DITariqMethod_conv8_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DITariqMethod_conv8_1.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/preritt/KProj/blob/master/DITariqMethod_conv8_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kdTAcjYec3s7",
        "colab_type": "text"
      },
      "source": [
        "## Load standard libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCo6LRTTcxmC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import gzip\n",
        "%matplotlib inline\n",
        "from keras.models import Model\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.layers import Input,Dense,Flatten,Dropout,merge,Reshape,Conv2D,MaxPooling2D,UpSampling2D,Conv2DTranspose\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.models import Model,Sequential\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.optimizers import Adadelta, RMSprop,SGD,Adam\n",
        "from keras import regularizers\n",
        "from keras import backend as K\n",
        "from keras.utils import to_categorical\n",
        "from keras.datasets import cifar10\n",
        "from sklearn import preprocessing\n",
        "from keras import losses\n",
        "from keras.models import model_from_json\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.utils import np_utils\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.datasets import cifar10\n",
        "from keras import regularizers\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sg_ZApCOPN9u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.utils.vis_utils import plot_model\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oG3txkjndHWQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "keras.backend.clear_session()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rDrYIgZc20A",
        "colab_type": "text"
      },
      "source": [
        "## Load image data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85Dlxs7adKVS",
        "colab_type": "code",
        "outputId": "4c593e01-f2bf-4008-e9f8-caffd9536eb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "source": [
        "from matplotlib import pyplot\n",
        "from scipy.misc import toimage\n",
        "from keras.datasets import cifar10\n",
        "def show_imgs(X):\n",
        "    pyplot.figure(1)\n",
        "    k = 0\n",
        "    for i in range(0,4):\n",
        "        for j in range(0,4):\n",
        "            pyplot.subplot2grid((4,4),(i,j))\n",
        "            pyplot.imshow(toimage(X[k]))\n",
        "            k = k+1\n",
        "    # show the plot\n",
        "    pyplot.show()\n",
        " \n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "show_imgs(x_test[:16])\n"
      ],
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: `toimage` is deprecated!\n",
            "`toimage` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
            "Use Pillow's ``Image.fromarray`` directly instead.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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O8NF2er2620g5XXfgeE5cX60xHA39KCJ8t8/127cBmE7G5EJIU5YlzVZM8RHA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\nvdxDuLu8zOOPPwLA0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c+VRdkF7nXkQczKNn1DO7O2do/z52VeHNvaz/PpmNHaoM0ipbMr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\nS4+xitZisVh6zP8CB+zi+GJTgIQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dQQPlF6RdS2m",
        "colab_type": "text"
      },
      "source": [
        "## Create Function to get data corresponding to three labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GFyk3xzNdYNW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# This function takes the entire traaining data and test data and corresponding categorical label and returns the corresponding data in class indices\n",
        "def getThreeClassesTrainTest(class_indices,train_data,train_labels,test_data,test_labels):\n",
        "  all_train_data = []\n",
        "  all_train_data_label = []\n",
        "  all_test_data = []\n",
        "  all_test_data_label = []\n",
        "  for ind in class_indices:\n",
        "    this_class_location_train =  np.where(train_labels==ind)[0] # train_labels[train_labels == ind]\n",
        "    this_class_data_train = train_data[this_class_location_train,:]\n",
        "    this_class_data_train_label = train_labels[this_class_location_train]\n",
        "#     print(this_class_location_train)\n",
        "    \n",
        "    this_class_location_test = np.where(test_labels==ind)[0] #test_labels[test_labels == ind]\n",
        "    this_class_data_test = test_data[this_class_location_test,:]\n",
        "    this_class_data_test_label = test_labels[this_class_location_test]\n",
        "    \n",
        "    all_train_data.append(this_class_data_train)\n",
        "    all_test_data.append(this_class_data_test)\n",
        "    all_train_data_label.append(this_class_data_train_label)\n",
        "    all_test_data_label.append(this_class_data_test_label)\n",
        "    \n",
        "  train_data_three = all_train_data[0]\n",
        "  train_label_three = all_train_data_label[0]\n",
        "  test_data_three = all_test_data[0]\n",
        "  test_label_three = all_test_data_label[0]\n",
        "  \n",
        "  for j in range(1,len(class_indices)):\n",
        "    train_data_three = np.concatenate((train_data_three,all_train_data[j]))\n",
        "    train_label_three = np.concatenate((train_label_three,all_train_data_label[j]))\n",
        "\n",
        "    test_data_three = np.concatenate((test_data_three,all_test_data[j]))\n",
        "    test_label_three = np.concatenate((test_label_three,all_test_data_label[j]))\n",
        "#   print(train_label_three.shape)\n",
        "  train_label_three = np.reshape(train_label_three,newshape=(-1,))\n",
        "  test_label_three = np.reshape(test_label_three,newshape=(-1,))\n",
        "  return train_data_three, train_label_three, test_data_three,test_label_three"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ex0j41PLde2x",
        "colab_type": "text"
      },
      "source": [
        "## Get data for three classes using getThreeClassesTrainTest function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IpqbOTfBdhHk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train_three_data,train_three_label,test_three_data,test_three_label = getThreeClassesTrainTest([ 0,3,5 ],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddFucSl_YW5B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_three_data,train_three_label,test_three_data,test_three_label = (np.load('train_three_data_saved.npy'),\n",
        "                                                                      np.load('train_three_label_saved.npy'),\n",
        "                                                                      np.load('test_three_data_saved.npy'),\n",
        "                                                                      np.load('test_three_label_saved.npy'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDQiwalsYIGo",
        "colab_type": "code",
        "outputId": "1ac7ea1b-a655-4fbd-c45c-fdd440ecec05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_three_label"
      ],
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 3, 5, ..., 0, 5, 5], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCQfV4TvZh1r",
        "colab_type": "code",
        "outputId": "459eaca3-3043-48a5-bd6d-c097b5d348dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_three_label"
      ],
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 5, 0, ..., 5, 0, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gK_tiIWd2Nf",
        "colab_type": "code",
        "outputId": "23db80af-a9d4-45bd-a189-8b64809cad8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "source": [
        "plt.hist(train_three_label)"
      ],
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([5000.,    0.,    0.,    0.,    0.,    0., 5000.,    0.,    0.,\n",
              "        5000.]),\n",
              " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 239
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PAb8f90RGrIDvJjnYvh1hWazEAKgjSV4LfB34aFX9etzzWW5V9UJVXcTgU/Sbk6zYU35J\n3gMcraqD457LGLyzqi5m8M3Ju9pp3iW3EgOw4NdNaGVo58G/Dny5qr4x7vmMUlU9A9wNbB33XJbR\npcB72/nwfcBlSf59vFMajao60m6PAt9kcGp7ya3EAPh1Ex1ob4jeAhyqqs+Mez6jkGQiyZq2/BoG\nFzr8dLyzWj5VdX1VbaiqSQZ/j++qqr8b87SWXZJz24UNJDkX2AIsyxV+Ky4AVXUSOPV1E4eA/Sv9\n6yaSfAX4T+AtSQ4n2THuOY3ApcCHGPxW+GD7uWrck1pm64C7kzzE4Bed6arq5tLIjqwFfpDkR8B9\nwLer6jvL8UQr7jJQSdJwVtwrAEnScAyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXqfwHd\n/gESZomJ0QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEYcIMeqd4i1",
        "colab_type": "code",
        "outputId": "cf7f1dd0-2dd8-4f1f-a59d-708e8e4892d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "source": [
        "plt.hist(test_three_label)"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1000.,    0.,    0.,    0.,    0.,    0., 1000.,    0.,    0.,\n",
              "        1000.]),\n",
              " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qqpuXRnZkDfBwkm8BXwe+VFVfXo4TrbiXgUqSTs2KewYgSTo1BkCSOmUAJKlTBkCSOmUA\nJKlTBkCSOmUAJKlTBkCSOvW/lK9vO4tP9cMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4n_WiMNd_Hf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train_three_data, train_three_label = shuffle(train_three_data, train_three_label )\n",
        "# test_three_data,test_three_label = shuffle(test_three_data,test_three_label )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VT_AKqQmeBk_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "le = preprocessing.LabelEncoder()\n",
        "le.fit_transform(train_three_label)\n",
        "train_three_label_cat = le.transform(train_three_label)\n",
        "test_three_label_cat = le.transform(test_three_label)\n",
        "train_three_label_one_hot = to_categorical(train_three_label_cat)\n",
        "test_three_label_one_hot = to_categorical(test_three_label_cat)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWEjK_exeEaJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# split train data and to create validation data\n",
        "train_X,valid_X,train_ground,valid_ground = train_test_split(train_three_data,\n",
        "                                                             train_three_label_one_hot,\n",
        "                                                             test_size=0.2,\n",
        "                                                             random_state=13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOFKlZWKeH25",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_classes = test_three_label_one_hot.shape[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lr931MtCYGoN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "3eca74c3-db5d-4e25-b10a-ad0a01f64912"
      },
      "source": [
        "train_three_label_one_hot"
      ],
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 0., 1.],\n",
              "       ...,\n",
              "       [1., 0., 0.],\n",
              "       [0., 0., 1.],\n",
              "       [0., 0., 1.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ssi32yj-d1iX",
        "colab_type": "text"
      },
      "source": [
        "## Define the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUISHV2weN0t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x, y = 32, 32\n",
        "inChannel = 3\n",
        "input_img = Input(shape = (x, y, inChannel))\n",
        "ep = 10 \n",
        "weight_decay = 1e-4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ich0RbYnebwW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cifar10Classifier(input_img):\n",
        "  \n",
        "  conv1_1 = (Conv2D(64, (3,3),name = 'conv1_1' ,padding='same', \n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:],\n",
        "                    activation='relu'))(input_img)\n",
        "  conv1_2 = BatchNormalization(name = 'conv1_2')(conv1_1)\n",
        "  pool1_3 = MaxPooling2D(pool_size=(2, 2), name = 'pool1_3')(conv1_2) #14 x 14 x 32\n",
        "  conv2_1 = (Conv2D(128, (3,3),name = 'conv2_1' ,padding='same',\n",
        "                    activation= 'relu',kernel_regularizer=regularizers.l2(weight_decay)))(pool1_3)\n",
        "  conv2_2 = BatchNormalization(name = 'conv2_2')(conv2_1)\n",
        "  pool2_3 = MaxPooling2D(pool_size=(2, 2), name = 'pool2_3')(conv2_2) #14 x 14 x 32\n",
        "  \n",
        "  conv3_1 = (Conv2D(256, (3,3), padding='same',name = 'conv3_1',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool2_3)\n",
        "  conv3_2 = BatchNormalization(name = 'conv3_2')(conv3_1)\n",
        "  conv4_1 = (Conv2D(256, (3,3), padding='same',name = 'conv4_1' ,activation= 'relu',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay)))(conv3_2)\n",
        "  conv4_2 = BatchNormalization(name = 'conv4_2')(conv4_1)\n",
        "  pool4_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool4_2')(conv4_2) #14 x 14 x 32\n",
        "  \n",
        "  conv5_1 = (Conv2D(512, (3,3), padding='same',name = 'conv5_1',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool4_2)\n",
        "  conv5_2 = BatchNormalization(name = 'conv5_2')(conv5_1)\n",
        "  conv6_1 = (Conv2D(512, (3,3), padding='same',name = 'conv6_1' ,activation= 'relu',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay)))(conv5_2)\n",
        "  conv6_2 = BatchNormalization(name = 'conv6_2')(conv6_1)\n",
        "  pool6_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool6_2')(conv6_2) #14 x 14 x 32\n",
        "  \n",
        "  conv7_1 = (Conv2D(512, (3,3), padding='same',name = 'conv7_1',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool6_2)\n",
        "  conv7_2 = BatchNormalization(name = 'conv7_2')(conv7_1)\n",
        "  conv8_1 = (Conv2D(512, (3,3), padding='same',name = 'conv8_1' ,activation= 'relu',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay)))(conv7_2)\n",
        "  conv8_2 = BatchNormalization(name = 'conv8_2')(conv8_1)\n",
        "  pool8_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool8_2')(conv8_2) #14 x 14 x 32\n",
        "\n",
        "\n",
        "  flat = Flatten()(pool8_2)\n",
        "  fc1 = Dense(4096, activation='relu', name = 'fc1')(flat)\n",
        "  fc2 = Dense(4096, activation='relu', name = 'fc2')(fc1)\n",
        "  fc3 = Dense(1000, activation='relu', name = 'fc3')(fc2)\n",
        "\n",
        "\n",
        "  \n",
        "  out = Dense(num_classes, activation='softmax', name = 'out')(fc3)\n",
        "  \n",
        "  return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_0LHS-Xf7aN",
        "colab_type": "text"
      },
      "source": [
        "## Define functions for calculation of DI score\n",
        "getDIScoreOfLayer(layer_output, true_labels), \n",
        "getDIScoreOfLayerCNNLayer(cnn_outputs, true_labels)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsLk-f7sf6zd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getDIScoreOfLayer(layer_output, true_labels):\n",
        "  # computeScatterMatrix\n",
        "  rho = 1e-6\n",
        "  mu = layer_output.mean(axis=0)\n",
        "#   print(mu.shape)\n",
        "  mu = mu.reshape((1,-1))\n",
        "  centered_data = layer_output - mu\n",
        "  S_bar = np.matmul(centered_data.T,centered_data)\n",
        "  # compute between class matrix\n",
        "  total_classes = true_labels.max()\n",
        "  S_B = np.zeros(shape = (mu.shape[1],mu.shape[1]))\n",
        "  S_W = np.zeros(shape = (mu.shape[1],mu.shape[1]))\n",
        "  unique_classes = np.unique(true_labels)\n",
        "  unique_classes_list = np.ndarray.tolist(unique_classes)\n",
        "  for l in unique_classes_list:\n",
        "    class_l_index = np.where(true_labels == l)\n",
        "    class_l_output = layer_output[class_l_index[0],:]\n",
        "    mu_l = class_l_output.mean(axis=0)\n",
        "    mu_l = mu_l.reshape((1,-1))\n",
        "    total_examples_this_class = class_l_index[0].shape   \n",
        "    ss = mu_l - mu\n",
        "    S_B = S_B + total_examples_this_class*np.matmul((mu_l - mu).T,(mu_l - mu))\n",
        "\n",
        "  DI_Score = np.trace(np.matmul(np.linalg.inv(S_bar + rho*np.eye(S_bar.shape[0])),S_B))\n",
        "  \n",
        "  return DI_Score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubrpaIpFgBzn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getDIScoreOfLayerCNNLayer(cnn_outputs, true_labels):\n",
        "  # computeScatterMatrix\n",
        "  number_of_filters = cnn_outputs.shape[3]\n",
        "  cnn_di_scores = []\n",
        "  for filt in range(0,number_of_filters):\n",
        "    outputs_filter = cnn_outputs[:,:,:,filt]\n",
        "    outputs_filter_reshaped = np.reshape(outputs_filter, newshape=(cnn_outputs.shape[0],-1))\n",
        "    this_filter_score = getDIScoreOfLayer(outputs_filter_reshaped, true_labels)\n",
        "    cnn_di_scores.append(this_filter_score)\n",
        "  return cnn_di_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NObSly34oADP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getDIScoreOfLayerFCLayer(cnn_outputs, true_labels):\n",
        "  # computeScatterMatrix\n",
        "  number_of_filters = cnn_outputs.shape[1]\n",
        "  cnn_di_scores = []\n",
        "  for filt in range(0,number_of_filters):\n",
        "    outputs_filter = cnn_outputs[:,filt]\n",
        "    outputs_filter_reshaped = np.reshape(outputs_filter, newshape=(cnn_outputs.shape[0],-1))\n",
        "    this_filter_score = getDIScoreOfLayer(outputs_filter_reshaped, true_labels)\n",
        "    cnn_di_scores.append(this_filter_score)\n",
        "  return cnn_di_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQ5H0p_KjVCw",
        "colab_type": "text"
      },
      "source": [
        "## define a function to get and plot di score of a layer. It takes inputs - trained model, layer name to get DI scores and returns a plot with some statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1e8IHOtx-xRX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loaded_model_low_accuracy = keras.models.load_model('vgg16lowAccuracyModelDataV3.h5')\n",
        "loaded_model_high_accuracy = keras.models.load_model('vgg16HighAccuracyModelData.h5')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_2COPWm_7Mx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def compute_di_score(features,Y):\n",
        "    '''\n",
        "    Computes DI Score of a Neural Network Output\n",
        "    For convolution layer, the DI score of each channel is computed\n",
        "    For fully connected layer, the DI score of each node is computed\n",
        "    \n",
        "    Input:\n",
        "    features: shape: convolution: Ne,H,W,Nf\n",
        "                     fully connected: Ne,Nf\n",
        "                     Ne = number of example\n",
        "                     Nf = number of channels/filters\n",
        "    Y: shape: Ne,n_class\n",
        "                One ho encoded array. Each row contains one example.\n",
        "    \n",
        "    Output:\n",
        "    dis: shape: (Nf,), di sore of each of the filters.\n",
        "    '''\n",
        "    fshape = len(features.shape)\n",
        "    \n",
        "    #Taking the features to Appropriate Shape\n",
        "    if fshape==4:\n",
        "        Ne,H,W,Nf = features.shape\n",
        "        features = features.transpose(3,0,1,2) #Nf, Ne, H,W\n",
        "        features = features.reshape(Nf,Ne,-1) #Nf, Ne, HxW\n",
        "        features = features.transpose(0,2,1) # Nf, HxW, Ne\n",
        "        d = H*W\n",
        "    elif fshape==2:\n",
        "        Ne,Nf = features.shape\n",
        "        features = features.transpose(1,0) #Nf, Ne\n",
        "        features = np.expand_dims(features,axis=1)\n",
        "        d = 1\n",
        "    \n",
        "    #Normalizing the Y\n",
        "    Y = Y.T\n",
        "    N_number = np.sqrt(np.array([np.sum(Y,axis=1)]).T)\n",
        "    Y = Y/N_number\n",
        "    \n",
        "    dis = np.zeros(Nf)\n",
        "\n",
        "    rho = 10**-8\n",
        "    for i in range(Nf):\n",
        "        X = features[i,:,:]\n",
        "        \n",
        "        dis[i] = np.trace(np.linalg.inv(X.dot(X.T)+rho*np.eye(d)).dot(X).dot(Y.T).dot(Y).dot(X.T))\n",
        "        \n",
        "        \n",
        "    return dis"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6Jl20SD_93r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "layer_name = 'fc1'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQ2ZfrgZAuDA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6d8af7c9-65dc-4f89-b609-26cf3ba07edf"
      },
      "source": [
        "train_three_label"
      ],
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 3, 5, ..., 0, 5, 5], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "moMtjkXn-4tm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = loaded_model_low_accuracy\n",
        "intermediate_layer_model = Model(inputs=model.input,\n",
        "                               outputs=model.get_layer(layer_name).output)\n",
        "features = intermediate_layer_model.predict(train_three_data)\n",
        "discores_tariq =  compute_di_score(features,train_three_label_one_hot)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lsR1xgsw_ZbW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "b1b609f3-460f-46a3-acd9-b015b5100912"
      },
      "source": [
        "plt.plot(discores_tariq)"
      ],
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5cde1ecac8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 256
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7kxf3ovNkf6Qj+jH1viuTJd/ZTcL5219vwdpdR1M/Tjn8xLCMhUS9KFE1LK1J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\nEEIKyP8H4H/Ccy3jpKMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9MHI1LFjUZs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def createCNNLayerDIScorePlot(trained_model, layer_name, data_to_use = train_three_data , data_label_one_hot =train_three_label_one_hot ):\n",
        "#   print(trained_model.layers)\n",
        "#   get_cnn1_output_function = K.function([trained_model.layers[0].input],\n",
        "#                                   [trained_model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "  \n",
        "  model = trained_model\n",
        "  intermediate_layer_model = Model(inputs=model.input,\n",
        "                                 outputs=model.get_layer(layer_name).output)\n",
        "  features = intermediate_layer_model.predict(train_three_data)\n",
        "  discores_tariq =  compute_di_score(features,train_three_label_one_hot)\n",
        "  plt.plot(discores_tariq)\n",
        "  print('Mean of DI SCore: ',np.mean(discores_tariq))\n",
        "  print('STD of DI Score: ', np.std(discores_tariq))\n",
        "  print('MAX of DI Score: ', np.max(discores_tariq))\n",
        "#   plt.title('DI score - Data - D_L, Network -N_L ' + layer_name)\n",
        "  plt.title('DI score ' + layer_name)\n",
        "\n",
        "#   plt.ylim([0,1])\n",
        "  return discores_tariq"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENOv0x-tktXu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# def createFCLayerDIScorePlot(trained_model, layer_name, data_to_use = train_three_data , data_label_one_hot =train_three_label_one_hot ):\n",
        "# #   print(trained_model.layers)\n",
        "# #   get_cnn1_output_function = K.function([trained_model.layers[0].input],\n",
        "# #                                   [trained_model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "  \n",
        "#   model = trained_model\n",
        "#   intermediate_layer_model = Model(inputs=model.input,\n",
        "#                                  outputs=model.get_layer(layer_name).output)\n",
        "#   cnn1_output = intermediate_layer_model.predict(data_to_use)\n",
        "# #   cnn1_output = get_cnn1_output_function([data_to_use])[0]\n",
        "#   di_scores = compute_di_score(cnn1_output,data_label_one_hot)#getDIScoreOfLayerFCLayer(cnn1_output, data_label)\n",
        "#   # plot DI score\n",
        "#   y_pos  = np.arange(len(di_scores))\n",
        "#   plt.bar(y_pos, di_scores, align='center', alpha=0.5)\n",
        "#   plt.xlabel('FilterNumber')\n",
        "#   plt.ylabel('DI Score')\n",
        "\n",
        "#   print('Mean of DI SCore: ',np.mean(di_scores))\n",
        "#   print('STD of DI Score: ', np.std(di_scores))\n",
        "#   print('MAX of DI Score: ', np.max(di_scores))\n",
        "# #   plt.title('DI score - Data - D_L, Network -N_L ' + layer_name)\n",
        "#   plt.title('DI score ' + layer_name)\n",
        "\n",
        "# #   plt.ylim([0,1])\n",
        "#   return di_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DCCl7-vOfSZ9",
        "colab_type": "text"
      },
      "source": [
        "## load pretrained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqIkmDGkfRtD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7J98O5qfi8Z",
        "colab_type": "code",
        "outputId": "b4fe3f4d-482f-427e-b261-e1f20e7e3c99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loaded_model_low_accuracy.evaluate(valid_X, valid_ground, verbose=0)\n",
        "\n",
        "#[0.3284807046254476, 0.923]"
      ],
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4554041403134664, 0.8660000001589457]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rddbxdrtXcR7",
        "colab_type": "code",
        "outputId": "4ef71e04-86f9-42c5-9180-6a5d78b9f9a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "temp_model = keras.models.load_model('vgg16lowAccuracyModelDataV3.h5')\n",
        "temp_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4554041406313578, 0.8660000001589457]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 260
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsXuB6NbXmGB",
        "colab_type": "code",
        "outputId": "1b592383-1f5b-4662-f405-00085643156b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "valid_ground"
      ],
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 1.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 1., 0.],\n",
              "       ...,\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVSvyoZ8Uw_y",
        "colab_type": "code",
        "outputId": "e9c3c0e8-5926-4248-c79c-e20938c23b1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loaded_model_low_accuracy.evaluate(test_three_data, test_three_label_one_hot, verbose=0)"
      ],
      "execution_count": 262,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.45106768830617266, 0.8799999998410543]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 262
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAY0LGCNSMBc",
        "colab_type": "code",
        "outputId": "e2ee7d2e-f47b-401d-aaec-b9d2a961f9b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# loaded_model_low_accuracy.load_weights('weights.hdf5')\n",
        "# loaded_model_low_accuracy.evaluate(valid_X, valid_ground, verbose=0)\n",
        "loaded_model_high_accuracy.evaluate(valid_X, valid_ground, verbose=0)\n"
      ],
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.825879515329997, 0.3953333332538605]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jsm88PV3TlmL",
        "colab_type": "code",
        "outputId": "8e5cc7d5-ff73-40ce-af89-80d89e061a24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loaded_model_high_accuracy.evaluate(test_three_data, test_three_label_one_hot, verbose=0)"
      ],
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3.126735850016276, 0.3673333334128062]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-dQDj8vPnXC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7c-A-a4SS6S",
        "colab_type": "code",
        "outputId": "67c8cb55-5f73-4757-91f3-52ec3b349791",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loaded_model_low_accuracy.evaluate(test_three_data, test_three_label_one_hot, verbose=0)"
      ],
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.45106768830617266, 0.8799999998410543]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzbIXvcEoNMj",
        "colab_type": "code",
        "outputId": "eaedf71f-9f44-4e3f-f872-10b1f9a1ca19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1074
        }
      },
      "source": [
        "loaded_model_low_accuracy.summary()"
      ],
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv1_1 (Conv2D)             (None, 32, 32, 64)        1792      \n",
            "_________________________________________________________________\n",
            "conv1_2 (BatchNormalization) (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "pool1_3 (MaxPooling2D)       (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2_1 (Conv2D)             (None, 16, 16, 128)       73856     \n",
            "_________________________________________________________________\n",
            "conv2_2 (BatchNormalization) (None, 16, 16, 128)       512       \n",
            "_________________________________________________________________\n",
            "pool2_3 (MaxPooling2D)       (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv3_1 (Conv2D)             (None, 8, 8, 256)         295168    \n",
            "_________________________________________________________________\n",
            "conv3_2 (BatchNormalization) (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "conv4_1 (Conv2D)             (None, 8, 8, 256)         590080    \n",
            "_________________________________________________________________\n",
            "conv4_2 (BatchNormalization) (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "pool4_2 (MaxPooling2D)       (None, 4, 4, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv5_1 (Conv2D)             (None, 4, 4, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "conv5_2 (BatchNormalization) (None, 4, 4, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv6_1 (Conv2D)             (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv6_2 (BatchNormalization) (None, 4, 4, 512)         2048      \n",
            "_________________________________________________________________\n",
            "pool6_2 (MaxPooling2D)       (None, 2, 2, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv7_1 (Conv2D)             (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv7_2 (BatchNormalization) (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv8_1 (Conv2D)             (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv8_2 (BatchNormalization) (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "pool8_2 (MaxPooling2D)       (None, 1, 1, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              2101248   \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "fc3 (Dense)                  (None, 1000)              4097000   \n",
            "_________________________________________________________________\n",
            "out (Dense)                  (None, 3)                 3003      \n",
            "=================================================================\n",
            "Total params: 32,214,051\n",
            "Trainable params: 32,208,547\n",
            "Non-trainable params: 5,504\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UijDrRL2khSY",
        "colab_type": "text"
      },
      "source": [
        "## Create a plot for DI score using function createCNNLayerDIScorePlot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lyk2-GAGaPnV",
        "colab_type": "text"
      },
      "source": [
        "#### On train data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EN1yVJGzUzXX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# layer_name = 'conv2d_2'\n",
        "# model = loaded_model_low_accuracy\n",
        "# intermediate_layer_model = Model(i4nputs=model.input,\n",
        "#                                  outputs=model.get_layer(layer_name).output)\n",
        "# intermediate_output = intermediate_layer_model.predict(train_three_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_GyLE1oSbGn",
        "colab_type": "code",
        "outputId": "b0a2e102-2cbf-455b-ef64-98fd128dd76c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "# layer_names = ['conv2_1']\n",
        "layer_names = ['conv8_1']\n",
        "all_di_acores_low_accuracy = []\n",
        "for lay in layer_names:\n",
        "  this_di_score_low_accuracy_model = createCNNLayerDIScorePlot(loaded_model_low_accuracy, lay)\n",
        "  all_di_acores_low_accuracy.append(this_di_score_low_accuracy_model)\n",
        "# discores_low_acc_model = createCNNLayerDI8corePlot(loaded_model_low_accuracy, layer_name)"
      ],
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  1.3071405137299335\n",
            "STD of DI Score:  0.959610188272944\n",
            "MAX of DI Score:  2.5684471674581775\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AuM8V0DJLaN2n07iLG615poyylVvMcKTsiZz9tmF6tSwX3OomU9TJOiUTp2X8\npLIHqj4WBkbV17aplHF0htbp1taWrt2wo0buCi0T4dwB/+UOaRn9+zhSwrnvnF9oAtWlpEHuRDAw\nHu0x6ZfZ9vMbapQqS88ZSbmaOzWhmovHdMfGnU3Y47Zydr1TFdcjJba9H3ZgfLLU7r9gb3nq3pfG\nt0lFhZQ2xnHuCi3TIPfOuLcS3bib1tuHxZ536oHEO4T0Q1QNnWLd+6TTMjKp1WN+OFQ7lmZla8u2\n1XYD4rXrd1R5FJVL8WEY1nUyyJ3nLKFldkWRu/+74dzrc/OMfpv0BJocxcl2lqNY5N4EBIrmKYmk\neuN9YXiGEpFFZ5G7nXhPtCK+pqX6jBfX9rlo9crOw3rwzajIHZ5uSyFTadzlbZ5X+PSF4bB1qN7Y\njU8bd/24dth3v1LytELu+m+7Yi/U03ifKYnF7iiROzftRMS2651Os7t8nHPn9ziHhEv1LUHuu+YG\nKnKXHV5qJDlKBMSUSE494P8zNjIw7uT2DJai+WtPSqQxz9FJMaqPi13sZe9Jqq0Xc+71pz6h6pdh\n59S0a2k20FnC2GLTadzFndbiuCwMTOtQvbG2kFvEpJ8ID/HhuuoWWH++8bmPao5Fjbuo1zPIijol\ntIw9N445soZNexm0ujVvBJnV85bJvJSlyGhhaJKG1xiDnfM+cn/KUYfgIQfOhsidfTGoIlo2+k4Y\nqMkRv9Ql5/mibVoT49x37FnA6hNOXRTfbFlnbk2BMQYEShp4O9dRMkr1vWXi72KKqpWjv2Gi3lF3\nRxtHssadiI4lonOI6Hoiuo6I3q6kISI6kYhuIaKriehZi6NuXZ/4HePc2yL3NqtYrcSRe3hCWxWn\nCTcoceNepofTJ59uYRA2zrZN0Zh4ntQ0SIpz5/rkXo42HPdHv3drcOyH92/DB067AXsWqlAP3Fvm\nzc9/NJ772IcpaNnSMpV1f9VHLijWt61oKJu3z5xxDzh3xCkRO2E4yQlhK7LOAlameG9gezuSyN0Y\n3PXgzuwqZ21OyIpsanI1MpfloGVK/NwXALzDGHMFER0M4HIiOssYw5dkvhTA4+q/nwTw0fpzUaRs\nQrU9co/TMnED32YSlrtCao3F5uETMv0eqa5VQV6+dFqpu9lgITXJpCLrdo3RwDQoq6Qs9/K4Yyn7\nlLPdkktNXe8PlXC+v/lfl+K+rbvx2uOOAeB7l/Rqzjd2T4gUNDdhWkZrCnYhWKVDS86dqGgjm0kI\nX2AlO6ESV0hCmYEvoSCHQ4Of+vtzAAAvevyq4LytRkaFdHWEZTbGHQaXrN2I06+9D+975VMA7KWu\nkMaYe40xV9TftwG4AcDRItmvxLFXAAAgAElEQVSrAJxsKrkYwGFEdOTEtW1E0DIK5z4/GLbe+zR2\n34dG90Cx59SyMrSM1kZtWRyp94nUYXPAuZvU2biR5mEInGuXTvEU2QDjG5u0Vk64UUq9wFlaRpzn\nHb+8Bcc+9MAgvwQKuR2zAN/P3Vb/gscdjtUPO3Di3jIaMudtMOsKqSD3SSxiKpEZ0a65lHHulJxQ\nteIoyHgaDgKSi5hsmuZ3dUSbkHehJoDXfeJinHTh7ayOvdwVkohWA3gmgEvEqaMB8EDpdyHsAEBE\nbyGiNUS0Zv36sqXKuh7+b81neWFoApSSkzg6jxumOOUeR6lABLnXnzMCuZfQMh6VougU0/NfvhMG\n5YqVVeI9Yeq/VOflp6+OxaJClpTBRb48vG3IZ5Ia4dhzwV6tFJ/E5Gr/v194Ig5cMTPxYXiq3QC+\n0dRc8vaT70TCFXLSwkcIcs4pj9xNMXJ3m9UkaJnCic3Ye6Y5cdi2pz1z++rsVZy7FSI6CMBXAfyJ\nMUYPUpERY8wnjDHHGWOOW7UqHAoV61KQZmEwbLY6e8sLH1NUbpQ/N2YitAwXtZFa5M541RgtExiq\nCNrO6cnFDSu9ghspMu71KEcz0BpF0UyoQhjRiGSNu+RBEzOaWllWRTs5F3DDgHKDGedey6EHzKLf\noyVBavwy+H3XFr9JCmZJkTurRyL37IQqAFChcS9A7qVGNnzPKtH2qHXGPSzHecvsZcadiGZRGfbP\nG2O+piS5G8Cx7Pcx9bFFkZIHPFfvWvSUow7B6oetzGdAakJ1FOSelpQB46f6PcKsFn5AVMBV1xFp\nRiG4hudPqLofJSsZTZ0n0XcF6QG3OAdID9FziCugZRK+Z6lO0LYFf+Us1QuVfNGQe5V+aThWj5Zh\n91FdTCMOEU0mtkyJeLSM7GQKVCCk/dytyBj6mpSHH6g/RdnaNo1u5BuW7QKHFVU7ESnxliEA/wXg\nBmPMhyLJTgHwxtpr5jkAthhjRtklokhKHvDCoDJLPSJ1EkqTlJ97WyM+CnLXssRombC+dIUlQMVy\nhjE/95Il4sbUeZSkqQnbUuSeu045oZryZ0+5ZtpOgSPMmpUJdCDxadP2xoxcqUmuNF6d5qeuIeSl\nmlBN0zKZzHaEVzShaj/LjHuqSGfU/c/khKpyznZmSxk4rMRb5vkAfhPANURkgzL/BYBHAoAx5mMA\nTgPwMgC3ANgJ4M2TV9VJyQNeGA4xNKZ6yQp9qOKTpjqHXp2LmvfWdTUUBdO3H/FmCP3cec1xI5oS\nDc3wIyVGwKbXOfd4Ps/PfYwJVXk+GYZCfQYWmdW0jODcKcG5w+sIqpWfE6dlEu1Gfi8BBanwA5Om\nlVK0TJZzh6knVPOS8jeXaXJS4lZpRcbu52IvfSknVLPG3RjzfWRoblO9EX8wKaUmIfMDx/2WBkYa\nJZTAqD2xlsuhWCf9XkUFzPbJ34kpyKt/t1LSOBcUzp1f34Er+jhgtp/c5d3OT2gIUVvy3dAyiRWq\nuTK4SFrGm1ANqKywLJt8QVmMRXAhBrhQw7n7aXuLwLl7NFmPqsVYkXuiGXd5a1Oc+4p+D7uGk9s5\niNMycjpgkq6QJQY5u9K5Kav8+Wmcu30XXGyZ4uLGlildoZpPszCoGj2hHLmPQstoD8sk0vM0MZGc\nOxC+qMEsfsbPvaRNqd4y/Lwx+IWnHhEdQjdDT0RopwQN4nPbcR1zE1IhLZPg3BOdoO0UZKdjg4Nx\n0Th3ogqdTpyWUZB5rAadcw8nMmPGfdJ7gaZGfrF3dG5hiKvv2szca9tw7vE0/LmkwISkY1KPU+Pc\n7de9coXq3iglEQftcLyXGHZKiQcOS+SJzJqP8gg1SiNm3AM9MrRMCWLgizDUOoZ2DkO/nzM9ak/L\niMYvvwc6joHcS/SxxS8kOXc/j+PcWdp6E4zFRGqWU/eNifuuInfltzS61hc+Fxu+rXgjaHFfYlX9\n3Wk34JUfvgC3rt+eDT8gi04BgRxyt9W4kBzpd4OXyQ24G5nmdZq0TKVx1yyH5PDmB5Zzj0e9k5Jy\nd2yD3FMrWnmZsbI8zj2G3AOf7Qxyb8O5RyiewdCg34sb39l+D3YRkyYpP/dSzj0XeKnNhKreCVbH\nnCukO9f4uUvjrihMVP1NnJZhxVnj7nfsTjTj/oQjDvZ+a3NSJ//O8VVHPWE7xEMnyKJjz/zau7cA\nADbumGs615yUcO58QFdSZhvkLmkZwNmnjpbJiH0Y/uy7n2ZhWBnkHpUjkLife7zHjkVgzD3DlAGW\n7neA2wIwlj9XX8lwUAscxq9kYKotzVITcKb+p++6FK+7lHPPL2Lyf8dcIfsR42UP2U7BWzmLtKeW\n5OfH3edVE/48mvUPkc5Y0io/8aiH4BVPP0qUGM5JrVwxg2MecsDEPTu4PrLokkVMQN4fHmgXJK8q\nM1GvKDN1R1Rvs6YO8tIshUynca9vFG+UsnEsjIDcYyirPXLPc+5nXBffKZ5ra69xdkYid19yyL0k\n1KhKYbBDw6FJ0jKzfWpcIUsH9A3nzo6l+uKcEZAvNEfu/FRl3OPIvaFlEgBCCokfi+Etw1W2+xX4\nC9jitMyTjjxYvX8yGFk16gj9+ccVfi/lc4oZbXt4aMoMOzCZCVUrAS2TdK7wP/n35XCFnE7jXn96\nKzkDWsZUDQLjr8CrkLsuMXol9wh/cOdmtR4AHpSw15WdUGW/NbRaFsc9LIvnyiH3mV6vuVeF76Hq\nLVM6Aa5JEH6Ax5Zhx6vJTkUfY/OFtIzlfAM/d21CFdTaW+ahK1dk0/DSVFpGoW08ncSttRO/8hjJ\nyiYgMwnjHntF7UjJOkeUUSghPSKlGEHXyb502TqsPuHUZkOOlPicu9+OugnVjNi26CF3bULVVIZi\n3ImhConHUb2UwTCePiUN/8yOWUMaehr45fOGvGc+NO5t1Il53gwsco8Y34aWMUApdteoqFTO7CKm\nYIWqnn6mF3q9cLHIXQY0I8T5Ypm2rbfMU446BG963upkGtVbRqEB+HmukxZOIVgtinpuoVhzFD1u\nHg6hmJYhl97OY+TEgZQELdMSudvN2NfXe9em84S6uMBhRdVORKbTuNeffRGDhYv1/yUafwVeCrmP\nOnmZKktzhZQozKa94d6teOf/XOUZNbsLu5e+xavqI3ff0Kc6S0vLAKY1cvf2bS1IHxNp3AcRPqqX\n8WSxE6q8XVG9iCmgFBTLRqhonLasTJtRS+Mtw45x1bQ4MoGfu/J+OK+gVoggKxyMyfYYR+51egsC\niqJCFiB3dlKjI6NZC65TDxxG0XOLJdNp3BuqgtEXCnI3qI1Ry8k9KenwAzHkXl6+K6sS3oBzfu5v\n+ewa/M/ld1Xb29WyWxk6tjEyMWNhvWVinWW/doUchXPnRabuXe6+yuH2fMRbJucNssBcaa3YeO7y\nXup+7lW7a0PLVD7nuVSuPNv+fYPBzktapvbT944hpNkIi8O5e7SM7HOzK1TLkbu95fze/9yTHoEn\nMk+h3O5eDfoP9CinN/n3foPcO+NeJF5oXPHU5xZc+AF1M+oW1neYgO4aMEyFK2grzrjrrVq7DG0F\naRvEEBvmD4xBr6d3lpWOPTeh2nKwxNOn7l3uvgZ+7t6Eqvvei0yoNvm0RUwxzp2d58f6PVL3GogJ\nocRrxH1fUUc9jXnL7Ddi+IGYP/+4wkfaxZw746rLgg+4NpLyiPGQuwoA8ug/Wr/CuWs6LbZMpXHn\nL5NtmLKB7p4f1kYmnEQC2vGJbUOHlqxQTYlHy8QmVJvZ+1CP3WNy7n4+l3E4NOgnvGUqw1S7QrZ8\nEblRGwu5B4uY9D1de5QuSzPuvRo5hsidgjos575HochiQpR32+VVr8isUA33KVXqhAZ+bAydyRoi\nzxVSnIt1am5CdRTOXZTFMnN7nkLTqRhOMdE495hOiylTadyt3eCoQz703QsDpAL8t0Wy8cBh4bHB\niMZdcwtsOPeMtwxHiLsV5N5mpBKlZYS3jMbftkbuDS1TxrnnkFSMlpFUUi8z2anRMtWDCfOR+Ky+\nV3MTe1oj93Qa/hwbV0huTFjassBhCi1T00+23O/dvB6rTzgVd2/elS0vJbwe2R5LokJauignWshf\ngv98hh7nHgdDKa+0mGgrVO3nXhU4bG8UHqhppkeYQ4g+9sxbzl1vEO0493h63Vd6NNRjG4U2oSpR\nWNNo6jyecVfQYqs2Fb1WO4dR60aEBTH0tb9a2na/M1Du6YfOvAlEhA0Zb4WQlrFGOjTuqVsyn4gK\nGXhaKJy79XNvRcu0WJMBuDbhGRP2PfCWiTyV2MSrvD83378NRx92QLF+Urg+sdFPTAwqxFByd7RF\nTBL1c4OusTL2kOzITzz7h2HiSP2Aex7aCHuxZTqNe/MyMVqG+4b3CLsXBs0iJq1BtLJ1Jm6q1UVM\nI06oWmkzoWqr2cN8rLQJ1VbeMom0NkolUBs+Ly42Je+VWlczWnHXfOv6HUG6E8++pag8+aJaekWb\nBE4i92HYKVj0F8vmce5UecvMtfR9K9qRKHLsDz5/BR7cOd8cl/M0UW/DwM89RpGMJ6lFTFFapuHc\ndW8fTTRvGRmXhoN1LbhczBhffdeWvALeojL72XHuRcKfr31pOcI6YLaPPfODGmnqjTV2k3UjEKc1\n9EVMoz1A4yxdI1FaRjSWPYyKUSdUW9gYbbaf62PV0xa/GFhapp0p4LedbyzcVmLIXY7sej0ke3iV\nc+/FXCHrT/KP9ahdfBad//ZFeza23Zx6jb8/TrDyVKtT4bGbn9FObDTxFzH554omVKnMFVKOam05\nPC+n73RvmbCDKJUU597mPRxXptO4W+QO14C5odl/to/d88P6JkeaA3sAfKipxX5PxYoZdYWqqlJo\n2xvjEvCnooI9Huc+2gpVVafAH9khoGC1I2rOneV59bOOKSp/UhEI+Ys6tzDEphrJztSePE7XHOdu\njTvPU49ORFo9cFj77etinl1cjPI9dhma/7pWpzxMZF0hRSdWFzAq9vTWMhS2R/v2mhbIXTPMshPL\n+bl/9uI7sHt+MFIUR69dmcS5RZbppGXgrLttwPyl2H+2h93z1YRqj/QGwW/yM449rJksmu31sBu+\ncbSThKUyGNG6O/7ZKWyvT+6jKl/s/IRqCz2SyN3d/8AgWx7buHv+j695Gl737GPxqx+/KFvXJIQj\nsrd+dg3OuWl9rbfk3NOPSOPqG+OmU+7BsfZhFPIhbbXwviYyspTPR+2EoPu+p+inUYWrI41crCrf\nA6mQcx+GdZAAefycRst86+p78ciHHjiSMeYdgoxJ0wUOy4iP3DXj3seeBesKqWN3e4+ffsyh+PXn\nPLI5riH37XsWsO7BncFxIOEKOYJ1T3nLhJx73Wjq3x5yVybx2nHu+nfAR+7a4hfLuTfovkeNV0eq\nrkkFVOIvljXsADCrGLrkClVLy3grVKHTMrY9kp92JOTeokN4xrGHAaierXb7AtosUqd2TJtQjcl7\nvnEt1m4I50mk8M5O0hPFj78EuWv8tsiXQ+4A8ODO+ZE2tNaps/DcYst0G3dy4Ur5e3TAbL9C7jCe\nMeJi7/Ernn6UmIzVb8mv/MeF6vHoIqaRkHuVSaIVIO7WZuvh/tS7xl2hGvG+AHyDpXHuNo+cXMzV\nNalGH0NGIYpNdyiDSDx3DdHq2+zp6ytSUor2n/XIw3D7B1+Ohx1UBRqLDRSDJqMZcuVwY9wjcwtS\nTr7ojqzOtlwrUt8S8BGr/yEHzuLlP35k83uoGFISCnDj3sbPvUT2FlfIqTTuYC+TNTZyQrXylokb\nFud26A9L224tFg0c1qoUXzSdpV6y+ezJ0DKt/Poj3wGL3Ov7ryBDUxsaf3JxMnx6icReHo1KSd2S\n+Zi3jOIf78CGf4y3yYP2yzOgMo8mct6gOlYWqC72HIIJVSJ1bmFc8ZC7pGUilYWB28JreNKRh+DA\nFf2gLLmVXpSWGWPHJk2890eAl46WKRTinDufUF3Rr1eo1q6QGnKv77Hk5Es307Zid4rhkooimRLN\nLdBK1BVyiTn3KvxA9V0iQzsJZye/3PF8XZNq8qlOjHe5PWXCkIt9qbnuuX0w5WXyNvnHP/tj0bpc\n/jza5xuhNCMllN0/nYLReHiL3P3joxom67DgIffiCVX+XX+XieQKZ0vLhOmslCL3kTh3Bbk35zrk\nnhbHuVNDo3DEs/+MnVB1Hg5SXJQ5f+g9m9uRQchn2JDUdjQWvbYVm0dH7vpmHRrnPm5sGV2rSvqc\nc08i93ad5MRomShy93lporRbmvOW0Th3P21sJONt9FF4P0pcIW0KN8Fr1OerjayCNABWilGF5kED\nOKqqrdhXykfufpqYsfcuIaJX4MNujbu3BgMCubvvqU3UJ+YKuQx+7lPqLVN/kkOP0hVyzwKLCqnY\na2dI/cfeFrlzsYt6Rg0/wDOd9acvxLX3uFFBGH7AeJ/cz31cWsYjfUS2mLfM0Ycd0KA9G/bBShly\nn0yjjxp3+Pcgh9znVW8ZHSjw9sjT8rwxo/2SJz8CAHDW9fcHedTrYFQjsWMlj1ctmoDHrjoIr3z6\nUTjlqnvqQ6QmbuL05KvyxL6bSW+ZiP7nsklxS4tJkW6OBsDmnXN480mXiTQuUTFyH4WW8ZC7T8t0\ne6hmhD9Ii9y5UT5gto+5hSEWhsOst0xAy7RE7lzcJrjlpurg/V3/yvM87hEH45ef6XzEY5y7/eQr\nIccNHJZKG/OWueCEn6l52oZbaiTFucvGP67wF5X7eWsGsCTeN+9TY7SAO++n9SafI8adQ4sS427L\nBlza0luX6ph+/ilHuGMRhGy56baPyoKAJHIvKCemV3XOL3v9Nj9MhUTuJd4ygD4iyolXnKAdO1om\nI413ApFbocoe7gH15Mru+WHUW6Z5aGKipe2EKhery7CFuwyvzakU6iBjczeNpv7kuy89uHMuyN/K\nuEe+A+kVqvaEgTB0Bcg9J6WeJ55xF5tDBMg9Ufd8w7kzaqUXoQUU6F4Zap4mYtwZuKg6BP+8vG4O\nG2y+kj17efpnPfKwQC9/Alx/ZqN6evTJva9WRom2GOfc/bkKYwxmgl2odOoGSF/XaK6QCudeH0tN\n3k5aptO4s2GpM+7u/P6zlXHfNTeI9vYNLQO/0clG0Ub6TSCnFmiKfANkdZLy9GMO835LDs8i94cc\nOKvu89guCqb+HagRqB1mq3HyEVx8Cd2cm2CTO1Fp8phVK73NOeS2bnLFYjKe+yCkIFITemFa//7E\n/NcJbHkNhZ2ARPKG9ZweLaO0uEjfi6/9n+fjjc99lHeMREIVuY/KuWu0jDByRa6QkVE4AWJCNRIM\njeUtQ+7pmP8x0Tl3e64z7knhQMm+9HzYa4/tnh9EX0hHy/i9/jibaTfIfUTOnQ0mAnnq0YfiVc84\nKkhruWHr5/6oh61Uy27nChlyhlasrzcQmVCF79FRHU/RMv5nTI46NB2N8MePPhSPf/jBXrQ/L8Qs\n/Bc6FxXSGjIPuSvGF+AjSXaM/AiPqWbFOwd5TwPjDt7+a1qmdKDo6UOxU9BWrQKjI3dOy3z3HS8C\noE2o5ssR02PecX54aEz4Hou8pe/DuMZY0o6jdpCjyHQadzaUtF4kclMFoHow1XcNYdqX1zc+49Ay\njavcsHyFqpwIAuLGUDtqXSAtLfOohx0YpBln8xCtw7HftYVBML5HR778POc+288vywcq6ooj92Ce\nwvN7Tr+488rkYUwHTqs0x4Ayzp3TMsrkf1Cn4emrTznH8/NPeQQ+89vH68pGdM8dA0anFPqNt4y7\nn6XhB0p0k6NvzVtNvlOlHdW4G1ovp7fMVBp3KwRqXmBt5WRq95ZFmVDtuXpLhauWQu7Vcb8BLwyG\nTV2Wljn2IZpxb7tCNfx+0Ipq4nfn3EKjcwiOmJ+70hmodRXokwsVYOuY7VEzkgFytIzj3LWht+bn\nHottwkeSXJ8SbxkOLiS9EMunrYjlRuOxqw7Cix6/KjBoWkfl0vgdk6ZtbLPxnHDOXY4IDj1gtni0\nLOPDNMcVPj1YXSvQfalxT7lJlkgzMhX0zFJI1pIR0aeI6AEiujZy/sVEtIWIrqz/3jN5NWWd7tNy\n5N6mCnKDBaWMZoWqODsOcreNtI0rZCnnLo8bGM+f3SL3A/frQ0rbRVX+hGr166Daq2fHnkFjgLQd\nfCz/K1FstC6BbDTpUzqCo61jpk/esNd3a/UnVAnVfbl9ww5ctHZjUN78QDHuzX+ycsUAC7ovt42c\nLSYMcCZpGeNGThFaJjfC4PXKUYDVXSsjttl4VhrO3V0tB1emkFeqgJrW2YU7ecniCBAdQIniBjv2\nLJQkjJdgR6ZcuSWSEj/3kwB8GMDJiTTnG2N+cSIaFQhHLjYgFOcqJYJSd2JyhXkNYxzkPgrn3ga5\n88TGwJs4tZy7NnE3aOGaCQCX3rYR92/Zjd974WManexCl51zC4FxadQj92KVIncraffLguEsEWb7\nPY9zl66QHIPZUx8/71acd/OGoDg3oSqBgm5c+KeVfgRwqJlZHd5pcV4z4rEN3MMJVf0dkWqQSGul\nQbsjGijNz93OfRTTMtox8svWgIykX7Wt9TTZPrZxF59LaN2zxt0Ycx4RrV58VUYTi9xnBFq3Et0g\nm02oerTMuIuYUHPchXk8NNHAiTzKM/CNu+XetSFuRUmUN6rPXXwnAOD3XviY5piNjbKrnqTW6orv\nxJTE7ux/XXo9ym5yQKjmX3gYBu75VHU6nHOvRgNzC0bd57ThlwOgoNStoF+rd/O9wLZLBAqE99go\n9dlJbFdOpP3IUQgc8JExXLQinJ97OwNli7KbnQAOOcfCKKvlREYUEBPAw0h7t0kO2m+m2GiPa9yt\nOM59IsUVyaQ49+cS0VVEdDoRPSWWiIjeQkRriGjN+vXrY8myQq5lqjsxaRssSOHhB3iDKdlUOCZW\nl8Ew79rna1iJa/CRlAK58yHjXGSv0KpcM/LiCXsZb37+avzKs47G773gMY3KpTsxJTl3gWw0qXzS\nM7QMVfefT/r5yF36uVs6w6hc8rziChmj+BxyF4bZGxHGDK4fiE0mk/eYR9zk4QeKaBmEabRRR4zb\ntvdp1DlBjp75fsExV84gP/R3WfLpmgMB95r75WcejZUrQvpSitvwZ3SRekwqtHWJTMK4XwHgUcaY\npwP4dwBfjyU0xnzCGHOcMea4VatWjVwhb5BayF/fcyYd8rfX8xtM7CUskVxgKU3kRBBQxrkDxkPu\nthFq6n/se2uxdfdoCMS+dCtXzOBDv/oMHHbgCobElPSmyiURabz88JuUOqpDUqq20Ity7gYy1gg1\nXiaaF4ijyNI0iZdGUh2RNin15u05nMfQjRmvTq6r0Ay21Cd5XZERyqgrVPnkbTPaaGgZ+7u8HP2c\nD5Jkcbxj6VHZepZJoPam09obJ1RzYozZaozZXn8/DcAsER0+tmYJ8SZUe6ErZAlq5BOq/H0ah5ax\neUd1PXTumflhtTHAjjnX+Nxy+TDvid/N79ieE41DD/lh5zueMiRcSpB7v0dFEQlX9Alzg2FzH6W3\njES3Frlroxqto+31YpgWQdqqDh9kRPMxayzThStUtfLFhHm0/bD3IpFU8tNWbBtr27a1uhwYqd+Z\nwnJUfSE4d4QImVNqpVsgjjuZCoReMksI3Mc37kR0BNWthoiOr8sM3Q8mKM0wFs67xV+gAvY9vYhJ\n8qhtoxlysXFuBsNyXtIbTmbT+rrt3BOuRJ3UXqRWVD/3KOdeZQgmVFvUo0mPKEsrVRu32PtvjTtD\n7oKWscZ9aHTkzmmDJg/ixsXqoB0Hcq6QNn3YVtUVqs05d8xz81Rr0pXW5gti1zky566ALdl5lox2\nU6Dndc8+Fg9buQKPe/hBRfM+JfdoIsbdfjbrOfYiWoaIvgDgIgBPIKK7iOh3iOhtRPS2OslrAFxL\nRFcBOBHA680iXwF/meQLDZQNbY13LkQ0o0ifNdxyV0j3PUvLCHSys0bufCMIbgx+5VlHlymREO0y\n4sjdTu7p6fXy8waj38u7QgJu5GSNkKRlOPq3USEN4pucA36HGuXcBU3ijusjSN4+PTaE8jSQYeU6\n/lpMqCoGW/52Hmf+p9VbNe6D0Th3/xqdzvx3ES2DWOdKeNTDVuLyv3wJjq33PdWRO7Hv+fq2TQS5\n+6OdpaRlSrxl3pA5/2FUrpJLJ+xlsq6Q8x6f6iTv565TDqOIpQHaRIXk2rWbUHWc+8r9+g0/6IVS\nGOdiWD1Sz6ifOxzV4fu5x/Xg7+ARh+yP+7buDtL0KL6I6TmPeSguXrsJgAuLPD8YYv/Zvj85LtEt\n2e0QjbqgxY4UgrahceCgIG2lt/vO79XDD94P927ZXefxDX3QQcjKjJvP8PhrD7lHEK5nwP1PnoUi\nZYy6QlUbxTWce/2ISif8YxOqVtxEucwny8i/GxNF7vZzmmiZ5RD+Mjnk7jwegq25tOfIOL/wwY8m\nTUNVkENMJI8uj4nULi0c574ygtzHiZPD6xFVZzh3f4PsbPmMc4/l6fV0dP3EIw7G8x7rpncsDWMX\n28jJcSORez3C0uyKNoqKXlMBUvbjzMQNunZPvWtg5RI75nPx1efxj35YVB9p08MRBAIZDA0uvHUD\n3v11dT1jkYS0TI3c6/OvePpRSq60Xv4xSkyopsuRskOhPdvKcvq5T6dxbxq3mxjhXhIyzoxmsDmn\nKjdkGFWa8APD8uEXr05DyV5a0RHs3DNAj6r49VIHYDL8e9PhKOeCbfbgUFORUQSnZeJYKhWelyNB\n29Fb+oBvdl7RL37eK+7cjLNvfEAtV1tyEF3+zs77xwueBbPumtEp4fiDyeL68xnHHoYf/u1LcUi9\nulh9Jg1VoXc+XBaGBv9y1s36dSREG8W5dSbV59AYHDDbx8t//AiZPVKSftSueE25Qpbhdgeexlm1\njqZ9V58jRnAYSabTuLMv2oSqt8FCbkIVOqIZRZoJVaNAh4j4LlwJSyoOV5z7ACtXzETR+iRoGXsh\n/mio+q5y7vbSW9YtfVOfpcAAACAASURBVOO5xMIPyGdr28J8ZEJVIndA35IQYLSMMBwp5JhE7l6n\ny9KwLkPrPALOnd0nTsvEECGnpjTO3f3Oy6jb7PE6HHKvPnkIhZwm2pyEzGKBgLaJeSngsGKL2H8m\n7xOfK2M5OPfpNO6sB7aNl/OBJbSMYUZLTiZd8hc/izc9b3VrvdweqqMNvpRFkVGpOPcFHLCiL3hh\n3dCPKlp/Y7+HaJQaN7Ri5F4wXCXSAz35dZBrC3ZBl0+5B5x7SlRvGYph95jefsfgvuvtUzNesj5/\nJao9Frp5qvponLuSJ2ZER95oQgFPRtzfoTHZZxJD3LKtaQ4NVcfiOsU2z7E/BnL/rU9dil/9+EXN\n773KW2ZvFp9z1ydUvc0QmPDJS9moHnHI/lipBODKCY8KOVqQ/xAlc5GHd88PauOuo/XowpkWbbVh\nJ8TLL+uyx+VLW30vgUlxvfo9nZaRRmiGTagCvsGT+XPb2WkdbQy5O33iaFgurNPSaW0x8HNnlJcL\nd+Ej1dDjJlRaM+ruXHqFalsh5QePLWN/c2SvlhMdOfn3tpqDiIOBeBgDXcYZAd+zZTcuvW3Tkhp1\nK1Np3Pkw1nrL8EBAAeeuIXfWuGINvK3YRtBmUwPJo1d1R9LyCVUD7FkYYkW/56XnlFRsEV7JPp2h\nTgz1NeXrRirg3AvKT03C9khfxCTTz4oJVZvl5U87MhymJ3QCnNuk3HREyxd7b2N0WZwG1GgZgdxZ\nJ2jPtN1mj/9oDJ5A9dqzmJ+It4wDQIBPy8RCKvO8qY7K1qUid/aex0YAMZmkY0IXzz0jfBib83OP\nu0LWZSCkcfhnG+mPQMvwejQqIJYWqIz7frO9KEqOTeK1aaspJB7n3I16T9Xy7YSTcJ/09Y1w7vA7\nZjeh6iaxnnjEwTjmsAPUlz0l6nX3MshS/mYHpIcMT8PbXLABSqImm0/zDMkJR7EyT8z4jcq5a3SQ\nbOt2Qj31XEreCx6nX9PEfrRZrDhOSBIrkntfCikJ+bvXCR9SzoqFK/Y4/649SN64fJTpI5o20sRz\nH7YJ+etq0hbO+GmdGBjMLQyxn5js4UPI6L6dRGg7taMhcVk8gdxchjgek5KJpiQtw8q2bcEGUeMe\nLxL5595tjZaJGZ8YIOA/Y7SMHBEFpYsDfIQTDRwWaz+JDpdEukly7tpIxbn9umsAKdcfKcc7zr5b\nbxl1QrWx7e3e7nH2VZbSTajmpBlSEh676iAAwLNXP7Q5LePMqChkqNMyzff6y9OPPQylMsM599Lw\nA62QuztR0TIDhZYh9TuXVsi9qTzUQ0OqjSukdk8zEqdlIulZHmNMMKHqhvoh8s/dA20nphydFXig\nCGOqdQLyuKwiDD+gTKiK9lZiBIO3ogS5j8q5K20nCD8w9J+nWk5JpwU75xXm5a92m5H5RJB7E/K3\no2WSwm/1U48+FBec8DP4zec8qjkWLBJRno1hqE4bMtvPQ/afwa8ed0yRXr2ea7jlyJ0rVZYHcJx7\nSMswfSItuHSCiCPC5OpGdlx6blR5E3U0dcXTxX3E/dd9ppl/aUptnr/2speJbqAj6kR/8xFizFum\n+i06iIRGDeqFRO55/XinWOXJX+fI3jJch/qzmVBlC/+yVEkRcncL6bw0svNqYa8nuV5kKaH7dBp3\nca+PPuwAYdT4d30ajNMH/pA0rKN0AnKknZg4GlcoDT8tvLQVLdPzyijblLnUuGe8DiLTgXKbvZQd\n5duQxfRKhstlp2ZnQm8ZSbs1ZRa2fNkOWkWF5PURNdfhX6drgeV+7rZMd8wk8mj6pOqR8ZasLIzr\n5846DbeIyb4z7RG1K5jXUW3sEsSWgRghtaBmJhLGQ3wuhUyncVcejLbIpvoO9e3mtIxOOYRINSc9\nRsuMIs2oN/pyso7AesvM9BPcbqScwusxzX866gvKIYb229SB0DeeS3zuwL8nsz1r3HmHoV9v6cvd\n5rUOkTtrQ3DPpifupU/L6B2mdox7nvj7w+Y79UDXzLUA5VRjUK83r+C/I5xzJ6SfS8mzINsG698P\nOXC2OT7KO91nu0eNI25CtaNlkqK/rE585K4bOc/P3Wt8Wh1lT5e7QhbHlvF0CofIXloPuYMhd3fc\nM+5Rzr0UubuXxEd9FoEqeeA8H2R6vQ6XL5YshrK9OohcVMgGuZvgWn/lmUfju+94UYt5AN5Z5l50\n/6Q04vZ01HMGYVsNkDtM0LmW7h+Qon+CtMqxUe2SXpU/Sm1Gbgm1ous/wJ+RpQar8u3o1XeFLMft\nk1nlDdjrncZt9pZUtNvNn4HvU6zP/A8ZctdsoG84yvQaxRWSV5SNCsl/2AnVmV7Unzo2EVRKIXL+\nXEd9EmWise6xeYBUZVHknlDYo2VE+IGhsaSHS/TEIw/GY1cdVExNyXaQypXi3AHXzgID3qQPzY7m\n5y7LN+x/TQ9Zj3qu4H5MBHTW1dhRKo8tw/q/VNbwOG9rIEGLUvN/aoQUk16vPG1KSlZiT1qm0rhr\n4vfeviHSHo0f8tdrHUrZZeKMe/mLwMvm+7qqaT3kbrBnPkTu3GsrOqFaaN2r0MWh10jUeJBbHaiN\nhnSxpik+oRZfaeuOc2+Z+XrDa1MrK1/+qsykUqwOX4/Uey5PBZOUzYhHHE/cW1mm5woJBiZK2luK\nlsn8ruoe1RUyrDdcoZo3uiWdVq/nz0E0z5nff+TapJM+laH8nP1fDj/3qTTuGufr3VzxMms3nvtA\n+1ntC+iOldIYXviBYldIV3Y+/IB/fM+g8nOPdWwTmVBtvGVY/ggtQ3CGppQCKGn08eF4SJsAzqfd\nKGjQGcYyCf3Qw5ya0eZ1Wd3s45CdDb+fOSPrb5Btj8kJVV0P7Rnya9OuicsEQss03+UK1WE9UTM2\ncifyXCF7/s12GQobQK9Hycl3W7ylb571yMNU4OAW65XVOwmZUuOuvWBOSkL+xsIPuI4jjnJiwhvq\nKMi9DS0zNBXnviLFuUeRcJlugDMa6j2S+hHj3JV3KlV+yhUyFrdJvqPNHIOkLrxnaZF72U2Q151C\ntOH98DuGnlI3L1OLgxT4ubOK+P6jI7tCKue038BkJgO9RUtcj4KySzh3CzAsUGo4d5aqDXIv9ZCy\nz2Km38PPPyUMW9xNqE5I+pGXh4sXfiBDIRSjPPbClD5Crltumz1+Yq6mHqQrZIm3TKlh8zcdCQ2k\njtx9dKml46JxyKX6ylGZQ4UOJckXmQ/T20p2ElK5H965xij7afwOKl0mL5dTHGWLmMJ2LrnpVBmT\nmAyUz6gBRMP4u6pryI4LcOPPFYVpcvQPl5y3TAMWek5Hrb0uhyvkdIYfyFjgWHhVLo0rZI9AXvz3\nsI42k29ELThQ+C+cbJAp2V3HId9vphc1DuN7yzA9FSOqTf6pHH2BJQ1847m+iSWqvuF2SNaWGYzM\nRNqchIi2vFeQYQY0P/eAf8/QJdVkNXnnjPE3gSg1grFzVl+l6pEk8BpCOEo1yLtCxk5Jw807ux67\nVw6mtHuGqdSyPfFOnEvHuRdKivcEwhdQRyFsQlUpp238CcAtVBn1AeqOh6FuALB7wRn32F6dUVpG\neeo63QBVI60DtGkk/xtklnVwlB1JF/Vzh/+suecFwNBgom3kJGts+bmgI/C/x+gsXnauM9G4dTuJ\nHcvDy4/pmkxs6x6xYcvImkAYaqNkEVP8nfTbvGENV53noPRz5NLPeMvYUyV0KNB5y2RFHaomkLvO\nubt8uaiQpfbANpohb10FeaxY9BUdVrPje+YtLROP5x43iuFxrUEOmaWW/DH/5CfsJGx5VEj/U5P4\nxDBHfk4hiwrtaEBrG6kYH6ln38b4SGMa49z9NOn6+GKv5trFSDFFD8V1z+cZ2c+df290rj69kL8j\nQaoYcvfLl2WXvtN9KqNlXMetX4OLLVNW7yRkOo175ljJTkzcMyVXXvEQHiHn10ZynDs/3tAys73o\ntcfoDO1ytI4gGn5AoRcq/RySjOmt1WE/Szcpib1IPWE57EpZ0jInnk9qT91kS0igbt7OYry6tuxf\n1scnq+052dai7cczgqKezG+gPOjVG44/NlqvLMtboRpefrYciCwEf5s9FawhNQrwpddrT8uor11H\ny5SJjtz1F5LH9ODCDakaOEw0hlLFJHLIZ3Gl5/J4yL2eUF3Rj8eWaTWhGmmQuitkXD9TQ/fSe2YY\ndo/lkci9z14krpP96ZA7AqpDcagJ61NohJRw/2nvuNRPQ+6si5K6AlCNrDQoMnBYTk9V18xvoNww\nhe0rfDcdcq8+3SKm+P0u6bTkBtmcc+fpS5F7NWeT0Kk+5VbC6s/MKN8WW6bTuKuce+S7RG61cD9b\nkhlEHcW0DKyBK+cnedFGoJkwrTveGHe5iKmA+yu37cajr2RiDdWqrpAFNzDFucvr8Cevwutt7r1S\nJl/4E5M0LZN60eOoO4boPEND2rX66fkIxyYdDsUIqwDiygFMCf1UitxTk7WSc/cXMaXbSfyy/Hd1\nyOZ9Gl3EKKpU+lnkbjts91srns8tLZVMp3HPPJtgOJxoqLFhd1vjBPAJ1VF3YvJ1SKW1MtOXtEw6\nfYkeXB/Nw4DzmADwtGMObcpoOHdedqpi433ouonfzu1Mp2U45y6RV2MQExV64SukLomLCfUUCJ18\nPW0e4mmCMv0j/gbZFrkX+rkndC2ZOC5H7ol6xf2352KdTEpHWWZVdx3yV9IyQZ7Sd7qMluWAI+Vp\ntJTx3KfSFVKTmFGrwF14s3mQrpRBTC2u0XQg6JsFpPIEOuWBVyOy8RWF/FXLURqkCV8Snp8IuPH9\nv+AtFDH1v9YTqsbE04njnJbRRhSNt4yC3FvTMglDFaiZSMufk+YaaL8nGA0Afnt0FIe/81C0/STQ\na4mtG2XVtXecmT05SrXPv839deX6davIXZRT+k73e/HERO6e5NaXmAIQM2nZd4x7hE+M9brcM0Xb\n9iz1IsR1cK5Yo7iN5bbZ01q3pJU84x6lZZQGr6SL+fzw7PvP9r3jOnKP3z8fcerpYrSMzBNSGKG3\njKQFNEk+bq0ThAMK0aTMRsidwnhbCSgNWZdxB51xFy6SMYSbvgyhuwaI0nliZb/peatx24YdeNUz\nj2rKdbQMmt9V5xZXrKzT0kGJbAOlo9qUnzs/rkWf5OJATFm9k5CpNO5qAyD9fGxSMbqHqm0MyrG8\nXlXGVhOqrCYNJftp9Tqjo5ZIQTr3q73MOueemkC0FEEpPdTE3Eikk4d7yotk2DU0K1QRDpObOlLe\nMt4NShtb75xE7qIT0ryMiGXUylaRpwAgwU5MBdgg16TVzr54HsnPfexDD8RZf/YiAC4aaxBbZlhi\ndPMvYo8BDIC/z34baGXcCzsVq2FsFMw/l0Kmk3PXjrGDsWEvFz6ho/pwt3gRXF2Ocy+17rye3HPX\n+zSfqfX83KO0TBlyr64hRKUaGrKluEsvu2ueK2QkTYxaIVEL35mIf2odXqr7TdIyicsKOjuRz84V\nyL6Dt7mS+uwx/3o5Fx/RL4FYcnSQqycvyXvEkLr8PSot46WBH/LXdYQ8TXx0I6WaUI29R048zn0v\nQe7TadzTwF0YIv3B+Jt1ZMprgdybzQLKsnj15Dn38ERPNKYSV0i1/ESDlHmccdcMhMLRF9w/k0go\nr9t3O3PHwwnVGt17ZdXnEg9I6+xjuoiTyXKcZ4VM6MrOhQXwFjHBXW8Rco9Ql/Kc9hsYrU1LfRzH\nXv3uid9tOk+t/B4JRwAGBGJ5UpKKCslHhR7gSFj3peTcs8adiD5FRA8Q0bWR80REJxLRLUR0NRE9\na/Jqijp1Pdj3dFqAUSCSk1UMV2kvb4fefsCtXCZXdo5z19pMrydHHty4F7Zg6PcpRsvY1BrYs/MN\nsc5WijehGtMtaTTDXEMG3WXnxzdkjknfizWUQbhMB6mLP4IcMeSvKFPrcOWG0CXtx9FTEa8xpYhS\nT4/U87LSaMyuoby1ivIVMCcXBMprL62rT4n7yd81ThUqaV3I372LljkJwC8kzr8UwOPqv7cA+Oj4\naqVl1OBNXNweqrJjcC+aO1aqF+P8CvXjRY/CufPJfCI/UXyFanhcS8uvQ+WtFYOgubSl7h9v7CVc\nMcC8ZYQO8llrOzE5P/e4Tv4iJqFLPFsgcrJXXcTEDLrU1Z7nwuczuH02vnUXZYRa5+pRDVQxXhFl\nB+f5TkzWGNt88Ttc0j6aDk+gkgAQFL7UyaiQStsj6DZnObxlssbdGHMegE2JJK8CcLKp5GIAhxHR\nkZNSUJMcLZNaPm6lTez0Nt4yAAXD5GQeVnSWllGP8+3b/C0D4ytUtVJCsZOjsbTaUF6LiJm6e5yL\nTLzW3i+HgH0j3PC3QzuhWrlkai9/yvMj5caZpA0Sab0Ronec/aRwy8cwcBjfrKPuqFCGqpPXlTH2\nKKyjKkvWGy8rWKGaur9RBO2k2bBlGJ7j+pR20qnRLz8Tdc8VspR+7pPg3I8GsI79vqs+FggRvYWI\n1hDRmvXr149cYeyBad/jrpAOk6rlJNBbVC+yjVUOlBN5uE5ZWkZB3AwRkkgTd4UsK7tajBV2ON4K\nUaXcVPjesBKXJybS4DVDYDD0yvRKdRjuOhP1cVqmBVZPpiTu564bWVIKSbV1jlLHXsSUMfZVPZGC\nUxUpZRFCP/ShqU6k7mEJ6LFtZSCAUptOmkssfIk9F5anp5eT/EshSzqhaoz5hDHmOGPMcatWrRq9\noMwws4QSkMhBKydXhqYDH3K2lRxy14T74UpEEqVltGtU0nkuZTwthcfsb90VMn9BfFl9oJs07h5K\nIna8+vQ264gYy2JapqB9uLT+uYB+EXra8iTC98v064jSMtxbpuA+5mgYrYhRXSFDPZxvP++gJDgJ\ny42XJ78PG9rVAgE/TekrlqJliMJFTDHkzl1+l0omYdzvBsDDwB1TH1s0ySJ3hA9biomc1wxX8YRq\njc5abbPH6x/hyXNXTuuKyc/F9Cw55nHuEmEqeaqXNvRZT909fzm9LjFvGXnGTabZsus0yj1J3ero\n5iApJZFGh4QC5E7KKCWgZcJz0vVW63QDXbMGODxWDNwzKJkQ+rnbzj2N3GNgJayLz6lJHXKdCJdU\nVEh+vO+NJuPIfSmt+ySM+ykA3lh7zTwHwBZjzL0TKHdkkduYaSIfvkxfgv6lNH7uGNMVMlq+fox3\nSNoQtb0mlfAYHfy8o4G0RmyalaEpvV1691ky7Oa/NZRE5Ao11ltGyZviPkcN+ZtCv3wxTKzjI9ER\naWVWajuUCPiuf5rOvPxYwWGesEMdlS/W7ots6+N4y0DcZ4DRMpGReDFyp9RIiIMGV7a+QXYle1Vs\nGSL6AoAXAziciO4C8F4AswBgjPkYgNMAvAzALQB2AnjzYinrdEofky+VJnKRQ1AewgeX1av+HAok\nlcyjAPeil5OlTSFpvc7wuHaNfNKxGO3D+qzn9QC4F0Gcp5dHubdMChXaDkPr8FLvmHRh9MpP0QYJ\n1M0ppJSrYm6zDsDF4Gk8f1Do554EPfI6/bN9ovLwA5kD1UKj6nurPVQL3ouGmhORyfxOtByw9Xvx\nndU4SufPNjYKBpaWlskad2PMGzLnDYA/mJhGBZKnSfKoMRoVUkNXhS2BqJ6MExxoWlNX9lBByZpu\nXPiETyrOjF9nWdl8ok5Dv2oZtXUvRUZ88jOWKcllIzzn+M06toxfGoAWyF3qEs0VnpWo3Hk18RPk\nfM2hte24GbZ7//KJb1tXTjvuaWPVSEnKyAX1KMZcKiI596GxzyrReRa8FxK58zQjrV1J9ja8XleP\nDijdaHKpZB9aoaoj7RjqlrPpspxS4+TntaiknHPnFWW32VOOecN9+A2rlbeMSsu4Tkp7MUIz5Cip\nNmsRbF2lOWzRktsH7JxHfd4oL3VBJUlapgWylKNJlXMHG0USgpugx3N3ee2x3Cgr0Eeey/yujPto\nE6r66MpvV9ooKyi34L0IJ1SdTl4kylLkTjoSl/XyVdNa2Xuln/veKDnk6RsW/cm4F0pviDGaJyW9\nHo0VfsAwBKemVZG7Oy4j2MX0joVBleItMNLSKvcudw3ROlpw7tqIhA96gsBhCrJLr1CNI7xCIFen\n9Y/YiVrP1VK02+weqkr5BgZnXHtfXDFFwxyFF3aaY0yoKufdIqbqcxwuWqPdFoY+5y7f59L2mdqs\nQ/PSMSayiMl+LqF1n9KokMqxyPccXx6Nv8JfhNIhHBxyLEY5HLlHOpyUHh4yFSi1FS2jHPNcIRXU\np6G9iv9NxGaXdTSfBhTBGvJlsdeluU/2rBJWD3HXONqNibfJRmCo4heWmgzlC8xS9EBYhv+bT1bb\n8s65cT0uWrsxWoY7rn+Xumr1tplQzXVydnQL+J3tDPWS7SZqZL1Re/XdOkzEbEUbwEYR12aPAuUj\nECUttwfV+1HavYwuU4rcNSOnv5C5mxh9uUZA7hYStAn5y2U0bxnm5w5JSekl6chdRxvaxLM2urG/\nG1dI/RLCOoz/qYksKzXy4IajoW1Y+l7DU8frS3lbjYrcfVpGL0+LryODVnEqyj6TjTv2xJWK6NfW\nFbIN5x6M6JTTtiy+eQpl9CoZ2cmRgGur/k0vBWzp2DLMz922K5gkcgeWDr1PpXHX7rVESe57pqgI\ngmmD/nneHlE1oVrsWeAKz4VE0IRz7jKIWFRvtZMIj+WcfrSOsXSzZlZL838pLeOuSzGG5HcY0nfa\neZiUTagW91JJPf2ON9hmL9Ehh8idtVFyx/w8Mf10AKT+Fnnt+o0Syd0ygs65Z8uNjmjDNAMRu4ZE\n+lZUazQqZDhiQKRsfn1LxcxMpXHXnkupYcidlz19m6X0lstrwx9K5AYkkIJyMXz7thD15V8GTQ8r\nPLqlPmoIy7WrJUuHnWUufP6JfoI28SdUlXglDbKL6+QHDstfh0TSWt4qxo1iaDJGVu+IyUsr21v0\nPuqHA115HVb6LUj3bMdBFHjpuJ2YUjrmK4zNqaQoqZT0EihfA5SpldZWlsrXfTqNe4ZWSCGhIF/B\ngyvu5YlaIRxZ9ih7qPKml9qOLlYnL0dKHrmHByyV0+L9qesq93P3PCzkWQonVLk09yRFAyUMQRuu\nVA4ANM49W4b47U9yWwAi8+Sfu0TMOZXatuuUPhwAcWOcM+4ljaqZUB34IwO/f497wEjpW8SmqUPu\n2nILJztaplCyw77IcEmTkhWqbfQiKt9rssrjKspli6FnLUwxEHeFLPaWgbPuZcidHCVVeP8M+4ze\n88h18TyOw2X3s+5k/MnxSsq9ZZKq+OcSiJWIjbCEkeWq5Dpo31tGOZjQow3bFHSKvTYeYJGXiv2W\nnHuzzV6Kc2+BoN0K1TqNAH+lo/FU+AFecy7shzehukTEzFQa95xwgx3zGLESW4GooZycWEM7bGHd\nPeSeyadp0fMCG8lrydeZEkuxVHnSoyVbbmyz6FQd/FOTYKESW7wTonqO3MOQvyWxZXzaJ39PoysY\nyS9H83OX6bM0DXMZjV1LvI8MDZAW9VOmBewK1fYeYHrZIXI3Bci9pD03NI/iLdN2vQNQbdxSMgLm\ni8K05PzeLRVy32dcIb3zKH+ImnGQZZRPqFbubqMOXyUPGZSvnOhFhoZAinPXkIWiD3eF1FUS5do8\nbVwhOYXiZ/rkG4/DTz/x4fjnM2/yjqcmr3oMCTcLY7iODdKPPyPfVRH419c9Axu276l/J3BcDrA2\nRrlFHvHbMB1io5CSEVCObgqRe4sVqrnfrKPwjKKSNlWOOx52WgPRebQZtXBJLWLyQaR+3Mpg2Bn3\nIskabAWpxST0TgjLKG4MZJFjaQb/pYotrHJpw2OxgFRAnJbRRBsq8kXtRbQMwa1QLa3XuC8yT79H\nashVH7EKVM+Re83Ja88y9Yik8f2lZx7t/Y6JFgrBO68g99I8/jH/MzQWsU49niL3u09jhB/QRniC\n7jM15B0XudtRV+PnbtOAOSxQ+Wg8HRUy7FRiE6rcJnS0TEKyPrrse46WicIr71BZQyCMN/GU83OP\n12m/h4ZOzaMc11TmO0pJzw+tPrvE2yLmNqLesch1pbxlAOEtQ7ruo3rLJJGl0tlpeWP0gDbikb+9\nDbIFtRLLo2nd9vm0WsQUAUz8txwRDpXOvVS8Tqv+ESxiEs+0tC4eUjuol4PIZJv0KdduQjUheeTO\ne9R02viEavph6WWRxyeWCC96lM06uItdMS2jVKDTMswzo2AkY5F7labsIhhwTxhD/ziP7ChrsTth\n2f9JKNyUlXhG3n3LGNvUOV230LMixgtX5/3fAb9O4eYw8ecTx+65TqXfo4nhTU7L+Huohnp5+WIj\nEm5kLS0jdlrzclL5O5YMPwC2iMk1LFVPj3Mvq3psmUrjnpPUyxKm1Rt5/DVI10tEHr9WksdKbps9\nTXi88sA3OYY4lGMaB11RLPHRhGbMnJ97Uu2gXi2et+bGBqSpNm7sNM7d29YtIilOPIWCc14i+mR9\nXA/tvOwE26BdDeFqIzOuq5V2yD39Gwh3QlPXJGTK1XSNhR+Q92wyyF0HkSrnzm5d5+eekHxIASc5\n3tmejVEbdYWFeulIqqR+wOcEc2mt+DsxhediepYIn1Atehs83rGsDj+7RK1+uVb8CdXwXBDyVxmF\npXjPpCtkC+MTm8+RnHub0AtygZhGAxbFlkmc087zieqcpBZz2bJlHKXKzz0Na0qaVLCHqlZOwmBL\n6ff8ydJQJ6rrTbd9P7ZMUdVjy3Qa99x5pSePScw9zR/qleo1yiImV7jYX6Awv45OADTxvkM9Q9E0\nruKE2zx5rdzANFaLVof7jBmcwNA0cTx0I8RD/vZ6+igs1QFr0f5kfjVf7nfznPJI0Iq6JkGUWRx+\nQJl7iOYRB3iwtpzkkDvXmU+Ok6JXsiDlcBN+YCCQO792lL9jfaJ4AD7PRqRtjmcTOuMel1ynmx5W\n62VJl6mRokJSVbfcKCAmxzzkAJVzb2Pd/cBhfsZ4PPdM46vFAK1ghhvqt2+9OueeH5Gkhvy2k9Fe\n/tcff2yQTy1flDQUEgAAIABJREFUnEsZn9CI6eZ+lPZpRS4Q4/x1rswknRO591b4wrES+e47XpQ4\nG26QPRzaye9Urthxblirz+w2e8Wj8cIJVdv2I+UMGJjoaJmE5L1l2iN3iRLb8KIuXTUPX0K5r+j3\ncPrbX+CVHeM/U+LFlkkYQZlHStTPvf5ecg+s3kPFUMfE3xVeHz3FVm1q3iV2J6zYefv7jc9djT/6\nmR9TdeqP8Ozr1EJPve5cm9TyWKlsu2/MZHsrofViHWnsPF84lhMC8NhVB8XrIo1zB3JhAUo6Lbdt\nX0hxciP9N7/0VLz+2cfiMatWJq+l36PE3BV56aQOXPwVqksjU2ncs8Lubc4Vkm+P5X3qxWWrJcqv\nNAWAIw/bHwfvP6uX08KgcD932Qhj0ey0K9I05jtKlajUoLAWbm0mYYibcsXv5GYaYCF/jXsmVlJ8\nfZMmWX5csgaz0YHnyQAVzboz6RNhIDimKDhoM2IQv+09LwEuJfdB49yz5Uauix+1bV4uYvK2ISTg\niEP3xwdf/TSsqAn1FzzucDzrkYcFZdu1Fmq9ynOMtWN/hWqH3KOS5y7d9xxfbh+ci6wYouBy5O6j\nksJcBUcyddbfpevjRJC7WEmYykPseDFyZ2XILNooChDXFZxjG2TD3h9Sk8dUHHWbvUAiessVsCmR\nHfZQGI9eTxkpRm17otPKXGdJ2IY2ZWuukKAwb6oc7bgL+Rt3TlDXbEToF7kvsV+OE3+FapjBX8S0\nNDKdxj1zvgSdVefc+SZPk7ysDF+vakJ1VFfItvUBtd51emkI2uyhqjU5b4WqV4Cui4/cy66B+7mH\nBdpyxXX1giRNOZW3jC3TesuEOtq0mqQAQZI2yJSjP2v3XUN0wR6qoh7VWyamn9KzxUZmsYVjRbRM\ngNzDsuUKVTvaa3N/NV1DV0h3TvNGI/apld/vpeeuQj93/Rr4aL7j3BNCTcPUb5Lfo8Zbi78SUXzG\nwWFSr7bIvQ0Q1FoNUUgtJZLXdeZRuD0mX8K0etYAtES40H3jYx1Ecqk3+ROqwcKuzEsIpFfAppGl\nRKw6gk1N2EqRozGJ3Pu9MFBddEQb+Z7StamH3fNxxUfu1TE7yhq1PCvNhKoIP5DLyzCSJz1K0DIi\nHWCvQ0PunbdMoaQfWSktoy0Z1jdUKGt1dnKzXWwZ5Vh5dvDNBHLGJVWn7grZfsUp0JZz5/y4bmAC\nisCiSGXhk90JqykzMtyu0uo6+bRJBoYmTsXQcIxz1/SUqFHepx5R4J0VR+7xunK69xrOvQS5Zzo5\nVo69Fm2UFZabrZrRPAlahihIT9DbeGoRU2wOQ0vd0TKFknvIpd4yqdDAbXhRr2YqC/nrRgo6Gi8V\nb5u9wnzqsFF1hXQ7MWk3IbYQyC5IKanbJM7F7hH3sAjTumvRYvWUUHbeiK7ctqfnBtj5Ns835xDQ\n7yFLA2r1hsg9o0djNNPp1LKDuohNqKIpl9OkarkFN84maYvcY4mrCdVIXvAO241sNJuzHFEhp9O4\n585z5F5KywjEbj/bUAxEvnFJp7VoWznXqjuJ0zLxPGHCGC3T5PEMQ9pwpy4/aPhi8tMvT79HKePr\ncdCNwQh1TEl6hWrC+GQnEilIl9NHa78eLUOE+YGJnvf1S+gWjFD832Nx7howCCZU837u8fpCMLcg\nOPecp01sdWyvR1GAyI+ng9n5963j3BOSdx9z30tpmZhLpPyerBe633Eyj1Z2rD6lUXC0U+w/XZiM\nN8KSLG6IHb9nAc1g/dyVSdjYiMTj9pU8hncY8GMA5lYS8np5Xc1vNUeYTz2vlZ/Ook7m+UZaO19w\nXRm6SZbQUGEj2CWNz7fviPRzT5cTOc71lLRMXrkmnVZ+n+LGXbMR2jwP4C9s7GiZhGSReyEto+24\noxn3UqNp+d1xe+ZWI4UCmiHIpxyLBg4T/sg5fYC0t4z0veeGOGZgSkL+NnMDpIT8jXTUsUtKr1DV\n8+TKiebNFKiOPCOIMV9k+YghHC1VnyWxyLPvJzHkLp5lSq+yqJDVZ0ODZFA0B3Va+VVsmRjqZ8+h\nARyxqJB6/Ysp02ncWzTMpHFXXuJmcnKEAWI1oVq2iMnVm0Zm/gn/jIyL015jJ2p7M+mXOebnntyf\nNPY8TFz/kHqxWbQJ1bowOBTlIV2lnEDHkb1lMr+b4/HOI9Alg9xV4575rabJXKebUNX19MvKIHBw\nzp2Cc/FyY+WFddt1Xal4On5efTK3R5SYePfTpfTs4rkXSpan9IbeqXJChN8cIj1dXq/QeyGdKa1X\nSVZttNFWNI1HX6Fahop5vZoLWbSMTLx1Hjgs5bmRc7WU6WPi7r80VDJd2AnzLBqi0ybzsrRjgTGy\naYz/U08LH5nmpA34koY5jdxj5YXvu1uhWv2OgRQ+Yo8a9wJPhZ7CAnBZjj1Ui4w7Ef0CEd1ERLcQ\n0QnK+TcR0XoiurL++93Jq6rqpR9n39O0DE8XL6PUZlaLmFpy7oXHNJGIvU3MEikqLcNdIb3OTi8j\nthKUi3xRePgBrcSqPn3EIuu057i3DBXq7pcRP6flj3WAZVs4ZlBuRmEdueffi1z8dllCq6iQLc5L\nCiyVN9ruFARtJ1Rz7wTHcjotk4gtQ3wRU3Ws8pYJ03reMkvEumf3UCWiPoCPAHgJgLsAXEZEpxhj\nrhdJv2SM+cNF0DHUqcXES2tvGfEpy8soVhmXVitUlZezsD6bbhK0jKayx7kXlF5iRKUx8jbIjpQX\nGs06rzJCICKFx+fILn8dKffDNsYnqEq5Hv+e6cYl1CF9PXEjGG/TOeTu1hYUSM6gRvSgjHUvii3T\n0DKhn7u+AjiD3BPeMtFOKgEA5PfFlBLkfjyAW4wxa40xcwC+COBVi6tWRrLDvnCYponnLSPuRCn6\nl3kqWqAN564dK6tPUknjIHdNhsYFIGhbdHzhUAy5xw1OjCLgafhvzxVScKkxw1qiu1qhdyqHhvWR\nSEpUzl1BqiUqUiKN1klqepQF+Co/X7o2o9Ipf8J+TcWW0ZSJvXP9BC1D5PJx+keNChn5vphSYtyP\nBrCO/b6rPibl1UR0NRF9hYjUYNlE9BYiWkNEa9avXz+CunU5LdKWestI9BszCEm9aFKxZUbLOx7n\nHqFlWrl1xo2ulfSuNnp5odeJQ5EhZeO+25AGJQY9Vkao4+g3WVuPkCtN9XNn33VvmbgxiqXJXZet\np6RtlxpUqQcV6JEpLnCFdGl4BxC20/hI0w8B7ZXJO9lML8Xv27T5uX8TwGpjzNMAnAXgM1oiY8wn\njDHHGWOOW7Vq1YSqTktqiK25vDkqID3M0surCmrDS46DtQNvmTEK099Z5udeUDZFfzgJ/dztZ7iq\nNYrcU7QJ+SF/eyRHcvkLSW3NmAT1maJH6cg1w9LGqHj5Em0691vSHaX15EROXo/Shv37UX0OhmX7\n+OZoMUohd4Uei61Q3VsnVO8GwJH4MfWxRowxG40xe+qfnwTwE5NRb3xJPWD+zGTIX1FKWV0gNdZH\nrM62EhtKt6VlMnbcHTIskl5bzj2SPnhRvNgyennyeFOEQuVUrqjNaVArUxPRkeuUyJdF4crcSCli\njp5v0WG0GTGEawuqz1HiuYdlx78n72+U+w6N7GBYFt/I49yV86WLmPx4QWFaH63vPcj9MgCPI6JH\nE9EKAK8HcApPQERHsp+vBHDD5FQMpXVI3Iho/szyU35P61U1kNTQNRXDpm19E/WWURocv4y2yD2W\nPuoKqcyoNi+tpF4y4QHstTRL2lsi3dR9TD6vUuTewsrq4QfcsRI/95Qu0ciqstO0tMwEYKc3kuLh\nmyn9bsfOaEZ2kIhvxK+Zj9S15MnNOtj3/B6qvH61uIlL1lvGGLNARH8I4NsA+gA+ZYy5joj+GsAa\nY8wpAP6YiF4JYAHAJgBvWkSdW0lqiK09kNFxe5WuR+mHVzU41rjUcspqtG3Ovnhjce6qt4zu594A\nZ8lrFnHuwrjzDqTAGwLg+3lqYYKFnzvaIdZKx/i5JC1TOJUo72XqXU+130qfcnDgH5cgI1mNm1Bt\nERSv5LxvCNN3MHpdXpo4ck+Gaogg98pbJq+QF4ZaSepz7pHyJixZ4w4AxpjTAJwmjr2Hff9zAH8+\nWdUmI6lGm0LuXNrEbGlNyyhFl48U4h1SW1EXqHp+7uG9CvXRv3OJvSgagow9E7+DEKiexIQ2+cRM\nyb1NIvdEvlLO3XjH0plKAocpNen1pzh3aexF3n4L5N5m7kG2q3TnmS+Pzw2UvLec+lNdUVO0DNyI\nVwtlEpOl8nOfyhWqVspWy7VD7loTakWTUHo2PFzYoqGJ0vp8Bdu4bErR7iVfoTpquVJC5M783KPI\nTKJMhpKkkSKrt50r8BUre+ETxn0E4xOcFwHZUvl0V0d3rE1sGc0IxvKEcxm1cR+UIPfMaMPjyPnx\nTLkFnLvt7AYxCB0pMx4VMt5mNDpIG03K9HvThOpeJ5MKvONHhRzthfbS1eW0CXmrlV0e7sAa9XhZ\npRJTufFzL1PIfY1NqCY492BQE0PuDAFLg2lXqLqAZ6LM5AX45beV0milbVpviiIC8q6SvN4UPRX+\n9o+4DbLHX6Ia62QqbDRGI2Zll06ockgXG7XHOPfYRjm5EMGdcS+QNhOrmvCXOEXLlDa4WO/vp2kK\n5R+ivkzeWhqjjnbIXZNYPPeYkVT1498j6aOLmBLb7Ml3y4WfVdwnqV5Zy+rz9cpfSHrSNTxXGn+H\nh0R2+mR0yXDqurdMHuGGSD0NOlrRMtkUelqKEd+1REEd7yyYP35Rm/Xe+zBDv5dyhWT1Zjh3oN1C\nsEnIVBv3cSW1iMmTUuROeQPbhgeUEkZhtKjBlp0tupVUK1StTu30jqUOQv7y/JFJPi1+TFyHKuIg\nj+fdNpRE0rQnThZz7oq3Rkxy3jCtVqh6z6ddY3Fcdj5tfgSjP48c5x6biPTvR/VZIXcJJJR5HfYZ\nQ+4lUSFzIyyZfinkR9q4a9EjtYbZhsuWxitIEyAmLU1ZfY23TJN8sq3HGLQaQ3ovU+QaYt4fMf68\nLtiTBiUhfGF7VBXGRxw+OsxLW3e8WCcUy5vq0KRkww8UcO6prRxjo44g/IBdHDTh8APBhGoiXwzx\napTI0MTBjh4NVn8SycBh3kjItcncqLWjZZZAPKMdQe6F8zJ1EaSUoNfp7NboBtl5y/gIflJi6Y3i\nOYci5K6jKa0eF1vfFxUlsfs6NGyugHz/5XFGTra8+MlcueHLbWmkmOicujuWc5UsVS83N8EXB2Xr\nKRzB8HKrOmkkqtXvLKrPheGw/WhTSZ6KLcMrLnkOufDDk5YfaePu0zLVp0NhTtoYtxw1EoQWLkDy\n0bLs0wsQ/GTE1Ag4Vmxssw4gfh0p5C5rcnyoPB6/UDmhWpXqo8OcJDeMSOUrvP+tJlRznHubkL8J\nQ1bKuU9CfOPOT4yK3Hl5NXIfptqtP3ay/2v3LektI9JVhSeQe4sNTyYh/397ZxpkV3Hd8d95782i\nDe0gaUbSSEiYEgiQPGgB4QC2QSHBdjCUWcqAA1G5CmLscsUxlSqXk3xI5UucpOxyZbHjihMvFcdx\nCOWKQ7CrnC8Bi8WsBgRhEZsAjZCFBmY00/lw733vLt19u9+7M6N50/+qV+/evr3e23369OnT58xp\n4q7TltGKSTy463KuxT6IfIZQfiO1U02DPJQy2XxxIB6GOCZ77thUyPJ56Ja3qsWp5znjLFErLqUL\ndbRy7pZn5kdxvkm90zJ3e6oyouoilkk9SV3Z8y2uliocA6ncfZQAjPupGuKuU4Vs53RxpC3jXq4N\nuu8/lZjTxD2jLdP89xksRZRvqBZyd87bxOUnwWXy/gSuXctLDRI3DrlgWqbpIFuXnz6veubIenGy\nTHPuRW0ZQ+Ud41RhFTK7qihJU0bcPapTJoLIxs1G8CHCXo6uc2PQVox5QzU9WUT/7rZlWnloxTJW\ne+7F8Mj1o51pmCbGfY4Td1c9d8f8xCFu+eERn0GUzaNT1dA8ElVIU7aFTqqZLPMwmR/QmJZJaQHp\nCY1ukCTZp7VlXOqVKdcmlrFy9W5ErWiO1gwd8c7YlikR25jK8ZGLQ9WcewsFq5CWt2F2lZe+To3p\nmv3MSVQXaeahq3ekLeOwUvXi3EujVoI5Tdy1BoxKBpNPnjqYXK+Z7l3KqsITkw6TqmVZMVuuS930\n4aaBopROLNMaeNm8bROx5FQ4/cUA7c6RZcnSGhWuZZU5yPYy+dvB5O+zcetQkeZl/oSqrRgXomg6\n8Wqc8FKrX6ObPYuzDl1Zpo7Q2lAPYpkpR9ajj5lAVimW8eWYrHE9y/ZF03CYY7bZweSWSKX+XSeR\nlsnf4iCp1eJJKWWq2EccEcVpl3MvyTf+dzGWlqA92zL28tMwcsMO9TCWUzYG0vlmOHd7vmYLlvrJ\n20dbRgzLbrttmWK59tPp0X8Qy1hQ1cvJiGXiN6G3HOcGF2cDJrl5qyx/6t6uK7wyJBuqztVpowK2\n05rSDM+vdmzEVyItH0OeTpx7aQxTOjfqnn6nIiVWIdvYUDUWn+E0/TqqyyGdVt4e9aiAczdOFi5p\nm6tegycHsenLp8uNy8Hc/iCW8UCntMzdtox7SWVjrWh9z0DRPJCcGqycc4/NQjpPbu2UkdpQzac3\nWb1MD6SC2WGyZhPaqZdtY7pdeXw6rdeGagmzoSO6ZiLYPpfvtaHquILJ16lM5m7cUDVNFjlmy3pC\nVfTj3CaWydahXOTS2lAPYpkph84uR/7F26y86eC6qWa8dy+qtcxvI60LOjrE5JimuaGqsxNjyKtM\nz11B86XUJHswxignzZTbPgG3QZe2VOZe4qyjSqJri+u1oVqqLZOufzqdHUY9d8OeilnOXhTdRHsz\nRdRr5oNVZjMK+vhBz30aoTP5a9PAcIE7IdRzpX0N8ycxm75VmfuqoGLZtWmwFg4xOSz7jd5xtHH1\neaVVCk2qkE1tGckvn1s3pjHWrlzdXeZePERjQqn5gaplcUkZuXpVuqGaLic329rFMi6HmDLZleaT\nWUVoEtisQkouHpScNm7GCZy7EVV1s6xYJvovLvPFq0TfwyF54tTfU/coK0JLp9s5qROaJ1QdVxft\nOBVvFWbjtPL3ds49oy1DXgxQjjJtnHaeRflG/15WITUjNJ3ETWTgVpYuTase1a0QskQxHW5/g8bJ\nOJOfuQ/q7UalSy/CZlumJvqzIKY2tBQBDBEqxqwk7gk6fUfpj9aynVGM58yN405gTdH6etw/SVKv\nNJfaSdl5tFQh/erjU0batkzxWfRfMPmbEElNKpHWiiO6N9uWMU9SZnTGuRe5uzLWQUu8UwX56Z/7\nMCm5enSQtlgPfZ1Mcu8ELuYH8qvHUj33tFhGJ3PP9R9d2nS5Vm2ZWvH7TyVmNXHvFBlHy/HX0XUg\nHya0XBWyKEZIo69h5tzzxCxPLKpXhdSLPlzQlszdkdzatA6SAZ3VlvFbUVgJuC2dY74++2lVmJBu\nBx3J3B0nOdDouVvSOfkJSRHjTN4mAl1SttTMbY/StCaHsrJqFhozFZjTxD2taaA7Gp7A7xBTyfOS\n9DaZu6ksX87dhnQ/zriqc6pPmit2S5XRc3cWyyT1K8atSUucFNXDzCma4LuhqvPGo0+bTMYqE+Yi\np82Wl35uLbIQPw9XzRofsUxpjzGtpMTeh80bkfrJ32ncSqtsXbXrNpm7pl/pzmu06hX9B1XIaYB2\nQ7VDzr20Q+UJVS7AR+aeoFXlzql7ZsMx0XN3zDYdzXeisVmfzIfr3nGaUy+Y/PWrSgnBtBB+x3wz\n/rtL0pT5SG3XWmPp98lz7lPE4ORN/trgYn4gytNefjauNMs2nVA15yeaq/KyglhmGqDbfNFxB87O\nOsRhaa5Jk4aPtkyCllimpPBcfG0Zac6dEpO/eTFRG3TGZXPRpCKZv4bUhmrCuYt5wnVdKbg+K0NL\nzz17iMkGvfmBIlNSNQrvdRoOMUX35tQuh5jSeXgw7lZRio/FR7RmNLJxgp77NEBnOMy0SecCobwj\n2AgV+Iplsp2lioGerl+LQBrkIvm0Gdm2q1hGNf/Nvj+zsHFAiZijJZYx84PmMdYed17KsWqW5WVp\ntNoyFXDuvvDj3EvGgGEPpKwIF/MD0GJyfPZXxBDf/RCTe1lBLGNBVe+mXit2sk4PGPjK3H02VE15\nTU5Ojcw9baNFB5ueuytaG6oWsYxlbsnXTiSWuXdgksGXXqZFQDYkTzMy9xI+t4yw6J5XQTyKbvY8\niHtp3um4ekKvg4sPVWhN/gURo1bkmnD5+i9RE1eTFeUimqDn7oFOaZnOyFCnJ1SdVSGb3EU2QX8b\nqpBVastkD/kog/s7Q30840RlpP5F/8xkJll3GCXaUMW4oarLpxhu4dw7eMdazq0tsUwxzzSqIB5F\nsYwP5+7+PLsysSd0UYWEtBzdb2WkYyJEzG72Mu3IjUVtWYFznz7UNB9nMk0VSC4rXJKWEEovzj23\n2qiGc89zO+6TqM8SO1MIaEeF0nyL6NYmNrGb/HWBVfTilVMurUacVJZflVYhfWAimJXkbdgzKBfL\nGPIzrOyKXrl0BaQ57txqJY7v9I4dOJtgfmAakV5qJh9wUnOIyWszqcMx4HWIKbdBV7WbPbDLwnU1\nKl55lGNI5ZNXTXImf6XcUmchD5vhsApesQ/nVrah2qnM3Xjq03G15pLW9tzHtoxR5m5ShXSoU2si\n0ExotUS8U1Ix0mPRHCdsqE4jMtxInnMn/ci9a3tzOLno7ei5qwo593THm5y0a8uY6hNdu6VqimWU\nu5hEL4poxc3rufvC18SAa/9oidGyeu42aPuTlDx3qUvpcz3BdMvbaypoXZUkM3K8hVWGW37ppDo3\ne82J1SUfh5V+sOc+jdBryxTh2q8jEYAffPTcTdxUQiwqty2D/XBRHi6cUqGMlFTGlMQUrjcTnJO5\nWzh3YxUtde9kAm1+aw8997JV41Rpy9g2scsTl+WdHnfpZPaEzo5FEs7dodKtuMWc6k3O3b3xtv2O\n1r6ec3YdIRD3GDZby35iAb/BVtSW8ZcBTaYIWadIt75FJF05U3+xTMueu3nj2kdEkJj8TRt0Kjsf\noMtjKpDfAE+HmaA9xJS6niZNSC/iXr4qaMFH5m62555bZdSKYhljXVLMuWlC83XwYopebxL3k0gs\nIyJ7ReQpETkgIl/UPO8Tke/Hz+8TkaGqKzoV0C2ldB3Iq2N3JpXx2lBNUKVYJo2ODJK5imUyXKw+\njZk46yfi4iEmp6pk8jA+64hzj5DV8vEXy+SdQHcCd91xD7GMh1KBRjJqhOshprbEMppVd91jkkhg\nNxwWx/HIrxOUEncRqQNfA34T2AJcJyJbctFuAUaUUpuArwB/XnVFpwIZq5DJi9e++eo6djF+9t5v\nQzVCQoSnwnAYqPZk7m2U5cy5W9pZq2m0ZTzFMrbX2MnqqGU4yq0sKOfcfbRlfE7G6sRdrvDh3H0O\nvrnYc4esSYEytE6zmmXuTpOEw6p1ut3sNRzi7AAOKKWeAxCR7wEfBZ5Ixfko8OX4+gfAV0VE1BSt\nPxpxh+/1EWFokB44CcecBCUftq9R99JdL4ubfOB5sWw93wFtYpmk3YlcObnviV1K9To6urSV0deo\ncXxsAoB/+t8XODExybze7Goiee89OVdWJo7MVvZ373+Re5543e5mLz/o4nb399Sb10m+IvDu+CQ3\n/8P90T3mM6qN+H01asKJFMW1ulx0aJMJyXtr1LOEoKeuf5+mumS0TbTEPxuWfD+dByLTHo+JYLqg\ndOJIfVefSaOhc51Gsb1pOXryzus1aX4n3eFF0eWTyNwNg7omML+3zuj4RNMIYV+jZq5nXNif3v0E\nr749yq0XbdTGqwouxH0AeCl1fxDYaYqjlDohIm8Dy4E305FEZB+wD2DdunVtVhkuPH0Ft11yOp+6\ncEMm/Os3bG921q9ev41F/T3NZ9/ft4uXRkap1+Dp14/x8sgol5x5avP55y87g76eGldtHwTg/KFl\n3H7JJm68YD3L5vdyw8519DXqNOrC0dFxNqxYwNpl8/nVa7+O2gZsWX0K83sbPPTiEXobNW7aPcTd\nj77C4WNjLOhrcPidMT5x/lp+8fxhrhleC8Des1fxwuHjnLqoj7eOvceezSuN7f7YtgEOvHGMK89Z\nw9/9z3N85Nw1ANy4e4i3jo3x6d84HYDv3LqTQ79+D4B//N0d7H9hhEV9DTadtpDRsQkuOH05Kxf2\nISL0NmpctuU0lIKHXxphUX8PP3/6Deb31Tn8zhgAw+uXZepxy54NHB0d55Y92c65be1SPr59kBOT\nk1y0KduOv79xmAmlGF6/lI0rF6JQjJ9QvHZ0FID3rVrE5Wev4gNnrOTnz7xJXYSh5fMBOHtgMde8\nf5Al83tYuqCX7euW8LkPncHVw4OsWdzPZz64met3RP3pt7au5uDIKEoptq9byq6Ny1m2oJfrdqxr\n5pfgmuFBXjp8nKu2D/LDBw8ycnyMrQOLm+VtW7e08A1WndLPzRcMRYSjXkNEuGVPqx9++cotnL+h\n9b6+dv12FvRFffLaHWt55cgon7l0M+uXLeDBF0c4a2Ax561bwnvjE9y4e6jwzeo14T8/exE/eugV\nDh19l0ml2LVxeTP/Czet4GPnreG0xf1NBuXsgVMydf7Wp3bwo4deZs3i/mbYxhUL+NyHzuDj7x9o\nhv3ZVVs5c9UiAHYMLeOqbQOsXNTHmiXzGFq+gOt3ruPt0XHWL5tPo15jfGKSV46M0qjVWLmoj0ml\nOPzOGOcMLAHgO7+3k9ePvlt4h9eev5b+nhqbT13EmiXz+OSu9Rx+Z4wr4/78B5e/j4Mjoyzqb9Df\nqHHZWav4j0de4bZLNgHwg0/v5rk33mFCKR54YYStg4sz+e/7wEbu+7+3uPiMU9m7dRXzeuv8zrYB\nLt+yihUL+9h71qpm3CvPXcObx8a4YutqxicmOTI6ztql82jUa833dUp/D3+490xGxyOm58TEJAdH\nRrlmeJA0LhoIAAAE/UlEQVQv7D2Te554ne3rlnLHBzfzifPXsuqUfn7/0k0cH5vg2LsnADg2doKb\nLxhicOk8jhwfY8XCvsJ7qRpSxlyLyNXAXqXUrfH9J4GdSqnbU3Eei+McjO+fjeO8qcsTYHh4WO3f\nv7+CJgQEBATMHYjIA0qp4bJ4LuvJl4G1qfvBOEwbR0QawGLgLbeqBgQEBARUDRfi/gtgs4hsEJFe\n4Frgrlycu4Cb4uurgZ9Olbw9ICAgIKAcpTL3WIZ+O/AToA58Uyn1uIj8CbBfKXUX8A3g2yJyADhM\nNAEEBAQEBMwQXDZUUUr9GPhxLuxLqet3gWuqrVpAQEBAQLuY0ydUAwICAroVgbgHBAQEdCECcQ8I\nCAjoQgTiHhAQENCFKD3ENGUFi7wBvNBm8hXkTr92OUJ7uxdzqa0wt9o7VW1dr5QyH2WPMWPEvROI\nyH6XE1rdgtDe7sVcaivMrfbOdFuDWCYgICCgCxGIe0BAQEAXYrYS97+d6QpMM0J7uxdzqa0wt9o7\no22dlTL3gICAgAA7ZivnHhAQEBBgQSDuAQEBAV2IWUfcy5x1z0aIyDdF5FDs9CQJWyYi94jIM/H/\n0jhcROSv4/Y/IiLbZ67m/hCRtSLyMxF5QkQeF5E74vCua6+I9IvI/SLyy7itfxyHb4gdyR+IHcv3\nxuGz0tF8HiJSF5GHROTu+L5r2ysiz4vIoyLysIjsj8NOir48q4i7o7Pu2YhvAXtzYV8E7lVKbQbu\nje8havvm+LcP+Po01bEqnAA+r5TaAuwCbou/YTe29z3gUqXUucB5wF4R2UXkQP4rsUP5ESIH8zBL\nHc1rcAfwZOq+29t7iVLqvJRO+8nRl5VSs+YH7AZ+krq/E7hzputVUduGgMdS908Bq+Pr1cBT8fXf\nANfp4s3GH/DvwIe7vb3AfOBBIv/DbwKNOLzZp4l8JuyOrxtxPJnpunu2c5CIoF0K3E3kXrib2/s8\nsCIXdlL05VnFuaN31j1giDvbcZpS6tX4+jXgtPi6a95BvAzfBtxHl7Y3FlE8DBwC7gGeBY4opU7E\nUdLtyTiaBxJH87MJfwl8AZiM75fT3e1VwH+JyAMisi8OOyn6spOzjoCZhVJKiUhX6ayKyELgX4HP\nKqWOikjzWTe1Vyk1AZwnIkuAfwPOnOEqTRlE5LeBQ0qpB0Tk4pmuzzRhj1LqZRE5FbhHRH6VfjiT\nfXm2ce4uzrq7Ba+LyGqA+P9QHD7r34GI9BAR9n9WSv0wDu7a9gIopY4APyMSSyyJHclDtj2z3dH8\nhcBHROR54HtEopm/onvbi1Lq5fj/ENHkvYOTpC/PNuLu4qy7W5B2On4TkWw6Cb8x3nnfBbydWgKe\n9JCIRf8G8KRS6i9Sj7quvSKyMubYEZF5RHsLTxIR+avjaPm2zlpH80qpO5VSg0qpIaKx+VOl1A10\naXtFZIGILEqugcuAxzhZ+vJMb0i0sYFxBfA0kezyj2a6PhW16bvAq8A4kRzuFiLZ473AM8B/A8vi\nuEKkMfQs8CgwPNP192zrHiI55SPAw/Hvim5sL3AO8FDc1seAL8XhG4H7gQPAvwB9cXh/fH8gfr5x\nptvQQdsvBu7u5vbG7fpl/Hs8oUcnS18O5gcCAgICuhCzTSwTEBAQEOCAQNwDAgICuhCBuAcEBAR0\nIQJxDwgICOhCBOIeEBAQ0IUIxD0gICCgCxGIe0BAQEAX4v8BxnthRWngSekAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBUWcfwaYwsx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# discores_low_acc_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siJntOdpYoNZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# np.sort(discores_low_acc_model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQC6i-0MVmWk",
        "colab_type": "code",
        "outputId": "06cdea59-d09e-4f2b-f0c0-33e71c4365ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "#layer_name = 'conv2d_2'\n",
        "\n",
        "# discores_high_acc_model = createCNNLayerDIScorePlot(loaded_model_high_accuracy, layer_name)\n",
        "all_di_acores_high_accuracy = []\n",
        "for lay in layer_names:\n",
        "  this_di_score_high_accuracy_model = createCNNLayerDIScorePlot(loaded_model_high_accuracy, lay)\n",
        "  all_di_acores_high_accuracy.append(this_di_score_high_accuracy_model)"
      ],
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.4516594534366701\n",
            "STD of DI Score:  0.4035461046682847\n",
            "MAX of DI Score:  1.1941331930053427\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+5dPcc4VOG809PwIno2VsXsXglBOBsbhfJ9i7OKk3TMWzNcYP/vWXceJnv12W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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGjrlhMbVmUv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHVud4ykeRn8",
        "colab_type": "text"
      },
      "source": [
        "## Pick top 10 DI scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8k5Ob6gWeQ94",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TyI8gOZuftmZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjMoET8nasck",
        "colab_type": "text"
      },
      "source": [
        "#### On validation data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPKQgmyXbACY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "valid_ground_actual = np.argmax(valid_ground, axis=1)\n",
        "# valid_ground_actual.shape\n",
        "# data_to_use = train_three_data , data_label =train_three_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nbg5-HKhaxxj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "#layer_name = 'conv2d_2'\n",
        "# discores_low_acc_model_valid = createCNNLayerDIScorePlot(loaded_model_low_accuracy,\n",
        "#                                                    layer_name, data_to_use=valid_X, data_label=valid_ground_actual)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zaH4FbSFaxuc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "#layer_name = 'conv2d_2'\n",
        "\n",
        "# discores_high_acc_model_valid = createCNNLayerDIScorePlot(loaded_model_high_accuracy,\n",
        "#                                                    layer_name, data_to_use=valid_X, data_label=valid_ground_actual)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMpuiVdSf60g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# np.sort(discores_high_acc_model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZueWb5hY7zZJ",
        "colab_type": "text"
      },
      "source": [
        "#### Pick top channels from the layers selected"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9HwamF674hP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "topChoose = True\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmggM8xc8c8G",
        "colab_type": "code",
        "outputId": "46fcf80c-0ecd-4c97-bb3a-24b089beddc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "for i,d in enumerate(all_di_acores_high_accuracy):\n",
        "  print(len(d),i)"
      ],
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "512 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1cAvZpx9G8_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Ks = [10]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3zlCJ3R9jjk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "copied_low_accuracy_model = loaded_model_low_accuracy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZY28-KyEAhdg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "copied_high_accuracy_model = loaded_model_high_accuracy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mCeCjHCzbJj0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# low_accuracy_model_weight.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5dHmH8KoxXn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for l,DIs in enumerate(all_di_acores_high_accuracy):\n",
        "  this_layer_name = layer_names[l]\n",
        "  this_high_accuracy_di_scores = all_di_acores_high_accuracy[l]\n",
        "  this_high_accuracy_di_scores_sorted = np.argsort(this_high_accuracy_di_scores)\n",
        "  this_high_accuracy_di_scores_sorted_list = np.ndarray.tolist(this_high_accuracy_di_scores_sorted)\n",
        "  top_channels = this_high_accuracy_di_scores_sorted_list[-Ks[l]:]\n",
        "  \n",
        "  # do same thing for lowest DIs\n",
        "  this_low_accuracy_di_scores = all_di_acores_low_accuracy[l]\n",
        "  this_low_accuracy_di_scores_sorted = np.argsort(this_low_accuracy_di_scores)\n",
        "  this_low_accuracy_di_scores_sorted_list = np.ndarray.tolist(this_low_accuracy_di_scores_sorted)\n",
        "  bottom_channels = this_low_accuracy_di_scores_sorted_list[0:Ks[l]]\n",
        "  \n",
        "  # now replace the weights\n",
        "  low_accuracy_model_weights_bias = copied_low_accuracy_model.get_layer(this_layer_name).get_weights()\n",
        "  high_accuracy_model_weights_bias = copied_high_accuracy_model.get_layer(this_layer_name).get_weights()\n",
        "\n",
        "  low_accuracy_model_weight,low_accuracy_model_bias = low_accuracy_model_weights_bias[0],low_accuracy_model_weights_bias[1]\n",
        "  high_accuracy_model_weight,high_accuracy_model_bias = high_accuracy_model_weights_bias[0],high_accuracy_model_weights_bias[1]\n",
        "#   print(low_accuracy_model_weight.shape,low_accuracy_model_bias.shape)\n",
        "  low_accuracy_model_weight[:,:,:,bottom_channels] = high_accuracy_model_weight[:,:,::,top_channels]\n",
        "#   print(bottom_channels)\n",
        "  high_accuracy_model_bias[bottom_channels] = high_accuracy_model_bias[top_channels]\n",
        "  \n",
        "  copied_low_accuracy_model.get_layer(this_layer_name).set_weights([low_accuracy_model_weight, high_accuracy_model_bias])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgoNV1oG8BS4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# for l,DIs in enumerate(all_di_acores_high_accuracy):\n",
        "#   this_layer_name = layer_names[l]\n",
        "#   this_high_accuracy_di_scores = all_di_acores_high_accuracy[l]\n",
        "#   this_high_accuracy_di_scores_sorted = np.argsort(this_high_accuracy_di_scores)\n",
        "#   this_high_accuracy_di_scores_sorted_list = np.ndarray.tolist(this_high_accuracy_di_scores_sorted)\n",
        "#   top_channels = this_high_accuracy_di_scores_sorted_list[-Ks[l]:]\n",
        "  \n",
        "#   # do same thing for lowest DIs\n",
        "#   this_low_accuracy_di_scores = all_di_acores_low_accuracy[l]\n",
        "#   this_low_accuracy_di_scores_sorted = np.argsort(this_low_accuracy_di_scores)\n",
        "#   this_low_accuracy_di_scores_sorted_list = np.ndarray.tolist(this_low_accuracy_di_scores_sorted)\n",
        "#   bottom_channels = this_low_accuracy_di_scores_sorted_list[0:Ks[l]]\n",
        "  \n",
        "#   # now replace the weights\n",
        "#   low_accuracy_model_weights_bias = copied_low_accuracy_model.get_layer(this_layer_name).get_weights()\n",
        "#   high_accuracy_model_weights_bias = copied_high_accuracy_model.get_layer(this_layer_name).get_weights()\n",
        "\n",
        "#   low_accuracy_model_weight,low_accuracy_model_bias = low_accuracy_model_weights_bias[0],low_accuracy_model_weights_bias[1]\n",
        "#   high_accuracy_model_weight,high_accuracy_model_bias = high_accuracy_model_weights_bias[0],high_accuracy_model_weights_bias[1]\n",
        "# #   print(low_accuracy_model_weight.shape,low_accuracy_model_bias.shape)\n",
        "#   low_accuracy_model_weight[:,:,:,bottom_channels] = high_accuracy_model_weight[:,:,:,top_channels]\n",
        "# #   print(bottom_channels)\n",
        "#   high_accuracy_model_bias[bottom_channels] = high_accuracy_model_bias[top_channels]\n",
        "  \n",
        "#   copied_low_accuracy_model.get_layer(this_layer_name).set_weights([low_accuracy_model_weight, high_accuracy_model_bias])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OodsSlYDCesc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# high_accuracy_model_weights_bias.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AtLgL9ICGJE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# high_accuracy_model_bias"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzN_W73aldFU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# # (discores_high_acc_model)\n",
        "# K= 50 # how many channels to pick\n",
        "# high_acc_di_sorted = np.argsort(discores_high_acc_model)\n",
        "# high_acc_di_sorted_list = np.ndarray.tolist(high_acc_di_sorted)\n",
        "# top_channels = high_acc_di_sorted_list[-K:]#np.random.choice(32,K)#\n",
        "# print('Top Channels: ',top_channels)\n",
        "\n",
        "\n",
        "# low_acc_di_sorted = np.argsort(discores_low_acc_model)\n",
        "# low_acc_di_sorted_list = np.ndarray.tolist(low_acc_di_sorted)\n",
        "# low_channels = low_acc_di_sorted_list[0:K]#np.random.choice(32,K)\n",
        "# print('Low Channels: ', low_channels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_BDN-T-BaGz",
        "colab_type": "text"
      },
      "source": [
        "#####  get low accuracy DI channels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5DgBrzXCGB8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# discores_low_acc_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5B34pIwCbXN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plt.plot(discores_low_acc_model,'-*')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRrtIpLVBdK4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SD8nswMnxWVC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# total_channels = len(discores_high_acc_model)\n",
        "# total_channels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MsCtxzRVokuR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "enable_training = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sk_0a6JlFux5",
        "colab_type": "code",
        "outputId": "5fcfb52e-3574-4620-e825-a208c9f6f128",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# returns \n",
        "'''\n",
        "def highAccuracyModelTillConv2(input_img): \n",
        "  conv1_1 = (Conv2D(32, (3,3), padding='same',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), \n",
        "                    input_shape=x_train.shape[1:], activation='elu', trainable = enable_training,\n",
        "                    name = 'HighAccuracyConv1'))(input_img)\n",
        "  conv1_2 = BatchNormalization(name = 'HighAccuracyBN1',\n",
        "                              trainable = enable_training)(conv1_1)\n",
        "  conv2_1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',trainable = enable_training,\n",
        "                    name = 'HighAccuracyConv2',kernel_regularizer=regularizers.l2(weight_decay)))(conv1_2)\n",
        "#   conv2_2 = BatchNormalization(name = 'HighAccuracyBN2',\n",
        "#                               trainable = enable_training)(conv2_1)  \n",
        "  \n",
        "  return conv2_1\n",
        "'''"
      ],
      "execution_count": 291,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\ndef highAccuracyModelTillConv2(input_img): \\n  conv1_1 = (Conv2D(32, (3,3), padding='same',\\n                    kernel_regularizer=regularizers.l2(weight_decay), \\n                    input_shape=x_train.shape[1:], activation='elu', trainable = enable_training,\\n                    name = 'HighAccuracyConv1'))(input_img)\\n  conv1_2 = BatchNormalization(name = 'HighAccuracyBN1',\\n                              trainable = enable_training)(conv1_1)\\n  conv2_1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',trainable = enable_training,\\n                    name = 'HighAccuracyConv2',kernel_regularizer=regularizers.l2(weight_decay)))(conv1_2)\\n#   conv2_2 = BatchNormalization(name = 'HighAccuracyBN2',\\n#                               trainable = enable_training)(conv2_1)  \\n  \\n  return conv2_1\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 291
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZchwmWh7la9",
        "colab_type": "code",
        "outputId": "46aff099-4203-4525-b31c-599c876a3386",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1074
        }
      },
      "source": [
        "copied_low_accuracy_model.summary()"
      ],
      "execution_count": 292,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv1_1 (Conv2D)             (None, 32, 32, 64)        1792      \n",
            "_________________________________________________________________\n",
            "conv1_2 (BatchNormalization) (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "pool1_3 (MaxPooling2D)       (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2_1 (Conv2D)             (None, 16, 16, 128)       73856     \n",
            "_________________________________________________________________\n",
            "conv2_2 (BatchNormalization) (None, 16, 16, 128)       512       \n",
            "_________________________________________________________________\n",
            "pool2_3 (MaxPooling2D)       (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv3_1 (Conv2D)             (None, 8, 8, 256)         295168    \n",
            "_________________________________________________________________\n",
            "conv3_2 (BatchNormalization) (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "conv4_1 (Conv2D)             (None, 8, 8, 256)         590080    \n",
            "_________________________________________________________________\n",
            "conv4_2 (BatchNormalization) (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "pool4_2 (MaxPooling2D)       (None, 4, 4, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv5_1 (Conv2D)             (None, 4, 4, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "conv5_2 (BatchNormalization) (None, 4, 4, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv6_1 (Conv2D)             (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv6_2 (BatchNormalization) (None, 4, 4, 512)         2048      \n",
            "_________________________________________________________________\n",
            "pool6_2 (MaxPooling2D)       (None, 2, 2, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv7_1 (Conv2D)             (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv7_2 (BatchNormalization) (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv8_1 (Conv2D)             (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv8_2 (BatchNormalization) (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "pool8_2 (MaxPooling2D)       (None, 1, 1, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              2101248   \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "fc3 (Dense)                  (None, 1000)              4097000   \n",
            "_________________________________________________________________\n",
            "out (Dense)                  (None, 3)                 3003      \n",
            "=================================================================\n",
            "Total params: 32,214,051\n",
            "Trainable params: 32,208,547\n",
            "Non-trainable params: 5,504\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OA-0pVGXvMeE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Dense, Concatenate, Input, Lambda\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41smOd7GTX5c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# def cifar10ClassifierTransfer(input_img):\n",
        "  \n",
        "#   conv1_1 = (Conv2D(64, (3,3),name = 'conv1_1' ,padding='same', \n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:],\n",
        "#                     activation='relu'))(input_img)\n",
        "#   conv1_2 = BatchNormalization(name = 'conv1_2')(conv1_1)\n",
        "#   pool1_3 = MaxPooling2D(pool_size=(2, 2), name = 'pool1_3')(conv1_2) #14 x 14 x 32\n",
        "#   conv2_1 = (Conv2D(128, (3,3),name = 'conv2_1' ,padding='same',\n",
        "#                     activation= 'relu',kernel_regularizer=regularizers.l2(weight_decay)))(pool1_3)\n",
        "#   conv2_2 = BatchNormalization(name = 'conv2_2')(conv2_1)\n",
        "#   pool2_3 = MaxPooling2D(pool_size=(2, 2), name = 'pool2_3')(conv2_2) #14 x 14 x 32\n",
        "  \n",
        "#   conv3_1 = (Conv2D(256, (3,3), padding='same',name = 'conv3_1',\n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool2_3)\n",
        "#   conv3_2 = BatchNormalization(name = 'conv3_2')(conv3_1)\n",
        "#   conv4_1 = (Conv2D(256, (3,3), padding='same',name = 'conv4_1' ,activation= 'relu',\n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay)))(conv3_2)\n",
        "#   conv4_2 = BatchNormalization(name = 'conv4_2')(conv4_1)\n",
        "#   pool4_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool4_2')(conv4_2) #14 x 14 x 32\n",
        "  \n",
        "#   conv5_1 = (Conv2D(512, (3,3), padding='same',name = 'conv5_1',\n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool4_2)\n",
        "#   conv5_2 = BatchNormalization(name = 'conv5_2')(conv5_1)\n",
        "#   conv6_1 = (Conv2D(512, (3,3), padding='same',name = 'conv6_1' ,activation= 'relu',\n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay)))(conv5_2)\n",
        "#   conv6_2 = BatchNormalization(name = 'conv6_2')(conv6_1)\n",
        "#   pool6_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool6_2')(conv6_2) #14 x 14 x 32\n",
        "  \n",
        "#   conv7_1 = (Conv2D(512, (3,3), padding='same',name = 'conv7_1',\n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool6_2)\n",
        "#   conv7_2 = BatchNormalization(name = 'conv7_2')(conv7_1)\n",
        "#   conv8_1 = (Conv2D(512, (3,3), padding='same',name = 'conv8_1' ,activation= 'relu',\n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay)))(conv7_2)\n",
        "#   conv8_2 = BatchNormalization(name = 'conv8_2')(conv8_1)\n",
        "#   pool8_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool8_2')(conv8_2) #14 x 14 x 32\n",
        "\n",
        "\n",
        "#   flat = Flatten()(pool8_2)\n",
        "#   fc1 = Dense(4096, activation='relu', name = 'fc1')(flat)\n",
        "#   fc2 = Dense(4096, activation='relu', name = 'fc2')(fc1)\n",
        "#   fc3 = Dense(1000, activation='relu', name = 'fc3')(fc2)\n",
        "\n",
        "\n",
        "  \n",
        "#   out = Dense(num_classes, activation='softmax', name = 'out')(fc3)\n",
        "  \n",
        "#   return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sf3PN5YIyba2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# top_channels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzUmKtVGFusU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# #high_acc_output = highAccuracyModelTillConv2(input_img)\n",
        "# modelFilt = Model(input_img, \n",
        "#               cifar10ClassifierTransfer(input_img))\n",
        "# #data augmentation\n",
        "# datagen = ImageDataGenerator(\n",
        "#     rotation_range=15,\n",
        "#     width_shift_range=0.1,\n",
        "#     height_shift_range=0.1,\n",
        "#     horizontal_flip=True,\n",
        "#     )\n",
        "# datagen.fit(train_three_data)\n",
        " \n",
        "# #training\n",
        "# batch_size = 64\n",
        " \n",
        "# opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
        "# # model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
        "\n",
        "# modelFilt.compile(loss=losses.categorical_crossentropy, optimizer=opt_rms, metrics=['accuracy'])\n",
        "\n",
        "# modelFilt.summary()\n",
        " "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cty76ahJDRhj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#data augmentation\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=15,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    horizontal_flip=True,\n",
        "    )\n",
        "datagen.fit(train_three_data)\n",
        "# #training\n",
        "batch_size = 64"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WE7b72OOFups",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plot_model(copied_low_accuracy_model, to_file=\"FiltModel.png\", show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-yqdp6_rQkI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# loaded_model_high_accuracy.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GkUnIIAqH0k",
        "colab_type": "text"
      },
      "source": [
        "## set predetermined weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKVw7k8QLwoQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# top_channels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hWcRyTNtaXW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# top_channels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JhmWPU7RA9N3",
        "colab_type": "text"
      },
      "source": [
        "#### # get low accuracy DI channel - stored in low_channels\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n",
        "s"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mL5R24pGA9KP",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H0JBUK8KA26B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# loaded_model_high_accuracy.get_layer(layer_name).get_weights()[0].shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PmS9J6gmDFIL",
        "colab_type": "text"
      },
      "source": [
        "## Get filter weights for top K10 DI scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0FN66utr5VE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # set weights of high accuracy model for channels not used to 0 [9, 25, 19, 29, 5, 7, 26, 28, 17, 23]\n",
        "# high_acc_conv2_weights = loaded_model_high_accuracy.get_layer(layer_name).get_weights()\n",
        "# # temp_weights = np.zeros_like(high_acc_conv2_weights[0].shape[0])\n",
        "# temp_weights_top_10_DI = np.zeros(shape = (high_acc_conv2_weights[0].shape[0],high_acc_conv2_weights[0].shape[1],high_acc_conv2_weights[0].shape[2],K))\n",
        "# h,w,d,n = high_acc_conv2_weights[0].shape\n",
        "# j = 0\n",
        "# for ch in range(0,n):\n",
        "#   if ch in top_channels:\n",
        "#     temp_weights_top_10_DI[:,:,:,j] = high_acc_conv2_weights[0][:,:,:,ch]\n",
        "#     j = j+1\n",
        "    \n",
        "  \n",
        "# # (high_acc_conv2_weights[0].shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bF9DJY3BD6Wf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# temp_weights_top_10_DI.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lR-sOsQD6UV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# top_channels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Esv5wQmYFby6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# temp_bias_top_10_DI = np.zeros(shape = (K,))\n",
        "# # temp_bias = high_acc_conv2_weights[1]\n",
        "\n",
        "# # bias_shape = high_acc_conv2_weights[1].shape\n",
        "# j = 0\n",
        "# for ch in range(0,high_acc_conv2_weights[1].shape[0]):\n",
        "# #   print (ch)\n",
        "#   if ch in top_channels:\n",
        "#     temp_bias_top_10_DI[j] = high_acc_conv2_weights[1][ch]\n",
        "#     j = j+1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPV28w1jFulD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# temp_bias\n",
        "# weights_to_assign_fromHighAcc = [temp_weights_top_10_DI,temp_bias_top_10_DI]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pYigm1qFJmK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# temp_weights_top_10_DI.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1t2Yj3-E01G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# high_acc_conv2_weights[1].shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQKKEkR2F-tx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# modelFilt.get_layer('lowAccuracyModelConv1_1').set_weights(loaded_model_low_accuracy.get_layer('conv2d_1').get_weights())\n",
        "# modelFilt.get_layer('lowAccuracyModelBN1').set_weights(loaded_model_low_accuracy.get_layer('batch_normalization_1').get_weights())\n",
        "# # modelFilt.get_layer('HighAccuracyConv2').set_weights(loaded_model_high_accuracy.get_layer('conv2d_2').get_weights())\n",
        "# modelFilt.get_layer('lowAccuracyModelConv2_1').set_weights(loaded_model_low_accuracy.get_layer('conv2d_2').get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "knA2xBI4jrZF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# loaded_model_low_accuracy.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZ9nBYl-h9SD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # preset the weights\n",
        "# for l1,l2 in zip(modelFilt.layers,loaded_model_low_accuracy.layers):\n",
        "#     l1.set_weights(l2.get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGIUVWh3G4x4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# layer_to_replace = layer_name\n",
        "# low_acc_conv2d_weights_bias = modelFilt.get_layer(layer_to_replace).get_weights()\n",
        "# low_acc_conv2d_weights = low_acc_conv2d_weights_bias[0]\n",
        "# low_acc_conv2d_wbias= low_acc_conv2d_weights_bias[1]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cVFju9iFKJID",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# temp_bias_top_10_DI.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlkRaotsHceY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # now change weights for layers corresponding to lowest DI scores\n",
        "# orig_weights = np.copy(low_acc_conv2d_weights)\n",
        "# j = 0\n",
        "# for ch in range(0,orig_weights.shape[3]):\n",
        "#   if ch in low_channels:\n",
        "# #     print(ch)\n",
        "#     orig_weights[:,:,:,ch] = temp_weights_top_10_DI[:,:,:,j]\n",
        "#     j = j+1\n",
        "# # orig_weights.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ye2_DU60K0nL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# orig_bias = np.copy(low_acc_conv2d_wbias)\n",
        "# j = 0\n",
        "# for ch in range(0,orig_bias.shape[0]):\n",
        "#   if ch in low_channels:\n",
        "# #     print(ch)\n",
        "#     orig_bias[ch] = temp_bias_top_10_DI[j]\n",
        "#     j = j+1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WDX113kegLSi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RERDrTNALkUA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# new_wt_bias_conv2d1 = [orig_weights,orig_bias]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMc3GS52iEv0",
        "colab_type": "text"
      },
      "source": [
        "#### Overwrite weights fow low DI with high DI"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTUKj97VLfIu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# modelFilt.get_layer(layer_to_replace).set_weights(new_wt_bias_conv2d1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ji5jVmbej-LM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# loaded_model_low_accuracy.layers[5]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0w2grlRlKQhJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7EozUm-dSIx2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# modelFilt.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRWAVstvGhht",
        "colab_type": "text"
      },
      "source": [
        "#### # now replace weights in conv2d layer for filters correspondng to lowest 10 DI scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcnUQrPtGay_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fev8cENxbr-P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "file_save_name = 'weights_FCtop_k_model2.hdf5'\n",
        "mcp_save = ModelCheckpoint(file_save_name, save_best_only=True, monitor='val_acc', mode='auto')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BTKJa7Zbwtc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def lr_schedule(epoch):\n",
        "    lrate = 0.0001\n",
        "    if epoch > 75:\n",
        "        lrate = 0.0005\n",
        "    if epoch > 100:\n",
        "        lrate = 0.0003\n",
        "    return lrate"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdiZwWSth97S",
        "colab_type": "code",
        "outputId": "f689c631-b65d-4d11-92dd-04f8b5e76747",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "copied_low_accuracy_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 321,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6.50756958134969, 0.4876666668256124]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 321
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIOMXQsjdSNX",
        "colab_type": "code",
        "outputId": "b85c3057-0c16-4469-8349-6a554d6a68f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2114
        }
      },
      "source": [
        "copied_low_accuracy_model.fit_generator(datagen.flow(train_X, train_ground, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=train_X.shape[0] // batch_size,epochs=60,\\\n",
        "                    verbose=1,validation_data=(valid_X,valid_ground),\n",
        "                    shuffle = True,callbacks=[mcp_save,LearningRateScheduler(lr_schedule)])"
      ],
      "execution_count": 322,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/60\n",
            "187/187 [==============================] - 11s 61ms/step - loss: 0.3126 - acc: 0.9306 - val_loss: 0.4598 - val_acc: 0.8840\n",
            "Epoch 2/60\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.2808 - acc: 0.9388 - val_loss: 0.4605 - val_acc: 0.8810\n",
            "Epoch 3/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2719 - acc: 0.9440 - val_loss: 0.4467 - val_acc: 0.8813\n",
            "Epoch 4/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2599 - acc: 0.9468 - val_loss: 0.5130 - val_acc: 0.8820\n",
            "Epoch 5/60\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.2568 - acc: 0.9503 - val_loss: 0.4583 - val_acc: 0.8823\n",
            "Epoch 6/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2542 - acc: 0.9484 - val_loss: 0.4903 - val_acc: 0.8843\n",
            "Epoch 7/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2488 - acc: 0.9505 - val_loss: 0.4755 - val_acc: 0.8800\n",
            "Epoch 8/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2434 - acc: 0.9545 - val_loss: 0.5105 - val_acc: 0.8870\n",
            "Epoch 9/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2378 - acc: 0.9544 - val_loss: 0.5060 - val_acc: 0.8803\n",
            "Epoch 10/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2311 - acc: 0.9571 - val_loss: 0.5212 - val_acc: 0.8820\n",
            "Epoch 11/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2398 - acc: 0.9534 - val_loss: 0.5358 - val_acc: 0.8877\n",
            "Epoch 12/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2275 - acc: 0.9590 - val_loss: 0.5169 - val_acc: 0.8873\n",
            "Epoch 13/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2198 - acc: 0.9603 - val_loss: 0.5093 - val_acc: 0.8950\n",
            "Epoch 14/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2181 - acc: 0.9628 - val_loss: 0.5173 - val_acc: 0.8900\n",
            "Epoch 15/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2149 - acc: 0.9631 - val_loss: 0.5289 - val_acc: 0.8930\n",
            "Epoch 16/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2171 - acc: 0.9607 - val_loss: 0.5839 - val_acc: 0.8797\n",
            "Epoch 17/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2113 - acc: 0.9614 - val_loss: 0.5101 - val_acc: 0.8923\n",
            "Epoch 18/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2149 - acc: 0.9614 - val_loss: 0.5013 - val_acc: 0.8900\n",
            "Epoch 19/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1995 - acc: 0.9681 - val_loss: 0.5820 - val_acc: 0.8910\n",
            "Epoch 20/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2062 - acc: 0.9676 - val_loss: 0.5832 - val_acc: 0.8820\n",
            "Epoch 21/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1956 - acc: 0.9697 - val_loss: 0.5765 - val_acc: 0.8867\n",
            "Epoch 22/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2034 - acc: 0.9676 - val_loss: 0.5806 - val_acc: 0.8840\n",
            "Epoch 23/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1974 - acc: 0.9686 - val_loss: 0.5537 - val_acc: 0.8900\n",
            "Epoch 24/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1970 - acc: 0.9679 - val_loss: 0.5776 - val_acc: 0.8823\n",
            "Epoch 25/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1918 - acc: 0.9713 - val_loss: 0.5875 - val_acc: 0.8780\n",
            "Epoch 26/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1942 - acc: 0.9671 - val_loss: 0.5657 - val_acc: 0.8883\n",
            "Epoch 27/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1909 - acc: 0.9697 - val_loss: 0.5291 - val_acc: 0.8913\n",
            "Epoch 28/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1955 - acc: 0.9664 - val_loss: 0.5388 - val_acc: 0.8857\n",
            "Epoch 29/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1921 - acc: 0.9715 - val_loss: 0.6260 - val_acc: 0.8870\n",
            "Epoch 30/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1857 - acc: 0.9723 - val_loss: 0.5978 - val_acc: 0.8847\n",
            "Epoch 31/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1802 - acc: 0.9730 - val_loss: 0.6014 - val_acc: 0.8863\n",
            "Epoch 32/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1746 - acc: 0.9751 - val_loss: 0.6802 - val_acc: 0.8907\n",
            "Epoch 33/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1773 - acc: 0.9731 - val_loss: 0.6000 - val_acc: 0.8890\n",
            "Epoch 34/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1809 - acc: 0.9723 - val_loss: 0.5769 - val_acc: 0.8853\n",
            "Epoch 35/60\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.1751 - acc: 0.9747 - val_loss: 0.5820 - val_acc: 0.8860\n",
            "Epoch 36/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1720 - acc: 0.9753 - val_loss: 0.6387 - val_acc: 0.8837\n",
            "Epoch 37/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1831 - acc: 0.9725 - val_loss: 0.5859 - val_acc: 0.8850\n",
            "Epoch 38/60\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.1640 - acc: 0.9779 - val_loss: 0.6661 - val_acc: 0.8870\n",
            "Epoch 39/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1672 - acc: 0.9784 - val_loss: 0.5772 - val_acc: 0.8893\n",
            "Epoch 40/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1674 - acc: 0.9773 - val_loss: 0.5860 - val_acc: 0.8907\n",
            "Epoch 41/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1657 - acc: 0.9789 - val_loss: 0.6598 - val_acc: 0.8800\n",
            "Epoch 42/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1695 - acc: 0.9772 - val_loss: 0.5654 - val_acc: 0.8870\n",
            "Epoch 43/60\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.1651 - acc: 0.9783 - val_loss: 0.5742 - val_acc: 0.8840\n",
            "Epoch 44/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1657 - acc: 0.9775 - val_loss: 0.6332 - val_acc: 0.8863\n",
            "Epoch 45/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1646 - acc: 0.9777 - val_loss: 0.6769 - val_acc: 0.8797\n",
            "Epoch 46/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1614 - acc: 0.9780 - val_loss: 0.5898 - val_acc: 0.8827\n",
            "Epoch 47/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1578 - acc: 0.9815 - val_loss: 0.5685 - val_acc: 0.8910\n",
            "Epoch 48/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1633 - acc: 0.9792 - val_loss: 0.7071 - val_acc: 0.8840\n",
            "Epoch 49/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1571 - acc: 0.9809 - val_loss: 0.6617 - val_acc: 0.8780\n",
            "Epoch 50/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1568 - acc: 0.9800 - val_loss: 0.6686 - val_acc: 0.8840\n",
            "Epoch 51/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1540 - acc: 0.9816 - val_loss: 0.6280 - val_acc: 0.8817\n",
            "Epoch 52/60\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.1584 - acc: 0.9810 - val_loss: 0.6440 - val_acc: 0.8907\n",
            "Epoch 53/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1569 - acc: 0.9812 - val_loss: 0.6832 - val_acc: 0.8813\n",
            "Epoch 54/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1527 - acc: 0.9816 - val_loss: 0.5666 - val_acc: 0.8893\n",
            "Epoch 55/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1523 - acc: 0.9822 - val_loss: 0.6975 - val_acc: 0.8883\n",
            "Epoch 56/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1449 - acc: 0.9841 - val_loss: 0.7617 - val_acc: 0.8823\n",
            "Epoch 57/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1488 - acc: 0.9839 - val_loss: 0.6712 - val_acc: 0.8903\n",
            "Epoch 58/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1447 - acc: 0.9852 - val_loss: 0.8419 - val_acc: 0.8867\n",
            "Epoch 59/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1481 - acc: 0.9848 - val_loss: 0.7423 - val_acc: 0.8820\n",
            "Epoch 60/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1433 - acc: 0.9855 - val_loss: 0.6561 - val_acc: 0.8900\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f5bfa48b438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 322
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAnK7D_Mm_dz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "copied_low_accuracy_model.load_weights(file_save_name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8ZXIw1Um_ao",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# copied_low_accuracy_model.save('final1_'+str(K)+'_'+file_save_name)\n",
        "# copied_low_accuracy_model.save('final1_Transfer1')\n",
        "copied_low_accuracy_model.save('final1_FC_transfer')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZsEELS6m_WM",
        "colab_type": "code",
        "outputId": "ca7446ff-edc8-4685-a8ce-a51548447e4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "accuracy = copied_low_accuracy_model.history.history['acc']\n",
        "val_accuracy = copied_low_accuracy_model.history.history['val_acc']\n",
        "loss = copied_low_accuracy_model.history.history['loss']\n",
        "val_loss = copied_low_accuracy_model.history.history['val_loss']\n",
        "epochs = range(len(accuracy))\n",
        "plt.plot(epochs, accuracy, 'b', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracy, 'r', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'b', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 325,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V1YHRFT97liyBWbPgoosOVImOEz8sWbKEhIQE+vXrx3HHHcc///zDgAEDSEpK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\n/wGCsPMpTVIMGwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6Rd5z377dxDwtzbY7dChcz/3VV00Ae/U68ljduvb9ROJ3D6b6LQq3325ZJM8/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\nu+M4Thzi4u44jhOH/H86wbjzJ4yiIgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5S9iJ-HGnRFW",
        "colab_type": "code",
        "outputId": "bf87f127-fb82-4d89-e920-6e96dff6339f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "copied_low_accuracy_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 326,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5093064686457316, 0.895]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 326
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8o16S6NPmfn",
        "colab_type": "code",
        "outputId": "7fa88522-c961-49a3-c3b4-d549a1ef374c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "#testing\n",
        "test_eval = copied_low_accuracy_model.evaluate(test_three_data, test_three_label_one_hot, verbose=0)\n",
        "print('Test loss:', test_eval[0])\n",
        "print('Test accuracy:', test_eval[1])\n",
        "\n"
      ],
      "execution_count": 327,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.49601475381851196\n",
            "Test accuracy: 0.8983333334922791\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z06QupNsYeLZ",
        "colab_type": "code",
        "outputId": "bfd3b001-2dbb-42da-ea2c-694eb97b952f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "layer_names"
      ],
      "execution_count": 328,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['conv8_1']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 328
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRuSLttWnRCR",
        "colab_type": "code",
        "outputId": "3f087696-283d-49af-96c5-9416e5a7726d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "#layer_name = layer_to_replace\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_to_replace)\n",
        "\n",
        "plt.ylim([0,1.0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.5702386197235612\n",
            "STD of DI Score:  0.059415098660331\n",
            "MAX of DI Score:  0.6984114818796965\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iufJCEXEWcBbAhAkTor+L7DR9ef52O5wL3un/ZNZe/H5qrMpQWASMKY2PztPKFgK3RsTr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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MI4AzYZDnQ_X",
        "colab_type": "code",
        "outputId": "bf40213b-79ab-434c-9780-1b9e161b182b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "#layer_name = 'lowAccuracyModelConv2_1'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_to_replace)\n",
        "\n",
        "plt.ylim([0,1.0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.5441455605104493\n",
            "STD of DI Score:  0.07125697376575997\n",
            "MAX of DI Score:  0.6857804932152856\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZCguAMaXx0bmtbD5wa0S8BTwq6SFSSNxeYV1mLeXgsHbW1DEFSStJ+tASLvt2YJyksZKG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\nUDAzs4JDwczMCg4FMzMr/C+wNKsqDlWHzAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9AxL2k2cPvs",
        "colab_type": "code",
        "outputId": "05b2cbc6-ec55-4ffc-f3b7-47a0d819c614",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_1'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.5324752079826454\n",
            "STD of DI Score:  0.08518245611608953\n",
            "MAX of DI Score:  0.6952950607026254\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmomNWqXclu8",
        "colab_type": "code",
        "outputId": "3a234d4d-d4e1-4831-c7a8-59acc185d0d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_2'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.5472756918767483\n",
            "STD of DI Score:  0.07193005751902407\n",
            "MAX of DI Score:  0.7008227385646286\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1iC11tO-c1ME",
        "colab_type": "code",
        "outputId": "d5e2ca0b-926e-4343-8a92-b002380a5ba9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_3'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.4283726311323016\n",
            "STD of DI Score:  0.11606436878312644\n",
            "MAX of DI Score:  0.7211856134234298\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PYfByEs+iaT6ivbjdXX0WpfExzl/exTBpm/7S/pprS+HzSd5Kczbyw4GPAgyeyCZJS4kr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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3SsO1CXc09Q",
        "colab_type": "code",
        "outputId": "80ab8bbd-d50a-4858-f8aa-e0e29cf392d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_4'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.4676357866426307\n",
            "STD of DI Score:  0.129639936407154\n",
            "MAX of DI Score:  0.9082884744169848\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Unii3onHcpx6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2XDOWtOP2p7",
        "colab_type": "code",
        "outputId": "5ba31b93-776d-4b0a-87ca-a773a09b26d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        }
      },
      "source": [
        "modelFilt.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "lowAccuracyModelConv1_1 (Con (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "lowAccuracyModelBN1 (BatchNo (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "lowAccuracyModelConv2_1 (Con (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 294,947\n",
            "Trainable params: 294,051\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxnMxP6cTGLO",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    }
  ]
}