cnn_inception.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "cnn_inception",
      "provenance": [],
      "mount_file_id": "1LfmEB6rcN7jiOMn3VCV_-pFHUSKvpvGk",
      "authorship_tag": "ABX9TyOq2gFAcOKUw0RfGVxf6AJR",
      "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/ninjab3381/CNN_Robotic_Inference/blob/master/cnn_inception.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPNwWsB-Uxxc",
        "colab_type": "code",
        "outputId": "00bd266c-e93b-435f-a9b7-46b2c17d6b69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "try:\n",
        "  # The %tensorflow_version magic only works in colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "2.1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZlXLPQJIV_pf",
        "colab_type": "code",
        "outputId": "61ffe86a-015a-4bb9-c49d-507e5e711da8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "IMAGE_SIZE = 224\n",
        "BATCH_SIZE = 60\n",
        "\n",
        "datagen = keras.preprocessing.image.ImageDataGenerator(\n",
        "    rescale=1./255, \n",
        "    validation_split=0.1)\n",
        "\n",
        "train_generator = datagen.flow_from_directory(\n",
        "    'drive/My Drive/Synopsys/pics/',\n",
        "    target_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    batch_size=BATCH_SIZE, \n",
        "    subset='training')\n",
        "\n",
        "val_generator = datagen.flow_from_directory(\n",
        "    'drive/My Drive/Synopsys/pics/',\n",
        "    target_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    batch_size=BATCH_SIZE, \n",
        "    subset='validation')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 396 images belonging to 11 classes.\n",
            "Found 44 images belonging to 11 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XatHr76XLWR",
        "colab_type": "code",
        "outputId": "adbee1d1-e3b6-4f26-f70b-6902a8a51493",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "for image_batch, label_batch in train_generator:\n",
        "  break\n",
        "image_batch.shape, label_batch.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60, 224, 224, 3), (60, 11))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cBKJtjk4XQnN",
        "colab_type": "code",
        "outputId": "dc9ec823-ee6f-410b-c2fa-b30db19ce7c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "IMG_SHAPE = (IMAGE_SIZE, IMAGE_SIZE, 3)\n",
        "\n",
        "from tensorflow.keras.applications import(\n",
        "    vgg16, \n",
        "    inception_v3, \n",
        "    resnet50, \n",
        "    mobilenet, \n",
        "    MobileNetV2\n",
        ")\n",
        "import numpy as np\n",
        "\n",
        "# Create the base model from the pre-trained model MobileNet V2\n",
        "base_model = inception_v3.InceptionV3(input_shape=IMG_SHAPE,\n",
        "                   include_top=False, \n",
        "                   weights='imagenet')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https:/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 3s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uVTFhD-YYvi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "base_model.trainable = False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgW5VoWZYkuN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "model = Sequential([\n",
        "  base_model,\n",
        "])\n",
        "model.add(Conv2D(32, 3, activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(GlobalAveragePooling2D())\n",
        "model.add(Dense(11, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGvVynDOyCYX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A66l0FUvY5--",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(), \n",
        "              loss='categorical_crossentropy', \n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1J1lW-AZCEO",
        "colab_type": "code",
        "outputId": "ad47a615-c534-422f-9fd7-6fc35fc80a61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "inception_v3 (Model)         (None, 5, 5, 2048)        21802784  \n",
            "_________________________________________________________________\n",
            "conv2d_94 (Conv2D)           (None, 3, 3, 32)          589856    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 3, 3, 32)          0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 11)                363       \n",
            "=================================================================\n",
            "Total params: 22,393,003\n",
            "Trainable params: 590,219\n",
            "Non-trainable params: 21,802,784\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gpPI2NNZLYQ",
        "colab_type": "code",
        "outputId": "be16828e-1294-44e8-970e-baf9a85b418e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('Number of trainable variables = {}'.format(len(model.trainable_variables)))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of trainable variables = 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWikE5sqZM_t",
        "colab_type": "code",
        "outputId": "42244fa1-a92d-4a19-b821-d5db0eeb2435",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "epochs = 200\n",
        "\n",
        "history = model.fit_generator(train_generator, \n",
        "                    epochs=epochs, \n",
        "                    validation_data=val_generator)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-10-6d60b2acb63c>:5: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "WARNING:tensorflow:sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n",
            "WARNING:tensorflow:sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n",
            "Train for 7 steps, validate for 1 steps\n",
            "Epoch 1/200\n",
            "7/7 [==============================] - 231s 33s/step - loss: 3.9156 - accuracy: 0.1540 - val_loss: 2.2292 - val_accuracy: 0.1591\n",
            "Epoch 2/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 2.1268 - accuracy: 0.1717 - val_loss: 2.2680 - val_accuracy: 0.1818\n",
            "Epoch 3/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 2.0319 - accuracy: 0.2197 - val_loss: 2.1703 - val_accuracy: 0.2045\n",
            "Epoch 4/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 1.9162 - accuracy: 0.2045 - val_loss: 2.1850 - val_accuracy: 0.1818\n",
            "Epoch 5/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 1.8154 - accuracy: 0.1869 - val_loss: 2.0297 - val_accuracy: 0.1818\n",
            "Epoch 6/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 1.7020 - accuracy: 0.2677 - val_loss: 2.0387 - val_accuracy: 0.2727\n",
            "Epoch 7/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 1.5671 - accuracy: 0.3283 - val_loss: 1.8767 - val_accuracy: 0.3409\n",
            "Epoch 8/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 1.3976 - accuracy: 0.4217 - val_loss: 1.8813 - val_accuracy: 0.3636\n",
            "Epoch 9/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 1.2935 - accuracy: 0.4621 - val_loss: 1.8939 - val_accuracy: 0.4091\n",
            "Epoch 10/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 1.2412 - accuracy: 0.4520 - val_loss: 1.7598 - val_accuracy: 0.4091\n",
            "Epoch 11/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 1.1529 - accuracy: 0.4672 - val_loss: 1.6941 - val_accuracy: 0.4091\n",
            "Epoch 12/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 1.0719 - accuracy: 0.5581 - val_loss: 1.8912 - val_accuracy: 0.4318\n",
            "Epoch 13/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 1.0468 - accuracy: 0.5707 - val_loss: 1.6197 - val_accuracy: 0.4318\n",
            "Epoch 14/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.9814 - accuracy: 0.6010 - val_loss: 1.8628 - val_accuracy: 0.3409\n",
            "Epoch 15/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.9263 - accuracy: 0.6566 - val_loss: 1.6579 - val_accuracy: 0.4545\n",
            "Epoch 16/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.8820 - accuracy: 0.6692 - val_loss: 1.6671 - val_accuracy: 0.3636\n",
            "Epoch 17/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.8102 - accuracy: 0.6970 - val_loss: 1.6829 - val_accuracy: 0.4318\n",
            "Epoch 18/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.7456 - accuracy: 0.7399 - val_loss: 1.7624 - val_accuracy: 0.4545\n",
            "Epoch 19/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.7203 - accuracy: 0.7525 - val_loss: 1.6705 - val_accuracy: 0.5227\n",
            "Epoch 20/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.7009 - accuracy: 0.7626 - val_loss: 1.8260 - val_accuracy: 0.5227\n",
            "Epoch 21/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.6552 - accuracy: 0.7677 - val_loss: 1.7546 - val_accuracy: 0.5000\n",
            "Epoch 22/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.5990 - accuracy: 0.7929 - val_loss: 1.8008 - val_accuracy: 0.4545\n",
            "Epoch 23/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.5719 - accuracy: 0.8056 - val_loss: 1.7592 - val_accuracy: 0.4773\n",
            "Epoch 24/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.5649 - accuracy: 0.8131 - val_loss: 1.8502 - val_accuracy: 0.5227\n",
            "Epoch 25/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.5333 - accuracy: 0.8005 - val_loss: 1.7653 - val_accuracy: 0.5682\n",
            "Epoch 26/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.5226 - accuracy: 0.8232 - val_loss: 2.1065 - val_accuracy: 0.4545\n",
            "Epoch 27/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.4864 - accuracy: 0.8081 - val_loss: 2.0663 - val_accuracy: 0.4545\n",
            "Epoch 28/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.4695 - accuracy: 0.8434 - val_loss: 2.2055 - val_accuracy: 0.4773\n",
            "Epoch 29/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.4620 - accuracy: 0.8434 - val_loss: 2.0277 - val_accuracy: 0.4545\n",
            "Epoch 30/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.4503 - accuracy: 0.8586 - val_loss: 2.4059 - val_accuracy: 0.4318\n",
            "Epoch 31/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.4445 - accuracy: 0.8460 - val_loss: 2.2436 - val_accuracy: 0.4091\n",
            "Epoch 32/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.4412 - accuracy: 0.8611 - val_loss: 1.9059 - val_accuracy: 0.5227\n",
            "Epoch 33/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.4291 - accuracy: 0.8889 - val_loss: 2.1344 - val_accuracy: 0.4773\n",
            "Epoch 34/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.4260 - accuracy: 0.8788 - val_loss: 2.1983 - val_accuracy: 0.4545\n",
            "Epoch 35/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.4194 - accuracy: 0.9015 - val_loss: 2.1878 - val_accuracy: 0.4773\n",
            "Epoch 36/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.3996 - accuracy: 0.9015 - val_loss: 2.5503 - val_accuracy: 0.4545\n",
            "Epoch 37/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.3765 - accuracy: 0.9141 - val_loss: 2.1745 - val_accuracy: 0.4773\n",
            "Epoch 38/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.3647 - accuracy: 0.9066 - val_loss: 2.3660 - val_accuracy: 0.4318\n",
            "Epoch 39/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.3397 - accuracy: 0.9268 - val_loss: 2.8479 - val_accuracy: 0.4318\n",
            "Epoch 40/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.3395 - accuracy: 0.9167 - val_loss: 2.4643 - val_accuracy: 0.4318\n",
            "Epoch 41/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.3322 - accuracy: 0.9596 - val_loss: 2.4575 - val_accuracy: 0.5000\n",
            "Epoch 42/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.3373 - accuracy: 0.9369 - val_loss: 2.6483 - val_accuracy: 0.4545\n",
            "Epoch 43/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.3397 - accuracy: 0.9192 - val_loss: 2.2578 - val_accuracy: 0.4545\n",
            "Epoch 44/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.3530 - accuracy: 0.9167 - val_loss: 2.7133 - val_accuracy: 0.4545\n",
            "Epoch 45/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.3400 - accuracy: 0.9394 - val_loss: 2.2829 - val_accuracy: 0.4773\n",
            "Epoch 46/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.3267 - accuracy: 0.9318 - val_loss: 2.4942 - val_accuracy: 0.4773\n",
            "Epoch 47/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.3053 - accuracy: 0.9419 - val_loss: 2.9261 - val_accuracy: 0.4773\n",
            "Epoch 48/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2909 - accuracy: 0.9369 - val_loss: 2.8754 - val_accuracy: 0.4545\n",
            "Epoch 49/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2838 - accuracy: 0.9268 - val_loss: 2.7138 - val_accuracy: 0.4318\n",
            "Epoch 50/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2846 - accuracy: 0.9394 - val_loss: 2.8037 - val_accuracy: 0.4773\n",
            "Epoch 51/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2876 - accuracy: 0.9369 - val_loss: 2.6348 - val_accuracy: 0.4545\n",
            "Epoch 52/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2562 - accuracy: 0.9419 - val_loss: 2.9043 - val_accuracy: 0.4773\n",
            "Epoch 53/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2638 - accuracy: 0.9268 - val_loss: 2.7809 - val_accuracy: 0.4318\n",
            "Epoch 54/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2409 - accuracy: 0.9369 - val_loss: 2.8660 - val_accuracy: 0.4773\n",
            "Epoch 55/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2450 - accuracy: 0.9318 - val_loss: 2.8047 - val_accuracy: 0.4318\n",
            "Epoch 56/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2319 - accuracy: 0.9217 - val_loss: 3.0901 - val_accuracy: 0.4773\n",
            "Epoch 57/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2365 - accuracy: 0.9268 - val_loss: 2.9781 - val_accuracy: 0.4545\n",
            "Epoch 58/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2217 - accuracy: 0.9293 - val_loss: 3.0269 - val_accuracy: 0.4545\n",
            "Epoch 59/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2233 - accuracy: 0.9217 - val_loss: 2.8469 - val_accuracy: 0.4773\n",
            "Epoch 60/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2316 - accuracy: 0.9394 - val_loss: 3.0890 - val_accuracy: 0.4545\n",
            "Epoch 61/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2048 - accuracy: 0.9242 - val_loss: 3.1598 - val_accuracy: 0.4773\n",
            "Epoch 62/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2220 - accuracy: 0.9167 - val_loss: 2.8713 - val_accuracy: 0.4318\n",
            "Epoch 63/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.2047 - accuracy: 0.9293 - val_loss: 3.0935 - val_accuracy: 0.4773\n",
            "Epoch 64/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1930 - accuracy: 0.9318 - val_loss: 3.2655 - val_accuracy: 0.4318\n",
            "Epoch 65/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1689 - accuracy: 0.9293 - val_loss: 3.5642 - val_accuracy: 0.4091\n",
            "Epoch 66/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1846 - accuracy: 0.9369 - val_loss: 3.1783 - val_accuracy: 0.4545\n",
            "Epoch 67/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1853 - accuracy: 0.9268 - val_loss: 3.1539 - val_accuracy: 0.4545\n",
            "Epoch 68/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1635 - accuracy: 0.9495 - val_loss: 3.2313 - val_accuracy: 0.4773\n",
            "Epoch 69/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1709 - accuracy: 0.9242 - val_loss: 3.2537 - val_accuracy: 0.4545\n",
            "Epoch 70/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1649 - accuracy: 0.9318 - val_loss: 3.5003 - val_accuracy: 0.4318\n",
            "Epoch 71/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1744 - accuracy: 0.9242 - val_loss: 3.3757 - val_accuracy: 0.4545\n",
            "Epoch 72/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1610 - accuracy: 0.9571 - val_loss: 3.5310 - val_accuracy: 0.4318\n",
            "Epoch 73/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1602 - accuracy: 0.9318 - val_loss: 3.9148 - val_accuracy: 0.4318\n",
            "Epoch 74/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1570 - accuracy: 0.9419 - val_loss: 3.1791 - val_accuracy: 0.4773\n",
            "Epoch 75/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1580 - accuracy: 0.9520 - val_loss: 3.1950 - val_accuracy: 0.4545\n",
            "Epoch 76/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1456 - accuracy: 0.9470 - val_loss: 3.7464 - val_accuracy: 0.4545\n",
            "Epoch 77/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1515 - accuracy: 0.9520 - val_loss: 3.7682 - val_accuracy: 0.4545\n",
            "Epoch 78/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1429 - accuracy: 0.9495 - val_loss: 3.5505 - val_accuracy: 0.4318\n",
            "Epoch 79/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1442 - accuracy: 0.9621 - val_loss: 3.5656 - val_accuracy: 0.4773\n",
            "Epoch 80/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1392 - accuracy: 0.9697 - val_loss: 4.0102 - val_accuracy: 0.3864\n",
            "Epoch 81/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1417 - accuracy: 0.9798 - val_loss: 3.6668 - val_accuracy: 0.4773\n",
            "Epoch 82/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1269 - accuracy: 0.9823 - val_loss: 3.8828 - val_accuracy: 0.4318\n",
            "Epoch 83/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1228 - accuracy: 0.9899 - val_loss: 4.1822 - val_accuracy: 0.4091\n",
            "Epoch 84/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1247 - accuracy: 0.9874 - val_loss: 4.1630 - val_accuracy: 0.4318\n",
            "Epoch 85/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1192 - accuracy: 0.9949 - val_loss: 4.1932 - val_accuracy: 0.4318\n",
            "Epoch 86/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1339 - accuracy: 0.9874 - val_loss: 3.9906 - val_accuracy: 0.4773\n",
            "Epoch 87/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1227 - accuracy: 0.9874 - val_loss: 4.2418 - val_accuracy: 0.4545\n",
            "Epoch 88/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1184 - accuracy: 0.9924 - val_loss: 4.1530 - val_accuracy: 0.4318\n",
            "Epoch 89/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1248 - accuracy: 0.9924 - val_loss: 4.2460 - val_accuracy: 0.3864\n",
            "Epoch 90/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1221 - accuracy: 0.9848 - val_loss: 3.8341 - val_accuracy: 0.4318\n",
            "Epoch 91/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1160 - accuracy: 0.9899 - val_loss: 4.4563 - val_accuracy: 0.3409\n",
            "Epoch 92/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1060 - accuracy: 0.9899 - val_loss: 4.2625 - val_accuracy: 0.4773\n",
            "Epoch 93/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1044 - accuracy: 0.9924 - val_loss: 4.2075 - val_accuracy: 0.4318\n",
            "Epoch 94/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1054 - accuracy: 0.9874 - val_loss: 4.7511 - val_accuracy: 0.4318\n",
            "Epoch 95/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0940 - accuracy: 0.9848 - val_loss: 4.3923 - val_accuracy: 0.4773\n",
            "Epoch 96/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.1038 - accuracy: 0.9798 - val_loss: 4.4966 - val_accuracy: 0.4773\n",
            "Epoch 97/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0857 - accuracy: 0.9874 - val_loss: 4.6950 - val_accuracy: 0.4773\n",
            "Epoch 98/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0738 - accuracy: 0.9924 - val_loss: 4.6871 - val_accuracy: 0.4773\n",
            "Epoch 99/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0828 - accuracy: 0.9823 - val_loss: 4.5387 - val_accuracy: 0.4773\n",
            "Epoch 100/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0670 - accuracy: 0.9949 - val_loss: 4.6430 - val_accuracy: 0.4773\n",
            "Epoch 101/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0643 - accuracy: 0.9924 - val_loss: 4.9951 - val_accuracy: 0.4318\n",
            "Epoch 102/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0745 - accuracy: 0.9874 - val_loss: 4.6244 - val_accuracy: 0.4318\n",
            "Epoch 103/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0795 - accuracy: 0.9874 - val_loss: 4.1725 - val_accuracy: 0.5227\n",
            "Epoch 104/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0543 - accuracy: 0.9924 - val_loss: 4.8725 - val_accuracy: 0.4545\n",
            "Epoch 105/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0554 - accuracy: 0.9899 - val_loss: 4.7047 - val_accuracy: 0.4773\n",
            "Epoch 106/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0455 - accuracy: 0.9924 - val_loss: 4.7992 - val_accuracy: 0.4545\n",
            "Epoch 107/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0458 - accuracy: 0.9924 - val_loss: 4.9734 - val_accuracy: 0.4545\n",
            "Epoch 108/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0657 - accuracy: 0.9874 - val_loss: 5.1713 - val_accuracy: 0.4318\n",
            "Epoch 109/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0553 - accuracy: 0.9924 - val_loss: 5.0255 - val_accuracy: 0.4773\n",
            "Epoch 110/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0507 - accuracy: 0.9924 - val_loss: 5.1834 - val_accuracy: 0.4091\n",
            "Epoch 111/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0539 - accuracy: 0.9874 - val_loss: 5.3272 - val_accuracy: 0.4318\n",
            "Epoch 112/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0656 - accuracy: 0.9848 - val_loss: 5.5314 - val_accuracy: 0.3409\n",
            "Epoch 113/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0495 - accuracy: 0.9924 - val_loss: 4.7108 - val_accuracy: 0.4318\n",
            "Epoch 114/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0526 - accuracy: 0.9874 - val_loss: 5.4920 - val_accuracy: 0.4091\n",
            "Epoch 115/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0496 - accuracy: 0.9899 - val_loss: 5.1590 - val_accuracy: 0.4318\n",
            "Epoch 116/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0478 - accuracy: 0.9975 - val_loss: 4.5699 - val_accuracy: 0.4545\n",
            "Epoch 117/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0426 - accuracy: 0.9975 - val_loss: 4.8063 - val_accuracy: 0.4545\n",
            "Epoch 118/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0429 - accuracy: 0.9949 - val_loss: 5.1172 - val_accuracy: 0.4091\n",
            "Epoch 119/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0426 - accuracy: 0.9924 - val_loss: 4.5673 - val_accuracy: 0.4091\n",
            "Epoch 120/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0417 - accuracy: 0.9924 - val_loss: 5.3217 - val_accuracy: 0.4091\n",
            "Epoch 121/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0291 - accuracy: 0.9975 - val_loss: 5.5629 - val_accuracy: 0.4091\n",
            "Epoch 122/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0455 - accuracy: 0.9874 - val_loss: 4.8299 - val_accuracy: 0.4318\n",
            "Epoch 123/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0445 - accuracy: 0.9975 - val_loss: 4.8873 - val_accuracy: 0.4545\n",
            "Epoch 124/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0473 - accuracy: 0.9924 - val_loss: 4.8381 - val_accuracy: 0.4091\n",
            "Epoch 125/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0394 - accuracy: 0.9874 - val_loss: 5.3720 - val_accuracy: 0.4545\n",
            "Epoch 126/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0376 - accuracy: 0.9924 - val_loss: 5.8846 - val_accuracy: 0.4091\n",
            "Epoch 127/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0447 - accuracy: 0.9899 - val_loss: 4.9116 - val_accuracy: 0.4545\n",
            "Epoch 128/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0332 - accuracy: 0.9975 - val_loss: 4.5605 - val_accuracy: 0.4773\n",
            "Epoch 129/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0269 - accuracy: 1.0000 - val_loss: 5.0000 - val_accuracy: 0.4545\n",
            "Epoch 130/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0259 - accuracy: 0.9975 - val_loss: 5.6641 - val_accuracy: 0.4318\n",
            "Epoch 131/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0307 - accuracy: 0.9924 - val_loss: 5.6800 - val_accuracy: 0.4318\n",
            "Epoch 132/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0293 - accuracy: 0.9949 - val_loss: 5.1158 - val_accuracy: 0.5000\n",
            "Epoch 133/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0260 - accuracy: 1.0000 - val_loss: 5.4353 - val_accuracy: 0.4318\n",
            "Epoch 134/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0289 - accuracy: 0.9949 - val_loss: 5.3036 - val_accuracy: 0.4318\n",
            "Epoch 135/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0424 - accuracy: 0.9924 - val_loss: 5.3882 - val_accuracy: 0.4773\n",
            "Epoch 136/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0409 - accuracy: 0.9899 - val_loss: 5.0183 - val_accuracy: 0.4545\n",
            "Epoch 137/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0374 - accuracy: 1.0000 - val_loss: 5.2196 - val_accuracy: 0.4545\n",
            "Epoch 138/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0425 - accuracy: 0.9899 - val_loss: 5.6017 - val_accuracy: 0.4091\n",
            "Epoch 139/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0506 - accuracy: 0.9899 - val_loss: 5.3692 - val_accuracy: 0.4773\n",
            "Epoch 140/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0325 - accuracy: 0.9848 - val_loss: 5.0669 - val_accuracy: 0.4091\n",
            "Epoch 141/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0467 - accuracy: 0.9899 - val_loss: 5.5151 - val_accuracy: 0.4545\n",
            "Epoch 142/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0529 - accuracy: 0.9848 - val_loss: 5.6821 - val_accuracy: 0.4091\n",
            "Epoch 143/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0327 - accuracy: 0.9949 - val_loss: 5.2068 - val_accuracy: 0.4773\n",
            "Epoch 144/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0240 - accuracy: 0.9975 - val_loss: 5.9342 - val_accuracy: 0.4318\n",
            "Epoch 145/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0226 - accuracy: 1.0000 - val_loss: 6.2981 - val_accuracy: 0.3864\n",
            "Epoch 146/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0300 - accuracy: 0.9975 - val_loss: 5.1375 - val_accuracy: 0.4091\n",
            "Epoch 147/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0281 - accuracy: 0.9924 - val_loss: 5.0021 - val_accuracy: 0.4318\n",
            "Epoch 148/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0201 - accuracy: 0.9975 - val_loss: 5.9309 - val_accuracy: 0.3636\n",
            "Epoch 149/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0214 - accuracy: 1.0000 - val_loss: 6.5494 - val_accuracy: 0.3864\n",
            "Epoch 150/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0320 - accuracy: 0.9949 - val_loss: 5.8668 - val_accuracy: 0.3636\n",
            "Epoch 151/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0459 - accuracy: 0.9899 - val_loss: 4.8068 - val_accuracy: 0.4773\n",
            "Epoch 152/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0281 - accuracy: 0.9924 - val_loss: 5.3221 - val_accuracy: 0.4545\n",
            "Epoch 153/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0253 - accuracy: 0.9975 - val_loss: 5.7660 - val_accuracy: 0.5000\n",
            "Epoch 154/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0311 - accuracy: 0.9949 - val_loss: 5.3905 - val_accuracy: 0.4773\n",
            "Epoch 155/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0240 - accuracy: 0.9949 - val_loss: 5.7415 - val_accuracy: 0.4545\n",
            "Epoch 156/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0344 - accuracy: 0.9899 - val_loss: 6.6182 - val_accuracy: 0.4318\n",
            "Epoch 157/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0319 - accuracy: 0.9949 - val_loss: 5.7239 - val_accuracy: 0.4773\n",
            "Epoch 158/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0304 - accuracy: 0.9949 - val_loss: 5.3344 - val_accuracy: 0.4773\n",
            "Epoch 159/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0435 - accuracy: 0.9823 - val_loss: 6.5034 - val_accuracy: 0.4318\n",
            "Epoch 160/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0303 - accuracy: 0.9899 - val_loss: 6.0211 - val_accuracy: 0.4318\n",
            "Epoch 161/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0260 - accuracy: 0.9949 - val_loss: 5.4864 - val_accuracy: 0.4318\n",
            "Epoch 162/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0219 - accuracy: 0.9975 - val_loss: 5.8520 - val_accuracy: 0.4545\n",
            "Epoch 163/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0229 - accuracy: 0.9975 - val_loss: 5.8390 - val_accuracy: 0.4318\n",
            "Epoch 164/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0204 - accuracy: 0.9975 - val_loss: 5.9397 - val_accuracy: 0.4318\n",
            "Epoch 165/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0466 - accuracy: 0.9798 - val_loss: 5.9538 - val_accuracy: 0.4318\n",
            "Epoch 166/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0373 - accuracy: 0.9899 - val_loss: 5.9297 - val_accuracy: 0.4318\n",
            "Epoch 167/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0344 - accuracy: 0.9924 - val_loss: 5.8185 - val_accuracy: 0.4318\n",
            "Epoch 168/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0241 - accuracy: 0.9949 - val_loss: 6.4674 - val_accuracy: 0.4318\n",
            "Epoch 169/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0330 - accuracy: 0.9975 - val_loss: 6.5048 - val_accuracy: 0.4091\n",
            "Epoch 170/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0190 - accuracy: 0.9949 - val_loss: 6.3090 - val_accuracy: 0.4318\n",
            "Epoch 171/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0197 - accuracy: 0.9975 - val_loss: 6.2169 - val_accuracy: 0.4318\n",
            "Epoch 172/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0140 - accuracy: 1.0000 - val_loss: 6.2719 - val_accuracy: 0.4091\n",
            "Epoch 173/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0151 - accuracy: 0.9975 - val_loss: 6.4089 - val_accuracy: 0.4318\n",
            "Epoch 174/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0165 - accuracy: 0.9949 - val_loss: 6.1684 - val_accuracy: 0.4545\n",
            "Epoch 175/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0203 - accuracy: 0.9975 - val_loss: 6.2445 - val_accuracy: 0.4545\n",
            "Epoch 176/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0145 - accuracy: 1.0000 - val_loss: 6.6988 - val_accuracy: 0.4091\n",
            "Epoch 177/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0171 - accuracy: 0.9975 - val_loss: 7.1917 - val_accuracy: 0.3864\n",
            "Epoch 178/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0189 - accuracy: 0.9949 - val_loss: 6.9480 - val_accuracy: 0.4318\n",
            "Epoch 179/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0240 - accuracy: 0.9949 - val_loss: 6.4522 - val_accuracy: 0.4318\n",
            "Epoch 180/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0174 - accuracy: 0.9949 - val_loss: 6.8241 - val_accuracy: 0.3864\n",
            "Epoch 181/200\n",
            "7/7 [==============================] - 9s 1s/step - loss: 0.0213 - accuracy: 0.9899 - val_loss: 6.1195 - val_accuracy: 0.4773\n",
            "Epoch 182/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0179 - accuracy: 0.9975 - val_loss: 6.3826 - val_accuracy: 0.4773\n",
            "Epoch 183/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0160 - accuracy: 1.0000 - val_loss: 6.6626 - val_accuracy: 0.4318\n",
            "Epoch 184/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0221 - accuracy: 0.9949 - val_loss: 6.6001 - val_accuracy: 0.4773\n",
            "Epoch 185/200\n",
            "7/7 [==============================] - 9s 1s/step - loss: 0.0269 - accuracy: 0.9949 - val_loss: 6.3948 - val_accuracy: 0.4545\n",
            "Epoch 186/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0156 - accuracy: 1.0000 - val_loss: 6.1144 - val_accuracy: 0.4545\n",
            "Epoch 187/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0160 - accuracy: 0.9975 - val_loss: 6.3292 - val_accuracy: 0.4545\n",
            "Epoch 188/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 6.2419 - val_accuracy: 0.5000\n",
            "Epoch 189/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0139 - accuracy: 0.9975 - val_loss: 6.6125 - val_accuracy: 0.4545\n",
            "Epoch 190/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0101 - accuracy: 1.0000 - val_loss: 6.9561 - val_accuracy: 0.4318\n",
            "Epoch 191/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0154 - accuracy: 0.9949 - val_loss: 7.1259 - val_accuracy: 0.4318\n",
            "Epoch 192/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0222 - accuracy: 0.9949 - val_loss: 6.3594 - val_accuracy: 0.4773\n",
            "Epoch 193/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0361 - accuracy: 0.9949 - val_loss: 6.0085 - val_accuracy: 0.4773\n",
            "Epoch 194/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0118 - accuracy: 1.0000 - val_loss: 6.3557 - val_accuracy: 0.4318\n",
            "Epoch 195/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0165 - accuracy: 0.9975 - val_loss: 6.6659 - val_accuracy: 0.4545\n",
            "Epoch 196/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0224 - accuracy: 0.9949 - val_loss: 6.2155 - val_accuracy: 0.4773\n",
            "Epoch 197/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0153 - accuracy: 1.0000 - val_loss: 6.5509 - val_accuracy: 0.4773\n",
            "Epoch 198/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0137 - accuracy: 0.9975 - val_loss: 6.9503 - val_accuracy: 0.4545\n",
            "Epoch 199/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0212 - accuracy: 0.9924 - val_loss: 7.0957 - val_accuracy: 0.4545\n",
            "Epoch 200/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0147 - accuracy: 1.0000 - val_loss: 7.0869 - val_accuracy: 0.4773\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gq7J7X8qZuOf",
        "colab_type": "code",
        "outputId": "f9361a3a-2f70-4bb4-9560-40e0237f8e4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(acc, label='Training Accuracy')\n",
        "plt.plot(val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([min(plt.ylim()),1])\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('Cross Entropy')\n",
        "plt.ylim([0,1.0])\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lO6moayIq3MXNJ41i8pBEDtQ08sTyHRRXNjAhLZ7xgxN4e10hMzIGUFnvZpf33MNcwrVz\nh/PAhVPbWO6Nbg/ZuWUUVtQDMG5wPNOGDWBLURVX/vNzKuvd/PWamVw8cxh7y+t46L0tvJa9F2Ng\neEoMl2ZmkJNXTnltI7+9ZBrTM5JoTV1jM1/uOsCUoYmkJUa3WX6guoFPt+/nk237WbW7lO+dPo4r\n5wynpLKebz6bRV5pLY1uD9UNbuaMTCa3tJYSbwdnbGocO/bVcFnmMP585QxcrsCNfu6BWqob3ExJ\nT2yzrLiynoff38rK3aXMHZ3CGZPSOHPS4Db7qmlw8+9PdrJkQzGXzxrGDSeMJCo8jDfXFHDXgmyu\nnTuclbvL2L2/httOGc13TxvHgJi2BegOVDdw6h8/5LRJg5mansgf393CjIwBnDk5jVPGD2J6RlIL\nj0woqG5w88THO1i8voj7L5zC1yZ0VBjxyKGdi8aAz9GhIiJZxpg5AZeFMDt9AXAaWuC/GL+yhMaY\nf3prSz+GVmKqBW4xxnSqna0St/RHmj2Gt9YW8OHmEpZuKiEy3MU/rptFXGQ46wsqOHPyYAYnRNPs\nMbySlU9JVT0iwhmTBjN5qK9x31BQwbeezWJgXCQ/O38yx49pWc7bGMOHW0pYlFPAJ9v2c6Cmkdkj\nkymvbWTPgVp+fsFkbjxhFFuKqrj6X19Q1eDmwUuP4RvH+cqz79xXzbsbivB4DAnREVw2axgJ0e1X\nHC2vbeT9jcWcNG4Q6Uk+N2xto5uFX+WxbHMJq3PLuHD6UH5zyTRcIixeV0hUeBgnjhtIYgf7bo8t\nRVUUVtRx2sQWheDYUFDBjn01nDs1jajwsHa2Prw0NXtY8FUu//hwB8OSY/jpvEnkltby0HtbmJo+\ngMevnxUwRHGk+MnLa3hxVT5JsRE8ft1sThjbugR8Sx56bwt/W7YdgItnpvPwVTPb7YBYDg89osRD\nhVXilv6GMYYH3tjAs1/uISUuklPHD+KH50xkeEps5xsHwOMxQTWq7mYPL2Xl85f3t+Ju9vCPVg34\nmrxysnPLuOnEUYfVNRgIY0zIj9HbcNrenj7v2kY3/16+i0sy0w+6uzuioq6JMx/6mKnpifz7xjkd\n5mFYDg9WiVssvZhHP9jGX97fyh2njuG+eZOOuFVT39RMY7OnWxav5eikusFNXGRYj3dAjhY6UuJ9\nIjvdYumPeDyGR5Zu5dFl27l8VgY/O29SjzSK0RFhREccGdeypX/QeoSEpeewd8Ji6QHqm5r5/sIc\n3t1QxFVzMvjfS4+xVo3FYukyVolbDpk/L9lCclwkt508uqdFOeJU1TdRWe9mWFJM5yv78atFG1iy\nsYgHLpzCLSeFPuZssVj6JzYjwXJINHsM8z/bxSPvb6W20d3T4hxxfvPWRi589BPqvcUpHIwxlNc2\nHpz2eAwHqhswRrPQF67M47unjeXWk0dbBW6xWLqNtcQth8T2kmpqGlWBvbmmgKuPHdHJFn2LZZuL\nEYTTJw1us8zjMSzbvI+y2iY+3FzCeccMPTj/3hdzeGttIdfOHc7pEwfz6AfbWJNfwfCUGMpqmsgc\nkcT3z5pwpE/HYrH0M6wlbjkkcvLKAC348cKK3B6W5vBS39TMD15cw49fXnuwzKI/Gwsr2V+thTte\nzdZig8YYfvP2Rt7IKeCEMQNZ8FUetz29ipKqBu45czwT0xLJSI7h0Wsye3RssMVi6R9YS9xySOTk\nlZMYHc6dZ4zj/725kfV7K5g2bECLdfZVNVBR18S4wfEA/HXpNj7aWsLdZ47ntAmpB93J1Q1usvaU\nkTkiCWPgHx9t58sdB/j3jXMYHKBaVjBsKqxkbGp8t8ayvru+iPLaJgA+3b6/TeGQj7dqSclLZqbz\n9rpCymoaWbgyj/mf7ebWk0Zz/4WT2bm/hvV7Kzh36hCbAW6xWA471hSwHBI5eRXMGJ7EZbMyiI5w\n8dyXbUv83vnCauY9spznvtzDfz7ZycNLt7K9uJpb5q/k9qdX6ccmjOHuBdnc9ORXZP76fU76/TKe\nWL6T9d7a3ADr91Zw21MreW9DEcHUN8jOLeO8v37Cz19bF9S51DU2c//r6/nPJzsBeGFFLiNSYhkQ\nE8Fr2W3L+i/fuo/JQxO549SxNDUb7l6YzR/e3czFM9P5xQWTERHGpsZz8cxhVoFbLJaQYC1xS7ep\nbXSzpaiSs07XOsuXz8rgxVV5fO/0cQerjW0rrmLFrlLSEqP4xevrAZg3dQiPXDOTZ77Yze/e2cxd\nC1Zz/JiBLNtcwre+NoYIl4vCinpuOWkUSzYU8bdl2zlh7ED+tGQLB2oa+WBzCXNHp/DYtZltLPSm\nZs9BN/VD720F4KWsfK4+djhzvB/aAM0q/2z7Ab7YsZ8BsZHMHpnMn5dsYd3eCgAKyuv5ancp952n\nJTJfXZ1PdYObJeuL2F/dwDVzR5C1p4zbTxnDlPREJg1J4JNt+zll/CD+dEX7dbAtFovlcGKVuKXL\nLN1YzNCkaGoamvEY/RoVwJ1njOOlrHwe/WAbf7pyBgAvfJVLRJjw5l0n88zne8grq+UPl08nOiKM\nO04dS0xEGPe/sYGlm0o4c9Jg7pvXsuDJ2NR4Xsvey09fWUdybATv3nMqK3eX8uDiTdw0fyX//dbx\n1DU28+Rnu/h4yz62Fldx/fEjOWfKED7dvp8fnD2BBV/l8ovX1/PWXScTHuaipLKeC/72KfuqGoiN\nDKO+Sc8jLjKMf90wm5dW5fHkZ7uICBOumJ3B7v01vLAil+v+s4I1eeUAvLgqD7fHcOqEQQDce/YE\n3lxTwB8un27LUFosliOGVeKWLlFQXse3nssiNiKMs6emATDDq8SHDojh+uNG8tTnu/jOaWNJT4rh\nlax8zp06hMEJ0fzo3Ilt9nfDCaOorHezeF0hf7pyRpvhVjGRYTx46TH86s0NPHTlDCYOSWDikARG\npMRy61Mrufwfn5NfVkdTs4e5o1O4aEY6z3yxhwVf5ZKWGMUdp45h/OB4vvP8an791kbuv3AK976Y\nQ3W9m6dvncuJYwdS0+Bm5e4yJqYlMGJgLKeOT+WuBasZPSiOQfFRDIyLZERKLGvyyrnj1DEMS4rh\n129tJDYyjDkj1bo/d+oQzp06JMRX32KxWFpia6dbusT/vr2RJz/bTWp8FEWV9WQkx/DpT884uHx/\ndQOn/OFDhqfEMGlIIovWFLDgm8d3+mWk7vBGzl5++OIazp02hJ+cO/Hgxxtezsrn/tfX85tLpnHF\n7AzNGH9rE09+tosRKbHkltby+8uO4Zq5wQ+Hy84to7y26eBQs6w9ZVQ3uHvNZxAtFkv/pcc+gCIi\n84C/AmHAf4wxv2+1fCTwJJAKlALXG2PyO9qnVeJHlmaP4cHFm8gckcSpE1I58XfLOHPyYO46YxxX\n/PMLzpg0mL9cNbPFNm+uKeAfH+1gU2ElE9LiWfL9U0NW0KTB3Rzwk5LuZg/hrYZwvbAilwfeWM+5\n04bw2LWZtsiKxWLpE/TU98TDgK3A2UA+sBK41hiz0W+dl4C3jDFPi8gZ6DfFb+hov1aJH1n+unQb\nDy/VBLEZw5NYk1fOW3edzLRhAyitaSQ6wkVsZOCozL6qBiLChKTYyCMpcocUV9YzMC6yjYK3WCyW\n3kpPfcVsLrDdGLPTK8RC4GJgo986U4AfeP//EHg9hPJYusjK3aX89YOtXDwzndjIcBZ8lcuJYwce\nHAeeEtexck5NiDoSYnaJtG6ON7dYLJbeSCiV+DAgz286Hziu1TprgMtQl/ulQIKIDDTGHAihXJYO\nqGts5op/fk5RRT1VDW6Gp8Ty20umER8VzlmTBzNpaGJPi2ixWCwWLz2dnf4j4DERuRlYDuwFmluv\nJCJ3AHcAjBjRv2pz9zbeXFvAhoJKLpmZTlJsJNcfP5KE6AgAzpyc1sPSWSwWi8WfUCrxvcBwv+kM\n77yDGGMKUEscEYkHLjfGlLfekTHmCeAJ0Jh4qAQ+WvFPAnthRS7jBsfz8NUzbeKXxWKx9HJCmd2z\nEhgvIqNFJBK4Bljkv4KIDBIRR4afoZnqliPI3vI6Zv92KX9duo2NBZXk5JVz7dwRVoFbLBZLHyBk\nlrgxxi0idwJL0CFmTxpjNojIr4FVxphFwGnA70TEoO7074VKHktg3l5bQEVdEw8v3cqbawuIDHdx\n+axhPS2WxWKxWIIgpDFxY8xiYHGreQ/4/f8y8HIoZbB0zOJ1RUwemsiwpGiWbirhssxhvWpImMVi\nsVjap6cT2yw9yN7yOnLyyvnxuRO59aTRPP7Rdq6cM7zzDS0Wi8XSK7BK/CjmnXWFAJx/zFBiIsP4\nwTlta5tbLBaLpfdiy1YdxbyzXl3powfF9bQoFovFYukGVokfJXg8hlW7S2n26Ai93ftryNpTxgXH\n2C9vWSwWS1/FKvGjhCc/28UV//yCR5ZuxRjDr9/aSFxkmI2BWywWSx/GxsSPAoor63lk6TZiIsJ4\n7MPtNLo9LNtcws/Pn2xriVssFksfxlriRwG/fXsTjc0eXvnOiYweGMe/lu9kQlo8N580qqdFs1gs\nFsshYC3xfkrWnjKu+8+XNLo9eAzcc+Z4pqQn8ui1mfzopTX876XTiLCf47RYLJY+jVXi/ZQlG4rw\neOC7p40jJS6S647XD8dMGzaAd79/ag9LZ7FYLJbDgVXi/ZTPd+wnc0QSPzrXjv22WCyW/or1p/ZD\nymsb2VBQyYljB/W0KBaLxWIJIVaJ9xPKahp5aVUexhi+3HkAY+CkcQN7WiyLxWKxhBDrTu8nPLps\nG/M/242IsDa/nNjIMKZnJPW0WBaLxWIJIVaJ91Ea3M3sr25kWFIM9U3NvJKVD8DvFm8iNiqMuaNT\niAy3jhaLxWLpz4S0lReReSKyRUS2i8h9AZaPEJEPRSRbRNaKyPmhlKe/UFhRxxWPf8HX/vgh2bll\nvL22kMp6N7+4YDJltY3kldZx4ljrSrdYLJb+TsgscREJA/4OnA3kAytFZJExZqPfar8AXjTGPC4i\nU9Bvj48KlUz9gQ0FFdw8fyW1DW4Gxkdy98JskmIiGTMojttOHk1+WR1Pfb6bk8bZpDaLxWLp74TS\nnT4X2G6M2QkgIguBiwF/JW6ARO//A4CCEMrT56msb+Lbz2UR7hJe/e5JVDc0cdW/viSvtI5fXDAZ\nEeG+8yZx1uQ0pqYP6GlxLRaLxRJiQulOHwbk+U3ne+f58yvgehHJR63wuwLtSETuEJFVIrJq3759\noZC112OM4eevraegvJ7HvpHJxCEJzB6Zwk/nTWRYUgyXz8oAIDoijJPHWyvcYrFYjgZ6OvPpWuAp\nY0wGcD7wrIi0kckY84QxZo4xZk5qauoRF7I3sGhNAW+uKeDes8Yze2TKwfl3nDqWT35yOslxkT0o\nncVisVh6glAq8b2A/3cuM7zz/LkNeBHAGPMFEA1YMzIA//fpLiYNSeA7p41rs8zlkh6QyGKxWCw9\nTSiV+EpgvIiMFpFI4BpgUat1coEzAURkMqrEj05/eQdsL6lmbX4FV8zOIMwqbIvFYrF4CZkSN8a4\ngTuBJcAmNAt9g4j8WkQu8q72Q+CbIrIGWADcbIwxoZKpr/J69l5cAhfNSO9pUSwWi8XSiwhpsRdj\nzGI0Yc1/3gN+/28ETgqlDH0dj8fwWvZeTh6fyuDE6J4Wx2KxWCy9iJ5ObLN0wsrdpewtr+OyzNaJ\n/RaLxWI52rFlV3spxhjeWV/E79/ZTFxkGOdMTetpkSwWi8XSy7BKvBfS7DHcszCbt9YWMjEtgf/c\ndCyxkfZWWSwWi6UlVjP0MowxPPDGet5aW8iPzpnAd04bZzPSLRaLxRIQq8R7Gf/4aAfPr8jlO6eN\n5c4zxve0OBaLxWLpxdjEtl7EV7tKeei9LVw0I52fnDuxp8WxWCwWSy/HKvFeQnltI99fmM2IlFge\nvOwYRKwL3WKxWCwdY93pvYTfv7OZfdUNvPKdE4mPsrfFYrFYLJ1jLfFewsdb93Hu1CFMz0jqaVEs\nFovF0kewSrwXUFxZT2FFPZkjkntaFIvFYrH0IawS7wXk5JUDMHO4tcItFovFEjxWifcCcvLKCXcJ\nU9MTe1oUi8VisfQhrBLvBazJK2fy0ESiI8J6WhSLxWKx9CFCqsRFZJ6IbBGR7SJyX4DlD4tIjve3\nVUTKQylPb6TZY1ibX2Fd6RaLxWLpMiEbyyQiYcDfgbOBfGCliCzyfn4UAGPMvX7r3wVkhkqe3srO\nfdVUN7iZYZW4xWKxWLpIKO8K4/0AACAASURBVC3xucB2Y8xOY0wjsBC4uIP1rwUWhFCeXkm2TWqz\nWCwWSzcJpRIfBuT5Ted757VBREYCo4FlIZSnV5KTV05CdDhjBsX1tCgWi8Vi6WP0lsS2a4CXjTHN\ngRaKyB0iskpEVu3bt+8IixY6jDF8ueMAmSOScdkvlVksFouli4RSie8FhvtNZ3jnBeIaOnClG2Oe\nMMbMMcbMSU1NPYwi9ixbiqvYub+Gc6ak9bQoFovFYumDhFKJrwTGi8hoEYlEFfWi1iuJyCQgGfgi\nhLL0ShavK8IlcO7UIT0tisVisVj6ICFT4sYYN3AnsATYBLxojNkgIr8WkYv8Vr0GWGiMMaGSpbey\neF0hc0enkJoQ1dOiWCwWi6UPEtLPZRljFgOLW817oNX0r0IpQ29lW3EV20uqufGEqT0tisVisVj6\nKL0lse2oY/G6IkRgnnWlWywWi6WbWCXeA1TUNrFwZS7HjkphcGJ0T4tjsVgslj6KVeJHGGMM9726\nln1VDfz8/Mk9LY7FYrFY+jBWiR9hFnyVxzvri/jxuRNtqVWLxWKxHBJWiR9hHv94O8eOSuabp4zp\naVEsFovF0sexSvwIUtvoJq+0jlPHp9oKbRaLxWI5ZDpV4iJyl4gkHwlh+jvbS6oBGJ8W38OSWCwW\ni6U/EIwlnoZ+RvRF7/fBrQnZTbYVqxIfNzihhyWxWCwWS3+gUyVujPkFMB74P+BmYJuIPCgiY0Ms\nW79jW0k1EWHCyIGxPS2KxWKxWPoBQcXEvSVRi7w/N1rr/GUR+WMIZet3bC+pYsygeCLCbCqCxWKx\nWA6dTsuuisg9wI3AfuA/wI+NMU0i4gK2AT8JrYj9h63F1RyTMaCnxbBYLBZLPyGY2ukpwGXGmD3+\nM40xHhG5MDRi9T/qGpvJK6vlslnDeloUi8VisfQTgvHrvgOUOhMikigixwEYYzaFSrD+xo591RgD\n421Sm8VisVgOE8Eo8ceBar/pau+8TvFms28Rke0icl8761wlIhtFZIOIvBDMfvsizvCyCXZ4mcVi\nsVgOE8G408X/W99eN3owsfQw4O/A2UA+OkxtkTFmo98644GfAScZY8pEZHCXz6CPsK2kinCXMHJg\nXE+LYrFYLJZ+QjCW+E4RuVtEIry/e4CdQWw3F9hujNlpjGkEFgIXt1rnm8DfjTFlAMaYkq4I35fY\nWlzNqEFxRIbbzHSLxWKxHB6C0SjfBk4E9qIW9XHAHUFsNwzI85vO987zZwIwQUQ+E5EvRWReEPvt\nk2wuqmT8YOtKt1gsFsvho1O3uNc6viaExx8PnAZkAMtF5BhjTLn/SiJyB96Ow4gRI0IkSujYsa+a\nvNI6bj/ZfvTEYrFYLIePYGLb0cBtwFQg2plvjLm1k033AsP9pjO88/zJB1YYY5qAXSKyFVXqK/1X\nMsY8ATwBMGfOHEMf44NNxQCcObnfhvwtFovF0gME405/FhgCnAt8jCrjqiC2WwmMF5HRIhKJWvOL\nWq3zOmqFIyKDUPd6MPH2PsXSTSVMGpJARrItt2qxWCyWw0cwSnycMeZ+oMYY8zRwARoX7xBjjBu4\nE1gCbAJeNMZsEJFfi8hF3tWWAAdEZCPwIVoN7kB3TqS3UlbTSNaeMs6ektbTolgsFoulnxHMELMm\n799yEZmG1k8Pyi9sjFkMLG417wG//w3wA++vX/LR1hKaPYYzJ1slbrFYLJbDSzBK/Anv98R/gbrD\n44H7QypVP2LpphJSE6KYPszWTLdYLBbL4aVDd7r3IyeVxpgyY8xyY8wYY8xgY8y/jpB8fZptxVUs\n3VjMWZPTcLlC9Bn24o3wzk/B4wnN/i0Wi8XSa+lQiRtjPNivlHWL+qZm7lqQTXxUOPeePT50B8p+\nDlb8E2r6bZ0ci8VisbRDMIltS0XkRyIyXERSnF/IJevj/P6dzWwuquLPV81gcEJ05xt0l4Js/Vuz\nP3THsFgsFkuvJJiY+NXev9/zm2cAW7mkHTwewwsrcrlidganTwzh2HBPMxSu0f9r+1VSv8VisViC\nIJiKbaOPhCD9ibLaRhqbPUxLTwztgQ5sh6Ya/b/WWuIWi8VytBFMxbYbA803xjxz+MXpHxRXNgCQ\nlhhCNzr4XOkANdYSt1gslqONYNzpx/r9Hw2cCawGrBJvh+KqegAGh1yJ50BELDTVWXe6xWKxHIUE\n406/y39aRJLQz4pa2qGkUpV4WmJUaA9UkA1DpsP+LdadbrFYLEch3fm4dQ1g4+QdUOJ1p6cmhFCJ\ne5qhaC2kZ0LsIJudbrFYLEchwcTE30Sz0UGV/hTgxVAK1dcprqonOTaCqPCw0B1k/1ZoqlUlXpBt\n3ekWi8VyFBJMTPzPfv+7gT3GmPwQydMvKK5s6H5S245lsO4V/X/4sTD75sDrOUlt6TMhbhCUdvHj\nb81u+OQhmPtNiLXD/i3doLoEVj8DJ/8AXN1x6lkslkMlmDcvF/3m98fGmM/Qr46NCqlUfZySyvru\nJ7V9+jCsewk2vwVv/xCqigOvV5ANkfEwcBzEDuy6O714HXz0IKx7uXtyWiwb34Blv4GSjT0ticVy\n1BKMEn8J8C/M3eyd1ykiMk9EtojIdhG5L8Dym0Vkn4jkeH+3Byd276a4soG07sbDy3bDlIvg9qXg\ncUPOc4HXK8jRpDZXmCrx2gNgTOB1A+EMSfMfpmaxdIWqQv1btrtHxbBYjmaCUeLhxphGZ8L7f2Rn\nG4lIGPB34Dw0jn6tiEwJsOp/jTEzvb//BCl3r8XjMeyrbmBwdzLTm5ugIh+SR8Gg8TDqFMh6uu3H\nTZrdvqQ2UHe6aYb68uCP5WSzF+Z0XU6LBaCqSP+W7epZOSyWo5hglPg+EbnImRCRi4FgfLdzge3G\nmJ1exb8QuLh7YvYdDtQ00uwx3YuJl+eC8agSB42Hl++BnctarrdvM7jrfUo8dpD+7UrBF8f9vm8z\nNNZ0XVaL5aAS392jYlgsRzPBKPFvA/8jIrkikgv8FPhWENsNA/L8pvO981pzuYisFZGXRWR4EPvt\n1RR7x4h366MnTmOY7B3BN/nrqqBXzW+5nmM9p8/Uv7ED9W9XMtSddY0HitZ1XVZ/ti+FvJWHtg9L\n3+NoUuLuRvj8MXA39LQknVNVDCuesJ8nbo+sp9Xj2U/oVIkbY3YYY45HXeJTjDEnGmO2H6bjvwmM\nMsZMB94Hng60kojcISKrRGTVvn37DtOhQ0NJ1SEUejmoxEfp3/AomHa5Kslmt2+9gmyITICUsTod\n5yjxLiS31e6HMK+MBYfoUn/7R/DeLw5tH5a+hxMTLz0K3Om7Pob3fg7b3u9pSTon6yl458ew66Oe\nlqT3UZ4Hb96tn2/uJ3SqxEXkQRFJMsZUG2OqRSRZRH4bxL73Av6WdYZ33kGMMQeMMU7X9j/A7EA7\nMsY8YYyZY4yZk5qaGsShe46SQ6mbXrZLFWvCUN+8jGPVdb5vs29eQQ4MneEb1uO407tiidcc0Mz2\n+LRDS25rdmsYoGitFqCxHB24G6CuFFwRev/7+713Oip9wevgvM+tPXgW37U5VMOlFxGMO/08Y8zB\njCljTBlwfhDbrQTGi8hoEYkErgEW+a8gIn7aiouATUHst1dTfCjV2sp2Q/LIlmNuHZe540JvblL3\ntzMffO70rgwzqz2g48PTMw8tua0iT5Pqmmq1AI3l6MBxpafPBE8TVBb0rDyhpqwPKfHCHBAXbFns\nu08WxVHihWv7TbghGCUeJiIHNZKIxACdaihjjBu4E1iCKucXjTEbROTXfolyd4vIBhFZA9wN3NzV\nE+htFFfVMzAukoiwbhS/KN3ti4c7pIxV17nz8JVsguYGX1IbQGSsfgilSzHx/ZrVnp4J+7ZAQ3XX\n5YWWjVo/6t1aOsFRDiOO17/9PUPdec57+3lWFmqYY85tOkQ1+9melqh34RgsDRW9/14GSTAV254H\nPhCR+YCgijZg7Lo1xpjFwOJW8x7w+/9nwM+CFbYv0O1CL8ZoQzHyxJbzXS61dg72IJ2ktsyW68UO\naqnEG6q1aMysG3UseWtq9us2Q2cCRq37kSeoHGtfhDGnQUKarrvpTXXfJ41oux/nRRCXyjjz2rbr\nFOSoVeDP4Ckw9ZKW86qKYfcncMwVOl1XDpvfhpnfABFoqod1L8LM6/W6NHvH0U+/BiL8rrkxkP0c\nTLrAV41u/Ssw8iRIGKLTW96FwZN8+QftkfslIDDiuI7XcyjPg4LVMKWTgRhNdVpoJ/N6PbdmN6x5\nAWZcC2ERHW+77mUdfujcn87Y8wVExcOQY1rO35sFW5cEt4+UMTDjGt90taPET4DP/6bP7rA5sPa/\nek6BzsEYWLMAJl+k8vQmakv1WXPuR2vac6cbAznPw6QLISYp8L63vAtpUyGpnbzdPV9AZBwMnd49\n2SsLIO8rfZ+c9mHa5fphpCynop5fG9BUrzHhploIj4bjvq2GQF+ioRq+ekJDjZHxcPx39JnzNOu7\nf8yVbc/JGG2jnFLVBdkwcGzbfW95x9feTrqw7X3J/VIrawIMnwvjzmq5fMeHamwde5vmNYWYYL5i\n9gevpXwWWkN9CTAy1IL1VUqqGrqX1FZbCo1VgZXK0Bnw1b/Vlb51CcSltrXY41pVbfvyH/Dh/0L0\nAJh2Wct1m906pjx2IGTMAQmDre+oEt+7Gl67Qxuzi/8OB3bAf6+H2bfA1x9pK1vZbgiLbN8tbwy8\n/l0o2dByvoTB8OMg0S+isuJxrViXMgaGzYJP/qwKYuh0VUAbXoNFd2lnYsxp+iK9eQ801sIJ3/Xt\nZ+eHsOhOfZHmPQiFa+DlW+Gke+DsX6sC/e91qpQu/ntbmQ9epyZ48UbtoHx/PYQF0ef97BFY+R+4\nMwsGjWt/vXUv6bmkTdVz3f2JTkcnabGf9qjIh1du047MJR3I7s/bP1QFc0urjtSSX0Du58HtA2D8\nOb5OkWOJD5sNrnBVcl89AUt/CRExLRW+Q8FqeP070NzYfjnhnmL5n/SdGZABY09vuczpYAOU7VFF\n4SjFonXwxvfUm3XOb9ru192oz9qcW+H8PwU+9pt3a4f61ne6J/uq+bD8j5D6pXaYxaXvy5xb4aWb\n9T0Zf7Zv/W1L9D45JI3wdZz7CtuWwAf/zzedOhEmnKvv0Zt3a77Gyfe23KY8F+rK4LSfQfFGVdSt\nz9vjgVfvgIZKnc5fCTe85re8GV65XcOIAFED4IebW3YYPntE283jghnEdegE6/MtRhX4lcAZ9IPY\ndagorqwnrVvDy7w9/ZQAH4hLz1QX+o5l2kuc+Y22tapjB/qy0z3NOowCICtAcktdqf6NG6S/iedB\n9vPa4GQ9qcvWvwr1FbDau5/2kt/KdkPSSG3MC9e2zKIHfQlKNsCFj8CvKvR312qNo2e3qkbnHCNr\nviZO5bzQcn57f7Pmt6xW5yT0rHlBrQ5n2lm/aL26Gjtz/295B6qL1T259d2O1w10DsGs52R4OzHl\nzpIMneXrX1FPRTDUHtCOjH/ymadZ5839lu++tPe78Y22slUValJb3GAYMFxr92c9pcvaS6hytu9t\n2exNdb5nLdB9qy4Bdx2kTWsb/3fOKef5wMPPKvL0WWvvnD3N+g61vj9doaZE/66ar/IMmqCejokX\naIe/9f0oyNaO18/2qiXeF6s2Vnrfm3s3AOJ7lw++f0+1jXk7yzKOhSHTAr//pTtUgV/0mHoxC7Jb\nti3bP9B7euVTcPNidctveNW3/MAO2PlR+x7QENCuEheRCSLySxHZDPwNraEuxpjTjTGPHRHp+gA1\nDW6M9ybv3FdNSVUDw5Jjur6j1sPL/HFc5+/+TJVfICvG352+fSlU5uvDumu5PlgthPYqe8eqmn2L\ndgBynlflnXGsutqyn1PlDlC8IXAjVbpLZU7P1IZu/5aWy1fNV3eXf4934Fi1pFc/7Wu4jPG9VOte\nUVlqW5WGbe/v/q2wx2tRVhWp6z7jWO115zyvVi9AwRqfSw3UUm+qa3tODlnzIXEYJKR3rpTBm3S4\nXv/P8XYg2qO1Enf+BqvE3XUa9giG+nJorIYDfiND92+Dppq2YZlADJ3RVraqIg1NuFza8dy6RDui\nGcdC3pd6bduTvbfFIje+odco41h1qbf+XoEj75jTvNO7fcucc6o9oGGn1nQWS68sUM9EU43ek+7g\nvM9rFmqIxLmn4ZHqUdv6DlT4DQwqyNZwlhNi6YtKvKpQOyCJw7TT0rpNKNvddohdQbZ2PNOmej2H\na9pX9OmZ+qsr04JbDlnztWM08QINfQ6a2LKTtPpp9TJm3nA4z7ZDOrLEN6NW94XGmJONMX9D66Zb\nvFTUNjHnt0v51aINGGN44I0NxEeFc+3cALHjznBe8qQAkYqUMeq2Kd0BY07X6dbEDfJVbFs1X4eO\nXfmU9rhbKyBHOTpD08aeoS61d+9T5X3+nzRW/sGvVbln3qAWSOsPXThuxuRR3tg6LXu3dWXaS51+\nFUQltNx29i3ao92+1Hv+u7UhzbxBG7R3f6b7HXmy7rPZ7StK4xyjMMcbXx0Aq7wehOxn1fK55J+a\nFLjkf1SBZd6gvebSnT63v2n2Kd3WlO5Sz8esG/W3/YPOM5OdpMPMG9TbsWlR4PXcjdopAp/CqC72\nnVNHNfALciDtGL3erT0QgWiq07ihs61D64JBHRGTrOEb/3BJVaE+Y6D3yV0HMSlw1TMaXglkjRes\n0b+9LcN71ZP6rFzyeOBkMEdex83ur5ALczQ/IXlU4HN21m1vGJ7/tejuKJHaUg2bNVTo++rfMZt1\nkxZ0cs7J6Sw7933ozMDKrLdTXaydSBE9F+faFeTAxPPVM+m0CQ6FOZA2RePUQ2dq+LK0lYFTkKOd\ng9RJbdu0inz1yGXeoB0kEZhzC+xdpV5Id4MaPhPPaxkmDDEdKfHLgELgQxH5t4iciSa2Wbyszi2j\nrqmZp7/Yw83zV/Lp9v386JyJHQ8vK8huaxmDZqbHDwmcYCIC6V5raM4tgfcbm6LK75OHNF6Ueb3G\n9yaer9a0v1XouN3jvErc5VLr3l2vD256ph7HXa+dilN+4JPdn7oydT2ljNYx55Hxmrj0+WP6e+c+\n3cfsADJPukBdsa1d3cfeptaBu15lGpYJxev1q2uOS7N8j8a0qgq1NzzjGlWYnz2q+xt9qsajnXNK\nmwZz7/AdpyBb5wU6p9wvVfYlP9fYYuYNqsRFfCEK0Hu454uW2zr7OvleVXqf/dV3LT5/DNb8VxvR\nko1qfUFbS7x1z9+fg4k5M/T+lGyEvBWB13Xwd7n7n2tBto5oGDSh4+0d0jNbdgIcSxx83qOZ34DE\ndE3qW7NQz/mrf2vj1lTn7QRK95T4ruW+61iyufP1HcpzYfdnvunqEnV3OhR7r+GcW3zfK1jd6nsF\npbtU7hEnqJXlyO90xtIzVVnu+RQ+/hN8+bjPOnbWbW70ueFzV2hsHfw6BNJ9i7h2v3oJBk3U6aF+\nHbOU0TD2TP1kbLPb11l2FH16ZlsvjT+b39ZOQqgo3em7r10ppFNV5KunkZ6p70/xRn13hh+nz+Lm\nxfDpI7797832XRvn/Fu71Auytf0JC1eL3RXhuy+rn9V3cPZNvvVnXKNKf+kv1fCoPdB+Gx0i2lXi\nxpjXjTHXAJOAD4HvA4NF5HEROedICdibydpTRphLuGhGOh9v3cfU9ESuP76TnL9X72iZVAL6YOR+\nodnS7THubBg4XpVyIAZPBUSt5/Bon8t9zi1eq9DP1XfQnT7QNy/zBrW4TrxLp6ddoS/JCXeqQopO\navvAOw1Q8ijtCIw5TRNL3vu5/tYu1EYxUNZtWATMukE7HBX52ksOi1Q334l3qywzr/fmAzRCzgLf\n+YAv/pqeCcferuf+/v1QuReO9ya5zbxOz/HEu2DwZC2ks+dzLZwz8XztRLRuOF++TWXf8jZMvQwG\nDNPf+HO1l93cpOu99m147nKor/RtW5ijXoGUMXDC97Tz4VyL936uCYO5X/ishpgUX4JYVZFOQ/uN\neUWe3sv0TL0/kQmdF/Tw/yiOv6V3sGBQkHG79Ew9vvPs+DeiGXPVEpxzq04f921VDO/9HBb/SBVI\n0Xr1fIw4QXMtuqIY6ivhhWt81/HdNh9EbJ9Fd8ELV/ms4E/+As9eCg1VOr35LUB0VADo81We68s+\nBlV8icM0gzxpuE8xO52x9EztNEcNgA9/q/I5FcH8Y+Flu/Vdf+EqeP8B3zxXuCaYdleJOyNNTrxL\nvSOtRyHMuUXfi+3vtx3d4vwN5AUoyIaF34APH+yeXMHw1g9893XBtR2HoPypKvR1Ip1zONgmzNRn\nMSxS21pn/w0VPm9K6iSISoRt7/n26Wlu+WGp8ChV5IVeT+DqZ9Rr6R/yjEnWdmbHMlj1f5A6Gcac\n0d2r0S2CyU6vAV4AXhCRZDS57afAex1ueBSQtaeMKUMTeeiqGUwcksC8aUMIc3XirKgqVovVn13L\n1a3ztZ+0v91Jd+tLGmj4C8DEefA/e/VBDI/yDW0YfZo+dFnzYfqVOs9pQP2VePxg+Olu33RUvGZd\nOjjDMvxxGignU/6qZ7Xx9qf1ufoz6yZtVFc/44vThUep+336VbqO03Nes1CV1tTLNNt6zUJfFm5k\nHNyXqw2qK9znzYgbCD/Z6TvekGk6PMt4fNn0LVzExZpLcOYDcOw3W4YA5tyiscXNb6vFlv+Vzl/3\nknoPwGcli8Dcb6piMF6Lzt0Af5vlzRGIVaU3fK4vVllVBGO+pvsvyIGpl7a9Xs71H5qp92f6Vdqx\nmPc7X35DaxxLfNBEX/KUMdpYzbop8DaBcNyvBTkw6iTtHDiN6MgT4Kd7fM9mxhx9Fpub4OkL9Zyd\nTuXUSzUjvmx3+zK3Zt2L6mW6+W3NNdj8tp5De++Cg5NkBBpvHjxJM+SNR92fo07yJoKN93mlJnm/\nV5A1H8Z7hw6V7fIlnCaP9j33B+OnM/X9+fF29fw8ea5fbHaPKox9m72dgXS9dgWrvct3a2LgsDnq\nAWh2BzcKwsHTrN6buEHaKQ40RG7CPPXyrXpSO7OuCH3XQD0x4TEqr/POOTgdxLX/hbP/n75nh5PS\nnTqS5NSf6D149ZuaBDssYOHOllQV6WgJ0DZAXNomgHZOY5Lhvj0t83hcYb5zCAvX8139LJz3B30W\nD2zX9svfk5E+Eza8rm70qgI4/49tZbngITjrV/p/RGzbpOMQ06WjGWPKvCVQzwyVQH0Fd7OHnLxy\nZo9MJiLMxfdOH8fY1E7GvjY3aW+wdRWlrPn60HU2trizRisyDqITW45NdLnUnb3nMx0GA+p+ix7Q\n+Xhkf9Iz1fLw7ykfTMYb6TtWdGLLX0cPdPJIGOd19RWuCZxk5eQDNFToyxmbovMaKlQxOS9lRLQe\nr6PxrumZup3zf3pmy6+4OQp9+PG6L//rPe4sbWyz5mvjFhalnhEnLu3vWnWIivddh/hUmH41bHxd\nO23pmWrJVhXq9lVFGroYPKV9i8zJKk6bqtNzbtEYvNN4BcKxxMee7quqt3+r/h9MUpuDf3Kb8/z6\nlwdu/WxGxOh5z75FG+ZVT2pC0KiTdHmwLnVj9HoPOUbH+WfM0XMKZnvHMnPkbnar8namnb/+jbaT\nDLblHZ/726mkCNohdo5dkK3vkdOJDY/Uc06f5ctqLtutckuYdgac45bnamfaPzG0O1UP68oA4+uQ\nB2ojDnq93veNWXfaiLBw9ZS1fubqK7XDm3aMhszWv9I1uYIh6ym9LnNu9RUNCsYb0VClytbJyYiM\n07agoULbhphknR8e1bItat0Jme19f1qPgvF/L9Iz9Xlb9lt93ifMayuPiO8YXemAHSaObJehH7G5\nqIq6pmZmj0wOfiMnoay62Ofeqy5RV/eMb2jDFwpmXqe9b6dRqz3Q0goPhvSZmvTjP967bLe+SIfS\nQ599iyqy+orASsVJXHFkgLZxrWBx1o9P08SToTNbfsWtIAeQwO5/V5harjs/0oz3qZdogYmidTq2\n3t+12h5zbtF1SnfqsROGaoequlgTBxOG+jwEgRLWCnJUyTuFbYYcoxnVq55sP8HNscTHnO7bR1eS\n2hyiB2jyV2GOnxIPotjMMV63/75Nes6OKzLYDPX8VRqWmHOrPgvOve8sCczdoPdp4vlqHRXmqIJ0\n1/m2ryrSZ6/1PZt9k7r+Vz+rNQiqi32KOmW0hjTqK3Qf6ZltFWf6TO+wvhxNnho03ueGbxHSyPYl\nhh70dHTRpR4oNBaIWTepnPu3tL3vQ2d6y5D6Jd453o+vP6KehMNdh93dqLk6ThLYgOEaTgqm6qOT\nDOrfiWzdNgTDkGkaCsp6ypfwFx7TMk/E2d++TRpy7Irhc4Q48t2GfsKq3eqSbleJ7/7Mm/koWmgh\nYYjvhTPN+n9CmrpDPe7QFr+IT9XPmuY8r65iJ4bWFZyGbuWTvszq/FWdVzzrjAnzfBZpe0olfaZ+\nRco/jrfh1a4pIWir/P2TW0Yc73Otts6kd8i8Hj76nVpMs29Ri+a9+7WoTmJ6y2MEIm2qJt3krfD1\n8EG9EKDPSES0Wvdlu9SqMEaHzNUeCFwJbvYt8MZ31dMy6mSdV7Re3asJQ7yWGqrsI+Jg/cs6HRmv\nyYhdIT1T74PjRk8IIgM3KkHDOKue1O0j4zQXwbFm93yunRHnmud9pffAsaaynCGK3lCQf7JRoJCD\nw6Y39Zode5teg4JsGOLtnDlDkhyF0VqJp4zRTk/WU76cAec5d/5++U9NpDrhe4GvE2hxImcbxw1f\nXaIenAPb9FrWlbZMDC3MgczrWu6vIl87DY4Hxp+DI006UeJJwzWvZtuStuebnglf/Qs+f9S3nxVP\n6H0ZNlufsXd/qomaMUEYLaNO9o2gKdnsCz2lz1LFCbD5Te3AOkmvIm2TJ0E9Apvf0jYyLlWVvpME\n6jyHzjmsWdD1jv2cW7QA0cd/VNf+0OktrenBUzS27nFrgmsvxCrxbpKVW87QAdGkJwWwnquK4ZmL\n1boCrWN84V9alkWtLCjXsQAAIABJREFULlIlvvltbWBTg8wS7i5zblHFt+F1lWNAFz/dPmC4untz\nntOfg5P13V3CwrWy0Rd/16SQQIw5HVb8y+dyG32qNuSjTunasVInqeJxrNLEoZCYATs+gOO/rQ3o\n6FPb3z5xqJazPLBNZRHRBverJ3T5gOGdd2pO+J5aPcOP07g0+KyvhKGqwECHtM0do4lwC7/h2771\nOU+9FBb/WMc6jzpZlf4zF2tjd/Fjvo5CTJK6sp1EnvHndL0YxehTtBOw8j/aIQj2GTr2drW6xnxN\np5NHqUIrWg/zz4N5v1evRrMbnrpQKwxe+k/1Iqx/FWZc7VPyTrJRZxZb1lP6vI45A7Yt1em9q3wd\ngg//V8MaSNtEMNDkvAVXw7LftFwnbZrGXz/yJnsFegbTpmrYo4USH6XTxqOxWONpudwVpiGLQJb4\nK7er1+CeAOfceqRJRxz/HVVUI09qOX/E8erWXvqrlvMv/rs+4zOu1uQ2JxmvM9JnwR0faob/wmvV\n8wT6fDuVD1fN12GtY/2SwNIztdpZU53PK7nq/1rKddfqwOGcUadom9DR+xuIqZfC+7/03c9Tfthy\neXikXq/oAe2Xze1hrBLvJqv3lDGrPSs85zlV4Lcu0exL5wP0/t/7rirSl7Zsl1rJoWbUKdrbz5qv\nSryrVqwIfPdLX7U3h4T0Q5ftpO9rRnl4ZODlY0/XxDUnjpc+UxOnulqXOCwc7lnb0iU24xodlpe/\nSnv4nbnjLnlcG2DHhTrvDyo/Rq2UzvIWplysme4R0b5KWweVeJoqniHH6HC2Y29XCzZqAHzrI1Wc\nrV3YkbEta+uX7dbnzCkLWVeuDZArDK5+3nfMuMEdyxmIWTep7KZZM3ujE4PbLm2q3j8nDJAyWofn\nOfULyr2y1pRojHLDa3Dug5o06K5rO0QxfSasf6395LZ9W3WUxJm/9H57IFNL+m54Xd+59Fm63poF\nWq4zUB33ifPgR9tVnohYXxLewLHw4x3qjQmLUi9XayJitENa7A3TJI1URe10qIbOVO+AE2d2XPVD\nZ6rS8k9uK9mkHTlxqQu69Tty0J0ehBIfezrcl9fyOwOg9+PH2/WcHFzhvphzTDLcu95XirQj1r6o\n5VALcvQcS3dq4ldEHLz+bU0QS53ovT8PtP1io8etnr6MOTpvb5Zeu6//VTune1cHtsTTpnSvTYiI\ngbuzvfdGfB41f657uWv7PMLYmHg32FfVwN7yOjKHJ7Vd6PFoAzzqFO3hDsjwPXQ1fpZ4VaG6imoP\nHLpLOhhEtDHMW6EdiK6600EVxoCMlr/DkYkp0vnL13p5dz8s4BRpcHDGfL7t7YF35o4LC2/ZkLpc\n3mFoGcHnBjiNqGNJOAo43lu8Ys6tqgC2LlELe8bV6p5sLwadnqmx+Wa3XyU4r7VSX67DA0Hldu5b\nex2mjhDxxi8zglfgDv6KI3mUjgJY81+vrK3GyrvrNVnPccG37nA6CYqlOwlI1lOqhDKv960PvuF5\nzv6c6faIT9VzbZ1FH5ui8wMp8IMyeo/h1H7wL6fsJFU6OO9/eqae+z6/USFOLNp4fB0zfw6ONAky\n07+1Andwzsn5OYVUHKIT2777gX5zbtW4ctZ8vX8xKTpU9JgrtcO/6knf/Zl5fUsZDoa3/OsZ5KhL\nf+TJvhKx1cXasWod9upumxAV75V/WDuJgeE9krAWLFaJd4PCCk2OGZESIBN65zItOOCMZ04Y4mtQ\n/d3pVUV+2d2jOSLM/IZaD/7ZrEc7SSM0Z6HQm9QWyLUaKmIHqRuzulitHaeBPeZKdfu+dof3YyGd\nFI8YOtPX+DuJUweLx5S3/3WtniJ5tCqlxirtYDjV6pyEpegk+Pj3ej7O2HN/Okpua6rXmvmTLtRh\nX+CLNzvbxg3yhQK6kgjVFRwlfnBo2ij927oaWOxAX4eotRJrrNXOjNM+BEoGrN2vXpEj8LWsoIhJ\n0nDI2pc0l2PmN/S5DgvXDPkdy3Q0yqQL2nZKE4fpO+GESmq8HqX0TN1+yHRvQmJh207GUUxIlbiI\nzBORLSKyXUTardAgIpeLiBGROaGU53BRUqljDwcnRmuFrw2v+36fPqIP4iSvizxhKNTsUyupdr82\n1nGp+iB2VC89FMSm+BKjgomhHS04SrI912qocLl8Lkv/+F5UgmZ111focLe0KR3vx7/xdxRAfYXG\nFv0t8d6C87ynTtaYaGtL/MQ7Vf6oRM1BaI2TbOT/3jm/j3+vblz/qlkul2+I3MGkxm6OcAgWZ7+t\nE+KcamCOPP7vfsoYzeR3OicbXlWPw5n363Sgj6jU7A/eCj9SzL5FM9s97pYdUKfyYUNl4I6pk9zm\nX0IV/BJSZ2oSaMXe4JIqjxJC5iMQkTDg78DZQD6wUkQWGWM2tlovAbgH6KR+ZO+hpEqV+FBXOfx7\nHvqBNz9O+aHPXZmQpstrSrwv3EB1N1UVt6x4dqSYe4cmJ3U1M7k/M/4ctXZaJ/wcCRKGaBEJ//ge\naDx89bNwXBCJgyljVOEVrNb65JEJauVWFalCG9xOwmBPkTpRLdLjv61FWLa+6xsrLy5NKlvxL69H\nIkCIIjxSk0E3LQpcn37QRBjVKsFp1CmqBJ2s6VGnamJbqDwvadPUlewoIGc8uZMIF52oCsvfE+By\ntcxvWDVfz2XKpRD+3cBj42sPdC80Fkoy5mjeQUxyy8/xDshQD8n+rTD6a+1v+/Ef9CtlB4sb+XXA\nvnpCn/MjkUfURwilo38usN0YsxNARBYCFwOtvqLBb4A/AD8OoSyHlZKqekRgYMV6wMDl/+ergCSu\nlgrS6TFWFfpeuKgEnyUek3xk3Z3Dj9XEnN7We+9JwsLh/7N373FR1fnjx19v7nITAS8hKqSVggIi\na15Ts8ysNMtMM7ttWe522Vr7xbfaatt2t936tlbfsrub5SWrtWzVaitL20pF84oaqFgIKqIiijfg\n8/vjDDggl0EZzgy8n4/HPJg5cy7vM4eZ9/l8zud8Pnctd1xqaGIV/x/VSxYdesG0LKvXufpUlDQ3\n/9squSVcbXUqU7zbUZ3egL4MmkJwJDyw2Yrr+/+zGlQdP2R9J0LaWd+PuzPqbmNww3unGsRVFx5z\neluNi6ZZdwZUTP/Vr61W4nV1DnQ2/AKt1uTOPRbetdw6ealwyyLr2rCzc5Kt/uZ3rbFa01/2Vyvm\nNnG1JPF9VjW0JxGBmz+p+e6Ha163Gv3W1pam13XWbZw/vmsl8ahupy43VJzwlJ3QkrgTdybxjoDz\ntywXuNB5BhFJBToZYxaJSK1JXESmAFMAOnc+gxHCGtmeQ8eJDA7Ab/dKK2lfMKr2H4OKElbxbiuJ\nV/QotHvDqY4empom8NPVdm+4u1X8f4TW0GjNlQReISbFavEL1u1lmR9ZJXxPrE6HU/+DlSe5u6sO\nqlLfiW1gWP2XGZz5+le9K8HH1/0nz0Gtq76u/j9W00lKTG+rRfyn6VbCT55gTW8TX0sS33/q/ndP\nUttlKf8goJbGdVBtmOLSU30fgHV/v3+wddJXveaqBbOtYZuI+ADPAb+vb15HV69pxpi0tm3raBHa\nRAqKj1kjleWvtRqp1HU271wSr7h+FXaOVb1emN10jdqUZ2pIxyl1qbgG6xt46j74AzlWqcXTGrY5\nqzh5Kd7t6Duhhf84VxzHX1ZY9zBXnOxU3Fvv3DOfMacu0TUnFcMUV+9Nr6JxG1it/hXg3iS+C3C+\nOz7WMa1CGNAT+FpEcoB+wEJvaNy2t/g47cICHQNe1NMwJqStVVqvKIkHO3rSMuVW/8l2lMSV56hM\n4mf5o1RR1dihp9Uq2zfg1JCdnlgSr1BbSbylqhgrAKo2/moTZzUWO7IPjh+2BlY5cdgqtTe3RqoV\nwxTD6XcPVDRIbOn/J07cmcRXAeeJSLyIBAATgMpWKMaYImNMtDEmzhgTB/wAjDbGZLgxpkax99Bx\nzmtVZLU6ry+J+/ha/5AFW6xOMiq6w6wQqSXxFq2in+azbWgYea6VEDv3t65JhnWw+nsGz7sm7qzi\nNqOin63vU0u/1ili9S/RIcka5a5CpNNtZounwSuDT41J3txK4r7+1t0F/sGnj2PQZYB1W6b+blZy\n2zVxY0ypiNwNfAb4Am8ZYzaJyJNAhjGmhmalnq+83LDv8HESKy73u3KfaViHU/2NB0dVTeJaEm/Z\nOveD+zdZLXfPhgjcuezUddfQDqf6ZPfk6vTAMMdtVU79x7d0175etVdAOPU7sWuN1RVt2fFTY5Z7\nWuv0xnDR/7NuSavejqDHaLhv3dl/X5oRt3ZDY4xZDCyuNq3GDniNMUPdGUtj2V9ygtJyQ9eTP1ln\nhBUd+tcl7JxTP1LB0VVLG5rEVWP9IIU6daUa1uHUwBOeXJ0OVqx5FUm8hZfE4fQGcWB1SgTw7T+s\nBB52jtVtLDS/kjhY179r+l6IeGwf5nbRHtsaqKKjl5iSLY5hIV0YPjSsA5X3kgdHOq73iNVhv6fd\nHqKaB+dk6MklcbC+H0U/W89raqWvrN+ZsBir8V+nC62+KMpLrfcacheDanY0iTfQ3uJjgCHiYCbE\nJLu2kHMVYUi0dZYZ2s46u27oSFJKucL5f84bSuKVz7UkXquKWrs+t1r3uPs77oppjtXpymWaxBto\n76HjnCe78D++/9RoSPVx/pGq+MJFnut5PWmp5qMiGYqP1ZubJ6v4fohv82tp3ZjadbeqzhOvtqrc\nk8Zbf+3q40B5BM8dmsVD7S0+xkTfrzA+/kiP0a4tVPGD6tfq1D3l1719em9NSjWWisQY1LpxRppz\np4rvR2h7rZmqyyVPwKD7T13Cu+yvMOBeHQikhdMs0kD7iw5xk99ypMdVdQ9F6KziB9W5lFHbsJJK\nNYaKxOjpVenQePfKN3dBras2egsItno4Uy2ah5+ie55O+Z8TzpGqoyTVp+IHtTm2IlWeqeIk0dMb\ntcGp3rc0iSvVYJrEG6j/gY/J84s9NRqRKyrGjdYkrppKUITV97aWxJVq1jSJN8SBnXQ/uZlVba5s\n2HUoHx+rJXprvZ1MNRERqzWzN7T2DjvH6iY2oovdkSjldfSaeAOYfVkIUBTtQi9t1d34Yc2dOCjl\nLhPnWr2hebqAYLjjq1NjfSulXKZJvAGO7s0mGPCLOoN+e7UBimpq3pQUO/SyOwKlvJIm8QY4umcb\nPsafsGjt9k8ppZT99Jp4A5zct52fTTvi29Yy4L1SSinVhDSJN4Bf0U4riUeH2B2KUkop5d4kLiIj\nRWSriGSLSHoN798lIhtEZK2IfCsiCe6M56wYQ1hJLvv8YwgJ1KsQSiml7Oe2JC4ivsBLwOVAAjCx\nhiQ9xxjTyxiTAvwdeM5d8Zy1I/sINEc5FtrZ7kiUUkopwL0l8b5AtjFmuzHmBDAPGOM8gzHmkNPL\nECrH6/Q85sAOAHyi4uwNRCmllHJwZ71wR+AXp9e5wIXVZxKR3wIPAAHAxW6M56wczs8iDAjucJ7d\noSillFKABzRsM8a8ZIzpCjwEPFrTPCIyRUQyRCSjoKCgaQN0KMrPAqBtrCZxpZRSnsGdSXwX4HxD\ndaxjWm3mAVfX9IYx5jVjTJoxJq1tWxdHDmtkJwu2k28iieug/Z8rpZTyDO5M4quA80QkXkQCgAnA\nQucZRMS5WHsFkOXGeM6Kb9FOck07YtsE2x2KUkopBbjxmrgxplRE7gY+A3yBt4wxm0TkSSDDGLMQ\nuFtELgFOAgeAm90Vz9kKK/mFLYHJ+Po0YOATpZRSyo3cesOzMWYxsLjatMecnt/nzu03mpNHaVNW\nyNFwvb1MKXX2Tp48SW5uLseOHbM7FOVBgoKCiI2Nxd/f3+VltNcSF5QW5uAH+ETG2R2KUqoZyM3N\nJSwsjLi4OKQhwxqrZssYQ2FhIbm5ucTHuz7Ilu2t071BYb51j3hIuzh7A1FKNQvHjh0jKipKE7iq\nJCJERUU1uHZGk7gLivZat7u3aa/V6UqpxqEJXFV3Jv8TmsRdcOxAHgDRHbrYHIlSSp29wsJCUlJS\nSElJoUOHDnTs2LHy9YkTJ1xax6233srWrVvrnOell15i9uzZjREyAHv27MHPz4833nij0dbp7fSa\nuAvKi/I5ZIJpF93G7lCUUuqsRUVFsXbtWgCeeOIJQkNDmTZtWpV5jDEYY/DxqbmsN3PmzHq389vf\n/vbsg3Uyf/58+vfvz9y5c7n99tsbdd3OSktL8fPzjvSoJXEX+B7ZTaG0IdDP1+5QlFLKbbKzs0lI\nSGDSpEkkJiaSn5/PlClTSEtLIzExkSeffLJy3kGDBrF27VpKS0uJiIggPT2d5ORk+vfvz969ewF4\n9NFHmT59euX86enp9O3blwsuuIDvvvsOgCNHjnDttdeSkJDAuHHjSEtLqzzBqG7u3LlMnz6d7du3\nk5+fXzl90aJFpKamkpyczIgRIwAoLi7m5ptvJikpiaSkJD766KPKWCvMmzev8mTgxhtvZOrUqfTt\n25eHH36YH374gf79+9O7d28GDhxIVpbVjUlpaSn3338/PXv2JCkpiZdffpnPP/+ccePGVa53yZIl\nXHfddWd9PFzhHacaNgs6VsAh/2i7w1BKNUN//GQTmXmH6p+xARJiwnn8qsQzWnbLli3MmjWLtLQ0\nAJ5++mkiIyMpLS1l2LBhjBs3joSEqgNSFhUVMWTIEJ5++mkeeOAB3nrrLdLTTxt9GmMMK1euZOHC\nhTz55JN8+umnvPjii3To0IEPP/yQdevWkZqaWmNcOTk57N+/nz59+nDdddcxf/587rvvPnbv3s3U\nqVNZvnw5Xbp0Yf/+/YBVw9C2bVvWr1+PMYaDBw/Wu+/5+fn88MMP+Pj4UFRUxPLly/Hz8+PTTz/l\n0Ucf5b333mPGjBnk5eWxbt06fH192b9/PxEREdx9990UFhYSFRXFzJkzue222xr60Z8RLYm7IOzk\nPo4G2tPdq1JKNaWuXbtWJnCwSr+pqamkpqayefNmMjMzT1umVatWXH755QD06dOHnJycGtd9zTXX\nnDbPt99+y4QJEwBITk4mMbHmk4958+Zx/fXXAzBhwgTmzp0LwPfff8+wYcPo0sVqsxQZGQnAF198\nUVmdLyK0aVP/5dDrrruu8vLBwYMHufbaa+nZsyfTpk1j06ZNleu966678PX1rdyej48PkyZNYs6c\nOezfv5/Vq1dX1gi4m5bE62MMkeX7yQ5pb3ckSqlm6ExLzO4SEhJS+TwrK4vnn3+elStXEhERwY03\n3ljjLVABAQGVz319fSktLa1x3YGBgfXOU5u5c+eyb98+3n77bQDy8vLYvn17g9bh4+ODMadGvK6+\nL877/sgjj3DZZZfxm9/8huzsbEaOHFnnum+77TauvfZaAK6//vrKJO9uWhKvR/HBvQRIKT7h59gd\nilJKNalDhw4RFhZGeHg4+fn5fPbZZ42+jYEDBzJ//nwANmzYUGNJPzMzk9LSUnbt2kVOTg45OTk8\n+OCDzJs3jwEDBrB06VJ27twJUFmdfumll/LSSy8BVjX+gQMH8PHxoU2bNmRlZVFeXs6CBQtqjauo\nqIiOHTsC8M9//rNy+qWXXsorr7xCWVlZle116tSJ6Ohonn76aW655Zaz+1AaQJN4PfblWf8YAREx\nNkeilFJNKzU1lYSEBLp3785NN93EwIEDG30b99xzD7t27SIhIYE//vGPJCQk0Lp16yrzzJ07l7Fj\nx1aZdu211zJ37lzat2/PjBkzGDNmDMnJyUyaNAmAxx9/nD179tCzZ09SUlJYvnw5AH/729+47LLL\nGDBgALGxsbXG9dBDD/Hggw+SmppapfR+55130qFDB5KSkkhOTq48AQG44YYbiI+P5/zzzz/rz8VV\n4hycN0hLSzMZGRluWbcxhsIjJ4gODayctu7rD0n++ja2jJpP976XuWW7SqmWZfPmzfTo0cPuMDxC\naWkppaWlBAUFkZWVxYgRI8jKyvKaW7yc3XXXXfTv35+bbz7zsbxq+t8QkdXGmLSa5ve+T8mN/ptd\nyOS3VvDPW/sy5HyrIdvRwlwA2rTXjl6UUqqxHT58mOHDh1NaWooxhldffdUrE3hKSgpt2rThhRde\naNLtuvWTEpGRwPNYQ5G+YYx5utr7DwC3A6VAAXCbMWanO2Oqy6qc/RgDj3+8kU9/dxFB/r6cLLLu\nRYxq38musJRSqtmKiIhg9erVdodx1mq7t93d3HZNXER8gZeAy4EEYKKIJFSb7UcgzRiTBHwA/N1d\n8bhiU94hQgJ8ySks4bVlVqtHKd7NIULxCwqpZ2mllFKqabmzYVtfINsYs90YcwKYB4xxnsEYs9QY\nU+J4+QNQeyuDJlC0awvzw/7B9T1a8dLSbHIPlBBwdC8HfSPtDEsppZSqkTuTeEfgF6fXuY5ptfk1\nsMSN8dTpwJETXF3yLxKP/MAj56zAAM9/kUXoiQKOaEcvSimlPJBH3GImIjcCacAztbw/RUQyRCSj\noKDALTFs2ZnHGN//AhC+aQ6T+8by4ZpcIsoKOdmqnVu2qZRSSp0NdybxXYBza7BYx7QqROQS4BFg\ntDHmeE0rMsa8ZoxJM8aktW3rnlLxyXXzCZVjHOtzFxT9zL1xv9DKX4jmICZMO3pRSjUfw4YNO63j\nlunTpzN16tQ6lwsNDQWs3tKcB/xwNnToUOq7DXj69OmUlJRUvh41apRLfZu7KiUlpbIr1+bOnUl8\nFXCeiMSLSAAwAVjoPIOI9AZexUrge90YS73ic94nS7oQdPmfIKQtrTe9y9S+bQiQMvwjNIkrpZqP\niRMnMm/evCrT5s2bx8SJE11aPiYmhg8++OCMt189iS9evLjK6GJnY/PmzZSVlbF8+XKOHDnSKOus\nSUO7jXUXtyVxY0wpcDfwGbAZmG+M2SQiT4rIaMdszwChwPsislZEFtayOvfatYZOx39iReQY8AuA\n3jfCT58yddf/ABAf382WsJRSyh3GjRvHokWLOHHiBGCNEJaXl8fgwYMr79tOTU2lV69efPzxx6ct\nn5OTQ8+ePQE4evQoEyZMoEePHowdO5ajR49Wzjd16tTKYUwff/xxAF544QXy8vIYNmwYw4YNAyAu\nLo59+/YB8Nxzz9GzZ0969uxZOYxpTk4OPXr04I477iAxMZERI0ZU2Y6zuXPnMnnyZEaMGFEl9uzs\nbC655BKSk5NJTU1l27ZtgNWDW69evUhOTq4cec25NmHfvn3ExcUBVvero0eP5uKLL2b48OF1flaz\nZs2q7NVt8uTJFBcXEx8fz8mTJwGrS1vn12fKrfeJG2MWA4urTXvM6fkl7ty+q0pXvskJE0hRN0e3\nfn2nwO4N+J48BueNoNW5jd/VoFJKAbAkHXZvaNx1dugFlz9d69uRkZH07duXJUuWMGbMGObNm8f4\n8eMREYKCgliwYAHh4eHs27ePfv36MXr0aESkxnXNmDGD4OBgNm/ezPr166sMJfrnP/+ZyMhIysrK\nGD58OOvXr+fee+/lueeeY+nSpURHVx3iefXq1cycOZMVK1ZgjOHCCy9kyJAhlf2dz507l9dff53x\n48fz4YcfcuONN54Wz3vvvcd//vMftmzZwosvvsgNN9wAwKRJk0hPT2fs2LEcO3aM8vJylixZwscf\nf8yKFSsIDg6u7Ae9LmvWrGH9+vWVw7PW9FllZmby1FNP8d133xEdHc3+/fsJCwtj6NChLFq0iKuv\nvpp58+ZxzTXX4O/vX+826+IRDdtsdawI2fQhC8v6c15nR//o4TFw44dw6yKY9D6E6QhmSqnmxblK\n3bkq3RjDww8/TFJSEpdccgm7du1iz549ta5n2bJllck0KSmJpKSkyvfmz59PamoqvXv3ZtOmTTUO\nbuLs22+/ZezYsYSEhBAaGso111xT2ed5fHw8KSkpQO3DnWZkZBAdHU3nzp0ZPnw4P/74I/v376e4\nuJhdu3ZV9r8eFBREcHAwX3zxBbfeeivBwcHAqWFM63LppZdWzlfbZ/XVV19x3XXXVZ6kVMx/++23\nM3PmTABmzpzJrbfeWu/26uN9fds1tvXz8S09yjxzCa91apxrMkop5bI6SszuNGbMGO6//37WrFlD\nSUkJffr0AWD27NkUFBSwevVq/P39iYuLq3H40frs2LGDZ599llWrVtGmTRtuueWWM1pPhYphTMEa\nyrSm6vS5c+eyZcuWyurvQ4cO8eGHHza4kZufnx/l5eVA3cOVNvSzGjhwIDk5OXz99deUlZVVXpI4\nGy27JG4MJ1a8yUYTT48+Q2kXHmR3REop1SRCQ0MZNmwYt912W5UGbUVFRbRr1w5/f/8qQ3zW5qKL\nLmLOnDkAbNy4kfXr1wNWAg0JCaF169bs2bOHJUtOdQMSFhZGcXHxaesaPHgwH330ESUlJRw5coQF\nCxYwePBgl/anvLyc+fPns2HDhsrhSj/++GPmzp1LWFgYsbGxfPTRRwAcP36ckpISLr30UmbOnFnZ\nyK6iOj0uLq6yK9i6GvDV9lldfPHFvP/++xQWFlZZL8BNN93EDTfc0CilcGjpSTx3FQGFm3mvbDj3\nXKyN15RSLcvEiRNZt25dlSQ+adIkMjIy6NWrF7NmzaJ79+51rmPq1KkcPnyYHj168Nhjj1WW6JOT\nk+nduzfdu3fnhhtuqDKM6ZQpUxg5cmRlw7YKqamp3HLLLfTt25cLL7yQ22+/nd69e7u0L8uXL6dj\nx47ExJwaNvqiiy4iMzOT/Px83nnnHV544QWSkpIYMGAAu3fvZuTIkYwePZq0tDRSUlJ49tlnAZg2\nbRozZsygd+/elQ3ualLbZ5WYmMgjjzzCkCFDSE5O5oEHHqiyzIEDB1y+E6A+LXoo0t0/zKdkyWO8\n13sW/3N130ZZp1JK1UeHIm25PvjgAz7++GPeeeedGt/XoUgb4L/+A3hSnueL4cl2h6KUUqqZu+ee\ne1iyZAmLFy+uf2YXtegkfm2fWEb27EBIYIv+GJRSSjWBF198sdHX2bKviYMmcKWUUl6rxSdxpZSy\ng7e1R1LudybDxsDZAAAgAElEQVT/E5rElVKqiQUFBVFYWKiJXFUyxlBYWEhQUMNudda6ZKWUamKx\nsbHk5ubirqGVlXcKCgoiNja2QctoEldKqSbm7+9PfHy83WGoZkCr05VSSikvpUlcKaWU8lKaxJVS\nSikv5XXdropIAVB3j/wNEw3U3jmud9F98Uy6L55J98Uz6b6crosxpm1Nb3hdEm9sIpJRW5+03kb3\nxTPpvngm3RfPpPvSMFqdrpRSSnkpTeJKKaWUl9IkDq/ZHUAj0n3xTLovnkn3xTPpvjRAi78mrpRS\nSnkrLYkrpZRSXqpFJ3ERGSkiW0UkW0TS7Y6nIUSkk4gsFZFMEdkkIvc5pj8hIrtEZK3jMcruWF0h\nIjkissERc4ZjWqSI/EdEshx/29gdZ31E5AKnz36tiBwSkd95y3ERkbdEZK+IbHSaVuNxEMsLju/P\nehFJtS/y09WyL8+IyBZHvAtEJMIxPU5Ejjodn1fsi/x0texLrf9TIvI/juOyVUQusyfqmtWyL+85\n7UeOiKx1TPf041Lb73DTfWeMMS3yAfgC24BzgQBgHZBgd1wNiP8cINXxPAz4CUgAngCm2R3fGexP\nDhBdbdrfgXTH83Tgb3bH2cB98gV2A1285bgAFwGpwMb6jgMwClgCCNAPWGF3/C7sywjAz/H8b077\nEuc8n6c9atmXGv+nHL8D64BAIN7xO+dr9z7UtS/V3v9f4DEvOS61/Q432XemJZfE+wLZxpjtxpgT\nwDxgjM0xucwYk2+MWeN4XgxsBjraG1WjGwO87Xj+NnC1jbGcieHANmNMY3ZO5FbGmGXA/mqTazsO\nY4BZxvIDECEi5zRNpPWraV+MMZ8bY0odL38AGjZklE1qOS61GQPMM8YcN8bsALKxfu88Ql37IiIC\njAfmNmlQZ6iO3+Em+8605CTeEfjF6XUuXpoERSQO6A2scEy621FV85Y3VEE7GOBzEVktIlMc09ob\nY/Idz3cD7e0J7YxNoOqPkTceF6j9OHj7d+g2rFJRhXgR+VFEvhGRwXYF1UA1/U9583EZDOwxxmQ5\nTfOK41Ltd7jJvjMtOYk3CyISCnwI/M4YcwiYAXQFUoB8rKopbzDIGJMKXA78VkQucn7TWHVRXnMr\nhYgEAKOB9x2TvPW4VOFtx6E2IvIIUArMdkzKBzobY3oDDwBzRCTcrvhc1Cz+p6qZSNUTX684LjX8\nDldy93emJSfxXUAnp9exjmleQ0T8sf5xZhtj/gVgjNljjCkzxpQDr+NB1Wh1McbscvzdCyzAintP\nRVWT4+9e+yJssMuBNcaYPeC9x8WhtuPgld8hEbkFuBKY5PiBxVH1XOh4vhrrOvL5tgXpgjr+p7z1\nuPgB1wDvVUzzhuNS0+8wTfidaclJfBVwnojEO0pNE4CFNsfkMse1ozeBzcaY55ymO19fGQtsrL6s\npxGREBEJq3iO1fhoI9bxuNkx283Ax/ZEeEaqlCi88bg4qe04LARucrS47QcUOVUheiQRGQn8P2C0\nMabEaXpbEfF1PD8XOA/Ybk+Urqnjf2ohMEFEAkUkHmtfVjZ1fGfgEmCLMSa3YoKnH5fafodpyu+M\n3a377HxgtRT8Cevs7hG742lg7IOwqmjWA2sdj1HAO8AGx/SFwDl2x+rCvpyL1Zp2HbCp4lgAUcCX\nQBbwBRBpd6wu7k8IUAi0dprmFccF68QjHziJdb3u17UdB6wWti85vj8bgDS743dhX7KxrklWfGde\nccx7reN/by2wBrjK7vhd2Jda/6eARxzHZStwud3x17cvjun/BO6qNq+nH5fafoeb7DujPbYppZRS\nXqolV6crpZRSXk2TuFJKKeWlNIkrpZRSXkqTuFJKKeWlNIkrpZRSXkqTuFJKKeWlNIkrpZRSXkqT\nuFIuEBFfETksIp0bc147iUg3EXFLRxHV1y0in4vIJHfEISJ/8LRxppVqKprEVbPkSKIVj3IROer0\nusZkUhdj9VEdaoz5uTHn9VQi8oWIPFbD9GtFZFdFV5iuMsaMMMbMrn/OeuO6RERyqq37T8aYu852\n3TVs63YR+bqx16tUY9IkrpolRxINNcaEAj9jdddYMe20ZOIYfEGd8jYwuYbpk4F3jTFlTRyPUqoG\nmsRViyQiT4nIeyIyV0SKgRtFpL+I/CAiB0UkX0RecIxQhIj4iYhxjBmMiLzreH+JiBSLyPeOwSYa\nNK/j/ctF5CcRKRKRF0Xkv46RtmqK25UY7xSRbBE5ICIvOC3rKyL/EJFCEdkOjKzjI/oX0EFEBjgt\nH4XVL/Qsx+vRIrJWRA6JyM8i8oc6Pu9vK/apvjgcJeDNjs9qm4jc7pjeGvgE6OxUq9LOcSz/6bT8\nWBHZ5PiMvhKRC5zeyxWRB0Rkg+PznisigXV8DrXtT6yI/FtE9otIlojc5vRePxFZ4/hc9ojIM47p\nwSIyx7HfB0VkpYhEN3TbSjnTJK5asrHAHKA11vCHpcB9QDQwECu53FnH8jcAfwAisUr7f2rovCLS\nDpgPPOjY7g7qHqbUlRhHAX2A3lgnJ5c4pk/FGiEuGfgVML62jRhjjgAfADc5TZ4ArDfGbHK8PgxM\nAiKAq4D7ROTKOmKvUF8ce4ArgHDgDuBFEUkyxhQ5tvOzU61KleFpRaQH1sAg9wBtsQafWFhxouMw\nHrgUa+CdPtRc41Cf97COVQxwPfB3ERnieO9F4BljTDjQDetzBLgVCMYafjIK+A1w7Ay2rVQlTeKq\nJfvWGPOJMabcGHPUGLPKGLPCGFNqjNkOvAYMqWP5D4wxGcaYk8BsIOUM5r0SWGuM+djx3j+AfbWt\nxMUY/2qMKTLG5ABfO21rPPAPY0yuscZofrqOeMGqUh/vVFK9yTGtIpavjDGbHJ/fOmBeDbHUpM44\nHMdku7F8hTUa1GAX1guOIYUdsZ10rLs1cKHTPNONMbsd2/43dR+30zhqUfoC6caYY8aYNcBMTp0M\nnMQa5jjKGFNsjFnhND0a6OZoN5FhjDnckG0rVZ0mcdWS/eL8QkS6i8giEdktIoeAJ7F+dGuz2+l5\nCRB6BvPGOMdhrGEFc6mFizG6tC1gZx3xAnwDHAKuEpHzsUr2zmOk9xeRr0WkQESKgNtriKUmdcYh\nIleKyApHVfVBrFK7q9XOMc7rM8aUY32eHZ3machxq20b+xy1FRV2Om3jViAB2OqoMh/lmP5PrJqB\n+WI1DnxatC2GOkuaxFVLVv22pleBjVglpXDgMazxf90pH6t6FQAREaomnOrOJsZ8oJPT6zpvgXOc\nUMzCKoFPBhYbY5xrCeYBHwKdjDGtgTdcjKXWOESkFVb181+B9saYCOBzp/XWdytaHtDFaX0+WJ/v\nLhficlUeEC0iIU7TOldswxiz1RgzAWgH/C/woYgEGWNOGGOeMMb0wBqHeizW5QilzpgmcaVOCQOK\ngCOOa6t1XQ9vLP8GUkXkKkep7D6sa7nuiHE+8DsR6ehopPaQC8vMwrrufhtOVelOsew3xhwTkX5Y\nVdlnG0cgEAAUAGWOa+zDnd7fg5VAw+pY92gRGeq4Dv4gUAysqGX++viISJDzwxizA8gA/iIigSKS\nglX6fhdARCaLSLSjFqAI68SjXEQuFpGejhOLQ1jV6+VnGJdSgCZxpZz9HrgZ60f/VazGS25ljNmD\n1TDqOaAQ6Ar8CBx3Q4wzsK4vbwBWcarBVV3xZQMrsZLrompvTwX+Klbr/oexEuhZxWGMOQjcDywA\n9gPjsE50Kt7fiFX6z3G08G5XLd5NWJ/PDKwTgZHAaMf18TMxGDha7QHWMTsPq2r+A+BhY8zXjvdG\nAZsdn8uzwPXGmBNY1fD/wkrgm7Cq1uecYVxKASBWjZlSyhOI1YlKHjDOGLPc7niUUp5NS+JK2UxE\nRopIhKMV+B+wqllX2hyWUsoLuC2Ji8hbIrJXRDbW8r6I1VFFtoisF5FUd8WilIcbBGzHqv69DBhr\njKmtOl0ppSq5rTpdRC7C6gxiljGmZw3vj8LqkGEU1j2czxtjLqw+n1JKKaVq5raSuDFmGVbDlNqM\nwUrwxhjzAxAhIue4Kx6llFKqubHzmnhHqnb4UL1DBqWUUkrVwSt6CxKRKcAUgJCQkD7du3e3OaKG\n2Xf4OPlFx0g4JxxfH3f3HaKUUqo5Wb169T5jTI39R9iZxHdRtdemWntVMsa8htVHNGlpaSYjI8P9\n0TWiLzL3cPusDN78zQBSO7exOxyllFJeRERq7SLZzur0hcBNjlbq/YAiY0y+jfG4zbltrd4Ztxcc\nqWdOpZRSynVuK4mLyFxgKFYXibnA44A/gDHmFWAxVsv0bKxBCG51Vyx26xQZjJ+PsGOfDliklFKq\n8bgtiRtjJtbzvgF+667texJ/Xx+6RAXz0x5N4koppRqPVzRsaw4SYlqzZucBu8NQSrUAJ0+eJDc3\nl2PHjtkdimqAoKAgYmNj8ff3d3kZTeJNpGdMOJ+sy+PAkRO0CQmwOxylVDOWm5tLWFgYcXFxWKPb\nKk9njKGwsJDc3Fzi4+NdXk77Tm8iiTGtAdiUd8jmSJRSzd2xY8eIiorSBO5FRISoqKgG155oEm8i\niTHhAGzMK7I5EqVUS6AJ3PucyTHTJN5E2oQE0DGilZbElVLNXmFhISkpKaSkpNChQwc6duxY+frE\niRMurePWW29l69atdc7z0ksvMXv27MYImUGDBrF27dpGWVdT0mviTSghJpxNWhJXSjVzUVFRlQnx\niSeeIDQ0lGnTplWZxxiDMQYfn5rLkjNnzqx3O7/9bYu4walOWhJvQj1jWrNj3xGOHC+1OxSllGpy\n2dnZJCQkMGnSJBITE8nPz2fKlCmkpaWRmJjIk08+WTlvRcm4tLSUiIgI0tPTSU5Opn///uzduxeA\nRx99lOnTp1fOn56eTt++fbngggv47rvvADhy5AjXXnstCQkJjBs3jrS0NJdL3EePHuXmm2+mV69e\npKamsmzZMgA2bNjAr371K1JSUkhKSmL79u0UFxdz+eWXk5ycTM+ePfnggw8a86OrlSbxJpQYE44x\nsDlfq9SVUi3Tli1buP/++8nMzKRjx448/fTTZGRksG7dOv7zn/+QmZl52jJFRUUMGTKEdevW0b9/\nf956660a122MYeXKlTzzzDOVJwQvvvgiHTp0IDMzkz/84Q/8+OOPLsf6wgsvEBgYyIYNG3jnnXeY\nPHkyJ06c4OWXX2batGmsXbuWVatWERMTw+LFi4mLi2PdunVs3LiRSy+99Mw+oAbS6vQm1LOj1UJ9\n464i0uIibY5GKdUS/PGTTWQ2cluchJhwHr8q8YyW7dq1K2lpaZWv586dy5tvvklpaSl5eXlkZmaS\nkJBQZZlWrVpx+eWXA9CnTx+WL19e47qvueaaynlycnIA+Pbbb3nooYcASE5OJjHR9bi//fZbHnzw\nQQASExOJiYkhOzubAQMG8NRTT7Fz506uueYaunXrRlJSEunp6aSnp3PVVVcxcOBAl7dzNrQk3oTa\nhwcSFRLARm3cppRqoUJCQiqfZ2Vl8fzzz/PVV1+xfv16Ro4cWeMtVgEBp/rW8PX1pbS05kuSgYGB\n9c7TGCZPnsyCBQsIDAxk5MiRLFu2jB49epCRkUFiYiLp6en85S9/cdv2nWlJvAmJCKld2rByx367\nQ1FKtRBnWmJuCocOHSIsLIzw8HDy8/P57LPPGDlyZKNuY+DAgcyfP5/BgwezYcOGGqvrazN48GBm\nz57NRRddxObNm8nPz6dbt25s376dbt26cd9997Fjxw7Wr19P165diY6OZvLkyYSFhfHuu+826n7U\nRpN4ExvQNYr/ZO7hl/0ldIoMtjscpZSyTWpqKgkJCXTv3p0uXbq4pQr6nnvu4aabbiIhIaHy0bp1\n6xrnveyyyyq7PB08eDBvvfUWd955J7169cLf359Zs2YREBDAnDlzmDt3Lv7+/sTExPDEE0/w3Xff\nkZ6ejo+PDwEBAbzyyiuNvi81EWscEu/hjeOJO9u6u5jLpi/j7+OSGJ/Wqf4FlFKqgTZv3kyPHj3s\nDsMjlJaWUlpaSlBQEFlZWYwYMYKsrCz8/DyzDFvTsROR1caYtJrm98y9aMbObx9KVEgA328r1CSu\nlFJudvjwYYYPH05paSnGGF599VWPTeBnovnsiZcQEfp3jeL7bYUYY7RrRKWUcqOIiAhWr15tdxhu\no63TbTCgazS7Dx1jx74jdoeilFLKi2kSt8GArlEAfLet0OZIlFJKeTNN4jboEhVMTOsgftiuSVwp\npdSZ0yRuAxEhISac7L2H7Q5FKaWUF9MkbpP46BB27DtCebl33eKnlFL1GTZsGJ999lmVadOnT2fq\n1Kl1LhcaGgpAXl4e48aNq3GeoUOHUt9txtOnT6ekpKTy9ahRozh48KArodfpiSee4Nlnnz3r9TQm\nTeI2iYsO4XhpObsPnd7FoFJKebOJEycyb968KtPmzZvHxIkTXVo+JibmrEYBq57EFy9eTERExBmv\nz5NpErdJfLTVf7C2UFdKNTfjxo1j0aJFnDhxAoCcnBzy8vIYPHhw5X3bqamp9OrVi48//vi05XNy\ncujZsydgDQc6YcIEevTowdixYzl69GjlfFOnTq0cxvTxxx8HrJHH8vLyGDZsGMOGDQMgLi6Offv2\nAfDcc8/Rs2dPevbsWTmMaU5ODj169OCOO+4gMTGRESNGVNlOfWpa55EjR7jiiisqhyZ97733AEhP\nTychIYGkpKTTxlg/E3qfuE3OjbaqjbbvO8LAbtE2R6OUUo0nMjKSvn37smTJEsaMGcO8efMYP348\nIkJQUBALFiwgPDycffv20a9fP0aPHl1rnxkzZswgODiYzZs3s379elJTUyvf+/Of/0xkZCRlZWUM\nHz6c9evXc++99/Lcc8+xdOlSoqOr/rauXr2amTNnsmLFCowxXHjhhQwZMoQ2bdqQlZXF3Llzef31\n1xk/fjwffvghN954Y737Wts6t2/fTkxMDIsWLQKs4VQLCwtZsGABW7ZsQUQapYpfk7hN2ocH0srf\nlxwtiSul3GlJOuze0Ljr7NALLn+6zlkqqtQrkvibb74JWGN+P/zwwyxbtgwfHx927drFnj176NCh\nQ43rWbZsGffeey8ASUlJJCUlVb43f/58XnvtNUpLS8nPzyczM7PK+9V9++23jB07tnIktWuuuYbl\ny5czevRo4uPjSUlJAaoOZVqf2tY5cuRIfv/73/PQQw9x5ZVXMnjw4MruX3/9619z5ZVXcuWVV7q0\njbpodbpNRIQ4R+M2pZRqbsaMGcOXX37JmjVrKCkpoU+fPgDMnj2bgoICVq9ezdq1a2nfvn2Nw4/W\nZ8eOHTz77LN8+eWXrF+/niuuuOKM1lOhYhhTaJyhTM8//3zWrFlDr169ePTRR3nyySfx8/Nj5cqV\njBs3jn//+9+NMmKblsRtFB8dzJb8YrvDUEo1Z/WUmN0lNDSUYcOGcdttt1Vp0FZUVES7du3w9/dn\n6dKl7Ny5s871XHTRRcyZM4eLL76YjRs3sn79esAaxjQkJITWrVuzZ88elixZwtChQwEICwujuLj4\ntOr0wYMHc8stt5Ceno4xhgULFvDOO++c1X7Wts68vDwiIyO58cYbiYiI4I033uDw4cOUlJQwatQo\nBg4cyLnnnntW2wZN4raKjw7h8017OFlWjr+vVooopZqXiRMnMnbs2Cot1SdNmsRVV11Fr169SEtL\no3v37nWuY+rUqdx666306NGDHj16VJbok5OT6d27N927d6dTp05VhjGdMmUKI0eOJCYmhqVLl1ZO\nT01N5ZZbbqFv374A3H777fTu3dvlqnOAp556qrLxGkBubm6N6/zss8948MEH8fHxwd/fnxkzZlBc\nXMyYMWM4duwYxhiee+45l7dbGx2K1EYfrM5l2vvrWDptaGVrdaWUOls6FKn3auhQpFr8s1F8dDCA\nNm5TSil1RjSJ2yje6TYzpZRSqqE0iduoTbA/4UF+WhJXSil1RtyaxEVkpIhsFZFsEUmv4f3OIrJU\nRH4UkfUiMsqd8XgaESG+bSjb9+lAKEqpxuVt7Z3UmR0ztyVxEfEFXgIuBxKAiSKSUG22R4H5xpje\nwATgZXfF46m6tg1h214tiSulGk9QUBCFhYWayL2IMYbCwkKCgoIatJw7bzHrC2QbY7YDiMg8YAyQ\n6TSPAcIdz1sDeW6MxyN1axfKv9bsovjYScKC/O0ORynVDMTGxpKbm0tBQYHdoagGCAoKIjY2tkHL\nuDOJdwR+cXqdC1xYbZ4ngM9F5B4gBLjEjfF4pG5trcZt2wqOkNKpeY6yo5RqWv7+/sTHx9sdhmoC\ndjdsmwj80xgTC4wC3hGR02ISkSkikiEiGc3tzLJbOyuJZ+/V6+JKKaUaxp1JfBfQyel1rGOas18D\n8wGMMd8DQcBpQ3oZY14zxqQZY9Latm3rpnDt0TkymABfH03iSimlGsydSXwVcJ6IxItIAFbDtYXV\n5vkZGA4gIj2wknjzKmrXw8/Xh7joYE3iSimlGsxtSdwYUwrcDXwGbMZqhb5JRJ4UkdGO2X4P3CEi\n64C5wC2mBTan7NYulG0FmsSVUko1jFsHQDHGLAYWV5v2mNPzTGBg9eVamq5tQ/l0426Ol5YR6Odr\ndzhKKaW8hN0N2xRWSbzcQM6+ErtDUUop5UU0iXuArm21hbpSSqmG0yTuAbq2DUVEk7hSSqmG0STu\nAVoF+NIxohXZ2rhNKaVUA2gS9xA9zglnzc4DlJe3uMb5SimlzpAmcQ9xZdI57Dp4lB+2F9odilJK\nKS+hSdxDXJbYgbAgPz5YnWt3KEoppbyEJnEPEeTvy1XJMSzemE/xsZN2h6OUUsoLaBL3IOP6xHLs\nZDmLN+TbHYpSSikvoEncg/TuFMG5bUN494efKdMGbkoppeqhSdyDiAj3XNyNDbuKmPV9jt3hKKWU\n8nCaxD3M1SkdGXpBW/7+6VZ+2a/dsCqllKqdJnEPIyL8ZWwvfH2ExxdusjscpZRSHkyTuAeKiWjF\npAs7szyrgGMny+wORymllIfSJO6h+nRpw8kyw8ZdRXaHopRSykNpEvdQqV3aALDm5wM2R6KUUspT\naRL3UNGhgXSODGb1Tk3iSimlaqZJ3IOldo5gzc8HMUbvGVdKKXU6TeIeLLVLGwqKj5N74KjdoSil\nlPJAmsQ9WGpnvS6ulFKqdvUmcRHxbYpA1Om6dwgjOMCXH38+aHcoSimlPJArJfEsEXlGRBLcHo2q\nws/Xh6TY1loSV0opVSNXkngy8BPwhoj8ICJTRCTczXEph+ROEWzOP8SJ0nK7Q1FKKeVh6k3ixphi\nY8zrxpgBwEPA40C+iLwtIt3cHmELlxjTmpNlhqy9xXaHopRSysO4dE1cREaLyAJgOvC/wLnAJ8Bi\nN8fX4iXGWJUem/IO2RyJUkopT+PnwjxZwFLgGWPMd07TPxCRi9wTlqoQFxVCcIAvmZrElVJKVeNK\nEk8yxhyu6Q1jzL2NHI+qxtdH6N4hTJO4Ukqp07jSsK2diHwiIvtEZK+IfCwi57o9MlUpMaY1mfmH\nKC/XntuUUkqd4koSnwPMBzoAMcD7wFx3BqWqSowJ5/DxUn7eX2J3KEoppTyIK0k82BjzjjGm1PF4\nFwhyd2DqlMSY1gBk5muVulJKqVNcSeJLRCRdROJEpIuI/D9gsYhEikhkXQuKyEgR2Soi2SKSXss8\n40UkU0Q2icicM9mJ5u689qH4+gib8nRscaWUUqe40rBtvOPvndWmTwAM1u1mp3F01/oScCmQC6wS\nkYXGmEynec4D/gcYaIw5ICLtGhh/ixDk78t57UL1NjOllFJV1JvEjTHxZ7juvkC2MWY7gIjMA8YA\nmU7z3AG8ZIw54NjW3jPcVrOXEBPON1sLKCs3+PqI3eEopZTyAK509uIvIveKyAeOx90i4u/CujsC\nvzi9znVMc3Y+cL6I/NfRpetI10NvWS7u3o7CIyf4bts+u0NRSinlIVy5Jj4D6AO87Hj0cUxrDH7A\necBQYCLwuohEVJ/J0V97hohkFBQUNNKmvculCe2JCPZnfkau3aEopZTyEK5cE/+VMSbZ6fVXIrLO\nheV2AZ2cXsc6pjnLBVYYY04CO0TkJ6ykvsp5JmPMa8BrAGlpaS3yZulAP1+uTunInJU/U1RyktbB\nrlSGKKWUas5cKYmXiUjXiheOjl7KXFhuFXCeiMSLSABWQ7iF1eb5CKsUjohEY1Wvb3dh3S3SuD6x\nnCgtZ+G66udCSimlWiJXkviDwFIR+VpEvgG+An5f30LGmFLgbuAzYDMw3xizSUSeFJHRjtk+AwpF\nJBOrf/YHjTGFZ7IjLUHPjq1JOCec2St+puREqd3hKKWUspkYU3vttIj4AP2A1cAFjslbjTHHmyC2\nGqWlpZmMjAy7Nm+7T9blce+8H+naNpSXbkjlgg5hdoeklFLKjURktTEmrab36iyJG2PKsW4BO26M\nWe942JbAFVyVHMO7v76QgyUnGffKdxw+riVypZRqqVypTv9SRK4VEb052UMM7BbN/93Qm+JjpXy9\nVW+tV0qplsqVJH4n1qAnx0XkkIgUi4h2HWazX8VFEhUSwGeb9tgdilJKKZu40mObXnT1QL4+wojE\n9ixcm8exk2UE+fvaHZJSSqkm5kqPbV+6Mk01vcsSO3DkRJn24qaUUi1UrUlcRIIco5RFi0ibilHL\nRCSO07tPVTYY0DWasEA/Pt242+5QlFJK2aCukvidWLeWdXf8rXh8DPyf+0NT9Qnw82F4j3Z8nrmH\nwsN604BSSrU0tSZxY8zzjhHMphljzjXGxDseycYYTeIe4qYBcRw9UcZ1r35P3sGjdoejlFKqCdV7\nTdwY86KIDBCRG0TkpopHUwSn6pfauQ3v/PpCCg4d57pXvtee3JRSqgVxpWHbO8CzwCDgV45HjT3H\nKHv0jY9k+oQUdh08yvfbtNdapZRqKVwZxSwNSDB19c+qbDfovGha+fvyzU8FDO/R3u5wlFJKNQFX\nOnvZCIXex3MAACAASURBVHRwdyDq7AT6+TKgaxTf/NQyx1tXSqmWyJWSeDSQKSIrgcom0MaY0bUv\nouww5IK2fLllLzn7jhAXHWJ3OEoppdzMlST+hLuDUI1jyPltAfjmpwJN4kop1QLU1dlLdwBjzDfA\nD8aYbyoeOJXIlefoEhVCXFQwy7RKXSmlWoS6ronPcXr+fbX3XnZDLKoRDDm/Ld9tK+TYyTK7Q1FK\nKeVmdSVxqeV5Ta+VhxjV6xyOnizjlW+22R2KUkopN6sriZtantf0WnmIC8+NYkxKDC8v3Ub23mK7\nw1FKKeVGdSXxWBF5QURedHpe8VoHQPFgf7gygVYBvjz8r42UlpXbHY5SSik3qSuJP4g14EmG0/OK\n1//P/aGpMxUdGsgfrkxgZc5+bnh9BflF2qe6Uko1R7XeYmaMebspA1GNa1yfWHx94NEFGxn1/HLm\n3NGPHueE2x2WUkqpRuRKj23KS43tHcsn9wwi0M+X29/OoKBY7wxUSqnmRJN4M3du21DeuDmN/UdO\ncMesDA6WnLA7JKWUUo1Ek3gL0LNja/5xfQrrcw8y5JmveWP5dnIPlKBj2iillHdzZSjSv4tIuIj4\ni8iXIlIgIjc2RXCq8Yzs2YHF9w0mKbY1Ty3azKC/LeXCv3xJRs5+u0NTSil1hlwpiY8wxhwCrgRy\ngG5YrdWVl+neIZxZt/Xl3/cM4k9X9yTAz4ffv7+Ooye0dzellPJGriTxihbsVwDvG2OK3BiPcjMR\noWfH1kzu14VnxiWzs7CE//18q91hKaWUOgOujGL2bxHZAhwFpopIW+CYe8NSTaF/1yhu7NeZN/+7\ng5/3l9CxTStGJ8fQu3Mbu0NTSinlAnGlcZOIRAJFxpgyEQkGwo0xu90eXQ3S0tJMRkaGHZtulg4f\nL+WRBRvYnH+IX/Yf5ejJMoZe0Ja/jO1FTEQru8NTSqkWT0RWG2PSanqv3pK4iFwHfOpI4I8CqcBT\ngC1JXDWu0EA/np/QG4Ajx0uZ9f1OXvwqi8c+3sQbN9f4P6OUUspDuHJN/A/GmGIRGQRcArwJzHBv\nWMoOIYF+TB3alalDuvLF5j2s+fmA3SEppZSqgytJvKLp8hXAa8aYRUCAKysXkZEislVEskUkvY75\nrhURIyJa9PMAtw2KJyokgGc/0wZvSinlyVxJ4rtE5FXgemCxiAS6spyI+AIvAZcDCcBEEUmoYb4w\n4D5gRUMCV+4TEujHb4d147tthfw3e5/d4SillKqFK0l8PPAZcJkx5iAQiWv3ifcFso0x240xJ4B5\nwJga5vsT8De0xbtHmdSvM23DApn53x12h6KUUqoW9SZxY0wJsA24TETuBtoZYz53Yd0dgV+cXudS\nbRxyEUkFOjmq6JUHCfTzZVyfWJZuLWDPIT2/UkopT+RKtfh9wGygnePxrojcc7YbFhEf4Dng9y7M\nO0VEMkQko6Cg4Gw3rVw0Pq0TZeWGD1bn2h2KUkqpGrhSnf5r4EJjzGPGmMeAfsAdLiy3C+jk9DrW\nMa1CGNAT+FpEchzrXVhT4zZjzGvGmDRjTFrbtm1d2LRqDPHRIVwYH8n8jF90sBSllPJAriRx4VQL\ndRzPxYXlVgHniUi8iAQAE4CFFW8aY4qMMdHGmDhjTBzwAzDaGKM9uXiQCX07sbOwhO+3F9odilJK\nqWpcSeIzgRUi8oSIPIGVbN+sbyFjTClwN1ajuM3AfGPMJhF5UkRGn0XMqgld3vMcokICmDZ/HdsL\nDtsdjlJKKSeudruaCgxyvFxujPnRrVHVQbtdbXobdxVx81srEYE5d/Tj/PZhdoeklFItRl3drtZZ\nEhcRXxHZYoxZY4x5wfGwLYEre/Ts2Jr37uxPuYGnl2yxOxyllFIOdSZxY0wZsFVEOjdRPMpDdWsX\nyoRfdeLrrXtdvuXMGENZuTaIU0opd3HlmngbYJOIfCkiCyse7g5MeZ5xfWIpN/CvNbvqndcYwx2z\nVjP+1e8pLStvguiUUqrlcWU88T+4PQrlFc5tG0palza8v/oX7hpyLiK136Qwe8XPfLF5DwDv/rCT\nWwbGN1WYSinVYtSaxEWkG9DeGPNNtemDgHx3B6Y803VpsTz04Qbe/HYHe4uP06tja65Kjqkyzy/7\nS/jL4s0M6haNCPzv5z9xRVIMbcMCbYpaKaWap7pK4tOB/6lhepHjvavcEpHyaFckxfDHTzJ5atFm\nfATKDfw3ex+DzotmwZpdbMo7xL7Dxwny9+Vv45I4frKMy6Yv4/GFG3lxYiq+Pq50MaCUUsoVdSXx\n9saYDdUnGmM2iEic2yJSHi000I/Zt19IyYkyeneO4KWl2by0dBvzVv3COa2DGHReNO3DA7mkR3s6\nRrQC4IFLL+Bvn26hrHw1z0/oTZC/r817oZRSzUNdSTyijvdaNXYgynv07tym8vmDl3Xnkh7tKTlR\nRr9zo2osaU8d2pVAPx/+tCiTKe+s5u1bf1Xn9XSllFKuqSuJZ4jIHcaY150nisjtwGr3hqW8iXNS\nr81tg+IpKzf8efFmVuUcoG98ZBNEppRSzVtdSfx3wAIRmcSppJ0GBABj3R2Yan5u7NeFl7/O5rVl\n2zWJK6VUI6g1iRtj9gADRGQY1mhjAIuMMV81SWSq2WkV4Mvk/nG88GUW2woO07VtqN0hKaWUV6u3\nsxdjzFJjzIuOhyZwdVZu6t+FAD8f3li+3e5QlFLK67nSY5tSjSY6NJDxabHMz8hleVaB3eEopZRX\n0ySumlz65T04r10ov5m9hm06vKlSSp0xTeKqyYUG+vH6TWkE+Ppw2z9X8cv+kirvFx4+zub8QzZF\np5RS3sOl8cQ9iY4n3nz8+PMBbn5rJYH+vvx1bC927i/h6617+W5bIcYYvp42jM5RwXaHqZRStjrj\n8cSVcqfendvwwdQB+PkIt8/K4E//ziT3wFEm9+tCuYHPM3fbHaJSSnk0V0YxU8ptzm8fxsK7B5GR\ns5+kThGVXbX+sL2QzzP3cPvgc22OUCmlPJeWxJXt2oYFcnmvcyoTOMCIxA5k5Oyn8PBxGyNTSinP\npklceaQRCe0pN/Dllr12h6KUUh5Lq9OVR0qMCSemdRCfrMsjZ98Rvt5awFu3/IoOrYPsDk0ppTyG\nlsSVRxIRRiR2YHnWPl7+ehubdx/ide3lTSmlqtAkrjzWzQPiuKZ3Rz767UDGpnRkzoqfOXDkhN1h\nKaWUx9AkrjxWfHQIz12fQkqnCO4a2pWjJ8uY+V0OK3fs56l/Z3KwRBO6Uqpl02viyiuc3z6MSxPa\n89LSbF74MguA/UdO8Nz1KTZHppRS9tEkrrzGA5eez+6iY4xJiaGg+DivLtvOVSkxHDp6kv/7Kps/\nj+2l45QrpVoU7XZVeaXjpWVc8cK3/LK/hOOl5fgIdI4M5tPfXUSQvy8AG3KLeOKTTfz+0vMZ0C3a\n5oiVUurMaLerqtkJ9PPlmXFJhLfy5/eXns8/b+1LTmEJL36VxbGTZcxb+TPXvvIdq3ceYMY32+wO\nVyml3EKr05XX6t25DaseuaTy9bg+sbzyzXbeWL6D46Xl9Ds3kgvahzHrh53sOni0So9wSinVHGgS\nV83GI6N6UHT0JB0jWjH4vGiGnN+W/KJjvP39Thasyf3/7d13eFRV/vjx92fSe08oCSEJofcgHcsK\nCugXy4Kiq7iKgj/xZ3vclV13/fpzH13XtsX1UVHcFURRii66KCjSFGmhhF4CxCSkkt6TmfP7Y4aY\nYBIDkkyGfF7PkyczZ+7cfE7O3PuZe+695zBzZA/uW7ST6wd3Y/b4OGeHq5RSP5smcXXJCPHz5K1Z\njU8bxYT6MiY+jGXJGWw4ksfu74vYk15Ej1BfJvWPclKkSil1cbTpOXERmSwiR0TkuIjMb+L1x0Tk\noIikiMg6EYlty3hU5zQ9KZq0MxXsTCvkpRlDGNgtiEeW7uZoTqmzQ1NKqZ+lzZK4iLgBrwFTgP7A\nbSLS/5zFdgMjjDGDgeXAC20Vj+q8pgzqQp+oAH47uQ/Tk6JZMCsJH0937n13p44Ap5RyaW15JD4S\nOG6MOWGMqQGWAjc0XMAYs94YU+F4uhWIbsN4VCfl6+nOmkcv54ErewHQNciHN+9MIru4innv76LW\nanNyhEopdWHaMol3B9IbPM9wlDVnNvB5Uy+IyBwR2SkiO/Py8i5iiKqzSooN4bmbB7El9QyvrT/u\n7HCUUuqCdIj7xEXkDmAE8GJTrxtjFhhjRhhjRkRERLRvcOqSNT0pmon9olj0XRrVddZGr1XU1FGn\nR+hKqQ6uLZN4JhDT4Hm0o6wREZkIPAlMM8ZUt2E8Sv3IrDGxFJTX8MX+bIwx/PXLo1z54noG/O8a\nHliyy9nhKaVUi9ryFrMdQKKIxGFP3jOB2xsuICLDgDeBycaY3DaMRakmje8VTmyYL0u2fk+t1fD3\ndccY1yuMnuF+rD2Yw8n8cuLC/ZwdplJKNanNjsSNMXXAg8Aa4BDwkTHmgIg8IyLTHIu9CPgDy0Rk\nj4isaqt4lGqKxSLcPrIH208V8OTH+xgdH8qie0bxwvTBeLgJi747BdhnTMspqap/3970IjYd1esz\nlFLO1aaDvRhjVgOrzyl7qsHjiT96k1LtbMaIGF7+8ih+Xu78feYw3CxCZIA3Uwd1ZfnODG4c2p05\ni3dSWFHLfRPsI729viEVEeHDOaMZ0VNnTlNKOYfOYqYUsOFILlGB3vTrGlhflpxWyC9f34KbRQjx\n9WR0fCifpWQBMCMpmm0nC7DaDKsfmkCQr4ezQldKXeJamsVMh11VCriyT+SPyob3CCYpNoTTRZUs\nuXcU8RH+3DuhiPLqOsb1CmdPehHTX9/C9De20CXIGx8PN3pHBZAY5U/3YB8SIvwJ8fN0Qm2UUp2F\nHokr1YLy6jrcLFI/R/m5lidn8O6WU1gsQllVLafOVGC12bcpHw833vn1ZYxJCKO4spb9mcWMTQhD\nRNqzCkopF9fSkbgmcaUuoqpaK+kFFWQUVfLcfw+RWVTJQ1cn8s43J8ktrWZGUjTP3jQIT/cOMUSD\nUsoFaHe6Uu3E28ONxKgAEqMCGNAtkJkLtvL854fp1zWQqYO68u8tpziUXULXIB8sAveMi2NUfBil\nVbXsyyhmdHwYFoseqSulWkeTuFJtJDLAmw/njGFLaj5TB3XFw83CkJgg/vn1cdILKjhTXsOaA1sZ\n3iOYg1klVNXa+N2Uvsy9IsHZoSulXIR2pyvlJJU1Vt7afIJP9mQyJj7MMV1qAWsfuYIeYb7Nvq+w\nvIZgXw89t65UJ6HnxJVyAdnFVUx8ZSNDY4JZPHtkk0l6T3oRt7zxHdcMiOIfM4dp17tSnYCeE1fK\nBXQJ8uaJKX354yf7mfTXTUwd2IXUvHL2ZhQxIymGO8fEMm/JLjzdLXyWkkWYnydPTxuAiFBrtbH1\nxBl8Pd0Z1D1IL5xTqpPQI3GlOhBjDCt2ZbJ0+/fsTCskKtCL2DA/tp8sIMDLnao6K8vuH8t/U07z\n1uaTxIf70SPMl73pRRRW1ALg7WFhXEI41w/pyuQBXfHxbPr2OKWUa9AjcaVchIgwPSma6UnRFFfU\nEujjjoiwclcGf/rsIH+c3J+hMcEM7h5ElyAfdpws4NSZcsb2CufGod2x2mxsPVHAmgPZrDucy4Ku\nJ3n/3lE66IxSlyg9ElfKRRhjWn0xm81mWHswh4eW7iYx0p/37x2tQ8Mq5aL0wjalOqn1h3OZuzgZ\nPy83bhzWnS6B3uz+vojeXQJ4dGIiIsJHO9LJLqnioasTnR2uUqoJ2p2uVCd1Vd9Ils4dzcLNJ3lv\naxq1VkO4vxdfHMgm1NeDLkE+/HZFCgDjeoWRFKszsinlSvRIXKlOoriyljqrjRBfT+a+l8y6Qzl4\nulvo0yWQ00WVxIX58eHc0Xr/uVIdTEtH4nofilKdRJCPB2H+Xlgswt9uHUqfLoGE+Xnx1qwkHro6\nke2nCvj6cK6zw1RKnQdN4kp1Qn5e7nwybyxrH72cyABvZl4WQ88wX55dfYiy6jrAPplLZlFls+sw\nxlBZY22vkJVSTdAkrlQn5eXuhp+X/bIYDzcLz900iFP55fxm2V6yiiu58bVv+cVLGziRV9bofcYY\n1h/J5ebXtzDsT2tJL6hwRvhKKTSJK6UcxvYK5/dT+/H5/mwmvryR9IIKPN0tzF+5D5vth2tnnv/i\nMHf/awe5JdVU19lYtjPdiVEr1blpEldK1Zs9Po4ZSdH4e7vz4dwx/OG6fmw/WcAHO74HYMm2NN7c\neILbR/Vg/eNXMiExguXJGVhtLV8g62oX0CrlKjSJK6XqiQgvTB/MlvlXM7B7ELeMiGFsQhhPfryf\n0c+t46n/HOCqPhE8M20Anu4WbhkRzeniKrak5je7ziPZpVz27DpWJGc0+Xp5dR3zV6Sw9cSZtqqW\nUpcsTeJKqUZEBDfH7Ggiwmu3D2f+lL6MTQjj5mHdefX24bi72Xcdk/pHEezrwUc7m07QAH/98ij5\nZdXMX5nCjlMFjV6rqbNx/3vJLN2Rzrwlu8grrW67iil1CdIkrpRqUYifJ/dfkcArtw7lxRlD8Pf6\nYYwoL3c3bhzanc/3ZXH1yxuY/LdNJKcV1r9+4HQxXxzI5tdjexId4svcxcl8l3oGYwz5ZdU8+tEe\nNh/LZ95VCZRV1/Hb5Xs5XVTJiuQMktMKL4lu+PyyauqstkZlxhheW3+cL/ZnOykqdanQEduUUj/L\n7PFxZBdX4eYm7E0v4s6F23h71gjGJITxt6+OEejtzqOTenPnmFhuf2srt721lT5RAZzML6fGauOJ\nyX35P1cmEOHvxdOfHmTs81/Xr3tg90CGxYTgZhFGxoUyZWCXVg9GU1JVi7e7Gx5u4rQBbNYfzmXu\ne8ncNLQ7f5k+uL58waYTvLjmCMG+HoxPDG/0xUip86EjtimlLprckiruWLiNY7llCGAz8Nik3vXj\nslfVWlm2M53lyRkMjg7mrrGx9IoMAOxHp69+fdw+lWqvcHZ/X8QH278nq7iK6lor5TVWhkQHcc/4\nOEb0DMXTzcKx3FLySqspr7YyNCaY/t0CAZi/IoWlO+xXzbtZBF8PN2JCfbl3QhzThnTD3c1CTZ2N\n19YfZ/OxPF771XC6Bvmcd32NMaw5kEPXIG8Gdg+qPw0B8MX+LP7vB7txt1ioqrPy+cMT6NslkHWH\ncrh30U6SeoSwM62w/kuMs1TU1LHouzR+OTyaiACvVr2nzmrDIoLFoqP7tQedAEUp1W4Ky2v495ZT\n1NlsRPh7MXNkD7w9ft6c5labYeWuDF758ihZxVVNLuPr6caqB8eRVVzFnQu3M21IN3pH+VNRY6Wy\n1sp3qWc4nF1KRIAXQ6KDySis4HB2KR5uQkKEP8vuH0OA9/nN9LY8OYPHl+0F7CPiDYkJJjHSny2p\nZziUVcKQmGBenTmM61/dzPDYEGZe1oOHl+4mMcqfZXPHcv97yezLLOabJ67C1/P8j8atNsO/vj3J\nzlOF/GX6YIJ8zi9+m83wwJJdfHEgm+E9gvlgzmi83Ftuq8oaKxNf2UhFTR0j40L5Rd9IJg/set5/\nW7WeJnGl1CWhzmrjcHYpyWmFWG2G3lEBdAnyps5m4463txHi60md43a3Lx6Z0Cgh2WyGrw7l8FlK\nFgdOF1NrNfzhun54e7hx9793MDYhjH/ePrzVySivtJqJr2wkMdKfWWN7suV4PnszijmaU8qAboH1\n88L7errzxsZUnv/8MCIwJDqYhXeNIMzfi+S0Qn75+hYev6Y3D/7i/GaRyyyq5JGlu9lxyn4Nwsie\noSyaPbLZL0zfHMvnxTWHSYj0Z1xCONEhPnx1KIe3Np/kukFd+e++LG4b2YM/3zyoxb+7YFMqz60+\nzJSBXUjJKCazqBJPdwtzL4/nsUm9L5mx9ytq6nCzyE9+qWkPmsSVUpe8b4/nc8fCbRgDi2ePZEJi\nRKvf+9GOdOavTCHUz5PHJvUh0XEE/8nuTDYcyUVECPR2Z9rQ7tw5OpY6m42nVx1g/eE8Vj88gV6R\n/vXrstnMj7qZq2qt3PDPb0mI9OPlGUPx8fwhMcxZtJO1B3OYP6Uvcy+PR0Qoqarlz6sPk5pXxuWJ\n4Uwd1JX4iB/+Rm5pFTPe+I4zZTX8P8ftfg8t3c34XuE8cGUvwvw9eXntEbadLOC+CfEMjQlm9rs7\nCPPzoqKmjsKK2vp13TYyhuduGsQLa47w+oZUxiaEcfuoHkwe0KX+LoSzyqvrmPDCegZ0C2Tx7FEY\nY0jJKObtb07y6d7TzL08nrvHxbFiVwYxob5MG9Kt1W1wMVXU1PHlwRwm9ouqH5XwfHx7PJ957+9i\nRGwIb991WRtEeH40iSulOoWPdqSTW1p13ke1APszi/nDJ/vZk15UXxbg7c41/bvg42khvaCSjUfz\nGr3nN9f2Yd5VvVq1fmNMk0ep1XVWHl+Wwqd7TzM6PpRB3YNYvS+b7JIqEiP9OZxdiqe7hWemDeDW\ny2IoKK/hjoXbOZVfzpL7RjG8RwgA72/7nqc/PUBNnf1KeH8vdwZHB7El1X7/fUKEHx/OHUOIryep\neWXklFRRZzVMSAzH3c2C1WZYsOkE721NI7OokvhwPx67pjcB3h4czy0jMsCL/aeLeXPjCVY+MLb+\n756t21P/OcDirWmIwNm08tT1/blnfFzrG6EJVbVWPkvJYn9mMbPGxDb6MtPU/3jlrkxeXHOE7JIq\nJvaLZMGdI1p97t4Yw8JvTvLnzw/j4+FGWXUdH84Zzaj4sPplih1fgIJ8G/fYFFXUUFBe02J8F0qT\nuFJKtYLNZjiYVUJhRQ1Wm2F0fFij7unUvDI+25tFqL8nvSP9GRkXelG6j202+y1nq/dnk5pbRnSI\nDy/fMoRhPULILq7iN8v3svlYPj3DfEkrqMDdIrx912Vc0btxb0N5dR2bjubxfUEF05OiCfP34quD\nOXyacprfTelHlyDvn4zFajN8eTCHl9ce4Vhu2Y9ev6pPBP+6e+SPys9emFhVa+Xm4d15ac1RvjiQ\nzcieoRRV1hAT4svvr+tHgiPJVdZYOZFfxrfH81m5K5OiiloenpjILSNisDmO8D/de5pVe09TUF6D\nRewXKd48LBqbMRRW1BAR4EVMqC/jEsKJCvTmiRUpbDyax5DoIC7rGcrb35zk4asTmXtFPJmFlcSG\n+eHp3vSd1WfKqvnN8hS+PpzLtQOiePamQVz3j81Eh/iy/P4xZBRW8uamVD7amYGbCHMuj2fuFfH4\nerqzJ72I+xcnk1taxX0T4nl0Uu+ffR1IQ05L4iIyGfg74Aa8bYx5/pzXvYBFQBJwBrjVGHOqpXVq\nEldKXcrqrDbcLI1vi7PaDK9vOM72U4UM7xHMpP5RDOgW1KZxWG2GjUdz8fFwp1ekPzklVRzMKuHy\nxIhWfRmos9p4dvUhdqUVEhnozdYTZ6iqtZIUG0J6QWWjGfKG9QjGIkJyWiFBPh6UVtViM+DpbuEX\nfSKZNSaWXlH+vLTmCB/vziTUz5MQX0/yy6rJL6sBQAS83C38fmo/7hgViwg8viyFFbt+GIgoIsCL\nmZfFUGs1HDhdTP+ugcwYEcPmY3m8+vVxyqrreHJqP2aNiUVEWLItjSc/3s+4XmFsPVGAReDmYdGU\n1dTx35QsPN0s9O0awOHsUiIDvBgZF8rKXZnER/jxn3njzvtCyeY4JYmLiBtwFJgEZAA7gNuMMQcb\nLPMAMNgYc7+IzARuMsbc2tJ6NYkrpZTrySut5qU1RziUXUJ8uB/xEf4kRPgzsHsgsWF+GGP4LCWL\nzcfy6BLkQ0KEH1f1jSTwnER47mmJwvIaNhzN5UBmCbeN6lF/pA/2rvg3NqZiEaFLoDer92ex4Uge\n7hb7HQnH88rqx/0fmxDGU//Tn75dAuvfX2u1ce3fNpFTXMWvRsdyz7i4+i8wyWmFrD2Qzd6MIsL8\nvfjTDQMJ9fPkm2P5fJuazxOT+160/52zkvgY4GljzLWO578DMMb8ucEyaxzLfCci7kA2EGFaCEqT\nuFJKqQuVV1pNgLc73h5u5JRU8VlKFr2j/BnfK7zJUyMlVbUIXLSj6gvRUhJvy2GCugMN5yjMAEY1\nt4wxpk5EioEwoPnZFJRSSqkL1HBAm6hAb2b/xIV35/YEdDQuMdafiMwB5jielonIkYu4+nAunS8N\nWpeOSevSMWldOiaty4/FNvdCWybxTCCmwfNoR1lTy2Q4utODsF/g1ogxZgGwoC2CFJGdzXVTuBqt\nS8ekdemYtC4dk9bl/LTlLGY7gEQRiRMRT2AmsOqcZVYBdzkeTwe+bul8uFJKKaV+0GZH4o5z3A8C\na7DfYvaOMeaAiDwD7DTGrAIWAotF5DhQgD3RK6WUUqoV2vScuDFmNbD6nLKnGjyuAma0ZQyt0Cbd\n9E6idemYtC4dk9alY9K6nAeXG7FNKaWUUnZteU5cKaWUUm2oUydxEZksIkdE5LiIzHd2POdDRGJE\nZL2IHBSRAyLysKP8aRHJFJE9jp+pzo61NUTklIjsc8S801EWKiJfisgxx++Qn1qPs4lInwb/+z0i\nUiIij7hKu4jIOyKSKyL7G5Q12Q5i9w/H9pMiIsOdF/mPNVOXF0XksCPej0Uk2FHeU0QqG7TPG86L\n/MeaqUuznykR+Z2jXY6IyLXOibppzdTlwwb1OCUiexzlHb1dmtsPt982Y4zplD/YL7ZLBeIBT2Av\n0N/ZcZ1H/F2B4Y7HAdiHuO0PPA087uz4LqA+p4Dwc8peAOY7Hs8H/uLsOM+zTm7YRyGMdZV2AS4H\nhgP7f6odgKnA54AAo4Ftzo6/FXW5BnB3PP5Lg7r0bLhcR/tppi5NfqYc+4G9gBcQ59jPuTm7Di3V\n5ZzXXwaecpF2aW4/3G7bTGc+Eh8JHDfGnDDG1ABLgRucHFOrGWOyjDG7HI9LgUPYR8C7lNwAvOt4\n/C5woxNjuRBXA6nGmDRnB9JaxphN2O8Uaai5drgBWGTstgLBItK1fSL9aU3VxRiz1hhT53i6Ffv4\nnrbdHQAABLNJREFUFR1eM+3SnBuApcaYamPMSeA49v1dh9BSXUREgFuAD9o1qAvUwn643baZzpzE\nmxoW1iWToIj0BIYB2xxFDzq6at5xhS5oBwOsFZFksY/QBxBljMlyPM4GopwT2gWbSeOdkSu2CzTf\nDq6+Dd2D/ajorDgR2S0iG0VkgrOCOk9NfaZcuV0mADnGmGMNylyiXc7ZD7fbNtOZk/glQUT8gRXA\nI8aYEuB1IAEYCmRh75pyBeONMcOBKcA8Ebm84YvG3hflMrdSiH2Ao2nAMkeRq7ZLI67WDs0RkSeB\nOmCJoygL6GGMGQY8BrwvIoHNvb+DuCQ+U+e4jcZffF2iXZrYD9dr622mMyfx1gwL26GJiAf2D84S\nY8xKAGNMjjHGaoyxAW/RgbrRWmKMyXT8zgU+xh53ztmuJsfvXOdFeN6mALuMMTnguu3i0Fw7uOQ2\nJCK/Bq4HfuXYweLoej7jeJyM/Txyb6cF2QotfKZctV3cgZuBD8+WuUK7NLUfph23mc6cxFszLGyH\n5Th3tBA4ZIx5pUF5w/MrNwH7z31vRyMifiIScPYx9ouP9tN4WN67gP84J8IL0uiIwhXbpYHm2mEV\nMMtxxe1ooLhBF2KHJCKTgd8C04wxFQ3KI0TEzfE4HkgETjgnytZp4TO1CpgpIl4iEoe9LtvbO74L\nMBE4bIzJOFvQ0duluf0w7bnNOPvqPmf+YL9S8Cj2b3dPOjue84x9PPYumhRgj+NnKrAY2OcoXwV0\ndXasrahLPParafcCB862BfZpadcBx4CvgFBnx9rK+vhhn8gnqEGZS7QL9i8eWUAt9vN1s5trB+xX\n2L7m2H72ASOcHX8r6nIc+znJs9vMG45lf+n47O0BdgH/4+z4W1GXZj9TwJOOdjkCTHF2/D9VF0f5\nv4H7z1m2o7dLc/vhdttmdMQ2pZRSykV15u50pZRSyqVpEldKKaVclCZxpZRSykVpEldKKaVclCZx\npZRSykVpEldKXTQicqWIfObsOJTqLDSJK6WUUi5Kk7hSnZCI3CEi2x1zNL8pIm4iUiYif3XMi7xO\nRCIcyw4Vka3ywxzcZ+dG7iUiX4nIXhHZJSIJjtX7i8hysc/bvcQxqpVSqg1oEleqkxGRfsCtwDhj\nzFDACvwK+0hzO40xA4CNwP863rIIeMIYMxj7KFNny5cArxljhgBjsY/CBfaZnB7BPq9yPDCuzSul\nVCfl7uwAlFLt7mogCdjhOEj2wT5Bg40fJp94D1gpIkFAsDFmo6P8XWCZY6z77saYjwGMMVUAjvVt\nN47xr0VkD9AT+Kbtq6VU56NJXKnOR4B3jTG/a1Qo8sdzlrvQMZmrGzy2ovsZpdqMdqcr1fmsA6aL\nSCSAiISKSCz2/cF0xzK3A98YY4qBQhGZ4Ci/E9hojCkFMkTkRsc6vETEt11roZTSb8hKdTbGmIMi\n8gdgrYhYsM8mNQ8oB0Y6XsvFft4c7FMpvuFI0ieAux3ldwJvisgzjnXMaMdqKKVAZzFTStmJSJkx\nxt/ZcSilWk+705VSSikXpUfiSimllIvSI3GllFLKRWkSV0oppVyUJnGllFLKRWkSV0oppVyUJnGl\nlFLKRWkSV0oppVzU/wcc0u2EQm7eCQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tfpe8NbOZxfr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "base_model.trainable = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXGTS0pSZ0Db",
        "colab_type": "code",
        "outputId": "ec30815f-5b25-48f4-f010-500e576441bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Let's take a look to see how many layers are in the base model\n",
        "print(\"Number of layers in the base model: \", len(base_model.layers))\n",
        "\n",
        "# Fine tune from this layer onwards\n",
        "fine_tune_at = 100\n",
        "\n",
        "# Freeze all the layers before the `fine_tune_at` layer\n",
        "for layer in base_model.layers[:fine_tune_at]:\n",
        "  layer.trainable =  False"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of layers in the base model:  311\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfDoHOzZaIp5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer = tf.keras.optimizers.Adam(1e-5),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0P8ZxqEeafKX",
        "colab_type": "code",
        "outputId": "896c3b0b-ee8e-4bdb-ba84-23c611535624",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "inception_v3 (Model)         (None, 5, 5, 2048)        21802784  \n",
            "_________________________________________________________________\n",
            "conv2d_94 (Conv2D)           (None, 3, 3, 32)          589856    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 3, 3, 32)          0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 11)                363       \n",
            "=================================================================\n",
            "Total params: 22,393,003\n",
            "Trainable params: 20,216,587\n",
            "Non-trainable params: 2,176,416\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgW9GRTQD16O",
        "colab_type": "code",
        "outputId": "828ea255-8475-470f-e334-4f8f30e19b92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history_fine = model.fit_generator(train_generator, \n",
        "                                   epochs=200,\n",
        "                                   validation_data=val_generator)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n",
            "WARNING:tensorflow:sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n",
            "Train for 7 steps, validate for 1 steps\n",
            "Epoch 1/200\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.0119 - accuracy: 0.9975 - val_loss: 6.5866 - val_accuracy: 0.4318\n",
            "Epoch 2/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.1514 - accuracy: 0.9646 - val_loss: 6.2431 - val_accuracy: 0.4545\n",
            "Epoch 3/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0736 - accuracy: 0.9848 - val_loss: 6.0064 - val_accuracy: 0.4318\n",
            "Epoch 4/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0678 - accuracy: 0.9823 - val_loss: 5.7288 - val_accuracy: 0.4773\n",
            "Epoch 5/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0418 - accuracy: 0.9874 - val_loss: 5.6207 - val_accuracy: 0.4773\n",
            "Epoch 6/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0374 - accuracy: 0.9924 - val_loss: 5.4822 - val_accuracy: 0.4773\n",
            "Epoch 7/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0454 - accuracy: 0.9848 - val_loss: 5.3015 - val_accuracy: 0.4773\n",
            "Epoch 8/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0371 - accuracy: 0.9874 - val_loss: 5.1112 - val_accuracy: 0.5000\n",
            "Epoch 9/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0361 - accuracy: 0.9874 - val_loss: 4.8374 - val_accuracy: 0.5000\n",
            "Epoch 10/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0357 - accuracy: 0.9874 - val_loss: 4.7680 - val_accuracy: 0.4545\n",
            "Epoch 11/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0326 - accuracy: 0.9949 - val_loss: 4.6824 - val_accuracy: 0.4318\n",
            "Epoch 12/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0353 - accuracy: 0.9874 - val_loss: 4.6853 - val_accuracy: 0.4091\n",
            "Epoch 13/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0372 - accuracy: 0.9899 - val_loss: 4.7421 - val_accuracy: 0.3864\n",
            "Epoch 14/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0494 - accuracy: 0.9848 - val_loss: 5.0025 - val_accuracy: 0.3636\n",
            "Epoch 15/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0491 - accuracy: 0.9848 - val_loss: 5.3607 - val_accuracy: 0.2955\n",
            "Epoch 16/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0439 - accuracy: 0.9874 - val_loss: 5.9589 - val_accuracy: 0.2955\n",
            "Epoch 17/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0287 - accuracy: 0.9949 - val_loss: 6.6108 - val_accuracy: 0.2955\n",
            "Epoch 18/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0449 - accuracy: 0.9899 - val_loss: 7.3394 - val_accuracy: 0.2727\n",
            "Epoch 19/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0447 - accuracy: 0.9874 - val_loss: 8.2752 - val_accuracy: 0.2500\n",
            "Epoch 20/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0377 - accuracy: 0.9924 - val_loss: 9.1853 - val_accuracy: 0.2955\n",
            "Epoch 21/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0244 - accuracy: 0.9924 - val_loss: 10.0933 - val_accuracy: 0.2955\n",
            "Epoch 22/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0458 - accuracy: 0.9848 - val_loss: 10.6915 - val_accuracy: 0.2727\n",
            "Epoch 23/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0392 - accuracy: 0.9874 - val_loss: 11.1837 - val_accuracy: 0.2727\n",
            "Epoch 24/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0303 - accuracy: 0.9874 - val_loss: 11.5530 - val_accuracy: 0.2500\n",
            "Epoch 25/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0277 - accuracy: 0.9899 - val_loss: 12.0476 - val_accuracy: 0.2500\n",
            "Epoch 26/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0296 - accuracy: 0.9924 - val_loss: 12.6569 - val_accuracy: 0.2500\n",
            "Epoch 27/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0186 - accuracy: 0.9949 - val_loss: 13.3393 - val_accuracy: 0.2273\n",
            "Epoch 28/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0374 - accuracy: 0.9924 - val_loss: 13.9408 - val_accuracy: 0.2045\n",
            "Epoch 29/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0220 - accuracy: 0.9975 - val_loss: 14.4100 - val_accuracy: 0.2045\n",
            "Epoch 30/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0255 - accuracy: 0.9924 - val_loss: 14.9346 - val_accuracy: 0.2045\n",
            "Epoch 31/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0328 - accuracy: 0.9874 - val_loss: 15.3147 - val_accuracy: 0.2045\n",
            "Epoch 32/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0231 - accuracy: 0.9949 - val_loss: 15.6886 - val_accuracy: 0.2045\n",
            "Epoch 33/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0332 - accuracy: 0.9848 - val_loss: 16.0603 - val_accuracy: 0.2045\n",
            "Epoch 34/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0550 - accuracy: 0.9823 - val_loss: 16.4099 - val_accuracy: 0.2045\n",
            "Epoch 35/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0247 - accuracy: 0.9949 - val_loss: 16.6136 - val_accuracy: 0.2045\n",
            "Epoch 36/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0264 - accuracy: 0.9924 - val_loss: 16.6500 - val_accuracy: 0.2045\n",
            "Epoch 37/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0232 - accuracy: 0.9924 - val_loss: 16.7139 - val_accuracy: 0.2045\n",
            "Epoch 38/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0489 - accuracy: 0.9848 - val_loss: 16.8544 - val_accuracy: 0.1818\n",
            "Epoch 39/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0345 - accuracy: 0.9924 - val_loss: 16.9826 - val_accuracy: 0.1818\n",
            "Epoch 40/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0109 - accuracy: 0.9975 - val_loss: 17.0575 - val_accuracy: 0.1818\n",
            "Epoch 41/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0145 - accuracy: 0.9975 - val_loss: 17.0780 - val_accuracy: 0.1818\n",
            "Epoch 42/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0423 - accuracy: 0.9848 - val_loss: 17.1645 - val_accuracy: 0.1818\n",
            "Epoch 43/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0207 - accuracy: 0.9949 - val_loss: 17.1847 - val_accuracy: 0.1818\n",
            "Epoch 44/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0258 - accuracy: 0.9924 - val_loss: 17.2026 - val_accuracy: 0.1818\n",
            "Epoch 45/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0354 - accuracy: 0.9949 - val_loss: 17.2647 - val_accuracy: 0.1818\n",
            "Epoch 46/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 17.3277 - val_accuracy: 0.1818\n",
            "Epoch 47/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0188 - accuracy: 0.9949 - val_loss: 17.3925 - val_accuracy: 0.1818\n",
            "Epoch 48/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0166 - accuracy: 0.9949 - val_loss: 17.4414 - val_accuracy: 0.1818\n",
            "Epoch 49/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0115 - accuracy: 0.9975 - val_loss: 17.4819 - val_accuracy: 0.1818\n",
            "Epoch 50/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0205 - accuracy: 0.9924 - val_loss: 17.6051 - val_accuracy: 0.1818\n",
            "Epoch 51/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0131 - accuracy: 0.9924 - val_loss: 17.7406 - val_accuracy: 0.1818\n",
            "Epoch 52/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0276 - accuracy: 0.9874 - val_loss: 17.9492 - val_accuracy: 0.1818\n",
            "Epoch 53/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0286 - accuracy: 0.9874 - val_loss: 18.1789 - val_accuracy: 0.1818\n",
            "Epoch 54/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0232 - accuracy: 0.9899 - val_loss: 18.2744 - val_accuracy: 0.1818\n",
            "Epoch 55/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0430 - accuracy: 0.9899 - val_loss: 18.4152 - val_accuracy: 0.1818\n",
            "Epoch 56/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0337 - accuracy: 0.9899 - val_loss: 18.4906 - val_accuracy: 0.1591\n",
            "Epoch 57/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0418 - accuracy: 0.9848 - val_loss: 18.5022 - val_accuracy: 0.1591\n",
            "Epoch 58/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0128 - accuracy: 0.9975 - val_loss: 18.4834 - val_accuracy: 0.1818\n",
            "Epoch 59/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0323 - accuracy: 0.9874 - val_loss: 18.3856 - val_accuracy: 0.1818\n",
            "Epoch 60/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0331 - accuracy: 0.9899 - val_loss: 18.3539 - val_accuracy: 0.1818\n",
            "Epoch 61/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0123 - accuracy: 0.9975 - val_loss: 18.2989 - val_accuracy: 0.1818\n",
            "Epoch 62/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0281 - accuracy: 0.9924 - val_loss: 18.1751 - val_accuracy: 0.1818\n",
            "Epoch 63/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0414 - accuracy: 0.9874 - val_loss: 18.0251 - val_accuracy: 0.1818\n",
            "Epoch 64/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0163 - accuracy: 0.9949 - val_loss: 17.8993 - val_accuracy: 0.1818\n",
            "Epoch 65/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0624 - accuracy: 0.9848 - val_loss: 17.7789 - val_accuracy: 0.1818\n",
            "Epoch 66/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0293 - accuracy: 0.9874 - val_loss: 17.6890 - val_accuracy: 0.1818\n",
            "Epoch 67/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0302 - accuracy: 0.9874 - val_loss: 17.5581 - val_accuracy: 0.1818\n",
            "Epoch 68/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0142 - accuracy: 0.9949 - val_loss: 17.4006 - val_accuracy: 0.1818\n",
            "Epoch 69/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0258 - accuracy: 0.9899 - val_loss: 17.2946 - val_accuracy: 0.1818\n",
            "Epoch 70/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0135 - accuracy: 0.9975 - val_loss: 17.2200 - val_accuracy: 0.1818\n",
            "Epoch 71/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0175 - accuracy: 0.9924 - val_loss: 17.1195 - val_accuracy: 0.1818\n",
            "Epoch 72/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0123 - accuracy: 0.9975 - val_loss: 16.9832 - val_accuracy: 0.1818\n",
            "Epoch 73/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0146 - accuracy: 0.9975 - val_loss: 16.8393 - val_accuracy: 0.1818\n",
            "Epoch 74/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0144 - accuracy: 0.9975 - val_loss: 16.7719 - val_accuracy: 0.1818\n",
            "Epoch 75/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0167 - accuracy: 0.9949 - val_loss: 16.7780 - val_accuracy: 0.1818\n",
            "Epoch 76/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0334 - accuracy: 0.9899 - val_loss: 16.7417 - val_accuracy: 0.1818\n",
            "Epoch 77/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0133 - accuracy: 0.9949 - val_loss: 16.7405 - val_accuracy: 0.1818\n",
            "Epoch 78/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0155 - accuracy: 0.9975 - val_loss: 16.7389 - val_accuracy: 0.1818\n",
            "Epoch 79/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0098 - accuracy: 0.9975 - val_loss: 16.7142 - val_accuracy: 0.1818\n",
            "Epoch 80/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0158 - accuracy: 0.9949 - val_loss: 16.6867 - val_accuracy: 0.1818\n",
            "Epoch 81/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0143 - accuracy: 0.9975 - val_loss: 16.7163 - val_accuracy: 0.1818\n",
            "Epoch 82/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0139 - accuracy: 0.9924 - val_loss: 16.7404 - val_accuracy: 0.1818\n",
            "Epoch 83/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0159 - accuracy: 0.9949 - val_loss: 16.6172 - val_accuracy: 0.1818\n",
            "Epoch 84/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0150 - accuracy: 0.9975 - val_loss: 16.6320 - val_accuracy: 0.1818\n",
            "Epoch 85/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0184 - accuracy: 0.9975 - val_loss: 16.6457 - val_accuracy: 0.1818\n",
            "Epoch 86/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0244 - accuracy: 0.9899 - val_loss: 16.7243 - val_accuracy: 0.1818\n",
            "Epoch 87/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 16.7967 - val_accuracy: 0.1818\n",
            "Epoch 88/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 16.8402 - val_accuracy: 0.1818\n",
            "Epoch 89/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0107 - accuracy: 0.9975 - val_loss: 16.8361 - val_accuracy: 0.1818\n",
            "Epoch 90/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0184 - accuracy: 0.9949 - val_loss: 16.7746 - val_accuracy: 0.1818\n",
            "Epoch 91/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0208 - accuracy: 0.9975 - val_loss: 16.6899 - val_accuracy: 0.1818\n",
            "Epoch 92/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0170 - accuracy: 0.9899 - val_loss: 16.5751 - val_accuracy: 0.1818\n",
            "Epoch 93/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0171 - accuracy: 0.9924 - val_loss: 16.4878 - val_accuracy: 0.1818\n",
            "Epoch 94/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0415 - accuracy: 0.9924 - val_loss: 16.5154 - val_accuracy: 0.1818\n",
            "Epoch 95/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0052 - accuracy: 1.0000 - val_loss: 16.5453 - val_accuracy: 0.1818\n",
            "Epoch 96/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0402 - accuracy: 0.9949 - val_loss: 16.5466 - val_accuracy: 0.1818\n",
            "Epoch 97/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0059 - accuracy: 1.0000 - val_loss: 16.6060 - val_accuracy: 0.1818\n",
            "Epoch 98/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0113 - accuracy: 0.9949 - val_loss: 16.6512 - val_accuracy: 0.1818\n",
            "Epoch 99/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0061 - accuracy: 0.9975 - val_loss: 16.6830 - val_accuracy: 0.1818\n",
            "Epoch 100/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0226 - accuracy: 0.9874 - val_loss: 16.7785 - val_accuracy: 0.1818\n",
            "Epoch 101/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0468 - accuracy: 0.9924 - val_loss: 16.9334 - val_accuracy: 0.1818\n",
            "Epoch 102/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0100 - accuracy: 0.9949 - val_loss: 17.0221 - val_accuracy: 0.1818\n",
            "Epoch 103/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0143 - accuracy: 0.9975 - val_loss: 16.9656 - val_accuracy: 0.1818\n",
            "Epoch 104/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0123 - accuracy: 0.9949 - val_loss: 16.9335 - val_accuracy: 0.1818\n",
            "Epoch 105/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0216 - accuracy: 0.9949 - val_loss: 16.9539 - val_accuracy: 0.1818\n",
            "Epoch 106/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 17.0487 - val_accuracy: 0.1818\n",
            "Epoch 107/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0143 - accuracy: 0.9949 - val_loss: 17.2315 - val_accuracy: 0.1818\n",
            "Epoch 108/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0069 - accuracy: 1.0000 - val_loss: 17.3343 - val_accuracy: 0.1818\n",
            "Epoch 109/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0069 - accuracy: 1.0000 - val_loss: 17.3819 - val_accuracy: 0.1818\n",
            "Epoch 110/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0140 - accuracy: 0.9949 - val_loss: 17.5010 - val_accuracy: 0.1818\n",
            "Epoch 111/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0070 - accuracy: 0.9975 - val_loss: 17.5448 - val_accuracy: 0.1818\n",
            "Epoch 112/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0209 - accuracy: 0.9899 - val_loss: 17.5215 - val_accuracy: 0.1818\n",
            "Epoch 113/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0144 - accuracy: 0.9949 - val_loss: 17.3670 - val_accuracy: 0.1818\n",
            "Epoch 114/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0418 - accuracy: 0.9924 - val_loss: 17.3251 - val_accuracy: 0.1818\n",
            "Epoch 115/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0225 - accuracy: 0.9975 - val_loss: 17.3169 - val_accuracy: 0.1818\n",
            "Epoch 116/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0239 - accuracy: 0.9975 - val_loss: 17.3019 - val_accuracy: 0.1818\n",
            "Epoch 117/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0191 - accuracy: 0.9975 - val_loss: 17.3224 - val_accuracy: 0.1818\n",
            "Epoch 118/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0316 - accuracy: 0.9949 - val_loss: 17.4912 - val_accuracy: 0.1818\n",
            "Epoch 119/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0309 - accuracy: 0.9924 - val_loss: 17.5243 - val_accuracy: 0.1818\n",
            "Epoch 120/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0333 - accuracy: 0.9874 - val_loss: 17.3576 - val_accuracy: 0.1818\n",
            "Epoch 121/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0107 - accuracy: 0.9949 - val_loss: 17.2049 - val_accuracy: 0.1818\n",
            "Epoch 122/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0114 - accuracy: 0.9975 - val_loss: 17.1410 - val_accuracy: 0.1818\n",
            "Epoch 123/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0080 - accuracy: 0.9975 - val_loss: 17.1084 - val_accuracy: 0.1818\n",
            "Epoch 124/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0190 - accuracy: 0.9899 - val_loss: 17.0952 - val_accuracy: 0.1818\n",
            "Epoch 125/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0062 - accuracy: 0.9975 - val_loss: 17.1856 - val_accuracy: 0.1818\n",
            "Epoch 126/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0099 - accuracy: 0.9949 - val_loss: 17.2231 - val_accuracy: 0.1818\n",
            "Epoch 127/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0091 - accuracy: 0.9975 - val_loss: 17.1648 - val_accuracy: 0.1818\n",
            "Epoch 128/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0210 - accuracy: 0.9949 - val_loss: 17.1251 - val_accuracy: 0.1818\n",
            "Epoch 129/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0209 - accuracy: 0.9899 - val_loss: 17.1569 - val_accuracy: 0.1818\n",
            "Epoch 130/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0359 - accuracy: 0.9899 - val_loss: 17.1232 - val_accuracy: 0.1818\n",
            "Epoch 131/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0242 - accuracy: 0.9924 - val_loss: 17.0522 - val_accuracy: 0.1818\n",
            "Epoch 132/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0099 - accuracy: 0.9975 - val_loss: 17.0090 - val_accuracy: 0.1818\n",
            "Epoch 133/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0200 - accuracy: 0.9949 - val_loss: 16.7911 - val_accuracy: 0.1818\n",
            "Epoch 134/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0123 - accuracy: 0.9975 - val_loss: 16.6856 - val_accuracy: 0.1818\n",
            "Epoch 135/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0073 - accuracy: 0.9975 - val_loss: 16.6239 - val_accuracy: 0.1818\n",
            "Epoch 136/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0189 - accuracy: 0.9949 - val_loss: 16.5763 - val_accuracy: 0.1818\n",
            "Epoch 137/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0076 - accuracy: 0.9975 - val_loss: 16.5632 - val_accuracy: 0.1818\n",
            "Epoch 138/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0108 - accuracy: 0.9975 - val_loss: 16.6137 - val_accuracy: 0.1818\n",
            "Epoch 139/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0215 - accuracy: 0.9949 - val_loss: 16.6975 - val_accuracy: 0.1818\n",
            "Epoch 140/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 16.8088 - val_accuracy: 0.1818\n",
            "Epoch 141/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 16.8931 - val_accuracy: 0.1818\n",
            "Epoch 142/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0278 - accuracy: 0.9949 - val_loss: 16.8813 - val_accuracy: 0.1818\n",
            "Epoch 143/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 16.8139 - val_accuracy: 0.1818\n",
            "Epoch 144/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0134 - accuracy: 0.9975 - val_loss: 16.7828 - val_accuracy: 0.1818\n",
            "Epoch 145/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0119 - accuracy: 0.9975 - val_loss: 16.7898 - val_accuracy: 0.1818\n",
            "Epoch 146/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0087 - accuracy: 0.9975 - val_loss: 16.8567 - val_accuracy: 0.1818\n",
            "Epoch 147/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 16.9152 - val_accuracy: 0.1818\n",
            "Epoch 148/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0131 - accuracy: 0.9924 - val_loss: 16.9532 - val_accuracy: 0.1818\n",
            "Epoch 149/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0064 - accuracy: 1.0000 - val_loss: 17.0059 - val_accuracy: 0.1818\n",
            "Epoch 150/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 16.9974 - val_accuracy: 0.1818\n",
            "Epoch 151/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0194 - accuracy: 0.9949 - val_loss: 17.0153 - val_accuracy: 0.1818\n",
            "Epoch 152/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0071 - accuracy: 0.9975 - val_loss: 17.0158 - val_accuracy: 0.1818\n",
            "Epoch 153/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0270 - accuracy: 0.9975 - val_loss: 16.9613 - val_accuracy: 0.1818\n",
            "Epoch 154/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0067 - accuracy: 1.0000 - val_loss: 16.8911 - val_accuracy: 0.1818\n",
            "Epoch 155/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 16.8538 - val_accuracy: 0.1818\n",
            "Epoch 156/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0067 - accuracy: 0.9975 - val_loss: 16.8393 - val_accuracy: 0.1818\n",
            "Epoch 157/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0163 - accuracy: 0.9924 - val_loss: 16.8814 - val_accuracy: 0.1818\n",
            "Epoch 158/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 16.9881 - val_accuracy: 0.1818\n",
            "Epoch 159/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0097 - accuracy: 0.9975 - val_loss: 17.0194 - val_accuracy: 0.1818\n",
            "Epoch 160/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0067 - accuracy: 0.9975 - val_loss: 17.0320 - val_accuracy: 0.1818\n",
            "Epoch 161/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0147 - accuracy: 0.9975 - val_loss: 16.9775 - val_accuracy: 0.1818\n",
            "Epoch 162/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0140 - accuracy: 0.9949 - val_loss: 16.8798 - val_accuracy: 0.1818\n",
            "Epoch 163/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0154 - accuracy: 0.9975 - val_loss: 16.8268 - val_accuracy: 0.1818\n",
            "Epoch 164/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0129 - accuracy: 0.9975 - val_loss: 16.8355 - val_accuracy: 0.1818\n",
            "Epoch 165/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0107 - accuracy: 0.9975 - val_loss: 16.8062 - val_accuracy: 0.1818\n",
            "Epoch 166/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0158 - accuracy: 0.9924 - val_loss: 16.7463 - val_accuracy: 0.1818\n",
            "Epoch 167/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0106 - accuracy: 0.9949 - val_loss: 16.7071 - val_accuracy: 0.1818\n",
            "Epoch 168/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0059 - accuracy: 1.0000 - val_loss: 16.6881 - val_accuracy: 0.1818\n",
            "Epoch 169/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0109 - accuracy: 0.9949 - val_loss: 16.7302 - val_accuracy: 0.1818\n",
            "Epoch 170/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0143 - accuracy: 0.9949 - val_loss: 16.7877 - val_accuracy: 0.1818\n",
            "Epoch 171/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 16.7765 - val_accuracy: 0.1818\n",
            "Epoch 172/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0106 - accuracy: 0.9949 - val_loss: 16.7966 - val_accuracy: 0.1818\n",
            "Epoch 173/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0174 - accuracy: 0.9924 - val_loss: 16.9030 - val_accuracy: 0.1818\n",
            "Epoch 174/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0067 - accuracy: 0.9975 - val_loss: 16.9514 - val_accuracy: 0.1818\n",
            "Epoch 175/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0057 - accuracy: 0.9975 - val_loss: 16.9500 - val_accuracy: 0.1818\n",
            "Epoch 176/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0105 - accuracy: 0.9924 - val_loss: 16.9691 - val_accuracy: 0.1818\n",
            "Epoch 177/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0151 - accuracy: 0.9924 - val_loss: 16.9874 - val_accuracy: 0.1818\n",
            "Epoch 178/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0182 - accuracy: 0.9949 - val_loss: 16.9011 - val_accuracy: 0.1818\n",
            "Epoch 179/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0246 - accuracy: 0.9899 - val_loss: 16.8518 - val_accuracy: 0.1818\n",
            "Epoch 180/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0750 - accuracy: 0.9899 - val_loss: 16.9686 - val_accuracy: 0.1818\n",
            "Epoch 181/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0150 - accuracy: 0.9924 - val_loss: 17.1981 - val_accuracy: 0.1818\n",
            "Epoch 182/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0092 - accuracy: 0.9949 - val_loss: 17.3463 - val_accuracy: 0.1818\n",
            "Epoch 183/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0148 - accuracy: 0.9924 - val_loss: 17.3999 - val_accuracy: 0.1818\n",
            "Epoch 184/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0116 - accuracy: 0.9949 - val_loss: 17.4515 - val_accuracy: 0.1818\n",
            "Epoch 185/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0155 - accuracy: 0.9949 - val_loss: 17.3637 - val_accuracy: 0.1818\n",
            "Epoch 186/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 17.3922 - val_accuracy: 0.1818\n",
            "Epoch 187/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0111 - accuracy: 0.9975 - val_loss: 17.3883 - val_accuracy: 0.1818\n",
            "Epoch 188/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0169 - accuracy: 0.9949 - val_loss: 17.5032 - val_accuracy: 0.1818\n",
            "Epoch 189/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0057 - accuracy: 1.0000 - val_loss: 17.5940 - val_accuracy: 0.1818\n",
            "Epoch 190/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0144 - accuracy: 0.9949 - val_loss: 17.5605 - val_accuracy: 0.1818\n",
            "Epoch 191/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 17.4953 - val_accuracy: 0.1818\n",
            "Epoch 192/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0069 - accuracy: 0.9975 - val_loss: 17.4402 - val_accuracy: 0.1818\n",
            "Epoch 193/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 17.3610 - val_accuracy: 0.1818\n",
            "Epoch 194/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0094 - accuracy: 0.9975 - val_loss: 17.2950 - val_accuracy: 0.1818\n",
            "Epoch 195/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0085 - accuracy: 0.9949 - val_loss: 17.2269 - val_accuracy: 0.1818\n",
            "Epoch 196/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0110 - accuracy: 0.9975 - val_loss: 17.2027 - val_accuracy: 0.1818\n",
            "Epoch 197/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0071 - accuracy: 0.9975 - val_loss: 17.1996 - val_accuracy: 0.1818\n",
            "Epoch 198/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0099 - accuracy: 0.9975 - val_loss: 17.1751 - val_accuracy: 0.1818\n",
            "Epoch 199/200\n",
            "7/7 [==============================] - 10s 1s/step - loss: 0.0066 - accuracy: 1.0000 - val_loss: 17.0881 - val_accuracy: 0.1818\n",
            "Epoch 200/200\n",
            "7/7 [==============================] - 11s 2s/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 17.0440 - val_accuracy: 0.1818\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gk6elOOesHrB",
        "colab_type": "code",
        "outputId": "83635d2f-0923-41c6-e25c-b76ca322e235",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "acc = history_fine.history['accuracy']\n",
        "val_acc = history_fine.history['val_accuracy']\n",
        "\n",
        "loss = history_fine.history['loss']\n",
        "val_loss = history_fine.history['val_loss']\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(acc, label='Training Accuracy')\n",
        "plt.plot(val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([min(plt.ylim()),1])\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('Cross Entropy')\n",
        "plt.ylim([0,1.0])\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K/GlOfw7oDkwC/ouTbI/48bzdQG+f6RR3nq8cYKmqVqrqDmALTlI3xhhjTDP8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\nDbrEOZe+/sXWidUY0y6ddtp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sTzaQVG/eQ8Bc9/Fc4MFgx3mc6xQG7AX6hsp+AcYDGcCX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\nnrcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ICTbiUjavFzq",
        "colab_type": "code",
        "outputId": "9ac6a87a-874e-4206-964b-abdf15350c4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "inception_v3 (Model)         (None, 5, 5, 2048)        21802784  \n",
            "_________________________________________________________________\n",
            "conv2d_94 (Conv2D)           (None, 3, 3, 32)          589856    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 3, 3, 32)          0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 11)                363       \n",
            "=================================================================\n",
            "Total params: 22,393,003\n",
            "Trainable params: 20,216,587\n",
            "Non-trainable params: 2,176,416\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7icNbbasus7l",
        "colab_type": "code",
        "outputId": "e13d3f85-c367-4589-9c73-7c2a37203d9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "from tensorflow.keras.applications import vgg16, inception_v3, resnet50, mobilenet\n",
        "import numpy as np\n",
        " \n",
        "#Load the VGG model\n",
        "vgg_model = vgg16.VGG16(weights='imagenet')\n",
        " \n",
        "#Load the Inception_V3 model\n",
        "inception_model = inception_v3.InceptionV3(weights='imagenet')\n",
        " \n",
        "#Load the ResNet50 model\n",
        "resnet_model = resnet50.ResNet50(weights='imagenet')\n",
        " \n",
        "#Load the MobileNet model\n",
        "mobilenet_model = mobilenet.MobileNet(weights='imagenet')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https:/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5\n",
            "553467904/553467096 [==============================] - 16s 0us/step\n",
            "Downloading data from https:/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels.h5\n",
            "96116736/96112376 [==============================] - 3s 0us/step\n",
            "Downloading data from https:/keras-team/keras-applications/releases/download/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n",
            "102973440/102967424 [==============================] - 3s 0us/step\n",
            "Downloading data from https:/fchollet/deep-learning-models/releases/download/v0.6/mobilenet_1_0_224_tf.h5\n",
            "17227776/17225924 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_cx0ePuSuJYx",
        "colab_type": "code",
        "outputId": "bb6d8ebd-cad4-49a8-9829-e8519447fbbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "!apt-get -qq install -y libsm6 libxext6 && pip install -q -U opencv-python\n",
        "\n",
        "import urllib.request\n",
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "# image loader\n",
        "def get_image(loc, resize=400):\n",
        "  \"\"\"\n",
        "  downloads an image from url, converts to numpy array,\n",
        "  resizes, and returns it\n",
        "  \"\"\"\n",
        "  img = cv2.imread(loc)\n",
        "  # img = cv2.imdecode(img, cv2.IMREAD_COLOR)\n",
        "  img = cv2.resize(img, (400, 400), interpolation=cv2.INTER_CUBIC)\n",
        "\n",
        "\n",
        "  # cropped = img[50:274, 50:274] - spoon\n",
        "  cropped = img[100:324, 100:324]\n",
        "\n",
        "  return cropped"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 28.2MB 1.4MB/s \n",
            "\u001b[31mERROR: tensor2tensor 1.14.1 has requirement tensorflow-probability==0.7.0, but you'll have tensorflow-probability 0.9.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NB3tHzr0uHAV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loc = 'drive/My Drive/Synopsys/pics/alfoil/alfoil_10.png'\n",
        "# loc = 'drive/My Drive/Synopsys/pics/milkcan/milkcan_10.png'\n",
        "# loc = 'drive/My Drive/Synopsys/pics/spoon/spoon_20.png'\n",
        "loc = 'drive/My Drive/Synopsys/pics/glassbottle/glassbottle_10.png'\n",
        "# loc = 'drive/My Drive/Synopsys/pics/cokecan/cokecan_30.png'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "demUjN-9uNHv",
        "colab_type": "code",
        "outputId": "8b2d1d93-aa99-4a7f-bf6b-e12f14b4c9c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 841
        }
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import load_img\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "from tensorflow.keras.applications.imagenet_utils import decode_predictions\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        " \n",
        "original = get_image(loc, 224)\n",
        "print('PIL image size',original.size)\n",
        "plt.imshow(original)\n",
        "plt.show()\n",
        " \n",
        "# convert the PIL image to a numpy array\n",
        "# IN PIL - image is in (width, height, channel)\n",
        "# In Numpy - image is in (height, width, channel)a\n",
        "numpy_image = img_to_array(original)\n",
        "plt.imshow(np.uint8(numpy_image))\n",
        "plt.show()\n",
        "print('numpy array size',numpy_image.shape)\n",
        " \n",
        "# Convert the image / images into batch format\n",
        "# expand_dims will add an extra dimension to the data at a particular axis\n",
        "# We want the input matrix to the network to be of the form (batchsize, height, width, channels)\n",
        "# Thus we add the extra dimension to the axis 0.\n",
        "image_batch = np.expand_dims(numpy_image, axis=0)\n",
        "print('image batch size', image_batch.shape)\n",
        "plt.imshow(np.uint8(image_batch[0]))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PIL image size 150528\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C34UNGWei/Hwl2lRJ1215x0SAiPYB/BKAf8jMdwD8MwDPAPgwRFL4J1vu+3ki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GG7hM\nEGQTCfRX4T+OQNu2EjocQt4ga7/OBTOMEeNkelYagw5WqblBQkESfTAZmSw4jsFRpHOTqjpxIjYz\naxq1BcZzg/EAACAASURBVIJiF9i2hSUGFgus+gv0F0uQ9aDlEl3XIWnvXdOCyOLKtWu4cfMmuv09\nHJ8c4cb167hYXuCJJ57AtZMTvPPW22gMsGgsOAZQMFrEo9048CL81+LudMOOB8xI7kCc58rlHGtO\nWnPZfG9ksKXJuDKhqg7PXChwek5Z76L8572Hc26Sjr5tLPUBKwlNfX1bK5OSakJVr1dJRJKqUF4j\nIpg4jcr8Z0YEiOjHIXie/5teegvAc8x8j4j+AIC/S0QfYeaH9Xe5qDvwxPUbnOvGK/cGSoQamxcj\n+aNtMvhV/YYQCso6gmcwy82CpiOVhtKT0iKH6KXGnuYJgAirtUTeGWsxDIrrz2IHmLwcFY9LSm2K\n4JlxQwYV7d2YJJM2P0QaiEH07yRpJNeT5BNQBmENwwDXOiy6Ds4ZDC6g78VTYVoprOIa+W7XLnCs\nmAQGjNP77+Or/9+X8cKLz+Fg0eH8/vtYdC0MGIajYBhGD6YGB8bm8utZ1L7E2DTZvDxiGMzdt6uP\nbb8n1cnYzTTi8p2mta8PZWnwK9d4oqIVOH91f6mPbfOYO8C1JPSoc09SU2pzLsbNMV6u0uT+dn66\noxHRvwPgjwP4DOvTmXkNYK2//79E9BUA3wbg1y/vUfj7SAXlmrwgVx04ZP3T6OXAAutlqs0i/UHL\nm4mVn4cI19ocZZgSmATibOQMxlg4COwWsVTMCZrAVIqO1jkJxmGGaxz6fihSTC1C8HleCfQzPUMX\nbmPDlIRktVrBOcEKzGJxZDRtA9WLsNctsL/vsFquEbyMddEt4BrZPF3XwYcB7ANW5+eIHLA8P8Wv\n/9rn8Ynv+hiu37gOo54ZazRnwgDOJiyHFiCtFcCSbcnMlTdmfEWlNLaNw19WUGOuZc6W7C92XK98\nD5AxttMz02GfUwFGgh03n1PcmyHCCjWh7qv8ue3a3N/1QS/vK9WC8mBvc7PKeLYHQ9XtGyICRPRD\nAP5TAJ9i5ovi+hMA3mPmQEQvQSoTf/VR+2Xm0XdPUSWAhFg7ijpieKPRaBg8yOoBVYyBONl4Yl0X\nClrnjhMM7CSWwIeAxWIhAUXGoiHKzx/6HpZGGCkiqeIzDJqt5QO8H9A07Q59cnpdQE3VCJrGQSMa\nTVIFRg4QARRGUSi4BoDF3gLO2DH8eBDk5eFiCbdYIPo1mC0Gv0TXNXD2AHfeeQtHx0dojMHAYmmP\nBuicgcOehF4DCAw4Nhnlac7gl9a8jm57VNddOgy7AneINEJ0tELmdw5gQpgAZNtD+T4mKFSV5b80\nyNW2i8kYqGRa856Ieg5zkkH5/Tk355z+X0o39dh+r+1RXISfBfAPAXyYiL5OUmzkLwM4AvALNHUF\n/ksAfouI/gmAnwbwHzLze486GE46vk507Xv1fRdUNzJgCH3wWCt+H5FByu5jjjljrlyoHApLhcUV\nSX2wsFp9qO06NK6BMYS2bUGGsFjswWilmbbtNsQs770cWN2ci7bLHod6gzBPucfG/FFwTgCRRtCU\noOXFiKyqCwm4pNBDI8MySR3FKCjLwyCpwmG9xunZGXy/krk0DZrW4Pr16/I96yA1EwexORgH5yxg\nDBiJAI0uwrmDbaoNX89zm55bfl7+nPtuPjAC7zwJUqrXctv6zt2Xwo9LEJKaE8+OY8bIt0sULwlk\nPe6Sw9eSU91XSShLSScB6W6zrdTtUbwDc4VH/uqWe38GwM9c1uf8g3InACSD0EpoGYJGqSEaNMbC\nrzxIdWZWfYBpKnqWgR6knBZG8g5ABERoHT7WBKSQ3YVN43ISUWutVDfScRABTWMzcIkcaiNhzMha\nioQq+7EOorwk1tDhghAo8qzUUjGi0jAhGgLYSnLTEBEUI4FZ4MM4iqegtWnxGAGCP7AGobEGxhK8\nB1pVp2xr4BqRIJxz2NvbQ9ddx17XwRjAh0GKkNgWtnNo9lrYbg/ONEJ0ImnQkhgORdenPGuxNqQ8\nDUDirMaMyLSRSzSeMmsUmOfaJbfNBzn9nvopDHhZbAYmBzVzekDyOtJBzftP7VIhju+lkA6chkNR\nZFinxWqLMabnlESjJpSlejInRaRrNTFJ61TbLrJ3Rd24g1akDhDQW5Cgb7sdgthjFTY817wa+mIU\nuO5kNZ+j6sYK5Fgy+KTFTLUKgNH6arNeJy82gTOGGHMVYlFPUpnvkD8XF6Qed4ogiko0KHNwQAya\ndUBJvblLyUQ2pt6fXJtWATmNwcVyOTkIMk8JkjEArBFcQmsB4ww8IvYO9wBHMK0TV51hhKKUeN68\necwSL+HaFmSdPmcztbn+Wwc9NkW3QcJHpu16c9keVW0ox74rXbaWrsp7YpIiqMzKowlU90T/Tu8J\nmrK+RaJIbZeLsb6+XXUcx16nSKfPxjRrk6Voo9IxHkEVe6yIQC4BXvydUoIBTLhIuUATQlCJUiO1\nLewAelATxmD9MhNYaTrwUYueCv7AWBNhMvY4iuZizEyx22O/SQooW0p+kgOdahIkA51sItc06LoF\nFl2Hi4sLFfkkOjGyVGcmACaaUY1QTjcW/Uy4BynKz+RMzLyeOkbbjJmBRCTqRk7oGjETp/PY3NQc\nk9QznfMuPbq8Z+MQFu94LhGnjK6rOfLkvmL/bIyApv2UGIXTpLaYaxtsa9vmWOr0u67NrcUu4sHM\nWT0yEGRpocW7qxo/NkRgxOMbCUGaXlDRfhjG4iTMvBEemuC9AeRN773PPm0iwQGIMeaDnvoDRjhy\nxkgIxgMqBkjXJGkjTKDGMsxzEHSjEWk4bmyGuZcvvxt4X4CTADBGuLEhSQ2+uLjAer2WnAXpDIgx\nQRAIqjAoB0wNim+w0WYOJhFpijWLuF0exBTBGFV14Sn68uw7jSPM+7Y2l+23XWqa/ivvre+r+0qt\n3F+P0ibqRDWPOTTpbd+vx1db9ks1ob6W7i/VhLr/klG6MoZgZi3r9tgQgVmeoDq30d+tleiAHBGI\nwijDYx+yaHYSQ2CIQEzgoFSRMHEHMkfRc7Pta9P6nUp6C5ewGZPAEIFJCEHTNnlsWbzc4srJcydS\nXdwql1eQSB1X4xpYZ9C2LY6OjnD37l2JdVBgyUmmHDOi93AKfyaIzJIyXa90KtOePTCZ85t8G5FU\nOJpm3kk/6Wfa53Midz3P9HOOq9X3lK08INvE8PT7Lgjyce6U5z5ek4Id26TLEspbPt+eEg7UMSub\n89ywc3DJxDaZxrZ1TYTUGCMEgEYDYynRbF2LrZ/8PrckCZTUy2qAComlDTFG+BRRhYqLQg55ySmc\nMZIlqI2USyaxvm5EKUIuom3byeJbayWIiDl7A9LB8cxFqLJ8Z7SSzxt/8rwLcTJvCjJgEGIESFNi\njCItOUUOeu2118AslvxkiMzYBprxaG0jM6aEh0iQgNp0wpFtJUBhJEsHmYCmacbNz5r7EFUtSGAo\nqT9GltDynLZIAXObfC4ldtd36xTh2uhWtrqvhGVZ9kk22Xm2RyyWamXd9xw3T/Oam3d5vYZQKwnl\nNm9C/dzEv+q5J3VmW3tsiEBq6cXEcsKl/p/yumc4zmQTzPSdN5s1uR6cPMNkcV42vNQytG6aidW2\nLYhI04+F8zvXwNI0fz0ZlvSb02dXFXvKF5tAR0q7htUMN2saRRVu0TQNjLF48OABGIAPEX6IkHBr\nJ/PD9JCYMpWv3KOUojTTBhorAznXoV3sSxAVQQx9bDYO3vZ2uVQgYxt17zlRufxObRuYswmUfdbP\nSjYho9LixLYUdxvRjDFwjYNtRnczMF3nOnW6DDCaayXR2DXv+t66xRLVKk77vMz+8tgRAWYJ1km6\nWzJmRRaMQaPgmdsWJAN/lOJjHP9O3I55tFiVhzYdxtIImZ4LaMhmcZCZOeeiJzgxoCBiAJyxUkKs\ntF8Ukk/57HEMBTDm5HeD/f19tG2L09Mz3L//XubaCXjVqlW/LHoyp4MWDy4IZ9owFlevXIE1DQAD\nREEVzt/foedLIJHIXYAks+SMwqKV+nEJETauwaZkUHp+5kT2OgioXLexTzPh6JkgQ42ZlzgojH7f\n2PmqRuX86jGV18v5b9sH6ft18FWpepV7SlC04sR4XUoTs/PZPd3f35a4PhFlblVvtXor1XRAXoou\nFqbRY0SE4AUuzDDgGHBGXGyusP7WhqP8UtJnVYQZACwWi0x1pSBkueE2Y+fHF70pXqa/64PbdR3a\ntoVzDm3TYW+xj/vvPcCbt99GGFLY86AGVFILsQGxuA4pCihJvZrJzkFECJHhh4ibN26AbAvwaDBN\na5Nca7s4065WH/T6AMxdL1va0CXaTx2mnFoKEivhvyZrTchwdgTaOKB1f/W4t3HYOe47d9DLPpLa\nubFe1dxL6XGqWiogLjaraO1qjwWyUBHzVRz+MTU3UVtbVLGR2HEJ3uGJ2ClIQJN0TQKMVuQBAAoA\nJwCTgoo7EhsECjFyooc1DThBlSsUmokEkAMhIBjR43Nx4VKSMWJhJwBMo+uufE5pKGKIJyIGhjGa\nF2FGaQQA2BCO7VWs+xW+9NUv44UXXsSN6zcknyKqnQTKhIkgKYgSR2AZcCBYFnIRYUCwODo+wQsv\nPAfbLAAAgQJihAZsTUNWofEGApBqML68iE3yPW312m47aLXen99F0XLsx7iLQGpQjphyeyIqIBEU\nzLXgrnVcR7oPGAODmFnev0qVNQBKvqd4p3X6MDASgG3qVRpvAHI0aspere+XGo8AOJXo25SMtrXH\nggiMjQuCkHLhR1dgacwhMpCS3pCDS6X1tkB/IZqI5mVLasF4GOVqev+pD2vERkDMQCOln51phF6w\nAJ2EKLBlAQTbOIRhAGlyj4CdpJEVs604qWx8yX1h5c4xDLKjo4GPQAQBZEGO0ZJBoIC+H7Bo9/GV\nV17B3Xffxcsf/BCOjo4k+Uk3H8kDJadBaqljtV5hsVhoUlPAtSdu4NlnnoVxHQBJypI8ARH/IzRW\ngoXKMEOxGTcP8zcqJZRrU9tNynUq7wFE97aT727eP17ABo3KXLX6XnmQd8X112Of+/wyINN0Ty1h\n2EYOeCz20ZzkmJ+P0T6RxrIrTuAxIQJK8YLAYgNiyGLInOvkkG1GQbVtIxUqTfdYxQFMtQwASfpJ\nLjGnBsC2bRFDRB+ksAiRHA5jJZU2JpeioVyGK/ZB8v9hQMRiXygkGAAIRbRcqRqMxMciRoEAF25E\niCwbMuuxkIjITLgCARaIVqShIQwge4zziwt8/vOfx1O3buHWzVt48gNPwHuP4+NjBDCarkPTOURi\nCZsmQiTg1jPP4Klbt0BqMA0FJxP1CuDAIDONFqwDuuZazQG3HaRth/1RD59cTwlnO/pUOw5Vnpv0\nfhL+RArJrXP1y++U18uDVhKOueSj8nml+3WCg6HPNTBiWqmklTmDYkqLLnEQLmuPCRFIh2TUP1P6\nsDMGPkY1Biq3FrY28duKqMyZc0vklx5iYwEjfYrooBVzyCjOfiPSsrFo2w57zmYON3iPGITbOpUC\nmCMcWQx+EKNhlL99AKzqzCRvDTEiz4VmRFtrE4CpzdyLmWFgEL1cN4aylmedQJ01TtOKrXgcjDew\nlkDMOD44woMHD3D/vffxO1/6XZycXMHJyQm6rsXh8REiAZFl7Lc+cAvPfeR5PPfsc/ARMExghXkb\n305e9A2DoEhmcUIs5F1OD2F5kOo1mDfwbh7edDBqw1l9HyfucUkrD3SSAhLqdH1wSw69jQDlXJWi\n33K85ecpK7SeRykFpDgDAYh1G+OtU7VTX3Oqx672mBCBUexKlm0JwJEsOqMiqNFQ3BSpxXHMk3dW\n8AFG7i+HnIyB1Y0h2xVoGgerFmnXSJ6+SAQCcGmcUlIiWOvQD2s0TQvTOHiNFAzDAOMsLAwulhfw\nnkBkc/4+x5ALnkKfGUPMwU5UAKamPZDEcpmDASwkUo8srAEiYk4RJgIa18mmNcBifwHvPeJhRBgC\nFos9IRZNg+VyiXfv3YNzDvvLc3SLBV548Tl8+Nu+Dc8++xyeuPmk2DeshVe7RVJLiChLWGVjAIkq\nqxalB2j+YJYcs4Tw2rojKs45x3mzIXZG/C6564b6QGN+B5jzYSMi4bgFvmPZ79yz6nEmnX1OjZkz\nXNbjrg+6Fzy6CTFJlHYuN6H8fkkcdkkEjwURSMtoraLwpuuk+rklNE2H5OqwTrL8uq4RS7yxE8oq\nkzdSh49I02+BxjUYgkfXdioBiBTAqisTCWcmTS1mMNqmweHhIRgiIhunUX2Dx2q1goVBt98hRsa9\ne/cR/IDGCXKQ7wewYTgA677Pc2XCBh5hmq+1TeYUzFGkAHVxAhKD0JArPB+MrpPAprZtECMhWA+O\nDbzWPlgsFoiGsOgW+NCHP4Qnn7yJZ597FsfHxzg5OYY1NqX5ABwlHIAhgUVKpMbKUOP7YiQz7Ohy\nLQ9QbQ2vD1H6uxa353T+sp/UkqidryulL0Fo5vpAujcPBJN7uTIYpLls2m/Gw5n07/rQzRGzdK3m\n2PU4M7Gs1oNoc171uqX1Kdd5W3ssiAAglmzBgBex0jUiolsyYt1mXSRr0CpgB4EALYvVLhbiupIw\nOzhrBWMfshiN5gx0htC2DYDkiweapoVzFiEIkIm1mo0FERETcYlE8GK1g7UGewsB8GgGBx88bt26\nBdYipP16jZPDIwxeCpKAJf5htVrKxg9j7MIw9HAOOp6EnyAWdwJgNJosMsMaC7KEYQhg0sxJFWUH\nP4Bh0LQL9MMKVw6vwjqLo6NjXLl6Fd1ige/4yIfx9NPPYNkLDqHo/gnEVaIzIwOGxzRtqchcbdZi\nU40GqKkhL2Ewlq0W50s3X9n/HMctD3ZqiRAYY8T7QpIUNScF7Nh80z6tEWzlSgef4+71YdwmlUwe\nNyNBlOux8Vkl/teyVn3/nCqwTYUBHoEIENFfg8CIvcPMH9Vr/xWAfx/Au3rbf8HMP6ef/RiAPw2B\ndP+PmPn/uuwZAOAal6ltMtpZK0CPTsVkACray0K3bYcQAhYLAfHohx6uG0E/Ihhd04LVINiadvIC\nmqZB0zRFpFqEbZwCjJTWVuSY7CYvmQMswBzRgGFbgSEnMPzQw1qLzjXoFSq9dBkl1uMVhajvewAk\nFZS9lyrLXmoeNIrvJ+pRUJ++JDGdXVygsQ5WvUN+8Oj2DtB1HYwFhn7A8ckx9vb2cP36dRwcHODk\n5Bi9ipiBoyAuaZ1HsiaHP2fdGgBVMfIiMWgcRvkSeeoamzt4pZFv1+HZJTLXbQw0mrcl1NJH7ivp\n7apSlfdvIx61zr0tVmDOeFj2URO9kpiV3ycigc4rPUyFuD8nEZTzqNGc59o3WncAAP5HZv7vqsl9\nB4B/A8BHADwF4O8T0bcx804wOSIJxWWIkSuEiCZlz+kiW+vEBRcCukWHECIie5hGOKGxBntuPy+i\naxr44FWi0LRTSEBF07TK4Rs0jYiOhgi2leo8thKtUsx+A0KvhiNHBiEGRA9Y14ENgTAgRI/WLSRO\nIEY0JO62YejRGgcfgngrWMKQgT0YMoKU1PdwJIhGfvAqXidB3aDrnNZBFLDS6wroQSYlDwkx5QDs\nH+3DOIujvQO0ncCQt22LZn+RszE1jwp+GKabB6pqKDDro9jZshj9iF7BrO9qqzn3HCHYdU0kgEd7\n9sZYUopw5EwMspqDZIdWr5S6bg0k3qDc2HMqQLpe2kFmVZTqexMCWagacp0nhssyvT55B9JncwCq\ndfuG6g7saH8CwE+yAI6+SkSvAPgXIfBk21uhKxkimEbRhQ2JGmBH3b5pGyz29rBer9F1HZbLJZq9\nBWIIaFo58GAGGYO9bj+/AInEigiBYSzDQFB6oYFFcC7n8jOR2AWQAEcKaq/uPoa8KNc1woX9Gra1\nWDQLDGsR/4cQ4Bqx8u65PcQYhdhxzCg1UcW9vbZBAiEJwYM6JV4QiSaGgMiEPkZ0ews4MhLSGwNs\n69C1HZrOwhpRe06uHIGNgQVgnYVrWjCAQUuPB6gOX1qWY8xSx/T1zJCAQiWQJCWDEMW1yjwmWWXo\nNZXyLjsI23TduYO1cS3Np9xXW7ZceV8dbViqHyVnDlzEH6hxNj2h/M4uFaEe/5z9Y8MAyZr2xWIo\njlvWrvxuki7Smn+zksC29qNE9G9BkIT/E2a+D+BpSDGS1L6u1zYaFXUHjg4PQc7BNSL+N00LQHL+\nnWvA6g60ZNEsOgSOME0DWIuD4+P8Ep21Ei1lBVXFGMo6M5GBRxCvgDEg6zTqj1S3NuC2hWUtZaWL\n5ihljvGEoscgB5pIsght18FEKTVurYVhhiPS8mYuh9qmisAESFlyAOLONGhI4tIb6pBSekIUAFFr\nhQvtGQO7aLHfiSrUkUN0QNM6GDJwKZWZRvirRou6ZIOe0VDieqPuODSXt8p1aMbElYn1/ZJWB8vU\nB6U8IJcZDpPKUreJyF9wb6b5DMT6e3M6dwoj9jUUPeazHes2R/wusz/Uc56zr+yaT2rfKBH4nwD8\necib//MA/ntIEZJHblzUHXjy5pPsnCi2i8VCKVfy42t8gCE0auhz1iEijMCQxsA0DpY1L9+IISvE\nIKXESEJKI0cJnTVGLOIcpa8gIBrIB2EzhjwvMIlkYpN/3hotvadqBRg2CKEIShSYgxrcImCbfAAt\nA3BWRExrYIdhtGwz51NrdQ5wDp21gCWxoUSgJYc+9jCNQeMaqAVV1jgbysaya8bafDAYIs5KzQdC\ntovnIJX8rjY3UmWcKzlO6f5LgStl4k+9wWsuuk2srvbPTgKw0xBY3FtKGtHHHJRTfpbmU4YxJ4ZQ\ntlJEn5Nc5g51PZc5qaeufFzbJco+khT2eyEE3xARYOY76Xci+l8A/Kz+eRvAs8Wtz+i1nY0IcG0j\nh4EoW6K7TvzgKcvPEAHWonUOA0mkH5FgBkQCEBnGCWqwNQaWrcYWCGdukuhmCGGQVOHIokOBSKrk\nSKD9dHyGQCwRgUj6FrMaMy2sIeHcXiIeqRGf+eA9iBkhmjGuXdWOmCIYaTQeDa7JKgkAWNvA2PEe\nz4zWiV3BkcYRkFVU4GaD23KKVAQQIcFUxXvLm0M2lM33YmPD8OQ7c22b37/cuJdBY28zGs71Wfa9\n7f7SKFZKJvk7GN90VK/SnCg0py6Uv5dqzraxzKkJ2+4v4/6ZGc65fLDnbCdz61ITnW/KJjDXiOgW\nM7+lf/6rAFLF4r8H4H8nov8BYhh8GcCvPUJ/cCZV+RlgjQTe5AgtJAOhyRuUiHJcARsJKBIBQAhA\nwgsowT4bYxFikIIjXCDKkrgioVxbFDBRA5LuLwlLasklyQ9wVgqWgiTfPmCEdkJkWOcROaJh1kQW\no4SLCpSi0fCWuLZTePNky4ghIpKBSUZKI0VRLYBIgCULMiyGMasHNXLO3vURcC4FTEmJs5JT2Euj\ny5RQEY3eAH0v5fZNG60O802i8rbw35K47DrUNZec45qbxG1etahbbZWfe3apW6eDXwc+zc2xXJvL\n1I3yvZR91YFRta0k/Uzf/72oYI/iIvwsgE8DuEFEXwfw5wB8mog+DmERXwPwH+gEf5uIfgrA70DK\nk/3IZZ4BeYaU2h4t5ik4hfVAK/fVw0iG0Fpx96W4fmMNXF5E+W65SMaS2hZkYVzjcj5Cig0wJknh\nVLgIhfAYY2Bbl+0MUsxUCEw0BiaKZGE1toBDhI1O7Bd6baTkBJtrE0oaawwRrlFiYDS1VdOQjTUa\n567GKKNBVBEwjsCsEXAlzgGN9gZLo7VYXI0pt77eIPObf1sIKiedOxYBO8V3Uh58OpjlgZno5cWz\nHmXTzo2xHFNqNajIrPrALPBrMcweVCJJPy9jD76RVh7my+Yw990yH6Dsp557/ffcd+v2La07oPf/\nBQB/4bJ+y0aUNqeKbZmCm5z0YxTxBhnVh0T9VQ6TOCnyz5K7ACjQdohIOLWi/1pnVCdGxvobKapw\n36Zt0TibiYMxijJDBj0ABM04TGMwmlmYxMBifEn8lr70cwNQHDMYoapR8Ur1MyEcAGviErTkOk3E\n+LRp09qNvaTed4v347sZvTZlE64kYcJz5sQy5z+tl0hv8y6rOR39UQ7dNt275pTps42/MSVINSHg\nwjZTPrOUeuYqK9fSQCnKb5MG5qIHjRGA3bn1mSNq2yIX59KdU3ssIgaJCE3bIvggfnsgW/SzLpeg\nsa3otinHwDZObVTpwE9FIFEnKJerJhVpiTk/S4qUWinkqUFBKU+ha0Xkd1aBRUk2fxJviYzo5wiI\nEG9CZAZZyc6jtOHLuHEaw3BTtJrYAkZRzxBlXzRB+ppZOJV5eHLQkdeviJEHJpuaWUu+TaSBwr1E\nBLL6DmY2bKkHz6kTY0XpUTQv2y60nW+27TIs1gZJAFlFDJq3MZelV4vipSpQP29OHaiNhtuMhPXh\nLiWp8vNdKsXvtT0WRACQABl2nHX5NJnErfOd+kJy/b7kQYicC2qK6gA5QGrtJVHuASB/P4UF54jB\nJAVYsbS3bYuoen4iLlE7ZSLA6M9k0FT5W5CNi4ivUpdEzJZ/ebmbol0WY3XObEZeO3EBaaBbUNk/\nbRQLgrObySVzB2D3W5ny+BKyDRhTipMBstyo36joXFrg67ar/9qoWHLCrcSgkABq9USkrKmxz8wQ\ns0ctqiro0PPG05JQNk0zIRj1+Mr5b7NdlES3tmXMju2RZvD70JjEwBf19zTkxjWAFk9IhzXh4VtD\nAIn3gI2WK2dCtBJjL1BLmB5+VSHSIWy7FqksunEOIEK7WMiiWQeDqA4DATwhHSenMUNEf2IArCK7\nEgYgCgKyRUYYQkCeA3Rj5cNbuZy4DF1WwpLmQUSIBqPEUIgK0RAiF2W3imdE/V8kbEgfqW+jNhbk\nT6WFIEjDcuAgtggWpKJS/N9GBJK8UJKWUk0ySly3fb88BEmtAEZVYyOhKD2jEo3zTyCD1jhnYGKd\nH7ExhA3XW/2sdE9tLJy7L+MFVJJHPWentSjrw1waPme/Cylm4/3u1OLHgggQaTCMkVDSxjmY5Hc3\n66A0vQAAIABJREFUBjEG7O0tMgBp4rrWulycl0iAOAyNonHeXDw+B4YypJRrHKyxaJxFYywijbrT\naJuwGjcgAr8xYlgzNIroBD2wgRESdj8znBHqb0GaFjweEIlKJFCc+sZLXXSyaRIRKMTBEWWpPjjT\nv1P/ZUhplpaqjZn+HLlT6dYTo2bqrz5UaeOXOvL8XLbo8pT8JOO18j1u7pvtnLB2p10mmdQ1EQEl\nSqoipd9rd+G2sdT6fSJac1WLMrE2ZvLdmvunVns96nsmagvvlgKAx4QIpBefNg9HRoAffdekPmaI\n/pk5sB6kzBHAcjCZRRw3BpZ5DDsmEXBT0FGC8HZtAy6hoUk0bUnlT8HkpuDEUdJsgcwt0liSqJ+u\nlRZqA+SIskCJC86vSPk91niCaAAKo5W9PGSZI0Dj/mncRCKkTP3UyZgKHcO2Q5I27ngPbei3yYdd\nptbWRCjfj3kOm4lKQQQn14HJPNPfZUsHqXbl5XWYEaG39QWMthb+/9v7vtjbl6uuz5rv3uckLVxL\nvaRpSrWtqST1pVwb0kTogxq1fbnog9YHqYaEkNQEEnyo8MKjmsADiSHBQCyGAMai9EETsSEaE1st\nUG5bammLGNpcWoTYEkzu2d+Z5cOaNbNmfdd89z730rv3yd3r5tzf3t8935k1M2vWv1mzBiMzIaKW\ng+JRveEpTBCqzNaNv2dKVqOx7ehnHzsxO3Hp5xeoQoe2pqGF22ACBIAJeQVe9SqJsc/11l9Rqhlc\ng7VzVWMJEh2oGXc0eWRiNK+6DHj1Sh/qnXI1mxCScPp+644J3AFATFjR5VLJWbbCiEBLEv9DlgIE\nyI4Fd/+DLnqo9MZoz5ciacmWQ/foajIKS1CWeBIlcDJ3MS5L341oiwyNQar5kkAoKBu1VLWB2tJm\nWmRRCq6tYmy1AItjaAZYFVrcshvp1RbqmaAb+9wvJLvAvJdcwZ/U8+/b78JALZ6xj8VqQsqErDPR\nfrZjp+/YMVJtzeNmGaCPHdB+qdkw4rg/lsCtMAGGeOhrJF3OKzRHTYbYsYnEwXY8HtrCZqNKU0pY\nDgeUsnbiJgkx1j39pQbqWHPBpnjS4WuJSRktu7BcYiqhzd2m5FETqOoXGC2c2Ir6XoZroFHPmMSF\noRgMNrNZqIflgExZ/uYsWopKJxMRl/RvC4YicN5KEckjYKVKv4ikl6l39zV/YBl+s9IuckZZ5lAq\nU2bmTQLUhoMjjT3i7SZLX3R+gVv8VFMI/QWQeVemWmrmJ0okW71Im1iByOGmY6COwEhbiZhTZF5Z\n5mrPBeh3P8Z7Zs/NMwFBkOpRUG6RgGDx+KeDZPqRgzTLQCiJJNFH445LwqG+3wJ6qrd/OSztlhki\nwnLs0XvJ4dMmKhekQw1NLnINWkI9BwA0htLsNIxENxCN2sLcHYVtchLwoB5xJoOjMA3U7bpqcqSE\nY0rdJ0HUtrgUp5RS2/LScYFpr0vTzggGaVU9//bSUa+mnpPIYzsj4atvwnv0fTkrIds4BnHzkZ3t\nr/LeWyT+3dYWd/vfjpPFT+38ZnLS+RBp354dJx934JmDtuvPNFgQuq5mxhk0boIJ9Gi6McAnl4KV\n5Ox+YUmoUUrPvgtIJCAKS4afoxzT1fvzjjWePgqfJJJTiWova+LsXJOIAmiLhtS86C8Pi8ITupfk\nfuH4BaLvtpuQ9DyD6aNKuiUtyKwEh7qN1yVc0waWBMoixaiaA4pXw4EIzJLGTN5D03SKW9gevDrv\nf/N982Al9yz33p7ne7btpc/PhcxG8xfRyKj1XZZG3dv/vh3bLz9WPh+Ar9dKfx/q7LcWzZvRBVAN\nboMJAC1RaOskmVtWGeDMSIdOMItV+4jrzTxHpCSqmPWCqwSVsk6K6e06dVEM6n21SbTsqtyWu/NP\nvceFCxZlHpcJgNZOaxM9tFn7prg2EyaJGbSkpanRajI0LaTibO9bXGr2oE3bsHcRbm3WPQZgv59b\nHF478lLtkndte3sL3PtUbB2z7bhGE5O9/Ogd++7s7EEUBWjLzeIM/G8DQzKaT9MWnfNQ1lGN7Uj7\n4/Uic7H8yYOeqJPOlrYN2ExR9NRc9pS45g44Ho9ycvDYGUAVlcOibItKpa5Z5AS0ew4tiL1YtpFz\njibUjhye7Ti6qhHUiagu5HZ1X5LPjZBU/Z9xGddXC7r3HzmnWney3myskjmOfOvPq/+mlHB/PoJS\nyuAD2GMedux8KW83+9/2TJSGZ8AIIvDRmB5mGknU7t67kdof4T9j0p6ZaqRrmFTBwM1oAqWUeqyX\nAGhocLUHa5gupX6y7nCQPX5KhFe/6tWSjOPBUezkts2HJuGFfVS1+yA5BSSZZKwGAmLDHx8ckXMB\nU4JGBHKRHYpc5MRh5jxwY/U9rMXYdkuqpxdZ9t613dq0/mYj/xLFUiRV3PfU2CiQpOS+ADW9eV7X\nDY2UXJBrwlNhUCJZc14hQVMAwMhrrhsG0v6hJlnx4KUxM/ftVtc/teXV8ZVSqpGZ6Lse3HdiIq2C\niFpGIx+0IwlfJCmrZf4F1ddgx0vf1TNp2EpeD1611yA3GwNg64+ku1X7beyF3XnQctYs2Ow05L6r\nhLmCcyNMgEeJ2BbBIYka6+ypw3Joi0YXSjrqab/Y7vaEuCe5etlUb92pGkph5PXUzi2MXWg6vUwe\nl+GmIW3F+hgA1TA607K3yT6OY8maMT6fjrVJB6dXjahkZhD65S6UEkqWPh0Oai44/4dGY16Kn/uu\n9yfA7fvPnHybfpgyqv5bP4BnACOz0JgOg5vDQdvTZ/lRRnrQ5937gWYQHWe2dVgn4OAMLmUINbbv\n23MZba1MHKg2Df8MboMJEADqkYC6f697+stBY+kJCzS+Xxa8LqCFa/agwqBD2nDJmSMrcsxYtSqX\nFcRULzyJb9FhZhxwaIuRVlGtIwKRBVc9cKjRjNXUSEQ4sWggidJm8V5CeH2R8GDsyZ2CZTiD0P92\nZ6yOAQDIhoIygNIYjJoNCrODNBan6LPnIpFj8HEhcrYp9H4tSEg96UrVFEsp7tZm48Mwqcc8M43A\nnoFY17VpSVaia/3eJLA06KW/D43WOpURWqYwO4Ph4TaYADrxamfsNp8EBonkPy7HmmFI4t655t7L\n6r1NfZ/cq1uzyfNEE5ftUgRAu66KuO4GLMaBmNdNvUqca17rAaPAHtSoOxbTiNg6ecgt3B0IVD+N\naRgXJNAyKRl1t7WDBN2HZBbpeSDCWlZJy8Z9f96Pl9fAtr6Sup25nF/okdpPGOfJq+jRGOk45rJ1\nxjXmEfmEKnNXz330ru1ztDMxY5A2kMgv5NZXpz1Y559vIzrbcA5e7L0DvwDgW2uR1wD4v8z8diJ6\nE4DPAPhs/e2jzPx9F7TRk4OSnNO3TIC5esOBZp+mtECvxtZryrTjXporzCSE1Rp8mfaZxJFEJIuh\nrLl68wPJh5FwhYgYp9NJcAtsZ6sKppr9R/vUjrjqSca6E7E3yVucxsXoJeZ5Ihw1BzJtRM4tr11F\ni8f2e9YXNZ2IqvqOmNF4bekSGGz/CV1EfYuYnYKnO2veqLnrd65aPwPGYSW/jqE9eBS1p+8Noecv\n0Rz4l3D3DjDz39HPRPSjAL5qyn+Bmd9+Qb0NCHJaUM/wa3SfhNUuLXBFCEIcfdZh5cESoh+AkMtb\nXAL1jKh7kkvJQOl5+RKN5xdse9a+yyW3ycs5D7awllU7T4OKlBGUXBdK6sSeS7x9ZGMBfD+bH8Iw\nHFuHf8+WadLN7ukTNu9EWoGqpnuMwONhwY8p83gztWc8M6KfMQg1B2xf9xa6hY1H3piLS02X57UU\n1Xj9oSXfRxszYJmJlrF+BM/cH0cjeEn3DpCMzt8G8JfP1bMHRAkPHj5ox38PxwOOh2N3yFG1tROB\n64UbzFyvxR63z7wjpuI5Xfx+sM5JktE7W6CGd7ut19BEsyebqilmQCkZcJJXzZ1SipwwPIhdv57W\nFrV2OMgtTTb5qu2L2uzKCDaSi8by4xyMTGG2v50r4TaH4s54WYbAzD3JCI/v2gCfWT0W/wj3x4UN\n43D1NFxU+6Ato4ykt9dOImZstbCoTNSnWT1RX/TZLHTZw0uNE/hOAF9m5s+ZZ28mol8nov9MRN95\nSSVENcDn0DUAroPeAmdIFp1EuK3Qveo+ifL3UA8JEVDj8TGUU7jIXjpDW6z/1cAMmFgG7/Uq9RRk\nLdYWhU6SbokxM/K6Yn3hBK7OSBRZOHnN4FxQckY+yWcuDBQxN1r99a/tv6J0brFFKq/iJdK/n7Gw\nzqeNzV/7yzUuwMZf+AUwm4vGFGsdBMhY7E3KBPa0QaAfOgPiBRip7rNxtHOZ0ngK0eNg67FtDKYK\nRsajmspsm1LLqaZxjkm+VMfg3wXwc+b78wD+DDP/ARH9RQD/joj+AjN/LUCyXT7y9NNP41hvB7bJ\nLEglpdp7NZpOLh0tYNayXfppJKEQTEE6LM1P5onQ4LJZAN0ODtRmYoC0TGkXhfbFH0tie1x+praJ\n+p8r8cg76v1dksT/gxnMGaBlXORQ00VuEoJZRJrpiLHdPptpSn48SilixiCWgjDPS73aXRgYg3MG\nN2IHaEf++AWgYyfZjrlJZ6+270EkLQfJX81MheICpZZlQZ7UYSW7Ze4yF11j9DtLfkflnO0+7Frp\nsXtDyxFdXwIvWhMgogOAvwXgF/QZM7/AzH9QP/8qgC8A+PPR+8z8k8z8DmZ+x5966ilxBh67NhB1\nhBvxq1jb7iuXKh3zag7POI69R7wzFVdOLso/Xbze7GiL2AV92G0hn1RCpUbzXJu8Blq3dQjZd+1e\nsdUmbNteUp9T370GYH+bjV8oWdPSLlXdg5na7HGaqbyzdy4BW69f9ApeAtvn3nzw39UEjHIg6Nxp\nuUh1t0zYzq0NINLnnq6slnkOXoo58FcB/E9m/qI+IKJvJpIAeiJ6C+Tegd++tMJEEgh0qME4dnGo\nt1NiBJYa0LINrNEBsxMSDYYvr8/2F4k9UbZ1oOkijtqzxOwZ1+yZ3Q9uqnGgPfh6tM9KaNt+XLZI\nIpD7DRiggpxP83I6voWxHORylN16zXx4ZuTt6FmfXnjhhcHPcEl7+q6CPR0qZiWFYcN7TMtKfM/w\nmXm4qszSjQ8qskzC0ojX0iIzLtI2ZnCWCZDcO/DfAHwrEX2RiL6n/vRejKYAALwLwHNE9AkA/wbA\n9zHzH55rIwLljD5QgkxEoD0uqx21C+RwPB8tVfu4+5sdUN3fjojVb9n4CYgIQutq6nZt0xOxZQQz\naW/f9ZL7nAYQwdzDLKaS7uaE7y6iNZWS+yEtHQNTlddmtK2Iifq+e0ZxPB43AiAaA6vxnRsb/57H\nSZ9HsQF75spA044BWoh8RrYPkbZn8Yuee3ix9w6Amf9+8OxDAD50rs4ZRPbMsACT3LUnCz8hl4JD\n2xwg8eAmAkpfDF6Fm0mRyBRgFoknn8fyngBt6Krl2lG8t3UE6V/L8LwzSLeTIoKxfWr2uFkss4V8\nib2oW1TbLUWGKHyMUraRmXaHhtkQozrI6i4J0M9H7M3Bngbgx9GPR2RODIy7SvtTPkm2aZKr6Rjc\nzqaAu0/AS9qoXTt+zN1f5X+zWY72wGt4lgH42I4ZHnvt3Mwpwkjl0nRJXVJ2KUw0TnLhvm3mOfeM\nS2qZGbeMFsqsrLXl7VXcUXtAkFnYEZVv49yiHSTtBLxPwde9Jznab6Vu0zLBB975Ps5wnLV/CeyZ\nCb7OvXoH0ws9GA1UNbp6YIxdgpeIqfp2rLYSgZ1fq+L7MlYg+D5bsJm4Iy1RGc4MboIJ7Ckrkfcc\nGKOmdAK58KB6MvM0Bv9xCM/j6O00i5+CTqJKyb12I0L2/Y6iwxYixOE3W0KcqYoed4+HN2Fmfgbt\nszVHBG+VVNu0ZH4RWfXcMib9vLe16cfWm1S6KHQxNCdbDdH2EZVJk9Oaw2Mzxuy11mE8nBagGlIp\nBSBxYM9gZoow93yUtm8vxuQDboQJABJvn0sGgwdPrUrMdjmn8YwCXYOwEp2IGhfPg/awNTe8jTnj\nmn6Bn3M82Tbsdy9NZpLZmwrRQgEwHOSZ1XdOQu+9Y5md1zb2bGo9W9H79Phn8mcw01T2ytt/euZh\n47OpDAFw25P1Cvpozz5iAAOtpJGB2fnv+TNik2Wm5VihQDTewdDare1omb3xvRkmUJh7iA1R3dtG\n/cft85ISDvV68mNwb7t+t6Dvs1nw0QLdB8Eupb5TMfzqCC1qI4KIMfl6gXlorVw+In0rxslm7UZb\nl9+r9hqAleIzyTczGSwUAEx1XomG9FaFWTJIU6/XL6zZ2Y9Q2k4kcRsjI31lTLhd/sKYHbWngSYZ\n47gPzMM57AbaoPG59kvKiKZhjwZH9du+Rn0n6mn0w56cocObYQKMShzMAFGLfiOgRQAS5ArxpW7f\naARcI6C0JQhJpIF26qy1t0NMA152ISkSwDChUflz0tficIl5YvHQBau3IgOVQQa+BC1vmVLUd2Ye\nAlA8jjPcvSpupa0wgLrt5hZQY/iTNs5pJ75vsz5HeLZ29V/0PmFgAJYJEPV4jmhLuNPGyCj03f6v\n7xBY/1dkqnnVXyFioLNxm8HNMAFglHreUeilw7CnGgWklFHF1/o9kdu6dfKiHHWRje3rORfBNiPW\nSzQG7YcPMeXK4FIigPoFIHZrKVI3PeGojbkXCuzxiWx6/3tUl297pvLOxu+c/etxiBiyZQoz55sv\n78N1ozsMbN+iE34WInNqMFsmwVo6BlrO3jeg50usGXBuB+ImmIAl8D3i0+ee0M9xOlNBKA2VEOyW\ni99iixZURNha1jKbwYYP9v/1HW9ieOmq3xWnQpD7G0n+6XkLi1vbpnLMT4lD69Q99kFiOimq4BmF\ntT898SrYZ5ZAI3U+Mn1m4xdJzdn8+ChPryk0XLkMklt/t9tzfQt0qzV5uz+58fH06se3nSYN+mp3\neHQOvCkRLfonYovQDu4scUMjfj9BJZYa2wl2EssTOkvuOmK5ZEzvMLQMwNYXSbEZQ4okl5cCVsW0\nYBenTrhqLZ7T67u+/9HC8Li025nPOJMiIta/5ySgj5HQhWUJP5KYHtZ1nQYOeZw8Mw6ZazAuakJ6\nepuNg8KQ7qxqqTbE1zOkmSYUjYX93ZaZjfslQvImmMDAOYO9UR8VqJl7Isgk9/wVt+XDkDPoi/l3\nIEIqDCqy6G0b9lIRBYYwhsgGs5IC2N5Hb/96mBFBpMYqTlZi6/j58nvRdTPpGe1ERFLE7ktb7SNS\nb+07lvB93IJfnLbOvUUe9SNaAFGd+m9luXuiAMgsOR1OhbEWRmYeAoUi8NoeM9cTpj0VWxSnYfsc\nmRY+UMtqFLZsdHWd/b5nPt0EEwBEHT0ej+HtsAramcNy6KonGbUYqLHeNReeJcB6E9GmTvd9dpBk\nqemb96SkVz/tJRK+vP0e1RWZO35ibTDIUD7AK7KjI6L0C97W7beiolDZPbBMS/tlx3NP6u3VacvM\ntIKoDsv4ot8KZ6AeF2/Xkk3mM2Y24xbubLxmjMAzdisovf9pXVfknMNbj/e0OuCGmIC1a/yEaWZh\nq/4wc79bcDK4djnQEkja8njpqCzM9so9I/BS9ZxWYN/16qD9bCfbEvmRCAszDrQNJIrMA1/vnrmg\nfz0j8Aspqm8WaeeZqmcSvr4o5Hq2wO3ncybbntpvy+jitEzX/94Y58Ssm9GIlrFbmsp4bdvelND2\n9GBSpKk+EUxAO7PxCdBIQPa+dS6MQ0pivwNAHo/lDpInx44WorpXO0lVZhlMPuPpjvo07GI44p7B\nngPJgmcCXNVWaWu+z76nMnuV3II/5mwXRFSX1hfZ4b69aOcjYqy2fS3r+2LtbpvPz+MUjY3XhGa2\neqTZWYbIzAiODDTwtrxedW7b9Gq/7uDYvtn6IsZkx2gGN5NtGOhOomVZwMlkka2DmaBHPM2EMsTB\nV+SiUGaAWe/dG4mIMM89V7j0oJEi+9pe6oAZHGxHzhat4l0YcnEKaLClz9lqCpEkm9n9lBIyy9kK\nrgzSjm3rr7HlIynpbdEZXsw8ODIjn46v26q6th0v8VpZBlDpYTkcUNbcxvbRo0fy3Ozq6Pc988Li\nrGWt+bagzhGoXXQCswCt2arv9FDeAg0SsrRnx8y+a+dDx2/YfaCCXCMwGRVvktCLVNPBA3OHrNY/\ng5vQBAh9IG2G1EvVGvXiMnPb3mFsuToQq+ues+eJTWXr85rGLn7GFLlUG9jDO3reJBDQAq8snnt1\nzGDGBGZaD3A+aWikXXjpFRJsjd0vNVEJEQ0ed9tmJA3Pfff2NxEBDKyn04ZRWgayp1VF7fhnKuFn\npojgkuo2owi73k69bCc9nnnk4SaYABwDALZcbOhE0J9NJ3lbjpnHZ9SlgOXs1j6M0Y1j5veI3quj\nCqVsD+VYCWmf7x2giUyFLoXmzrFL6vNMxNqhe3b0bMtqtmi1fs9sW9s82u2ROWQl6F6flYn4ZzMz\nyM7TnvkjgmjrnNX6vaZk++Bx6/MvGYoKc7tuXMvZxe/jLy6FS5KKvJGIfoWIfpOIPk1E31+fv5aI\nfpmIPlf/flN9TkT040T0eSJ6joieuQgRp+broPibWYHR4edwbacGqdr6/ncPpZoOVv20xB614Qc5\nKre3x2txsarwXn2z66q1DqsO24VTSpE8f84m3yOSvYVkGcC5hRb1KWIu3pa1bes89sSycYSeD146\nN/Z+PLTfdlFqOG/EqLwj1PaPud/LMFuYfjxsWduH2XV5vT5JvW/bsCHt5+YIuEwTWAH8IDO/DcA7\nAbyfiN4G4AMAPsLMbwXwkfodAN4NSSv2Vkgi0Z841wApIoUlOSiPEXsqpfeI1058OwuOoDz18gpp\nSchrrFpaiA7fREQ9cGhzh99sIiOi9Fx+SEkeTGrETCyuxI+XmNMzAa8FRP2PtBGv9fiyg9N35ksY\ngsFGn4UuVvu+hs7OYMacvNal46V90C04Py5eI9LyiQjLklrE4N782M+WFqxfKqXR5Dwn7S81A88y\nAWZ+npl/rX7+I8gNQ28A8CyAD9ZiHwTwXfXzswB+hgU+CuA1RPT63TYc0gBa9F5i4EhVygH1RBq1\n+IBCQEYP9Gj18FZl1MYI28WoZSLitNw/mnwvxcbKOyPIYCClZv4MY3CGsfiyUbnj8bjBm7leZuKS\nTnim4QnbM6FIDT4nYfY0qtme9qwOMnkhuIwSMmI0keSNvnvNxtYT7QJY022m7WmosMJyOEAS5Pb7\nLK2GYAPLdOHbm5nbOJumNrs/JA5ESb6bARQhtfqvpXIK4LF8AkT0JgDfBuBjAF7HzM/Xn34PwOvq\n5zcA+F3z2hfrs4thswjsPHSn/6AiR5y/5HpunEdGoBqC9eqnRTzpCwgLaGBCS/UQJwZSOb8IPAFq\nPMOS4uChPSL2BLfH/bVP/pllVj523pazzM3idW6hzxaDnZs9xnapdqLFSsnT8YsSds40MB9fEmk9\n9ru/eHVPqktD+rwyOgYePHy4mSdliPaeABsYpHVTApZlNIn0olhhLON2aDG475p/018cENE3QPIH\n/gC7ewRYWrpsJnt930tEHyeij3/ta706Ao02v8F9WZYm4T1h6W6An1Ql6MzzDC4Vn36Fl8Z7B9eE\nF0fMc1vNqPt6iSmPBHROnZup2pEpYH+LtIHZe36hzzQSX99sUXvtwga7ePxni99rIlE7CipVZxDh\napnAuQNF0ZgpXdkFutFoSMwYuc2aBzM0Mv2UAdiTiY3p1IxZWvF4l2HMYIvr9+GlHiAioiOEAfws\nM/9iffxlVfPr36/U518C8Ebz+rfUZwOwuXfgqaeeEnW5ZhYaCNHYgzln6HVQnhiVPCLiAbC5wNNO\n5FAX7Tlh+rv2bzBeu4Tp69F3ovasJqB/vZPQtme3WCO8tI4ZI7FjO+vbrG6vFTDzxgOvz6067hmX\nLbc1F/ZNqZTS0KZqQDPTY2ZuRTsWUaTejMGpsLKmLiXCejqd9c1o5F8zC7R/Rf41p2OgxUTjAswS\npwhcsjtAAH4KwGeY+cfMTx8G8L76+X0Afsk8/24SeCeArxqzYQKMQgV6sciyEJgzCrIER1Bp/9Te\nAeRzZvEHZGAI5LGTa1OQKah/oUDs5YIa3FODbdTHsHJBof63bGlGeuBMBKJqVqBqKZQk6KUUIFgs\nEd4ANpJ0Jp3t+9FOh37f2JkGLmEEGp1pD2LZ9/07EcF7RtKkmjFHhm20Ov5CDS1cZgOWyfl/M83C\n/+bHyJZdiHBc5BLaw2FMZ2/Vbo1jCP0dxCi8tp5Ib8R+B3GntdpvdhooJcJa/RI2nmVGS6KJ7sMl\nEYN/CcDfA/BJkvsEAOCHAPwTAP+a5B6C/w25mBQA/j2A9wD4PID/B+AfnGuA6+JIJk6AJpL0rKRK\n1M4EzBZXyQVY4vMG67pu0jjbhW3VwLgvzgZjRMJrKOvx8/6A2bszKXbuXf/+TLrvvbv3bBNpeQHk\nLJe0wjACrSPaS7fgxyuij2jObP/1Pd9e3IeqMSZJkwZ9f6kmkEamEkl/ikRwajlhMPUrG98JEexG\nCLPEBxzTsV8EWxhMBRRotm28JibMDC65d+C/YkrG+CtBeQbw/nP1urfkmG7AqTP3Y77AGM7qFxzV\nQZcvvZ60pMGsYOaajWcb7hs5zqK0Wx5CE4WoXqQqnl0NRbWhoVE99q9frJpeehg97ja09V7vLUJP\nQJEJEr1TdBw1Q7cSOs+3IH0MvJ87NXGisueY7iUM05cFxhB1z0D8VrBCk+xEEOm9QB1zTJ3uiBIy\n9+Cz0uZfFvW69p2NDc1Xn5hed59SQnFH58lptjKmAMAbzUY1E5/41cLNnB0oVICVtxPDLFd113JW\nKqeUujPPqD6FJT1ZO+2Vt3HydkFZG9Im5bD7z97Obyq/1V6cSgkAtKTK9buEidRErxVEQVJEsg1o\nPeAWRx8kMmg/pW9N2Tq1bxHxAMDpdGoBMx3Xgrzm8Gh2pNGklPDo0aNQHfd7/PnRIwnSqQJgDcFa\nAAAPn0lEQVSAg774+u0YzjRAf4GnXujy8OFDMDNOp1MrZ30unhHkNYMWkn8QU1PpFEnMJCsQUkpV\nwlf1vXRcS8mg5TCM44LODLXcshyQ+YTCaLEC27GQ27jAwpy070ute9lhpLfBBOxkknhKkzkAYrll\nSglrzvWwEAOp6N3BQE0+CnRVyntEvO3kNYFIEttynhgjwvN+AdU84BZItB3o241uALJEZhnK3gUT\nFrzUtwxvNg4KElgl+91lrTrcMkYYRgvTB97MFrXWoachZ0zN1mE1MPtspkF45mgZhP9nx3dRpsWM\nxezT66U4VFgSq9IYBu7V8z1gZNEw6u3LFWGk5QCUMZ4AVZNoNByMfZR1y8NtnB1oKnyVTjkjo0tk\nC8J9t4SkuwpWtZ3ZzOfRiSVPFCpqvc5eCkbtenNB6/VEbN+fbWM9LjTtyYyPD1zxoFrA+Hv1jvMY\nouz7qn0DalKWw1bmeMaluKhzdtYHX3/EEKJ3fT02AlAZQRTI1IKTUt3CZkZxQT69rDDJGfQ5TqIF\nsPZ92SxiAGAC0mEbNt5mo81lTWNumdwFtHIbTMDBYnKoE1FL90xJ7h+8xNb1C81L+72DL7MFDHTJ\nEbUbhZpGOFhbd4ZfVP/G32C+P45N7BmM/vXS0L5j8aJq96aU+k097p8Few7eM0NvTnltZ9YHP562\nL1aKR1paBDPNMGqfq2rfHdm88TnZ8RreN5+3moriUHp8gWu7t+FOX9I2+Kn1Z9qbiseZ319WGDrA\narsCqEcCufRMK1vbz5w8c1uFIYHWP5fs51vwi9VLJRsCugeW2KPJs+CzCHnH4DTmfoeI7O+zOxSm\n77JEQS6H+QUsUR8i0PGyOzJNCw5wUik9c975GIpIq5uFBNdWp0zglDPWUkAp1SO9KkiWsJ1LhBUY\nw5ZiKQW5ZJSSp7c4CR2P0afqi0DAtLHD1IBb8QkAsui5d+BADFL0mKUfVHCyySASgU8ZhweiORAR\nsIwnwWYLYdEtmlJwMB5pOXwkg8nMLbFoNI4b+8sVUnXTM6BIU4h8DzNpGLW/p71EuFo8bDseFy+Z\nC4BMQLK/2XcDHLVPUSIOu/iPxyPyo0ftKC6hS6lS21G1l5mbw61KikGrUcefjZlvST+o+pEoAZBY\n+9YibZOctHlDaWMATiBO1TeVG400GUOxL6PtftR+r6VgYXPitVQnI1UpnxmJjjLGTeUfx1BvoQLE\nATjc5oUtbXi4EU1gS+ySIY/h8W+LiRlECWte5coyMtsttSrlpF5FpURtC6aU0mL6iSj0eINo0C58\nHIFvY6Yan06n5oX2i8+fMpsxsMjv0YjBSM6tphR7uyPGtUc0RITFBcrM6rLP9piZ1+z8tpnvg/+s\nppjtw6Y9NmNbJOZe5r/6BMrW1NrgzSSxXgVYUcRvUWrWn6axxvgN/bCaKmqIrwkNLiVjXU9YTyes\neUweOzO5hq4+hokI3JAmoN1MlIaLIRNGwtY0UwSq2WBFbbREroNJdfHa4CGRIJVLF27tDTkGuWyi\nsZrkOoz72Z7TD5LDLOQoMs1LWv88svlbH86o71HEm8cvgugdu70oY49BxeySMh6DvXa3MQQJetJu\nXVewboWewdkziO6EHBdCKXLSLgEDAyjMSGUrNe13e4BooapxmhiHaOw22hxVRlpjV0rVaHh4n2TH\ngYF1PWFpvpe0oZfNWLjvtKl7CzeiCaB5Wr0TZ3PohiWvGhjgLDnniDQ8t9ZVr5omMkESOmbc6wbP\nU4xHC9xy8EhV9gvVH731XNxqA7MkFXYxR4FSEc4ReCbjx3kGkVTxPohIOmmf9u1vhGPR8LNCeIrh\nti5tf9yNGGvQ/BE5b5n9sjjB49qw/VGaOKfB2Tb8M0pjaK83P0/rivWUN9mLba+Km9eBIQX9sHAT\nTIDIeHYxEqeoQtyecykyYDWvGgHI64pc5ALMsm5TTjFzYwbpYLqcMGgGekv9xslYUTosh+GORDvg\nmicuYgZ2EUex+7pgIulhF5dffL6PkVbi/SN+8e8xAMs4PJOyZTy+Wsbjck4LqSUlR2LU1y2GAMQO\npmDMm9Yy1NzrKlZjxMiQIhB/xJjBuJVl+Z+Mxc7Rahb6SpPs1u4FEBHyuuK0nnBat4eg7AIeWqz9\n17Mde6N+E0wAkAU2LFo9QlkXaYsos95hMupOKeCc281DAFAVBuMoGbmkTmDmglxv0c3GFAHkzgN7\n27GCx1Vxs797GDzgRlJa8Asv0gg8eK5vwTIBr5l45uTr24yTTaiRt5qLH5NZ2xHu+ptuuzVTLmBe\nuZjQaFTfH3cTKmJEnQK2/Zd2+5VhEgYQa0rW1IgYOSD7/Tm4JcuwxE2dxY95jYUtueYsrLsRzYTR\nca7/vCAQfHILutqDm/AJzPBk5marS4erfaon6wo3H0FKCzjnfu+8VppsNF6SzESOqBZcZrs+Xp/m\narlf2Faa7klVb3ZYh6BKKbIHb7B1ks0WoQ9DnkFLTsIMwGyNMUQCGmmqocw+NDk6J6C4JGPaxeZh\nnSfqtCFj04/cEpmwa+rPSsk4rYzjwYVOs/gDFNbJNu/GNCziV6KlzksGDgfxX+RcfU5L2oT6ynmD\nURPQnQ7x83PbAZGxFQGFvNb5WQdNQiU+tTrq8wuiBYEb0gRmkU2WCNQm0k6qhF4W4WXe3lawqlv0\nL5I2+l6pB7jPH8jch8fJLxCZE179jBZ2qSYRUTprB9r69V0rzSIbfzZOrQ7eSjj9biPz/DHq4XOR\nWPm9fgJi0+s+/cxUsZCINrjZ0VGJKvV1nHwwlbbh9/CbxlnrkYxWuc3DMHfF03VBNFWeCZbCyHkN\nIxKt+dPemfiONmNztsTLCBHCMqjVXjaS0m7pWFDzQD/7ZBCXtmufK7mwU+P2JCyAesOxr7Pu6zLa\nrccV2VZPtPhanUGQTHR0NzmGEf3b67N/ZpkEm+etLuqLx7erOHqTxjOeYezqfHPpkq1J7roA1zVX\npte3dlO7sm7cR1ewO0mgeNeBuacWn92NuZDI7GHrOFknr5z7t8KteTUI6IFG8igXHvGcLOB1zch1\nW1zMCvFk6X0EygQoiQZSSv4TySfwdQcigHIGpdFmHokpYyWgLfFSsLQQXfEJpJSQiqQYYYwqp3W8\nRep2zhnH4zE+B4DxvXbomxmlMpooLr6kzriICKCCJLYHKFUJlkX1SySEW7lEWzQeT//d4tnLo+Wn\nZ+bG6ZVB6ndmlsSn6EQ3c+a1MwAw3uY1g1KSQJ5EOCQCFyAtQKEx90LEBGw7rQ+kf0nSbRBw0khR\n1KvgRO5KRCERlgPACUilgKiAkPCgTkdB9zURDjiKtg2wHP9Ni2hNaWDo4iT25tFwlkC4DAgMogWH\nA2qob5EslUR4cHgILgBYYl6sEKstVQ2lQN16KQl5kTQ4mDhNq1qBlRhL4crUgIwC4gKmpTPput16\nwpzpAzekCTDQ2SJGrp9P1b5jbqp/ojGAB5UR9K/bhTOTgJZYvRkBoGkiG2mKraS24CWYxceq0KB+\nxtyW21PnvRbiJWz0rpWOAGCxjvLtbfrTJKgcj/aakfzUzbHuoBqZmd8JseUikyyaE6BL+HFe0P76\nd+yQsLjpZSvZ1buYQ1N2bDMyChXJgqUZryCagx/v0eTczpH/3A7Fpe7nsX0c+ykaymk9SXhxdaK/\nWLgZJtBy5KXtgRJLMMsksaImXFRVLFoUKS2VM/dLJaytamFjP+4sSD0z4BN6eLu31elUeWaxIWVS\nRy/6rO2ZGm1B39XDKMU5vJTAZvn9orY1YEX++cSu2/btCUVrmtlxAfaPQVvm1NpOvX0u3I7z7o3P\npj8sSVLUt8QQxrhMcATQzjPM/DOKkx3T6MZtpRkRJlXbqmOuZ/99XIqnaZuluPkjzHFjHaNzfoHb\nYAKVe4PH46ldlFZpLZpyy3PHhUHB6S1gK/mVWNrCOLNlpe8oPHr0qDqj4t+5TcJ4cs1KFFSci8FZ\n4w5Op9NuUs6ZGq2L1ZsKHpq6b98Pynm8zzFAC2lJY4KMup9uD335uuy4AePRYDtXficlufTtRNSi\nPKN5mc0bgOZf0vGJNI/2jC1u3Q8BdL+ENZ+0T7NEMkL3/f7LlktA+zUwgjKlOWWCqimMzA67mgKd\n4xIvBxDR7wP4YwD/59q4vAR4Gk82/sCT34cnHX/g69uHP8vM3+wf3gQTAAAi+jgzv+PaeLxYeNLx\nB578Pjzp+APX6cNtmAN3uMMdrgZ3JnCHO7zC4ZaYwE9eG4GXCE86/sCT34cnHX/gCn24GZ/AHe5w\nh+vALWkCd7jDHa4AV2cCRPQ3iOizRPR5IvrAtfG5FIjod4jok0T0CSL6eH32WiL6ZSL6XP37TdfG\n0wIR/TQRfYWIPmWehTiTwI/XeXmOiJ65HuYN1wj/HyGiL9V5+AQRvcf89o8r/p8lor9+Haw7ENEb\niehXiOg3iejTRPT99fl152ATCvsy/oOEon8BwFsAPADwGwDedk2cHgP33wHwtHv2zwB8oH7+AIB/\nem08HX7vAvAMgE+dwxlyn+R/gIRsvRPAx24U/x8B8I+Csm+r9PQQwJsrnS1Xxv/1AJ6pn78RwG9V\nPK86B9fWBL4dwOeZ+beZ+RGAnwfw7JVxeinwLIAP1s8fBPBdV8RlA8z8XwD8oXs8w/lZAD/DAh8F\n8BqqV9FfCyb4z+BZAD/PzC8w8/+CXJD77V835C4AZn6emX+tfv4jAJ8B8AZceQ6uzQTeAOB3zfcv\n1mdPAjCA/0hEv0pE31ufvY77Ney/B+B110HtsWCG85M0N/+wqss/bUywm8afiN4E4NsAfAxXnoNr\nM4EnGb6DmZ8B8G4A7yeid9kfWfS5J2rr5UnEGcBPAPhzAN4O4HkAP3pddM4DEX0DgA8B+AFm/pr9\n7RpzcG0m8CUAbzTfv6U+u3lg5i/Vv18B8G8hquaXVV2rf79yPQwvhhnOT8TcMPOXmTmzHMr/F+gq\n/03iT0RHCAP4WWb+xfr4qnNwbSbwPwC8lYjeTEQPALwXwIevjNNZIKJXE9E36mcAfw3ApyC4v68W\nex+AX7oOho8FM5w/DOC7q4f6nQC+alTWmwFnI/9NyDwAgv97ieghEb0ZwFsB/PeXGz8LJEf7fgrA\nZ5j5x8xP152Da3pLjQf0tyDe2x++Nj4X4vwWiOf5NwB8WvEG8KcBfATA5wD8JwCvvTauDu+fg6jM\nJ4h9+T0znCEe6X9e5+WTAN5xo/j/q4rfc3XRvN6U/+GK/2cBvPsG8P8OiKr/HIBP1H/vufYc3CMG\n73CHVzhc2xy4wx3ucGW4M4E73OEVDncmcIc7vMLhzgTucIdXONyZwB3u8AqHOxO4wx1e4XBnAne4\nwysc7kzgDnd4hcP/BwAdTIFee88rAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ADGGSOtpOZlMKUQaWnLm2eoKl7kQVyDJlKnKdy/qwmAaLM45cZ5xXIJQ3coRLGIFzhCEK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GG7hM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\nwysc7kzgDnd4hcP/BwAdTIFee88rAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "numpy array size (224, 224, 3)\n",
            "image batch size (1, 224, 224, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f8176aa0240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ADGGSOtpOZlMKUQaWnLm2eoKl7kQVyDJlKnKdy/qwmAaLM45cZ5xXIJQ3coRLGIFzhCEK\nMj7lN8BgDCFgd28Pq/Ua3jlEdsm0tZZ6nUsmoQHDZiO6JzMGR+jIFQquXC8EcOIEqZHSFwgGADA8\nOWxiNOBmkVqGYcgmPSsd6DiEOCD4iN4BHh1CCPjwj/wIYox4+eWXEaN4aW42G1y/fh3vec97sF6u\nis4+GoUJAh0hRuUzilUbW9Rf67QMw3LdSgrAmANbS4NiGgDgqWkUIW96lVJiFCnOobZK2GeoiW/h\n+4T9JNygM1YwXWM6PoYAxKRCOu8EmM7q81jK0t+nJA1bHhBJoIBUgIh+vhuvBOX+2wCgajGcxbxd\n7d6Zv9dNY5wsOC2KaB2MfP3qbFsNeKREilEmP3JxhJnNZknXFk4trr49nPPyW+KmkRmBkDh6RAjC\nHRVFzp5zWfSsOx9jzJtavN5i/k51eH1xjOAgxEaJQYgBIIbvHRgxuf8CFy5fwiNXruB0tcJnPvMZ\nrNdrAAKyiSmzz+Ntbd46X9mt1Y3186IP16pVvl8lg4m1bUV+YDuGoG7E+rs16uq4cBRMajNsqrYr\nWJzdiFP/MlYVY7UGR8SGi+RjuXr+7Op17tILULwElZux9y6t29JG52qmua08EJJAZlyuHiilsBmw\ns/bgpnMqQk6BPEBjDlKvv1R3viY9T7mEAkKtOOWMnu98B3BETCpMsTUUfRDc+gzUkofY6UMSowWh\nPz1ZZscfBYZm8xlWpyv4nS65wtYbJzKD4vYgHPFSAOCKBKVj2XWduKE6US0U0BNkO2EvyUXXey9O\nQSen+LVf+zVcvHgRs/kCy/UaIQQMIeDg4ACnp6dCcCHcZmp+YoxwqH3xGZyJmUuxDgrqqWPRWeCg\n9kf/zm4dzbho3AIgBLbrumoNbnNIm+KsaopWYtp66yl4mF3aUW/MGCNATsDgroMDiYjvKcfM5GtV\nyqnhCtgmqRRg1/228kAQAaRJz30gAtIGVc8v/duitJWObzzEpI5a1GzRYPU6U5AHAHhI9TkDCCXg\nT0vUDc3iigoM1fMiFwcXR+oUUnS/ltOFGDCbzTIRYRbVqEvBQLoQd+ZzDBsBsEDFd97WxZEREaqx\nqMaUBaX37MVv3YzLcrlC53sRK51aTAxKrZzbd5nDh80G3f4+dvd2cfvOHSyXS6yHDeJ6g9lshk0I\nEiTDLCaxe6wCy/29L0FiIGQCoDyTSKS54qA1rmdqA5d5jNWasL79MXnjZdPwtva6sY+KjfRsO5yZ\nywSCr+u57/vs5pzbGmI2I1bPUVUAhNbFolVx/woAgzogFV0bTUKMEWEIiVvX+uyUXnj/D0eDIHMS\n/0kwN9QvAcpkOmN1u0YLcua0Iuq5xnstcQxmdL3DJqwz53DeYWdngb7zcAmk9J2HcySoPkekIF0w\nMZgSZ1GA0hWAVcVMghgRCKhs5Vb9mc3mKVpNnmeJ6Gq9kkVKMuYnp6fYDAPmycPxxo0bAiaGIFIL\nEQJHDGGodXCC+FHUwz0Z8hxjrK7TTWmlGkHZ9fcisWVTmrnWMo2Y1pqdUy2d8/DWSxHjtbSNs1aA\nINUEPzKLm7VREav+sqzpEAM4Ez/xkdG6VSUtD4RhYq1kQiNCsK08MERAQW5kNJfALOa2og4AQ0yB\nIJxvADAG/vS9HYhtmEK6Akp8YogA1zp9/jv9y7o+kDc+O5kU3ZwC7tRTJCj7gMiM9XqFGAbAy2Lt\nZrNsMiMHdJ0DEBHCkAlB55J4nogAO6DvOuGY5OESiNV1XYnmI2NaTWKiWiSEABUUWYwNRUy22AaI\nMLBseCZCBONkucLJ8hRDDIBzcN5jsxlwfPe4uObqKw2I68TUOKWzKiiXpS5MSXNpjaSFo8CYcnQA\nVWCZLWUeC06Tx4ahVteM60yvldppSCMVs5NVY5VomYkFpm17yENcgBMt136HECeJZW4bAwQPtRQB\ntWXjLIvVg6EOEKGf9RIqmSi0Q1kYxac8k/1kzqvFffmpdNzaYFuCUJ5dqq2bJDqZlNrmKr+76hkM\ncVCxEI8G9Qi4Fqs6I4tddzabYVguM7ey3NklFWi93mA2m8umdi7p/RHMQxKZk6hOJOHPzHC+SyK1\neOPZfhERyBd7MgBRWwjilKOqS1rQ680GXTeD9wTXdVgsFuJvkLheiMUU5ZKFZhgGPP300zg9ORFT\npcVmiGvHMKPcTmE61ntRnLMiwCpZcQ75JlePoYxzzNJPiAGeCnbUbtTIcXKstpWSmwJANGPb6PCV\nGpqSyOgcVxJKiOBkLi9SJmeilOuLJpAq8sj6RKQxDhp8N51gRsuDQQQgi8a7bmTeElEqSQNc6zYh\nRASqs+r05BJFlIUWGHAkC0lFtcjikddOlhQRrWo9SqCtEnBS+5QzM7zrcoipEoKQcI7IJUBFJggg\nYnSxx+lyjb3dPaw2G7CPoG5muKMsnJ2dOYgl2Ge9WsF3DgwPYhKEmyM8PHzfmedJy/uunxzvqSUR\nohCAkKwDnXM5lsC7Hq4j9PMZ9g8PErEBTpZLhBhx+/g2nn76vbj+1nXs7+6im3e4c3Qn4wmKaMco\n+n4mnkZkV46mHJxc8kbUhZzGXCwGEoUHL27aMUSQR1YZhEnI52w+NolKtOh3OSFNyjnQUVcsFwZ7\nsRtaCRm5gh9Zj9cWlGMuamJOrBKmVQ4Zk0RfjPehI0IwBLMOUY9VX4QIbMcC8hjc84rvUOm6Li96\nziI2EJBiwQHJtsOMIX0eECulTgFDTXkFpA4mEYkjgEgg9hkUHE8UYNZIs1DHYitQ+wEwc85fwMzY\nTPiky4S6FGfA2Axr4ULem9+LCYi5JkrO+Sze+85jMV+AOyBQHHknnlWs2XAYBgwbCTYiFnOlS9mJ\nRM8GnPfwvsxTCEMG2M4dnsPt2zfR9Q6//hu/imff94wQiqYt1h1Yx8sCab73I8lN1oKM2di0q2MU\nwUHMeVNz1L6PiurXRmJBs9lypiBXMB4J9CnXt3EPk9apPI8lAYnt71QbC5BcfAPy+kURZhmFedzL\nKqDlgZEEOE4jmCryyd/5S+XXmfpZl9RhswZQD2reRFS4DhtdUK+TiQ0VRQeKR1ZbHzUEQBeGqAEq\nrs6aCZFNHsBYrgcwAlzfo/OdELhG7Sj92kAjK4k8JB7RIcRQJTWxJXDMxIsMLqAitgb7xCEFrPgu\ney12ncsmQed9Do4aUgq4YQhYDxvM5hJ9uFovcXx8jB/96Edx8+YdIQAmCYuK7da8l9U4EDqX0sF1\nXZIMx74iatbjSBUc5rwHcyjhvuk5zifCMeGAVHHfhuNnbs3lOv3dOYnZ4FB8KezQc+SMqYyek4qa\nQPNcp0sioXYismOUpCpmwHdeAGpNrZbMvtI+Da6bxsza8i1LAkT0JBF9ioj+jIieJ6L/Mn3/j4no\nFSL6bHr9nfupT+3pFsRQbg1g0rknt6UxdykBsI4zI+uB8TjLjh5enHV81wm3bV6qz9uFm9tv9MMQ\ng9nIxcmjFiUHeHLYWcxwslohhIC9/b26zsbmLyY6ghIRXfAWwLJSwDAMCBvh8JqkxGZi0gCjbE50\nTvRRZnR9V20oLcwBm2GDk5MTrFZLbDYbdL5DALB/eB7/7x99Go89+iSGdQRRh2DwgMixiKpVnYLE\nBy7gmnC4Zs6SxcISbu2tT6pLuxnV3DhV1GMwc3SUuc0qC7hWCVwJUVdnMcvt28xYU321zwFQjXFg\n8SS1Dl6qJlVMxtwvpsqyzlqScy+J4J1IAgOA/4qZ/4SIDgD8MRF9Mv32T5n5f7zvmlj1GUl+YXP7\nsZHPKQrQEUkBvyJO5esjMryrohLOGACNUNSB6vLkl6KutEPyofe+azADaacSqhaQLKKZtF8JFXPE\n/u4uTk+P0fc9giGCmhEmKcnYhJCTgShoqDq8eqwxMxxTlZ5LcQlgHDyj70SE+WJeHIFms5T9WDZl\n5qyxBDfZPq5WKyz2d/Brv/qr+E9+9j/FZrlO5kjCxozH1CxoHbrgXVckH+98YwZOi8XMi7YrMtD1\nNcGyGBCAEvWJkg5Ox8ESCvVJAIrUGF2sYgNsotvAEkqciZMv463ExHLzItDyiHDYudHSEkQlTl3n\nkYTbakwsuDzlQdqWb5kIMPNrAF5Lfx8R0QuQVOPfUiEgi9ACA+lGTpPPKfMvJSccJL9osZUB6X6r\n16WG5j9jFq8AYsouwaT7jUocty0hIe/WFk3lFmlp4OTg5MQvIPs5JCuBd0nMiylSksFhAIMx39nF\n6ekyB4v0fQ8wIw4BxBJK2vV9RuOzx1+KztOsymLIF5MhWHAWArL34WYIKSGJbkxg1svmJy+JTqBi\nZAI4iwnRIOXMGYAkIoQY8W//r/8b73/f+7A8OQEGwuHhPjbLVXF1tZsgi7oEGP8JdWPOPv/WgQz1\nYte1oFJRdsMhN7bBy+QXpD2tHyt1tURdv7POSZLpKun+hojO+ln1XYbzSTCslmu3liD7PMSYPDTJ\n7IEJT1hxMEzjkL/NbZLU8i4xRar62pZvCzBIRN8D4IcB/FH66heI6PNE9AkiunA/dTi4ZKPVTcZi\nLLSSAAACw3ntdxG5kTadlqlBthwZ6tudXmpbnxKdlIvlzdC0nTmZ5HL0G0GHnRhA0sud86CUCFVi\nBBz2d/fQ9zMQCJvVGgwhAl43B5ec/lV/SDIMaaJU4sQFHGXTonMO/WyG3f0DRAjB3AyDDGkUkXY2\nm2G2kPThlPT+IQREjLP4TiZQhVg6/vyLL+DHP/Yx3L55E8N6jcODQ9y9cyeNb8xz2AKtFXExU4n8\nJhMkxLb8I7Pw5W+205k2ugHMCPXnZp6tGlqaYkR2k/G4AjZzewqOoO0BkqXDtCsJgaXf2tf0ufNe\nnLYI6FKuBhAqjMoC34UNGWei/Hwl2lRJ1215x0SAiPYB/BKAf8jMdwD8MwDPAPgwRFL4J1vu+3ki\n+gwRfebu3bvSVtQbtw1zBUz/ACN+i6vOOP1SvWitbn6WhKT6ss1kmxFd5WytGhJiOkQkpqSQIjnI\n4HOFaQgan3ADIgxhwMHBIW7fuQNOkXg7i0UWUXfmCxzdvZtEx1ofFvUCxVZOyrld0ekJ2NnblQxG\nmpkGwHzeYzaboU8WgLKIC+eYFiXLfEi2ZMKFC+dwdOcI+7t7uHDuHObdDHduS38cSko2qyZVM6Wq\nUyy/Z+wiCJagQFgMsQIX7xW6PUXYLTjctqclDm1bFYfReoDiK2HFb+9dBW6OgEAag4YZwFUPUtKM\n2HX7fLP+2nbbZ00Bk1V/zvz1HoWIeggB+F+Y+f9ID3+DmQOLUvsvIEeSjQqbcwf29/flu6h6c01p\nzfNEDE3iddYlicFcHFbMUyYH4F46ElRamLg+p3yO9cKSfPwdoJOjASRJnbFEo7SJ07MYq5WI7KfL\npYyBiuHOYb1eYZ4yBhekWt9FipGmqMuwEAFxX3ToegE7dxY76PoZZrMee0kCkU7FDI5azKAlgKVI\np3L8v++x6OdwHOEAXLp0GV/68z/Hyd0jdGrOhNj323HT6rKapRtTcZpkNZoChitC4OulbAHIVqWx\neQLsvZYolIlF/s3q2rYeHa+qS6Y/dt7btkwRH42jkRgU6WPXdY03JCf1dPsWzhmwuDhcTZV3Yh0g\nAL8I4AVm/p/M94+Zy34WwBfut06b4qron+U3doRo9SuzaLQUJLq20bb3nNGv6rOqD5abtNJJBfwB\nZgNLXb7rsv3aEpfOz+DS8V7rtcQPaHAOJ1DJMUoOOowXmz39ZhRuag618Imz9l1vzIDa6bLZYHRY\naer4vAcrehLEfDrvO4AZj1y9jM2wxh/8wafwyOWLOQJSVCbX1KODXM+LRcFjmM6so+HTau0AUCwf\nqE2/CtpZhyGgjlmwal4OIU51tVzV+3K2hWZnclbySuPCzVojKkfLTRGJs0qMoah7W64fS1d2e787\n1oGfAPD3ADxHRJ9N3/03AH6OiD6cnvo1AP/gvmpjQdGYBbxR1cBGjnkSE9CGWTLqgAHIhPnOgzWE\ne0KnBzCiumUSEsVVjzIU9L4QJaNCTBKe8gwVzW3QEASzhJ6l4IjBEejSeQZqr1/4Xdy6eQvnDg7E\nUsJFxNcGiEOQup5SJg4MZEKQiSADxIyIDQACOQ3XjQlglfp9jh6EojF5LIuKoci2HKEVI+N4eYr9\n2R729/fRzzvMd3bh4fBDP/whIBEoGcfi2psJtxF9QZh0tJEuGwebZPfPBDgYhmHdkNM8tgiOftdu\nmCi6VZWlm0CVFUDnN3LNBAA0V9sAACAASURBVCwjYk75FIngvJe8uLrW1NmIaKS62rooqZPMJT7F\nAqx6rxAmrnCFus6SRO+s8k6sA/8GrdIt5b7PGtBCBFDnEIcAoDbBqJmImfP8KEiki9J5cd7wzphj\nzhD57YRN6aaAJpYctl6ri0ARcl2s9lqFbBQA4/xbIg7EIspDXKBXqzV29w4wn/d4+9ZNEDl0zqGH\nK9JFFi09MJHDIPc67WKRYmJqZ912VVtijPBON6GoMM55BBMFWBan8eEgyGGjMYJ6h3OH57AJjE/+\nzv+JS+fOo+/79CwPdhM4TNoYzCwHsyAUomnEcAXGJCFnaU/OOeDG+vG2+W25sIyfAQBdUTOdd4hD\nEqnd2c+x7xaj6DqPYVMzISXWgFiedN1n9+Mtbc9ttOpkHkpCe7k0h3AvIvDAuA37ZO9oF11LbS1Y\n54jg2Mkr1l1pRfZtpdWp2qw0dRlPTOv8wtyoH6Yvtl8uWRK8Yyzv3pVU3es1hiCHle7vH0j9ISC6\n5OFnuMG2YkHNGBVYrX+nZIYtY6DJT2SP2+Aee02r/rjExUOMePyRR3F0fIz9vQMcK4iZn2IbYLje\nRPursbNnQdgTiszctioEgBH3tiVHVTZ5JdvnOyfHqbXtVjWl3fgt3pPbp881/g/SRrZVC7Bt1l5M\nTC+rKFvXca1W1K9kK27GdTQmW3/5DhZmxjCE5FgRR8h713XiJjnh+CAbyY2SLmzj8Nv0sJwzkCOc\nkSjOMq0AyEk3be77Ci9ASS02VVZLOeHn+OgIuzs7uHvnjpw43JeU2TEEQdlNYo6tyVg13ZjBMaaw\njFad4az/JhUjo/QTfU6L1afYgtdf+QauPvYI7ty6hetvvonTu8cAAav1urIKtH79WvLcNGnIKInU\nbXiw9aYb9Z/rrMZTi98ylKnf7EZWX4k26KkdwwqbYg1iIwwKyOlPE+tX17vkDRwD0laCKPc5UANw\nTpXC5B5wIqAqQBiMf36KE5fz1Ahglzh/wsCTTmkDi4A6yYcGIbHmefdeYuBJdEABGpFfUrcDMYNi\nzEeQA7XI1xIjdXJJV+bvBzaHYQJgyKnEjABQzFzoYG8fR3fuSE4AyInDJ8tVEYmZsV6v0bXZZVgO\nmxgUdY9snqGBqHHr/NuNwEhqQxwwhA0iFymiWuzq3q0M1AFff+UbuHzlCp5//nm89NKLePyJx3Hp\n/AXEEBDYnP+UgrjyKwXPMyjPmxYNXCJXLAOtk40lvJTWhqcGBUin+bb7XfGSLMKzcWjSdQGAPaW8\nCWk9MWOIsaytZqOmIRGR0GRQbqWWyfmIXAGLldBPVK3p6pVOJ9bTivXFlDJbOQ+47Vv9ASECWdJt\nqGttixXkfWxWCSkLjXr2tSK5Fs0arKL4NJewAFW74Agh2FN0c6NhzTEZL4hFhNe664Ug2X/39nYl\nAGe5hCeH46MjxBAwDGsQEQKzHPBpBwqJ0MUAjkWXjrFCtqqx3SYFFQJniRub/9Vl2FcgZEwrdblc\nJt99hz/5kz/BLB380c/6jH2weRanrEx6+rKAh+Uaza2oJjhJtqppW1BxZsUMtEypAGYoSp+bLD3i\nDdjc48bmxIHD9CZu1YLR7/UpR5Xak9Sqvu8qt9/tvLusn1S1Ef9Lnfnl6MzKHhgiwMxJb6KUjCOh\nyU3rz9RtErVz3VivBCGbe7a3IcVjJ0DQpnNSJNaqB84Vk88Y2ZZYCP2s71aKIJJMQkMYMJ/NcHz3\nGDGdJrw8Pc2OJr3vMe9nGELIGIPWJ7q3ECeNdGyR63bspnwCto5JknKYGZwlDmWrErtxenqCYTPg\nypXLOH94Do899hju3j2GenSW5VpMl1BCmUw6CvRqEtGSIzHpyOnZOiaads2WKeKmerlutKk+xyBh\nyBxLYtNqvMxY2dTjti4FiNvnV39vwUOUkJ2NXrXz1IKCDRZhNv+9VIYHhAgkxweIS6Q2Xg8IqTnU\ndLGJSq03mC3O2aOpCgYgjkZF/wZKDIEF9lpKm+vN7ZTB996PknlYS0MFeAKIgbG3t4udnR0sVyus\nVqts/5e2DNjb2xOct1q8QdpENNr8QC2CniUBbCvq7VhCc9MhIFHi2ok8nEt/M+ODH/ggvvyVL+OR\nK1ewWOwAyT7PMG2bWLDK4TOxNBl5pf9C3IZhkLBpG7nXnFzUFks0nafsZu2IMve2bdG10wJ9o3qV\nKJgs19Yq8K2M97a5a5+Zr2nabuuYYgLbygNCBIRq6QZVoK9w2HpCrHTgXU21gTSATtxx8xOIYMV7\nJSzCYSxRSCKnbtTI8HDoyOdTjmruKgCNRhaq+W8I9eEpbWSkJpQYgpz4s5jvYL1eyWI3GYo4SQ46\n4fZUGZ/05mps7kH1RyM/ES5sRUk9dMU6cgEldZrzcjCqEuv9/QM8cuUKOt/J0V7k4CIBKSYkmtOE\n8uZMfvkqBYz6wMXrT8ziihupOXh7/7wndJ7QdQSfXHDzK9kvbMx+O5ZFtatPK9Z3Pbo+N1W5MFmt\nXisu12jRdUZEk0lg5W+NFLp3USemSh2gs3MMPhBEQHXljNqmlxIC54Uo5I1p7fFGtLUuvTHEzI3L\ngCpqHguBoe0UWtoj17sJk5m+yyDXkgKA3OYRBbf6aMIOmBnnzp1PgUIui8rWqrDebOC8x87urmT5\nyb4MNerVglBExaY+erVec3YDOBJAigQoy+NEogYhEVGfzIRHd+/i8WvX8NnPfx597xET2EaglDzT\nSACRM4Gx7Zb6ZYyyDk0lx4B6DroUiKXv1f12LBxJQlOfQMgEErMB/+zo5fk0AUOj9pmioG80f2eg\nOvU5r1GkdHBB5jWkjNNqclRzsyOxeNlV4xoX4dYyocV3Pnk0NjEmo5abus/47TtehFPoSTiCfJPB\nNBQA0Y3VitVaIpdQ13uJYHYBqclF21JzguJHr5ykrtvohwacCzHmkGWtV4uCTiHKCTd930v8wOlp\nBiBDOhpMk2Z45/DMM89AAVErFVnKb5+lJr1sCTCnFjHL+YP2+pYT53nRcUrvm7CS/IazGRxJBNwX\nvvAc3n77bdw5PsmqHflpsVTG0ujpAg7keVHToHc+6+sKELaHj9gsQNU4UFEPtS5b9PsumTtHKlVq\nkHVPtvdmqa4Nt27qr8yfVOvuOoX36w4s7SqqToXxcD2PU0yoLQ8UEQAAdXAKYAwcEEg8OXOuYSIM\nSbcEqaderR95opygo+U0hQMqml67VhbLQR2JV2UVSht3qmgykIqjarsSwfG9L7pkLO1Zb9bY399P\n5/kNSvXgnFB3VQ2+8NxzJWNP6p4lYHV7GhRcF42rVbB28bdjZscqBDkVKQ4isR0eSOLRb3zjFfzn\n/9nPo5/1uHv3KPfNHsFdqRVbnmcj9HTzKQg4GYCEMQHMJSrhMkTNUe1ujII7aD9rVa5WFe14xBAr\n/MpiFFNt0kN0sooFjOIimp5V6H6xmJXfaoJt77y/8sARgdrRo4hk28Qfq4Mp1Qs8tuWfWZrRUkIA\nIOfVmxLF2vpj4z24SSf8Rnve31DQcO2LI3ESWS1X2AxDzgfAKKnxVKoRqWEwaLRVQ84GoqaIhNUX\ny4Y3qchNPc65nPx14IgNDwAzzh+eB3PE1178Kg4ODgAGrl59NJ9nSAk41CaOgMBGp57iuqDtKea0\nD3r0mK3PHjVmi+tcNVxy4GptwtO5acskYZ1S+9jOcW2dsCUT15Fq5rIUZnEUNXNb6WMKSJTrXX5t\nKw8EEWCgWlxRxaMcspkAJCAfa5UBHaMOSC7/MuDOcFqbcabVydWIFV1EpCippDyDHGfnJGdEuLxZ\nzN/EDA8qSWXIpgmTXm6GiABgCECEQwQhgPN1RCRn7jGKGc25rLtK2yW6MOMNjsUvPzkhTXJ0k22Z\nIxDZ5Zc4YRVOFCMjREJkE5XIzcJncUkCObz66mvY3V2g7+a4cfM2uq7Dq6+9gaeeegpBVak0v4IL\nJGmJGN3MAyn8GByBIHNvIwSrkqREOW4t6fNGr1d9XNmh6tnbiGElCST3qnUY6nTsDXePHEc40pQa\npRzfXqPjWUsgsWJY0RDGjEcBII7wJECmB6H3Ht4V5zkbtarFkQSaEUIOOJsqDwQR0OJA93T/VZ3U\nDrxPUWgywON6MyHQ+0ci1AQ4ZZ9nf7fiaMNJCoo7ll7sc6YWkO+6fCJQiBFxM2R9s+TKkwXuq0Ai\nqUf1dX1tix8/6xAK7aui4ZP3p03H5LDYmeHO0S1cunwZRITrb72F1TDgkUevYrk83fKQNGedR+e6\n1C/jKhsl0KlYaVz+XhXhmiCN52+batPiAtqOqfMnKj8FlkCp7DhkzIhT3LdVH0btQLEK5H7VQ2Ta\nrNy+9o7UNGUaWkxu7Ox0v+WBIAKkdmQU8K/luhr2qiXz7wYU0DlpQRbmmA+wBBKl9nU+OmstUCuF\niscqMChnV1Hc1l+3TL+vN3ydElvq913xYLQiZN7oEOAqNmZHabNIO3bTqhmxfX5pd12qRecI5Fx1\nBFh7T0xJT1abNY6OjvDkE0+g63vcuHkTv/8Hv4+P//THsd6sc6x9Bq7U/yP1fxM2OXCpyuarU6ou\nt7HGd7Lubrw0LfBZ9bXZYEQlY5EWi1NMmUtVGnAu5SQwpsI2mYkjGsW+tPVVzIDGRGyKALdShfyB\nNA4KMJokpqa+v3LA4BQVZ8iE5yPBNbaeudKvNaQYwEjPL6APG/F2CzVnFUkF5NNjsc9GGERW1Umc\n5kaqwxegjBiYzxapbSEn0VACp8CiSghjSckhJF15k8BC7/0k2DQNHKKOlkNZqGcREucIO/MF7hwd\nZW58/sI5vPbKq7hx47qcr5gIahjqrE+W86qLcAG+CtGwlhvvCsemdEpQpa9vWedFzB5jRPdDIM9i\nr7ZPWQJLTmveSUZoK7WOqrZcvVmHRcnV9V9wGf0yH0dGlM2A8rzavHgvIvDAHD4CILuOTolRw2aA\n66Uz6oiT0XFzedSN0opYaWNFDiD2hvrX12RHFDZhoGqPz9dFKP1sxXL9TnW6IqEYh48E9pKTLDUh\nbMTWnnISqjcbpVh/PeJc05J3ZpN67+CjR4gDQowlI7Fpi7ZhWkRGFb2Wzy9s9dkUO4GEO3RdByZg\neXqaAbUPffCD+MgPfxTr1RJd76vnMXNG0u2CD0PIJzF1XYeBo8xJg7IHLmcRsJwtN+qL+C5M91Xm\nv6gYEbEmTIo7TfjfTxKLLfuqsiqQYUqjawj2iDArAY6en+uT51pMq7TfEkTBgADe2gdb3rEkQERf\nI6LnSA4a+Uz67iIRfZKI/iK93zPjcCsG51ca7VnKsQeoh5noxkxUnC6YR5yyrV/jAfImpfp3JSzM\n4vAREBFQDuzQxcwpg25LzbMXG7DVnAXU2WI6J8d7eS9eiVXac6OWFDAwHSGeJKMQAvQkWuljvC/r\niIjQIbcz5/5Xb03WEGhdVBZwi/lZi50dMA/46I/+CD7/3Ocwm/c4Oro7CtKxmEMGVPWzQz4PACkZ\najT3KVg4JeFs061HqlO6oMo/eGaOvnpjVucONs+xkl8G95r6WtDPei/q3AKl+qxC2WdDQEx1dgIR\nmFwFjjIgUYNpf3D6e1v5dqkDP8nMH2bmj6bP/wjA7zHzswB+L33eWqaoJaCTUAY2O+mgeFvZa0eR\nYSSutekhAsj4Ym+OydlG8QFABnvSFLVFVKzE/szljJSwBagEkMX4znt0nTjHeO+y7q9CzpBSj2k/\nnXPou048B416kQOomixE91NYYqtBNO3lWKQBKZEloGi9WoNAuHH7No5O7uJzn/9TDOs1XvzyX0AB\n6dg0w25qXdzVMeCox03nRo//0utHfbDERW3oxkuxdKb0yZapPAT3o9uXMWwCxCbvtHW40cumESNP\nlWSnJw913o8wrxHWoN9p387wRXi3MIGfAfCv0t//CsB/fK8bLNevKGvW5Qsn9lQQ8m0Irbgex4rb\nAw1gqHpVGItXVjJQc2ML4BRuaey79hgqI47FGMwriaJewKxASJJAB3hfjq5OaoFVfzp1F44R+3v7\nWG/W2Txq26eHjE6Ncw6WapxiRJKJOSS7uTH1O31kxjolgLl79y72zx3it3/3d3B44QKe+8JzODk5\nwWazyVw3i6WGY4sZVDZ9i23rEeJEacOTnJs4bLb1qcYcXDdBBFmwAU/+TCI5RQDty6XYFit1qoVK\n+zblrdqOqUpctl5yatOXMQkqmTYMbipHYXmeYY4Q9fVdVQfSc36XiP6YiH4+fXeV5YQiAHgdwNX2\nJpo6d2Aqa4wBRFQuqHy9z2oYowr80BJiFBs2kq4KczxXeop15lHkNT+zjSNQZ460oLVkjjxKi5Wy\n2SbHoSz+pUbP53Os12sMJqBKiZHvuqwCcIw5iKgdB+s8Mz02Yoojqu3LIbltT4XGyu+hAKvM2N3d\nBXUev/f7n8K1J57EI1eu4LnPP4e+n8E7j85PH42uY9ui4y2h1e86VZcmsICq3904IKpUXmIQtP6p\nRKJtO+5VlEF470vKsqmHm8IAVutVbgcg60qTqeTApAa3qGqexHiM2pakO/ETeXeJwL/LzB8B8LcB\n/BdE9DfqdhqZvv5+fO4Aj8UsS/Gs3jNwySZ0VlHzY3pm5sIz5yBn7wIuEuIqiDNN0EwtafByg8sm\ndoz8oihOHIyAyBtIhh7JHFTw2hQrjzTgsRADBeE0F0C7CVpgJ1N8ZpysV9jd34fr0gbZtnDlaCdI\nco8objEUwRRTOyUbkfMAI4J4qDgLg8EcEKPkLBggUsx6s8TT730vvvKVr+BrX3kJ7/+BD2CxWODi\nlUs4OHcO6DzIiXPL1KYiGmcU0pLHCgA7zZDjwE4CgexGz5vIy8nGI1zJjKFzDhyDmUNGRy4/zz7X\njnfb/rNSxhGNnYJmfpadfYjFuUwySdUWmExAuK4PgLjQs+QelCAoJ38TspNdIPkszk8EJg+iDmdt\n9XdsHWDmV9L7m0T0y5DDRt4goseY+TWScwje/GbqnOIMeQO6OnQTKOOlGWljs2EKly91DiEkKwJn\n3wSgiF3ahAKYjTen0VYMLpB8Ckx48pS8UkX+ZZ1eMIwhSI55BcEoia7e+xz1F2PEyfExDg4Pqzru\nt1i9GUC26UtV28FVnYPOdzjdnODGjdt44fkX8B/+Bz+Dt2/cxOWrj+DcwTnM+0XCplw+bdiWdlOF\nGCaPIpexTs5gcDlPWGtLz/1JaqWVIux6ySqJ3nfPkSoln/9gpMhU6ahf1FoGDDNmpHVGuk5jNeZ6\nzBhHzuZQdQZyRCXr9tScV31VSRQ1Q2vKO5IEiGiP5ERiENEegJ+GHDbyqwD+frrs7wP41/dRV40F\noAAkMBOqFDYjq97BJ09DSwAIhrpGHuloNg8BA8XCMNE2K5bV7azVFyUM+R7oBNfmsvp3Tv2jrLoE\nLu6/FoWHcrMUMx5jxHq1MnV8a0XVlikxVm3Q2oYqsxJ5fOOVV/Hjf/1voJ/18M5jsdjBfCaHnIJS\nSrB7SMfWhm8PEMm/hwg2YO2Uzq7v3MyzIzf6DkAGg1tsYJsa4JprpqSugvSnMTLPHDaSRdo+f6oU\n9XPqu+3tbPMtthLkWerNO1UHrgL4N0T0OQD/H4DfYObfBvDfA/g4Ef0FgH8vfb6vElKSjRhLzvuM\nemcApVkEKZ2YosjFtNUShVhScEHMhRlsVPAPU96G9/a6aq9XIiGbW/oSQ/EbYIwTjQAiKnJqt+jf\ncgoPoWQ9ciQSg/gvaMx9DQq2+QOmyv32SbmeAp2OCLP5DG+88SY+9KEP4cLFS7h86TLu3LmDi5cu\nwnuX0eg4bM8w3HJp6ZsbgYRCDEOJJGzmg0jcf7fmFzTSZPU9pjfJNqC54v7pOufGwTnFq/Sb3145\nQ5Zpams6bEsGqJvUdVkNxNnu4u9IHWDmFwH80MT3bwP4qW+mrnyIZz9DCAM6XyLCiAhdV8RVNeGp\nmcp7D/JpM3uRCIZhqLiJiPwOUK8+VldTQeZCjMn+nrgt2kVBIKoHUxex5D0oCy0vomBSlCEF0TDn\nbLScThomIkQOGDYSvNL5DoiJ+JHYzOXEH3PYBou5KOjEcz3ZSiAscm43nl3oSkiqjcniMZlVpgR6\nEoklY7PZ4Nmn34uDS+fwyjdexiurNd588w1QHLDbL3Bu/1weD/uuf9vjuJD6XLdNQDvvvMqEGDb1\nycwqDVk3XUZN9IZYWxNUkkKaDz1MRUPPY5obALle3WD52aZPs36WzMr6U8J5UIv48mfKj5B2eZwg\ndkTlhKN8HqMl5s5VBD/fm7JKRw75DA9A53M7ASit+ksu2qUhp85q0iNNBXgYDg+MuV3XdcX7Tp9j\n6rQPrxyMFJxU+7URF+0jrHrhjQQy+YxUcluoxJC7NFlyClqdSYm5DgSyOmiblUf715qdRvdijLNs\nkxaqE5DzXAiiJjZxwsnRCf7405+W04bCgJu3buHChQv31LX1ZB8Zg7FnnVgWuhHxaD9naXEi5Lwd\nm8nPRpWoCIwvKlLBgMbjxaznJcZqs9X5KIufwxTuohu19QSMiYkEQ9hbKXXSRZxKHcqEzioPBBFo\np63qFJXO6KEgyvHLgDQT3BxiAaRBnRCJlPLqYAsXHG+UyDWObSdDJYEpPW1q82V/iHSOoAJfROkg\nFS8BPDGETAQ0J4FyaF1Q3jkx01mbdUrH3R6aOYVLTOrLW9QEUQUcOhaVLHLAm2+8gf39Q8y8x3K5\nxE/82Mfw6quv5uuJtgTUNB9zkhcbUXiGGtES28kcBKnoYaTkir6e695CNO51UEr52+RdrMTyAibb\noDMl1G1/Rm02QU35M1qvw5oRWcxMC1sb9JbyQBABoJgH84dUVOeyVL9MfAHf7PeFO5TNNoVOaVRh\nHuhm46fGSB3pkyLQpW1Un/ALjMBCW1rJxnsP14k5gVx6bijEw2mAjfaXDFDqku49QWi0H/U4TnO9\ncZGx7p0HcTGbeRKXZd93mLkOCBGnRyfpxCHC5cuX8fbNt3B4cJjGoZy+o5w1t1EU7PJ5S3PaOSUz\np9YjcOoe7bd4mIYqXXh13T2kh6n3ojoF7WL1LtWaJCX5nerNSml9tgzEzhFXb2dIWbU8xZHAwSEG\nAp2h+T8QRIAAMAWQk43g07ltdmF432EYNoaCm01rTnoBmk1MgB4LpUdDgV127okmjz4RFd8DQso2\nQ4iQI9HZObA3nAAygMwMggOTq044ChAdbmrz2ZN+e5jjp4jBPGSMxJvFEUMY26eZcXxqYveZQHD5\nnZngorw6dvATU94SNQCgGBEoIFCokprMncMOHDwz4jAgbDZ46vEnsTrd4OjuMf7o05/B/t5BGh9Z\neBr77nw6et07RE8YUKSi6jBZAIFL4k4LdrUpxjN3NRmLW4DQnmw1VfJx91SIS4tZaLESYF5/eSrK\nuhsGxVkS5oCUi8Eh2/M9SyZmCoQOHo5rac0C3dX3zNnXwKVzJV108OzhI8HDy/mcFOAwwLkI5/6K\nJBVpCbLoM2OxXsToslkn/cO5XA+UVMxA4mydz0dMAcbko9JFqBsjYl6dpWZKXMwBG6ZTrkolrdF4\nah3gSi/tFSvQd+Oe2uqi+t5v8QsX4iRlyrll28ZgNvnzzP0OhkMxww1yStDjjz+OQIybt+/ixz72\n4zheLcHcgKSNnipZn2pAVS5LY+PHbZuyJIiePE3YVPT33Rg/sfW1FoK2TU3F1TUAihrZ1C3qQQEI\n83FqlLwLbUZgGs/hWQfPlhtt0xqPQqiEMZY0qufc+ynfuaILRI68mm60S66oI9HMO9jjxdo+y4JM\nHNhOJMuztnp/OUMcJi4ZmaswoVboc5NbqXNmIyjoZMAfIo8Q5IDWzTBkwFTrtJ9zP8xYbONgqptv\n2/zqp7ANMFTf9nw9R4Rhg6uPXIUnwl/7ax/D8ckJjm7fyVx6qrRYyRQmYaMxawmqALZT5kStTwlD\na82p4jwa5tI+y362Y5vTrpUHjtTV9nBRbRezsC9HTlLZEQMdEF3IxEIJiK0LwPi5dky9SwFovkrC\nYtu9rTwwRMC72s6bRXzTZ93k2V5tAKesc2lElskpoGCN1FEsDmKGUW5yH1T3myh5MU1Wm0Reo4ao\nCYpZgoQ2mw0cEfZ2dtD3fabyikaHYajUmJbqjSY9/Sy+Ci6pChBBBAVAtOZCsVJ4e3tVnHM4PDjE\n6eoUINmYly5exO07t0cSgCUoLcdti0Xk80ulPSh+47bW0QJqMJBQmy8ASAlrtojeStTdPdps67Sv\nyWvSu3dylJrmWdBU+3pNbMZApbmzNjQ5Gp12da/ygBABpVYAeJzoQ0XL8t29s8Qwi8vl1CBECQQA\nMUQkhVknZgHpYpLPlLmb5O2kfD2IygmwIDnZmCSGW4PmmIzDSdNm5ojVapU5ZD+bYW93txy0GkLm\nwMzlAFYtehAJtiw8JuTncp5xVbUKWj5aXEal6ryvNp4Sjf39fWw2A8gRLl64gJe//nLSp2Wctm34\njH6bYC35gRDBmhs1E4C8qcylUxwui/KGWGhEYis9KWFpQ4i3tpmS3EHFoqTvNh+AXT+B88iP2jxW\nR8q422dbIlThIyrq83ZLD4BJaanq15m/fgdLDLLRZMOX7yXhRUFZNRS3LXbQeMJX3V4TojgDEYq5\nZqMpsGDWmX5oFh65TiQJgtj25Yd0g9QL7QepS3JI7rBJXNVNkA5ZiUPIDj7DZoN+NpNFlCSEnLxS\nCYOGI6fF0HmPed9PJhjVE3EKjykWlXsdmuE7Xy1wBSydc+j6HvPFHOvNBruLPYQwIAwB+3sHxZLR\n1GlfXdchJNOnJQRVW4ypV9tqfQymypSYP1WmVBI9Zn5Kpcp1pnHQTWcPosn9M9dayUaLHreWz30I\n47R0djvH0eYf98OmJa+IzT28Nh4YIqBZU1u9ZySeker+Y5/wzAWaPo/PZlNdvuiUXecnKW7VRqKk\nbiS1Q75tfqfq/AGZjDiSZmyuee89zp07xPXr1xHTuQKtDpqfkiQYuwy8mgq3LH69v3AL49aqtGvU\nV1el68rtZolDdM5hps2YlwAAIABJREFUvrMAEeFwfx/Hd46wPFni9u1bWMx3sgpmF60mDslZodgE\nhCXu5yBnBlo3bmZOacg0IS1X50zeT5nc0FziJvLvqkLG7ZzVFp+YgFVLW9UhOwsZIDDGiMF6ajrC\nfD7PhIENN8z5BWk7JpC7FJWZ1lLGWeWBIQIAtujPLZePzYbWwa853BS4M9JJQTl3oI3718zGNgMu\nqLYQKIDXFnVbVa/H2jKQ0nnpYagx5ExJ5y9cwNtvv43d3V0453B6epoXgYSfpvf0N5nUZYQ6IKoV\naYkIIZSj1qf0xakYfPWhaH8r9wtBvnnjJt6+cQO97/GVr3wNi8WiSC4NB8zRcJCc+doOHbdJMM2a\nfxPoFYZg/CdiCTziaQC3lebavuj3GTx1Y47b3p8Po80pzF2eb5UGvPPJR8JX415JDpo2LLlAa55H\nK2m4xICAaWKQMzOjTn6qSV7PKg8MEagQzcYWnP8ku9EZFk0FjfVD/W2kC+ZJTGa6icV3z/aysOOc\nA85yEgXh0oZvpRc5LLU4bxAIu7u7WC6XuH37Nvq+F2DQSYRkMwyJO8YMbFrE3rk6yManGIyuc/dM\nyGH943P9hjPFhIVkVSFxtrvHd3HtsSfw67/+G3jv089kIFNfzmyUfAZiDkYaexPqxh/5fthTeJyT\no8MazhFiAKNYB6q5wVicbtebfY4i+eMyBf4ljMVRnmt7NNw2y4xdm3riddse21Y5eUgzYQBo+j/V\nt78yRMAes62lDHQazOToI84+6TMsgFcAw2oRkSTNICenCoFiOr0mQvxqqMpWZBHYnEDUbKq8uL1P\nfgFpotNr5vp0jLkHoWSL0Un0XYfjk5O0icuRY48++ij+9POfw+uvv45Z32f3U2ZGcKLbMxGiA4YU\n+tx1HeAcVmHAZoiT+fzyeAJwXHNnGSdAE5VGltOHhMwm7gx1flJ1ICW7jIxNEst3d3exXg14+umn\nZZwSp29DdXNqNSOytqY+TiqHFWlVWlDCq6L1ZtiM+rrtGLORlNTkIMzu302mY7uFClbS5bUh+EiX\ngegsURrp0t6rz25jBQKlzNJe/o4UDZBrJRDp/3w+R9ebVOzG8kVEiMSITpLlRtpOCB4MIkC1OKqd\ntA4mwFn6jRCKNg+b3NTeR8VsBFQTNoUvyPs0Zc76esN1QhO5Zg/9jMl8U4FdaUG8//3vh4PHi1/5\nCuaLBTabTXJBlk3T5gTUumtOa8NJ7TFoqNB+K45uBklzNQ2nNlKIkXjWmzU26w3e//0fwK/8yi/j\n/PnzcNTBRcP9tuh4kweF2GLamjeuRc3TplbTWr7NrIFtsQRTPgxEhFnfbc1WXdrS5LakUl/L6a3J\nT4ujElLeqmymeWnc6nuVadjANiVCOVGuJRQuwd9b1JrcpjN//Q4VgjrOoFpxzhXpYBsB4GSCcQoa\nWXGzSQ+t18IMTK4/gV1qbtOjnfSaqXa4ZhJLvgNfAU4WkHNeUkqV0GjK7WVmfOQjH8HtO3fwyU9+\nMoNH27iJ+guAUgAJKGchKn01bR5JCUIwhk1I6b4bt1UaRySSacM6BADyvLev38IPfeiHRzkiW/F0\n1IcJjCew5FGgpj36e+c7eJKTnfMRberh6LasleYrb88oiAXht8RxDMIVjtuCntbPQxhPTZgU7wgx\nZnypWuuTUvDY09ECltvUX3uPrsOzAMVvmQgQ0ftIzhrQ1x0i+odE9I+J6BXz/d/5JurMXDWj8Bgv\nHAGrVPdKUkAzIJPJJisqqU4wY/FPRb2qPqPj5Y2df2syHjk3QoiLpMG5zdp+510OI3722Wdx8cIF\nrJZLvPnGmzg9OcFyuRyZj2zEWm73LLlGJzWl5YOZk2Rx2GGz2eDWrZtpLMxQuZqztGWIEcTAD/zA\n+/GLv/iLWCzmOHfuXHXNVLowHYvMwSZcfjvXjURpoIBeVtwnUM5FoDEKMifTzj+5f1SczqRNBtDL\njKEGQa2o3dap6cZjiAhJRWJsf3Y1HhNjnDl5s+71WUQFq7HS2VS5l0XhWyYCzPxFlrMGPgzgRwCc\nAPjl9PM/1d+Y+Tfvt067wcyX5TcHSc5AyuVjggiEqobkbiyLh1JGXoOEGwoalMPafPfV34Wryxdp\nozkHuPHBqQ5lcplL0hIA1UKzsQO230qxV6sVfvJv/k3s7OzgrbfexPHJCW7dvIUwDBjMUef6HBWe\nnEtnF3QE7wm+c3BdwivUD58o+wtESPBU1xVu6s7Y+O3R7DEG7O7t4vnn/wzOOTz11FNYrZY5Gctk\nDggIuBg4At6dlfauxnYUhN2yWQAhOEUiUxTdtLnZkPaUJXEbr6UXIRK2PVGNNTn4zEpJWQptMBjN\nZjXVt20E1jHBMcHzWBKyOImGO1cYTyO5ee+2Rlvm52395ZsrPwXgK8z80jupxDsnnmmWU7hx/rup\neGwabcwUT5/OclfJotyXruJiq7V1W06dJZRm0nK8AaW4BGNiJCo2bpUQuhTnr9JCW7Sv58+fB5FY\nDF55+eXMWXIQUINL6Nh438thK0RAylXASfTUiDyoc5M0FOQcHrt2LS8wS7jUK1I9DrMlIsVaPP7E\nE/it3/4tbDZr/MSPfwwEhk/DZBH2MiNl3DIB27IRWmtAJeKaSEHbZnU9py3j2z7LbqrWycrmtixt\nCtU9NsDKqo66bvJvoMnDZKfaJNejrJ0JhtHiAFO/t2N2Vvl2EYG/C+B/NZ9/gYg+T0SfoPs4gkxL\noV5dTqKQ8/41dnAZoDpLbmuGUd28DEoxreRnkDnsgagSr4GyybQdNtBIvlMuq29kP1YTkX3BFXPI\nnMuj6zqxIgBYzOd43/d/P7q+x97eHvq+x6uvv46w2eSYfmfGIROa7F9fEqeGIeS/hxBw9+5dSb3G\nEoMA3ehmYwpFS9wjxbpzGl84wiYGfOAHfxD//F/+S1y4dAmPPHIVR0dH8Boc5ShnydUxtGPRSlu2\nTF2br2Mdx1ipBKX/hQAoeDa1vqpnGQCySm/PXB0BX8/fuG8WgNaTnfPamlAfpjYqEaV1WVQTZ7y5\nFFfSXduGX1fjthWSHZd3TASIaAbgPwLwv6ev/hmAZwB8GMBrAP7Jlvvy4SNHd48q//DSbdup8aKp\nuFYlNtWiq72+3dyaFsp+37QTQHHYqNKVpf+qxUblvqlFp8RLi0uLW1WR+azHEAK+93u/F8fHxwgh\n4Pj4GKvlEndPTjBwLCqSWhYSNrDZrLMLLoFw584dnCxPcLpaYrlcokumydl8Lmc7EmG9XldHurG4\n0VUmrGyiS8959tnvw+9+8pN46823cHR0hJ/4iR9PGZF8btM2/XTEjRunnJE9nYyn3YRKoCccWWJo\nCUG1XoxYnPXpqaQkQE7tZX/1E16qU5/bfsrf212Lqyen/hbMqq6LmbOLsYKqU8/fahmZKN8OSeBv\nA/gTZn4jNeINZg4s8Oi/gJxDMCpsDh85PDioKGw9gKaxZqO2OrmAJSXz6zZCcFappAyzEL3z2S5f\nPTPVHcApeKgs6lY1sM/Qd0clAQnBwZGHcx1653D16lU8/d73YrXZ4Oq1azg+PsaNmzcktDiG7LgD\nSjZ9jvn0oGEYsFqvcP78eWw2G3zhC1/A3aMj/MWXvoTVcomvvvgibt+5kwnAZr02rqrSzvrgVnmt\n12t83/d9Hz73uc/hD/+fP4T3DnuLOTbLVfKRkIQW28TQVhI4a/xrlL6R8Bp1YMon4H6fpyqondMi\naTpbXeHo6XFWZcmWhpQrkIgqorVNFZjy/LNg5FTR5+ZYiji2RGz7PFW+HUeT/xyMKkDp0JH08Wch\n5xCcXUhAPzBDUySTUk4iSTDBahNvEicwJfG3+KG31FGH0uUNUxZYvdgiQOKyqRQ4IgCoU3h5UDq9\nJ62HKGJzpKRHJsARSHpgurd3rqo7NOwyg5Bekk5cunQJt2/fxquvvw7yDpsY8dI3voFr1x5HuHsX\n165dw/7hIZbLJYZhABFwerICYsTqZInbN27j5ZdfBgXC1156KeUU8BiGAS99/evwzmEYBpw7dw6P\nX7uGgUiazhHMhBgJLs2Hcw6PPnoVL774In7pl34JOzs72N3bxTPvfS8Wu/M0bYxIPOIsmfOJrpE3\njGscZrYVdQcmmhZyz9L1mSVxaz7qKOEVtUXIATCOZgD6voM11VMDMqr5FNDTgKRvzK44bBksixo6\nxQmjEZG+5K9UKc72YchHpQEgjVVAzoJlGRAHQ0SiEFB1stpW3hERIDlw5OMA/oH5+n8gog+nXn2t\n+e2surIIJgk15XudMJtrT/W3NpOwnfi27iw5VL+ZWYYcghlYwZ3EdVjsyRo9Z+tE2tBqjqMkzuXf\nc0KS9FtDxByj8vDz3klwDgQ53zs8QARw59ZNuL6HJ8LR6Slu3z2C9x5ffeklHJ47h73dHdy5c4Tr\nr72OYRhw4/p1rFYr7O7uIoaArvO4cPEihs0GwzBgZ2cnc6HjkxPcvHkzBQTtYDbrMQwBfd9jPp/j\n8HAf/azHe97zJF577XV84hOfwP7BPnZ3dnH+/Hk8+uhVzHyXrS2OHDhGdI3JrSXMgHD0gBKxZ733\nJjnnfWu55hkJbI3pYFoiyuf8UZqLNh+PtNul68cOV+ps5rwbef217dd+Oz/2PMyRmSauYKuZr/L/\nGP+GhrHp8wPrCdfbx+6dnjtwDOBS893f+1bqismJYuzQot5yYesgEIoume+xeub9aQNy8g/09BiA\nPBAiwBRBLK6wLaHJBEkTnlR2JWQnJCKHAQE+CmVWca6AWxHDICpNTNFCB/sH2NnZEfF+GLDZbMDH\nx/jsZz+L97znPXj22Wfx2muvwXuPJ598EtchHOzSxYvwXYevf/3rePbZZzGbzdB1HWI6RyDGiOVy\nid29PTxy9SpOTk5wkoiBYgtXrjwCZsbjj1/DlSuX8cef/lP861/7FTz15FOgzmOxWGB/bx8H+wei\nnumR7xDdWcdGxWKlt4oXbN3ohmhkt1xy1aGx28rU2tADSzRnoA1smozRN/fGEBMnrXXyjHmISJfr\nVSaVj4+jcqpUp2rOqJ2MEhErcSZFhG8wBKMStdiDfra5EXN2JY5ZvZsq3w514J2XpIvWVKyOYLPm\noklC0Ezgtr+nSvYPSCJrpvROEn2GUAeqRF0DyR/BGYO3Xag+bTyL2GbhI20MZzCMHBjlBYmez2d4\n+unvwfPPP48ZdaAUdDSbzfDiiy/i9u3b+NAHP4jZfI5hs4GbzxDWawQvwUIvv/oqrj72KO7ePUbk\niN2dXYTNBkSE/f193Lh5EyEELBYL+L7DJjJ2dnfw2BOP47HHHsOFCxfw1o238cu/+D/jK1/+C1y+\ncgUDIvZmO5gv5rh4+SLiMMB1fU64OhoHoxfrArVOOnZOR45D90G8p8DX1r2aYbw7y42T9dT1JfWU\n7UaVdoZhgOu8VJOImqZ61zrqE7HlwvFx8Dah6XS0ZpvzQa+PKQzdftfuDyJJchNHrmOlPBBEwFLH\nNg0YCyseBVwoXmDv93mKqWAKqaj6UCOtRb1waTYpveckJ2QIjBIqGA/AlsKYRS/tTQvH6WEjDrFJ\nelIOpxhP6P7+ARaLHWzCgOVmk8W+rvO4ceMG/u0f/iEeeeQRXLl8WQ6iWCwwJ8J6tcKPfuzHcHx8\nDNd3IGasw4DZYo7d+UKSebCcY9DNZtg7OMDFSxdx4cIF3L51C6+9/jp+47d+Czdv3MD1t2+g6zwO\nDw/TQaMSq3Dh/EVJqppASh1uHSOdnOzSbTaInA8Zy7xy3W8QCriaqrJbxC7pkVShRFY/xpiTv+Z1\n0EybnuYknpK6mcR/YOq4OJXmrFVrm3QDFL1eCRKziYJNOIBVL1qpUlTUdOycAgwMZFySYmJmdXQm\nOQGuHE+DjMADQgSmHCIUqRb0lytuCySQLxGCWrOv68lFRSTYBSQORI4k15vgQw6Rh2yW8l4G3pED\n0qGgjpxEI0rF4GhcSu1zCdDDSDXRpyT9ZiD5rnfG4aMQFsEYFos5YmQ8+uhVvPrGG+gSBjEbhpw9\nOUZJTfaF559HYOE8ezu7mM16OOcwm80RY8Tp6SlCypqzM59DmkA4d3iI49MTvHXzBr769ZdwdHSE\n62+9JVaDGLC/s4u+83jyqadw/vz5PCbDMGB3Zwe9OS5Ox3ybtNYW5ZQV0VRr4JbNRERiFlQLAdW/\nZcuCZnCHem8WqXJbUtnsGq5YTiRZH2mO4qBnDKS+qglyS98rlVR1ehIrCswmJU6xCKAS+mua2BIX\nkR6Sj8fIFFiYWXZxJp7eIKk8EERgCgcAyqD6iUVFSUwHuYryT4lDQHL8SOipgnM6sN47ibunCAbB\ncQlcGkIQBw5yCCGaCRfx3fsEJipHMM/1fYcQhqQGEDw64YxdlwKUXEKQS6YfaW7IJjsF8bzzGMIp\nPDns7O7g9q3b4HxUOWOxmOPOsYj9R3ePsFqv0Xc9KBGVMAxCIPZ2cbJcIg4Dbt26iThE3L57hH42\ny+12zqGfzTD//5l795hLkus+7Heqqrvv957XznL2vUuuGImU+HAS2bIVUqJlCYkBO0DkJP/kZcSJ\nISH5I0ASRQEcwDCQIC/YMJBYgR8xkFCWpdgxBEGJLEWgIdkWFcmQKYkMl1zuLmd3Z3dmZ2e+x723\nu6pO/jinqqvr9r3fiKSFqcXs9319+1ZXVVed9/mdtsHe/j5efvll3LhxA2cPHkgQjbO4cnyCMHhY\n10w2f20Qm75YjCi8BjnLzRgDpkJSKKSpmhiktZgTbtPYN5/L2eic2pjbIZyfOeTDXXL4pJZaa6X2\nBEPh6KcHfFYVqsYm/+xku9eoyuNSjV6uEgFa+gCYxQBbx04wczbKgkab2j8z78C3tFWDTHp56fLY\n+AqR5IAbI5F0czMlGnV+RW5J7sb0HGsdstZOGl9PIqFY53KvxhoxFkE/sw5ghjWbiywGw5gTg5IE\n4RVNiIiUgLA+12SsfQPAQSSdtmlUN38Pvh/ARGi7BW7duoV3797Fcr2GXa3R9x7WGPTDgBAC2raF\ntQ5Ok4Rs0ygMmUPXdQCApm3x7rvvoutaQOcagsfR0RH2Dw/w/HPP46UPvoTVxRK3b9+GBdA2LcAR\nL774IhZdO7W9JN3fmFmsQzLCxUsfd/ovEQEAwrnytqDJz9T/XCryJEip0NXJyIGZ6ujSSk5aGgpL\nN91o4LNZBdwlqWyzWQFqFwlxtI9kdOGKiKq0wBzgvZ9gUuRJqhBSDkVUMcVrCAUz3ZE78HgQARV/\nYqE3jS+Zxllusf5mEbz+uOQoEL0VxUtMcOPj7eqis2ayl4ruhENEtT/wuFEiULwk4TAh+GkmGEn+\nAGgExXCNpsJm4yhAbGANYdE08MOAmzdv4vXbt8EhAMZifXGBe/fu4oMvvwznLL72tdcQIWmqi3YB\n4wyWFxcYmGGbFk3TZPXh1lNPgSBYAIv2Bp559hnEENC2HYy1ODw8wsH+HhZ7exiGHqenZzr0MQrv\n8PAIBiSqAI9xDxNuiHkJ1BmHoMgF5cEmUObEcy0b21gqNNfuubl3niVKHQtHBtOIEmyrg5z2HVFh\nfyoIW+ozVbLmyJPw6DTOOSIwSjnCMCKPCj3zZj/Zm0IWKR80j6FYp1laxIXKUhCFbe2xIAKM0eKe\nWyUixRinVnjVpbZZROVLADROhFKfqqOTGfXRzAEMKYBH1U8aZ3IrOajBECAjluJEEAR5KM1l9P+a\nIs5bLOQkCTzqQkRRY8HJYBGjB1lCt2ixvliidQ4cBCJ90bS4/frr+OjHvgvf933fh7feeRv33nkX\nfT9gtVphr+2wXq8zeOX+/j729/dxvrwA+4C2aTS5iuBcg7ZpAADLi3OcnZ0qQIjMabVaZZwFawxu\nXr+JxjlBNkrrx2NOfiqbnupGlOsY4mYiTRZ7k6GsQsEpeT4BG+hJdV95zyAiMfpSSkjvJNV9yPiM\nEEZkyU6eMXm+UauSqtlROszPTq3W40vXcoQYKy0216FcDzHqMcRWGUX1BVRKSSPebJcd+ro9FkRg\nrqUKPEkXEuOcDDfEiMa6HMAjGX9TlF9Ob6lqKeec4ighAIliji359us0UFT3pO+OySKqCoRUfoqz\nwYdA2cMAIHskxudVRiUWeu59wEsvvYQvfrHH+YOH4BjQWIvz5QW+/OVX8OwzzyIExtWrVzEMXtfL\n4+joOOMWDsOA119/HcMwgArJ5eDgAI21cE5i/5umwWKxwMmVIzAZ+MHj7OwMjXOwALquw8nJFViS\nSoNzGzn9K+P6y8/nDkjkWLhLVQ+v8AgywU8Rf6qa5/4jQHYk6nN2g0lwT3GvXgEgxUvFBkyT91Ie\n0lraSNcnIbyVuzD1NxkP82w2YO7TbD4n/b3LDpGkajsDTFK3x4oI1Bx9Qj1jBOYxKiaUb7owZQCR\nulD0OaUhcTIGTDfytvGVEohEbG0aBsf7iufM2A7mngUg1/4LRHj++efxhS98ASmWIIYA6xrs7e/h\nH//arwFgvPvuXQkMCRFt2wBEGJY9lstzeZYa/Bq1DzTOSXCPMbncWduJgZB9wIVf67opkRJ5GcRR\nS8WNad7Jsr+5nlPj1tya72rlAZwQjiSKE8C+2DOY58rbnptMfGmvbGACVATAAYCG+iauHrG5d2ef\nxVNEoPr+eq4JnixhF5Zl4sZnTI2Uc43jbkLzWBGBqQV0+hJrDAEOYxppQwZcqAeTlswJkaf6U8q3\nh7hsSP8WI+G8fppjzDHlCqzGvalVW59b/LH5HWQCcZkuycy4euM67nqPvu9BIWKva9EYB9c2WC4v\n0DUN1us1+nUP9kGw+UOAsQ7WmoxFaFQ3TgVEQhjQabETZs4c/OrxMc5XS+HIMWBYeVy5chV37tzB\ni88/O4EML117KV49l3rDdP6b60rCsdK6V2L75F5TLmwSjcfDk4hRKtqSfMJlRuFcUg2R2hk4ZhG/\n5Pb5ABaEPr/bLYbruqXCJlnCiPMo1+U6GWdy+PmUeGxGEG4jPJcR3W8VnsC3pJUW5clC1ASAR0AK\nSwQPyZtHtrKPkM9WXxwbg0CMAIwJF/o7GwLngB0qypeNBGlq9Ntc1BrUZELIeLOfXX0BmpNkHJgJ\nBha//Tv/FLDA/tE+IgVEE7EeVnCGsega0fOtg1+vsdd2MGA0RhBpW2uAIBw8hVd3GuW3Wq3yvJ1z\nGlvQom1bnJyc4MknbqJ1TlWdiC9/6Ut47dVXRVXDlKNNjaxJMho3ab6vigzMa6LrngJd5gkxZUku\nV45OQlYKSa7ul9Tc+XTvjSw+nkqD5cEbCfKYUZrufZS4iNRP+c7L/VBLCkClvlTqyNx+SvUpUx8p\n+WrX+B4bIlCKOIlilq2c7AYiLI8bK082sXn9rrU2b0xAkHUDZg6h/m2tUbeMEJMUNWatmwCZAOpm\nLKzHiUDljVdvNGDSZxpj+n4SA9f9GoBIKK99/Q0sugX66NEupPKP2IsYvB4Qvcf5+TmsdfBhULtA\nlOQHICdEIbk4SYygjoQjIdW5NwaLbpHzDRaLDh+49QEMw4Djg0M8eHgf7713VwyOeppKUNXpe7Jb\nXVM1IUjEKeMJgGaJxWStCtG/5oq12lb/Pu13tEUA02CitB9H9yLlCNUSW3Ku5fTsYo3quWz7Xpks\ntw2DcE6dqFUDUwY/bWmPhTqQItqSITAtgPcejVqtAUzciPkwsybvqOEtxrBVTZoc3LRxjEQCyiJq\n4gfLw5g5J6DkPqQjGU612TKnyDczrB1F7HoTiy1hFBPLl5/EWTYWd+/dxXvv3cfL3/7tOFqe4zQ8\nQEMWfd/jzrvv4ODoGA/PzrBcLsEMNM2IGZis3BM9kwhDv4ZrWjSNA2tVJWbGXrdAjBE3nngCJ8fH\nuPPOHdy7ew83b97EP/6VXwUi42x5hq997Wt4/tlnM7bf1D1IoyqQD9YIDJruL+HWs12Bpgk4k7VX\naSsXAXU2A3ymIKSy2EY6QBkRCCO2IJMcYmvdJsHgkWPXiNFB18rlMY7GYGatA4EKfRjT6Mj8/u2o\n36eKSqNaMj4/wbHV+0eYWwVGGjgHIBkyl5ajBx4TSYCZ4YcBw7rH0PeyIEEPhg8S+x0jfPSqryaI\n7ggCI5fK4Jj/iRleeQXz6M8u/gEYc9xRiJPMouaJeqk5JOL6UkUwf84sBrfUf/pOauULSIe8RA5O\nhz+EkKWfmObrPQbv8ebbb6NpGjx8+BCGDFzbwLUO3WIBshbL1QohRrjGgiEw5UyYQnwBihWYNqVM\noO97wR/UMVxcXGC5XOKjH/0I+mHA6cNTHB4d4Vd/9VdxcHiA5WqF8/NzPHjwYIP7p/kaZ/IiZ6mA\npqXn86ak0TaTC7ymn3qtbpMU8gJheFYnrv5h7kCQSiIYCUedX1BKCUQ0YUYEMa5O9hVtegI2GpHM\nn6XYinUWrnGT500N5TLYJClRtaNVNhQip/OVmAaaZYqpPTaSQL9ab4gtbdOiV78tESFQEm8cDAx8\n8HDkEPwwUmwwGCMmPKfDqU2WSRtPM9omwSckhz8TAEqfq48f+lKYIHk4RWIKQ8ufFzaMGa+H9z7P\nH9i8N8aIte/x1p234NpW3HtEaBcL9MslgvfoFotcOUciHVlyyItZEwTl16llOQIIwcOxk3qItsnj\nCiFgUITjN15/HcYYfP2NN9C2LfYODoQwhYA333wTn/zYxyfzqXXdyVqS1A+sD2AqcJpDA9LnZnwf\n9f4VW8/o559jZZQPF7J0lh6R1KE09mQzSPyAiGBYx1QYZlH0ESMDhmEIMIoKnPbWo1gHmHkEctV/\ncWYqzAxwUcSFR+lDhmvyPkPRFxkj68OAwbeg7gAJYOg7RPSF4to1IvoFIvqy/ryq14mI/hIRvUIC\nNvrJR1mQ9XqN9Ur+rZYrrJYrXCwvcH52jrOzM6zWK/TrXlB0eg8fPPqhR4gBwzDABz+rg8UQx4Ik\nkSclxtKq1ocvVUgGCmTcqihmynzLf7P2XWyBjQ1fSAEhhBFCvPpXutSYRJRuGiccmyWUuOs6rHsJ\nByYN5EnIyT62ewuqAAAgAElEQVQEHYtsrFC9f3kG0K97gIUYpbGkeX7uc5/Dai0xBl/96ldxdHSE\nF194ISMzrft+1g6Q5p1/N1PxlyquVL6LORTp+p7x89EOQzR17W1zw5VtjDHRcWLk8vPPG1WYEuS1\nXNN67EkayIlLMRWi3SzOMrFn1Nmy5bhnjOTlvSUDGT+YXYKxz90f5/Y3APxQde0/B/CLzPwygF/U\nvwHBHHxZ//0ZCPDozsYspaZ98Bj8IMEa3mN9scTy7Bzr1RLL5RLL1RLnF+d4ePoQD95/gPV6jYvz\nC/RDj76Xf3HwIGZwCIiDRx2NOSk2WW0azgEWNKkKmy3D0BdcF8XQz9PhKq+XJcjS36XuXx78dBAT\ngSAiUGRcu3INzIyu69A0jYCGWodu0eH84gL9MChwqIYia1LSNuo/DD0AqR3gg8/PA6S2X9/3ODo6\ngh88ukWHD33oQ3jr7bdwcbGEcxYhCvJQFDk4/wtg+VmsGYAcaZj07dJAm9qcKD8JxiqQlMuEq9Iw\nVxL7rQeifGZ9gVmiPzcuFzYGHZMpn8nJ/bwpCdFMnkMaU00sSltR+VmyB9TEtl638r7J2HfbBR9N\nHWDmzxHRC9XlPwHg0/r7/wrglwH8Z3r9b7Ks3D8iois0xR2ce4C6+AqKW3ws3LxXKD/dGIbAHugs\nK4SWAxyhcc0keMgYB0ociDmLm84ZNRUkfoncb/pr3EhiI8j+CmOzGjF+PhpvokwEjFHnLwkAgAkg\nRLpORLnSLnvGeljjzTtv44knn8ArX3sVi70F3jsVaLG1H3B8eIz79+/D2QbGNcUcTDaYTTeAbAhj\nrGLmiy+/BKZgBtpuATKEF198EXfu3MG99+7hM5/5o3j91VexXK5AkdB1Cyy6Dr6wY5gJZ46zFnFy\nlOO4Y0j5+yPBdG66JY0xeS8k/bvc5AmfYYMrVjiCZRtFdhrtFtqvdS4nGwnhnztw0sNm6TAd07ja\nesAthipcmkiL4/AYAyMBWTwxNOZ5F8RgNnmKRrwBIk1O03OR5retfTM2gSeLg/02gCf196cBvFHc\n93W9tp0IAMK9WTO+WCzYAodl88QIhGAZPKh1mA2WfKGTj6BAWK/XcOvRjdc2CyAtsjWaRjygja0u\nlhTUCCFIPHwQC3MMEaYQ76PqiPISPFJIMKCRXGljx6gAFQAoTgJEhmGAZSvQ4MwI5LU2wIDBB8QM\nYJFCVyNefeWr+PB3fidC8Fm1MbqBmr0GP/jHfhCf+9w/gB+8pFxbkwmq914NXqlYqdYHcqpnMlQK\niGhbhR+zDfb39nD7jTextzjA+/cf4uz0Ai8+/yLefP22xC4AuHrtOlzTwYc1iKZ1+fI7ndl8BMpJ\nPIAcfqvgp+nv+rCUPvnk+Em3JBLOIs/nQrY5hgCbUkbi9Rm4hghe+9qFzpsJVVGnQq4nINR5l6ax\nZiKRTqTLYh3KzNasmsiH+fCndSoTmrL9o4oRKFOQmbaTgW+JYZCZmWhH7eOZRkR/BqIu4Pj4OE+O\niLS8kg46qt/dBIl28042kSV4HjSgRkXttejC5Eg2OoDgx4UuKbdfSDpt17UwRgxkNfdwGmZcVv4h\nIpAV/buk1hEqsQQPSwYDGFDPhnB6xjD0YAD9eo3AjKFfI0ZG36+zcW+1WoGZMXgPBI/T+w+wXC1h\nrcPp6SlCDAJ/HiJWFyscX7uKvf09nL3/EGQM1sulHoKIbrHA8dExTk8lbNgHj0iETomey5xb1n3R\n7aFpOxweHuLtO3ewWCxw//59fPpTn8JXvvIVvPr6a4J5uO5xdnaGRbeAH4Z8SK2CwZbcbea953Wz\nbsQF2Kb/17H7yY1c3yMMhLJ3J9siZsTobIidGRsTAKbsvk1tojqqKJMyGbfQjIl4Xob7brM5zH13\n7rP6vmEYsiu9/s4cca7bN0ME7iQxn4huAXhHr98G8Gxx3zN6rR78TwD4CQC49YEPsA8ezrpsAefI\nCMpVjbVAlGq1ohJoPAAxIkTkbDiF8wIhehA38sKGYfLcRAj6vgcZwrBqRoOVsbDWoGlagcTCuGkS\nRzJEQFQEGg1zjTFIwA0E+hxgDMHD9724/iLj7PRUnq8EJXiPCGDQakCDVhcK3sMACOsezlqcX5wj\nhIjVaol1v4ZXvABmhmGDn/npn8aTN57AGVG2I8QQQMZgdXEhsOKDh2saOGsxJPekmJS1NFoUyYAI\ny+US79x9FyfHx3jt9dfx5JNP4qWXXsLv/u7v4sGDB3BEuH79GhiAKdxZSfLKG443xeXy7yzQFiKv\n7ot8z7Zr9SFK90nS7eW8aC6JZ3J4aAQ/mTucIobIO8z5KZxsFZeb2S4zPtYxBsmQvKuVNoasytCm\nDWOufTNE4O8B+LcB/Nf68/8srv8oEf0kgO8G8GCnPQB5H8jBT/h7nHKnC+svAWRKKz4DIYibyzVF\nuKfFoNZrgATfjRmk8fNEpLncjNB7fbEMsg5kDLq2FcKSMgmNIugS4FwD+ADQ6IYbggeC+Of7voch\ng8GLodL7gBC8lAMbPKyzEq67XosYqwATYRiAFE+uM1/3PYL3Wdy0xsIbA4oRfvAICLh27RreeOPr\naG2Ddd/rejIIDOMc1v0azjSjiAgNRFJxNnEQ5yzu3r2L8+USZAyunFzBanmOP/w934Pbt99E0zQ4\nP73A/l6LJ5/8gGAbyFuYHP5s07mE+0yCeKrvlz9r9WBrfxr0lVopPdRtF4cVSW8anRoqkbuMKGyc\nWv4VdarEGNg27m3Erb6+yyBYfp4ksPQzv4MkIckTtq7dIxEBIvosxAh4g4i+DuDPQQ7/TxHRnwbw\nGoA/pbf/HIB/GcArkErF/+6jPIMjEEzUwo2iF6fJiMilOdwhwDZNAcgYYaxD3/ewCtsF+DGBBEpZ\nARHPjRTnJFnFjKYTIsNYMZYNqxWcaxCCR9u0CDGi06y8xjWZkxqF91or9+6HAevVSvqMEcvzcz1s\nGooaPFb9WoxmTYOowUB71IJjlKAfH9D3K4QgbtODxQIX5xe48cQNrNdrNG2H86FH7wc8+eSTuP/e\n+zjYP8DD01MAhDB4CRcWOET0MaI9UDRiAFEt+30vVYO4adGv17g4XwFEWOzt6ZwDnn/+edy9dxcX\nF+f4lV/5hzi/uIAf1jg9PcX1q1Ji0lgCzyRuUeaM46YPNfcjeiRJoGx1TEWtkjEw6r/F95PKWBov\nZ/ehfk8kQ4kd2HDL6X25hoTaFSJDQU0pYyuAFNeyVEGSYeOSNmcfmbu+bT75Ck/o40Z7VO/Av7nl\no8/M3MsAfuRR+i1b8BE2WewjZ6itGCPIUbbkR2bYtMFYjHOGkEtuAyJiA2IAykAXYHGhMY8VYhng\nbJCKiF5x3w2wXq/QNk0O912t1xLFqPBggECKBx8k2MWYLI73fY/ofQYmDVpCGhpbYIzAXflhgCGD\n27fflPMAQVaOHOD7kKPhGmdx/dp13HvwAFevX8N7D+7DOIs777yDftnj5OQYF8sL+F5Up+zOBMGS\noAw5Z3F+doa2aeDBYhQDqZ2CBWexbRWCzGJ/fx/giIdnD/D0rafx5pu3s0Hy4cOHWC6XaBoph85s\nJofTGJNzFqTp5t0iqtei8S4OXnK/JCLn7xeqSOqx7iVxzNI2UT0kH/TSbVffm4yUqaIUSAmQnjiy\nNp91RhE2DCF8nOnUvCT0qG3b+ACRMsvai9vaYxExmKzh8OrSSmWwQsohEDtByLr5eKjIEta9GNxI\nFzp4L3YEaHXemJBgisKj6tajOHIJyUgMcNSg73tJVzaknLNH2yQpQMYQ1X7hQ8CwWknlHWsxZKMb\nKbQ3w6/WsM5i6CW552J5IfPLL53VOGmEGJAk+CyXF7h+7Tpef/vrAAFvvf221DPwHt4L8MpqtVZ1\nwcBZh54Eo1AI0qBiIqNpGokmDCO8WQxRjW0j4m3XNbg4P8f161exXC3xC7/0Czg43Mdq+QC2cXji\niRvY21+gaZuJ7jnZiEQAxNMzGmbnsQEnO2GLle0yQ2OZa7+tz23Gx23jKfveKjkU0kiJfViPIyed\nxRTyvkkAynuTR2kbQUzPK79f2g0ywzRbQDiK9ljkDgA6Keey/ptVABQUl0YoKynsEbLRJFmDgxe3\nW1ROOwyD6nFafdhoKmgM+lPdeDEisGTe+WHQRBVRT/r1GmCRMIL38H4YowRjBKIHcRAUcY5ipwgR\nFKOIxZrb//7993F6dob37t/H+fkFBj/kSsISy0/j/FmMnO/dfx8GwHK5RCpU4QdBMN7bW8BZi4uL\ncyEIzqFpHJxxeQM1jVOMQUFJylllNK67GBp1oxOwXC3x0gdfxNGVEzRtix/+4R/GnXfewen5GZ56\n+ml4H3DziZsgYjStm8QDJIJgGkVsTpzqkky2stVccY4AJOPottiLNLeyn/oZJVHYRhgehTPXB7XM\nDak/L5+yTQIoparLxlfGmpTjqfvflcfwmEgCiqDjxZIvL1eomrEVogqNRSLSJvNerN9938MlY06M\nQnUJWSqIzDBRNz2mC58oJzNPsOeGQXztztosmci9IcOX+xARA+fsrwhGWK8wDEo01GCIGIEYYa2o\nMTLPFIcghAk8jRBbLBbwfQ/LyGXJA0m22vL8AsSEtu3Qr3sYa9F2LY6vXpWIyRCwVJdjF1u4xmG9\nXoHMmGLqXJN1Rh8CTk/P8Oyzz+C7vutj+MVf/iUcHx/jZ37m72Bvf4E/+2d/FD/3sz8Lv17j+vUr\nSJSkXMeSGxpDQDCTz0oD2xxqcGpp/nMQ4nOifNl3CpqpIb3Kdb2sZTtDjLNjSK3ODixDhEuClLi0\nASZZgpdJJSNDmB9zGSo/PlfU6iTtJWlmW3ssiECeZIyi36OwkMYRoSbHewMjeEIIOD4+xjAM+WUl\n661xYi1NFnFA3IdpyVJEXbLyl64VjowhDllsS+NJ/aT7IgDDAJEBQkDwQd14Hut1L0E+ceRYRMjE\nJMUGsBblFPcHZ3YRo9QT+OorX8HeyQFWqyWieqn3Dw8RCViencMQcOXKFSxXKyyXKwz9gJOTEzGg\nkgCIhBhE2kGpWxsY18D3a6zXa5wtL/CJT3wCH/mOj+Bv/+2fwtnFOV544QX85m/8Jnq/xode/iAO\n9g9w9vAhnvzALTCNUZ6ppYOdrdZ2M602tXRo6lj6OU5WH4T6cMwZ0bZZ28trJSeeE+drS/u2/cvM\nVeRlZbRMa2AtWD0828af+irXrexzlkBKiigMOQ0osyiWY6ck8FioA3IQRByuXTAheNHnNflHAEcJ\nQ2EUWmsqbbmZkhEr3RNzeGslWmX35BitxRpOS5DKREk6mFBcjd4LIaBfrzD0PU7PznCheQ6r1ToH\nII3UevT31lyjJHr5GTHi6OgQX/7dL+Lo4BBWyVXXdVjFgMX+PqhtwM7i3A9o9xY4OTkBGYP7Dx5g\nuVzCWIulH0bphijPlcC4uDjHerXClStX8Ac++Uk4a/HZn/wsrG1xenaOf/qFL8BYg5//+Z/H//HT\nfwvnF6domgY3bz4BglQxSvNJc7LWShQgRFZIJeHTpiRKnHveJpAISEaDNiNoSXpWuje/7zpXv1BR\nykOU1B+v8Rl1f3PP2KZOlO9pYjQkmkgBk/mgwLKoiFbN0etxlESgPtQSryBetQRWs4v7l+2xIALM\njH7dYwiDYAXWGyMF5OhCDd6LNKCHM6joSyTGxBgjhmEQUVwj2qzGpHOUII/gPYZesuiSeMoxil/e\nT5N+UkvESaSNiBCkUnDfe/Ra9juo4Q9QQjHD/eq5Z0I0KSkl+AXeM3o/4PDgAE3TSHFRAM459UZo\n9J+1Gi7MODg4QNe2YGasVkuAGQZiT0l2D2YJUDJkcOXKVXjv8du/8zv44pe+hKtXr+K1r72KRbuH\nrt3H9/6R78Xnf/3zuHPnXbzxxht4+eWXcXh4CEcWMIRtTMZamxGcwAZgKyHMbDUF1s7nF1Tcr7xe\n3pc4YXng6pb6rw2Xc/aCOR19F/evMwRLA+I2daV81rbn1Htkw+iKeTuBCNNx1v6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GG7hM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\nwysc7kzgDnd4hcP/BwAdTIFee88rAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NRH2_a2ucdh",
        "colab_type": "code",
        "outputId": "a521fc7d-b078-474e-b756-7ade5117c5c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "source": [
        "# prepare the image for the inception_v3 model\n",
        "processed_image = inception_v3.preprocess_input(image_batch.copy())\n",
        " \n",
        "# get the predicted probabilities for each class\n",
        "predictions = model.predict(processed_image)\n",
        "# print predictions\n",
        " \n",
        "# convert the probabilities to class labels\n",
        "# We will get top 5 predictions which is the default\n",
        "plt.imshow(original)\n",
        "i = 1\n",
        "for i, prediction in enumerate(predictions[0]):\n",
        "  print(i, prediction)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 0.80311257\n",
            "1 4.9813336e-09\n",
            "2 4.291904e-23\n",
            "3 4.9493833e-15\n",
            "4 2.1976139e-06\n",
            "5 1.7071886e-15\n",
            "6 5.942985e-16\n",
            "7 0.00060332665\n",
            "8 0.19628185\n",
            "9 1.2042314e-22\n",
            "10 2.4010615e-33\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ADGGSOtpOZlMKUQaWnLm2eoKl7kQVyDJlKnKdy/qwmAaLM45cZ5xXIJQ3coRLGIFzhCEK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GG7hM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\nwysc7kzgDnd4hcP/BwAdTIFee88rAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2TOL_2458ZK",
        "colab_type": "code",
        "outputId": "b8c9a2ac-c889-4523-8678-4631a8d3e462",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "print (train_generator.class_indices)\n",
        "print (list(train_generator.class_indices))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'alfoil': 0, 'box': 1, 'cbcontainer': 2, 'cokecan': 3, 'glassbottle': 4, 'm_and_m': 5, 'milkcan': 6, 'plasticbottle': 7, 'spoon': 8, 'steelspoon': 9, 'straw': 10}\n",
            "['alfoil', 'box', 'cbcontainer', 'cokecan', 'glassbottle', 'm_and_m', 'milkcan', 'plasticbottle', 'spoon', 'steelspoon', 'straw']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Px6doLm37I1W",
        "colab_type": "code",
        "outputId": "76f1826d-23ae-4386-fb15-8d20f4201b0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "lst = list(zip(predictions[0], list(train_generator.class_indices)))\n",
        "print(lst)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(0.80311257, 'alfoil'), (4.9813336e-09, 'box'), (4.291904e-23, 'cbcontainer'), (4.9493833e-15, 'cokecan'), (2.1976139e-06, 'glassbottle'), (1.7071886e-15, 'm_and_m'), (5.942985e-16, 'milkcan'), (0.00060332665, 'plasticbottle'), (0.19628185, 'spoon'), (1.2042314e-22, 'steelspoon'), (2.4010615e-33, 'straw')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loBmCsvG7dqK",
        "colab_type": "code",
        "outputId": "5f30ae39-afd0-4616-f63a-e05edd470901",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from operator import itemgetter\n",
        "print(max(lst, key = itemgetter(0))) "
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0.80311257, 'alfoil')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ms6QQjnC_IFu",
        "colab_type": "code",
        "outputId": "713ccb60-5baa-4b73-b6cb-7596feba0c77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "path = F\"/content/drive/My Drive/Models/Inception/ninja_cnn_inception\"\n",
        "model.save(path)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "INFO:tensorflow:Assets written to: /content/drive/My Drive/Models/Inception/ninja_cnn_inception/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9m7RVFwKxLv",
        "colab_type": "code",
        "outputId": "fe30dcde-e689-4d70-8cd2-586ad38c7200",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.input_shape"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    }
  ]
}