8th_DNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "8th_DNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/AyusheeMittal/legendary-guide/blob/master/8th_DNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aNyZv-Ec52ot",
        "colab_type": "text"
      },
      "source": [
        "# **Import Libraries and modules**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3m3w1Cw49Zkt",
        "colab_type": "code",
        "outputId": "027beb80-c5c6-4651-de88-d30f2d2fe946",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "# https://keras.io/\n",
        "!pip install -q keras\n",
        "import keras"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eso6UHE080D4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten, Add, BatchNormalization\n",
        "from keras.layers import Convolution2D, MaxPooling2D\n",
        "from keras.utils import np_utils\n",
        "\n",
        "from keras.datasets import mnist"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zByEi95J86RD",
        "colab_type": "text"
      },
      "source": [
        "### Load pre-shuffled MNIST data into train and test sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7eRM0QWN83PV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4a4Be72j8-ZC",
        "colab_type": "code",
        "outputId": "e332775a-468c-4b5b-99fa-a0d9995133af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "print (X_train.shape)\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.imshow(X_train[0])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fd86b5a06a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J82zPtX2YpC9KWtuBPt7H9rTaiRPZniZpkbpvKuq1ki6u3b9Y0n0d7OVdumUa73rTjKvD\n713Hpz+PiLb/STpXQ2fkX5T0V53ooU5fn5T0RO3v6U73JukODR3W7dPQuY1LJX1U0jpJmyT9l6Se\nLurtPyQ9JelJDQVrdod6W6ihQ/QnJW2o/Z3b6feu0Fdb3jculwWS4AQdkARhB5Ig7EAShB1IgrAD\nSRB2IAnCDiTx/65XcTNOWsh5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkmprriw9AnZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train.reshape(X_train.shape[0], 28, 28,1)\n",
        "X_test = X_test.reshape(X_test.shape[0], 28, 28,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2m4YS4E9CRh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train.astype('float32')\n",
        "X_test = X_test.astype('float32')\n",
        "X_train /= 255\n",
        "X_test /= 255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Mn0vAYD9DvB",
        "colab_type": "code",
        "outputId": "db81bebd-e2bd-48dc-86fe-436d074f028b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_train[:10]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, 1, 9, 2, 1, 3, 1, 4], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZG8JiXR39FHC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert 1-dimensional class arrays to 10-dimensional class matrices\n",
        "Y_train = np_utils.to_categorical(y_train, 10)\n",
        "Y_test = np_utils.to_categorical(y_test, 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYlFRvKS9HMB",
        "colab_type": "code",
        "outputId": "8ab0d195-b185-42e8-c15c-3ce1821b3be5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "Y_train[:10]\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osKqT73Q9JJB",
        "colab_type": "code",
        "outputId": "3c8889a2-3489-47e0-ab53-2139b7755ae9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "from keras.layers import Activation\n",
        "model = Sequential()\n",
        " \n",
        "model.add(Convolution2D(12, 3, 3, activation='relu' , use_bias=False,\n",
        "                        activity_regularizer=keras.regularizers.l2(0.000000001),\n",
        "                        input_shape=(28,28,1)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Convolution2D(16, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Convolution2D(20, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Dropout(0.25))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Convolution2D(12, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Convolution2D(16, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Convolution2D(20, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Convolution2D(10, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Convolution2D(10,3))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Activation('softmax'))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(12, (3, 3), activation=\"relu\", use_bias=False, activity_regularizer=<keras.reg..., input_shape=(28, 28, 1...)`\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2041: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: 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",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzdAYg1k9K7Z",
        "colab_type": "code",
        "outputId": "891073ab-1fbf-43a4-ac93-a9a12aadff2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 26, 26, 12)        108       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 26, 26, 12)        48        \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 24, 24, 16)        1744      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 24, 24, 16)        64        \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 22, 22, 20)        2900      \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 22, 22, 20)        80        \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 22, 22, 20)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 11, 11, 20)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 9, 9, 12)          2172      \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 9, 9, 12)          48        \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 7, 7, 16)          1744      \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 7, 7, 16)          64        \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 5, 5, 20)          2900      \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 5, 5, 20)          80        \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 5, 5, 20)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 3, 3, 10)          1810      \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 3, 3, 10)          40        \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 1, 1, 10)          910       \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 14,712\n",
            "Trainable params: 14,500\n",
            "Non-trainable params: 212\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zp6SuGrL9M3h",
        "colab_type": "code",
        "outputId": "b9e4a4e3-edf2-41ab-a545-b341898d8dc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "def scheduler(epoch, lr):\n",
        "  return (round(0.0035 * 1/(1 + 0.33 * epoch), 10))\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "             optimizer=Adam(0.0035),\n",
        "             metrics=['accuracy'])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4xWoKhPY9Of5",
        "colab_type": "code",
        "outputId": "9b39db7a-6ed9-4ea3-8a55-1f7f6697df1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.fit(X_train, Y_train, batch_size=32, nb_epoch=20, verbose=1, validation_data=(X_test, Y_test), callbacks= [LearningRateScheduler(scheduler, verbose =1)] )"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "\n",
            "Epoch 00001: LearningRateScheduler setting learning rate to 0.0035.\n",
            "60000/60000 [==============================] - 38s 632us/step - loss: 0.1666 - acc: 0.9486 - val_loss: 0.0565 - val_acc: 0.9824\n",
            "Epoch 2/20\n",
            "\n",
            "Epoch 00002: LearningRateScheduler setting learning rate to 0.0026315789.\n",
            "60000/60000 [==============================] - 37s 616us/step - loss: 0.0617 - acc: 0.9811 - val_loss: 0.0536 - val_acc: 0.9830\n",
            "Epoch 3/20\n",
            "\n",
            "Epoch 00003: LearningRateScheduler setting learning rate to 0.0021084337.\n",
            "60000/60000 [==============================] - 37s 612us/step - loss: 0.0476 - acc: 0.9853 - val_loss: 0.0464 - val_acc: 0.9844\n",
            "Epoch 4/20\n",
            "\n",
            "Epoch 00004: LearningRateScheduler setting learning rate to 0.001758794.\n",
            "60000/60000 [==============================] - 37s 614us/step - loss: 0.0414 - acc: 0.9871 - val_loss: 0.0417 - val_acc: 0.9869\n",
            "Epoch 5/20\n",
            "\n",
            "Epoch 00005: LearningRateScheduler setting learning rate to 0.0015086207.\n",
            "60000/60000 [==============================] - 36s 608us/step - loss: 0.0362 - acc: 0.9888 - val_loss: 0.0275 - val_acc: 0.9911\n",
            "Epoch 6/20\n",
            "\n",
            "Epoch 00006: LearningRateScheduler setting learning rate to 0.0013207547.\n",
            "60000/60000 [==============================] - 37s 615us/step - loss: 0.0311 - acc: 0.9904 - val_loss: 0.0306 - val_acc: 0.9904\n",
            "Epoch 7/20\n",
            "\n",
            "Epoch 00007: LearningRateScheduler setting learning rate to 0.0011744966.\n",
            "60000/60000 [==============================] - 37s 620us/step - loss: 0.0269 - acc: 0.9919 - val_loss: 0.0254 - val_acc: 0.9916\n",
            "Epoch 8/20\n",
            "\n",
            "Epoch 00008: LearningRateScheduler setting learning rate to 0.0010574018.\n",
            "60000/60000 [==============================] - 37s 618us/step - loss: 0.0241 - acc: 0.9923 - val_loss: 0.0254 - val_acc: 0.9922\n",
            "Epoch 9/20\n",
            "\n",
            "Epoch 00009: LearningRateScheduler setting learning rate to 0.0009615385.\n",
            "60000/60000 [==============================] - 37s 618us/step - loss: 0.0205 - acc: 0.9934 - val_loss: 0.0221 - val_acc: 0.9926\n",
            "Epoch 10/20\n",
            "\n",
            "Epoch 00010: LearningRateScheduler setting learning rate to 0.0008816121.\n",
            "60000/60000 [==============================] - 37s 617us/step - loss: 0.0201 - acc: 0.9937 - val_loss: 0.0200 - val_acc: 0.9935\n",
            "Epoch 11/20\n",
            "\n",
            "Epoch 00011: LearningRateScheduler setting learning rate to 0.0008139535.\n",
            "60000/60000 [==============================] - 37s 613us/step - loss: 0.0187 - acc: 0.9940 - val_loss: 0.0238 - val_acc: 0.9927\n",
            "Epoch 12/20\n",
            "\n",
            "Epoch 00012: LearningRateScheduler setting learning rate to 0.0007559395.\n",
            "60000/60000 [==============================] - 37s 612us/step - loss: 0.0167 - acc: 0.9945 - val_loss: 0.0260 - val_acc: 0.9913\n",
            "Epoch 13/20\n",
            "\n",
            "Epoch 00013: LearningRateScheduler setting learning rate to 0.0007056452.\n",
            "60000/60000 [==============================] - 37s 615us/step - loss: 0.0168 - acc: 0.9945 - val_loss: 0.0217 - val_acc: 0.9926\n",
            "Epoch 14/20\n",
            "\n",
            "Epoch 00014: LearningRateScheduler setting learning rate to 0.0006616257.\n",
            "60000/60000 [==============================] - 37s 614us/step - loss: 0.0145 - acc: 0.9954 - val_loss: 0.0218 - val_acc: 0.9922\n",
            "Epoch 15/20\n",
            "\n",
            "Epoch 00015: LearningRateScheduler setting learning rate to 0.0006227758.\n",
            "60000/60000 [==============================] - 37s 620us/step - loss: 0.0139 - acc: 0.9955 - val_loss: 0.0197 - val_acc: 0.9933\n",
            "Epoch 16/20\n",
            "\n",
            "Epoch 00016: LearningRateScheduler setting learning rate to 0.0005882353.\n",
            "60000/60000 [==============================] - 37s 617us/step - loss: 0.0140 - acc: 0.9955 - val_loss: 0.0223 - val_acc: 0.9933\n",
            "Epoch 17/20\n",
            "\n",
            "Epoch 00017: LearningRateScheduler setting learning rate to 0.0005573248.\n",
            "60000/60000 [==============================] - 37s 614us/step - loss: 0.0125 - acc: 0.9960 - val_loss: 0.0185 - val_acc: 0.9939\n",
            "Epoch 18/20\n",
            "\n",
            "Epoch 00018: LearningRateScheduler setting learning rate to 0.0005295008.\n",
            "60000/60000 [==============================] - 37s 616us/step - loss: 0.0116 - acc: 0.9962 - val_loss: 0.0204 - val_acc: 0.9933\n",
            "Epoch 19/20\n",
            "\n",
            "Epoch 00019: LearningRateScheduler setting learning rate to 0.0005043228.\n",
            "60000/60000 [==============================] - 37s 613us/step - loss: 0.0126 - acc: 0.9961 - val_loss: 0.0178 - val_acc: 0.9942\n",
            "Epoch 20/20\n",
            "\n",
            "Epoch 00020: LearningRateScheduler setting learning rate to 0.0004814305.\n",
            "60000/60000 [==============================] - 37s 614us/step - loss: 0.0107 - acc: 0.9966 - val_loss: 0.0189 - val_acc: 0.9941\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fd86b0b2978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AtsH-lLk-eLb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = model.evaluate(X_test, Y_test, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkX8JMv79q9r",
        "colab_type": "code",
        "outputId": "62daa13c-7b79-4bba-cf20-80470a889ff0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(score)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.018910792756626323, 0.9941]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCWoJkwE9suh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ym7iCFBm9uBs",
        "colab_type": "code",
        "outputId": "34c42399-43bb-4836-823c-bf0395a6508e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "print(y_pred[:9])\n",
        "print(y_test[:9])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[4.22777768e-09 8.52803112e-07 1.60338473e-07 9.75489201e-08\n",
            "  9.75362241e-07 1.32468926e-07 2.11718820e-09 9.99992490e-01\n",
            "  2.72886203e-09 5.36939069e-06]\n",
            " [1.00795774e-06 4.43244971e-07 9.99998569e-01 2.48729881e-09\n",
            "  3.41102324e-09 7.89464161e-10 4.90922503e-08 3.41457751e-09\n",
            "  2.49247556e-09 6.71880121e-11]\n",
            " [1.70585612e-11 9.99999762e-01 3.47964907e-10 2.18840918e-10\n",
            "  1.97158592e-08 1.10071149e-07 3.94986355e-09 1.08503848e-07\n",
            "  1.81719778e-11 4.53311833e-10]\n",
            " [9.99997258e-01 1.26616453e-10 3.79499632e-09 2.83251955e-09\n",
            "  3.58326369e-09 1.51496273e-08 2.67570476e-06 4.37329017e-09\n",
            "  3.51982834e-08 9.76223014e-09]\n",
            " [4.06488298e-12 6.52973586e-09 1.67212789e-11 1.24816227e-10\n",
            "  9.99997735e-01 1.10185305e-09 1.22236079e-08 1.30450140e-08\n",
            "  9.82441350e-10 2.28904969e-06]\n",
            " [8.17159014e-12 9.99999762e-01 7.70831107e-11 2.21805820e-12\n",
            "  2.71043170e-08 5.34072075e-09 2.22220309e-09 1.88956179e-07\n",
            "  4.72910036e-12 2.11547904e-10]\n",
            " [3.30284439e-10 5.28749013e-07 6.26328822e-10 1.36759605e-11\n",
            "  9.99968767e-01 2.78992101e-08 2.10215248e-10 3.81858899e-06\n",
            "  5.53617213e-07 2.63433394e-05]\n",
            " [2.07157469e-09 4.09538981e-09 2.60143596e-09 6.55220855e-09\n",
            "  1.86067045e-05 4.77588635e-08 3.50623142e-11 1.61701019e-09\n",
            "  1.59167215e-07 9.99981165e-01]\n",
            " [1.28105551e-03 1.29157957e-02 4.00023737e-06 1.20372761e-08\n",
            "  2.10828162e-07 8.55915248e-01 1.29823089e-01 4.29883130e-07\n",
            "  1.67120161e-05 4.34895337e-05]]\n",
            "[7 2 1 0 4 1 4 9 5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CT--y98_dr2T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "layer_dict = dict([(layer.name, layer) for layer in model.layers])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2GY4Upv4dsUR",
        "colab_type": "code",
        "outputId": "b34b700a-7ee0-4d86-8dc9-5bd620be4cfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        }
      },
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from keras import backend as K\n",
        "%matplotlib inline\n",
        "# util function to convert a tensor into a valid image\n",
        "def deprocess_image(x):\n",
        "    # normalize tensor: center on 0., ensure std is 0.1\n",
        "    x -= x.mean()\n",
        "    x /= (x.std() + 1e-5)\n",
        "    x *= 0.1\n",
        "\n",
        "    # clip to [0, 1]\n",
        "    x += 0.5\n",
        "    x = np.clip(x, 0, 1)\n",
        "\n",
        "    # convert to RGB array\n",
        "    x *= 255\n",
        "    #x = x.transpose((1, 2, 0))\n",
        "    x = np.clip(x, 0, 255).astype('uint8')\n",
        "    return x\n",
        "\n",
        "def vis_img_in_filter(img = np.array(X_train[2]).reshape((1, 28, 28, 1)).astype(np.float64), \n",
        "                      layer_name = 'conv2d_5'):\n",
        "    \n",
        "    layer_output = layer_dict[layer_name].output\n",
        "    \n",
        "    img_ascs = list()\n",
        "    for filter_index in range(layer_output.shape[3]):\n",
        "        # build a loss function that maximizes the activation\n",
        "        # of the nth filter of the layer considered\n",
        "        loss = K.mean(layer_output[:, :, :, filter_index])\n",
        "        \n",
        "        # compute the gradient of the input picture wrt this loss\n",
        "        grads = K.gradients(loss, model.input)[0]\n",
        "        \n",
        "        # normalization trick: we normalize the gradient\n",
        "        grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)\n",
        "        \n",
        "        # this function returns the loss and grads given the input picture\n",
        "        iterate = K.function([model.input], [loss, grads])\n",
        "        \n",
        "        # step size for gradient ascent\n",
        "        step = 5.\n",
        "\n",
        "        img_asc = np.array(img)\n",
        "      \n",
        "        # run gradient ascent for 20 steps\n",
        "        for i in range(20):\n",
        "            loss_value, grads_value = iterate([img_asc])\n",
        "            img_asc += grads_value * step\n",
        "\n",
        "        img_asc = img_asc[0]\n",
        "        img_ascs.append(deprocess_image(img_asc).reshape((28, 28)))\n",
        "        \n",
        "    if layer_output.shape[3] >= 64:\n",
        "        plot_x, plot_y = 8, 8\n",
        "    elif layer_output.shape[3] >= 32:\n",
        "        plot_x, plot_y = 4, 8\n",
        "    elif layer_output.shape[3] >= 16:\n",
        "        plot_x, plot_y = 4, 4\n",
        "    else:\n",
        "        plot_x, plot_y = 2, 2\n",
        "    \n",
        "    fig, ax = plt.subplots(plot_x, plot_y, figsize = (12, 12))\n",
        "    \n",
        "    ax[0, 0].imshow(img.reshape((28, 28)), cmap = 'gray')\n",
        "    ax[0, 0].set_title('Input image')\n",
        "    fig.suptitle('Input image and %s filters' % (layer_name,))\n",
        "    fig.tight_layout(pad = 0.3, rect = [0, 0, 0.9, 0.9])\n",
        "    for (x, y) in [(i, j) for i in range(plot_x) for j in range(plot_y)]:\n",
        "        if x == 0 and y == 0:\n",
        "            continue\n",
        "        ax[x, y].imshow(img_ascs[x * plot_y + y - 1], cmap = 'gray')\n",
        "        ax[x, y].set_title('filter %d' % (x * plot_y + y - 1))\n",
        "\n",
        "vis_img_in_filter()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7d24Yj57bBx98MBx79913F2JZd5+oi48Uv2Zjx44Nx0bdRy6//PJw7GmnnRbG\n3/nOdxZiUfc3Ke4uJsX5leVidLzv3bs3HLt58+amtzFy5Mhw7JYtWwqx7Hw3FJlZ0x20svNAdN3I\ncj7qviTFx+CECRPCsdHrmZ0TjzvuuDAeHYN79uwJx+7YsaMQyzrLZdekqVOnFmJZl7aynZgiZboz\nDRV8MgAAAABUFIsBAAAAoKJYDAAAAAAVxWIAAAAAqKieKCAe6O28UZTd3j7y8MMPt3Em1ZEViDUr\nK3DKbvMeFdqtXbs2HHvPPfcUYqecckqpx4sKtrKCtmhfskLL2267LYxHRXRZ4VlfX18Y/9a3vlWI\nRbe2z7aR5Ub2eFEhXlRAJ8VFhlHxWzRuKIjmFcWy8/vjjz9eiK1fvz4cO2rUqDAeFabOmDEjHHvu\nuecWYlmx6plnnhnGo4LKLF+iQs01a9aEY6+55powHp1TssLJ7LmLCoizIuQpU6YUYtn+ZbkYvSbZ\nuXH48OGFWDe9H3D38JgvU/QfPTfZ8Z69xtHzeNZZZ4Vj77vvvkLs6KPjt5IPPfRQGI/Oi9m1ICoS\nz+aWNWb42c9+VohlzR2y83v0HI0ePbrpsWUbOXT6OO6erAEAAADQUiwGAAAAgIpiMQAAAABUFIsB\nAAAAoKKOuBgws2vMbL2Z3d8Qm2hmN5jZw/X/x7eqA3oEeQCQBwA5gF7UTDehBZI+Jen/NcTeI+mH\n7v5RM3tP/e/vbv30is4+++xCLLrVNAYm6wARueGGG9o4kyFjgQYhD8p0IMg6SERdVyTp9NNPL8Sy\nzjNRB5JHH300HHvssceG8Whfoq4LkjRs2LBCLOoyIknPfOYzw/iNN95YiG3fvj0ce9RRR4Xx1atX\nF2LTpk0Lx44fP74Qy577qFOMJO3Zs6cQi56LLB51E2qxBWpRHkTHQ9QZpUxXjez5zrqdRLLuTUuW\nLCnEotdLyo+zzZs3F2JRhyEpvtZlx83MmTPD+AknnFCITZo0KRx7yy23hPGom0u0XUnauXNnIZZ1\nOcteqxEjRhRiWQekKG870D1rgdp8LYj2ITtHReeB7Ly6bdu2ML5169ZCLDqfSXEuZV2usnk88sgj\nhdjcuXPDsVFnrqjDlSTdeuutYTx6P3P++eeHY7PnecyYMYVY1gEp20akzDW+ncf2Ec+y7v4TSYee\nwV4q6dr6n6+V9ActnhcwpJAHAHkAkAPoRf2tGZjq7geXgmsl8at5VBF5AJAHADmArjbgAmKvfW6R\nfnZhZleY2Z1mdudAHwsYqsrkQfRROtALDpcHjTmQfRUH6HZlrgXZ13aATuvvYmCdmU2XpPr/49va\nSXL3q939PHc/r5+PBQxV/erF7QgAACAASURBVMqD7K6FQJdqKg8acyD6/i3Qxfp1LShTmwe0U38X\nA9dJem39z6+V9M3WTAfoKuQBQB4A5AC62hFbLJjZf0qaL2mSma2S9EFJH5X0JTN7g6QVkl7ezkk2\neslLXlKIZV1GcGRZJ6asW0Qk6rjSazqRB1HXlKi7iiQdOHCgEMs6DZT5JKKvry+M7969uxCLun5I\n0jHHHBPGo25HWfebyZMnNz0268YQdRmKumZI0pe+9KUwvmnTpkIs65Y0ffr0QuyMM84Ix2ZdKKLu\nG1lniujYiDp9lOlWcSSDcT3Ius5Ev1XN9jX7DWzUnSfqsiXFHXuy3Mpy8bHHHmt6blG3rgcffDAc\nm3Uvio7rj3/84+HYefPmhfFvf/vbhVjWeSi6Fs+ePTscm3Wric6DWUei6PVudzehVuaAmQ14H6Jz\ncyY7XtetW1eIZcdadN5ZuHBhODbqiCVJr3rVqwqxqNOWJH3xi18sxFatWhWOzToSRefV+++/PxiZ\ndx6bMmVKITZhQtxBNtpGdM0+nGgbZboUlXXExYC7vzL50QtaPBdgyCIPAPIAIAfQi7gDMQAAAFBR\nLAYAAACAimIxAAAAAFRU8/doHyJOO+20psc+8MADbZxJb/jEJz4RxqPC4sWLF4dj6RlejpmFRXJR\nIVk0LhubFdhmBZhRcWxW/BsVjWXFaNkt6Pft21eIZfsXFcVlx1nWQGDs2LGF2LnnnhuOzYqhP/ax\njxVi2X5HhfTZ3CZOnBjG9+zZU4hlr2v03GXPZ7eI5j9s2LBwbFSQlxVTZoXj0TbKFBauWbMmGJkX\nQ0bHTtYkICqwzbb75S9/OYxHooJgSTruuOPC+E033VSIZUWW0fkj27/sWG22MF7K86jbRef3Ms9j\n9txm55LodbvjjjvCsdF5NXsdsqLZpUuXFmK/+MUvwrFRsfAb3/jGcOw555wTxm+++eZCLCuQPuus\ns8J41Dgiu09EdK2LnjcpLwqO4mWaQZQtpO/uKwcAAACAfmMxAAAAAFQUiwEAAACgolgMAAAAABXF\nYgAAAACoqK7rJlRGVg3fK7Lq9EsvvbQQu/zyy8Oxl1xySdOP9zd/8zdhPOvUgZi7p50hDpV1hYg6\nBWQdNzJPPPFEU7FsHtu3bw/HZh10du3aVYhlc167dm0hlh1nURcfSXrOc55TiGUdiV784heH8S1b\nthRifX194dioE1DWCadMV4+oM0Um2m7ZrhKDKeqWUea4zsZmx2R0vGedpaJtn3zyyeHYrItKlANZ\nzkXbeOtb3xqOLdNNKDv3PPbYY2E8yqNp06aFY2+99dZCbOHCheHYbBtRF5Xs+Yz2pds7amWy5yA6\nx2RdZ8p0nFu0aFHTj5d1ofv5z38exn/3d3+3EJs0aVI4Nsqx6FwrScuWLQvjP/3pTwuxmTNnhmNP\nPPHEMB6d97PzTTS/rNNZ9lpFr3fWeagVedCbWQMAAADgiFgMAAAAABXFYgAAAACoKBYDAAAAQEX1\ndAFxu25V/oxnPCOMZ4UgF198cSGWFa9EhTivfvWrw7FZgUhUvHL77beHY7PixKgw5q677grHojWi\nQs+saKxMUWh2nAwfPrwQy4oLowLMzZs3h2OXLFkSxqNjbfz48eHYOXPmFGLPfvazw7Gnn356GN+0\naVMhtnLlynDs3XffHcZnz55diGVzjl6r6Bb2Ul4At3fv3kIsK/rLisl6Tbb/0XG9c+fOcGxWFBy9\nZlGRb7aNNWvWhGOzPIoK488777xw7IQJEwqxqNBTkv7pn/4pjF933XWF2DnnnBOOza4FUaOKm266\nKRz76KOPFmLZ/mXH7/Tp0wux9evXh2OjOWfF4kORu4fHYJniz+hYy3Lm+OOPD+NLly4txLL3OVGh\nefacZ9epUaNGFWLZ8RAVMmc5mjV3iJpdZIX7t9xySxiPntOsicuUKVMKsaxgPjs3Rc9R9n6gFUXz\nfDIAAAAAVBSLAQAAAKCiWAwAAAAAFcViAAAAAKioIy4GzOwaM1tvZvc3xK40s9Vmdnf9v5e0d5rA\n4CIPAPIAIAfQi5rpJrRA0qck/b9D4p9090+0fEZHEHXhyCrWP/3pT4fx9773vQOaw9lnnx3Gy9wC\nPLs19YMPPliIXXPNNeHYO++8M4zffPPNhdi6devCsatWrQrjUXeA7PbkFbFAbc6D7PiJRJ2AslvC\nZ/kRPV7WhSLqWJHdij3rSrJly5ZCbOrUqeHYqAvK97///aa3K0nPe97zCrELLrggHJt1zohkXS+i\neNb9JYtH3YSy1zXqhlHmGOqnBWpRHkTHZTT/rDNP1IUjOyYzUceecePGhWNHjx5diJ100knh2Oh1\nlKSnP/3pTY+NjpF77rknHHvyySeH8agrV3bOnzFjRtPbeOYznxmOnTRpUhiPHHfccU1vo0y3lDKd\n1vppgVp4LRhoJ5joPVHWVSvLpaj7TXacRF0as/cXp556ahgfM2ZMIZZ1yom60z3/+c8Px0adGyXp\n29/+diH2k5/8JBz75je/OYzPmzevEMs6EkWvaXZuzq6X2WsViZ67st3mjngUuvtPJMU9BIGKIA8A\n8gAgB9CLBrIkfauZ3Vv/yKz46xWgGsgDgDwAyAF0rf4uBv5V0kmS5klaI+l/ZwPN7Aozu9PM4u+0\nAN2rX3mQfYQLdKmm8qAxB3bs2NHJ+QHt1q9rwbZt2zo1P+Cw+rUYcPd17n7A3Z+U9G+Szj/M2Kvd\n/Tx3j29DCHSp/uZB9N1joFs1mweNORB9ZxjoVv29FmT1KUCn9WsxYGaN9wv/Q0n3Z2OBXkUeAOQB\nQA6g2x2x9YKZ/aek+ZImmdkqSR+UNN/M5klyScslxeXXbfCWt7ylEFuxYkU49tnPfnZb5vDoo4+G\n8W984xthfOHChYXYbbfd1tI5HckVV1wRxidPnhzGly5d2s7pdJ1W5oGZNd1BIusoEHX9yboSZF0F\not/OZp1Nmp2DVK6bxy9+8YswHnVLyrp4ZZ0s5s6dW4ht3hzX/WXPXfR8RN07JOnMM88sxLKvAWTn\nkKjjRJmOFdHYVnZXGYzrQZYr2fEXyV6zqDNK9hWm6NjJuhdlx1l0XPf19YVjx44d2/TcNm7cGMaj\nrj/RPkvSrl27wvjDDz9ciGWdX6Ljfe3ateHY7HWN9rtMp7Rsbq3S6mtBdH6OjqusA2H03GTHX3Y9\nGTVqVCEWdRiSpHvvvbcQ+9GPfhSOfcUrXhHGp02bVohlHdZmzpxZiC1fvjwce9ddd4Xx66+/vhCL\nOjdK0j/+4z+G8YsuuqgQy87vp5xySiF22mmnhWOzfBw2bFghVrZDUBlHXAy4+yuD8GfbMBdgyCIP\nAPIAIAfQi7gDMQAAAFBRLAYAAACAimIxAAAAAFRUuXu3D1Ef+9jHBnsKQ94LXvCCUuO/+tWvtmkm\nyJS5pXhUCHbssceGY7MC1CieFZhFhbRZYeoznvGMMP6Vr3ylENu0aVM49s///M8Lsa997Wvh2Jtv\nvjmML1q0qOm5ZcVrs2fPLsSy4t+oEDR7/bZv3x7Go6LYrMg/KjaOCqGbLVbvtGj+0TFVpoA4O9az\ne3tEr8/JJ58cjr3//mKDmPXr14djs9apa9asKcS2bt0ajj3rrLMKsQ0bNoRjs9yP2hhnz+eIESPC\nePQ8RwWSUnyeGDlyZDg2ev0zUeF19nitLJhvN3cPz/tRLCuijorjJ02aFI7NCs2jJgxnnHFGODY6\nJ0YFyFJe8B4Vq2eF39Fxkm03awQRNXfICrJnzJgRxqM2sNF2JWnWrFmF2Pjx48Ox2bEdncuy/WuF\noXmVAAAAANB2LAYAAACAimIxAAAAAFQUiwEAAACgolgMAAAAABXVE92E0Hpf//rXB3sKPcvdw649\n0S3os+4oUfeGrKNDdsv0qINO1v0m6jSSzS3rZPFHf/RHhVh2+/hobvPnzw/H3njjjWE86lR03333\nhWOnTZsWxqOOLsuWLQvH3nHHHYXYlClTwrFRlxcp7iyTdb2IRJ1Uuq27SjMxKe4yknVeWr16dRiP\nOrFs2bIlHDts2LBCLOsKlXXbiboaZd2pnvnMZxZiUTciKd+/Cy+8sKk5SPFzIcX7nc0jOq6z1yQ7\nX0XnxqzzULMdqbpN1tUtEp2zs/N41uUq6lQUdWqS4o5sq1atCsdmXX+iYy3riHX22Wc3PXb69Olh\n/LHHHmsqJsV5J8XXuyz/r7/++kLshBNOCMdOnDgxjM+cObMQy/KgFR3j+GQAAAAAqCgWAwAAAEBF\nsRgAAAAAKorFAAAAAFBRLAYAAACAiqKbEDAImu2aknWViDob7Nq1q+mxUtyBIHu8qDvFvn37wrFZ\nd5T9+/cXYlnXlajbxLOf/exw7He+850wfsEFFxRi55xzTjj2Qx/6UBiPuihlnSyi7ihZ94fseY5e\nkzIdJMp0IRlszXaCyfZp+PDhhdi4cePCsVlHpqgbyMqVK5t+vFmzZoVjs45EUY5mj3fbbbcVYlkH\nnqybSNT1Z/bs2eHY8ePHh/Gom1Cmr6+vEMu6mWXnq0iZPMrGdpOsG9BAx0bHsBTnQbbdzZs3F2LZ\nOTHrXhR18lm/fn049t577y3EouNMkl784heH8eXLlxdiUce6bKwUdyq69dZbw7Fr164txLLcnTNn\nThiPrpdlOnCV7arFJwMAAABARbEYAAAAACqKxQAAAABQUUdcDJjZLDP7sZk9aGYPmNlf1OMTzewG\nM3u4/v8J7Z8uMDjIA1QdOQCQB+hNzRQQ75f0v9z9l2Y2RtJdZnaDpNdJ+qG7f9TM3iPpPZLe3b6p\noh2yYqtTTz21EIsK2iqk7XkQFQFlhYFRUXBWKHz00XGalyk2jYqFJ0+eHI7dsGFDGI+K17Iip5NO\nOqkQu+WWW8Kxb3nLW8L4hRdeWIhl+xwVCktxwVZWaJltI5Ldxj7advb6RQVwUT63sKCypTnQbIFb\nNi7a/02bNoVjs9cmOh4OHDgQjl23bl0hNnHixHDslClTwniUz1khfjTnLMd37NgRxqPjbMWKFeHY\npUuXhvFp06YVYs997nPDsXv37i3Esv0rc17KjuFBKhZu+7UgOubLFFGXaVAgxQXAkyZNCsdGr2f0\nukt5Me6znvWsQiwrII6uJxdffHE4Nsu7iy66qBDL5vzCF74wjEfNArIi+Og8ns1t586dYTxqohEV\nFUvSyJEjC7Hstc4ccbS7r3H3X9b/vEPSQkkzJL1U0rX1YddK+oNSjwx0EfIAVUcOAOQBelOppYOZ\nzZF0jqTbJU1194N9y9ZKmtrSmQFDFHmAqiMHAPIAvaPpxYCZjZb0VUlvd/enfPbotc+0ws9yzewK\nM7vTzO4c0EyBIaAVeZB9pA90A3IAaE0eZPdgADqtqcWAmQ1T7aD/grt/rR5eZ2bT6z+fLin8wpe7\nX+3u57n7ea2YMDBYWpUH2Y1YgKGOHABalwfZTfKATmumm5BJ+qykhe7+9w0/uk7Sa+t/fq2kb7Z+\nesDQQB6g6sgBgDxAb2qmm9DvSHqNpPvM7O567L2SPirpS2b2BkkrJL28PVNEO2WdOspWoldAS/Mg\n6gwRddfIXp+oq0DZ249H47Nb0B9zzDGFWNYNJDt2om5CWYeMqLPE2LFjw7GrVq0K46961asKsez2\n8Vkni6jLRvYVl6jTS3b7+Ox5jrqAZB1kss4Shyp7XBxG268F0fOSdUYp0wlo2LBhYTw6rqPXXIq7\nj2SdpbLXffbs2YVY1BVJirt1ZWOz+MKFCwuxefPmhWOz5znqMhR1OpHiriZZJ6fsuYu2kR3rzeZA\niw3Ke6IyeVz2+h2dy7PzXJnnvK+vL4xHnXVOPvnkcOxv/dZvFWLZ+TPrXhSJumRJ+f5t3LixEDvh\nhBPCsdExn3UeykTXxuw8Fj1e2U5bR1wMuPstkrKtvqDUowFdijxA1ZEDAHmA3sSvfwEAAICKYjEA\nAAAAVBSLAQAAAKCimikgRgVFtwtfsGBB5ydSIVkBXyQr3o204jb2UfFu1iM7K8CKitqyQsvNmzcX\nYieddFI4duvWrWE8Krx92cteFo7NihkffvjhQmz06NHh2CieFXaWKYotWwjWzcoUSUZFhNm/z+Jl\nntvo8aLjQ5JmzZrV9Dyy4zo6FrJi3Oc85zlhPCquP/PMM8OxWYH+okWLCrGsUDvK/awAft26dWE8\n2sdsbtHYQSoqHjTReTU7rrPXrUwziWjbJ554Yjh2586dYTwqTj799NPDsfPnz29qDlLcEECSTjnl\nlEJs4sSJ4disIUXUACM7Ljds2FCIZdeCbBtRIX2WS/v27SvEstcvwycDAAAAQEWxGAAAAAAqisUA\nAAAAUFEsBgAAAICKYjEAAAAAVBTdhCquSp1Khrqoe0h2W/moq0DWuSHrrhHdEj7rurJ48eJCLOuI\nk3VHiW6lvmXLlnDs1KlTC7Hsdu4/+clPwviECRMKsawLU9T9IYsfe+yx4disK0Qk6ywRvd7ZMRDF\ny3SkGorKdBOKZM9Vme3u2bMnjI8bN64Qy7r7ZI8Xdf3IuupE3UCyHMi6qER5tGzZsnBs1lErOq4n\nTZoUjo32+5FHHgnHZl25stcwEp0HuaaVz4Po3Jx1o4muJ1lnuaxbXPS6Zdep6dOnF2J79+4Nx2bH\n1MaNGwuxrONXdo2YPHlyIZadx6N8nDNnTjg2O4esXbu2EMtev+jxyuYBnwwAAAAAFcViAAAAAKgo\nFgMAAABARbEYAAAAACqKxQAAAABQUXQTqojvfe97YfxlL3tZh2eCTJnuMFE3hqwrQdYdIepWsHz5\n8nBs1Gkk6xqUdQ/ZtGlTIRZ1O5GkrVu3hvFItn9R15/du3eHY7PuG1FHhuw1iTq9RK+TlHeFOHDg\nQBhHUfQcDrQbUVlZF58yXZ2yrh/RNoYPHx6OzY6zqDNSNjbr/DJy5MhCLDtO+/r6CrETTjghHJvl\nbbSP2fOZbaPqyp5Hss5BkajzUHb+zDoERa9xNufo+Mm6v2Wd3qLuQ1knoCzHduzYUYht3749HBtd\nC7L9i7qUSfGcsxxtBT4ZAAAAACqKxQAAAABQUSwGAAAAgIo64mLAzGaZ2Y/N7EEze8DM/qIev9LM\nVpvZ3fX/XtL+6QKDgzxA1ZEDAHmA3tRM9c1+Sf/L3X9pZmMk3WVmN9R/9kl3/0T7podWWbBgQak4\nCtqeB1GRXFYQGRU5ZUVcWfFdVLg4d+7ccGx0K/YtW7aEY7Nb059xxhmFWFZcePvttxdiY8eODcdG\nBW1SXPiYFbQtWbIkjEdF2RMnTgzHRs9nVMAplXutyhT3RcdL2dvSH0YlrgVlipCz1zETHU9ZEWxU\ncJgVLGdFj5FsG1kDgjFjxhRi2ZyjXMyKOvft2xfGyxQQt/l4z/RcHkTPY1bwGj2/ZfMgkhX0RvHo\nmJTyc2V2bJfZRlS8G10Xpfi5K3t9zgqLm91G2SLyIy4G3H2NpDX1P+8ws4WSZpR6FKDLkQeoOnIA\nIA/Qm0ot58xsjqRzJB38td1bzexeM7vGzCa0eG7AkEQeoOrIAYA8QO9oejFgZqMlfVXS2919u6R/\nlXSSpHmqrZL/d/LvrjCzO83szhbMFxhUrciDnTt3dmy+QKu1Igeint1AN2lFHmRfqQQ6ranFgJkN\nU+2g/4K7f02S3H2dux9w9ycl/Zuk86N/6+5Xu/t57n5eqyYNDIZW5cHo0aM7N2mghVqVA9l3foFu\n0Ko8KPO9cKCdmukmZJI+K2mhu/99Q3x6w7A/lHR/66cHDA3kAaqOHADIA/SmZroJ/Y6k10i6z8zu\nrsfeK+mVZjZPkktaLunNbZkhMDQMSh5knTGizgRZp5wnnngijEddPrLOPMuWLSvEsluxT58+PYxH\nXSGWL18ejp0yZUohFnUHkvJbtEf7nXUNyjoERR577LGm55H9Bjzr9BC9htkxEHUAiTphlOmOcwRc\nCw6RdQIpI8vbSNZxJZtH1EUl66ySbTs6/rJtROelrHtRNucynVgGSaXzoBXdtqLXPjtOIlnHn+xa\nF3Woyh4v23aUB3v37g3H7t69uxAr0xEri2fXglZ0eGqmm9AtkqIZfLfUIwFdjDxA1ZEDAHmA3jSk\nltsAAAAAOofFAAAAAFBRLAYAAACAimqmgBjAEBMVI2XFRWUKpbJtRO1QsxapUbGWFBdVRTEpnvOe\nPXvCsWWKKmfNmhWOzQoi165dW4iVKfgsU/wrxQXcZYrG0NvKNBSQ4uNs165dpbYdFcFnx29UwJnN\nLWsIwHHd+8oUx0bKFu5Hj7dv375wbJkcy+Zx9NHFt9ZZYXK2jU4XzfPJAAAAAFBRLAYAAACAimIx\nAAAAAFQUiwEAAACgolgMAAAAABVlLbxV/ZEfzGyDpBX1v06StLFjD9557N/QMNvdJw/2JBo15EG3\nPIf9xf4NDUM5B6TueR77i/0bGsiDwcX+DQ1hHnR0MfCUBza7093PG5QH7wD2D0fS688h+4dm9Prz\nyP6hGb3+PLJ/QxtfEwIAAAAqisUAAAAAUFGDuRi4ehAfuxPYPxxJrz+H7B+a0evPI/uHZvT688j+\nDWGDVjMAAAAAYHDxNSEAAACgojq+GDCzS83sITNbYmbv6fTjt4OZXWNm683s/obYRDO7wcwerv9/\nwmDOsb/MbJaZ/djMHjSzB8zsL+rxnti/wdJredDLOSCRB+1CHnQPcqA9yIHu0qt50NHFgJkdJemf\nJb1Y0lxJrzSzuZ2cQ5sskHTpIbH3SPqhu58i6Yf1v3ej/ZL+l7vPlXSBpD+vv2a9sn8d16N5sEC9\nmwMSedBy5EHXIQdajBzoSj2ZB53+ZOB8SUvcfam7Py7pi5Je2uE5tJy7/0TS5kPCL5V0bf3P10r6\ng45OqkXcfY27/7L+5x2SFkqaoR7Zv0HSc3nQyzkgkQdtQh50EXKgLciBLtOredDpxcAMSSsb/r6q\nHutFU919Tf3PayVNHczJtIKZzZF0jqTb1YP710FVyYOePEbIg5YhD7oUOdAy5EAX66U8oIC4A7zW\nsqmr2zaZ2WhJX5X0dnff3vizXtg/tFevHCPkAQaiF44RcgAD0SvHSK/lQacXA6slzWr4+8x6rBet\nM7PpklT///pBnk+/mdkw1Q76L7j71+rhntm/QVCVPOipY4Q8aDnyoMuQAy1HDnShXsyDTi8G7pB0\nipmdYGbHSHqFpOs6PIdOuU7Sa+t/fq2kbw7iXPrNzEzSZyUtdPe/b/hRT+zfIKlKHvTMMUIetAV5\n0EXIgbYgB7pMr+ZBx286ZmYvkfR/JB0l6Rp3/9uOTqANzOw/Jc2XNEnSOkkflPQNSV+SdLykFZJe\n7u6HFtUMeWZ2oaSfSrpP0pP18HtV+45c1+/fYOm1POjlHJDIg3YhD7oHOdAe5EB36dU84A7EAAAA\nQEVRQAwAAABUFIsBAAAAoKJYDLSJmZ1mZneb2Q4ze5uZfdrM3l//2XwzWzXYcwTajTwAyAOAHBja\njh7sCfSwd0n6sbvPO9JAM1su6Y3ufmOrHtzMPi/pBZKOVe0GGB9398+0avtAkwY1Dxq2fYpqBV9f\ncffLW7194AgG+3pwk6QLJO2vh1a7+2mt2j7QhEG/FpjZK1QraD5etfdFr3P3n7byMboVnwy0z2xJ\nD7T7Qawmeh0/ImmOu4+V9PuSPmxmv9Xu+QCHGOw8OOifVWvjBwyGoZAHb3X30fX/WAig0wY1B8zs\nhZI+Jun1ksZIeq6kpe2eT7dgMdAGZvYjSRdJ+pSZ7TSzU81sgZl9OBj7OdVWqd+qj31XPX6Bmd1q\nZlvN7B4zm9/wb24ys781s59J2i3pxEO36+4PuPu+g3+t/3dSq/cVyAyFPKiPe4WkrZJ+2PKdBI5g\nqOQBMFiGSA58SNJV7n6buz/p7qvdvRdv8NYvLAbawN2fr1of2oO/iVl8mLGvkfSopMvqYz9uZjMk\nfUfShyVNlPQOSV81s8kN//Q1kq5QbYW7Itq2mf2Lme2WtEjSGknfHfjeAc0ZCnlgZmMlXSXpr1q0\nW0ApQyEP6j5iZhvN7GeNb6SAdhvsHDCzoySdJ2mymS0xs1Vm9ikzG9nC3exqLAaGpsslfdfdv1tf\nwd4g6U5JL2kYs6D+2//97v5EtBF3f4tqifEcSV+TtC8aBwxRrciDv5H0WXenOA3dqhV58G7Vfls6\nQ9LVqv3WlU+K0S0GmgNTJQ2T9N9Vez80T9I5kt7Xgbl3BRYDQ9NsSS+rfxy21cy2SrpQ0vSGMSub\n2ZC7H3D3WyTNlPRnrZ8q0DYDygMzmyfpYkmfbO80gbYa8PXA3W939x3uvs/dr5X0Mz31jRQwlA00\nB/bU//9P7r7G3TdK+nuRA79GN6Gh4dDbQK+U9Dl3f1OJf3MkR4uaAQxtrc6D+ZLmSHrUzCRptKSj\nzGyuu587gHkC7dSJ64FLspL/BuiUluaAu2+xWutSb2Z8FfHJwNCwTk8tePm8pMvM7EVmdpSZjbBa\nH96ZzWzMzKaY2SvMbHT9379I0itFASWGtpbmgWpfhzhJtY+E50n6tGrfO31RKycNtFirrwfj6/92\nhJkdbWavVq2TyvfbMHegFVp9LZCkf5f0P+vvjyZI+ktJ327hnLsai4Gh4SOS3lf/+Osd7r5S0ksl\nvVfSBtVWxe9U86+Xq/aVoFWStkj6hKS3u/t1LZ850DotzQN33+3uaw/+J2mnpL3uvqFN8wdaodXX\ng2GqFV5ukLRR0v+U9AeHK+IEBlmrc0Cq1Y/dIWmxpIWSfiXpb1s66y5m7nxSAgAAAFQRnwwAAAAA\nFcViAAAAAKgoFgMAAABARbEYAAAAACpqQIsBM7vUzB6q3975Pa2aFNBNyAOAPADIAXSrfncTMrOj\nVGvR9ELVWljeIemV7v5g9m9GjRrl48eP79fjDZb6zYo6pszr0YpOUNn+RdtuxdhOW7NmzUZ3n9yu\n7fcnD8aMGeN9fX3tmlJPiI4fOp/1z6ZNm7Rjx462JmTZPOjGawG621C8FowbN86nTJnSril1lez8\nPlTeS/SKJUuWhHkwToGh0AAAIABJREFUkDsQny9pibsvlSQz+6JqfWDTA3/8+PF685vfPICH7Lyh\nsBjIkmT//v1h/Mknn2z68Y466qim51FmMfC0p8UfOmXxdj3PV1555Yq2bPg3SudBX1+fPvjBDza1\n8TJvgNt5rLZikVrmOImOy+y4PnDgQBiPno8qXliuuuqqTjxMqTwYP3683vSmw91MFGitq666ashd\nC6ZMmaJPfvKTA3rQ7BwaKfPeoBVzKPN42dgy5/FWzKPXXXbZZWEeDORrQjNUu/HDQavqMaBKyAOA\nPADIAXStthcQm9kVZnanmd25e/fudj8cMCQ15sHOnTsHezpAx3EtAJ6aB9u2bRvs6QCSBrYYWC1p\nVsPfZ9ZjT+HuV7v7ee5+3qhRowbwcMCQVDoPRo8e3bHJAR1yxDzgWoAeV/paMG7cuI5NDjicgdQM\n3CHpFDM7QbUD/hWSXtWSWQ2C7Dto0XfNynxHL1Pm+8/Z9/qHDRsWxst837/Md+my/Y4eL/sedw/q\nVx4MtOg6GtuK47LM42X27dsXxqNj4uij41NQ9HjZsVqm7gVt01PXA6AfBiUHWnENH+jjZdf77Pwe\n1TtmY6Ntl92P6BrRzutDN9Yo9Hsx4O77zeytkq6XdJSka9z9gZbNDOgC5AFAHgDkALrZQD4ZkLt/\nV9J3WzQXoCuRBwB5AJAD6FbcgRgAAACoKBYDAAAAQEUN6GtC/dFs4WSZG1xlsiKTqCAl2260jexm\nX2XuoJfNLdr2448/Ho7NilSeeOKJQmz48OHh2Kz4Mtp29nhR4U9WUJQ9XhTPxlbpxlHZMVWmGDcr\n6I2OiWwb0TyysdmxvXfv3kIsO06OOeaYQmzHjh2lHi9S9piKivSzsdw1GUArROe0VhQKZ+9donNX\ndn6Pzs1lrhuSNGbMmEIsa4gSteLOrhvtPN9Gj1nmvchQvyEanwwAAAAAFcViAAAAAKgoFgMAAABA\nRbEYAAAAACqKxQAAAABQUR3vJtRs9XVWFR5VXmdjs3hU+Z5VspepFs8q9cuMjeaRdYMZOXJkGB81\nalQhlj0XUWeAwz1mJOrQUqaTk1Suo1QviF6P6NjOjssonj3nUecGSdq1a1ch1orjMjr+pLgbUPZ4\n0XGSHe9btmwJ4319fWE8knXDiI7Bst3EgG6QnT/KdBKbOXNm09vN8nb79u2FWJlray8o02GmTOe/\nrKtgmceLjofs3Ldu3bowHp1XJ02aFI6N3qNk883eM0Tjy5zzpfialL2fiY75Mu99BgOfDAAAAAAV\nxWIAAAAAqCgWAwAAAEBFsRgAAAAAKqrjBcSRMkXBUdHHE088EY7dvHlzGI8KEbdu3RqOjQond+/e\nHY4dMWJE0/HRo0eHY6P9K1ssExX0ZsUrWWHWscceW4hFhV1SXEiaPRfZ6xrNOdu/XijUbLZoaM+e\nPWF848aNhVhWEJUdr+PGjSvEsmLj6PXJ8i4aK8UFvdkxFcn2IyuKi/YlK1osU0CX7R+Grqz4PHrd\ns+Os11/3rNA3ajQQxSRp/PjxhdgFF1wQjl2+fHkYj57/qhUQR69FmaLg7LyavQ+ItpFdT/bu3VuI\nbdu2LRybNXHYsGFDIZadm6P3YNn7i+wYjq632XUju65Fz3/2fEbniuz8Ueba0058MgAAAABUFIsB\nAAAAoKJYDAAAAAAVxWIAAAAAqCgWAwAAAEBFDaibkJktl7RD0gFJ+939vP5sJ+oOk1WFl6nojjob\nSNLOnTsLsVGjRoVjo1thz5o1KxybVd9HVetRBx5J2rFjR1NzkPKq9+j5zLpplKlwz7YRKXNb8Cze\nLV2DWpUHjz/+eCEWdVKQpBkzZhRiWYeqrGtC1BEk69oRdTXKXp/s8aJtZ8dfFI+eHynvGhZ1Jcke\nL+uiFMWzeUTHfLccw63QqjwYiOz1zbqrTJs2rRDLznNZx7luk51TsnNzdF5Zt25dOPb+++8vxCZN\nmhSOHTt2bNPzizrYDEWtyoHotcjeE0Xno6gboBS/98ni2Tai89yECRPCsVkuRZ2DsuMyimfn1ew5\nit6bZR2xsvN7dAyW6XKV7V+Z97rt1IrWohe5e7HPIVAt5AFAHgDkALoOXxMCAAAAKmqgiwGX9AMz\nu8vMrmjFhIAuRB4A5AFADqArDfRrQhe6+2ozmyLpBjNb5O4/aRxQT4grpPiOp0APKJUH2V0ZgS53\n2DzgWoAKKHUtmDx58mDMESgY0CcD7r66/v/1kr4u6fxgzNXufp67n5cV6QLdrGweZIW+QDc7Uh5w\nLUCvK3stYFGMoaLfnwyY2bGSnubuO+p/vkTSVUf4N2ElelTpnXWjKdN5KFt1RxXgWbeCqIvKpk2b\nwrFz5swJ4w8++GAhNmXKlHBstC9ZJ4yse1HUfSibc9apKOoOUKZyPhubva7ZvkSGUueh/uSBFO9D\n9AYp6ogjScuXLy/Etm3bFo7NtnHWWWcVYlnOZJ0lIll3r+y1j0SdHs4444xw7H333RfGN2zYUIhl\nF99s/6Lx0TlByjt1VEF/86AZZXI7O49nx2T0mnW6i0enZV1NsvNH1B2sTEei6FwlSSNGjAjjUUe9\nbtDKHIiO+ey4jK61WaemrAtU9HjZNTzqhJhdN7LOPBMnTmz68aKORNn5Ons/E50XsmM46nQkSRs3\nFmvCs3PT7NmzC7Gs01nWFbLTBvI1oamSvl6/wB8t6T/c/fstmRXQPcgDgDwAyAF0rX4vBtx9qaRn\ntHAuQNchDwDyACAH0M1oLQoAAABUFIsBAAAAoKJacQfiprl707fZzgq+ooKNrBAkK8yItpEVd0TF\nJCtWrAjHZgUwJ554YiGWFbpFxZ7Z7dyzgsyoaOfMM88Mx2bP3eLFiwuxrFA7eq2yguDsOYri2eNl\n8W4SHW/R65YVpUbFd+ecc044NnuNI4sWLQrjUWFWVqRWppA2K4qLCrCmTp0ajl25cmUYHzNmTCEW\nFYFJ0sKFC8N4VEgfFb9JvXFcdpPo/JcdTzNnzgzjUdFsdu4aKoV+A5UVSK5duzaMR7mfXXuixhjZ\nte6RRx4J41ExaplzWC+IjsEy186oAYOUv5eIzq0PPfRQODY672d5l80jmnP2Gkfn2+OOOy4cm117\nouczO+dHBfNSfM3OxkbvMbNi6jKNNdqJqxcAAABQUSwGAAAAgIpiMQAAAABUFIsBAAAAoKJYDAAA\nAAAV1dFuQlLzt9nOqtPLdB7Kbq8eVYX39fWFY6NK9qx7SXaL7K1btxZiWeV81Lkhu217tn+33357\nIRZ1KZKkt73tbWH8/PPPL8SWLFnS9Dyy23SPGjUqjGevYbMG+u87LXp+ss4Lkagbw89//vNwbHac\nRLd5nzVrVjg2ypmsM0/WZSjKg2xsNLd9+/aFY7Nb0Ec5tn379nBsdr7JOkBEoq4QWR5kouc5m1uk\n7OMNNdH8s32KnpfsvJp17Bg3blwhlp2jHn300TDebfbs2RPGs/2eM2dOIZadb6Pzx3333ReOjbp9\nSXHOVa2bUPQ+J8uDMte+7Fx59913F2LZe4bx48cXYtmxk8052nY2t2gbWYeqpUuXhvF169YVYtn1\nJHu/tWnTpqbnEZ1XRo8eHY4dKl3K+GQAAAAAqCgWAwAAAEBFsRgAAAAAKorFAAAAAFBRHS0gNrOw\nQC4qEInGSXFhTXb7+GisFBeeZcUrUVHwhAkTBvx4ZQpsyzwXknTRRRcVYtdff3049itf+UoYv+SS\nSwqx6JblUlycl93yPit2ygpxmn287BgYitw9LBqKjsGogF2K9zcrxo0KvqS4UCp7HW677bZC7Pjj\njw/HrlixIoxnxcmRqJh60aJF4djsmIqKrLP9O/XUU8N4lI9RcZgUFydnxWFZ/pcpJovyPzpXDJXb\n3fdXdp6L9isr/ssKHKM8yo7TbiwgjppdZPmSnSfmzp1biC1fvrzpsdm1IDsuV69eHcZ7VXR8R+8Z\nsucryvnsWpC99lFx68knnxyOjeaRNVqICnezeZx44onh2ChHv/nNb4Zjhw8fHsYXLlxYiJ1++unh\n2PXr14fx6DmaN29eODa6bmcFy9lrUqZxRCvwyQAAAABQUSwGAAAAgIpiMQAAAABUFIsBAAAAoKJY\nDAAAAAAVdcQWLGZ2jaTfk7Te3c+qxyZK+i9JcyQtl/Ryd49bBjShzC3oo8r77FblWTyq3s5uvR1V\np48cOTIcm1XUR7d/zzoERV0vottgS3mXhjPPPLMQO/fcc8OxW7duDeP33HNPIZZ16oi6K2XP0bZt\n28J49JpkHZ6y7iLt1Mo8MLOwQ0J0TEQdaqT4tutZJ4WsQ01fX18hlnWMOuGEE5p+vOc+97lhPOpw\nkXXIiLqVZK/74sWLw/jMmTMLsaxrUJb/GzZsKMSy3I26F2Xbzfal2W5r2TbKdC7rj05cDw6VzT/K\noeyckXWRio736FiXpJtuuimZ4dAV7fezn/3scGzUOU+Ku65E3Vkk6TOf+Uwhdumll4Zjb7311jCe\nnVeGilbnQJSzZc4D0XU5O79EXdqk+L1S1qEvulZn71GmTZsWxqNr0ubNm8Ox0bUu6+KTdZy78MIL\nC7F77703HJu9z5kxY0YhtnPnznBs9Fpl1+HsfWqnu8A1865qgaRDs/k9kn7o7qdI+mH970AvWyDy\nAFgg8gDVtkDkAHrMERcD7v4TSYcu2V4q6dr6n6+V9ActnhcwpJAHAHkAkAPoRf39vsVUd19T//Na\nSfF3C4DeRh4A5AFADqCrDfjL1177clT6pVQzu8LM7jSzO7PvqwHdrkwe7Nixo4MzAzrncHnQmANZ\nLQXQ7cpcC7IaOqDT+rsYWGdm0yWp/v/4/s2S3P1qdz/P3c/LCpSALtWvPBgzZkzHJgh0QFN50JgD\nUaMEoIv161owbty4jk0QOJwjdhNKXCfptZI+Wv//N5v5R+4eVs5HVdNZx46oSjurnI86gUhx9X72\nBi3qEJR1eNm4cWMYj6ryozlI0qpVqwqxrGI96lIkSXPmzCnEsqr+qKOMJK1Zs6YQy57PaL+zbkLZ\ngjA6BqKuBVL8ene68r7u/7N379F2ldXd+L+zIffk5J6TkJzcE3KTAoaIBQWkYLQV5KWijIrUUYW3\nyq9q1YqM2tpRW60XtG/VdmDFoFVaBaW0gIIEBAUxAbkkhJD7PSf35ORCQmD+/jib9z1kfWeyn7PX\n3mfvvb6fMRgkM0/Wftbaz1xrr+wz5+pWHkTdhNhai9773//938/Eoi4ImzdvpvFp06ZlYtExX7Ro\nUSYWdbdgawcA2IVv/fr1dOyRI0cyMdbVBABuueUWGmf797a3va3suQF8vUbf7LD3Lzo3RZ0louPP\nsHMI66ZTg9zoVh4wKecBdryjNck6kgC8u1nUUavW2JpkHbmA+PrFzsPROTjqMsReMzovsW4us2bN\nomOjzk8NKrccANI65rGcYedPIP7swl4v2gb7zBCdE1esWEHjbM4bNmygY7dt25aJLVu2jI7dvp3f\ng61atYrGmdNPP53GWZfGaCxb89F5uIc+u2ScdMWZ2W0AHgNwmpltMrM/ReeCv9jMVgL4/dLvRZqW\n8kBEeSCiHJBmdNJvBtz9quCPLsp5LiJ1S3kgojwQUQ5IM9ITiEVERERECko3AyIiIiIiBdXdAuJc\nsQKKqMBu0KBBmdiePfyp38OHD6dx1uI0KogaOnQojTNtbW00zgrdou2y4t/p06fTsVFHjoULF2Zi\nURHeyJEjaTwqOGZYAXFUzBo9ap6931FhTcqj2uuRu+PYsWOZODuOUTHjypUrMzFWfP7q6zF79+7N\nxKICRVY8NWHCBDo2Kh6fO3duJsZyA+CFiFEB+xve8AYaX7p0aSY2Y8YMOjbKJfaaUfE/O85RI4SU\n9rLR3IYNG5aJRUV/jYIdw5TCu2gsK9gH+HkxKoBnRbpR4W60VlluRMXr7Lq2a9cuOnbdunU0zvI5\nOgdHa/XZZ5/NxKL1y/Z7yZIldOzUqVNpPKWIvlmxc0xUVMxyvnfv3nQsu+4A/FoQYWs4agQRNShh\nxcLnnXceHfvQQw9lYl/96lfp2CeeeILG2ZqKzglR/Mwzz8zEomsuk1IU3hPqe3YiIiIiIlI1uhkQ\nERERESko3QyIiIiIiBSUbgZERERERApKNwMiIiIiIgVV825CrFsE6yQTVcMfPny47NeKOtps3ry5\n7G389re/zcTGjRtHx9599900zirZt2zZQseyTg8plf4AsGDBgkyMVcIDwJo1a2icdTCKOnWwx8pH\nlfNRZxv2vqZ0bWmkbkIAP5ZsXUWdDdj6YZ22AOCSSy6hcbYuo85c7e3tmVj0SPioE9g//dM/ZWI/\n//nP6dioO0ql3v/+99P45ZdfTuOsG0aUj2zNR+syOr+xbW/cuJGOnTZtWibGOn00Wm4cL6Wr2P79\n++nYqIsKW6vROZG9N6xjFQBMmTKFxlevXl323Fj3rW3bttGxkydPpnE2nnVyAYB9+/bROFvX0Zxf\n97rXZWKzZs2iY6MOflFXo2bFjm9KRyX2mWHgwIF0bNSdbvny5ZlYdC3Yvn17Jha9l8899xyNs+5D\n9913Hx374IMPZmKf//zn6dg8sM8zAPDOd74zE4uO8+/+7u9mYlGnvlGjRiXMrnr0zYCIiIiISEHp\nZkBEREREpKB0MyAiIiIiUlC6GRARERERKaiaFhCbGS0OSikYSimW2b17N42PGTMmE4uKOFgRa1Rg\ncuWVV9L4I488konNmzePjmVFz9HcvvSlL9E4c+utt9J4VAzJHusd7TcrUps4cSIdy96/aNtR8Wyj\nF0QCfF2x/WKFXQAv4Gtra6NjWdEiAPzmN7/JxCZNmkTHXnDBBZnYaaedRsdGhd+syPHjH/84HcvW\nT1R0/+yzz9I4y6WoyDo6V7z44ouZWLSGjx49molFxdSDBw+mcbbtAQMG0LEsZ1hhclSA2yii6wPb\nV/Z+AbzoEeCFmkOGDKFj2XFk7zkQF/oePHgwE4vWE5tbtB//9m//RuMdHR2ZWFQsv3btWho/77zz\nMrGoGPKss87KxFasWEHHRueJohUQlyvlurdr1y4ajwrs2WeoqCh45MiRZcUAYPz48TR+6aWXZmJR\n3rFzdvS55c1vfjONr1q1KhOLirSjcwgrsI8apbDjHBXd1wt9MyAiIiIiUlC6GRARERERKSjdDIiI\niIiIFJRuBkRERERECko3AyIiIiIiBXXSbkJmdguAPwSw3d3nlmKfBfBBADtKw25093vKeUHWkYFV\ndUedBlj1dtTlhnWbAHg3oagrBOuAElX1z5w5k8afeuqpTCzqsMC2cdttt9GxX/7yl2k8BesSAwD/\n8A//kIlFj7Fn3U527txJx0aV+qw7QFTtX24nnjzlnQdsHR8+fDgTi7o0TJ06NROLuupE78Xs2bMz\nsWHDhtGxy5Yty8SWLl1Kx0a5yzpcRHNja+3pp5+mYyN/9Ed/lIm97W1vo2OjThbsPYnGsu5XR44c\noWOjPGCdM6JzE+sAEh37vOSdB8djeRzlNuvOMXz4cDp29OjRNN6/f/9MLOoQwq4zUVeo6H1g24jW\nAot/8YtfpGP//M//nMbvvffeTOzQoUN07IQJE2icrUnWbQ7g5+xo/6KORPUu7xyo9HrGxkbXzui9\nZ+eulGt41OVq8+bNNP7cc89lYtHnC9aZK4/rfXSuOP/882mcfW489dRT6ViWS9Hn1KjDXa2V883A\nQgALSPyr7n5G6b9unfhFGshCKA9EFkJ5IMW2EMoBaTInvRlw94cB8CbcIgWhPBBRHogoB6QZVVIz\ncL2ZPWNmt5gZ/9kCkeanPBBRHogoB6Rhdfdm4F8ATAVwBoCtAL4SDTSza81siZktYU9fFGlg3cqD\n6MmOIg2qrDzomgPRzy6LNKhuXQvYU21FekK3bgbcvd3dX3b3VwB8C8D8E4y92d3nufs8VvAm0qi6\nmwf1UjAkkody86BrDrCmAyKNqrvXgqgZgUitnbSbEGNmY919a+m3lwPgbUWO4+60yp11hWDV7ZEt\nW7bQ+LRp02icXYj2799Px7L59urVi46Nvvn4vd/7vUxs8eLFdOxb3vKWTGzUqFF07N13303jb33r\nWzOxqGtQ1AXgiiuuyMTa2troWNYN4Q1veAMdG72vQ4cOzcSijghRl6ha624enGB7mVh048C6L23c\nuJGOnTt3Lo2fdtppmRjrngPw7g+sGxHAO7QAvPNC1CmH5e499/CavOgYzZs3LxOL/iVuzZo1NM7W\ndkdHBx3LzmMpXdEAfg6JtsHeKza2Bh2GcssDltspnZeiDjV79uyhcXb+i85RO3bsyMTOPfdcOjY6\nN69duzYTi87NrHvJDTfcQMey7mIAsGjRokzsjjvuoGPZOQXg3fCicw3rQDN27Fg6tplUkgPsXMCu\nfS+99BL9+2y9RjmT8g+y0ZpiHbTYtQSIryfXX399JhZ1i7v44oszMbYmTzQPdozY+RqIz5fsc+bu\n3bx0hF0Do9eLOiOxzwPVVE5r0dsAXABgpJltAvA3AC4wszMAOIB1AK6r4hxFepzyQER5IKIckGZ0\n0psBd7+KhL9dhbmI1C3lgYjyQEQ5IM1ITyAWERERESko3QyIiIiIiBRUtwqIu8vMaLEMK+6IiidY\nW8bW1lY6NnqcNiviigqI2bb79etHx+7atYvGWVHgpZdeSseyApioFeUf//Ef0/h5552XiUVdC377\n29/S+Kc+9amy5gYAmzZtysSi9y8qGE0pnGSFOFFxTr1ix4cVT0bHi7VmPOuss+jYqPC7vb09E4vW\nGsvbFStW0LFRgfd///d/Z2IrV66kY1khaFS4O2vWLBpn+5JSjArwgrvoXMGOZyRqClDuHKI4y5mo\n4UE9SmnYwN7fI0eO0LHR+Y/lYfT+snl84hOfoGM/+clP0vi2bdsysaVLea0pu85E6zQq5l+2bFkm\ndsstt9Cxp59+Oo1PnDgxExsxYgQdy87jtS6EbAZsrUUFxKwpQvQZJcqPqAkLwz7nRIXC0XW5T58+\nmVjUVOHRRx/NxJ588kk6Nspddj2JGgW8613vovGZM2fSOMPOY/VSKBzRNwMiIiIiIgWlmwERERER\nkYLSzYCIiIiISEHpZkBEREREpKB0MyAiIiIiUlA17Sbk7rSimlVTR1XoLB51CHnkkUdofPz48ZlY\n1KWBVclHHVfWrFlD41OmTMnEWLU5wLtN7Nmzh46NKudXr16diUUdXlhXGoB3HXjhhRfoWNYNI+o2\nEenbt28mFnXSYZ0WGqljStRVK2Vts+MVranNmzfTOFtXUR5Mnjw5E2MdIQBg7NixNM66/kSdFFg8\neo9Tur8MGDCAjo26b7Dx0ZzZMbr//vvpWJbnAD83RbnEunqw9yRaF40iOnexHIrGRt2E2PsbjWXr\n6YYbbqBj3/Oe95S9jaiTC1uTUQ6wjjIA7y71xS9+kY5973vfS+PsWsA6DAHAnXfemYmdf/75dKx0\nKrcDWP/+/enfZ9eIlM5DADB8+PBMLOrSyHIs6lIUXb9Y7m7cuJGOnTt3biY2btw4OjZal+y8Gn2u\nirDPrtH5ho2NuiPWy2cXfTMgIiIiIlJQuhkQERERESko3QyIiIiIiBSUbgZERERERApKNwMiIiIi\nIgVV025CEVZlHVXOM7t376bxt7zlLTQ+cODATGz79u10LOva0tbWRsdedNFFNM668PzP//wPHcuq\n70899VQ6Nur8snz58kxs7969dGxUfc86lUTdfSZMmJCJdXR00LGsyh7gnXSiLihRF5tG4e5031ge\nRJ1N2LrcuXMnHRt1lmDbiLpCsI5E0XscrTW2z9FY1kFnzJgxdOzQoUPLfr1o/UVrm3XfiI4n68hx\nyin8FLt161YaP+ecczKxqHsZO1dEr9fIUrohRe/Nhg0b8ppOWb71rW+VPXbw4ME0Hu0L89xzz9H4\nu971rkzs7LPPpmM3bdpE42xNtbe307GrVq3KxNRNKB3rthNhnwOiLjdRZzI2Puq2wzoQRued6Box\natSoTKylpYWOZdfFYcOG0bFR5zx2jYi6JUXXwJTrJfs8E3XIjK5JUde6atE3AyIiIiIiBaWbARER\nERGRgtLNgIiIiIhIQelmQERERESkoE5abWZmbQC+C6AVgAO42d3/ycyGA/hPAJMArANwpbunPd/5\n/71GdmJBQQortoqKZdij5gHg2WefzcSiAjNWyPzkk0/SsXfccQeNs8KRyy67jI5lBZXR/rHH1QP8\neEbFxlExLiuojAr52Hs1cuRIOjYqomHbjubGiquiR33nJe88YO8R26+ouOjw4cOZWLROojgTPRqd\n5V1U3By9x6wAK+V9i8YePHiQxlnxWnROiNYaW9vRMWJF99OnT6djo8L9bdu2ZWLRfrMiOjbfvArR\nanEtkO6JCtJZbrz+9a+nY9n6BfhafeKJJ+jYqKCymdQiD1jxeHQtYMc8ZSzAzzFRUwXWyIFdjwB+\nzgf4/kXrj80tOgdH2DUpargRNa9h24jOzeyam3Id7gnlfDNwDMDH3X02gHMAfNjMZgO4AcAD7j4d\nwAOl34s0K+WBFJ1yQER5IE3opDcD7r7V3Z8s/boDwHIA4wBcBuDW0rBbAbyzWpMU6WnKAyk65YCI\n8kCaU1LNgJlNAnAmgMcBtLr7q99NbkPnV2YiTU95IEWnHBBRHkjzKPtmwMwGAbgDwEfdfX/XP/PO\nH1CjP6RmZtea2RIzW8IeViHSSPLIg+ghUiKNQNcCkXzygD3UUKQnlHUzYGa90bnov+/uPy6F281s\nbOnPxwKgj/B195vdfZ67z4uK90QaQV55EBVzi9Q7XQtE8suDqAmDSK2V003IAHwbwHJ3v6nLH90F\n4BoAXyj9/7+6OwnW7eLFF18seyx71DQQdxmZMGFCJnbhhRfSsawq/5e//CUdG2GV9lHlPOtqEnUG\niCrqTz311Ews6s4UdRphnViif9FmVfJRF4HoXwRZpX6030zK2O7oqTxIGRt9wIrygHVCiN431nkh\nWlNRF4qU94ifO9QGAAAgAElEQVR1mxg4cCAdG3W5Yt222HZPhOVY9D6xc1aUoyy/AGDdunWZ2MyZ\nM+nY6PhXSy1yQLpn/PjxNM6uVW1tbXRstJ7YB1bW9Q6Iz0HNpBZ5wM6V0XmHnRejjjjR+YidQ6NO\nOazrXSQ657NtRNce1jUtpYMcwD8jRnOLridsfHQs2Dby6upWLeVcTc4FcDWAZ83sqVLsRnQu+B+a\n2Z8CWA/gyupMUaQuKA+k6JQDIsoDaUInvRlw918CiG5pLsp3OiL1SXkgRaccEFEeSHPSE4hFRERE\nRApKNwMiIiIiIgVV2wq0ACvMYAWlAC/CiIpJooINVigVPc6dzWP69Ol0bMpjrKPCE1aoGT3GOiq4\nYfsdFcVEhdqseCgqDmPb3rFjR9lzO1G83Lk1mnKLaaPjklJgFq0fVkwWFZilHHNWuJuKzTlaw9Hc\nUh9Zz6QUgrF5RHNgRXEA3+/ovMLOIdUupJf6NH/+fBpftGhRJnbffffRseeffz6NP/nkk5nY4sWL\n6dgNGzZEU5QEKddDdo6JmjhE59CU12PnmKi5SPR67DNY1FRl//79mVhrK3+EQ9Spjx2j1ALiZvjc\ncSL6ZkBEREREpKB0MyAiIiIiUlC6GRARERERKSjdDIiIiIiIFJRuBkRERERECqouugmxrhhRdTur\n9I464qR01jh06FDZc+vTp0/Z2wX4o7Cjx2azTizR/kVz3rNnTyYWPWo+OkYp3QVYt6TUzjZMVL3P\ntt2sXVRS9ivl0e+RPDompHTxSXnkfercUvY7OnaVdiSKtht1AkuRxzGS5tDS0kLjV1xxRSb2gx/8\ngI7dtGkTjb/73e/OxCZOnEjHrlq1KpqiJGC5HXW5YfHoOptyvo1ej50To06D0TzY55GoCx273kfd\nJl966SUaZ/udcn0oAh0NEREREZGC0s2AiIiIiEhB6WZARERERKSgdDMgIiIiIlJQuhkQERERESmo\nuugmxDpupHRGiarCo442rJI92garTo+2e+DAARo/fPhwJhZ1KUl5vWgb7Nildvdh1fdR96L+/fuX\nPbeoQwGbXzRndUzhUrov9cQ8UtZlynscnStStpHHPPLoXlTr90qaw913303jN954YyZ2zz330LGL\nFi2i8YMHD2ZiF110ER0bda2TNOy8k3KOijoQRttI6VrHruHR3z969CiNs/lFnw0GDRqUiaV2eWPH\nKKWzUhEUc69FREREREQ3AyIiIiIiRaWbARERERGRgtLNgIiIiIhIQZ202sfM2gB8F0ArAAdws7v/\nk5l9FsAHAewoDb3R3XllUjdEhS4phaZRkUmlBSJRoQsrpI3mwYqyAF6IEz1iOyr+HThwYFnbBYCO\njg4aZ4VgY8eOpWPZo8GPHDlCx0YqLfZMKYDqjp7KgxR5HIN6KWyt5r6kSNlvtoZTj2fKuanWhfSN\nkANF1adPHxp/+OGHM7FLL72UjmWFmgAv9rz99tvp2MsvvzwT27JlCx3bqBohD1LP13mcu5joMxjb\nRnTuY+sv5fNhqqI2KCmn9P8YgI+7+5NmNhjAE2Z2f+nPvuruX67e9ETqhvJAik45IKI8kCZ00psB\nd98KYGvp1x1mthzAuGpPTKSeKA+k6JQDIsoDaU5JPy9jZpMAnAng8VLoejN7xsxuMbNhOc9NpC4p\nD6TolAMiygNpHmXfDJjZIAB3APiou+8H8C8ApgI4A513yV8J/t61ZrbEzJZED60SaRR55EH0cDqR\nRqBrgUg+ebBv376azVfkRMq6GTCz3uhc9N939x8DgLu3u/vL7v4KgG8BmM/+rrvf7O7z3H3egAED\n8pq3SM3llQdRoZ5IvdO1QCS/PBgyZEjtJi1yAuV0EzIA3waw3N1v6hIfW/rZOQC4HMDSPCcWVbJX\n+tjs1G2kdC/av38/jbNuO1H3BzbnqCL/8OHDZcejSv2oI9GOHTsyse3bt9OxQ4cOzcSix9KnPA69\n2h2CUvRUHtSLar0X9fQeM0Xdb6boOVDPog4ov/rVrzKx6JvJ3bt30zjrOLdu3To6lnWyazaNkAeV\ndkw8EXYNjz5rRfEU0WeJFEXtEJSinKN8LoCrATxrZk+VYjcCuMrMzkBna611AK6rygxF6oPyQIpO\nOSCiPJAmVE43oV8CYP+cqz7SUhjKAyk65YCI8kCak55ALCIiIiJSULoZEBEREREpqMorM6okj8dK\npzxOO3o9VogTFaNE24iKhRlWLBMV+UbzOHLkSCYWFRRFc2NdDo4ePUrHsuOceoxERKR7omsEi69Y\nsaLi15syZQqNR00tpLai629KYXEeRbd5XO9V/Fsb+mZARERERKSgdDMgIiIiIlJQuhkQERERESko\n3QyIiIiIiBSUbgZERERERArKoo47VXkxsx0A1pd+OxLAzpq9eO1p/+rDRHcf1dOT6KpLHjTKMewu\n7V99qOccABrnOHaX9q8+KA96lvavPtA8qOnNwGte2GyJu8/rkRevAe2fnEyzH0Ptn5Sj2Y+j9k/K\n0ezHUftX3/RjQiIiIiIiBaWbARERERGRgurJm4Gbe/C1a0H7JyfT7MdQ+yflaPbjqP2TcjT7cdT+\n1bEeqxkQEREREZGepR8TEhEREREpKN0MiIiIiIgUVM1vBsxsgZmtMLNVZnZDrV+/GszsFjPbbmZL\nu8SGm9n9Zray9P9hPTnH7jKzNjN70MyeM7NlZvaRUrwp9q+nNFseNHMOAMqDalEeNA7lQHUoBxpL\ns+ZBTW8GzKwXgG8AeBuA2QCuMrPZtZxDlSwEsOC42A0AHnD36QAeKP2+ER0D8HF3nw3gHAAfLr1n\nzbJ/NdekebAQzZsDgPIgd8qDhqMcyJlyoCE1ZR7U+puB+QBWufsadz8K4D8AXFbjOeTO3R8GsPu4\n8GUAbi39+lYA76zppHLi7lvd/cnSrzsALAcwDk2yfz2k6fKgmXMAUB5UifKggSgHqkI50GCaNQ9q\nfTMwDsDGLr/fVIo1o1Z331r69TYArT05mTyY2SQAZwJ4HE24fzVUlDxoyjWiPMiN8qBBKQdyoxxo\nYM2UByogrgHv7N/a0D1czWwQgDsAfNTd93f9s2bYP6muZlkjygOpRDOsEeWAVKJZ1kiz5UGtbwY2\nA2jr8vvxpVgzajezsQBQ+v/2Hp5Pt5lZb3Qu+u+7+49L4abZvx5QlDxoqjWiPMid8qDBKAdypxxo\nQM2YB7W+GVgMYLqZTTazPgDeA+CuGs+hVu4CcE3p19cA+K8enEu3mZkB+DaA5e5+U5c/aor96yFF\nyYOmWSPKg6pQHjQQ5UBVKAcaTLPmQc2fQGxmbwfwNQC9ANzi7n9f0wlUgZndBuACACMBtAP4GwB3\nAvghgAkA1gO40t2PL6qpe2Z2HoBHADwL4JVS+EZ0/oxcw+9fT2m2PGjmHACUB9WiPGgcyoHqUA40\nlmbNg5rfDIiIiIiISH1QAbGIiIiISEHpZkBEREREpKB0M1AlZnaamT1lZh1m9udm9q9m9pnSn11g\nZpt6eo4i1aY8EFEeSDFp3TeOU3p6Ak3sLwE86O5nnGygma0D8AF3/3leL25mB44L9QfwTXf///J6\nDZEy9HQeTALwTQBvBHAEwO3o7At9LK/XEClDT+fBLADfAPB6ADsAfNLdf5LX9kUCPb3urwfwJwBe\nB+A2d/+T4/78InTmxQR0FgD/ibuvz+v1G4m+GaieiQCWVftFrFPmfXT3Qa/+B2AMgMMAflTt+Ygc\np0fzAJ03AtsBjAVwBoDzAXyo2vMROU6P5YGZnYLONof/A2A4gGsB/LuZzaj2fKTwevr8vwXA5wDc\nQv7OSAA/BvAZdObFEgD/Wc151jPdDFSBmS0CcCGAr5vZATObYWYLzexzZOz30HlX+t+lsX9Zip9j\nZo+a2V4ze9rMLujydx4ys783s18BOARgykmmdAU6PxA9ks8eipxcneTBZAA/dPcX3X0bgJ8CmJP7\nzooE6iAPZgI4FcBX3f1ld18E4FcArq7G/ooAdbHu4e4/dvc7AewiU/xfAJa5+4/c/UUAnwXwu2Y2\ns+Kdb0C6GagCd38LOj94X1/61/kXTjD2agAbALyjNPaLZjYOwN3ovKMdDuATAO4ws1Fd/urV6PwX\nnsHo7Gl7ItcA+K6rj6zUUJ3kwdcAvMfMBpS29zZ03hCI1ESd5MHxDMDcbu2QSBnqdN13NQfA013m\ncBDAahT0H4t0M1Cf3gvgHne/x91fcff70fkV1tu7jFno7svc/Zi7vxRtyMwmovNHI26t7pRFcpdH\nHjyMzpP7fgCbSn//zmpPXCRHlebBCnR+M/xJM+ttZpeg85owoCazF+me3D4HBQYB2HdcbB86bywK\nRzcD9WkigHeVvhrba2Z7AZyHzp97ftXGMrd1NYBfuvvavCcpUmUV5UHpZ0h/is6fCx2IzidiDgPw\nj9WbskjuKsqD0oekdwL4AwDbAHwcnU9KVScXqWd5fg5iDgBoOS7WAqCjgm02LHUTqg/H//jORgDf\nc/cPJvydyPsAfKFbsxKprbzzYDg6fw716+5+BMARM/sOOr92/suKZipSPblfD9z9GXR+GwAAMLNH\noW+Lpb5U83MQswydP0INADCzgQCmogYFz/VI3wzUh3a8tvjl3wG8w8zeama9zKyfdfbkHZ+yUTP7\nPQDjoC5C0hhyzQN33wlgLYA/M7NTzGwoOk/+z+Q+c5H85H49MLPTS39vgJl9Ap3/urow32mLVKQa\n6/4UM+sHoBeAV7fx6j+C/wTAXDO7ojTmrwE84+7P57Q/DUU3A/Xh8wD+qvRV2CfcfSOAywDciM6e\n0BsBfBLp79c1AH7s7oX82ksaTjXy4H8BWFD6+6sAvATgY7nOWiRf1ciDqwFsRWftwEUALi59WyZS\nL6qx7v8KnW3Vb0BnDcLhUgzuvgOdnRb/HsAeAG8A8J58dqXxmBrMiIiIiIgUk74ZEBEREREpKN0M\niIiIiIgUlG4GREREREQKqqKbATNbYGYrzGyVmd2Q16REGonyQER5IKIckEbV7QJiM+sF4AUAF6Pz\n4SWLAVzl7s9Ff6elpcVHjRoV/XHDqGbRdR7bZtuItvs7v1P+/WDK3MwsKV7p2MiaNWt2unvVFl13\n8mDIkCHe2tparSmJvEZ7ezv27dtXeTKdQGoetLS0+OjRoyt9zYr+fk+o1rUjj2MRzS1l2/XckGT1\n6tW6FlTJK6+8kjQ+5XNHimit1vO6rLWVK1fSPKjkoWPzAaxy9zUAYGb/gc42UOHCHzVqFD7/+c9X\n8JL1IVr40YJLWYgvv/xy2a8XJdSLL75Y9jb69+9P42zOx44do2NZAkZzO+UUvuRYPI8Txrvf/e71\nFW/kxJLzoLW1Fd/4xjeqPC2RTh/+8Idr8TJJeTB69GjcdNNNFb1gtT5QVFPqh6Zy5XEsUq8zKduo\nB5dddlldXgu++c1vVvSi7Jinfvhl1/CU9XDw4MGyxwJAv379KppbtF3dDJzcxRdfTPOgkjPIOLz2\nUdCbSjGRIlEeiCgPRJQD0rCq/k8rZnatmS0xsyX79++v9suJ1KWuebBv376eno5IzelaIKJrgdSn\nSm4GNgNo6/L78aXYa7j7ze4+z93ntbS0VPByInUpOQ+GDBlSs8mJ1MhJ80DXAmlyuhZIw6rkZmAx\ngOlmNtnM+qDzMc535TMtkYahPBBRHogoB6RhdbuA2N2Pmdn1AH4GoBeAW9x9WW4zq2NR8crRo0dp\nvFevXplYVEjLvjbcuXMnHTtgwAAab2try8S2bdtGxx44cIDG2b/cRQXEbM5jx46lY1OKr/MoaKu2\nIueByKvyyoNa53ZKwWsec2PbSJlDVAjJGk8A1eu4VOtC4UYoYs7zWsDet5SOgNFnkZdeeonGWQHw\njh076Ni+ffuWPbehQ4fSOPtM1KdPHzqWidZDPRew17tKugnB3e8BcE9OcxFpSMoDEeWBiHJAGlX9\n/BOriIiIiIjUlG4GREREREQKSjcDIiIiIiIFVVHNQDOJCk9YoUtUlDVo0CAaP3ToUCYWFdyMGpV9\nWvrevXvp2Oipf+wJxMOHD6djX3jhhbK3zbYL8EKjPXv20LEDBw6k8dGjR2diUds19l7VU1GxiFQf\nOw9EBZKR3r17Z2KbNm2iY0eOHJmJRefV6FxZaQExux4BcQFxuXNInUett1G0wtCoWQfD3vvoeEXr\nZPDgwZnYiBEj6Fh2vY+u1Zs3ZzqrAuDXe/Y5CeCNUqL9iz6bsf3WZ4bX0tEQERERESko3QyIiIiI\niBSUbgZERERERApKNwMiIiIiIgWlmwERERERkYIqZDchVnEeVZaz7g1RJXtHRweN9+vXLxOLul6w\nbc+ZM4eOfeihh2j817/+dSb2xje+kY5llfoA8Mwzz2Rihw8fpmP37duXiUUdkKZMmVL2PNhjzwHe\nGSDqziQijYOd/6LcZvGoW0p0zmbntKgr3M6dOzMxdm4HgKNHj9I409LSQuOsI9GBAwfoWNYVCQBO\nOSV7iY+OUdSJJUUe5+FKOy41q+h92717dyYWXatZJyCAd9CK1tr27dszsaibUDSP008/PRMbNmwY\nHTt9+vRMLMq7SErnoGgsW9vN9LlD3wyIiIiIiBSUbgZERERERApKNwMiIiIiIgWlmwERERERkYJq\nigLiPAqfouIcFo9eLypqYdtghV3RtqNi43nz5tE4K+iN5hwV9LJHgy9ZsoSOZUVCBw8epGOjfWHx\nqAhPjxHPR8pxVAGf1ELKmmTnh2idPvroozTOii/HjBlDxw4fPjwTmzp1Kh0bFRay83B0nmPb6NOn\nDx0bXXui8y2TkuPRPFK2G8XZGojWRbOel1hBeFQkztZlVPw7a9YsGmdrbd26dXTs5MmTM7HHH3+c\njh04cCCNz507NxNbsWIFHctyJmp8En2OS8mDlIYFzUSfqkRERERECko3AyIiIiIiBaWbARERERGR\ngtLNgIiIiIhIQVVUQGxm6wB0AHgZwDF35xWtIk1MeSCiPBBRDkijyqOb0IXunn1Oe5WwyvKogjzC\nOhNE3XZYVf6xY8fo2OiR3CzOulhEYzs6OujYqKPDqFGjMjH2aHsg7mp01llnZWKsSxEA3HfffZlY\n9GjxlG5CEbbfUUeOGqppHjApHUwA3omjV69euc5JCqdmecDO41FXneg89/zzz2diS5cupWOnTZuW\nic2ZM4eO3bVrF42z+bW2ttKx7JwYdc+JridHjhzJxKLrVLTtvn370jijDmUAqpQD0fFiHXs2btxI\nx65du5bGn3766Uzs3nvvpWNZJ6Bf//rXdOzs2bNp/OKLL87Etm/fTseyvBs3bhwdyzodAXxdRp/j\nIuwcktJhqN67EenHhERERERECqrSmwEHcJ+ZPWFm1+YxIZEGpDwQUR6IKAekIVX6Y0LnuftmMxsN\n4H4ze97dH+46oJQQ1wLAyJEjK3w5kbqUlAejR4/uiTmKVNsJ86BrDrAfZRRpAroWSEOq6JsBd99c\n+v92AD8BMJ+Mudnd57n7vJaWlkpeTqQupeZB9DO7Io3sZHmga4E0O10LpFF1+2bAzAaa2eBXfw3g\nEgC88kqkSSkPRJQHIsoBaWSV/JhQK4CflDqVnALgB+7+0xP9BXevSgeBqANK1GWIdVdJmVc0du/e\nvTS+fv36TIx16wGA9vb2TCzqhHHw4EEaZ8cj2gbrlgTwH+k655xz6FjWzSDqbjF+/HganzJlSiYW\ndcGJ9ruHJOdBtUTHS6QGkvLAzGiHD3ZuTelQE3VYO++882j8sccey8Si8ws7N0eddoYOHUrj7Dwc\ndTVJ6XoXdVFKGbt//34aZ/sYXQPZvkRjo2tSA8vtWpDyeYR1jIq6XEXX+3nzsh1QP/OZz9Cxhw8f\nzsSi7oFRZ67evXtnYlHesc9P7DMHEHclHDBgQCZ26NAhOjZal+w9yeOaG22j1t2Hup2N7r4GwO/m\nOBeRhqM8EFEeiCgHpJGptaiIiIiISEHpZkBEREREpKB0MyAiIiIiUlA1reCJisZYoW9KUXBUYBYV\nYLAip6gghRXL7Nixg47t6OigcVZ48pvf/IaOZYW7w4cPp2OjYrkUrLAGSCuWmTlzZib2/PPP07FR\n8RqbR1QMFBWGS5qUwkyRWij3+gDw819UeDlixAgaZ9eC6Nx15ZVXZmJbt26lY1mBJMDPc1ERMrsG\nRte06Frw4osvZmIrVqygY48ePUrjs2bNysSiaxJrohEVhkbnn5TzUrlF6M0g2i+2JqLPT9F7vHnz\n5kxsyZIldOzixYszseg9jgrpx44dm4ldeOGFdCzb7zVr1pS9XQCYO3duJhblzL59+8qeR3ScmXop\nFI7o04CIiIiISEHpZkBEREREpKB0MyAiIiIiUlC6GRARERERKSjdDIiIiIiIFFRdPA+cVVlHldes\nAjzqOhNVaad0aWDdH6Kq/lGjRtE4q+CPKvXZNmbPnl323AD+aPBozlF3H/ao7qj6nnXq6N+/Px0b\nHWf2mHTW6eNE25Dait6HPB7RntIlpJrrge1LHl2YmrXjSaUq7dgRdTWJzs2f/vSnM7G/+7u/o2Pn\nzJmTiUVdiqKuLSn7ws63rLtdNBbgxzPqXsTOwQDvnhflwCmnZD9SRPusPOo8v1R6vmTnv6jjIesu\nBQCTJk3KxFpbW+nY0aNHZ2LRmors3r07E4vW34IFCzKx5557jo49cuQIjbPjEXWQTMmlPNZfvXQZ\n0jcDIiIiIiIFpZsBEREREZGC0s2AiIiIiEhB6WZARERERKSg6qKAmBUSRUXBrDCLFS0BaQUYUREH\n2/bZZ59Nx27bto3G77nnnrLHPvbYY5lYVOQ7efJkGh88eHAmFhWeRQU37D1hRcUAf0+WLl1Kx7Li\nI4AXmfXu3ZuOVQFxmqhQL6U49uWXXy7r7wNxURWLR0XibF2mFF8CvMA+j6JFdixS5VFk3cjcna4H\n9v5Ex5uNjRoXRNeI+fPnZ2Kvf/3r6dihQ4dmYtG5aOTIkTTOCi2j/WM5EBUmR4XTGzZsyMSi82rU\nkIIdu+h4sqLMKG+ja3yKlEYD9cjd6Rpi+xC99+z9jAp6o88SKe8FO2dHr7dx40YaX7ZsWSYW5czY\nsWPLigHx5yqWH1Hesc9PQFrjGRav93O+vhkQERERESko3QyIiIiIiBSUbgZERERERApKNwMiIiIi\nIgV10psBM7vFzLab2dIuseFmdr+ZrSz9P/vIW5EmojwQUR6IKAekGZXTTWghgK8D+G6X2A0AHnD3\nL5jZDaXff6q7k2BV3VFHgJSOIlGFe8qjs1l3hOix2VGHhde97nWZ2JAhQ+hY1kFi3759dOx3v/td\nGp82bVomdu2119Kxl1xyCY2zx5Z/7nOfo2N/9rOfZWJXXXUVHRt1smDvd9Qhg62X6NjnaCGqnAf1\nILVDEJPSkSh6JDybR9S5Icpnto3W1lY6NuqmwbYRdfVgxyjabtTJotzOIj1oIWqcByldOKK1Fx3D\nHTt2ZGLjxo2jY1k3oTFjxpQ9NwBoaWnJxHbt2kXHsrUTXXuirnArV64sa7sAcM4559D4hAkTMjF2\nLABg06ZNmVg1u6jk0R2sGxaiyjnA9ivaV9blb/PmzXTs9OnTaTwlD9hamzVrFh0bdfcZP358Jhat\ny5///OeZWNR5KPocwNZrdO2Jzs1sHUevx7ZR710QT5pJ7v4wgN3HhS8DcGvp17cCeGfO8xKpK8oD\nEeWBiHJAmlF3b6tb3X1r6dfbAPB/bhNpbsoDEeWBiHJAGlrF37F553cf4fcfZnatmS0xsyX79++v\n9OVE6lJKHkQ/9iXS6E6UB7oWSBHoWiCNqLs3A+1mNhYASv/fHg1095vdfZ67z2M/LynSwLqVB1G9\niEiDKisPdC2QJqZrgTS07t4M3AXgmtKvrwHwX/lMR6ShKA9ElAciygFpaCdtwWJmtwG4AMBIM9sE\n4G8AfAHAD83sTwGsB3BlOS/m7rSrA+sQFFVps0r2qLtHFB88eHAmFlWWr1+/PhOL7uajCv4LL7ww\nE1u0aBEd+8wzz2Riy5Yto2Mvv/xyGl+6dGkm9oUvfIGOjebxvve9LxOLOhGwDhd79uyhY6NuQux9\njf71kHUZqnZXiTzzoNaiTiopHStSttvR0UHjrFsE6z4CADt37iwrBgBPPfUUjV9wwQWZ2PPPP0/H\nnn/++TTO5jxq1Cg6lp2z+vfvT8dGedBD3VHK1hN5EB2TY8eOlT026mizevXqTGz27Nl0LOsGsnfv\nXjo26nbCOr9E6zrqHMSw6xQAPProo5lY1JEv6hDEOiZFnd5YZy/2PlVTI10LzKzszmlR1zT2eWbA\ngAF07NixY2l8+PDhmRjrKAjw/JgxYwYdG61h9hll5syZdOzGjRszMdYlCwD69etH4+yzYHQ8Dx8+\nTOODBg3KxKK1za4F0Tko6jKU0lEvDye9GXB33iMSuCjnuYjULeWBiPJARDkgzai+/xlKRERERESq\nRjcDIiIiIiIFpZsBEREREZGCOmnNQJ7MrOzinujR1KxgI7WAmBURrlmzho5ta2sre7tRYTHrJfzm\nN7+ZjmUFxFGh5plnnknjrGD5Bz/4AR0b9ftetWpVJsYeIQ4A3//+9zOxqKDtYx/7GI2zIqgIWxvR\ne1Ik0TqJHq/OCgmjbbDCrKh4KipeY1hxGACce+65Zb9elEus2OrKK3ld3+jRo2mcrauoOJ4d55Sx\nUTylwKwZRWuSFelF15eonzs73sOGDSt7btFajwqL2XkxKuhlRbo/+9nP6Ni77rormmLZzj77bBpn\nhaStrfyZWqz4MmoGklJYHK0Bpt6L8Ltyd5rfbB9SjkF0PdyyZQuNT506NROLmi20t7dnYlHTiMWL\nF9P4tm3bMrEo7x555JFM7KyzzqJjowLiHTt2ZGJRI4iocLpPnz6ZWFSEzAqn2d8H4vN4dF6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SjYPKLuAlFFfUpHqdQq+ZxUPQ+YlK4S0ZqKOkikdCtg3RiivIs6HbGuKVE3EPZ60didO3fSOOuG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\nUmVpAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x864 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}