(Conv2d1_Layer)cifar103ClassAccuracyHighDIScoreCrossCrossVer04.ipynb

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  "metadata": {
    "colab": {
      "name": "(Conv2d1_Layer)cifar103ClassAccuracyHighDIScoreCrossCrossVer04.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/(Conv2d1_Layer)cifar103ClassAccuracyHighDIScoreCrossCrossVer04.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "FgwWyMb6iCkO",
        "colab_type": "code",
        "outputId": "4071fbcb-a886-44a6-f690-e6ff3dac0a41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "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"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "txdR9fEabt2P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "from keras.models import model_from_json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PYdWJtFsiF1a",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "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": []
    },
    {
      "metadata": {
        "id": "9m3ehGh2iIBA",
        "colab_type": "code",
        "outputId": "e4997503-ef13-427d-8811-819b941740b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "cell_type": "code",
      "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": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 6s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "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": {
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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": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hVAjMdeSrloa",
        "colab_type": "code",
        "outputId": "e2360c0a-423e-4726-d383-210e84fc2ef8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "ngfK-2Qewhtq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "3ZJV35fkrj3O",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2ekn-CkLikYJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Get only three classes of data and train the model on it"
      ]
    },
    {
      "metadata": {
        "id": "VPYv1L-uigzL",
        "colab_type": "code",
        "outputId": "e187fc9d-c49d-4b86-8072-d2efaa47a65e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "train_three_data,train_three_label,test_three_data,test_three_label = getThreeClassesTrainTest([1,8,9 ],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[    4     5    32 ... 49993 49998 49999]\n",
            "[    8    62    69 ... 49968 49976 49985]\n",
            "[    1     2    14 ... 49963 49971 49997]\n",
            "(15000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9luxTYeM3aZO",
        "colab_type": "code",
        "outputId": "8995f5ff-0db3-4bfa-be46-a4aabb7b2cf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "cell_type": "code",
      "source": [
        "plt.hist(train_three_label)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([5000.,    0.,    0.,    0.,    0.,    0.,    0.,    0., 5000.,\n",
              "        5000.]),\n",
              " array([1. , 1.8, 2.6, 3.4, 4.2, 5. , 5.8, 6.6, 7.4, 8.2, 9. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VwTckrAR+AXwT+KuqGp+N473srgCq6tvAnlH3sb+qerKq7m7LPwW2Mw8+BV0Dz7XVw9vP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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Dni1oqIcyPai",
        "colab_type": "code",
        "outputId": "97cbb913-0b6b-44ff-8feb-1927a1eef6d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "cell_type": "code",
      "source": [
        "# test_three_label.shape\n",
        "plt.hist(test_three_label)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1000.,    0.,    0.,    0.,    0.,    0.,    0.,    0., 1000.,\n",
              "        1000.]),\n",
              " array([1. , 1.8, 2.6, 3.4, 4.2, 5. , 5.8, 6.6, 7.4, 8.2, 9. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YfavtJW3acWgTwBnA1VV1OvATYMl83Xqbknon8C9D1wKQ5HhGX0R5CvBa4NgkfzlsVVBV\nO4Arga8xmv65D/j5kRrPAFhEbY79JuD6qrp56HoO1KYM7gLOHboW4GzgnW2+/QvAW5L887AljbRP\nj1TVXuBLjOZrh7YL2DXr7G0Lo0BYKs4D7qmqJ4YupHkr8L2qmqmqnwE3A388cE0AVNWmqvqjqjoH\neAr4ryM1lgGwSNrF1k3Ajqr65ND17JdkMsmytvwq4G3Ad4atCqrqo1W1sqpWM5o6uLOqBv+EluTY\ndhGfNsXydkan7YOqqseBnUle35rWAIPeYHCA97JEpn+ax4CzkhzT/m2uYXRdbnBJfqe9nsxo/v/z\nR2qsJfdtoONKcgPwZuDEJLuAK6pq07BVAaNPtO8D7m/z7QAfa09GD+kkYHO7Q+NlwI1VtWRuuVyC\nlgNfGr1nMAF8vqq+MmxJv/QB4Po23fIIcPHA9QC/DMq3AX81dC37VdXWJFuAe4B9wL0snSeCb0py\nAvAz4JIjeTH/JXcbqCTpxXEKSJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktSp/wO6\n8/vXYIPl/gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fuM8CeuRsDzW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "EshrdJ78fp7U",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mbK7iwsZx8QW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jYJt52bU0ooK",
        "colab_type": "code",
        "outputId": "23e6683e-a192-4371-b0ca-2012d722c657",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "train_three_label_cat.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "r3Ydm3cykUFn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_three_label_one_hot = to_categorical(train_three_label_cat)\n",
        "test_three_label_one_hot = to_categorical(test_three_label_cat)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_O4_VLoJ6vSF",
        "colab_type": "code",
        "outputId": "05a27297-3e53-4f11-8a1f-cb8103316e67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_label_one_hot.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "UIrWUPz_lGvF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "H6AkQaz-kRh0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "num_classes = test_three_label_one_hot.shape[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "duFF_pGbj2i1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "Pt1coy0mkFrj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def lr_schedule(epoch):\n",
        "    lrate = 0.001\n",
        "    if epoch > 75:\n",
        "        lrate = 0.0005\n",
        "    if epoch > 100:\n",
        "        lrate = 0.0003\n",
        "    return lrate"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4IbtMUdhiscG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def cifar10Classifier(input_img):\n",
        "  \n",
        "  conv1 = (Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:], activation='elu'))(input_img)\n",
        "  conv1 = BatchNormalization()(conv1)\n",
        "  conv1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv1)\n",
        "  conv1 = BatchNormalization()(conv1)\n",
        "  pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) #14 x 14 x 32\n",
        "  drop1= Dropout(.2)(pool1)\n",
        "\n",
        "  conv2 = (Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop1)\n",
        "  conv2 = BatchNormalization()(conv2)\n",
        "  conv2 = (Conv2D(64, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv2)\n",
        "  conv2 = BatchNormalization()(conv2)\n",
        "  pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) #14 x 14 x 32\n",
        "  drop2= Dropout(.3)(pool2)\n",
        "\n",
        "  conv3 = (Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop2)\n",
        "  conv3 = BatchNormalization()(conv3)\n",
        "  conv3 = (Conv2D(128, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv3)\n",
        "  conv3 = BatchNormalization()(conv3)\n",
        "  pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) #14 x 14 x 32\n",
        "  drop3= Dropout(.4)(pool3)\n",
        "\n",
        "  flat = Flatten()(drop3)\n",
        "  out = Dense(num_classes, activation='softmax')(flat)\n",
        "  \n",
        "  return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-VPIvSX1jytM",
        "colab_type": "code",
        "outputId": "edb3ae64-1d8c-4549-8c68-37f4ccddefb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 972
        }
      },
      "cell_type": "code",
      "source": [
        "model = Model(input_img, (cifar10Classifier(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",
        "model.compile(loss=losses.categorical_crossentropy, optimizer=opt_rms, metrics=['accuracy'])\n",
        "\n",
        "model.summary()\n",
        " "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (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_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (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_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (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"
        }
      ]
    },
    {
      "metadata": {
        "id": "2jabvkmcr1KR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mcp_save = ModelCheckpoint('.mdl_wts.hdf5', save_best_only=True, monitor='val_loss', mode='min')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yBK4UwK8lffO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "train_X,valid_X,train_ground,valid_ground"
      ]
    },
    {
      "metadata": {
        "id": "0x-iDMcukCCT",
        "colab_type": "code",
        "outputId": "86196408-b2f8-4f49-b40e-e8142023730b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        }
      },
      "cell_type": "code",
      "source": [
        "model.fit_generator(datagen.flow(train_X, train_ground, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=train_X.shape[0] // batch_size,epochs=20,\\\n",
        "                    verbose=1,validation_data=(valid_X,valid_ground),\n",
        "                    shuffle = True,callbacks=[mcp_save,LearningRateScheduler(lr_schedule)])\n",
        "#save to disk\n",
        "model_json = model.to_json()\n",
        "with open('model.json', 'w') as json_file:\n",
        "    json_file.write(model_json)\n",
        "model.save_weights('model.h5') \n",
        " \n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/20\n",
            "187/187 [==============================] - 15s 79ms/step - loss: 1.2368 - acc: 0.6082 - val_loss: 0.9422 - val_acc: 0.6497\n",
            "Epoch 2/20\n",
            "187/187 [==============================] - 10s 54ms/step - loss: 0.7766 - acc: 0.7243 - val_loss: 1.1097 - val_acc: 0.6410\n",
            "Epoch 3/20\n",
            "187/187 [==============================] - 10s 52ms/step - loss: 0.6032 - acc: 0.7888 - val_loss: 0.7746 - val_acc: 0.7190\n",
            "Epoch 4/20\n",
            "187/187 [==============================] - 10s 52ms/step - loss: 0.5243 - acc: 0.8230 - val_loss: 0.4618 - val_acc: 0.8477\n",
            "Epoch 5/20\n",
            "187/187 [==============================] - 10s 52ms/step - loss: 0.4640 - acc: 0.8422 - val_loss: 0.5091 - val_acc: 0.8467\n",
            "Epoch 6/20\n",
            "187/187 [==============================] - 10s 54ms/step - loss: 0.4252 - acc: 0.8629 - val_loss: 0.4865 - val_acc: 0.8480\n",
            "Epoch 7/20\n",
            "187/187 [==============================] - 10s 53ms/step - loss: 0.4016 - acc: 0.8735 - val_loss: 0.3171 - val_acc: 0.9053\n",
            "Epoch 8/20\n",
            "187/187 [==============================] - 10s 52ms/step - loss: 0.3777 - acc: 0.8818 - val_loss: 0.3845 - val_acc: 0.8800\n",
            "Epoch 9/20\n",
            "187/187 [==============================] - 10s 53ms/step - loss: 0.3612 - acc: 0.8906 - val_loss: 0.4081 - val_acc: 0.8800\n",
            "Epoch 10/20\n",
            "187/187 [==============================] - 10s 54ms/step - loss: 0.3378 - acc: 0.8967 - val_loss: 0.3671 - val_acc: 0.8963\n",
            "Epoch 11/20\n",
            "187/187 [==============================] - 10s 54ms/step - loss: 0.3339 - acc: 0.9009 - val_loss: 0.3185 - val_acc: 0.9023\n",
            "Epoch 12/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.3167 - acc: 0.9066 - val_loss: 0.3026 - val_acc: 0.9143\n",
            "Epoch 13/20\n",
            "187/187 [==============================] - 10s 54ms/step - loss: 0.3056 - acc: 0.9103 - val_loss: 0.2862 - val_acc: 0.9203\n",
            "Epoch 14/20\n",
            "187/187 [==============================] - 10s 53ms/step - loss: 0.3061 - acc: 0.9149 - val_loss: 0.3089 - val_acc: 0.9083\n",
            "Epoch 15/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.3020 - acc: 0.9146 - val_loss: 0.3977 - val_acc: 0.8883\n",
            "Epoch 16/20\n",
            "187/187 [==============================] - 10s 54ms/step - loss: 0.2877 - acc: 0.9210 - val_loss: 0.2623 - val_acc: 0.9350\n",
            "Epoch 17/20\n",
            "187/187 [==============================] - 10s 54ms/step - loss: 0.2859 - acc: 0.9205 - val_loss: 0.3514 - val_acc: 0.8950\n",
            "Epoch 18/20\n",
            "187/187 [==============================] - 10s 54ms/step - loss: 0.2909 - acc: 0.9214 - val_loss: 0.3291 - val_acc: 0.9150\n",
            "Epoch 19/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.2691 - acc: 0.9279 - val_loss: 0.3004 - val_acc: 0.9167\n",
            "Epoch 20/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.2634 - acc: 0.9340 - val_loss: 0.2898 - val_acc: 0.9260\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "w4M2DfGjmT3I",
        "colab_type": "code",
        "outputId": "6ada77a3-cf3f-49c5-8793-556dd43a449c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "#testing\n",
        "test_eval = 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])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.30554428646961845\n",
            "Test accuracy: 0.9213333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "M1_boK_fprtB",
        "colab_type": "code",
        "outputId": "17f1afe2-6b99-4437-b16c-74d303c8f876",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_data.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "ZuH6igT7nbza",
        "colab_type": "code",
        "outputId": "17d161f9-a974-44b9-8d90-9f6807cbda1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "cell_type": "code",
      "source": [
        "# show_imgs(test_three_data[:,1])\n",
        "show_imgs(test_three_data[:16])"
      ],
      "execution_count": 25,
      "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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Out0eS8viaV9pNXnmO88C8NjxI9SqQre0M6C9sCS/rc6Su3vpQFHYg5TVzkSz+xXcfr/P\ni999AYDFpSXKFcm8y7J0pHZBTmNCzE+zc1MsXBV/hFaKrosCmZqsk2VCxyiyrK4sA7C2tu6p0GhU\naThTgzE5tYaYF9bXlrl08S0ZT6/rmUSn0/a+hu/duu6D1DBGmKHFMy+ltRedUq38Bl0KyyQuKshk\nAbow82nDaAbeEG/DdBnNdhzaYAf9ASYaRkf5CKcw8AKYnH/7qSBj08EYY4wxxjZjh51hGZ2uOFEC\nrSliio0xPiaQPGdxWSShdmuDffvEoVKtNnye/mjZwyhSaOdtV8FQcm1uNKnWqu78nL7znre6ORQF\n0Y3FjsbX+lR+hVYiwS4sdnjm20/R7rS3mhxbCmMMly9dA0AdCZiZkXjOi5ev0G53AZicmMAUpf6s\nISi8ZDan5DSBcgxHjogqvLi4QJoJHUI9w9qqSGiXLlwjHbiaFbrExSvX3SB6/MyPS9eSS08/x/J1\naRN19PCDPu9fo3xtBKwFc38E1AM8+ugjgMy/fiLPMzFRp+0qli2tLHHilMQRf/5zn+F3fvPrAPS6\nKVkq9Op22tRrksrbbTc5f04k1DRVTBb1KUxAqy2OtOnpSR5797sBWLhy3tdbqNWr3qzT67Zw0xtJ\nbB4mk/wASlDeMTY5w7CExkmQeU7mHiUPoJ8VJkVFpApzmCUvTCUmv0l4L/64NQ30pmiEEVopS68v\na2Z9bZ19+/fd4vxdnLDQ6XT4o29I5szx4yeYnZJQmOb6hif00tISr7wmKnCr1+bRk6cAOHH8UW9z\nXV1d5sKFCwAcO/Ywe/bMA5IF1XEq/pkzZ5ibk3Cc2dlZAufFnZqaIwicvVZBlkgUxKVLl3yhmn6/\nx/WFK4Cobm++eYaBCwjfrej3+pw58wYA168vMDEti3aj2SSOZXFWKhWmpiRCQNmcOBaa7Nu3l6BQ\n0kyXciCrVucdWmuy6Z3u9Gi1hAaXLl1Fh0IrEkvuavcSRCy8Kfn3r//e1+i4DVPvneWRT39SPhtL\nWNSjCBTGjpYE3L0YDBLmpmTzevxv/U2eelrMJXEYMlmXcK2jxx/g6AMHAfj4kw+y+F2JTHjt5VdY\ntKL+r7VX2Dct7yA2hrKL0ohLETNG1sPi8iqtDQkZm5mucni/1DfYM32UUkUYzMc+9UEWL7wJQNZe\nQKXyPlIqqKI7iE4dI9jtG9kwkUkpRTMsBCpLxW1QOsnI2iKkkaSEjnUl6QZBVWirgjKjhaH8Z/Qt\n93JjjZ97WquRminK27+NzcU8BIRh4LMs73QDG5sOxhhjjDG2GTsr0babvPziUwCsr11h1rW2WVpc\npt9ziQOppT0QsT1N+5x74zQAiyuLlGMxBazcWKLZFM/t66df5MSp9wDwwMPv9g6eqb1zrDv19uJL\nL3LtkqhoDx172Bu0A63pNGWX/Pa3vz1SWDwmcWNIkoQkSSW1cRcjiiKOHBFpyhhD2TnDLl3Z4MYN\nkTInJyapONUzz3MWF8QzvnDtCu0N+fzIQ/tYWVoAYGVllX5f3svi+g0WbgjNB70+oYsGabd7lHsi\nUeydHTYvFA+x7P5RqCk56bnTbrpaCRBqRZbvdmlLYHLDN//0TwH42z/1t/n7/93PApANMiYmRaKt\nVAMfG/7W5TX2yWH2PHaYM33RMJ67eImWS1IwtYjVntD04Nw+1tZFclpfXfXO4Q9/6AlOHpOmm5eu\ntjlyWNTYh8MK+oOSjvva13+XdiBr6UUT0/N+pVt7y3cbrGWTRFsZuAiEpVXUiqxPnfSJM9E+FTlx\nUT0ujrDzswB0Jqd93G0QhCPF/uNheq4akUZtxsaGOHV7vZ6vpZKmw+qASikWFqQTz549eymXZK6b\nYYGF23rGHWW0Yag5elgmhDFNri8UD5kRhs7LX69RqsnD6NQw1ZDZup6m9FzwfK1cZ7omqlKv36Xi\nVOMsyems3AAgrVioyr2q5RKRyyl/9qk/oTEh95qb2cfakthek+6A+qyE1JSikq8Z2u6mpNnuV75K\n5ZiHjz8gn0slaq4P09yeeXpd8XJPT8/QbLsoDqXZcLbbq5cvUS3J5jMzVWFyQlTVfgI3Fp39sdlm\nw21K9UqF+RlRc68vXeX6mtiGD81Nc+IDHwAga66wcPY8AB/+9KfpObNPc9D1qU6DXpcc5cNqdjVM\nztJ1WXC//mv/Hz//P/4CAJ//kc+he0LHK99+issrovK/sGrZ8+h7ARgsX+VET+bo3OEjnD4nKv9E\no4rJnFDR6bB8YxGAXqfPvDN7ffqTH+Hxdx0D4Mj1fUy4MolxCvk+eQcBa2y4kpRnVY22cUzIGAK7\n+0t83tz4U7mSj6Y3oDNwCU4x5FOyPsO4hLYyX9MopOVqfJh+d1gESWtfGrVSblAqVdy9hkq8tdB3\ntther0s+wuwLs5pSwwauy8tL7N0rfEf7mr+394xj08EYY4wxxjZjR7e7arXK448/IX8oS5IUZfci\n4kh2nE677/O2m9dX0c4DOb1/3gd6R3GJuCiSXJsAd53WwkXoibdWZZqsJ9Jb2QTMT4iEMFGu+bhH\nk0HZqcD7ZucpunFGcUTiZNhqpUKv1+Ps2Te3gyRbhixLOXNGnIh79+5jNhN1c35uxqdzLi+uehXN\nBAGZo+FGq4tWIuUHYY0Ll0QrSAY5y2siUaQ2I3cOgunpSebnRVvITydU3e4f64BnXpGgezvTYOK9\n7wJgqdUmLA27WhR5/3mekdv7ZK+3BuXiX1cXl/jVf/vvAajUajy8X+bWs9/4BtcuihO18dEv8u5P\nfwaAV176c947ISp/u2NorYqT8IiNiCaFjte7Pbp90a5anSaf/pQkMrzr0WNeBZ6fmyd12sD0vhl6\nD8h83XfEMqHk3Uwt5Vxvuc4LVoFRu14dy/OcS5cuABDHJXIXORREwJxot0EpZOBU+9iG1IqSoGS+\nTGdo0+FFDeDmVqtpuLgqCTxhFDLtnPClOKDqirmHgfJOL2st2pemtL48Zq/fp9cX/lKp1LBW3zZp\nd5TRBkGJev2o+6xw40drfEjK9Iwlc9kvry1cpRQJQR979BDPvyBtUqZn9zE3sR+Ab/zBn3HomNiq\nDs7P0FsUYlWmJ8ndDdqrXd44LXbKo0cP8773vx+ANOv70nfCgEZKKBaJDElClmW89OILW0uMLYa1\nlrlZ8WZrNNdcqFdtss6NZRd+lWimpyR0y4YB2tkBJ8oVtJuU5y8u+OB3k0PgNkCVWEouu26QJpx+\nXTLD5ibmCFyYUZq2OffmGQAOP3CImrPBb3TbJOvCpGq1hk9YSJMeVsX3RfZSoCHKXVKAsixfFPr+\nxq/9Bv/Dl34OgM/+1M/y51/5HQCeeu05jr3vUQB+9LOfobcqNvAv//kzVObFHFaeqdN3dseJPdOc\nereEhi0vLrBwWXwTq4vXeeAhqfG7vNIldWpsb6pGeULswY986BF6rh36xDfAdooQvpBdz2URRvuX\n33oakCjPohFqFIXep1Ct1nzNkSzLOXhQ1r+xqf9cicsERc0UPWzCispJHJ073YQklc8YRegSnyqV\nmNRtpIN+n5WlFT+Ghx1/mWjUvXlhmC02LpM4xhhjjLEr8I4SrVLqMPAfgL0I+/4Va+3/qZSaAX4D\nOApcAP6OtXbt+10rt7AxcB7nUBPmrgKPsr6NstYapWVYi4tr6FR2nPeVqmhXGakcZdQastPdaLWY\ndKrDA4+c5M31rvttiw9+VNSvZxee56KT8HqDhM/9yN8AYHZ+im7fqcZpOiw4bgwmcZJunpOkCeVy\n5Z1IdcfYStpaC+WaOEryDK/mDwYDKm7sOtScffm78oM8JXL59PP1CQrnf7+beCkiDEKWXCysSTPK\nhUTb66FcEnq5XKbfFxPN+ctX+PinfwiAR06cHJpockgSMU102v1hkepBm42NFnk6bDu/ldhK+iqg\n6hJjUptjnJr61ksv8Wv/4dcB+IWf+1k+8/f+PgDRSy9y9ZJoUcHlN1h7UeokKBtw6pjEhk8fOMDp\nN8Tck24M+Mj75PhM2uTiWdEYApsSu2SS+fl5Bn3R9spG8+bLUldi/ZVrdJxE21+rYpXElWtrUGRs\nh1S7lbQNghCFjP+N11/3VfeMHUZNiGOriJHVvPHmRUBiW+fmxFwzPTlNzTnSq9Uq5bJow5VaRLki\nWkS1pr0jSzF0mIWxdrYK6PUSQpfWXyqVicsy74Mwxim6JElGENx+Ra/bMR1kwD+21r6glGoAzyul\n/hD4e8A3rLX/Qin1y8AvA//0+13IWkPfZRo1KjUqFedF1BFROOx823ehRq12Sq8vT7ay0qe9Jqrb\n3qmApevCAI4ePsxBl7Bw+vQZvvOyqK7z+/b7TKmLly5gXZB8Y2oKnI1QxTXKWhhAiWH7C8Wwqj04\nJhyVboNUd4wtoy0okryo16louMSEqZm6L9F37dwFplzTxlBFLK6Lh7yTZYSxvIvIKgY9l/OtLFki\ndsM8TXyXidymlKryvro9Q+pq0/b7Hb79nDCOJ973XsDVxCUG5L4LN65Trcgkbq3f4PLZsySDbasj\nsWX0VVhKbuOItcaEMhdDm3P6WUle+NfNFj/1sxL29fGPfpKeq7Vx/ZWX6Ree8evXWSnJhjg1McWJ\nh04AsHd+noYzY8WzJU5NnwRgolGmYJRxHFN2DGDQ67GxIELC4pk2Ay30navtJ86EaSXNAeqmwilb\niK2jrVKcOiXZb+fOXWbgoousGkk0MMOMN4shSYvuKZZuT+pFNFs5pZKYF8rlMnlWlKzEC1FhGPoC\nU7VqQK0qtNKBJXK+Bmsy6rUiwqHMwqLLZg1CjHFts0oRk5MTt72HvaPpwFp73Vr7gvvcAk4DB4Gf\nAH7VnfarwE/e3i3HKDCm7fZiTN/tw5i2d4Y7coYppY4C7wWeAfZaa52XhQVEhXhHBC7+bdBJqQay\n4zSmJ4fNGZXGuHjNU48/xsxhcQTkWB48JM6CA3uPc/YtUZsefeAhDk6L1/cPv/mnvOm8l4++5330\nXOHlQbdFxe1WR44cYnpWnDSNiQnyrCiY3PcByzoYKSas1aZGktuFe6atUj4RIzU5mfNOGzStpkg+\nrY016lMi3c7MT8KyxG2evbjE3klxkn34ve9BhBV47fTLdFzc7dryNdLUtX4v1cA1wWy3uxSaf5an\nPPOUSHfvec8pPvu5TwDQaSc88+xzcpKFdz8izosrB2Z48MCTPONSVbcT90pfxchisSlGy7wJdOQL\nq1848zL/9v/+NwCcP3eBT3z+cwA89rFPkh+VqIP8W3/BH78q8cVcOsfHTopEeyhPuHpVnL1r16/w\nuItHrk9MDiv7GyhKWPXTAT3nGAtthSAXKW3v5CT1tktnTzrkucGa7U1cuFfaWms55IrWHzlykNde\nl1RyHQabz/O1vIYV34wZll5db/YIQ1nDpVJK6EodBir0dT0SldHtCj3arYx9+yXZQWvI2qJZdTsd\n/07DMPSJTGEYeT41MVHnxCNlzFaXSVRK1YHfAv4Xa23zpnqPVhVVXr73d78I/CLAzMysF+GV0qys\nimdvZWWFw4eklur0zAyHnJd1en4PlWkxC9isS+Iyn5Sq8J45eX9xHPv2OD/8xS/yuZ8QG83UxCzN\nZRdqM1HjvR8Sprve3ODCWckSO/Kg5djDMtGNyWk6r2a73SZJR0Jktrkwx1bQtt6YoOnoafKAcEpC\nf5LMcMHVHwj6LV+7oD1o8uDxw4Bk5u2ZFNXzYx99L/1+0VFijTSVLDGygF7X2bDSkJUVoXm33fYR\nCwCp26F+5z9/mVpdxtBqtfmjb/wJAD/5418kQDjzjetXmJqZ9+UytwtbQd9KFPp+BRrrBQaNouJW\nUaAVrZaExv3W7/42Z8/L/PvUpz7Bxz8ikS6f/pt/l8PvE/vi0889x6tPfROAdZMw924Jh5t9+CT9\nstBOhRX8/LM51t03DgJKMy6ZJ9DoIvRusoZeF1WapItV21v1dytoOz09TSmWOfTE449x9uIFAHpp\nPizkYvFFqLQeCVlTlqyoFW0hK8IXgbhodxNm5CNmQW+jtZoieKBWqvh619iE3NVYyJKc1Jl9SrEm\n18IX+qHa1AjznXBbUQdKqQgh5q9ba3/bHb6hlNrvvt8PLN7qt9baX7HWfsBa+4G6y/IaY4itom1R\n53eMzdgq+sY3SVdjbCFfcG3Z/yrjdqIOFPD/Aqettf/HyFdfBv574F+4f//LO13LWuvVc2st6+vi\njDQmp5dItIA6r4gjUUujMII+UA/TAAAgAElEQVRQ1KlSrKmWZLhaDXylrRxL2JAXNVGdRTtDtzWK\nhovjnN077yt/ra2tEbn42jRJWHXxjcYY1tbW/GdcLJ+xFnNTW/KtwpbS1hi0i08Ng5DMefaff+Zp\nzjuJ9tEHD/iaEtcXl1i8JhJXtdxg1X3+rd/8jySpvIs87xM6uu3ff4C1VZEcWldapC6pJFDay/ta\nabod+e1LL73Cv/7X/w8A9XqNWl00jWTQ49xboll2uh1ePv0G7fb2lKDcSvqiFLmTSwyKoJCKlCZw\nx8ta+cLzC+vLvOoKha+uLPPaa1Iy8oc/8xkef0Ik1wcOHeXC81L7Y+PCmwTOBPaBT7yblkuP7vR6\nTLj1oEe6km602iy5yJi16oRXkx958gM8MStayJ8u/wlZL2Gbog62jLZpknL1iosiCHLmXeLQwo1V\nL8ViQPnW7GakZ1hAbopi3yHaOau0iVG5K2CvFcVGGQQB2mnVsQ5JrkhadR5ofzzAoMMixdZiXW0K\nbIx2JiM70PR7ndvmC7djOvgY8HPAy0opFxvE/4oQ8j8ppb4EXAT+zu3ccLQDbdlldyhtsbqoqq7o\nJfJgV65eRjlP79TUBOuudFwcVf11TG6ouKr+6ICWyzs3xjBTFQbcTwf0XbB5tVSmUZFFH4cxFy4J\ng9Fae7PG9MwMgesyoJSrpL496u2W0XYwGHD1qiywfr/P1SsScXFj4YYPyv7LGzdIXfZLr9/3TCGK\nhu1Agm8NW9lorXzwuEU5exjoIPJFN/J82JE0SRKMi0wIopCFK5KNMz09Ta0sdRh+97e/TNIR1TZN\nEtY3mr778DZgy+irlfIe/zRNhkVQ9LD6q0YTOkGiQU7alfm6eCOk6RjnufNX+NAHpQbCF7/wOT74\nw9Lwsd9+kqVlOX9i/0Ei14h0ZXmVdlts7PPz80ROkFBhyOxxCQermpB5F7S/5wMf5qFAsvOuX73K\n2TfOs7q4LdL4ltE2y1IWl8TkonXAMVeLZGZ9DesYXmoNqZujmTXDMobGoNz8Njb1NUpCmxK7cyZz\nqCLvLlIRkQvjUoFFFxmLWnkmmmpLGg9ba2nfNicgqrgux/U6eZ7fdkTHOzJaa+1f8PZGys/e1l3G\nuCXGtN1ejOm7fRjT9s6ws80ZRz4HQUBJu0o4wbBNeBRFJAPXr35liZoLnp9o1Gi6aIQoVL552tLy\nEpWKXCfLLL2uSA6zszOEzjO+trrMkquTMDc1zeSk7EqTU9OUXXxsGIS+JXmWDY3wOtDkWbLrExmT\nNOOlMxcAkW7Tgezm5foeYle6L0kScufkK8XGxxPmJvcVtHKMLwk5yHIin/NtiMuufbvW9Fzb+JJW\nhK4cX7lS9e8R8Op1NQjpb4jzrD8YsO684lmuiaN5UG9tNTm2HFpBXciIDRRdJ14lWT5shggELqZ4\nMtKsOxNMb33Jmx1SFF/7gz8A4KVXX+JvfPFHAPjsJz/J0RNFWimo2PV5K1dZXhIH5uLiIrXCnqkC\nnvzCjwJQ+uwXiBsiBV5d32Bp+Rsy5sjy8U98lOWF3V2nI4oi5uYl6qXb67LuJNEwgthJtwMM2sXO\n2t6A3En8FuVLIG6KAFDKN3lcLylajtNFsSKO5F2E5YCau76sEdd7LM+8GU7rwHe1KJUqxM5JWapW\nCKPotqORdryGms8/Bl8wRitFrGUWBwQMXFJDf5DQaEhwtyKmUhZnWr1W9+pmKa5ScZ1HdRgXafRM\nN6YpOQZT39igjSx0UkujKsSaaEziLYxqOLZIa894jDFUSqXvaWOx2xDFFab2Puj/DgoGmeNNAUEQ\n+lxt3+kOwA5NOmj8OXmee7U41JbATdzcGOac6SAiJ3Dnh9EwGHzUniiFZIQBT5QqxG1ZJN1uj9zk\nqGB315EAt4gdjeIA4pKYvXqJYdCTKIrMZCjXYiVUlobrntDNenTXRDU2eQJu4V64eI7/8O9/DYCX\nvv0dfuRzIgieePRRJqZFGAgrEfv2SWjYYDBgYUGuY7Wivk8ib1Kd8so5CXd85rkXaDq/w7Xrl3jo\n6DEJV9zNUMpnYtkeXHVdUpa7A1+XN8VIgRxAjyS7FXZVEHNV0TU7C2IvaKkw8tenXAY3F1UUoMO6\nO2doPjM6k/shvCmLXWGbqDTMYDU50R2IX7v8DYwxxhhj3P/Y2eaM1vrKUNGI2G1yQ7cnu1gcRSRO\nLZieGuYug6LmnFsVV7oQYGpqippLl9NhRCFFJYMBkQsurlYqzM5IYPLs7CxFOInWQ495nufDBnHW\nbvImWrttzrAtg0HRz4oYZbB50dJbYV2ca56kqED740We96YWzEZjTSHlh97ek2N8HGlmMu8ESCn5\nCI1ABajMqV/GopXLWTfGO9W0SrxpKMuMNNe7D+p+D3JYT+QZZkolyk60iWsRPaeKtvsD8qRob2Ao\nOedKHITErlV7b22RQcdFxkQlelY0rT9fWObFV0Syf/zx9/KJj0uyx/FHHmZ2VuZurVbjwaPiVByk\nKe2uq9PR7fLGOYksuXLtOitO6j10+ACn3nWSinM671ooGDgn7Xpzg4GTJk0UerNMrEKUi2fVsfLa\nZxAEKKdpxZWS748XRSUiJ9FWopIvdai08k7vOFSUi2gEBUWWQprbETOERRcJHyYnLzo7qM08452w\no4zWWstgMPCfiyHqQHvGlmSZNwvUajX/IHmeU3KRAKOMLwxDz3RLZeWZdxRF/pxSqcSevaJm7Zmf\n94RO09SXVRvt336z3WU7Qru2HBZwwex5bui76Is4Grb0EJPpMFogdbn4ZtiXg4AA4zoDh2HkmSgh\nGFuYDqw3L1iML91nrfXMO88McSgLPIpiEreQtFK+4E2Wpxhjdv0mBpAZuOBqbQwmSxyoCS0qKqfm\nbNdhHNLtujoR3Q7KFUUJlCJydCybnL4LZRwM+mSuA0I3CNjoS8jptYUrPPu0lA1897vf5ct6njjx\nCPv2ihmhUqvQcAJDUK/x6Y9+HICTDz/K+ZOSeaZLhqnZ/T4UcrfCGkvT2fA3NppELtJgsl7xPWOC\nQPvGPLk1BG5Oh0HgfRBhOfJ1ZKMoIHBMNIoDv6atMQQuS1THmsxlpxo97PRgMQQjrWyKOwfKDnmD\ntWT57WfcjU0HY4wxxhjbjB2OOhiK/HmekzvpyuSbVfOS8/5jjTc1jNYb0Fpv8m77OM408Rlx5XLZ\n32tjY8N/Lo9cJwpD7+TK89zvjKMSrR4tILzbYYv8e0UQFNJnhsmK4xpbSK9KeVUpioKhsy81PnbR\nmAzrSlYmSe5bLQM+NjfPBuQu7llrjXJSRIgic5XXAkq+IZ7VoymhGa4U/tbRYJtg0awkTvpcNyQu\n6uBQHRqOdtU4ojLl0pRDS78riRhZDtqpw2UNcfFuyHzHhJ5VdBNn7gkUy4sSg/yN/3qJ5510e/TY\nwzz0kPQP23twPwceOCJjOLCffXPitX/0oaOcfEBS2DfW1/izZ18gTXd3Y1Fr7XCOqsDX7CCOyJ3U\naEbMeVYpn4DEyJwLgngo3W6qURB704G11h8PAuUdvMV3AAZDpgpnmCZ3Dk6rNd71ZkU63p2mA6wv\ndoK1mILRWjuituthUoOxPndZ5zmlYKgaF+puEAaERVfbMKbpCsn0+32mXMuKqakp/8KCES9lGIbe\nRhvFw0r/wU2MNcvz+4IVFC1A8twMPeRx7GklZSAdw7OWkWnj88WVyXxtWoP2weChClCOCmLaKd5X\niNJF9+CSV9essRR9AZM0IywYsFKufgQEOiSOgvtmI7PO5tzNDBdaMp+6ac5RmWbMB5qKMyOUGmWy\nSOjV7CT0+sN573P2FYRFNwFCIndKtVah0xezTnPQorkqiSgvNde47IomzczNU5125RYnpzmwxyUs\n7JtiblbMC5OTU7z4nRfoOVvuboUFL1ANBoNh9uiIOU/qVLs5F2hiZ0bUSvnsxVEopfx6ztLUCxhK\nDW201lhUNFwDRaSRmCaLsQ0ZfJ7n4LPT7kwAuz9m+BhjjDHGfYwdl2gT5yDIsoyQIm51KNWY3NBz\ncXRJnvndrWIsPacChcPoV7Jef0RFSDftgEXl/3K57HexUcdAfzDwqgbGkLh7hWHoVWNj7R15F3+Q\nGCZ94Ok2mj4chrGX7JMk8TSP49jTM00TUqfGRaUyw3J0GcZJvVmWb9IMCgeYsZbURRRUSmVKLtbU\nGOOdoINB4t9REAT3DW0Fw3TkNjKPkl6PzLUMz4zlsGtrXQsDL93GYcSGlrnY7fV8tTKF9lpCREbo\nTDk1m1IO5XO1FpK4/P12mpE0JU23F4dYd36n3WV9TcwUTz97DZPK+9t74CgvvvIy7db21JLYMtih\n2h7HMdPTUrQ+UsprkjJnirTnoXkvDEMCl7yko7KPq42iyM9RcZAN51zhSNPKenMbDMeQZdkmB3jh\nTNbBkE9Za8mybJeaDkYICsMmbKA8kwu09iYFHQTExYMpNezJbodWvSAIh3Ung5C+6yQ6alvNsoyO\ny2SqVqsjjDn0BNVaeyaU5zkmGoYm3Q+ecQubCvaMjrf4XHwPMqFvdU4YDkO6RmL0wQ7pr7X1k7hI\nhpDj2k/KLM8ZuPKVWmt//dHPSkmUiGJoJ9vdGIbPedu+LrHsNqbOsmFgZZ6dnCvTcI8VRBpcZ4tS\nBO2uMN00twTFXLeWkmOcQa9N5K4/EWpyZ5rpaEMnld82Fy7T64iZrLHnADSEOTXqZZqu/dCF8+fY\nuHGdPBvpDrsLYbF+rZZKJZRbh3rELquUGkbJZKmvP2Cww8ghk9+yPqxVypsC8jwnLBJsAu1rqYzy\nppvXe8EXdJ77wjZ+jd0mWxibDsYYY4wxthlqJyU1pdQS0AGWd+ym74w5bm88D1hr57d7MHeLXUpb\nuD367mraAiilWsDrP+hx3ITx3N1ebNnc3VFGC6CUes5a+4Edven3wW4bz71gNz7LbhzT3WA3Psdu\nHNPdYjc+y1aOaWw6GGOMMcbYZowZ7RhjjDHGNuMHwWh/5Qdwz++H3Taee8FufJbdOKa7wW58jt04\nprvFbnyWLRvTjttoxxhjjDH+uuGeJFql1BeUUq8rpc4qpX55qwY1hmBM3+3DmLbbhzFtb4Ei8PZO\n/0eit98CHgJi4EXg1Pc5/wtIeMxZ4Jfv9r738j9wGPgT4DXgVeCX3PF/DlwFvuv+/+IPYnx3S98x\nbcdzd7fQd0zbt7nHPQzuI8DXR/7+Z8A/2wribyNB9wPvc58bwBvAKUfQf/KDeMn3St8xbbePtmP6\njmm7VbS9axutUupvA1+w1v68+/vngCettf/wFud+BPjnk1Mzn99/8FBxcJi+pvCVeWyWoF1ZMpSi\n73K7y9UqzfVmcTqNKalcFJZKjLZ93JTM+TaZnf6wervfKW7Orbt08SLLy8s7lit6u/QtaDs9Xf/8\nkcNS3DwIqyjlajiMNFsMwojMpeH2e11qE64fm03JB0JbkyQoVZQusvgqXUGEDV2dCD3slqSD0Bdb\nttaMvDvoufoGWZaRujGkWQqu8LVJ+wwGGe1eSj/Jdh1t3XcfAf55EOnPl2uu1xTDdE1rh4XTjcFX\nidIj5SADrQjjYS2PvOh+EViK9tiB0pi86GyR+ypoSimybDQfX/6VTOcilx/fRl4p/DWV1qRJSpYY\n8tTuCH3vhrbVavXzkxON4qBfetVqhWpVOqxorWm7xqsmz/16bW1sYN06bkw0iCKZ9/1B39cxKJXK\nbGxIY9dutztM62VYx8BiKZfLfgwDV7NDMSz8n6Wpq4AHYRQxN7eHxcVFNjY23pG2217rQCn1i8A/\nBSYq1Sr/7je/Dkh5Q1/qMFZErnnaYPkylYoQXUcxr379dwE49cQH+fpXfh+AEopP/Zh0AJ158CHQ\nRQm+YQ66xo7y0U0o8qp1oIu2Az7vXr5XoEa6KljLxz/6kXslxZZjlLbVSok//v1/BUBj5gNEpYMA\nmLzPxoo065ves4+l69cAOPPy83zks18EQKeLtM79EQD9a5cIlTBIZVJy5boET+zDzEktVCp7ffO6\nSn0v1bLUCUwHA0rlYsFozpyX7rYLq8vccGNYuHEVfeV5ALrX3uLNc4v83lPntpw2W4FR+upA8+5P\nHQUgDiypK2OY59DpuJKJPcugLcerE1XPjCerirkDQpdut0tzw3UQruekrsZtvVyj13Quk6jF5KR0\nT4iiiNUVKbKktCJ1BWOm5jShcR1ZY831JSmlGJcNg0TeWaVS5erVa1x7ZfeVSRylbRxH/PyXfg6A\nQAcUlVSfeOJx3vd+yReo1CZ4+tvPAtBqNylKQ33j936ftCK841M//CkOHTgAwBtvvE7mOj6fOH6c\nr3zlqwA8/50X6LlaE6FSzM1JmyCjcx5+ROa31hHnz12R8Sjt66QsL9ygVpb3Nb//AF/6hf+ZX/ql\nX7qt570XRnsVsW0UOOSObYK19leUUqvAF6ZmZr+kwyGT09q1TNHaF/vVylIUhipVaqyuyC62evEc\nTzz5IQD+4ve+wtPf+hYAPzQ9xdS8k+Ss8bU+7UjlHxhW+1FKDSVZpdDaFTjRaoQxb5ZmtfqBlD15\nR/qO0nZ2ZuJLWSavM88hNEXBZIjKFf+bpRuyIBevXvWFv/PMkBs5x0bTGFxPr8HAi1AmniKYlELT\ncVyjGriuxUEN64pu9Hst1palHcv6yipPvykZqz2gXBHJpF6NubEmWkq73aUxEfoi5TuIO567QRR+\nyTdM1gMC9/ydVkqr6Yp610ICt89YMorlZWyfpjun38+xruVQMjBI8XMY9BNKFXkHOiqTeQnVMDMr\ntGu3W3Q77jqdErhaMeW5iDAoNJgOgZXP+SCgMV0mCHt3Q6O7xR3TtlQqfanVdu2oSiW0Kzb/+utv\n0HdS7LufeD/TM6KBxZHi3DnZxOePHWFqRgrqzE41mK5LR+xKFHDhuszF7OhR0kTo3OslvmJaoxxT\ncm2Vjhw6wH7HRF979S1m23K8kWdkHWHMjwYRuI00W2/xwjN/Sbdze5XR7oXRPgscV0o9iBDyp4Gf\neZtzrwKHFSMSp1K+5bWOlC9B3dhz1PcMyvo9Oq5K/dLlKxz6uFSOf729wfk3XwHg0auPMz/jUo0D\n2GSPGMEoo930WRffj/6Wm347bJ29g7hd+l4FDltrh62Q9WgJQ02lWnV/GVavyE4dG4N2VaH6vYyN\n8zKB1jpVcmRhr127DKGoXCenTzAdzADQ62VYxzizboe1ayKRNhcvYwey+zebTQ7vEal69vBxryDo\nLOWbZ56S6yxfZXl9sEk13iHc1dwtnkHp0AsGSZpjU8fY+i3qdWdeCA0by0KL2f2zqFgWbjWGHFFR\n19tLxCV5aZVYUavI58XFHGuFizYmhoxSB5aZGZFibywuEztBZXa25stQTtVrRIEw7CzJ6a+nmPzu\nzIN3iTumrTHWm5aMyb3631Lw5vmLAPSNopDA1tdWeMsx2kZtwlelu3rlKjYVOvR7XTL3eXlpgcVF\n1+4dy0m3Hk51BlT7UgFtOrlEKP0tsSdOMr0hx/e89qpvdJp2+6w7htG6cp2lbz2Fad+etnDXjNZa\nmyml/iHwdYTF/Ttr7atvc/qzwPG7vddfR9wBfce0vUOM5+72YUzbW+OebLTW2q8BX7uN8wrif9Xb\nQcOAbGlFTshSwqMH3NmBt5tiLWttkWijTpOjTqicffAY005yeO4bf8L+WTEdzB49hMmHhm4v246Y\nBcRyUDgLRj5//yfg7aTd7cTt0NfTVqmvFm3FxZ/g6myaHOWcVRvr65x5UVpaHz54gMyIStRaXKX9\nlqjz3foMy23ZzduLKWlZpIv0fI9q/zwA5ZKi5to6B0qh3H1rJc2ykwRe+NNv8dCsvNMTf/dBus4G\nH6oI3PkbzQGXrrQYpDvfZfhu5m6/7To1d0FHhQTWIy659tX1Ci1nc03aPZQRS+JaB0xPzDHVSkjF\nOXjiQRPt6vkGcQJOk2uuW4wrkF+KYX6vvIN2M6VSlnvVqyWCoOhEHFBxjpz2hkJpuWbS6ZN142H7\n+B3CndLWWvvVwlRilSJ0poNWu8PZxUsAvPTqaYpVmiYDEmd/la7KQqv5mUmOHxWrhUVx/pzM1+ef\n+w7rHZFuD07UeMLZyPe2OuyfnZPzTUSyJoW6FvbMQ0e0iGq3S+bGY7OcnlMWK7nhyI1F4vT2av3u\nWOFva+3XTr3nvb7Yd5YN6K9JgeKoVB2qZSovTIeUK1U++sNfAGDj0htYZ0+ZmZ3lgb1CoEvfeo6l\nRbnO3INH8FXYR00EI62EtQJV2GVv8pbpUXY76gzb5R3DrLVfe8+7HgI19FQXtlXF0KTw0nN/wbXr\nonJNzExx9jURNAbrK3QPi0pamdnHjPit0JVZSvOi/idBhddfFxPBB594lBoycYMwolQVk0I8M8nl\nZZl4K4MyhxYkkmH92y9Q+rA4NWq1OjNzYuqZm5/i8Hqf1y6vbgNVtg7W2q9VGlUwwvDmaxVUwfDK\nZXDPU505ROQYXq83IBvIO6jUKvScnW/t/FmaN8Qck/YtDde2PE9KrLeFdnE5Z2pKjL0zc2VyI0xl\nbn6SxetyzcXrOXPzstmVSyXCSK6zutqiXhdb5vKNNsTDote7Edbar83Pz/m/tXKdD4C012VjVZhf\nv9cl9A9ifQ86E2h6rpPKtW6TmWmh2/z0LNlA6Hn9+iK1hszvdzW7HHHCQAvAdVwJ5w6gV0XwO/js\nC5TX1mQ8I8Xvw1Az6XhBohR5qyWhJreBcVGZMcYYY4xtxo63G1fOiaD6TZ569Y8BeOzUk5wqnwAg\nz3KU4//KWt7z4ScBuL6vQRTJ8XocMeGcAv/N//Ql7wzL83zYmXIkukApO+ygeZMz7K8KjMl9v65s\n0CN0/boG/SatDdmdl668ydFHHgHgwrWrXPnt/yTnpz3++C8kdGbf4eP85E/9NACd+iKZuQBAWeU8\nOOXaZ6/d4PKK6zac19l/SK5pbItaLJLuz/6jX6Rc9G/SJd/LKeu3US52MY4j4lLoYxN3MxRQikSC\nnJueojEpz9OcOkkrFgkyWenTbsnzl8sxZWfeykhpOCnz4sV1TE/MNI29DfJE1NhB1/ouu8ceqZNl\nIqWtr68QaJGkS3FEqMXsMOgP6PWLHmaWZCDaXqkcE1dE7Z2dD2gt5L7r8G6F1oqgMBeivSlpZnaa\n+T3SRj3PU98BuNVsgjMRDpJh365mu813v/sSAI16w8fIRpUKZl3WwIMbHeoufKyhNfqymCbsjUVw\nrXLm3nwDXUR9WO1b6wQGVCafW1nKIK6S3aa2sMM9wzK0kgnRXF+heV5617f3nGDh9BsyoGpMzXkF\naxNTBEWQfBSSGJmU/XaT8xcvAPDQ0aNUGnK+RvkFbcEbXrW1Q9F9lNGOjE0NfyUJFPcZE1YK3npT\nJk0ymODoMXnis2+9xvKiMMWgPInuu6aN6RqXrorKvrTW4tx5cbkOKNF2dqeoNMvDjz0AQGf9Ambh\nD+RenefZWJP3+Px3Bxw69n4ADh+5RKcr11xcr/uYT5MHmK587iUl+qmYGqZnpihXlrxJfjcjCjVT\nLnRoEKSkNVF3k9I0piWM7eozf8C5sxKOVK6WefBRMbsMWOTgPrFXl4M+tuGakkaWrFu0vlYYNwu7\n7aTgI+RpmUFfNs2uyqmU5Zzjj05TrcnaCAKNyYVJt5odGtPCmCdmUmq1mMWl3T2X4ygkCOWBa+WY\nOWdyyXRMVpONZaJa5ehREcYmGxO88fppAF595RU+/YlPAcJTei/L8TcXFmgVSRCpIY6Ehp2j+3ij\nLp9VtYJ2hG6HIYkzKc5cvcHDNyRKITAZmRMOU6UxiesZVmlw7eQx0r9Yu61nvA+m+BhjjDHG/Y2d\nlWhNzun/+jsAXF9fIUxkZ/mjL/8XQudcqe2ZplGXAOR9hw9yaL8EyYeVErWi3XU64PXXJfD+/e95\ngvxgoUakZIlIY9mgTzIQ9Wt27x6qPpb07Rxg1gfwQxE7O4LdLRTQafc5ffoCANPzj3D+/HUAVld6\nLN6QePE3zrzCsouj1XmKcnG0g0yx9/B+AOJSTHtdVNs///2vMnfgHwGw/4Ef5tqCXLN56Y9ZviFS\nXLfZwUQiOfSzd/Ha818BYKW9yJ4JkbhqM1P0XTrk2toVZhoiITSCEGMV7HLVFsDagImazInZyYDr\nVkxXSd/wUChS7KF3HWHtkmhmh/fXObRPsrvWV1fRTZmLc0fKLFyX511aaHHgAflcn65ScZEczY0+\nJndxur0OSSaaXLeb8OBRMUEMupamizGvVPqkfZf40InJUnkf1UqPUt2id3SV3zmsla7MAMfn6sxf\nehGAl5YPUZ6XrMNytcKeAxJdVKpUeXzqwwA88ZEfIjXCO946+zqRS144lSTkLqU5ikLftbkVBD6q\nQQfDztflqEx3VXjK1akp8pMiPTemZwgqhSajyZ3DOSTnVKNO5aUXb+sZd/QVZL0Bf/Qf/zMAG1MZ\ngbNJZXlGEMsD9JsRzYFM6Etra5x+XbKLpqoRJ5wt9oGaZsoIQS9/90XaZ94EoNds03bhYFm/j3K2\nmx/5qZ+keupRAFSusbs8iuBu0On0WLgsppg//eqX6SVCwxuLV1helAnU7ObUSjL5qrZPPyvs2TUi\nZ9tO2h32uDTGXmeJr/yWvK8v/tw/IK2/S+6VPMegqFegQ3p9mayqfpJKJMxFdduc+NTfAuBdH/sM\nZ14T29nv/e5XqJRkM5yua9fyeTsosrXIrKHkWtBPxhXWtNjDA5Mz4+oYDCbniKsSYH/s+EHqc44Z\np9O0luSZu0GP2M310p6c6ozLyLMZSglN65WYXsct6DDCRjKn65GmVJHrJHlEWJLxoBNqE7KUJ9LU\nZz51OymDTk6W7PL5rmCqUPPLU3ynL2tbNyaoaxHG4tR4U2O/20VHsolPlGLSlmxEtfU1Ss62PYfy\n67/U7aGdbVVZ0K5le5ilaOT4wGYol4TTO3CAlYHQvPbAA0TluhtDRuIYc75wlYu//xUGrhbLO2Fs\nOhhjjDHG2GbsqESbDhKWmGMAACAASURBVPp0AlFxavUa3bZIBXG5Suh2k6hc8QVmQgJyl5y70c54\nIxdHS5Tn1J10u95vsu4KpQzWWj4VUQE1F1OXNzcIenI8Lde8FUBbSxF3y0gJBMEulwJugjHwnW9L\nXGyZV6hOibPmzWsL9HoiKVksMw2h7Z5KTO4kNBPGTFSFzoN2wsy0SGKPnTzKX37zj/0NYvdeHqpX\nmJ4TUwPXL7KxKPR/ZnmZmWLHp0xlSgp2VGsNJiZFSinVS2jnmAjjCmmWe6/xbkY5jtm/72EAJusx\nYSqmk0O1SUoDoW8r7TI1I888N7eX2EV+2PkAtSY0WuvGTE4IHYNqRpK6KmA9RcXFbgcqJXQqahSG\nxJXC2WuZmozceALWnYmHOCWqym/3VDS1WCZycxWiSplA7255SjEslrNx/QYzLsqi3rlOelroptM+\nyplHGnbAWmHay/vMuDjjGZOjItHGTFCn56IIYg01VwuiVIqJ+6IlR5UeGJfWG1h0KJUFz6XzjjdA\nQO7j9xWGLJF7lXWPvbMJUXh7c3dHGW1YKnPqSQnXWli7SJ7JBM2N9fnEWZajQ5cVE4Y+wUGVqnQc\nccvViJqrbhSHIbEr91fbl7B4QWyQ6wuLvP8jnwSgMT8NLkhZhxEqdPnoashoJRpsdEKO2mt3v26b\nDlIGbuHZUomOkU2p2+uSpS70pxIRFmFuBESO0ZZKUVEAjXVytBL1NFCwdkPssi9/86s+Yy9/7BAf\nfEzsZaHpkLh88VffWuTUvASYT9Yq2FxUtGzQJHAe3Ua94hd+tV4nSVLMfcBo4zBmkIswsLiRM3D1\nBIwtMzErURR5HjMzL0kae/fsxQV4EC4nXHpDvNPxbIXFdUlY6PdT9hx1IVoDzcDZzPfM1oiLbC6T\nY0xRFCikuSILfW2lj3NBQJoxOSVj2Nho4vY69k7vIcv6P4iiPXeEPM/puoSO+cESR40kIDXKlo20\nSPoIKE0KHUrlBpPO7DhbCWi4zLzcGrLaKQC6/Qjj5jFpRv2gRM+El68S4eb9nog8ld9W+glhS+i0\nsbRBZlx0jlXkbv1gLAPHvFERlQPH0NHrt/WMu3urG2OMMcb4K4AdlWjjSplTH5KYy+U/XEBp2cWC\nkTqySukRyVL5RINAKV8MudaoMzsh3shet4dyElJpssGeByXX+cFHj3Hi8ZMAVKemydddTc/WBtoV\nDUfrEcF1d+/67wSlFbomqmqrZ6m7mE9rVike0qiA+qQ8eymK6LoNX6WGel3MLHHagUItPvYwT37u\nk+76lvOvimni5dNnODglP64GGR2X6ogF66owRaWExSWRbg+sr7CxLumNWbcLDXl3M1MTVMvxMMlk\nF6PV6fFnz34XAG0NpQfkGSam92L3ikmh0ZihXBWpq1KtolzZysnmKg+H+wB4sbdOx5nM6mVNkDiz\ngIqxiai33eaAqSnR2MphyNlzIuGleRXjnGfri33WlmX9DOYVg5asjYXrfQ4eclEgCfR6GrNzNdXv\nCjoIyJ1TqnbwODN7HwckfbxzSUwHtlpl3wExV/VTi+4KDU29gnWxtqabkLgyhoPuKomTgNNWD+vM\nZ/niMvqwVAFMoj2YQNbGIO9iQ5mjeXaFvqulAMpHLKgAn1xjwxq2fACcZP1O2FFGG2jFkSPykNXG\nBOvronJizOYkAlcQvDJRB1dcw2Y52oUIxWHg7YWtVovJogZlKfb2lPlD+0kLe6EOCBpO/ep0oOqS\nIMo1P7bRjLH/n703i7UsO+/7fmvt4Yz33Llu3bo1V/VIsUk2KbZIDaRliyISRxIkRTAMGDasQC8x\nYAcOYDnIgx79FMBIkAcBMSAncTxIsixAVBiFEkU2JY7NJnvurnm6t+507pn3uFYe1rfXqZZIdlX3\nrerb8fmARp97ap99zv722t/6xv//gyhhHFGXEDaYwLlzTs+3724RS/plrtlkroI3LAuvn72DLmcX\nnQEOCLj62usAdAvL/GmXt8qSDN10AxEHgwk7A6fnY6vzRFIZfqx1gmDijPSd7pjhGw5XIVMBhYAt\nd3f20dJ2s7e3S73Z/EAYWovFSiiqyIgCp69moBjKXLwKShJ5oPd2D0ite1g7gwPOL7i893e7mx4Y\nZmGpSSBrvdNqeBzmUZpjZQjCFBo5Jb2DPq2m0/XK4hxbt91GtrcXkMmUWG9/wuK8+95e0qU00fsB\nQ/lAkuUZ+2MZqiksYU86WjJDIdjFpmwSyURdXpboXHCts4BIrJgtWpSXJLSv1YjkeS4mKbouG9pC\nQaPhhhqUvebZEyKb+nWZZYVPk6XJhEgC/yAIKOV+1e2YML0ENr2vazz6K3wmM5nJTD7g8mg92kDT\nEjd/bvEY5oYAr1vrvViTF1iBfwsC5UGsR2nitwUN7O85Nz9JMowUfvTyEvWGoCeNRiTjim6kxIjX\nGxQFtiqMRbWKfOkDLxGaukDxtSPF4MCFXBtr8yxXSFALHe89ZlnBmdNOt+MsJJXRwrjeREkVZyGD\nqHDhbFoGzD/1rPvs0yFnT7uujxMdCKXQlRvD/r4rkvVGY4xUj/NhyUSKGo8//gyBeIa1QNGeq38g\nsA6shVIgEG2WYcX7GfQPmBN4w+WVVaJQimRKY2TQYL8zgW23ptv1kJFEFWlqMRIB1OMmsXQalCbk\n7l3n1Q17Y4pcEL6KjFGvGlKIicWV0wSetQGlGcm6375zQFwPKSSdc1Ql0JonLjhY2vKVF9h+xeFu\nKBWijItcD9YWufWirFELfekiULbEZu55NsEc5Z50cbQaHlelZkrSOzJSe7BJX7vXhJFHNhtZSDoO\nVyEtF3jrmkRvO3dpzQk/YRiRCKbEccacNjvY7IjBJIKE59IOcXenz4GQLS7MtfxQVjZJ/LSWySYk\neWUsFRWhzO7uLkFYcdZoTspU02g08oak3qiRSll2PBlTE/g606wRCG2GmQxRAvbxQU4bANhxwsL3\nHf5mrzRcEhzZc88+yfKSSyl05lpMUrdY8yIlQuhY9odkkkM9F7Y4/1U37dK0JTKgRFlYEFCfu5Oc\nzT1nRDq1kFVpEjdYLkg7nrFgBRd1K9/nJWnfeWpxiUAoW0YLUK83PhCpA5RlYd6tkfm4QdGUNqso\nZJILPdDeBKvddQ7SjNGeC3UX44BGy+n62MIKd7quk2O+s0xnwW1YWZKzfMy1hqV5Tl64zoRf+sVf\nYmnR5YNbrQ6djktBNFsNDrruHvzu7/4+r7zxXQCMygliAVOpNSHIjnz5odNZ5NOf+XkAbr12meEt\ngYusQVp3er585iRm4AzqqUyRDKWLI8kJR9IhoA78AILZtVi58BhLftk9GyYIKSoyUTXBynpNsaSn\nXF6c9QWPd5sVOaG0dKVZ7gcWJr0ByfYYm8xgEmcyk5nM5EjII/Vo0yzzPZ0rSwtszclAgQJbUfqa\nnF7X7VyN2I8ToOI6Wrgvx+OCUMKmKI7Zl4R5FEZ0ZJQvyzI/vDCajGm3XbgWRjF98XQbRUlTPF0V\n19628d87fq/skXcKmGD4rmC27fbG9AKJBF65TP8tYZkNC+biKjzVDAQJ6uqlfY5JF0dtLqL58rcB\nyBsrZEtu8KEWaZK+C2c7w7u8uuZSBF+M6yzITTJojDTdL5RjnhamBoo6C5nTeTkZkJ4+C8CXi19h\nezDHpPzmoevjsCXQivU1t1YWagW7Dbf+GrGiJzCUg92c06dcv2ZucoK2Q+9qDCfYhrv+9mqHf/AL\nvwDAV//yzwgip6OlpVXGUhAaTiacOusYWX/1v/41jgmDSD8b0xWvbjAc0s+crueW6yy6qBcbaIjc\nvZ9bgqyYdu4cVQnCkECGWGwcYaWQH2jDQum81dXxmIYQIeo096SqOQmZrbqXQt+Pb5tNrAyM7Kc5\nVpgUNrq7RGUVgeE9WqyiakAuTcGyYCacOXMKKymwoigZyuBTOJo8kJf6iA1tztauy6f+rf/y5zkl\n9DVf+sIfk1UTXQpiCX3GwwFx3RlXrfAwckFUe9vcVi7GO9CBf21K43NTaZqSSihQq9f51nddmFUO\nx3zqp38GgObqCq22Cx2stffiy3wgJLOW7wgebaYDZACMuK5BCYZmWjAWFP+ashyMpP1oqcbJFZn6\n+vgynR9zT+3gi1223nSV7d7TK5wUojz9s2d5Y8cp6LUb27Rb7jyTNCcTY398rcGpJ1yYO/7mNjvK\nGYjzCwF7P+Va8F69VmP7oCTJjn5gFWjlIfImZUYqE47dtGSSunVjgnVGYwEpaRRMZNBgqdkAga38\n+Z/7DB/+qGtxzMyYL/2pA1laOn2aUOb3l1dCzp9z2Bx7e7uMBJDmbm+fgUyhlVahxA1ZPbHEZlfy\ntXGdPJVK+qIhy7TvZjiqYoyBakBABUjjEGUQcCCdQavXrrIiuWcbBJQyvViW1ncOoCxGLEMZKkzL\n3ZfLSwH7584B0PnGCyx29/x5rBhsq+/BQLGWRr2ibK9T2lBOX6C0DPNojVXOVN+PHP0VPpOZzGQm\nH3B5pB5tWeTcuOx6K49trLOx4TzaM2fPcOmSe1+jQOAQszwnL5yXFoURcV26ERqWSNIIOggovMtv\nMNJrW5QFk8TtgJPJhIlUd9vtORpzbpf84te+Si5DEM99+qeYE16hwhRon7MAe8RDr0qq8dpSQV12\n5PWNdYoDF0U0dUCt4Y5pxQFlhdxf1wQCR7f82EfhjGuuV+3nyfoOdevayxknpHdRLZwnHLuCztPn\nJrSlR3mSJiTSjB/MN7ANiWfTbXTpfo9aXqB1wnlrz9QX6Ccd7nz//pq+30/RCqdYQDXm6TRdEXVp\n/jxKCn23t2+TSzHmb3/28+xtuSLZcJKwIH3KZy+co3vgBhA++YnP8qlP/BQAhYGFRReu5pMJQ0m7\nlEXBSEbVV9odjs+7wqZV0JNjljor3Nl2xbNAGRrS44syRFYdeXQ0pRSJhOSD8YimPMNlWZJVfatm\nytlqFFjz10fkEwVWnucoTQi6bt23V47RF4QvW+QQiNlTlsrXtCjvnBZFfg87iyYIZGQ/t28bFy9K\nc998bO9oaJVSp4B/DazhfspvW2v/pVJqCfh3wFngGvBr1tofCTcehwEXj7tFMBj3aS+4HMpTF5Y9\n3cpBf0AmsHtFlqAkR5OZCWNZWEEUEAmtSNBo0BCs2U67Q6RcLsa06h5rMi8Ln0ZIkgkf+/jH3LUF\nylOABFFEllfNx8pXLLEWZexDAT45TN1aFCdWnWFbPrZKZN111WsN7nbdgxoFsR/6GN/dRc/JZlUP\nKWUlmK1XIHI53bK3ybx1en5Wt5nXTj/dO5dZkeMvfrzDkuDO9vY1E8ED+P6VEckNt3nWg4Cz2t2j\n7O42nVtuwuxvf/5vsZWUfOk/xg+oufuTQ9WvVYxkKODHP/lxWgJqdPf2Aa/ecK1Ab129xIUzkqNN\nLRsbbmjkztYWK8urch7LZOJSAbu7uzzzIUcDtL23TybV7Xa75WsNgYJCGC+0nhItWqyvgA8HB6gq\n/aLmmD/hjPFguM+oN75f/sAHkkPVrTHsCKZGbzyiKawqFlgdu86kBiHGW9qpwSvVlJkisEwNYZFz\nUNmIZMLTr7ohnHaeY8MqFWAx97C2VrQ0pbE0paZTb7aIau51uw1aOmaagxFWqfsu3txP6qAA/qm1\n9mngJ4D/Vin1NPCbwJestY8BX5K/Z/JgMtPtw5WZfh+ezHT7APKOHq21dhPYlNcDpdRrwAbwi8Bn\n5bDfAb4M/LMfda60hD1JdGcEvPWmoP2bGCWN3lcuv4LSUlAINY242sUMVQQfZ4ZCiitqqEkkFBjF\nTfp3XaP+3uYSx05uyEVeZFFm+W1nnppQDH/84x/nzTed1/Wtb3+bn/mMFMaaDdKqEdlaolA/FI/2\nMHVbliWlEMqdXV8mHTsvdjwYsyu9fp31FY6J13vpa1/Djlw4FddCQkkLdL+0w2LNeVaT/QbbgTTX\nFwVtKdbkN4ZsSW7lzijiWMeFa3vdlEIqL1u3Un5y5I7JQs1AQr0oraNfc57x8LltJuMcU9xf0/eD\nymHqtyhLxpm7tief/CharifOIw6kGh6Gip967pOAS3vty4h5q9X2EdV4PPaeqzGGmzfdM5DeAxc5\nqdem0IbGehwQ7inYBEFATRgZAh2zsuhGpc+dfZb5ZZcG6h1s80rypw9lIOQwdWvLgtrEpT7SxUUu\nHXddFktaU5OCX7uEpnj2YZKSVj2yCh99BlFM8zEXIQQm57p0NWVFzilB9dNAKfTkFKX3SEulKcT1\nHw4GtCX1lufGM1TUaw0WBfEubDQprT281MG9opQ6C3wM+AawJsoG2MKFED/oM78B/AbA0vIyZ1fd\ng/ut777ON593+b/PfuY5apELH+txnZt3HJL6JBkRSAkyUIFHuG/UQmoyL96sxzSkRatWK8ileX44\n6rMtzAK3b95g86K7AR/7+Mc4dfYsAK1WgxMnXJO4MRkHXXezy9KAhNjWGmwcYu3DnRd/r7pVwF7X\ndQi8+cor1MQoxmXmc9V3bt5mftGFlc3VZbp3Xa6wTAwDCT37VyDZd/lsTnaY33D6yY0hlNx2a2eH\nBbmPW5M2I5lcqps5lFTmTzZKloRqJV5fpi0DJuGtbY+IP9m5wfVLV8nGo3ehsQeT96rfuF5ncdXl\nrtfX1xn23Vp59tgan/wpB/0Z6YBcugLSsmQo+epOu4Gu1nEUU6s7Axk1WiwIEJDRAT2BT1xZXPAR\naRRElIJVUGKIhPJFKeVbJS9cfAYrYXKns+RbljY2zjI8uMull7/xLjR2//Jedbu2doyVjbMAPPHZ\nGr2nHZNHPhoxEPbanV6XQgacQgvtnjAJ37jhOw10XGPub/4NAGqNmAvibAwCWPqMpKf6I/p/9AUA\nilvXUaF732jtDXaWTLhyxTlgk2F/igesQhIxxqcPeizb++dque+uA6VUG/g94J9Ya/v3/pt1W/EP\n/E5r7W9baz9hrf1Eu6KrmMnb5DB0+0GfbHuYchj6DeOjX7B7P+QwdLswv/AIfun7K/fl0SqlIpwy\n/09r7e/L23eVUuvW2k2l1Dqw/U7nMUXJnHg21y+9xVsvvwTA46cWESorPvbhH2NeYAyv3LxBd8d5\npb3+iMAIU4C2PhyKQ+WJ3eIopNl0r5uNJi3poxtPcgrpLTTG+BDhzPmz1MUbfuziBW7fdrtktzec\njuzaAlUUZPc50/ygcli6BesrsaNkzPK882i3bxwwFAyE/XzE9dsuWji/tMDOdnVag5H7YjXeQ4jn\n51lbc16cSXO0cFQ1btzgF8RDS01Eed6NQEfz8+jvvAxAeHUTsyREgqttigr1f3hAo+8873jcpR1M\n0OrhRQuHp1/FiXVXaO32DziQwYHuaET/pnQFdBY5f0rIRLUiTZ03NknGRJJSWVvu+IJNur9PqyNw\nlqVlnLhj4kY0HQk3+HRBr9cjTIUbrN3xYO2NZotWx0UqBNNR9Swv6feUBwI/bDk03SpN2Kq6Mjqo\nCw7e1GKmY/TjMZdecfbiy1/8E07L839Sayp/0QQB0YKQOc7PsybF7WY9olZhIxSWwVe/6o7PSw9I\nj7FUWOsrqws8+5TrjDl54qTnJ8vTgr2+20uil18hu3OH++2jvZ+uAwX8b8Br1tr/6Z5/+kPg7wP/\nQv7/n97pXHlpeO2aaxa+eesmly6/CsDv/8GQc6dcjmllZZW2GLmFVpt+14VEKkpcDwyO0qO6wKIw\n5IKfOjKa7n6/+t2E0pZRq99kadmFwLt7XSaZtI8Zy/q6i2y0CggEqb03yHjjkot+rCrJR2PGSYVP\neXhymLq1FpQY2slkxO077hpjaymMtL+Vhj2B9DvTavi2JIPFCnmd1dPu9l4tpjjp7kut1iAV7IL4\n5ZdZFaK8cm9ITyAAObZOc8uFv7o34FLHpRcGP/YEdskdk3YW2P+TrwFw40+f53aeMx4nPAw5TP2C\nZWPVtV8FWlFvuAd3kqRs78lQx/4B25sOsOT8hfOeedmY6WO2t7fnH83B4IAwkhRVVnIgXTXGFFTJ\nP2OtH+bJ88Lnd3sHXT9UkyRjtLAoNFsdT88SzAWsra15Jo3DlMPUrbGWoaSZtNLEkkaM45hFoUBa\nXFyk3HWprm9fu0O2LGtOhdTlOae0HhApihNqok994jh1cZRyHYLUaAzGEzgai+9qSNOcoeCh1GtN\n1jfcgM3iwjKB5G7vGs03v/fyfTNe3Y9H+5PA3wNeUkq9KO/9DzhF/nul1K8D14Ffu7+vnMk9MtPt\nw5WZfh+ezHT7AHI/XQfP88O7xf7mg3yZtZZM0Pg3Tp/zCFz7+12MjMjubG9Tk10jLQxGvDGlNaWE\nmMrid3Ot8WhfhAotYYTzeqV3dlJy547zCvYPuty+66KZ67c3+bEPu8T76bPnaEnvXJIYdqoxPQWT\n3pg8P/z46zB1C6AEZm+QGBLxgo5FmjComq9hIt5jP5n4ry7M9L6krSZWujiupmMmyzKmu74AsRTD\nvrHMsW2BuGyGNAWlSj3/ApFwKkWhIpYq8XhcYrTzEFojw1i+64VXL3PXwOQhRAtwuPpVSvO49Mi2\nwoBIybhss83jUlzttNqMRwL83ev6wqwi8J0DhTV+ZLQojO9xVUHoQ/7eQY9cdNdoNGhLCmxhvklT\nCjOB1qRyLy9fu0Gkpfe0sCgfDhuWlpcJg8OfSzpUu2AMowpRzxS0mlXxSVGtjKLIWBIw+4/8/b/L\n6DWXRti+cZtEUOXmB2Py/+f/BWB46gyhdF8EWUHjzFkAVKBRVV9yaQnEo7XKkguX4HiU8Lu/5xzx\nr37pS8zLIEmt1iAQAPGnRhNa+v77aB/pZJgxlt7APZQbGxucOe1c8suXr6OljSNLJkRiaIO45t83\nRelzW27RivEIAoqywts0VKSUCouqDLMOqHz8NJmws+NCkLdev0x3V6Aaj73B2ccdJcnpc4+zvFrB\nJ2rSRt2D2BxVUcrNiYPrlKjV3OIbFhYjJc9GHFKvJufSBGSKxmY5iTTR91E+99cuLTpzetabXZi4\nDao9TLBCjxIOx6i3HARdrmAiaDyDqEYqBrX1la8SCYVO4/IdJjKnvnpsiRNzq9ztff0haORwpVFv\nsLTi8tVWBT5k1DomE1qVbnrgI8koqlEXUBOlAp9nVQoPC9ntDdjZ2ZPzRH4acWV1za/p8B68ZGun\nm1LAlDRw48RJzgoOcxiG/l5meU5Dl/yn5h8cqi4OW8qy9ABQURiQSnokCCY+hVLkBaHUYn7i5z5H\n/gmHF/Gdb/4l39p1qZtGCU1Jn9UmB4RvuFbPerfLoqQdG4EilOc/XFoiED2PdMiOfG/Uijm+5lKN\nC62m79QJgpBR1ZpX5pggmA42vYPMsA5mMpOZzOQhyyN106wpqUkRod1p8su/8osA/Jt/8+/Z23Fz\nyaUpUBXSVqPESMlUKeV3d2utfz+MAmKpQBZFTiEesJ0mFAjQvktBKUU2cmFKMh77huM0y1Fx1d/Y\nYLmp/fFpLSYMjzYEUhAE5LI7l9agJG2SEhBI2DrXrNGSwkFcr5EIZKJNbVVnpKcUI+nc6AzHqD/7\nC3f+NMdKo3d3NCaVkdJ2rebBmbNGnUQKDVlesrfv7mmtlzFoS1P5mVO0xev43HjC62pA9DBmRB+C\nfPl553lrbYjC6dhwUA0E6GkDe6i1Dyubzbp/rQJ8kXbYnxBJN0ZZWiLr1tzlq9c9ZkeWZb6bROnA\np3gc5YM7pjfo05ehCWOMx0kYjceMh3t093/kBOz7Lhb89Vqmw0F5lvsUYZ7nGOm3DiKNFdN16umP\nUt9xBcjhaEB333m3+31DKus+nJujIe15NaB21qWAojQhGrpzDg967A9cIXdxZZ1TTzjGh3ocE9wT\nVQwFIW/x8g1GB4P77qN9pIZ2PBry5a+4BzdJJzSluvjMYxf51sjlXPrDAZHklIbDIYXkRi0lcewe\n4nocEUqYfPHcBot1d/zmzgG3BYZxPMp8uGZUTlkI2EQY0ZSHJMASShZoqdNkRRrya9b6/HEYRdy6\ndIUifzh5xMOSINAsCLjLQX/MKK1yyqYiRiCMLYUsvrvdA0/DYVGkwsgwaDS4LBM19ahOOZCWuiBw\nw944lHpd6WNpyefXqdfJnnU57/ZfvEBHOkCsMbxQd/cii2OelInAPM8ZXDxLecSpVsBt4pOJM1i1\nOMIIBodSmix3G1CeZ5i8omSyIAAzjVZMFFb4ySGh5AL39w9oyOx8lhXeNSiK0mPIpnlJIMdrzbQ4\nYUuU5ISG44R9YcjY29tnIvgdd7c2GfZuMa7YHY+oTBOBAlEqa7S0xoNBFUVOJo5E3s8ZSS68f7DP\ncOA2mTIvSGQYZtAbkJZVLrz0LYtYQ9WDocOIUKi1SlMy6TlD2wIakguv11vEtWqYqsayOFzN/T69\na1e535GFWepgJjOZyUwesjxSjzbPUv7gD/8YAK0MJ6W5+OTqAqtCnjgXhdSEHrsMNIlUsTvNBgtS\njTx5fImljvOu5tsNBtID2m63WGoKmWNi6AoCvQKsdvvYJMuoifdWqwX0ZGy129/nrnQjnDr3GEEs\nv2euw/e//11Gw4c/JvpeRKFYXZbqaBSzI95kkuWU0omd5CVl6q7jQEFd9FbTmkCKWGV/xEg8qKW4\noCY4BFFeckMA2WtlxoLM8Wc3b2OkQNPf2GD/U27WX/X61IT5IqvH9CQ0zPOUrjTvjyPNOAqnCEpH\nWPIiIx2566npOQrjvNgojJiT9aSbEZE0txeF9awhjn+qCoczStF7ox6TVnP31uEdgItOAhmw19ph\nKACUZU5dUjONepOs6tZQgHIFxrgWetjQMMi5NLx15NlBLJY8rwpgdd+VkWWZ7yEu8szzeI1GY0aS\nRjjY36cvnmiaZQyHzrvt9w5IvDdcTMlfjfUptuq7q/OXkhawL73OFcFGmJuboyURXrvZJJCi+NrO\nLnt5SfkwsA7eq8w1Ij5yyhnXMA5YXZBqXhix0pHcbb1OKVQTKXUmMju/urLCYugUtLYYEUk+dTKZ\nMJJjLq7P8/RpnRxTAwAAIABJREFUZ2xoLELNGeOAwJ/nzu4ufZlTD6Mm/bEzJGVZsnnHpR1ubX6b\nmlQ4szwlLSZ+QuWoirXWV8I7cy2U/LHb7XlCxklaVJCqpMYwlM82goBU8ta9svQAMKlVHoG+KC3b\ny65zoKUsbTEKaRBQShrnzmDIrjAbq06H8QmXxx3W6xiZXApbba4suOr92FoGYUB51CkAAFMWXL92\nDYC5dtvlYHE2LhDHIAgD6k0Xiqog8HlcNzDgFG+sRavqQZ/WDsqyQOkqT2kp5AkOlEVZaUeixNMP\nmABTCl1RMaFZc49yPW6RiPHeC0vpvDnadCHWTGsuWms/lJEkiTectizcIAcwHPYZC47BaDRkJMMO\nSZLQF7yI4Wg0NdJF4XPA5h5GBmNK391RGuNwaOX4qr6AnYL6RFHs0z5ztqRIM0bm/to+Z6mDmcxk\nJjN5yPJIPdrVxTb/8Fd/EoBJCsTOQ5rkiqWm21mKLOfutgsLuonh5tDtxvsqIjdu57q4OI+uO894\nb3fMJHXvjwvLgsxMR0GddSEW1CoiE2DxldV1Vo+7efRGq86wauA/6NGVnkYaDRo1lzoYJTmDXpfg\niHtd1lofWoU6oNWU0WUMez3RT1JSUBUaqlIN5NZQijfVtbAj77eUxUpKIdGwJV7vwMLXxUkalYaJ\nqGZ/MmHlmvNos1OnMBsOA6EMA4x4d6VWJMKakaUp6XB0xP0tJ0opHzmNuwPf6RJoTYVoqBSo/d70\ntXhCFoOS8c6ysGhZS1oF3rtyZINT79bzYBnjGRJMOQWg11qTZ86rM5TE0rNb5AWjoXv/4GCfWk0f\neXJGwFf2i6LwBbA0TX1/rSkyrHX6T8ZjxgLxmSZjX5gsi4zSVEXKaUG7NNMR87IsMRKxlab0A1HW\nTmFYa3HkEPyQsWcZcEjSzPfy71oDFrLy/jpmHi05YwHbE2fAwvYqhVzw/EqTBUH7f+31Kzz/xnUA\n1tZW+ennnpFPl0RaDElcYySV1eZKhzMn3Dm3tva4fMs96MvzJblApi8utFlYEbi/uwcOGR3AlhQD\nYXboDkitewDMeEIkzv5KM+J4s8lfREfb+bd4ZEdKjH/I67U6Sx1pD2LEaCLklW/7sGUoVvcbwCvy\ndgNLIg9/CuQCt5gyNdKFNT7Haq3h9C2HgaBjzXy7IeeJKYqK7aL0XQZZlpJOJg8dgvIwRCnFJ3/C\n0c4YaxgeuHzt7s4u40QMnilJpBpuyhItHQLNZo1QjGsYxr4JP8tSrxcwlBLGBkHghxryLJ1SuBiH\nBQCgC3z4XNqcvDqPBbFHKDRBoI58jhblVcJkPGIiXRJpmlFIGqHIM9/5U5bGs90qNR3qiKLIg0Rp\nrSiK6v3Qk7bmee51XhQFRVGldIzPqbvzyvvG4FVblj4LY0sjRnfWdTCTmcxkJkdC1MNgDvihX6bU\nDjACdh/Zl76zrHB/v+eMtXb1Yf+YdytHVLdwf/o90roFUEoNgDfe79/xV2S2dh+uHNrafaSGFkAp\n9W1r7Sce6Zf+CDlqv+e9yFG8lqP4m96NHMXrOIq/6d3KUbyWw/xNs9TBTGYyk5k8ZJkZ2pnMZCYz\necjyfhja334fvvNHyVH7Pe9FjuK1HMXf9G7kKF7HUfxN71aO4rUc2m965DnamcxkJjP5z01mqYOZ\nzGQmM3nI8p4MrVLq80qpN5RSl5RSv3lYP2omTmb6fXgy0+3Dk5luf4BYa9/Vf0AAXAbOAzHwPeDp\nH3H853F9iJeA33y33/te/gNOAX8GvIobgPrH8v5vAbeBF+W//+L9+H3vVr8z3c7W7lHR70y3P+Q7\n3sOP+xTwxXv+/ufAPz8M5T9Eha4Dz8rrOeBN4GlR6H//ftzk96rfmW4fnm5n+p3p9rB0+66LYUqp\nXwU+b639b+Tvvwc8Z639Rz/g2E8BvxWE4ecqrnvlUDeqI/z8e57nU1oL2QgqaQvqeV4UpIJTG2hN\nNW9clKWfEVfgh//NPee493IV0/O/TQt/ZThcKY01BlshrDwCuV/9VrqNo+hz7QpIRisPnGGt9ZCJ\nYRgQCl7qeJJQlhV7xVQq2hQ5+T2qsG+7FxU+QS2KCEJdHeJnxGGqxiAMPdQcSr/t+5RSHPT6jMbj\nI6db+bdPAb+llPpcUxgm1tbWaAtG6V99fH7Y02T9kLzyx/zVZ+/tf3vOAX98WRQMeoKJ22h4yL4s\nS6kJqMwkSYhrbh0EQYApS3a2bjM46D4S/b4b3S4uLn5u/cRJwOmgwm2wVM83hIFGFwJ7aFLCqMI0\niDBFBUiV+RsQhCFa2BB0GHiWChQYgUM0pfW4J1rrqT0yOVaAaoK4gRbqoUG/R1cwLowY0NFwRJIk\n76jbhw4qo5T6DeCfAR2tNc996ifcF0cRWpRlrKIr+I+bd++QCnhEmpZkgpgUKMNPfORZAHZ2d3nz\n2hUAFlptrHHH7/e7NOsODzTUClM4FKAsSyiEj8kaAYfA8ToVAjaRYaZgM0pN9wAUtSj2zKJHSe7V\nbRgGfP6znwIgjELSrNq4MgJBGVlZWmT1hONLeuGlNzgQCpCiNFQPdpalnvZaBwFTOqzcU2BrrUkT\np9tzJ4+xMO+MjjKWQCh0g0B5xKrFhQVP2aLDmKKyOSiiuM7/+q9+57BVcyhyr36VVnzkOQdq/o//\nu3/KT/70ZwHu9YowWIrqWcV6PVosVvBlC6P89TvM2WpDxIOdWIvXnTHGOwr9vbv86R//IQDnn/4I\nx44dA+D61cucfewpAF5+/U3OyevO/BKjXo//8R/+yiFr5r3LvbptNpv8m//grivPS+4KaH1RWk/P\ntDzfoLZ3GYCD/lVWTj0BQKN5nOGu0Phcv4kSKqG51RVaAoTfXp6jvbrmvleHjPccO+64l1GK41Gb\na6IFnKYcbVH23DGdk8/QnHcTtl/+k/+b3/9DR0M+zjLysuCPhMjgneS9GNrbuNxGJSflvbeJtfa3\nlVL7wOejOP71sUCgLTfq5LKzjCcJWeYM2dmLjzF/zCmlN0wY9AWe2hS01xzsXl5rcFJQemySkYwc\n2n8rqlVA6qSmIMndDmh16OGmjDH+ATBF4b09rRQEQuCI8jtjFITMt+fYTR85Z9g76vdtug2DX+8P\nnB601h442liDEsDkOAph2xHZ7e/cZSLXVBjQAl7tUIwqL9/6CKHIS4+qZMwUUi7LS3JhdlTGQnWe\nsvCb1WDQ94bZEGDEiNQbTfKinEICPjp54LWrg+DXL735GgC/97v/Fx96yj3o6ydOMPVjFVt7DmTy\noHdALJx4y8tLVHhpaTKhpIJYDBkNnFEZjgZcufIW4BDXbDklB+313TGj3j69bWdsJieW+PJ3vwrA\n1u1b9PYcatqLL73K3h13nlNnTtHdv81kfPAu1fSu5IF1G4fhr7/4/J8DsH72Aok4P93dXXriLN2q\nN2kIv9ekX+PunrvGNldIhs52lHlJGAnx5aiHecut72anycK5s4Db6HavOXTAySD1BI5ZqNFRBV85\nIRZQ9fX9nOOnzgHQ7++S5YKYVlhMWfz1kOaHyHsxtN8CHlNKncMp8u8Af/eHHHsbOBUEEcY4RZgi\noFEXGL1ai1AW5ZknP8TGY24Rp3nJaOwUbTFYwY5d04qPiBc12Nln87pTXGpKJgKf1h8PGfbcTpeP\nBhiB+MsnE4pUHvo8xRYVq+g0TFF2yqGrLWg0fy2f8PDlfvV7GzhlUWQC86gJsEGFxelg9AD2Jpbb\nXfdAbu7sYiqPS2lhAXAG16dTrCWS+2LCBkHL7f6xDqjV3PG2Pk9q3TFKK3JbbVaFZ4fNc1DiDZem\nwAi1jhoV6CAi81CBj0weeO1iLbmQMH71K3/K//G/bwDwK7/8y+ztu3XW7/W5cdPpN8syv4GEoWZh\n0elue3eXXPaVzvwiAyFV3Nm5y81bLkoLg4C9uwfy2Yi1Nee5UpaEQnHz0ne+xk35rmYt5NqrAiHY\n3ab7losOt1/7CjoMyMYVl8YjkQfWbZGmxAfuWhbi88wtOU+0nnaJr8rmc+pxskX3zJdJQFmlC+OY\n3ZHbiBZXFwkbLr3TbjZRpdOJUprdntPJ1p2bVM9yfzJhT8hcG515RuIETtKMzpw7z6C8zvaWu0fd\ngz3/vWmWQ3D/3BXv2tBaawul1D8CvohLav8ra+0rP+TwbwGPvdvv+s9RHkC/M90+oMzW7sOTmW5/\nsLynHK219gvAF+7juEIp9Y/yPP+j7W2HOlakKXXJ23U68zz9IQfwvfHkhyhiSXQXhUeOt1qRaLdz\ntefanHrsAgBpf8hYyNl2e/tsnHFu/pK19IVVYXjQpRTOL5NnFIlDZy+TMeloKO/nCD0WJi+JxLs1\nWUGoNfqg+6509F7kfvTrF7YK/ihoCh9bEEy91dKSpM6bzIMI1RYw6nXtj1FBSCgU74HWvnCllPIF\nF1Tg84Zaa19oLOKAseRllcWDJytCHwNEOva8WlppTNW9rQO0DrHvw9zMg65dY+wf5aLHLM34D//2\n3wLw2ivfJI7d9d++ucdcw3ljS0vzTBKXyukP9lldc95YrzegP3Ce0+LiEgtzLhoYDvvs7zly0HZ7\nnnGvAr9OQMLntbVlisJ9V/fOEEp3/jhqcXfT5RSTcZ+Ftjtm6/ZNUDXPwfWo5EF1Wyr1R2MhT420\nZiR6a8YBjbpbG3MNxbWhi1yz4ZDtwaY7Rxlza8fZlLWFVUrrIodWu40VhoXF5QXi2L2+M9mm2XTH\nDEzJjR2XSnt8YZ5I0pFJaQgE6N8CY+t+W9xc4ImLLv99+cp1SpP5KPid5JExLFhrvxDVGxwcuBxW\nOt4njt0F9zpLrEkexBJi7LSKXRk/bZXPbanCYGXRxyrw1chbV99g6aQj/ltaXqG0FfFaySR3Sgyt\nIUhc94LJJoSJewDyNPUEcbYoiarvKi02z+H20aWysdZ+obOwRKrcNWZWYcX4laEmkQqqURGRhPzR\natMX/0JCoqqIdU83iNUQyRKxpvDZE4v1XQ15oLDB1ND6nJWy/nWOlvSLy4VXnQlKa5QKMUd8QNFa\n+4Vavc7iomPpSNOMUc+tuauXrrK07Bho93Z61Nfd2ur19ohq7vqXlpqMJ25DH40H6IrM8uZN0o77\n7HgyZqfrDIwpG+S+eGa5IawhYRxgZZeajBU6dAZ00M/Y2xFW3lgxqr4rmWCt9vfqKIq19gtnzp4j\nrTnyzls7XXb3rgGwMt5hu+sM6mqSsK2FiXoUsTtwIX+eW/rCyGwP+sSB0+dWUTISxoooDlk+7u5d\nvRWiqDqTIlrinPT6A6xYQ6Uhm7hnZn/vgNGOO/9wOKAeOF02wggd1O7b0B7tFT6TmcxkJv8/kEfK\nGRYGATpwXmOWZiQT5xUMRimvvP4SAGuPP06tI5ThZsp9ZQCU8E7lEza3hELQWFZPu5alj7ZqzHUW\n5HhFQwo8WRxRSEGotIpSCkW2FoP02hGFjpQJCFG+IFQWpSN2C472nmQsjIwUrpQG8ZoMmkz6AC3B\ntBshqKHk/cCGhFK40gqqbuEiMOiiJscXnnrc8ZM5XWUoCr9fW08EqMF7wErhPVruaV3SaJTV2KPP\nasXc3Bwfeca1F165cpXJwHmQi/Orvl1wY32Fc+ddP2hRpozGLqV10NujLn23K8eXGQp54nC0g8ld\n+sxaTbvtdD2/tM7Jtjt+cXGeq1ddkSxNU2JpiTx+fAUVSKRSjCgnLrUVBIZQSjTLix3CeIm3Xrv5\nUHRyWNKem+fYOReS6zjkzIor/s31NhlsSYGwv8f60K25ehixvuCIV42xmAXnrcahBkndXMs1u1JI\nnwxHhMqZumaj7dNYYQjHz7rPjpMh/b67X3EAiXEdDgd7+wTixRY2oye9eUmuWDp2ASWko+8kj9TQ\nlsawKxXaKAiwYhjKIKNVpRTyCTU1L5+w/mEtjKXEhQJaa7J7wqG5Yy5d0Fg5Rla1jCUTmpLfzWp1\njBhaqwBpd8oKjdFCVpjneBa8IPA3AyNEcEedBRdFWllIrQkQQ3tvKgBV7SWEKnBpAsCGkIoRDQFt\nq3atAqucMVY6mA6SYKe6QvmWOqU05Q8guFNKoe45pjK0gXJN4kc3sJ1KWVpu33I51J3tAxY7LtTd\n35vw6U8/B8Bzz32Kty65POLXv/F1GlIBb9TnuXPDhcDLKw02Nlx94cLJkK0rzpCcO36Miz/mjM3a\nxkkiJUMHOuT0KXdMkoy5dt1V4aMoYuPkmhxT+jpFv7+HwaUUGnMDRpNkequOqGgFj1UblME7CUl7\njnjFdYo1yz7pWy8CoOo1ltrOoYrqLd+7XZiCoaTJNijYkGdgqbVKWHOb2HA8YDB0Od1xsk+WV2ms\nJlnqjLTFEobu+EDFtMR4x1GLwcClL77/4sucWJonuk+7cMRvwUxmMpOZfPDlkXq0KtC0lt0uHMUh\nQegKB0FcY/WM27l0LSKXXQljvCdkDG8bLqjGPh1Pu/OiSlNSCk+70opYdrFGs0nuPVpLRyrso2TC\nRBrpaTQpxRsOgsCPmCqcN3fPqNiRFAuU06lNryuF8kOcFgW66rWFugwa5MqSiwcfaE1LwohOFNOX\nHb9UAaa495zVmPS9Y7n3TEmp6fCossoPiWCs/6zWrjBm3uUY+KOU8XjM1WsuBN9YP8GnPum82G98\n/S+5ecMVq86d30ZLl8be3oDlZbf+2u063W3XF2uznJV5F7GtbZwglEm9pz72DM/97M8A0B+PSaUz\nYTRMCGPn3a4tHGNxyT0/+90dX2BbO3aC42tPuvOTE4TuGUiSAZfffJM4fPWh6OSwZG9vjy98wU1Y\naQWZdAiNJkPWV08A8NSHLtD+sNN5p7NCo+mKXmmWkUr/a5aXhNKjPV9mJHKeYV5SQzoHolUW5p2H\n2mpmFJL2KY3xxVsXobnXLrJ19qIoSjala2pre5eDXp+ksh/vII82RxvHrD/xOABRPUYFbiGGQcSy\nTIPZUJNXjcbGomzFu37P3L2yFGXVUWBQFaYB0wZiY60PdYMw9I33RVH4UErrgGbT5WKKPPc3OAhD\nbzCKPHd4AR8AQ1tdmAqmAxbGTjOgCoUJq4ECy5wENEmR0553+q/FIfWuS+881u7wkrQZ9bViYc4Z\niKzIGcgUGqgpZsI9ejJK++ZuW5p7MCimeixLi9L33/T9fkqj0eATz34McJNbw5ELIfOy4KVXvg/A\nYLTPh57+CACf/ZkfR0ktYGvrDp/+tGtfXFpaYOOES3UVec7ygguZ146vcuWSm/ra2rnL8VWXO7RG\ncWrDpSkWl45RSkfO62+WXH/hmjv+9g7zyy5nqRR+ynJ5fp4Tx54gkrzuURWlFEoMpA4DFubd9Z4+\neYpzZ91gSFCrkSTOSRiMRySZy0m//uYVtAxxtNsdVpfcGu3MzxHWXK63sxwx2nftb6NRj7CqWRjD\nFHnDUIiThgUrr9OkpBRbUxQFdUk1NmuaZrOO1vdnF2apg5nMZCYzecjySD3aIIym4A6RRkkIXw9q\nLEqlUWvtkXNQGivVwlBrwlh6OpWdphf0NPQ01viKuQVfmLFMK91lOe1kqNXqfj8LggClK+AZQ1D1\nhuqA0hRH3qN1OQ7xVu20a8JgXeoDKT5JIbBd0zxzQYY+Sksieh6Oh9TEs28fQJBXww4F7cCFsHGg\nCQSwqMzx3qp1aCjuNyhF5VUHwfT3WN7u1VY9pUddlpcW+bt/51cBeOmll/nynz8PQH94wFzHeYxn\nz50mEU//sWfO8rOf+wwASTbx3R6rK2seYOYv//LrLK/KWGlhfXHy/MYZtKzXuXabUNZiGIbcueuK\naiYZshw7z2xusUXjmCsObW3tkE9cRLg9vsvd8up9h7fvlzQaDY8d0W41WFpyaQGrLHXp+y4I0LHz\nVrfv7nHQc2mTNDMEoXTA5AUHI7d2CxXQalahK6ytu9Tk1rVLjAfOG47jkDyrkLxyn27LstynHYzJ\n/cBHWZSUosvVhXlq9Tqhvr/1+0gNrQ40dQk/rYaaoOXUVES74RZcYKEo5MJ0gJWHWFvr3fnClBSB\nO8ZYPU0d3JMuUIH2QCmBDWjUXT7YoiS0hsIYf3xZRtSqyZAk8RNROpRczxE3tMoqlPF/+E0jUNrr\n0KLoyM5yptPk3FkH0jMo4I1rLrRK84SOhLxhmjC/ILPj6xvE0i6XFjlWEMH2dgfeKFilfL7cWnw1\nWFl8Z4LWwRTVSnAk7jW8R1XyPCdNXTviE088hpYQ8vnnn+fgwFX8G7UWi6tVNTzi7PnzAGze3eIv\nvvktAL72zReIYrcW144doy0DC+kkYWvTdTUM+kOGUt1Os5RBlabIUrLUpXLqQciidIS0FTzxhPuu\n21cvYWTyMYhCWnPxfYe375tYSy7XtXrmOBcungVg8+4emejcGkOo5dnWxj/DAIXUVooiJU0rMB4Y\nD90k6f6eRpdurVulXRsYoIrUAy4V6YSxtIN1D3r0ZWI0LwtyMbRFkfuaUV4WdIcjcoFofCeZpQ5m\nMpOZzOQhy6P1aHVAQzzXoswIxFU3WUksoWustMc9zXVJKZ55aAMiCT+VYpqgLvEeLWjKe7xbLakJ\nba2fvI+Kwu+GyhpfdbTWTEGDg2kxLApjBxd4xD1ay73V/MAX/ExpsNJPWNOWk7H7hyfWlqnXnUe0\nu9ujKfP6pllHl+4epXHMmhQX4hPHmWs77yvPC25IcVGlJZMKA6AwlJIKCJVy8JSACiJC0Xkch2jx\ndEfDMda8D7ho70KMKblzx3UddDpznJQe1l/55f+KnR1XPGw0Wh53d+PUBgd9l0YYjBOfurpxe4sX\nX3oDgKWlZX7h538agFDB//I//0sA+t0ejYYr0q4cX6PelpFx7RCqAOJmHSsz+C++8n1uDJw3vL15\nh91d15OeW8PaiePk+SOH+HwgUUAuBbwrV66wsuqKYXPzC+zsumghSxN2bruoa3Nzi1JGkTtzTdJq\nHD+u0RL9F1nKeOQ+e/zYMqp0519dXWJ10XV65EniU5AH3QO+95Lrztjc3WMiiGdKOVxmgCiq0ajX\nmP6DIozuz4Q+WkOrFDWZpIjDkKiC0dM5Y8ErWCoanFtzlcZJWbAvYVNooBg7pTRbTepVA3KakkjO\nK7MFVs5ZUJJrmSSj9K67UmCouhQMRTUEEQZoqXzaKCJJpwwOb0NfP6JSjSQAWFv6wQ1dhkSS215t\n1Vm0Ljw6sbrE00+5BvnTkyl04d29HQdGggP1ONFxRjcJYyJp4jalZelJ1050/sRJhgKKfmtzhxu3\nXQ4xSVMSSQGFQUQsE3h5MiaXTdIBz3wwcrQAC4tuo2k2GmhJnZgoJBUQlP3uXZZXXa7x8Scfp5CC\nweUr1xkImPWkP6bM3TX3+xlagKd7ezukE7fWawH+/NYUaOkU6fZ7PPVR19Vw8cIFxluu1ehiFJMU\n7h6cCAMywVK9tbnFwcHAd4UcVdGBptlyBvLb33mBty67AY2z585ycsPZgvbCGiaooCYPuHbpGgBr\nG6dpt6uaS8y6bETL8w0/0dVsNWjWnd1pN2o0Gy5FGASB102apswtOYDvYyfPMeoLTKXNfMoIPU2H\noTTGGM9s8Y7X+GAqmclMZjKTmTyoPFKPFhRz7YpryaCl6DKOJiQ4DywMQj76xIcAKK2hO3aewM7O\nDtcvu12mHcVsbLhexElh2eq6EGFvOGAiXpQ1xoN6W1uimVbejRTVgkBV8AmU5dTrDcKQmk9TKGzx\nAZjGV8ojCTXigIqbbaG1QEuQ6U8uNDnTdp7DqXPnPF7BfLtBJtXX9dVFluUYypJmyxXDMtwYNEBe\nlr4xv16rsSQRxcrKKouLrhj02puXmAioMsEEpHhEkfuinQ4VuTEfiD7aMAxpC/D5wUGXTCr7tVrA\nslCm/NiHn+biEx8G4Pjx49zZdimFq5ev8NYrLl1w9fodjEAdnr64weKiC5Nf/d6LLAg62LHFpSmV\nTaD8IEqEZrLt1vptc51s6KKTWGvqcp+On1ynK8hW127fdgDkR34gxFIXjzNNx7zwovNo33zrLTpN\n5zE+++M/zrOfdDRYTz1xkVwisCBsgaSoOk3F8RWnh9WVBSKJekujPA9Zf1IwSlzhrdmsEUu0F9Wb\nnD/vEATXN05y5aaDT7x6bZOORL2BGft0RJmXPEjS69EaWoWf1sqLzE9iKaU8d9fiwiJNYV7o93us\nLzt33iQpp55zkyEn1o55xU3ykpbkSZZqISPJR20ddAklLJhY1/rhTjSdNtNBQE27G5yZHEMVrlmf\nuzXGuO6FI546wEIkedCf+dQnWV12udXIBrQlx1crE5486dpcjp86RWKc/qMwIBm7xTfJIpB0Qa/b\nY74ytGVOKjovdEwq1da5Zo1E+MlMkXNyzRmLPEt91TcMFZGuhkc0qdyK0lRJnKNuCCCZDHnpha8A\nsL5+hk//1N8EoFav+6r3qVOnmJOumqX5Bd/687c+89M8JTim+/sH9CR3e2JjnXZNHkFrWds4C7jU\nBMKDV5QFgaRdTq+fYnHRGfVaLabTqEgYNYXkGkfjsX/GTpw4wcL8Iq++8vpD0MghisVjBpw/f87j\niuzu7XPl+jUAbt3Z4lvf/CYAn/0bn+Fjj7sUTXds6I6EVWWQ8OZ117rVHeTMzzkj3Wo2PcHiJDV+\nYnF3MPLOSS0K/bRpLQo5vuzaTaOgzl++4CiMsgzOnDgNQD7cJZuM77vtc5Y6mMlMZjKThyyP1KO1\nlikWgVK+mhdY43eWZqvJ8eNuN1nstLl+6xYArThmTbiEVJ6D0I0n/SHDbSnADPssyChibWWFvnix\nB+Mxu0NXaBiMx5gKcd7Ye1gG9BTNykIgYXWRJkfemQXBFZDffGZ9laelrzIorO8nJp/QlDSC1Xh0\nsygMqAkxXUcpX6Da392jwjXevHmLdSlMLC8ssiOsxSYvMIL8pRsRNUGmn3vqIhtrLiwuy5JIWDP6\no5T9vrsXV67fYHu/9xC0cfhiypxYuYLT0lzMSDioJsOQvpAn7mxuVsQTXLhwwfcymzRlfdUV0uZb\nIcY4r3+f2T5qAAAgAElEQVR/f5cXv/U9APJJnxXpWa7VG3QW3PFznY4fCKnX6yhJ2UySCRMpQubj\nCSNp4L97d4uewP2NhyWRMpijXQsDIJGuA4WiLqhno/EN3+ee5jmXrlwD4MaN25w96yKz537iOdZP\nupA/IKInBfO3buwRSGpydbHDQtutv1o99kDzSVqQCX5HlqZEYUUaWieWnvpaVOfjzzwNwFe+8W2+\n+LWXAfjw6Xnm4vJ+uRkfdY4Wz7VuzBSYxGD9nPEkS8mkiTgMQ9e/BUzGCZvZFgD1cAo8kaQ5EyFn\nG4/7rG64xuTjayd46/oNd0wRsrDobl5iUkZFhRUYTPsRTAB+E3DN3gC6CPxk1VGXXHRSTIYsy8Iy\n44yiypWEDVIJc8t+lzh2aZMgCPwwiNKKQNrillYWiaQFrLPYpiZ5tFBDs8pt6RalVHGzPPfMxoWx\ndOou7TNOUu5Im85ud5/BWDbJJEHfA+ZxlEVriEP3O7/+ta/SfNnBFa6srvnJIWusH8z43gvfentY\n6YvVyrPj6kD75+H8uZPMC/RiGNXIKjbnbFr1zsqCJKnWfcpw6Izr7eu32Jac4ng89sYpDDX1RnDk\nBxaMNf5aymIKV9rv9el33Ya2sLSMkhbQcZryyhtO/9eu3+SJJxzt2LmLT5AYycvqJkiXzG63R02m\n6FYWO3SEyqbRaGLKqSHvDd19zHa6RHJfyiL1dYcnH3+cnbs78r1XefLcibeBKv0omaUOZjKTmczk\nIcs7erRKqVPAvwbWcFWL37bW/kul1BLw74CzwDXg16y1P5rBUE2dF3sPqHcQhf4fbu/c5ZLQKJ9e\nWaXVdp7opDQk44GcxnoA3jTPiYR7bLV1nGOCmXDu/HnWjzvv9rVLl3jlitsBm8owEi6xtLCYsCIK\nMn7W+d4qrQ4DP1Z62HKourXQlHBneWGe4yvOO0oHY4aSZtkfjECKhXVdYiRyQIX3jM4WfpdWOmAo\nUHydxXnGqXs9nowoyyoamfYiFllCIedPs4yxjDH2hhO+9hcvAHBz58BDNYa66s19OB7XYerXWtg/\ncN7k/sGExRNO1+un1nzTepKk7O2503TmOv59rTWheFStVstz06E0QUXnXipKCWOHkwmp3I+8KLzH\nbErj2Rnu3L7Nzo7zrnbubmMyd856rUGkXZGsVg9YXOpMQewPUQ5Tt8YYJuKpaw1F5ropyiIly4Sk\nMqkT6IrWfhphDscTXvy+C+e/9vzzPhI9deEpjp1yIf/88jEGQ6fbrd0u7YazFwudDq2G9PVHbvQZ\nYJQUU+o7ZdjZc91OUWBRuXtdr2mUDu67GHY/qYMC+KfW2heUUnPAd5RSfwL8A+BL1tp/oZT6TeA3\ngX/2TierJrdKU06hCM3UyN3Z3eYbL3wbAPPUh1gXw3ms3eHupsvXapOyfspV/3QQYCQVMNdqsSB5\n3L3bW9y64yZJst4BkSDQZ5ubTCRfy1ybSGbNc6YULtZOXythAHhIwe2h6dYNqrgF+Oblm5w6KfB7\nK8vEMmV0rFEHGQwxZU6Ry4M9nEyZEZSZwsgV0+ReQYapoCmt8SFyUeR+kq/ICwrpRihMia6quPWY\noqiQ7BveAFm0I5J8MJ09iByafpO04JsvuM16PC6ZiPFaWl9meUmI/9oN1iS9UpTldPtQCivHj/PM\nYx2XpaWUNjFs4BLnQF6U5BWuR1EwkLD67uYWm9fdM9Dv9T3wUafVwDZlKlBFaDFI1sDVy5t+cuqQ\n5dB0ay1+o1co6jKgceLYKqsyJJIXBeOxM8ZplvgBG6UDqn0rzVOWOs4xS4f7XHvN4UssHT/t6xHU\nO4yGbiPa7x7QjNz3TkYHIEMfpVUU2j0zo/4+E6EkGna3KRJ3L378E8+Spdl9O2HvuNVZazettS/I\n6wHwGrAB/CLwO3LY7wC/dF/fOBMvM90+XJnp9+HJTLcPJg9UDFNKnQU+BnwDWLPWbso/beFCiB8p\n1lpfLJnW+3FAu/JHv0j45uuuEtvd2eRXfu7zAJw4fYGljkt622KKk9CoxVyVxPhbL7+MkbTAn33l\nz3nzLQekHNfqGOkZ3R/06Uootnz+LBc/4gCZ9/OcQlfYCAot3kUchA6A/H6V9C7lvepWgR/E+M53\nv8tN6T+8eP48T0qx4ML5s8zPOQ8hiDW24Y5vtlNfcMmzcgopWZTeQ8XGaClGFKak9FBhU9jJKIqm\n8IwWAumdHaclaS6vJxk1+ajVGh1Ej6QW9l71Oxol3LjtQvX19TUf4rz4nZd4UsaRwyjyo6RxrUYo\nBRWtlL835q+0ABh5HtK0RAsaVzLJ2Jci0ObmHTY3HYPDYDBAyyNbrzeJZXxXBRpTpeF0QCjHtJt1\nPvyhi7z66nffWUHvQd6rbl0iccprtyAMFI8//gSpwBKOkwm7kirZ73ZJxEvP85xSwqswDKlJgbdZ\nrzNJ3DG3Lr/Ckli6PGxgmy6tduLECTIhi93b2WZNMBZGvX1ee/MaAP3eLlUtcX5+no70SW/e2WR+\nrvM2jrwfJfdtaJVSbeD3gH9ire3fm5uw1lql1A98XJRSvwH8BkBjfoFqhSo1xSjVKPKqFSsdkKcy\n6fXiZcKRy4n88i/+Ko895SbGrGqwv+umbvr7d/iLr/0ZAC9/77uowF34q6+9zl2pdIdRvUoLMhqN\n6EvqYGN1hZ97xhna127c4tK2u5HDe9q+luc6JKORw3J9SHIYuo3qTZAJlqWlZY9h+p1vf5cXX3Ab\n18ULFzl3xmHQPv7E42yccq1wcS2mKQvUFsqHtsbklJKW0XZK51Ma40OmwpYUkq+1xkzz2wpfub1+\nZ4eBD/tyqpWrg5A4jB86qsxh6FdrxfKKCyePn1hkJBNCo3Gfxx93rXR5VrLZc3WEPC/e1pYV16Yd\nHl6PZenTPUVu2BJm5/3/r703i5HsOu88f+fcNbaM3LN21sqd4iJKoknJlsdut0YY2N2DGaM1A48H\n8MDzYqAbmAHa8JMf/dDjQT8NoIEb6AEMyJ62YbtttyyJkihLMilS3ItVXGrfMiu3yNjveubhnHsi\nskSxssjMrKzh/QOUIm/duHHii3O/+63/b22NVmvFXL9nCZQq1RqOr11jlUtScx2Jg2sqRchTwqq+\n5mOPneSBU8cICyKUHcB2yHZmZsZm/5EOTtF1WKnaEFXN8ew5nufT6+t7uNXuMDB7S0lJ3TTYnDhx\ngquGd+P64hKemULc77cYrOiQsatiTFENS0vX6bQ0Mc9gOKTb1vIP/MDeSyhJz3CvTDXr+L6/ZbKp\nLWkPIYSHFuafKqX+0hxeEkLsN/++H7j5Ye9VSn1dKfW0UurpwNRwlhhhu2Tr+nt7XMndwnbJd6+X\nSN0NbJdsG8bL+v8ztlJ1IIA/Ac4opf547J/+Bvht4I/M///1Vj6waCdMklESReQQF9nqJLatcP14\nwDe/8w8AtNbX+Odf/XUADhw9bt1N15XMHdaJny9ON5g1radf/vIvsnhN/8bvfXCeVl9bxhsrq9y8\npF2x+2tVPjerPZvKlWWbmFiebXDN9EMHviTMAtwduNG2V7bKknHX6hXuO6iH2jWbNS5f0PXEL774\nQ175qW5jXNh/gEcf1VnZ+x84xYljRwGYbNSsywuSWJjkWZLZAnzJKHkJzoj4e2w4owJbp9vpDokK\ny9jzyIqqBnQP+lZrEe8U2ylfKSUTZsR1PMxYNpwDX/nKf03NuKKDwRCKaowkteGwbjQgE3o/jcvI\nskKZ914zzTlSOpZ4fnpyn6X7VEAqC+9BUthJEsfSgM7MTvH4YzpUtDA/oz/vtpK6c2z33i3gOI6t\nhnFdhyAoGOMy21o85XrUDGeK63msSx1miaLUng+j4ayOVCyYZpuJqX1cva4t3Y3OBjdM40m/38Yx\n3ojnhziGZbDb7VhqxEa9SsNYxoePHCG7g727ldDBc8BvAW8JIV43x/4ALcg/F0L8DnAJ+M3bX0qN\nMtRpOrZIYW9iz3XxjODTMOCmYYv//ve/w7XrWkH+wi/+V5Zg4tHHPsNhM+wuG3Tpd01XTGuN+YYu\nE3OSiPfO66aGmdlJ7je8qlOuyw/+7BsA3Dh/hX2ntFv9C7/wDK+ZuNiNy9dw2LEY7TbKFuuSJklK\nkujYVrXm8dAj+nsdODDN1ev6Zr585SyLS/r1Sy++wpEjuorj6c8+yiGjpKdnpqgVMceqZ7v30jgZ\nxW4zCQVjgWA0IFIoW73QanUZmNCQF1QRalTelGY5O1h2sG3yFQgi02gRDzIcob2zE8ceodnUCnh5\neYlkoPdNP27bsrc4ScA0eEjHsYz9/X6ftuniWlm5ydBMGXBdn/0LmjNVIZDom77WqOIGhlcih6IL\nQiCZMnHNpx5/hPk5/dqVEildW52wzdjevWsgpbR8xVLmeGaqRxb4DI3xUwkrVEz3mBCSwFSxtNo9\ne/76+pqNn+Z5zunXdSXTzNx+jp/UMfUHTx3j7TOa7Ce6OGDYMSHLaAllBzJG1KtaSU9OTTJvRnG5\njkucxNunaJVSP+TnR9F+ZUufUuJDUcp2Z1HKd+dQyvbOsMtcB8o+zYWU9kmbphkFWZ4jJPFAWw6R\nyoiLFbb7vGPqaxcvX2PxorZWVT/i6c89BUBjZpaqaXC4MYw4d/4CAOfeOUO8oYPbB+YXcE0//rVr\n13n7jC52lkHA//gbusLhVx5/ms8+oq/5/A9/yNuXztvExp6GqZQY9IesmkSg7znMzmjXtlYNmTah\nlThO2Whpb2FpaZVzH2iGomtXz1OpaLaven2CY8eOAnD46CHmZnXybKJWxzONHr4rrWykHL0WQuGY\nRM/6eoeBpewa4hYsacLB8Xx2xrndXjSbVT77tO6pb7W6KNcQVb/2PUtC7zgO169oLyGKIjttwQ99\noqG2rlobLTaKmu4osv34kBFWtRzqdahOaDe20fC4/5T+3COHjuJaPg6B55q4vJCEhk6wUakhi0kh\nUk8c2fPTQRSWI0IJPb6zOF7oCN/3Ld3ncDAkMMnFRqNpZ/0JZ822j8dpwqLxSvMsZaOr5bm6tszF\nC7pKKazUrJUs4+7YYFGXzNCtNup1Wyc90Wja0OdgOERKZ1tDB9uIUYhAKWVLXdIsJzWmejwYMujo\nouA4z3AntHDjtTaBcQVWF6/zrb/Ssfebl65y+Z//KgBPP/t5jhzVGeCZ/Uf4tV//l/pjM8XL3/sO\nAFeu3cQ3N8DEfUeYekS7Ecc+8ziP/LJ+EPtunRmjVx85/hAqCKkY6sa9DGVStJ3ekJppUqjXJmzH\nkesKKqE+Xq061Koj7s6iGHxtvUPbxKounD/H2bOaYk8JmDDNHQtzc0wZd7lRrxCYzrxmc9KeMz01\nSXNWh3Sur7RGwyJdiTC/oyMl90qOyfEEDzxqQlRZTlTwHqucKCrGnkikeQAtLq8iPXM88Em7en8H\n9YRjM/o3cESdSsUMKA18y43quOCYQvrAz5loaiOh0ZhkekLvb1eGeG4xcNQhzopxLj6h+T0EOra5\nQ6GDbcVoSvUopCilHCN08pG1Ue6gZ7oOq5UanqvlmU9mtFr6+NzMPtbXdHVBmsRIMep2zEzoam3t\nptU7rnQ4ZHI90pGsruj8TrVasXqq221bG95zXaI42TLX7z1gppUoUaLEvY1dtmjVpvlFxessz0e9\nxQjbCx6lCZi+ZNWs07qpn+x1BI6x0l77yT9x+ZJuTDh95jSf/eIvAXD46DHmjHX1q//tf8+MyTpe\nOn+Bkyd1VvaRzzzBuhnC1s0lXaWt1teuLNLu6WRSa22VSbe2o3W02wbztI1y6JmM9uVr12w/94GF\nOXzTcy+Fsu5mGHrWza3W6nQNRd/kZMOyKq2urnL9ima+v3rxA1tRIHBtt4kfhLaOcX5+juY+nWB7\n64OrNqTgkNnBkULkpoJh7yPPczqmdtZ1Xcvp4Louvhl42ev2qBir60A4x3pbW1TdQZ9BT/8eBxf2\nMz1hBl66rg2bOI5jZapUTs9MFomSnOuXNWtda3nI/Ky5zv6jTE/pe8P1XGsRxnEyqshxHELPuzem\nXxoIIez4eSkleV7MwRO2+qJeb4wqNwYDKlW9dxv1CRLTVn7l8mWWljSj2TAa2Fpkx5H2HvA9x5K2\ng6DTaZtXynppruPa90bxkDDVnxXHMVKOOFpuh12nSSxcAZUrO0oFpUZjZBjtC6UUqQneyMkGRFr5\ntdc27AYNhGTRVCN882/+Mxcu6tdPPfMMDz+kwwKff/pJnvm1fwbAZ7p99pnhj2kGi+99AMDKapuV\nSBeM3xz2LW2g1+8RphHZFue33zUoZVn2o2hAt2/iXHFEEhckGg2EcSND37d7JM+UdXkrVQ/XZG7r\njTpxpBXK1GSTqUmtIMYflmkaWz7WbrfN2rpRzOtL5BcvAdAhRJhfWGVipHQdF8917gm+X4GLynS4\nJEoyMJUAcZKPGhCSnMzMnXFknaSj5VRxZplb0A+1qeYklaJkSYFnQjlSjjUdCIUjdezQ932reISQ\nFBVh167fJI71bzk5NY1fUFi6nlVO/WhA5qTk2d6PgY9zBoyaHkYddUopy42sBFRM2CtLU9tt6nkh\nE6bsq9fr4Zoy0Xq9gTRlWY50bENOmqa2MkHrIvPQkxLH3A+u59m4rCOFpZxUKPI7GMN0D5hpJUqU\nKHFvY9ctWqcYB65y+4Qig9wwRak0tZMCRiXZgCupGWauQaromox5ksWEpl6TwYAzL78EwOKFc7z/\nkJ4rtHz1Ak8+9QQA8wsHiExReasfs9zTSaAbnQ4HpnVW/XCzjleMbH77MstLV0kNqfbehSLLtPXZ\n6ycMTUikUa1ZmV+/sUTDJBebk5NUTSF2GPhg3TVhaxGFlNbK8lyX2TktH4G07lSc9Fgz0xa63f6I\ntEI6DHNtCSx1EtqxqWlU0radCuHohMc94NtKGRAGJwF0Qb3S8s2z2FrowYQiNYkW36+wb1rPCcvS\nHIy34QWe9R5cz0c62hWVjqOJ7g0Kl1ZKMWp3TlM7/y3PoWWoQnvDxBbVNycnbdG+UA5Jku9YQ8i2\nQSnroUqJ9X7SNL/F0pX2HNcwfAVhSMckzx0nxzNhgfm5OSqGkL7b7dnqAt0opffuMBqSjA1zLXah\nrp7Rr13PJwxM9Yjnj5pzcm3RbtWk3d3yLpSNv+bZqKtCjNd3gP0yEsdOr83JwJjw1ekpYkNwMmx1\nbKF31fXxzXVWlm6wsqJjNO+9e4Y3nnwSgGd/8Rd59MmnAZjZd4D5aR2PlA4smGywi+LmZV0+1rp+\nHhV3IN/boQMFRIZ3NhUCzzE8pwg7JXQQJ6iOllW3N2DaMMdP1Gv2t9CuqoaQ0o4eylVuQwoCYScG\n+LJGc0r/Uakmo59R5lA8AFc7RKaTKsPVsS0KWkdxb4QOhMTzR5UnIjXyldmYkpBIUzwvVE5oFKdX\nkeRFwtwRYMYGKengKMPkn41CEK7n2SnDWZaObnrhkpgJuo4jRtNKFCMKwWiFuilxrAShfZjuZYwK\nujRG3MjKhhGEEGOlg5LBQIcRu90+wyKeIiRVw/Pgea7NF7iux2BQTNmNiKJRfL1QtNmYPpJCWg5f\nx/MITHu754ej8UTK6LAtatoydFCiRIkSO4xdDx3YMd5KjTJ2Qo0xOjmWdd7xfGSRFcxyzMMfGUpC\nU3gvQpe2YeNZ7w2pGmup6vhIkwRYvH6D51c129fbp0/zGWPdfvFLv8j8Pl0bSX/ItRVNFC7yFJnr\np+ThfQ3q4bSdO7+XIczPGfg+tZrpBfcDpFdUCOSstbRlqfKc1IRrup0uQaAtqGq1Yn+WJIltYqVW\nqeAYixah3X4AIZW1UJ2xJKzjKnxDttwcJoh83ZyP5UlQKrftlnsdeZ4zjLWL6rmunfvlBZVNBN/F\nLHvXEZhcDI4jR2TnUhCnRe2mi2es/mg4tMLL0nSTVTeOtOAHES7ZMLXrqZo67zRNGRiGqWgwoOd5\no3bpPQxhq3pGtJtyrKkpzzeHQArrNlMjisX+YGATtbVaze4txxlxJggh7DU9z7WJtDzbHKYoGh+E\nM+KdcF0fx3pjkjsp59hdRasY3YljgsvznCwreGqVzfj5fjgKNSBI1MC8Tsg982NMVKiZ7prheodh\nW7vGWTQgLATkOCSxvv7lCxdYuqEV6puvvszBA3rczeGDh3nsYU2yct99B5ls6nhwPOURRfGeL/oW\nCKvwhHRtIXWvP2DQ0w0IvsR2CQmhR69AkWU1RD6DgXWGoigadX0JYUMTg/7A0v4hcjtqRWfOjcsb\nKzxTrpTGMaLgu5PKxh/z3AyG3OsxRPQN6ptOLGk6rvRrZac5SynAxGilIxHGtVdCjsh2UoVr4i6+\n8FBZET4TNkab5zmyqDSQIzfZcZxRHBFFVnCCJAn9dDRdOit+M9ehPxxsqhLZi1AqJymm4Dpyk17Y\n/FqfLwRWVmHgbxqO2ev17fmFcnUdhywtuIGlDYEpITY96As5KYUdlyPkqMmq+Lu4/jAd2iah26EM\nHZQoUaLEDkPspjUhhFgGesDKrn3o7THL1tZzn1JqbqcX83GxR2ULW5PvnpYtgBCiA7x7t9dxC8q9\nu7PYtr27q4oWQAjxilLq6V390I/AXlvPJ8Fe/C57cU0fB3vxe+zFNX1c7MXvsp1rKkMHJUqUKLHD\nKBVtiRIlSuww7oai/fpd+MyPwl5bzyfBXvwue3FNHwd78XvsxTV9XOzF77Jta9r1GG2JEiVKfNpQ\nhg5KlChRYofxiRStEOIrQoh3hRAfCCF+f7vO3SkIIQ4LIb4nhHhHCHFaCPGvzfE/FEJcE0K8bv77\n6t1Y363YqsxK2d45yr27o2stZXsrivHHd/ofmlbkHHAcTc75BvDwJz13J/8D9gNPmdcN4D3gYeAP\ngf99t9ezHfItZbtzsi3lW8p2u2T7SSzazwMfKKXOK6Vi4BvAb2zDuTsGpdQNpdSr5nUHOAMc3O11\nbBFblVkp2ztHuXd3DqVsPwQfOxkmhPjvgK8opf4X8/dvAV9QSv3eLef9LvBvgYlarTb74IMP3vba\nxZqSJEYaHgMpJIkh1BgMB5YzNQgqdrLAeN9yHMf2fNdx7fmu51kaRjHGz3c7eoiLFy+ysrKya4R+\nW5HvuGyr1ersCTOiB0bTQxFqjDNTkBsuXoWwPLWI0XEYo6aTcqz/W22iMyy2zfjuGZenUvlomobK\n6ZiROOvra+QFl6JQSCEZ9obEUbynZGuO3/He3S3cSi34c89TcOnS7u3djyNbx3Fmw2KS7fg9OfZ6\nnGtkfNpymqb2PpdS2vtcKWXfI8RmTt/x946mfTj288Z1Yq1WY3p62p4z/m9KKS5fvszq6uptZbvj\npDJKqa8LIdaArzz44IO/8/LLLwObhWjOs8djQzCxuHSFWl0TQwRByOKinkx5+swb7N+nHzgnTzzC\nclfPVAorEyhD/Hn16iUWr90AYHp6ln0LCwDs27ePSqB5Zz1nNE9JWLphc8gqEq14nn56TzWtAJtl\ne+Lkqd/5228+D2gZNhqai1M6o6mieebQ7xn+zVTSqOvN7UhBr2/IOLLMblY/DNkwY2qSJLEMVACJ\nITHJUPbBNb5Zo3iIH5i5TsmQH/zoBQD+/C/+jP5Qs0u5fk4Q+Lz8D69st2i2BT+7d39i/kWMaTmx\neW6U3U/56I9bVKL6mRfFJccfVOY3Uz/7/uLfCxY0zaY2/hQcnZMreOYLn7/NN919jMu2Vqv9zmOP\nPQZoZVl892KGGugJzIXirFQqFIp5bW2NxUV9/9dqNWZnNTl9lmU0zPiaMAwtMc/Kyoolm2m1WvY6\njUbDEtWMc9M+++yzfO1rXwOgWq1uMjyyLOPLX/7ylr7vJ1G014DDY38fMsc+8txbFSxsJviFETOP\n5wsGQ32jS+mwtm5YqMIGBw/pWfdhtYmb6Bs3rE/juZou7sHJaU6cfASAJE4s8zpuQLFxFQpp1Kvg\nlu1srTFFL+qPrLDdw1bla84TVoaO42giaYAswynYnJQgMPRvaa4gKxifJL4hOs6FZawkjgZGYWja\nv4JEWkrJcDhiWxJjjGAFqbLIY9597x0AfvLKS3zvH79nzoewahiQVEaaDVHsWdnac3MFXcMvLSW4\nohiZjp0jBWo0YYIxtjex+aXYdLygjOTD95jCUi/K8QGhm7wLZf9USnsQep0OQsGH3HI7iTuWbRzH\nVhH6vm/3U5qmVKt6ooQQo8kfztg0iomJCTY2NPXnuBfreZ5VzBMTE0yYQa1KKauML168aIePAva9\naZra63c6HcsOFgTBzyjarUYEPomifRk4JYQ4hhbYvwL+h4869+ddSAhBZFjS4zihYibf+oHk6jX9\ntHrv3YtIX1tpj37mM4RVM8tdCvyKFmJvmCGlCRd4kqCiz/cqWDqzDMXquuZGDVyXqampYhWb1jQc\nmNE1Ek6/e4bBcNdH2WxVvuY8xhjohR01IwBlrE8pJZKCLxUwCi6KIzveI8/z0UA8BEleKGOIEy0D\nx3FAGPrKNCIIC/pAQZro46+8/GP+03/+UwDiLCKK9UNSiZxhon9r39chnY8bvvoEuOO9mys9kRb0\n94wL5SfBKx5wYxpNyrHQydiDxBHjD6aR2s2y1PLGep43ZniocTt39EqpW64zfs7PusC7iDuXbZ5b\nBea6rlWQvu/b13rasLZw+/2+VX5Zlm2S2ziPbzFiKQgC6/5Xq1V7vNfr0e+PhmAW19m/f79VzFmW\nsW70RbPZHHkauebN/TDD8cPwsZNhSqkU+D3gH9DB4z9XSp2+zbkltoityreU7Z2j3Ls7h1K2H45P\nFKNVSv098PdbPffpp5/eFIstrK5ev0+3qy2ed945w6lT2vjtDm5w+p23AZibOc4jD+tkRLURMhwa\nF1UKlJkfdnVxicDTrobrCaQqrDfXkol7rsPQTBmYatTtE9CR0toOAtho63NW19d4/nvfpW1mvu8m\ntipfpdTfP/7Ek5ssnMKYidPUsvJLmdipCgIJSUHGjX2aO9IhN9baYDiwwwalkNYSlY7DD//x+wCc\neZ6+H1MAACAASURBVOdNay08+OCDrLdaAHzzH/4eKU0CIoqIY+0aSk+QmsGXMlMkabKJ2X63cKd7\n98nPPr0pMWsiLQgxZlkCWOt1NOMKIey+V/no/HGLU0pp3dVxS1eMxV/F+HVusWjH76vNScndt2rv\nVLZhGI5Iul3Xxk2DIKBS0aHA8WRYpVKxFnCv17OWbpZlNqTV7/et/Nvt0b3b6XRomT067k1FUWTX\n4DiOfW+z2bSx3lunPIyVh90Wuz7KptgEGYquib++ceZ1ls2omQuXLnFh8TwASdxjfkHHUx7/3NPU\nzBfWsRTDRq9Go1Q817PuWp4L2makh+OM3BFUTs01o3IqVSKjhALXYVBULzgOvVQrhu/843d54Xvf\npdvubL8wthF5nm+KTxVyzgFltEKn37cKVToOXqDl9tabb9mEwvT0NCdOnACg3miAmQCQgw0pfP+7\nz/ONb+iwQLezamNn+185QGSmBd+8ucTklL5h2r0NgoqW+TAZ4Jmk2nAQU63W7okpuAJwiwG/Eqto\npSPs1GaUsj5ikuW0u9otbW2ss3pzWZ+SS+YX9FDMqYnGWJY8H02eyHKCijYYgjC0gwKVGjmgcoz1\n/9ab/+dl7vcqxuOvQRBsmjTh2mkro8RYHMc21BiGoVXM6+vr9Ow979jj3W7Xvr9SqWyKxRaKXCll\nP2t5edmGFPbt22cVdbPZ3LTmbre7ZSOhbMEtUaJEiR3Grlq0SZJw7ry2Vu87fpTVliYvf+/cu1xb\n0qPBhStZv6Kt2/tP3c+RozqM4AaBHX1dDAY0fxD62uSfajZotXQW0ZcBoRlYx9iMISkEhXGbSUlm\nrIErV67wTz99EYD5hQWuXdeJ0m9/51usrqzeEwPuxmsFiyf4Rq9Px4RlpqYmcTxTQ5ik/PjH/wjA\nn/2/f0an07HXOHb8OAAnT51kymRoA9/n/AX9273wwg/o9fRTvtEImWiYjK6T0x60zWvFjRvaSvYC\nl9y41KlKSSIznNAJSOL0rri3HweiqArIc7DVKsLmqFzPtVbsq6++w5VLVwEYRj2cXFtRjdBj7Yb+\nbWanJmmapEvoS4YD/d44jgmqpjyvNsGh+/Tv4Xvh7dd4qwV7D1i0gHX5gyCwe1cPWJT234t7MI5j\nez8HQWArE+I4tiGFMAw3hWKKqoZ6vW4t0+FwuOlzi/PHLdVz587x4otaL0xPT9tR7lmWcenSJZuU\nux12VdF2Ox1e/elPATh05BBvvvkWAOfPX2LdmOeTM1M88/kvAHD44EEqRojqFhNdMR7z0sekgL4Z\nRJj5CdJObRUMjSvQqNeZnNSCXl+5yd99X5cdXbt4gTPv63KkyekpVk0oY3Fp6Z5QBONVB91ul7Pv\n6qkry611Tp/RuYj9BxZ4/PFHAXjnzNt894XvA5ColGGqXTGlFGff1+/90U9eZMLISqBIE72pfN+j\nWtOyrVRd+ka5rm8MrUuXJglJqs+fmm6y3taZXiUFngkjNCcmSdPsHnFvwXVHBfCOiWMLlN2Lly9f\ntnv60vnzVE2s4eBUncMH5gEIglFcsNPrE/dN3Df3wShyPwhwfa1UcyAzcXW8j1rfz4nL3gN7N8sy\n6/LPz89z7Jgu3Ww2m1aRJUli3fki4w96zxfu/4EDBzaVgBX3Q7VatTJJ09Se43mezS9IKW11geu6\nVmHfvHmTM2fOAHD//ffz1FNPAXD16lVeeeUVu+7boQwdlChRosQOY1ct2vZGm0vnLwBw+cIlOm39\nNMgzaEzoBMHnP/cMTz35hDk+KhzWk8pH3TJFXawUAmGOT9brhMeOmvcqiiHLSZpa1yHPEqSxTJZW\nVvjb//J3APQ3WmRKuybLqyu2YnHv21ojRCazf/bsGd54400AgokKnZ62JpffvsiNm2cBiJMeE1Na\ntu1r6wxMnWslrDEwNcSe5+BIM7a8u4Ey1QIVr0I91J5GFnUZDkzDSOgzNB5FteIzP6tn1kkcsly7\nXMMkwXFNdtcXeIFvs/l7GYPBgJdNaCmsVGhtaOun0+0QmOTqxsYG3Q1t3T/wwAkm6trSkuTEJnRA\nrvA8fX4wUaExpS2qeqNpx7NL10eZdvKc3B7PN1mntzThqp89br2+T/ztdx4LpnPzq1/9qk3GFsms\nAsU9nCTJpjBZoReklDYEBmONT543at831yhe12q6Hr/dbvOjH/3oZ645GAxsovjs2bMcPXoUgFde\neYXXX3990/U+CruqaLM05bWfvgpAjuLo/dpFOHXqAQ4eOQLAkaP3Wbe9OTFqi3McabPTeZbZMqKN\njQ7CKE4/CPF9rQC8wLOpYQUwVRQg5wiT9c7H2mbSPKNvFMZ4cbTi3sjcpmnKmTO6FG6jvU6tYW5O\nJ2J23ii5IUSxjmFneUyzqRVBHDcJfC3DOMpxipn2dWfUpCBAGIXoOYJOSytv15FUitIcBxr1ohxH\ngNJun+MGeJ5+b6c/RHr6+mvLKzhS3hPx77X1Vb75vK5Ymp6a4uKlywD0B0P7MDp+4igiM+7qag3f\nhL2k4xbFGxA7pH0tlzgeMET/TlO4+H5ReJ/YPTfRbDIY6gecEAmuo93eQRTZkkXQVR76HEVqyiY7\n3TbT0/NbjiPeLQghrKINw9Aq0UqlYu//arW6aZ8UirDb7VrlOl5FMN7Kq5Ta5OIX8vB935Z0NZtN\nq9h7vZ4NHQghbBz3/PnzvPCCbiU/e/Ysi4uLVoHfDmXooESJEiV2GLtq0SqgZzLgp996m06knzJf\n+OKzHDyk26PbnS6u1E8TXT84eiolps7w1dde5wcvaDPf90OmZ7X7dejQEYLConU927QvXYfAVCYE\nYUhueA+Wl5eJzTWzPLdB8izLNmXhyXLrhu1VDKOIi1d0WKZaDUhyvf5Bv0uWF/Wcy9y8qd2g/Qf2\nMYi027O2dgPPqZjzBwx6WibDOCEMTNIrCJif1QmdYb+NU3AgSIfAnBPFfULjFksJfqAt1yRLaW3o\nCpM4SukWCctU4XveKNmzhxFFMRcvaiv25tIq6xvao9JWkJbXuQ/O40ptFb333kUmGtot7Q/bTJsQ\nQRBUyMyeG7a6VCd0C7gSSu9ZYHJqgvkFHXaZnV2g29O/U61WZ3ZGH4+SmNgkJyuVKmff1YlczxN0\nOvq+un7jMpVqg/XW+k6IZNtQqVQomNEMGxYAs7OzzM3p71utVjdxDhRW79LSkk2SDYdDe9zzPGsB\nZ1m2qXZ2vE23SIClaWorCsYTb0EQWO+i3W7z5ps6JDcYDDbxMtwOu6poc5XTM9ltum36fW2ST03P\napITIAN8WZRlKGLTjeS6ARhClOWVVU6f1m7y/Owsn31KZ9KfeOIJHMPe5aYxna6+GQZRTGzu5X67\ny7vn3wfg9TdfY9grXOkMYdYQj/EaOELuPuXJx0CapvT6+rvcXLmC4+pVZwzYaOti+SyPmZmZAQqF\npzeZ52QMeroTrt8ZgNLKcnpiAtctup4SAhNycSshQ9MxhoIs1pu4FoRUqkV3DUipN/owy2hMaqVz\nfHaBqQmtdJJIM1B9+/qLOyGSbUWe57TWdfy11+mC6XrrpRGua0qQ+hGBcf+FyLl8zTxckoTVNS3f\niVqNtrm5k37MzIH9+nUWk5tOveZknXffL0ppXFLjxiIVszNT5popUTyiBy04JqanJ1lb0/tgEPUZ\nDC9sOY54tzA9Pc0zzzwDwPPPP2+V19ItFT+F8TM3N2eVZVHlAlpBFiECIYTtAAuCwIYFxolgxjvq\nsjHWuiRJNoUOCgW/urq5zHO8W+12KEMHJUqUKLHD2N1kmFIMzNNBOA6pqRtMs8zwbupa8KIuPM1S\nS9nX7nTITAvisRPH+cIXPgfAytJNrhmGr8vXv0NmrIK6KwiN6xpUKoSGyataq3OfCVO89srLDMwT\nMFcZTvGky8e55dSeDxuAdpWKts3esEXHWLdKprZFuRI6nD6tqw4EDo5jrIEsYTAw7lSSMT+nQwR5\njq3EcKWgZyyEJOpZ0vC1lXU8k9wKQg9PaourF/WJYtNjHvisGHdQ4jBZ1a3UG6stFuZn7oma+omJ\nSX75l/8bADxfIR0tryRWFOEtKTXPL4DvSgreA4UkM5ZQvRrwve9+F4Brly5w3/3aZT51/wlSww4W\nBO4oq54rMqUtV6UypEnwDgcRRcQlTVNbhSNwqBhmu6BSoTKo4zi73ml/RxivZ42iyFYCpGlqmbbS\nNOXcuXMAnDx5clPrbGGJRlFkLU6lFJcuXQK0xXzgwAFgs0WrlLK1tuMMYvPz8xw8qPmuXde1Lbjr\n6+t86UtfAnTY8dy5c1tuwd3VX8B1XQ4d1NUFx06dYG6/zjRmKrcNCb7rMm6RS/PH+mqL1CjaMAxZ\nWNgHwDtvvUOidGzyvWtrrHVNaZIrcI0ycB2YqOofY3ZqgsDRrvGVa4vIonwszcgLrkmwhd5KmQkF\ne1zXCiE5dOQoADPzUwwG2s3qD3v0+/ph0u30mJvVCnij1Sb0tJsfD7p4Js74pWef4eoVTZh+/cai\nbe5whLJ8D0IlttLDk5JZE3/s9jZITIZ80O1RNxUIQymoFR01ac7br71j1jbAddU9UXUgHZcpEy8M\nfJfEVGMoBcVgjzAMbdeb73n4Jl49NTlJNDR8qwKeT7WizXLJZFVXwzx86lEqTV3i6HhQKyoW8EgK\n7l/PwSsaGbKMXI1i24WizVVuyZFylaNyeP5v/267xbGtGJ+GACOCo2azabu+Wq2W5Y5NkoTJyUl7\nbhE+GCekiePYVgvEcWyvPz6dQSllwwJCCBuamJ6etjHd+fl5e82bN29u4mGIomjLzUxl6KBEiRIl\ndhi7atF6rsfCnLZEH/7M44RmlIrreSOG+DSzyacsyyxz0WRzks5AP7miwdD2iEspCSvaFZ2aq5A1\nDK2fM3pyVTzBhKE92LdvmpVreiRO7njUTZ9+p7VMOpaNvBdqZ8eRK0VeJALdurUmqxVBUjcZ15mY\nQwfuB+DG4nXefUvXNLcHMDGtz3/i0cdp1rQ8pYDVFe3yh74kcIsQREBesIBVariG+X+mOWmTYXNT\nk0RmZE2SxoShtmg3Vtp4pqzzyNFD9O/O9Io7RhT1Of/+6wBUK1XWWwWrfx/HhBFqtQZ5qi2cqeYk\ntbreW+trvp1y0V5e48hB7cmpNCMyFRhrN87TSLS7Kj2JnNKehMS1beVhrabrk9G1nkW4zXUcW1ce\nhgG9rnZ1u90ujh+QJHu7jnZ8wsLCwgL79mkdUavVbBhBCMHhw4ftOYVrn2WZrRCoVCrWle90Opss\n1JMnTwKbicXTNLXnZFnG5cu6qmRjY8OuZ3p6elNt7htvvAFoHRGG4d6sOgCQBSGM46KM+6nEiMvA\nFdKWC+V5RmJMe9fzR90awwFrJnM7GPQRspgmkKDMpACVOviBFlDgeAjTm44TUGlqV3dqpk8SmTKu\nLLtlEOEtVHN7XO/muaJnZq3kSnfbAUgRgjKzwZwEXO1OHTp4jJrQx8+//y4PP6wV8MZ6i0cN7++T\nTz7B6bd1n3e3s871q5pU5sbViyRxQS9ZpdPSN/b0zIQNR0xONIgD7fbdXLpGvz8ikpmd0oo8qAZk\nykHIPS5coF5r8NQTmoMjCANaJm437HcJvKKnvsbQVNI0GxO2jCtJYmpVvRcXz1/iuWd1hh3xCteu\n6vzCzKUb3F/R8e2Tp05QqRojxPWQpnzR9bxRPLJaZRjp68dxxMDIUCUx0oQUkniIEsKOttmraLVa\nvPKKnhv35S9/eRMHbdFQsLCwYEMHDz30kI25Fi48aMVZ3MNSSi5evAjAoUOHKGaSjTdvjLv9a2tr\nthnhvvvus3wLQghb9nXy5EmWl3UFz7lz53ZnwkKJEiVKlNgadteiFQJpMqDCGzUUZHk+KlrPMpPJ\n1UmGzFYgDBHGRe31RjODDh8+xIF9Zh7QpGShZxJgysEpxoo7klSYVjnl4Bs3Nqw1LLlRlmWjDKJi\nE9dBnud7PhmmyImKKQm4OGbqRJoq8iIjnQLm+DAaEBiL8/6TD3DK9Jf/p7/4BmfP6GRVs16zG2R+\naop0QlMmdmttEs8kGoYd/IJgud9hEGmro9NeIzTTLohzhMmoCwRD89vJrq71vBcaFnw/4L7jmrLT\n81z2G2v1/TNvk5mki4/L0FhXSxsXWVkbMfkXe+vs++dYMuGY9kbPMpx9cOECTmGtJkPr3oZhuMl1\nteOxpSQv5sIJyE3yZnl1g4ZpN3/isceZmJqhVqvviEy2C0mScPq0Zpj70pe+9KEWbbfbtRUIgLXs\n0zS1ssqybKxl37FDG4djdfFKKWsBb2xsWHmurq5aa/j48eM899xz9r1Fcs73fVubu3//flqtlv28\n22HXO8MipxhwN2KsV3lKjsk8S0WcG4WhfJu5XVtbQ7p6w0zPzHHYuA5f/OIzlruzUZlG5oYmTUpM\nuIxL1xZ577wu9UgAx5Q7rTXqtJY17+zS0hJZrIUoGFVBSCFQ2d53bZN0SIreUDLzyMw02swFP9Q3\nXiVo2D74PM9JHFOhUfGo1bVS/Pznn+WFF/5Jv3djnZlQC3FiYoYbG/q9nzn5WaYWtMxXlt9DmgfX\n5euLdE3HVCUMaJqMepLnqFTLc63bZT3WN0C3nVMJfEZEAHsX0nGoNnTc1Pc9BmbU0dm332G6oRVD\nPuzS29BKdH1tldPnNR9trdZgYKoO1jc6XLh0BYCJeoMwLKgXU6JYxxrPnz9vpxhrJT1yh+34GpUh\nKRStsiVcyyt9aiY+f/zoMWaak7h3UFh/NzA+ESTLMm7e1DmU8+fPW5ffdV2OG57kZrO5qbtrvKSr\nUMBCCBteqFarm3htv/GNbwDw9ttv2yGMMzMztoxrnFQmyzKrmLMs49vf/jagR+UcPnx40+SHj0IZ\nOihRokSJHcZtLVohxGHg/wEW0Ebp15VS/14IMQ38GXAUuAj8plLqI5uq8zxlYNo+z7/zFrP7dDto\n5fgxO4NJoez4Zj/3bE1gtVZlGOkns+NIGk1tRU02q3Ta2hJoBi7VsGY/z9Tg89Jf/IhvfUcHunFc\n6iYbXG9UEFI/AWfm9nPDTA0QeY6JOpBnGUKJHQkdbKdse70OK2vagqrLJnMNXWc4HHZRxoJPE2wY\nIU5iUlNcv3azzY2lHwMwOTXNE09+EYDAARHpj1XRkCcefggAN4DlNW25udEkQpn22rlZ0qqWoe9k\nrPW1devIgH3z2mJ2KgGJSdb4mYI8x3F2phd/O+ULoyGgUkhapupgcekmLrPm8xTKWJ+x41rqz1qt\nZhtgkjS3NbIOijSLi6vrfQZ4bogwLb6u69pmBz3mXa9GZakNueRJxNS0vh+OHJqgP9Dhm7Di4rli\nRxpCtlO2SikeeOABQFuNf/VXfwVocu0lM3nl13/91611W6lUrJUZx7G1aKMoshUI1WqVL3xBJy+l\nlJY/4cUXX+QHP/gBAJOTk/a9/X7fViBcvHjRVilsbGzYkEK/3+fVV3WlTqvV4vDhw9s6YSEF/jel\n1KtCiAbwUyHEt4H/GXheKfVHQojfB34f+LcfdSGRZ7h9/WWuv/sOoGNec/PzllPW8zyUcXUG/SGu\nURKhX6HT0UJMUKysare3WllAxfp1uxfRzbVAp/ftp9PTP8Z7779n6f6qQZM818Lp90dTM13HxVWm\nPEpKXOOCxHFEJlIQO+Lebpts8zzlpZe/D8Bc4xBfelory1dfe5X7julNvDB/xG6MKBqQpsYN9Sd4\n/fWXAVhdfYkTx3XVwczCPh5+QFcjVKMOB+r6/NAZMjTcCDTneeRxzTpfCwP6azqLXg0d3jfxrPcu\nnmO1q3+Xm+11Jpu66uChEydob7R4860bdy65rWHb5KuUIjYhJJkJgqLsyPdYMbHA9Y0NO0E4rFSZ\nmtRVBEIIwjntzi8s7LMxRYWyN7ErNYkPQL9zjYohm8mdqm2IGJvHSC6FDRd40qdmqhQuXb6CF2h3\n1vdD0kztVH5h22SbZZlVeD/96U/54IMPAB2fvnpVGw+tVsuGAnq9Hj/5yU8ATUJjJ1Z0OvZ+fu65\n56yybLfbfOtb3wKw1Q2gY67jnLdFbHhxcZG339ZcKoPBwMZ6FxcXN/Hdrq2tbbnZ5rahA6XUDaXU\nq+Z1Bz2r/SDwG8B/NKf9R+BfbOkTS1iUst1ZlPLdOZSyvTPcUTJMCHEUeBJ4CVhQShWmyCLahfjo\n9yuFNGxcjtQF2wDRYEBgKOIa1RpFJXa/3ccVhlHHU9Zdq1ZDPBNryJKM/aY3f21phStXdNHx/KFD\nlrk/DD0ee+xhAIJKjfUNM79K5dx39BAArfU1OovXAei1WzaZpIAoTW5ht99+fFLZSqlYWta94B+c\neZ9FU3xdrU9yyi8qEGKUcdsdV1L4oQ1vgsPHtRy8qsu5K9qieP/6VS7c0DL53AMniRIzVnz/FA88\n90sAdForyEDL6r0Lp/ETw4YWDXntkrYultZXaM5pK3Z6chq3on+7y9cv0+t2d6Wg/pPKVylFZtZ5\nc63FyqJOorpSkpg9rbwAx8h0Y3GRwPBrNCYnbJJmvGAe6Y6MzTwnMrShaTpEGcu432/bGnDdjlq0\nhueWcNyTksxMv+j32wTKELpHKcNkxCOyU/iksk3T1LrznudtGrBYWJAvvfQSs2ZQaKvVsudfu3bN\nyjbPc9s6u7a2ZsnENzY2eP99zdiXJIm1etfX162V3O/3N1UgFAMZ0zS14Yssy+w5QohNvAm3w5YV\nrRCiDvwF8G+UUu1bhsEpIT7ctxZC/C7wuwCVSsBqR8fwwrRKYtyCKE2ZMaQSU9PTNgOplGJ2Vgtl\nOIxIpS71mJ6eZGZa37iDaMiP39FEKaeOnOTgcV2m9N7VK0wY92tycoLri7rQeHltjdbGsl1fx6xn\not7kC1/U7na/02YYmQy+4zCIBixfX9mqqO4Y2yLbukutYTrh3DqXTXPBiVOP2uJ3xWijIByUIStJ\nVJ8T92u6PjccUp/UimCjk7GyokMB/9jt0DY8p6JW46kndDjisycmObLfkNCsrOIIfZN3l2/ywo/0\nzTC7f4GKiam7bsVy3EonpVKr4XrnPp7gtojtkO/+A4dwTfz17Nuv8dor+kYUShGasT5BUEVlhgCm\n1qBmYrRhpTJGUiRQhSOZj9bhSMeWYdXrTctdMBz2GClXLL+BpuAwlQlS2PFM8wuzls5SpD2i3jp5\nvnNcEtsh2/GJJgsLC9ZVr1arrKzo++6HP/zhJo6CAocPH950vFCiy8vLXL+ujQTHcZia0rrA933r\n7idJsmmCwzjfwviU3SNm+kue55uGRQohtkyVuKWqAyGEhxbmnyql/tIcXhJC7Df/vh+4+WHvVUp9\nXSn1tFLq6WCLpRCfJmyXbP1wbzM03S1sl3wnp2d2Z8H3ELZLtlutRb2XsZWqAwH8CXBGKfXHY//0\nN8BvA39k/v+vb3etNM9Z6mjXspJmJKZ18/zFS7bDNQgCgsC0HzoeCwvaWnI8j/nDRwEYDLrkJ7Sr\nGw17/J//7t8D8D997bd5/ClNn/j8iz/mS1/QrY5TU03Ovv+BWYMCoa2CLMvZMPWQSgnCOf3Uc2pV\nDhhC5n379rO8uMz3g2/f7uvdMbZTtp7nYsTG0uoyA1MIf/3aZdrmO3puim9aZHNShLFoBUN8M9Or\nWlFEQ9MuKxyaofY0+pFDx9QoX2gPufyCLjA/+3qFo8d00uvowf08dEz3qc83evzSr5ricRfuf/Ao\nAL3BBgMzt+zi1fdQImen+pu3U74ChTT7xnN9PNOMEfg+CEOZmEeWUtNt1Gx1RbffHbFHCWGpDh0h\n7By8bGxcvBCZbc4J/Kq1hqUUSDNyNElSO3Gk6ntMGL6Po4/fh2tagi9d/IB+kjPob20k9p1ge/eu\nx/79+n6bnZ2l0TAt2kFg6RNrtZq1MiuVik1cjTeDjNe8bmxs2DCClHITe9e4RVu8d3zGmBBiU6in\nOGfcoi2qHbZaR7uVR8lzwG8BbwkhXjfH/gAtyD8XQvwOcAn4zdtdKM8VXUOjF2WKeFiwmANmA2V5\nSmoE1GhMsHxTh3tyoPdj7a5JKXnuWV26Ua2EXD+vXYSzp9/lc88+C8CRIweZMbR2M/PzBKEWyGS1\nymColY0SCkxVQ65chKs1VTJMuHRFx2WGseKNV35i+6y3Gdsn2yy3D5BUDRgk+uZab61x7ZoO0RzY\nf5RCqUmZQVHonSlSqWXuIgnNAEDHyXENGc9UPSQ0ZUnV6Tqdtt5wNy6tc+mKztB+O36NqVDf5I8/\nfJTAxG4vvv8Bi8s6BDEc9AgqppJkytXTCcSWHKuPg22Tr6bU0zLa2NggNSECN5MIoyBdJzCxb8AW\nJoJUI47lTIw6EHOBVbSaG7V4PRqdJITUvy0wjIbIomFBSlQREkg8HOOhJ0py7oI2Kq5cvkitVqVt\nXPFtxrbJ1vd9O/ljenraUiMGQWAV2aaBqUpZJTquXMcnJhQ0isU5hXLN89wqbCmljQfneb5pmm4R\nArn1eKGAi8m6Um5t795W0SqlfsjPNzl+ZUufUuJDUcp2Z1HKd+dQyvbOsMvE3x77FzTVWSWskJoe\n2TRLkLJ4avdZNgMEZ2enmJjQbY+9bo9rV3XSJM8VL/1Y19E1J5rUTJPCxUsXOX3mXQBEEPDuBxf1\n8Ss3qDd1WODw4cMMY/N0U7kdob28vMbMlLaATx4/xU3D0rO8tMTK8vKeJ6eW0mHKkHRHQ3BybbXP\nNI5w6JCmlKuEtdETXOWoyFhHqWcpIp0kwLeE0hGeY9o/0zaHzMSKuSBnFd1S2j0e0jKeycpaj6Vr\nur30r//LP6EynTHWtJZ6besra0ybuVfP/tpTzMxNseep0YAsV6y3daKlPxxyfVG3dIdhaFu6PdfF\nc4vhlC6+qaTxHR+/cEU9x44J19SJ5jcYE0GuUrJ81FZa5MyG0XCM30Da2vM0cXFC43q0Wrz6hvYw\nlm8u8sgDJ7Y8BeBuwXEca8UCm0IBxX0Xx7H1KtM0tS78rS244wx849MTxt3/8WuOy6Y4Pxtj9DbR\nLAAAC/hJREFU8nNdd5PVWli3eZ7fEZXqriraIAi574gugNf9ykVXRpeuKYDv9dss3dCKttPpUzUl\nMq7nE5hCbMfx7BeO4iGPfe4JANbaPV59U1cghLVJErMpWxtdhKPDAstrHU1hD+SZIjE96FFvwKWN\ni3oNs7OWzEJIQRBW7gEqP4HvFmVcPeYWtGJrVn3iTDfmDFsDwrBhzgaVFQ0agdV1UoZIYbhmZYYw\nZDy+p3Ay3TDiZhFHTupYbGX2AO9f1KGbdj+gdbzoxd/PhfO6BKq70aVuOG7n5iZHg/JySPc+Xw+g\nb77VDf39b66t0OrobHgQhQiTU3blWOmWwpIaSSVxZNHV6Nm95Xmj177v4xXDLz3fXsdxHBtznZp0\nGQ6KErDEhg6klKRmovFG3GfNDJFcXWszOXcAxyj8vQohxKZhi8XrwWCwibSlUH63TrItlG6aph+q\n/JRSm8bXjCvd4prApuqF8fDCuMIe50C4E0Vbch2UKFGixA5jd+sqlCAybubSYJU819bSemuV9Za2\nEAQZsXHtl2+uEg2LmT7S9ukfPLjPsq0v3VyiMaMD35Fw8H3tgkw254hNEXcQhLZgeXVtjWlDPB0P\nlR6vDSRxB8/X1sVwOLSB92qlwvFjJ3j/zSLevzcxGAxpGHLp2ek6q2vail1u97m2rEMrU/WjDNe1\n+zXVnEP5hvdAJSRKyzn3chzjhR7avx+3YD0aDLh4Uc9m87wq+xq6GLxRb3L+7PfMcYepKW2tRmnC\n/Sd1Mfi4RSWEqSsFlO/p8M12C2MHoJSi09MW7dy+Izz80GcBOPPOG7Y1NsnTEY2h49jmBVcIMmPR\nijRmaBoTBMIm0oQYuaKucJHGGnY9d6wgf+RKSwnSJMBc12HQNZSAgx5d8/rBE4eYnAhxnb1tTyml\n7ESDcQYux3E2WZbjIYXxEEGRGEuSZFON7YeFTBzH2WShFu8dJ/EeT4YVn1H8/7glfSfYXZpElY8y\nqwiU4Rbw/ZCKKfru9trUzJTUPM/oG76CJE1sIGtxcXFTFUDf3ABxNOSnP9HkKPPzB8mNU5qpnJVl\nrcjTdIg31PG1XkfR7upr9pM+NUODt7BvnxWokA5TUzN7nmouyzIWb+i4sut6o3n3SvHuezqcMl3v\nMTetH1C1aoASJjwiHLxiJ2SjIgCFYt1MshhGkc2jx2nGSy9rboQ4iUfTRqcmmZrWZXdhENKncNHS\nUXhAKZKkcItdzU98D4wNysYqZqZnj1EJ9B69cvkGw57eW47r2K7GLI5xTFhASYVw9PHAGYvzjf2v\nQlLohSiP7aDQvJdjGxbGXV2l7Hul49gRN56b8ugD+jc+dWQfk6GyHWR7FXmeW9IXKaU1csIwtIp2\nvIyrUI7Fe4u/x0MK4+Va46GDWxXtuOIsjhfhHPjZEMF4CdhWKw6gDB2UKFGixI5jVy1aISWVqnla\nBSGuq58caTpLFOvkSq/XJjKB/eFgwMaGDux3um36A225RlFkW+3q9TqBuU5n7QofvKdDBJer75Cb\nr+f5Ab4JC1RDyeFD2r1d7Pd495Kp75SKuWIigxjV16VJQpbm3KGncBcgiIZmSOBEQKWiPYThQNHr\nale1s3aehklKtTsOtQldZeHKKpl1s5Sl39tob4yyuygmTFWDkJKDh4vheCn7DxwxK4AkM56GcPFU\nkbzIbJE+SuEYMnfpOAgp74nQQZopothwc8R9pKPlePi+U7z/tmnpljnGsMTzHZrTOmTTqIZUK2b/\n+eN7ayTrJMtJTFggS3JL/J3ljvX88qxvyb7rtSq+W4QpXDsXrNn0mKmZiQxCMDU9j+Ps7WTYeCVA\nvV63FuR4Yqw4DzZPVRh/7/iUlPFQwLjlWpxXHB9v9/95Fur4dW49tlXsqqJ1HEloSrECP8B1Cxeh\ngme4ZoPAtzdeFMdMT2vl2uv1WFnWTQQbG+v0DO+kyCHpacXcWV23s2/63S6iGHGRDHDQnxtOTDHM\nNB9tOxqQ2RE3isAo4zyHKCoKnAXV+iRyz4cOcjZMVjxKh/ZGlcKl3zdx6MGAlVVdCRANV3igqWXi\neD7KbL5MJXbycDTGtSkcaYvl0yyjP9SfJaW0hDtZmuP6pj1NJjgmKy7kqEg8H8/oul5Rlr+tstgJ\nKJXbhgUpHQLzsGjOzNOY0UYCeYyhlWCy5tFs6P3UqFdt+VLoOxSPFom0kzzyPLf8Bnmu7O+XZqPO\nuSyLKbZh6Pt4RVWD4446zPKYQWR+G6fCzP6juN690/r+8xoHsiyzIYLxMq5biV3G47jjIYLiOo7j\nbLpmOsb1O949dit5TIEPa2TYCsrQQYkSJUrsMHaZzUEQ+Nqt8T3f9nMLqa1aACcV9mmOdPBMb36l\nWmOioWtqe92OTdJstFq8/bqetd5qt1HKMBc5Lsq4WWkW0+0Z5vsk5uZa3xxPSSmedIJqRVt4g35k\nXTGBIHADazHsVUghuXJZW/wTUwF+teind+ia0EHg1Flb1xwfrVaCMBmwfQv32WGOngzwpbGAhCQz\n7r8UgjQvMr05qRnrLgW4RX2zJ+xkgDTXfwMIJ7cegWRUr4gQevz8PZAMU0pblBoOqeEZCKs1Hnr0\nSQBqFQkmdOIT4cliWKbAMXJxckbekQRZJHuU1LIAdDt6kQDLrLeRZs7IkkszUpMME3mOZ8MxPq6R\n+9z8cSanD+C4e9+iLb7X+CBFz/M+1J0fb1IYt0SLaoXieh9WdaCUsuePW6u3koCPhyCKc8YTaeMJ\ntq1gVxWtlKMOEN/3kUXJC4os10tpbQwtdWGWZVYoQRAQhrqMa3Jqiv0HdWa1vdHiyiVddtQdxHgm\nwyoc1/b+K5WRGSUxiBL6se5YElLgelq4fhAgzHoGg75dp+6H3vsx2izLQRk6xNwhMhnywA/xjSKs\nh1VyMwgxTiMuXHwPgI12i9Azgy8n5jkwr2UrpSRVP9sLjpQIowhyNXKrshww41hyJUYPUkdgt7zC\ndigIJZHS2eOPsAIK3y0yzqkdvTRRD3Abmie14jtIpfeo6+QjdzGNGXb1Q5Asts8VHTowChVFbvg+\n8jG3VwqX3Lq9I+OBPLUVDkJIpFsMJXVwTQhs4cB9NBqTW6byu1vI89yWd40r1lsV27jiHFeW4yV1\n4xwIHxavHT8/z3OrXF3X3aRQa2aCRhRFm8q+bsVWY7Vl6KBEiRIldhjiTgtvP9GHCbEM9ICdY9G+\nc8yytfXcp5Sa2+nFfFzsUdnC1uS7p2ULIIToAO/e7XXcgnLv7iy2be/uqqIFEEK8opR6elc/9COw\n19bzSbAXv8teXNPHwV78HntxTR8Xe/G7bOeaytBBiRIlSuwwSkVbokSJEjuMu6Fov34XPvOjsNfW\n80mwF7/LXlzTx8Fe/B57cU0fF3vxu2zbmnY9RluiRIkSnzaUoYMSJUqU2GHsmqIVQnxFCPGuEOID\nIcTv79bn3rKGw0KI7wkh3hFCnBZC/Gtz/A+FENeEEK+b/756N9b3cVHKdmdRynfn8GmR7a6EDoQQ\nDvAe8M+Aq8DLwNeUUu/s+IdvXsd+YL9S6lUhRAP4KfAv0JM6u0qpf7eb69kOlLLdWZTy3Tl8mmS7\nWxbt54EPlFLnlVIx8A3gN3bpsy2UUjeUUq+a1x3gDHBwt9exzShlu7Mo5btz+NTIdrcU7UHgytjf\nV7nLm0QIcRR4EnjJHPo9IcSbQoj/IISYumsLu3OUst1ZlPLdOXxqZPupTIYJIerAXwD/RinVBv4v\n4ATwBHAD+D/u4vLuaZSy3VmU8t057KRsd0vRXgMOj/19yBzbdQghPLQw/1Qp9ZcASqklpVSmNDfi\n/412ae4VlLLdWZTy3Tl8amS7W4r2ZeCUEOKYEMIH/hXwN7v02RZCc5r9CXBGKfXHY8f3j532L4G3\nd3ttnwClbHcWpXx3Dp8a2e4KH61SKhVC/B7wD4AD/Ael1Ond+Oxb8BzwW8BbQohifvgfAF8TQjyB\nZkq9CPyvd2FtHwulbHcWpXx3Dp8m2ZadYSVKlCixw/hUJsNKlChRYjdRKtoSJUqU2GGUirZEiRIl\ndhiloi1RokSJHUapaEuUKFFih1Eq2hIlSpTYYZSKtkSJEiV2GKWiLVGiRIkdxv8HXCfvod2jNFIA\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cE1UG2RavZW3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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",
        "  return DI_Score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2_q12H9HU7n-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "mPTmOl-QERZN",
        "colab_type": "code",
        "outputId": "6d39f5b5-2836-4ad3-8af6-781d2f8e74fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# layer_name\n",
        "valid_X.shape"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "bRQ1YTnsd6zM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# train_three_data, train_three_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pe0g2T0IA7sG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "layer_name = 'conv2d_1'\n",
        "get_cnn1_output_function = K.function([model.layers[0].input],\n",
        "                                  [model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "cnn1_output = get_cnn1_output_function([train_three_data])[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4Yy-y5xIWdz6",
        "colab_type": "code",
        "outputId": "eee84c4e-c4a2-46aa-e703-85107036f070",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "cnn1_output.shape"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 32, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "EF3q_KOgYTxb",
        "colab_type": "code",
        "outputId": "07cb5ada-0ed9-4522-ba3b-75242bc3ec0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "cnn1_output.shape"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 32, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "B0Hfr4ZwB8dX",
        "colab_type": "code",
        "outputId": "69e2f6ee-b461-4871-cabf-c5ea427823f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "np.ndarray.tolist(np.unique(test_three_label))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 8, 9]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "pM8xfShbdHJV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# valid_ground_cat = np.argmax(train_ground, axis=1)\n",
        "# # valid_ground_cat\n",
        "# valid_ground.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jZ1IST7pBZ92",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "di_scores = getDIScoreOfLayerCNNLayer(cnn1_output, train_three_label)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RZnzBi-7BcfM",
        "colab_type": "code",
        "outputId": "c6586fa2-0352-44a5-8f1b-e901ad9afc5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "cell_type": "code",
      "source": [
        "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')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'DI Score')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k/c3+TyXZ0GU9kqS5dRYKScaAfcCVwGZgZ5LNA91eCzxQVc8CbgB+o6t6JEnz6/JMYQtw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\nSJJahoIkqWUoSJJa/x9YFGM+fZVyLAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gFs0BsyhzMXk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "d1LLCxe9aHpX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## DI on different data\n",
        "\n",
        "1.   List item\n",
        "2.   List item\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "WeSV3VJA6lm2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "c2b88af5-b3e6-4298-8d37-103ff77f9c61"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "train_three_data_low,train_three_label_low,test_three_data_low,test_three_label_low = getThreeClassesTrainTest([0,3,5],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[   29    30    35 ... 49941 49992 49994]\n",
            "[    9    17    21 ... 49979 49982 49983]\n",
            "[   27    40    51 ... 49964 49980 49988]\n",
            "(15000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Y3R57M0i6liE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# split train data and to create validation data\n",
        "train_X_low,valid_X_low,train_ground_low,valid_ground_low = train_test_split(train_three_data_low,\n",
        "                                                             train_three_label_low,\n",
        "                                                             test_size=0.2,\n",
        "                                                             random_state=13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0IlIKFz86le9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "be1e53f6-b6ca-4247-a235-a7ae4e301e3e"
      },
      "cell_type": "code",
      "source": [
        "layer_name = 'conv2d_1'\n",
        "get_cnn1_output_function_low = K.function([model.layers[0].input],\n",
        "                                  [model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "cnn1_output_low = get_cnn1_output_function_low([valid_X_low])[0]\n",
        "cnn1_output_low.shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "uZIf7RZ86laf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "di_scores_low = getDIScoreOfLayerCNNLayer(cnn1_output_low, test_three_label_low)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "q9pMSCcD6lUm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "d7574df2-b980-41c9-d20d-f69c3bb041ba"
      },
      "cell_type": "code",
      "source": [
        "y_pos  = np.arange(len(di_scores_low))\n",
        "plt.bar(y_pos, di_scores_low, align='center', alpha=0.5)\n",
        "plt.xlabel('FilterNumber')\n",
        "plt.ylabel('DI Score')"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'DI Score')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tQ0GS1DIUJEktQ0GS1Oo0FJJsTXI0ybEku+fZ/j1J3t9s/3SSDV3WI0karLNQSDIB7AVe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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "emT7m8pI6lP8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "outputId": "b635e050-c94e-4ab6-bcf6-79281ed884d8"
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (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_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (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_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (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"
        }
      ]
    },
    {
      "metadata": {
        "id": "OcsyCU-caFc9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# # not working\n",
        "# mat_save = {'diScore': di_scores, 'conv2d_1': cnn1_output, 'trueLabel':train_three_label}\n",
        "# savemat('LowAccuracyConv2d_1MatrixMethod', mat_save) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "n7Xmu2bLaFZa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Lxf2mXCG8nW9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "t3vXkMPaaFWT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1WLuuRgeFImF",
        "colab_type": "code",
        "outputId": "4877db7a-cdf0-41ae-a1e5-5204e7d3bbc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "sum(di_scores)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16.38424550143677"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "w27VKTFQUI6d",
        "colab_type": "code",
        "outputId": "608f2c47-ea99-4e11-936e-f0e224d175fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "max(di_scores)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6679229295993279"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "metadata": {
        "id": "8VzlOOa1YB0I",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from scipy.io import savemat\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HDtz_epHYCT_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}