU6tzu3hFWgSIKfk3wBZniALIQq4/v370759e37//fdk54KCgrC0fJsjaWlpSVBQUE4OT4ikGjWDLv3g+H7YviG3R5Mj9HV1GNTCEf8fv+DXkR1QKKD3wm2UH/wDY9YewC8gMNVrI6Ni+OPCbVYcOE9UTGwOjjr/kxSLlISFQiEj0JZfHiFEweXl5YVKpeLFixf07dsXa2trHB0dE88npPCELrUcSH9//wzdOyoqKsPX5DcFfY65Nr+qdShR7Romvy3nsRrC7Wpl263y2u9hjaI6bBj0CcdvPWXbuXss3HWa+TtOYaMywa1GGVyrleZVZDS+dwPxvRPEpYfPiVPHA7Bw50nmd2+IlfnbXTPy2vyy2ofMTyLAlEiRECHER0ClUgFgYWGBi4sLfn5+SQJkS0tLAgPfPp0KDAykWLFiKfZVuXLlDN3b398/w9fkNwV9jrk6v7EzYcqXlNq+FhxqgU32jCOv/h5WrVoFz/af8jwsks2nrvObjx8L92te/6pRzpLh7vX4tEZ5It/EMGDJTrovO8SaEe1xd6wI5N35ZZX0zO/ChQspHpcAOSVhIflmhawQQmTG69eviY+Px8jIiNevX3Pq1Ck8PT2TtHF2dmb9+vW4ublx5coVjI2NUw2QhchRunowZhaMG6xZuNfxM3DvAjo6aV9bgBQpbMjnrnX43LUOfwW+ZOefNyluboyzfblk+ytXL2dJl+824THrN8Z2aMzUbs65NOr8QQLklISHQhH5ISCEKLhevHjB0KFDAVCr1bi7u9OkSRO8vLwA6NatG05OThw/fhwXFxcMDAyYNWtWbg5ZiKRMzGDaYli1CDYsh6P7YMBIqJZ9KRd5mbWlOSPbNnjveZ9Z/Rmx4g/mbD3Bn7cfM+yTimibvEjSTkuhoGwxU5TKj3uZmgTIKQkLAWvZlFsIUXBZWVmxa9euZMe7deuW+F6hUDB58uScHJYQGVNEBaNnwsUzsGohTBsJDZyh91CwKJrbo8tzDPR0WD60LQ0qlWbY8j0cvXof2JesXXEzYzo2qELHhnbUr1jqo9xWTgLk/0pIkBQLIYQQIj+pWQ/sHGDHb5rdLS76Qtf+4NoBlMrcHl2e06eZAw0rl2bH8bOULFEyybk3MbHsvXCH5QcusNj7T0pZFKZDg6q0r1+F2jYl0NP5OELHj2OWGfHmNcTFgbEs0hNCCCHyDV096NwXnFrAiu9hzRLwOQhDRoF1xdweXZ5jW8IC9xplU1zE1t+lFmGvo9h99habTl1j6d6zLNzti462kuplLalboSSOtqWoY1uS8pbmmUrHiHgTzeMXYTx5GU55SzPKFjPLimllmWwNkH18fJg5cybx8fF06tSJQYMGJTn/9OlTxowZQ3h4OGq1mlGjRuHk5MSpU6eYP38+sbGx6Ojo8PXXX1O/fn0AevXqRXBwMPr6+gCsWrUKCwuLrBt0YpEQeYIshBBC5DuqEvDNt+B7TJN2MXYwtOqo2T/ZoFBujy7fKFxInx5Nq9OjaXVCIt9wxO8+Z+885uztJ6w5cpkf/zib2NZAVwcjfV2MDXQxMtDFUF8XHaUSbaUW2kotlFoKtLW0iI5T8+R5GI9fhBH6OirJ/epXsqJbY3s6NbSjqIlhTk83mWwLkNVqNdOmTWP16tWoVCo6duyIs7MzNjY2iW2WLVuGq6sr3bt35+7duwwaNIgjR45gZmbGsmXLUKlU3L59m/79+3PixInE6+bNm4e9vX32DPzfAFlSLIQQQoj8SaGABp9A9drw2y/gvRnOHIPew6BWfc3TZpFupoYGtK9fhfb1qwCgVsdz4/Ezzt15zJMX4YS/iSYyKobwNzFERGlesXFq3sTEoo5PQK2ORx0fj7ZSC5sS5jS1L0tJi8JYFTFBZWrEuTtP8PLxY/gvfzBy5T5capSnh1M1Oje0y7XFgtkWIPv5+VGmTBmsrKwAcHNz4/Dhw0kCZIVCQUREBADh4eGJ2wdVqVIlsY2trS0xMTHExMSgq5sDddfDQjVfJcVCCCGEyN8MjWHg/6BJc/j5O5g/CbR1wLYyVKkBVWtoSljrG+T2SPMVpVIL+zIq7MuosqQ/52rWjOnQmKsPgvDy8WPjiav0+n4rW0/fYN3I9hTSy4H47z+yLUD+b4lSlUqFn59fkjbDhg2jf//+rF+/njdv3rB69epk/ezfv5/KlSsnCY6/+eYbtLS0aN68OZ6enilWdspM5ZSoqCie3rpBCeDusxfExhW86jJSNSd/K+jzg4I/x4I+PyHypIp28O1KuHwWblzWvLZvgK3rNIv4iluBeRHNzhfmRcC8KFgURUtLP7dH/lGxL6PCvpcLM3o0Y4n3n3y1ej/NJq5hxzfdUf1nX+fslm0BcnpKlHp7e9OuXTv69evHpUuXGD16NHv27EncTuTOnTvMmzePVatWJV4zb948VCoVERERDB8+nJ07d+Lh4ZHsXpmpDOPv708JQ01+ko1DrQKZqyRVc/K3gj4/KPhzTGt+qVV1EkJ8IG1tqN1A8wLNovybVzXB8pOH8PI5PH4AIS8hXg2ArVIbHOpoto6r3RAK5X5u7MdAS0uL4a3rU7aYGT0WbKHR2BXsntCDSqVybuu+bAuQ/1uiNCgoKFkFpi1btrBixQoAHBwciI6O5tWrV1hYWBAYGMiwYcOYO3cupUuXTrzm39KoRkZGuLu74+fnl2KAnGnhoaCjKx+3CCGEEAWZQSFwqKt5vUuthrBX8PcTXu3bicWtK3D+tCY2cKir2SWjTmNNnrPIVm3qVuLIjL60nbmBRmNXsHVsN5zsyubIvbMt89ne3p6AgAAePXpETEwM3t7eODsnLWtYvHhxfH19Abh37x7R0dGYm5sTFhbGoEGD+N///ketWm8r4sTFxfHy5UsAYmNjOXbsGLa2tlk78LAQKGwif/CFEEKIj5FSCWZFoEp1gl07wbLNMONH+LQ13L4O302AycPh0f3cHulHwdG2JKfmDqS4mTEtp65j4W5f/gp8mWKmQlbKtifI2traTJo0iQEDBqBWq+nQoQO2trYsXLgQOzs7mjVrxtixY5kwYQJr1qxBoVAwZ84cFAoF69ev5+HDhyxdupSlS5cCmu3cDAwMGDBgALGxscTHx1O/fn06d+6ctQMPlSIhQgghhPiHlhZUste8+gyDI3/Ahp9gVD9w6wSd+hTIlMy8pJzKDJ/Z/ek093e+WrWPr1btQ2VqRP2KVtSv9M+rolWKa9IyK1v3QXZycsLJySnJsREjRiS+t7GxYePGjcmu8/T0xNPTM8U+t23blrWD/K/wUNnBQog85tWrV/Tp0weA58+fo6Wlhbm5OQCbN29O1w4348aNY+DAgVhbW6faZsOGDRgbG9OmTZssGbcQooBRKsGlNdRtDBt+hl0b4dRh6PMF1HOST5+zkZmRAQemfsa1h8H43nyE761H+N58xI4/NYueVwzzoE8zhyy7n1TS+6+wEM0m40KIPMPMzIydO3cCsHjxYgoVKkT//v2TtElISCAhISFxke9/zZ49O8379OjR48MHK4Qo+Aqbwudj4JNWmqp98ydBMUuwsgarsmBVTvO1ZBnQk50wsoqWlhbVylpSrawlg1s6AhAUEsHl+4HUrVAyjaszRgLk/woLkSp6QuQTDx48YOjQodSsWRM/Pz9+/vlnlixZwvXr14mOjsbV1ZVhw4YB0K1bNyZNmoStrS316tWja9eu+Pj4YGBgwNKlS7GwsOD777/HzMyMPn360K1bN2rVqsWZM2cIDw9n9uzZ1KxZk9evXzNmzBgePHiAjY0NAQEBzJw5s0DvvCGESEUle5i7HA57w7WL8CgArpyFuDjNeS0lVKsFjV00C/skFSPLqUyNaOFgk3bDDJIA+R2KuFjNti+SYiFE6o7t0+TgZSXnVtC0ZaYuvXv3LrNmzWLatGkAfPXVV5iamhIXF8dnn31Gy5YtkxQoAk1hIkdHR0aNGsXs2bPZunUrgwYNStZ3QkICW7Zs4fDhw/z444+sXLmS9evXU6RIERYvXszNmzdp165dpsYthCgglNrQvK3mBZrgOPCxJli+6w+nj8LimZrqfXUaa4Ll6o6abedEniW/O+9QvtZU9ZNFekLkH6VLl6ZatWqJ33t7e7Nlyxbi4uIIDg7m7t27yQJkfX39xPURVatW5fz58yn23bx5cwDs7Ox48uQJoNmneODAgQBUqlQpWd9CiI+ctjaUKqt51W8KPQbDrWtw4qAmWD55SJN2YVUWSlsnfZma5+7YRSIJkN+hjJQAWYg0NW2Z6ae92cHA4O2e5QEBAaxbt47NmzdTuHBhRo0aRXR0dLJrdHR0Et8rlUrUanWKff+7+E9LSyuxTXZvLSSEKGC0tKByNc2r73BNCobfeXh4X7O/8rufyFlXgBYe0OhTyV3OZRIgv+PtE2RRW+14AAAUvUlEQVRJsRAiP4qIiMDQ0BAjIyOCg4M5efIkjRs3ztJ71KpVi71791K7dm1u3brFvXv3srR/IUQBpqOjqchXu+HbYyEvNcHy/duaFLZl38K6pZoFgC3aaspgixwnAfI7tCPDNW/kCbIQ+VLVqlUpX7487u7uWFlZUbNmzSy/R8+ePRkzZgytW7ematWq2NraYmRklOX3EUJ8JEzNNa9qtaBNV/D3g33bYe9W2LMJqtSAoipNhd93X1blNLnMsrVctpAA+R2SYiFE3vfFF18kvi9Tpkzi9m8ACoWC7777LsXrvLy8Et+/m3Ps5uaGm5sbACNHjkyxfdGiRTl48CAAenp6zJ8/Hz09PQICAujXrx/Fixf/wFkJIQSaYLdKdc3r1Qs4vAfO+ID/FYh6o3nFxLxtX6UGfPY52MguOllNAuR3aEeGa/5wGhrn9lCEEHnU69ev6dOnD3FxcSQkJDBt2jS0ZTW6ECKrmVlAx96a17vUak2gfPIwbFoFYwdDw2bQfaDUcchC8q/6O5SvI8C4sKZSjhBCpKBw4cLZX9EzB6nVajp06IBKpeLnn39Ocm7btm18++23qFQqQJNe0qlTp9wYphDiX0olGBpp8pObuMCO32D37/CnD7i2A7tamjba2pot6JTaoK8PqpKgp5fbo883JEB+hzIyQtIrhBAflXXr1lG+fHkiIiJSPN+qVSsmTZqUw6MSQqSLQSHoNkCzB/PGlbBnM+zelHJbhQKKWkLJ0lCqDJQsi662AUiRoxRJgPwO5etwKRIihPhoBAYGcuzYMYYMGcKaNWtyezhCiMyyKApDx0LnvhDyQpOGERcH6jjN1zev4elDePwAnjyAa5cgNobyALvXa4qXNGoGRVS5PZM8QwLkd2hHRoBKFtsIIT4Os2bN4uuvvyYyMjLVNgcOHODcuXOUK1eOcePGpbog0d/fP0P3joqKyvA1+U1Bn2NBnx/k1zkqgH/TKwBdoJApWPy/vXsPivK+9zj+XhaRUy/oqqwaCCkXA1Gxjo1EZ3o8WbGGovEStT2xXqjRCROLitYYpdGTepvU2NhO7TG1XlOtDkGNLI5pxBRPrMYLDsZA6ppqgMiioqJRICx7/ljZQIs3BJddP6+ZHX32eZ7f/r7s49fvPvz29+sOvZ9xHVJTQ6srl2h9KpfOnx3nP979X5x/XsONsCjK+/TneuRTVAeZvH6GjAd5/1Qg12G8oSEWIvJo2L9/PyaTiV69enH48OEGj3n22WcZNmwYAQEBbN26lVdffZVNmzY1eGzMff6aNj8//77P8Ta+HqOvxwe+H2O+qQuh02bA+SIM//chbQ78lTa73nXt7GCCHj0h6inXnxFPuqaX8yL38v4dO3aswef9mqNDXqmm5laBrCEWIi3NhAkTOHDgQL3nNmzYwKJFi257Tt++fQGw2+2kpKTctt2TJ0/e8bU3bNjAzZs33dtTp06lvLz8Hnvech0/fpzs7GwsFgupqakcOnSIOXPm1DumY8eO7tUEx40bx6lTpzzRVRFpbt1CYOxkWPUurFgHU2ZC7Pfhyy/gz2tgYQpMTIDUybB6OezdBWc+h2++8XTPm43uINf6+hoGp1N3kEVaoMTERLKysuqtipeVlcXcuXPveq7ZbOa3v/1to19706ZNPP/88+4lrf/4xz82uq2WZPbs2cyePRuAw4cPs27dOlasWFHvmNLSUoKDgwHIzs4mIiLiofdTRB4igwGeiHQ9Eka7niu/Aqfz4R+nXEXxkY+/XR67VQB0e8w1drmzGToFuxY16RzsWsjEi7/XpQK5VvkV158qkEVanKFDh/L2229TVVVFQEAARUVFlJaWEh0dzaRJkygvL6e6upoZM2YQHx9f79yioiJefvllMjMzqaio4LXXXsNmsxEREUFFRYX7uIULF3Ly5EkqKysZOnQoKSkpbNq0idLSUiZNmkSHDh3YvHkzFouF9PR0TCYT69ev57333gNgzJgxTJ48maKiIqZOnUq/fv3Izc3FbDazevVqAgMDH+rPrLFWrVpFr169GDx4MJs3byY7Oxuj0UhQUBDLli3zdPdE5GFr3wH6DXA9AJxOKD0PZwrAVgDni+CiHWz5UH61/rmmzvB4BISFu4pu82Ou5bb9W4G/0TVO2r8VBHVscVPsNmuBnJOTw5IlS6ipqWHs2LFMmzat3v6vvvqKV199lWvXruFwOJgzZw6DBg0CYM2aNaSnp+Pn50daWpr7ztHd2my0q7cKZC/+tCPyMGzef4L1+3KbtM2kwX2Z8Oz3bru/Y8eOxMbGkpOTQ3x8PFlZWSQkJBAYGMjvf/972rZtS1lZGT/+8Y8ZPHgwhtt8sWTr1q0EBgaye/duCgoKGD16tHvfrFmz6NChAw6Hg8mTJ1NQUMDEiRPZsGEDGzduxGQy1Wvr008/JSMjg+3bt+N0Ohk3bhz9+/enffv2nDt3jpUrV7J48WJmzJjB3r17GTFiRNP8sJpBXFwccXFxAMyYMcP9fN27zCIigOsus7m76zHQUn9fZQVcLIULJa7hGWfPwJdn4NNjrtk0bqezGYYMB0uia4GUFqDZCmSHw8Ebb7zB+vXrMZvNjBkzBovFQmRkpPuYP/zhDyQkJPDiiy9is9mYNm0a2dnZ2Gw2rFYrVqsVu91OUlISe/fuBbhrm4127danHt1BFmmRaodZxMfHY7VaWbp0KU6nk5UrV3LkyBH8/Pyw2+1cvHiRLl26NNjGkSNHmDBhAgDR0dE8+eST7n179uxh+/btVFdXc+HCBc6cOUN0dPRt+3Ps2DHi4+P5zne+A8CQIUM4evQoFouFkJAQ9xdDevbsSXFxcVP9GEREWq7Wga55lh97HL7X/9vnq6td08yVlkD1N67p52qnoquqhE8OwNa1sH09xP0nDB3lWm7bg7NoNFuBnJeXR1hYGKGhoYDrP7d9+/bVK2YNBoN7cvpr1665x7rt27ePxMREAgICCA0NJSwsjLy8PIC7ttlotUMsglQgi9zJhGe/d8e7vc0lPj6e5cuXc+rUKSoqKujZsycZGRmUlZWRkZFBq1atsFgsVFZW3rGdhu4uFxYWsm7dOtLT0wkKCmLevHl3bcfpdN52X+0X2wCMRuNd2xIR8Wn+/vB4uOvRkOdGwVeF8NddsH8PHNwP3R+H6F63zrs1TCOo48PrcnM1bLfb6dq1q3vbbDa7i9xa06dPZ8qUKbz77rvcvHmT9evXu8/t06dPvXPtdjvAXdusdb/z3nWynSYYKCguwVl66b7O9SbeOafjvVN83u9OMUZHR5OamkpcXBz5+fnYbDYMBgM2m42TJ09SXFyMzWbj2rVr1NTUkJ+fj91up7Kykvz8fB5//HE2b95MUFAQ586do6CggLNnz2I0GvHz86OoqIjPPvuM7OxsQkJCyM/Px2g0kpeX515uuaqqitOnT9O5c2e2bNnCoEGDcDqdWK1WZs6cic1mc78euL7odvPmTff2o/Aeiojct+6hMGk6/GQqHMyGA3+Fowe//UIguH7L/0QkRMVAZIxrCroOptu3+QCarUBu6O7Kv965sVqtjBo1ip/97Gfk5uYyd+5cMjMzb3tuTU3NXdusdd/zFh76EEdAa6JjY+/vPC/j83M6Kj6vd6cYX3zxRaZPn87q1auJiIjAbDaTnJzMggULiImJITw8nMjISEJCQvDz8yMmJoZ27drRunVrYmJi3ItdzJ07l5iYGPr06cMTTzxB7969ycnJYc6cOYSGhtK/f3+6d+9OTEwMEydOZPny5XTp0oXNmzcTEBBAVFQUcXFxXLhwgbS0NADGjx9PYmIiRUVF7tcDOHjwIDdu3HBv3+09vN2cnCIij4TWreHZBNcD4EoZfPlP11jmL7+Af56GHVugxuHa36Wrq2D+75egW2iTdaPZCuSuXbtSUlLi3rbb7e4hFLXS09NZu3Yt4JqztLKyksuXL9/x3Lu12WjfjeRar35ogIVIyzVkyBA+//xz97bJZGLbtm0NHpub6/oiYUhICJmZmQAEBgbym9/8psHjly9f3uDzEyZMcI9bBtd0Z7WSkpJISkqqd3zd1wOYMmXKnUISEZE76WByPWL7fftcZQV88Q84/Zlr9oxzZ6C4sEkL5GZbKKR3796cPXuWwsJCqqqqsFqtWCz1v+3YrVs3/v73vwNw5swZKisrMZlMWCwWrFYrVVVVFBYWcvbsWWJjY++pzUb7rwTOj5rUNG2JiIiISPNoHQgxsfD8TyD1f1wLnHx/YJO+RLPdQfb39+f111/npZdewuFw8MILLxAVFVVvjs158+aRlpbGhg0bMBgMLF++HIPBQFRUFAkJCfzoRz/CaDTy+uuvY7w1P15DbYqIiIiINJVmnQd50KBB7nmNa9WdYzMyMpK//OUvDZ6bnJxMcnLyPbUpIiIiItJUmm2IhYiIiIiIN1KBLCIiIiJShwpkEREREZE6VCCLiIiIiNShAllEREREpA4VyCIiIiIidRicDa3r7OW0VKuIeLN+/frd/aAWRDlXRLxZQznXJwtkEREREZHG0hALEREREZE6VCCLiIiIiNShAllEREREpA5/T3egpcjJyWHJkiXU1NQwduxYpk2b5ukuPZDXXnuNjz76iE6dOpGZmQnAlStXmDVrFsXFxTz22GO8/fbbBAUFebinjXP+/Hnmzp3LxYsX8fPzY9y4cUyaNMmnYqysrGT8+PFUVVXhcDgYOnQoKSkpFBYWkpqaytWrV3nqqad48803CQgI8HR3G83hcPDCCy9gNptZs2aNz8VnsVho06YNfn5+GI1GMjIyfOo6bQxfy7egnOvtMSrf+kZ8TZlvdQcZ1wXzxhtvsHbtWqxWK5mZmdhsNk9364GMHj2atWvX1nvunXfeYcCAAXzwwQcMGDCAd955x0O9e3BGo5F58+axZ88etm3bxpYtW7DZbD4VY0BAABs3buT9999n586dHDhwgBMnTrBixQomT57MBx98QPv27UlPT/d0Vx/Ipk2biIiIcG/7WnwAGzduZNeuXWRkZAC+9W/xfvlivgXlXG+PUfnWN+KDpsu3KpCBvLw8wsLCCA0NJSAggMTERPbt2+fpbj2Qp59++t8+Ie3bt4+RI0cCMHLkSD788ENPdK1JBAcH07NnTwDatm1LeHg4drvdp2I0GAy0adMGgOrqaqqrqzEYDBw6dIihQ4cCMGrUKK++VktKSvjoo48YM2YMAE6n06fiux1fuk7vly/mW1DOBe+OUfnW++O7ncZeoyqQAbvdTteuXd3bZrMZu93uwR41j0uXLhEcHAy4kl1ZWZmHe9Q0ioqKyM/Pp0+fPj4Xo8PhYMSIEQwcOJCBAwcSGhpK+/bt8fd3jY7q2rWrV1+rS5cu5Re/+AV+fq5UdPnyZZ+Kr9aUKVMYPXo027ZtA3z33+K9eFTyLfju++yrOVf51rvjq9VU+VZjkHF9ivpXBoPBAz2R+/X111+TkpLC/Pnzadu2rae70+SMRiO7du2ivLycV155hS+++OLfjvHWa3X//v2YTCZ69erF4cOHb3uct8ZXa+vWrZjNZi5dukRSUhLh4eGe7pJHKd96N1/Oucq33htfrabMtyqQcX1qKikpcW/b7Xb3pw1f0qlTJ0pLSwkODqa0tBSTyeTpLj2Qb775hpSUFIYPH84Pf/hDwPdirNW+fXvi4uI4ceIE5eXlVFdX4+/vT0lJiddeq8ePHyc7O5ucnBwqKyu5fv06S5Ys8Zn4apnNZsB1bQ4ZMoS8vDyfvU7vxaOSb8H38tGjknOVb71XU+ZbDbEAevfuzdmzZyksLKSqqgqr1YrFYvF0t5qcxWJh586dAOzcuZPBgwd7uEeN53Q6WbBgAeHh4SQlJbmf96UYy8rKKC8vB6CiooKDBw8SERFBXFwce/fuBWDHjh1ee63Onj2bnJwcsrOzWblyJc888wxvvfWWz8QHcOPGDa5fv+7++8cff0xUVJRPXaf361HJt+Bb+cjXc67yrXfHB02fb7XU9C1/+9vfWLp0qXsKlOTkZE936YGkpqbyySefcPnyZTp16sTPf/5z4uPjmTlzJufPn6dbt26sWrWKDh06eLqrjXL06FHGjx9Pjx493OOpUlNTiY2N9ZkYCwoKmDdvHg6HA6fTyXPPPcf06dMpLCxk1qxZXL16lZiYGFasWOHV0/IAHD58mHXr1rmnHfKV+AoLC3nllVcA1/jGYcOGkZyczOXLl33mOm0MX8u3oJzr7TEq33p/fE2db1Ugi4iIiIjUoSEWIiIiIiJ1qEAWEREREalDBbKIiIiISB0qkEVERERE6lCBLCIiIiJShxYKkUdWTEwMPXr0cG8nJiYybdq0Jmm7qKiIl19+mczMzCZpT0TEmynfirdRgSyPrMDAQHbt2uXpboiI+DzlW/E2KpBF/oXFYiEhIcG9Xv1bb71FWFgYxcXFzJ8/n7KyMkwmE8uWLaN79+5cvHiRhQsXUlhYCMCiRYsIDg7G4XCQlpZGbm4uZrOZ1atXExgY6MnQRERaFOVbaak0BlkeWRUVFYwYMcL9yMrKcu9r27Yt6enp/PSnP2Xp0qUA/OpXv2LkyJHs3r2b4cOHs3jxYgAWL17M008/zfvvv8+OHTuIiooC4Ny5c4wfPx6r1Uq7du3cy3mKiDxqlG/F2+gOsjyy7vQrv2HDhgGucXLLli0DIDc3l9/97ncAjBgxgl//+tcAHDp0iDfffBMAo9FIu3btuHr1KiEhIcTExADQs2dPiouLmzUeEZGWSvlWvI3uIIs0ksFguOP+uuvZG41GHA5Hc3dJRMQnKd/Kw6YCWaQBe/bsASArK4u+ffsC0LdvX6xWKwC7d++mX79+AAwYMIAtW7YA4HA4uH79ugd6LCLinZRvpSXSEAt5ZNWOiav1gx/8gDlz5gBQVVXF2LFjqampYeXKlQCkpaUxf/58/vSnP7m/NAKwYMECfvnLX/Lee+/h5+fHokWL6NKly8MPSESkhVK+FW9jcDqdTk93QqQlsVgspKenYzKZPN0VERGfpnwrLZWGWIiIiIiI1KE7yCIiIiIidegOsoiIiIhIHSqQRURERETqUIEsIiIiIlKHCmQRERERkTpUIIuIiIiI1PH/m96FI1oovjIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd4ed4694e0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCJKZ3hSvd6b"
      },
      "source": [
        "with open(location+'/ft_history.txt') as f:\n",
        "    ft_history = json.load(f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTVtHXwtvd6b",
        "outputId": "76c5dc87-15de-4c01-baee-7002796aca19"
      },
      "source": [
        "plot_metrics(ft_history,stop=10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Model: \n",
            "epoch: 7 , val_acc: 0.9043478260869565 , val_loss: 5.6756150121274205\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd4ed469240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0e3_kRO2vd6b"
      },
      "source": [
        "def plot_acc_metrics(hist1,hist2,stop1=50,stop2=10):\n",
        "    fig,axes = plt.subplots(nrows=2,ncols=1,figsize=(5,6))\n",
        "    axes = axes.flatten()\n",
        "    \n",
        "    axes[0].plot(range(stop1),hist1['acc'],label='training',color='#FF533D')\n",
        "    axes[0].plot(range(stop1),hist1['val_acc'],label='validation',color='#03507E')\n",
        "    axes[0].set_title('Training')\n",
        "    axes[0].set_xlabel('Epoch')\n",
        "    axes[0].set_ylabel('Accuracy')\n",
        "    axes[0].legend(loc='lower right')\n",
        "    \n",
        "    axes[1].plot(range(stop2),hist2['acc'],label='training',color='#FF533D')\n",
        "    axes[1].plot(range(stop2),hist2['val_acc'],label='validation',color='#03507E')\n",
        "    axes[1].set_title('Fine-Tuning')\n",
        "    axes[1].set_xlabel('Epoch')\n",
        "    axes[1].set_ylabel('Accuarcy')\n",
        "    axes[1].legend(loc='lower right')\n",
        "    \n",
        "    plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p5lh-QyUvd6c",
        "outputId": "fe160bab-0e9d-49d7-f846-4262f90b99b0"
      },
      "source": [
        "plot_acc_metrics(top_history,ft_history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd4ecb20470>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHflkCjpvd6c",
        "outputId": "72eae90b-52c0-4232-df9d-cb3daaad7117"
      },
      "source": [
        "cm = evaluate_binary_model(ft_model,val_data_dir,val_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 460 images belonging to 2 classes.\n",
            "58/57 [==============================] - 534s 9s/step\n",
            "\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.97      0.83      0.90       230\n",
            "          1       0.85      0.97      0.91       230\n",
            "\n",
            "avg / total       0.91      0.90      0.90       460\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIlnmEcZvd6c"
      },
      "source": [
        "heatmap_labels = ['damaged','whole']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgDbmzkgvd6c",
        "outputId": "7f0952cc-ec53-4b75-a7c7-573c5dca6abb"
      },
      "source": [
        "sns.heatmap(cm,annot=True,annot_kws={\"size\":16},\n",
        "           fmt='g', cmap='Blues', xticklabels=heatmap_labels, yticklabels=heatmap_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd4ec8a5e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd4eca7d4e0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l5BRtlYMvd6c"
      },
      "source": [
        "### Making live predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Krh1Qz5svd6c"
      },
      "source": [
        "def car_categories_gate(img_url,model):\n",
        "    urllib.request.urlretrieve(img_url,'save_second_gate.jpg')\n",
        "    img = load_img('save_second_gate.jpg',target_size=(256,256))\n",
        "    x = img_to_array(img)\n",
        "    x = x.reshape((1,) + x.shape)\n",
        "    pred = model.predict(x)\n",
        "    print(\"Validating that damage exists...\")\n",
        "    #print(pred)\n",
        "    if pred[0][0] <=.5:\n",
        "        print(\"Validation complete - proceed to location and severity determination\")\n",
        "    else:\n",
        "        print(\"Are you sure that your car is damaged? Please submit another picture of the damage.\")\n",
        "        print(\"Hint: Try zooming in/out, using a different angle or different lighting\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsClJQbMvd6c",
        "outputId": "330fcc54-4c00-436c-ade7-211c9c197af3"
      },
      "source": [
        "Image('https://www.nerdwallet.com/blog/wp-content/uploads/2015/12/exterior-car-damage-384x233.jpg')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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81lKhRlfDsVIxiqVpthNd3SIqsAGyzY2UZ3NfTZcShFH5jjyOTZrca5VQfWOOp76fUYAwoHlRSw4Q1u6gikSxkCOoILkJ3eBNF7f0davIMu1rF5yZ+QrgbRvRVwSe/NdALDGDV2h9GcgKmfUo17iEiJ/EkUSh9G+noR2l5dP90Kv5GlvQ6ZnIjIPSlhD34rUIuCNDtxl4ZJcdTJKfyxWY8W+k7hjQdXNppGkQ3v0bZpkbCl+8ZOcgU4vc6Qmq7hjSeFdQ1TDJCVQ/xNVpsfRpGQDdXPmBWQXf+ULrxXksrKytl7sguR8qr996auM73P8A9WeEHuiUL8q0cJegWj6ctOBdBtRmRecj+Y1LnHC2hW0txOLaKOJS7EjJwPAHrXx3ececSX2fpGtX8me4zt+tCpdVu5zmW4lkPizk1Lwy9WNTRrmt+mPiG+1i5WymGnabv2IgCqcZ2yeuaBXXG2o3ZJutYu5fY9wf1rODMzdaT2jeNLyPmdcdZGHEYL7l6PEMHNlrhGPixLZpScYLb4Md0FIOQVVsj8KofMT3muZNPyIjfUJ1SSPoq1/yp2s9Pt7dtNnuZoolR5WlChyBjOMd9Rp/8q/VDnsNGgH35WPyxXz/AJr2aawwOJ5G3ZuM/wDlV8UspEVhYR+3Dn/moXcf5TXG82eV7KPyhz8zWR1yn5cfYW5mlT/5QnH03/acSfdgX9KgTem7juf7WvTr91VH5VRK5T2r2C2Wu49KHF95tPr96653UvgH4UDm1m/uZOaa7mck9WbJoeOtdY4o7dhk5L+ZW9Yh/vCp9pqvZSK6mSJ1OQynpQYnLZpxDWsWI3HgT0tXBaKw1h/pEXRbkfbX73j861iPU7R4DOJ07IdWz086+U7UCO1jH8Wc57xVx4Z4n1myWWS2vmnmj9fsZzntE/iw3XI2O+ds+FcORW24nowxwW2E+7N1/tRo8f8ArqbeANMycZ6NGc9uz/dU1nVpqWmcWWsdzDEul3jg5QkdlIwOCPYc1DuLae1kMcyFWH415+XLkx90fWdN6HoNYvyZXu9VxZos3pD05GxHBM48TgVHk9JEIH1dkf7z1nwVs17lNY/ipnuQ8KaJd039y53HpHuypENvAngTk/nUFuP9XPSVB5IKrRjOa6ImPdUPPP3OuHh/Qw/9aDsvGusSje8cfd2ofca9f3P7y6lbzY1EEDeBr30ds9Kl5ZP1OvH0vSY/hgv6HmvJ26yMfM000znqxPvp8WrH+GlCxdv4TUbmdccOKPZEJmY0nBNT2sXVsEGurYMe6lbNPyIH8prxQ4osmlSMRhTTlxo8sMIkZSATjpS5F5uNOrAgjNKERomunnwqRHphbG1BTzRQHEJPdSTCQcYqzwaJzeAqDdaf2dzIg3AOKdMiOphJ0gOIj4UoQE91FUsvZT62S+FKhvPFFPljAt3OP4D8qrWhyWiXsP0iEzxlsGMvyhvDcVbblMWkx/2bfKqVpQCXCTFlxCRJysT6+CNhX3Or9D+dtL6n1ToxuX0m2N5EkNxyAOiNlVPsPwok2IwPbQLhTWX13QrS9lg7CSVAxT34yPYcUbIyBXhvueijxYFQQO8UoMRTZ+yB7fzrKPTP6TP2NA/D2kzYvZRi5lU7wqR9kf1H8BTjFydITdAf0w+lppmm4d0GfEY9S6ukP2vFFPh4n3VibMW7667FiSetIIrvhBQVIwbsSa8EZhsM05HFzHmPSn8AdKbYEMow6qaQRU6uFc9RUtjIVexUiYL0AGfZTRXFKxicGvYpWK6EJ6DNIBOK9ilcpHUV4CgBOK5S8VzFACaSaVikkUhnM711ztSe+uvtSGhZP2T7BUmzQSShWzjHdUX+FT7Km2f1aFz1OwolKomuKClPnsExJuADsKnWF3JbTpNGcMpyKDxNkipsTYxWaVKh5JucnItPDuq24a6hlhZIDO5CofsBtyN+o3onoOpaksbvKhv7GJ2iaENl0weozv5b99VzSbGYahJbopZ5oo5gPAEEfkKtumwHQtXiibHZ38XreHbL+q5/4amaTXJpizZISUoOmiw6bZWmsBH06ftQ5wEIwynwI8asK+jq9Cuz8g5VDbZOcjONhVG4mE2lQLrmmEwTW7gz8n8cZOCSO8jr5USl9M3EYgENqLPKIMl4ssy4+0DnB2rzcmmiuUfXabxFrMkNtq13fuW+D0d9oIXN3GFkBLergjA7skZ32qXH6O4gk/PJJzxk8q4AyAOp3rLZfTDxXI3P+0I4nGwKQrjHwqLN6VuL5sg63MB/Sqj8qjyfZCn13UN/qf7ZqkPBDNuVCj21Hk4WTtWEZDhdsisjn494knBEmtXrA9wkI+VDn4g1OTPPqF0c/wC1b9ahaaZqvEEk7cjbBoEcRy5VfM1Iis9It97i6tkA8ZAPzrAZL+aQ+vNI33nJpo3AxuQfM1X4aRGTxBuVWzb9SvuHzdOyahaIgAABlWh76/w5B11O2P3Tmsca4T+ZR76ba8iHWRPjVLSsheImlSX9zZhxvw3bjm+k8/3YyfyqHqfpJ0i5jWFBKUVubaPrWR/tOFCcSp5da82r2/fy+4Gq/CGL8Qy3btqv/fmaU3pA0pB6trct7gPzppvSPbqCYtOkOP5nArOf2pZnqXH3QaSdUtwdhIR5VS0iJn4k1D7UaDL6TZyMRWKr5yf4UOf0gXhdm+jwgnvYk1SZdagQ47J8mkHVM/ZhPvNUtIjF+IdSuz/Yujce6m2eUW6YHcn+NRpONtZf/WVX7sYqom/mPREHmc0g3lwT1QeQqlpYmEuuap/zs1G+Xlsbg/7NvlVEtYlPrsSBnbA6+yrhqOrwnRDdJFIwnUIFwMgtsM0MtpoLO1gTskaE47RlBLjP2ipJ6+HnXvaiabR8np40maz6Jzc3tpdalcvJ63JAifwBV3HL8QPjWgBjvVFj1m34T9HwudNi5HjthPFDMeYkFxkkj7341QE9NPFN3B28UenRJjP7snFeW4ubbR22kuTS/SRxxFwXoLzIVa/uMx20Z/m72I8B+lfLl5eT31zLc3EjSzSsXd2OSxPfRfizizUuLtRF5qUqyOihEVRyqo9goGRXXix7Fz3MpytiCK7HHzHfpSlQu2KkKmBgVoyRAWvEU6ENdELN0Un3VDGMYpuR8bL1+VTP2XcTt9XGzd3vqKI15se6kMZ5a4fCn3gI3HSkBKAGgtPqgxhWWuhB4ZxShGp7qKDdQlo3A6D3U2RnqKf7IjoTXOXAIxRQbrI3LXOXNSuzwppJTHvooVkVlpBWpbqAuKZK0qKGMVyQU9yesPOm5Bg1LKQuJA6rzHG35mpAboB0FMRD6tT/APOtOr1qH3NVKo7USY2wetG9E05r6YM4IhU+sfH2CoWlaU10RJJlYvxarbZhIEVEUKo2AqZMSRO08iLi2EDAEtkyj+6wP50S4qdYtPWYOqzQETRZ6llOce8ZHvqrXWqLHxBYdg+ZEjmRmHdkD9Kd1aWW4g5pXZypByTWGTJSPV0XT5Z3b4RIvuJ7nU7doQVSCRcEJ/ECKD6WxlgNpIxE1q3KjjqF/hPw+VR7LKhou+NuUeXUfgaVLIbK/guR9mT6t/aeo/MVy7m24n0kNPixQhliu3D+j4f9+SJq7XVpPn1VU9QBtnxHsNDWv7jGe0+Aqy69e2E9n2QJecYK8vd4gmqhNcfRyDyLIGHRu6unBLdHk+c6pghhztY3afP0H/pk7dZXrhnlb/rJPjUb6fuFFvDkjPSux3TXEnKyooAz6q4rWjzrHJnkK/afOR304YwPtEZ9ppqXYbZyMUqSeCW3X6tjcZPO7HrvTUbE5UTIdIu7hQ8NpNIp6MsZINSF4b1Rumn3P/lmrz6P9VOpaebUWfYpbAAOv2W/x76tZhHhWywpq7OaepcXVGE6haz6XdmK5geNyoPK2xxTAnJO0RNX/WIEf0lafG6hlKJkEZHRqvy2NuvSJB5KKSxWOWopJ13MGUXD/YtpD5KTTqWuoyZ5bC5byiY1uq28Y6KBTogTwp+Uvcl6r5Hz1yST3KR8h7QuF5ehznpVli4P1WXH+bhB/U4oYFxxKB/+7/z1sAhHhSjjUu5eXM4VRn0XAt8x+smgT4mp0PAK4Ha3hP3Y/wBTV1EIpYgrTyoGD1EzPNbvEt+HtIt4jFmUozAEFsYB929R7e6kEkbyBZAmPUfdSB3Gq9o0Ftd6hbJIwWNnAY5xjw/HFXK+01tNvRHGDtuCd+/r31oouUXP0XAOoS2e5arS3kPo21m8nkViyqkKcxJRe0GcDOAM4+FZp9K7HRYoEA55ckn2ZrWhava+iue0mZfpmpXCrbxAgu45gf1PvoRaeiKF7eFZ72cSKuG5QMZ9lYwaTdmsoukzKTGa6sWeoNbFF6G9OPW7uz5cv6VMi9DGlYGbi9P95f0rR5USoMxdYuyX7JJNeBl35Ux4VuA9DGjtu0t9/wAa/wD9aSfRJw3EcPeTL481wo/KoeRMe0xBRdn/AK3lA8KdAuh0nJ91bR/0Z8Hx/b1FR967QV5vR/wYkbmK8Sd1UtyJdhiQPYKneh0Yq1vcls9sv40ldOxKGD7dT7TW06fwHw5fzRQw2/rSkBeeZ987eFC20jha04oGj3mnxQwKgeS7kumCqSvMBjx3HfTUr7DlCUXUlRmEkXLtjNMtZsBkZxW6pwx6OMA/TLBv/EH9afThv0dFf9J0zH9U5/WhZETtMCFv5/Clpas32Q3wr6BThv0eZBWfRvfOP1o1aejnhy8hWe10+ynibo8eGU+8Gh5UhbD5m+juNj5Ukwkd1fUq+jTRR/2Raf8AlilD0daOv2dHs/8AyVo85B5bPlcwkjGPwpBhY91fVy8BaWvTSbMf7hf0rx4J09emmWg/3K/pR5yDYz5Oa3Y9xPupBtJOvKfhX1g3CFovSwth5Qr+lNNwvAvS0hHlGKXmj2nyn9HZPWKnbfcVDmAz1FfQnpVtY9F4SuCkUaSXLrboQoB9Y7/gDWJyQoFBxgeypc+LOnBgeVtIExHKcoqfplvHJMTMRyr0BPU0zNCFOw/CuwwqXUkbd9LcmaS0s0Wb6ZFbR8zNt0276iXWrzS5iUGJe/xNKsrK1ReaeESDw5iMipc1lpc0mY4LqEH/AGwb/lFZzhJ8I200sWNqWRWC7JgNWsiTtzPkn7tWW9kiazl+sX1Rk70Pg4civLuH6Ldy8659SSMd/XcHwojqXB97BZyus8MqEYOCQRnzrJ4uOT0I9SrI/Ljd0Vp9Xit7ssoLgpynHeQdvnUa81ea7j5AFRQQwA65HSkXXDepQP6qCRR3j/Ghs0F3AeWWKSM+1cURxx4aM9RrdTTjJUmTJboBck9Tmh13IZTnGAO6pcFs90Oz50jbrmQ4A/8AmaLW3DtumnT3jXcdy6qSnZnK5GCQfdmtlGjy8mTc7YAR1A+wOneaVaSBpcYwQv50dn0a5jsIr9dNnWyuZOzjumiHZM2DsDjrsaFw3kBdlkgDcpIDRkLkfCqUeLRDkux6f7HwqOj4HcfPuopd6e0lqtzac00LddslCBkg+7fNBQp39Vs+W1JAzU/RPxJo+i2d9Bc3i29zOykGaIFMDIwDvjr34rSBFp89uZre8EwP2SgyCfOvn7hu3t2vFlvZ447dfthlJ5vZVwibRLV+0sL+4tJAcgwsyk+4006MpY9zCOt6fIvpM0+ReV0SKNnIPTPMAKvMiiJOZ15QehxWZHVtVu+LrS67f6dlUQTPHjZc4Bxjxq3T6hq8q4E0UIz1WPfy3NOM/UjJifCDPax9fWPkpp+ILIoYDY+IxVXa91cMee7bkPQxxrke7FOR3WqLuNQkIx/HEh/IVe8z8pmVgf8A3QB/+9/z1sMkDftDk7SQIkYYKNg25G/wrHZYLu31n6VJGwC3XNzY2J5s1run6mdTvAVLEdljJ8yaUZGmaPYnLHnupbJyKWPQDeounCdbrspnCoSx9dsnrgdaJtLFPZTPEHcRuYzkY3H5Ve5nPtPnExtG3MmxHh31YtG4kkkUWtyJJV7nxlk2wBnw6bVXAtwDkBs1N0e2mF8pZSFJyfnS3L0Z2Qg5NKjbfRzpMusSHVblfqrcdhbrjYHHrN5/qa0630xdsiqx6Ok+j8LWC5wWVnPvYmrhFK2NiaybNMr/ADV7DsWmqNuXapcenIO6mo5m8afWZuU+selZtklR1y6Gr3EtlbuyWcLckjI2DK/eMjuFDI+C9FJJeyjY/wBW/wAzVsf0T38ljA2lcSPauV5ik9ssi5O53GD86CXvov8ASZbktZ69o1z4c0ZQ/iuPxq4ZY1wRKLTGIOEdCDADTLU+cYpep6BpttZMtpZW0Esv1fPFGA2O8e+q/qOjelPSWY3l3plqinlV/UIc+A2oTOnpBvWFtLdWcqr62VjBwfPFN5I9rCMWuS0cJ6LeJqwvrq1migigdlYocI/LyqN/aR51dv7N6Pcu0k2mWUrnGWeFSTtjckVjsUXG9pIOW9tVfw7PNElvfSXEOZL61Y42zEv6Vlj2wvk79frXq5RltqlX+fqzWV4S0EAD9jad/wDx0/SnP7E8PyAltG07OAP9HX9Kya11z0qT8j/S7FAWwBIqDJ+FPR696YpoHuoYbeS2SQxdsEiCkg4PU56itFNN0mcDVehbvSB6MtJuOH57rT9Pgt7q1XtVMKBeZR1BA67b+6g3oHuJLfVb7RZSximjE8anorDY/EEfCmIdR9Kd/A8V3qWmWySqUb6pS2CMHoPzq4eizgttFml1a8vWurzBiXlQIig4zt1NXKdY3FkxX5k0aKNMXwGK9+yl8BTqzOD9qlidu81ypmxHOkr/ACikNo6nuFTe3NK7U4otCBb6KD/AKZfQs/wfhRkynxrxlI76dhR82/5SSm3m0DS0UguZrhh5cqr8zWKyq65iAJXwxWx/5Rt878fWMYbPZaevuzIT+QrIL1ZUfJbBO+RVvlJHqdP/ACxcvmJbTGMDCQYDD1T7aFIrc3ZAZIJHnRhdYaGHsnIdV8epqDZENM0mBnrWWPcr3Hs696dwh5L5rkL2NhczQJhVIHq5JojFppjAaZk5fBTXdHEq2pVCT653OKkXEVzImArg+xhiupyZ8o63Ox2C+hsAzRQjOOuaZk4ge/HI7Mq9QuOUH49aGXEckYKyhwSM71FAKsD3ColBy7s6sWphie6MefmG01G2LnMnKABuR1r1xq1g9u8RiWYsMbjp7+tQ1EfIG7MEeGKYyGujGtqrxhgGbvXPf5Vn+Gj3s7H1rLKO1xQIvbKKU84G464qLAt9aw3ENmUlSUYMff06gd5q3SaZBJEVCKhboRQttMNriZ3dTG4yVHTwIrdKjyHJSKq3EeqjSI9Ge9m+hQzdukB+yr4xn4U1a6dfTwNcQwSvEv2pAhKjzNaU/Dtol9+07dFkaYJcK2AQCd9h4Z7qtM/pD1vVOFzw/qNnpKZkZjNImEIDhlJ3x3kYx3UcrsLur9DO+HrafTtJ1KK7uEjMsQIt+bLMMEhh7ifjQNFDE8oJwK0LWDHDBPIr6WZxCpIhCkn1eqjH2em1AodbvBMnN9FCEgMRbRdP+GsIzbbdF0q5YChuHicOAo5DkLjIz7c1bOCbiW8u5vpDGXkUBebfHWp2s6vp50uG+06ezS9llKvZC0UrEgzg8xGSTgd/fTXCOoXN7dzi4WIBAOUxxKnUHwAzWGfJuxSaR7XQcS/iGJPnv+zI8jNHqlzIjOrwwzyxlTjlZYywPxFDYuJ5ZII4y0pmbJkk5sY8qlalcNbahLIuN+dGDHZlZcEHzBNAI5IkvQ5jXswfsDp5Vppn/wAaMusxT1uS16sK201/d8zSajLHEpwW52OfIDrXLi7sICVa61C5bH8Z7MH8ScUGvrktLlfqgR9lNhSrBxNcRpIOcA7c1dLXqeMghfODbIyggdopHfVu4Yt73UGlNpexwSxkYQxszMD3+qDgedALS0e7RxFCJXRgVQ4ANFrbTNQjvVhVQiNAZGdJMesEJwPfiso5NqZpkgpNWH00m8uVWSTUI45Byq0bxS8ySMfs7L35Hxp6TTNagspHt7tmUAns0in9Y+zK1WfoWsSaek6Qus3a45S+dgB61TP2fqo1NrVo5jEIeftBJ3hM4Hjv+FPz2iPJizPAkI8akWpSOTmXO1RDKD3CptjaPc21xKuMRAE1lGzvxtb0fQPCv1OhadGP4beP/wBoqyRS5xVZ0j6uyt0/liUfgKKpeRRuEeZFY42LAHfpXQzgl3bDkcm1SI5B0oTb3KSA9nIj468rZxUuOXB61DA0DTJOazh+4Knc+BnNAtDuQ9lFv02/GickuImPsrBoszz0iXZub2C0RvWGZCKrUUNwTkPj86J65aXOpcQXVyBIqxt2a5GxAHWkppc8YBVgwPWnCUUuWZTjJy7DXZLFAS6Bmz1qI940W6kZG1GZbVmi7Pl38aGXulPHEzgZI3wKtTi/UmalFWSLSHEc13NIroiluQjoR31ZLSA23BumwH7UiiRvM5b86oVvfyyaRdxMjK0jpCue8swFajqEASC1t1G0UYXHuA/KlVTHiyKeNSj6lcaHbpVu0aP6Lp8S95HMffQM2vM6qO84o8kgVQB3bVUmUiaJTn2U4JagiXc0oS7VNFWTTLvSu1qD221e7baihE7tqS045ahdt7aQ03WnQWfN3p4l7f0lyqDns7KBfxY/nWbTEu25J8cmr/6Xne59KWpBBzFYIVxn/Z5/OqFG3JcEuFwegNXJ0j1unxuC+bIV3ZMsZbG4OMd9NWWzEHY0W1O/tprd2CBWC4Bz18KC2TFmPiQSayhKUlyenrtPixZEscrtFt0ecIjLnq35Ci6uDVTsJiEDDxBqxQyZUb10eh8xk+NnNXjDwLIOqnHuNA2KRqWZ0GNsc29WGc5tnGxyO8UMhtrZ7jeJDIqlubAJFCYkrGVikUbhuU9/hUO7tdSS77Wzn5Y2AD1Ld5DegI7BQNwDsaIW8nqspHX2kVLbSs2lCKco+qORTokShnZiB0xTd5N9JVokDAvGVBPcw3BrhLEkE5x13pthkofb4E93tpbmZbUPR6ld6bFGknLOeXLHHLv7KZhv0upFaS3jkZCT2bjZh3ik3Q51B3yNs8uKasYy1wBnpvR37lwltv2YV1d0vLC4u4NP0+OEwcqyq2JVwoBAUt0z7KpjPyJlhgEAZzRi0uDc6jqNoYBNHa2wPNnHISd8+w5AolrvDE10jGy0VLY5yDDccyn3N0qU1F0TJccFP7WJQSjyZYY8qtvo9OZbo8zNsOvkaAnhbWIT61lKR/Tg1ZuBtPubCW5+kQSxcwGOdSM7GsdW15To9rw5f8Qx/f8AZg3XmAnmJ/mP5VXgwDEnpR7iRS30nHXP6VXCp7LHfVaf9NEdY/8ANy/VnHVp3JUMfKpdlp9/zho4JR7cY+dTdK1OCxtRE/ac2STyqPmaJrrVgTgzTHzH6Vu5PseNQqxSL6NcrcxzOBjPZEcwOeooukenxatbK1nfdoLL1SSMKOyPXbr3e+oGmSKI7uRLl7cEgrIoJI37wKPp2j6hYGPXmH+a4MTB/X+qPrDb/wCYrnZo+4EjbSZtEC8uoCOK6OTkFt1G3dt3UUH7NPEiAfTOY23qJgYA7Hr18NqjxC7GlsV1u2c9vsW6br0ORRO2W5k1qIyajZBFt15hhS2eyx4dO+hsSMjElWDQNQht9OvIJYnJnHKjY2B86D6Np7atqMVorBQ2SzeAAyauWoapa2emmC2hQQxJheZc5rojCylkUXbNZsZouyRVkQ4AGxp+70ex1SSOa4QtJFsjqxBXfOfOo3DGlcPXWjWJmtbN3aCMsxABJ5RmrDFwloEn7uFoz4xTMvyNUzAgaLo9poqyC1MmJMZ525umf1NGI5PbSBwZp+Pq73Uovu3JPzpQ4OI3i1zUV+8Ub5rUgGdJ1tLFDHKrMM5BXuohJxPzryxRpj+tiPyqsf2R1EH6viCQ/wDeW6H5YpQ4X19f3es2T/ftSPk1TSHbJ91J9JlMgR1J6iO5IHwIpjs8D9xc7eEymo/7A4pX7N1pEvmki/ma7+yuLY/9W0qX7s7L8xUeXD2KU5EtRgf6Pe+51NKSMZz2d+P7itUP6NxanXRbWT7l4B8xXQ3FCfa4ac/cu4zU+TD2Dex79i21xdWs05v+ztphP2SwKquw6ZI3q0NcR3knMFdPvriql9N15Nn4Y1D+68bfnXP2vqKfvOHdYXyiDfI1SjXYmyzzIsUgPMp8jmvCfbr31WBxFKn7zRtZT/wjH5V4cV26/vLTU0+9ZyfpVUxWWG+1GW1WPsLWS5eSQJyqQAuf4mPcBQVOL9TSeSOTh2+IjkCFlxg52yPEZ7+4UgcZaYv2muk+9bSD/lrv9tNFPW85fvRuPyppCsMRcQpd3kVvBYXsashd3ljKrH4AnvJ9majapxFc6fefR4tJvLpWj51eEArtnmByRj2DvzUIcZaG3/aUA8yRSxxZop6apae+QUUOx/ReJ21m5li+gXNsI0DEzIQCSegJFFmuNqCDiXSG6apZn/fL+tLGt6dJ9nULQ+Uy/rQBgfpFAuvSpqoOSAIth7IhQhuHY7w85MiEk4VRk0R4u1KC39KGr3UjhoudV5lOR+6AqNDxrb2FyT2uYSd1CZ229vsFXUb5O3HPNHElj7eoM1LhewS07W3lvWYYDiRAANs0z/Z+2s9Nhu4ZpCZX7NlbG21Wfiz0q6TqtpJZ2lgY4uTCHkCknGMnBqjScVRyWq2qxEBWDA+2pXMRKeVT5Y7agICo6DAo1av9WtAdLaSYSO6Mqk5GRiiSuQMA1V8HNP4mELy6WG2JJyfCo+kqxLzPuzbVBuWM0qRDoNzRezVYyi9wGa0SqNszvngiG2eK5YuQPGnO05J42AHJnBOfHv2qn8VcUSTXkltaSFY1OGcHcmlcK65I1yLG7kLxy7IzHdTWTto1tXfqXN425iRnB8GpL27EA4Gx7zmpcRUoC+5xvmm7q9ijXlyqjrjpWNl0RJbY43wPcaTaRmK59Yj7JIIrx1aEn96tMT6iiLLKpUhIWYkVUW7E0RuC4LTUda1kzLO8k0R7NYhk45x+VaXo3DqzsDDfzRE/6tOpI92cn8ayz0YXcthxjYSJn/OI5YyDtn1SfyFbiLmQsJJGWRwcjPRfIVjnbUjTHFSQ03C06d9ufMOKF6ppz6fLHz9n6ytjkJPzq2WmvTghZXQr93JoZxlKlzLaNHIsnqPnlXGOnWuPNL8jPd8PxrX439f2ZncfDl9qbTXttGkiRSlSrDO+B3YpscL6hJIWgsLR37wowfhV94F1SzsLC8huJI+1NyWEZQsxHKtHLqQX/wC50GKVu55iIvx61rjytRSOXqqvWZX82ZcvDmuqgVdGTnBJLFEwRTq6HxKoGNEjYDwRf1rQW0nieQ4hn0uyi/lAeZvicCpMFkYCP2ldahKw6hHCofcP1q3lPO2Gc6VwxrEDXDvYKjOObkmQcmc/Z3oyukaiuo2sp062YRwcpdVwVPIRgYNaBaWukEhoklDf1Ek/jTsul6dL1aWM+KnFS8ovLMvOlXAsJEk0CIjtR6g5xzbHfrSWtCmrxFtGkjJg5e0SRgB9X0OQa0aTQVGTb30xH9TDPyqO+mXMe/0yUD2qDT80XlHzbwVIBq7sDuttKf8A0GiWpKZLVx7KCcGty3t0e/6Mw+JFWJ4+0QjHUV6sUcknZd+C9R7TQbEMdxHynfwJFW63uDsQxHkay7hO6aC1Nt3xSEY9h3/Orzp94XwO+pkhJlmjvp0HqzSDyY1Mi1e9UbXMnvOaBpKSDT6SbDfuqKKDy8Q36Y+vz5qKkx8U3yncxnzWq52uAKWsu4pUgLPHxhcoCXiiIG/eKAp6etCVuV1fY4yAajMxZSviMVgMzSW8giHMW5ypHTFVGCfcTk0fS9t6ceGZcZnK+ef0qxad6Q9L1ODt7VmljzjmQg4NfIweYH7Ab3qa0j0SXb9lqcL5GHRwD3ZBH5UpYklaBTZv6cYWONxMP7v+NPx8WaecAyOPNTWeiXbNLEu/Ws9iK3M0iPinTzgfSMeamnl4k05v9bi99Zp23trwm3o2C3GorrmnP1ubc+bCl/tHS5Or2jeZWsu7XauGWjZ8w3Gp82kS9YbF/NUNIax0OUetp2nP/uUP5Vlb3BXfNIN6w6MfjT2P3FaNRbQOHJOujaa3/h0/SmZOD+F5uuhad7oVFZkL6UDaV/c1SIdSuAdp5R/fNG1+47Rn3HvCGlTcc60sVqLa1jkijjSH1RkRgt+JFCBwPo2PXWU/3qtnEMqJqkskgdyx5m36nzqv32uGLm7CIR+BPrGtlVci3SXZgHVeG9GsFzHal/vnNVyURRPiGNIx/SoFE9W1Ced2ZnO9Ay55zk+3zq7Vdibk3ywmtwjQ4JAak5xue6h/aEDapNxcAW2R1bas5K2WnQ5ZHnlaQ9Sdq5xFqp0uwmdDiR1EaeZpi0nKsAB31XuMr8z3iW+do1yfM/4VU+XSJivUrpJJydz1qVazNDNHKpwyMGHuplYGdciux5XY9aTKNRTU4mjXMn2t/Kor2cd5MX+kZB7hQKC4/wA3jJP8I+VeN+0O8bEHxrHaabvcOzW+n6cvNMec9yk5J91BNX1US2knKoQSYRQD3d9Dbq+dySWJPeSaHXN81wVXACr0Aqox9xORaeF7+3suJNCnuT2FvEDzyN03Vt62FOKeHJPsazaj2FwPnWCySI1vYANlxzcwPcMbU5zVE8KnyzTHk2o3+DV9LmI7LVLV/KVT+dEEljkwRKrjuPWvnHmp6K8nhx2c0qfdYisnpfmbR1Li7R9HxSGHeJwmf5RjNOfSrhj+95x4ZxXz3BxLrFvjstTu1x/tSaL6d6SNfspAZLpbpO9JlG/vG9S9LJdh+em7ZuaTDrKp9xzUiN4zsr8vsNZ5Yek3R7mFGuZZLWUj1kZCQD5iiUfHWhOPV1OD35HzrB45L0L3J+peELLkq3NkdfCli5nXbtDj2iq5Y6tHeIstrOsiHo0bZFT/ANpTp/EWA8azaGE/pcqnJZT5qK79PY9VBHsFCxqUknU8o9i09C1nKfrpXJ7+YbUmgPmLhhkgvZeZsBoWGfx/KrdDd2kSB3Xm8zgUNvNKsrC2EltFyuCPWySanaILC95UvIScbBlOCK9uMlKPB5ko06Y6bztJDJYA27kYLKNj55olpWoatb4LPDPgZJYYJ+FEBwvpzIDaah2bEfZlXA+IqO+h6vFkQiG7XOfq2Bz7qXI1QeteJWQYuLTB7+Rsj8amx8U6eDyydpER1BRjj4Cqc/7StjiewuI98kqppA1NBkMGUk9GXP4VFDNCg1jT7rAjuoCfAyKD8M5ogiF15kV2HiFJFZuLyCQv60bHHX/HoKdieGN45I2MZI+0jY/HqaQGgl1U4LAEdxNCbzhfQtQmaafToi7nLNG7ISfHAOPwquDXNRt0wuq6iqg9DMzD/hzTU/El6HCtd85O45ooy3vPLRyIOP6OuHJMkR30f3Zx+amiWgcM2HDrTmya4bt8cxmYHGM4xgDxqjnXL6YELqM6EHfkYKB+FTLTWdXibKao8q+MqAj3Y3p8iNGyaXzHaqPHxbqsQYyQwyhe/m5M+7lNSF40u1AaTT8ZxgBgSflSpjLlzEYrwkqovxxdA8q6Xzv/ACmQKfzpUPG07kltFlwOvJOpx+AppMRbw+1JZyOlV2LjKJl5n0u+VfEchH/up1+LrJMdraX8fnBn5E0hUE5nkweQZPhUFrtlJ51ZT4EYqOeNNKXZhcKf6oHGPwpa8YaNKpWaJ5o+n7th8DjanYUSI7rnxvip0UqIodnGMZPsqj69x3ZWCsul2Fw6lcKJMkqfPAyKCWXE/EGqyK8tlP8ARifVEICq396n35AtnEUomvmdN1YAiqrqRwDVn1OFo7WxlMRjLwAsuc8pBIIz7qqOrXcKMymReYDJGd6a7AAL1xk0MkfDrU64V3QyspXJ2B7v8aGyse2X1fV8aYx4MD1puaXIRM9K8DTNwSPWC5x4UIdEu2k5PWOMCqZezm9v5pj/ABuT7u6rDc3oSym5T9YUIAHWq1DG5b7LfCj1sCQDyLnvptyGcONs9alpbRvFhixmYZVR3CoWCrFPbSsYaWblgQeCj5VHebmJHMAB1J7qZmm6gHCrsT+lD7i5MnqrstFAOXV32nqJso7+80yrYpqug0xBMzYEBGCQtPrcsR0FDYixwcZxUgMwH2TQFsmi58V/GlrcJ4MKgh991PwpQlXPhTDcwirhgSDSgaiQzoARkb1IV9sYFItOx4NSlamgM11RjvoAPaBxDqejzBLC4EYkIBVwCpPvraNFmvZdOjbUTALhhk9j9mvn+EZPXp03rZOD9QluuH7dmfmdcqcnOcVx6pJJM6MLZa1I72FKyB1AoUtzMXwyLyY+1nf4Yp5XJ61x0bGVaxABp0hH8OD+NV2w1JLa45ecDBq2Xii5tJolBJZCB51mM7Mtw+diGOa9TA+Gjiyrk06HVOaFWD5HspKaw6SbORv41nUGoTQ/YkK++pUWuTqcsebzrakYs2PSeK72JVUXBZfB/WH41YIdas731bzS7G4HeTHyk/DFYpYcVrFgSJjyNWvSuLbFyOecxn+oH8qmSGmaWuhcIagMS6PJAT3wS/rST6OOF7lua31O+tm7udAwFDtH1e0uVXs7qFz4BxmrHB6wBFZvgpAOb0QxSEmy4jtjzf8A5QQflQ669CvEBH+a3VncAb+rMMn8auRTBrqlwdmIpbmFGczeifjC1ZmbTHlXwTcVFPCXEtj/AKRpNwSp29XYVqkd5cxfYnkXyY1Li4g1SIALeTAe1jRuYUZG0N/EwL2Vx2hG7FTgeVIE7pIVMcyK3V3HTyrZf7Ual0eRJPvxq3zFJfiJ2GJbHTpfvW60bgoxxbtCCoLxherHq1PpfxH12bs4gfsY3atRn1rTyPruHdJkPiIyPkaHM+gThjLw/bnxKsRinYUUaLUIOVXdlIAHZxcvTw2qUNQj5wzNFJcE+qN8L/j7atIs+EeVe30NlYjJKS9/wqDNa8DB2zY6jHjryyg0rArstxCcrGUeVgOdsnAHspktBLGyoMW6Z5iW3JqwtpPA7AlJdSTPcT0qBd6NwssTtaz6pMf5CwQH30+AA8GgPxL20cDpDBEwEsy7lcjPKP6sb+zNWPQOF+xeK206ae3iTALc2MAHNP8AD99pGlQfQoNMmgtSefIkDuzHqTvUbiDjIPE9hpMRhRsh5SfWIPUChtdhUJ1200zWboxgSSJAMFxKwHU7dcHvqq6pFbWYaK1hjT2qPzqYL2SK2ECAKOrEdWNCbwnqMHHfQuB0Ab9mOQ3f30InySApxmiN6zHJPShpxnNVYUdXYCuMa9ua4w29tTY6O220oO29e1G4a3gJj+2x5VriNyuDUe9mDXkaHcIVyPaTQ2FDRtpItXVCxICBj7dsfOhN8ogv3GNg2atNyqLcdoT65THu61VNRz2+W+0dyfGnFg0RppmlO/TwFNch7qcAroFWSIVPGnFjXPSugUsCmAtMDoKdVh0poUsCkMdBFLGKbBzSxQMWo32Ap0NimxilAigB0SUoPTQFLQFiAO/agdky2VHHrZznurX+FNNbTNAhDODznmI8Kz/g7Rf2pfKz7Qw+sxHefCtQunKW0SIRyjfauDPO5bEdOONR3D4lAHWumd13GTULtjikmYk5yRWW0uzJZNXuHG8z+40IvLL6XI0qEh23I7jUqG2kkICgk1Pj0/skaSaRVVVyVX1mPkK7E9vY52r7lVmtpbf95GQPHuprmqdqOrrckx26skX9X2j5+FNaZpV3q03Z20fMB9pzsq+ZroUuLZg1zSGQ9PRTEHYkUVvODNQtlLwtHcADOFOG+FBZIprZuWaJ4z4MMU1JPsDTXcMWuo3EeOWVvInNHbHjDVbLHZXUigfyuy/I1UYJ17zU2OQFftUNAi/2vpS1eIDtJXce3lb5ii1t6XZAfro4z5xEfJvyrMA3tr3NmppBZs1r6VtPlA7SKEf70r81/Oittx9pN0Ntvuyxt/zZrBQe+uFztU7B2fRMfEFjN9gXPuhZvlmuyazY780/IR/+RSuPiK+d0nkQ5R2U+IOKkR6zqUIPZ390o9krfrRtFZu82rWTdLu3P+8H603FcwyhsSp5hhWJf2m1fqdQnP3mz8683EuoqvM9yrDp60an8qKYG0ySNzDBSRe4g1HkAYHtI1XPeR1rHl4nvA2RNET9wD5Ur+110oIaRPiR+dFDpmoyW4GQUHL4g0lBHFgKwGf5mxWXf2vulJImTy52H506nGd/nnR+n9RYfA5oaYGqqyNiNZZULd6gMB5EGoMunCGXLczK3QnbNVLR+LF1MtHcKqToOYYGAw8RRlr+3YR+v2gx3Ocg/lUdgQ7cBU32AyRuaFX7IsBbtDudgBUi4uIMAgMMbkE0E1G6EikAHHsoGCrmTJbzqLnNcupdiAdzTUlxHAgaVwg9tMB7m7q4CKD3WuqMrbpn+pqFy31zMTzTPg9wOBTUWFotE11BCpLyKuO4mhMVwLq4HNIAzNzE93sFBtydzRTRYVluxzdFHNiq2pdxXYbkimlvJZmcFQp5VHXuFAdUwJ0Abm9Xc0dt0nll7bYJGxRt9/ZQXWsG+IGBgUl3G+xBApQFcroqyRQpQrgpQpjOilrSR1pQ60AOKcU4hA3xTa705y0gsXnNdFJApSg0DFDzqfpFmb/UIbdSRzNuR3CoKI8jBVUsxOAAM5rReB+F2si19dgGYgBIxvy58azy5FCLbKhFydFp0zTrbSbRILdOVQNzjdjTl3KysFVQnKOgp/kaNSWUgfzEbCot1KGmxnPt8a8nA27kzuzKqSOiQkEHBrnaEg4ByPGm1kQDJ8q6ZQc4PwroMTPLbTp2GXl5R7KnwWEcJHNk53pUPQVIat2yEgfd8N6XfyB5IORs7tGcE+dE7a2htIVgt4gkadAoxSB199S7b7L+6lb7AkhcFs8px6q+1jipSWFkiFr6RXUjZRg5qOe/yFD9U/ciott0iqRTOLdEhsr2S40z1rRjnkzkx+zyoAlwVO5NWXUOrVVpv3zedduO65OWa54Jkd1npJg+2nxcyDpvQnuqba/u6pkkxb5ujLS/pinGRiobdTSTQBPF1Hmu9vGf4qgNXu8UATxKhGzCklkbZtxnxqInWvCgCaFgbcJg/fpyOOCNmeSBZtsAM52+GKhD7NKb8qmi3OT4bJo+hyHBsEHlI361x4ogv1MYjGc45s1DXqfI17+AUhHZJ2tZo54zhkNHI+MbMqqv9IUY35QMjy3qsT99RO6nViui3zcXWTD1VnbHcyjf8aHXHEiyZ7OBgPaaA17uo2odj9zq9zKSFKoP6Rv8agO7OxZmLHxNeNep0Jia9XTXKYHB1ovoTBblydvUPzFCV60S0n9+/wBw/lSY0FnuTZK0qrziVxkHbuoLqMglvZGHSi+p/uI/vflQK4/0h/OlFA2cxXRXB0pXfViOilAVwV0daAFYpaiuClDrQMcUbUsUle6lCgLFCiOi6Nc61di3twB3szdFFDh1q/8Ao5/cXHmKzyS2xbRUVboMaNwtaaMnMi9rPjeVuvu8KtcVuFtIwVxjJznrQ5evwow37iLyNeXqZNx5O3CkpEYKFZfacCmLs9m7DORjvqRdfw/eqFqP78VGmX5Ss7/MMEIwCtGrZ8NsUl7WND6rugPTO9eT95T793nXR2Mkf//Z\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MnZvN1V7vd6d",
        "outputId": "ef04ccda-cfe4-4edc-c8f3-b7a77b5868c8"
      },
      "source": [
        "car_categories_gate('https://www.nerdwallet.com/blog/wp-content/uploads/2015/12/exterior-car-damage-384x233.jpg', ft_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validating that damage exists...\n",
            "Validation complete - proceed to location and severity determination\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLr02y1Avd6d",
        "outputId": "865fe600-21cb-4d6b-dc73-6ac178ab207b"
      },
      "source": [
        "Image('http://1.bp.blogspot.com/-ToQS-qIxYbo/UDNuV5OcVQI/AAAAAAAABdo/tjeQywWiOo0/s200/Key+scratch.jpg')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0pBiJNSvd6d",
        "outputId": "0dbaa0ea-8235-4ace-95e3-d98f72de2b34"
      },
      "source": [
        "car_categories_gate('http://1.bp.blogspot.com/-ToQS-qIxYbo/UDNuV5OcVQI/AAAAAAAABdo/tjeQywWiOo0/s200/Key+scratch.jpg', ft_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validating that damage exists...\n",
            "Validation complete - proceed to location and severity determination\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VsGBaT20vd6d",
        "outputId": "159a05be-13dc-4f04-c7e0-46d8162610a4"
      },
      "source": [
        "Image('https://i.pinimg.com/originals/00/e2/d2/00e2d282c9d51424c5c3a96e9e649e37.jpg')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60FEurgdvd6d",
        "outputId": "1333cb65-1738-4702-b04c-c9a4c6c82939"
      },
      "source": [
        "car_categories_gate('https://i.pinimg.com/originals/00/e2/d2/00e2d282c9d51424c5c3a96e9e649e37.jpg',ft_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validating that damage exists...\n",
            "Are you sure that your car is damaged? Please submit another picture of the damage.\n",
            "Hint: Try zooming in/out, using a different angle or different lighting\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2O_CvHC5vd6f"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}