LeviHessener_Retrained_Age.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LeviHessener_Retrained_Age.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "6t73ZmRfmxOy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import torch.nn as nn\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import os\n",
        "import pandas as pd"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTssdJKZnCdb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "77fea369-f299-4f69-b51c-623071b13e8a"
      },
      "source": [
        "device = device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcJ_u33wnErg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "age = keras.Sequential([\n",
        "                          keras.layers.Conv2D(96, 7, strides=4, padding='valid', activation='relu', input_shape=(227, 227, 3)),\n",
        "                          keras.layers.MaxPooling2D(3, 2, padding='valid'),\n",
        "                          keras.layers.BatchNormalization(axis=1, epsilon=0.001, momentum=0.9997),\n",
        "                          keras.layers.Conv2D(256, 5, strides=1, padding='same', activation='relu'),\n",
        "                          keras.layers.MaxPooling2D(3, 2, padding='valid'),\n",
        "                          keras.layers.BatchNormalization(axis=1, epsilon=0.001, momentum=0.9997),\n",
        "                          keras.layers.Conv2D(384, 3, strides=1, padding='valid', activation='relu'),\n",
        "                          keras.layers.MaxPooling2D(3, 2, padding='valid'),\n",
        "                          keras.layers.Flatten(),\n",
        "                          keras.layers.Dense(512, activation='relu'),\n",
        "                          keras.layers.Dropout(0.5),\n",
        "                          keras.layers.Dense(512, activation='relu'),\n",
        "                          keras.layers.Dropout(0.5),\n",
        "                          keras.layers.Dense(9, activation='softmax')\n",
        "])\n",
        "age.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy', 'mae', tf.keras.metrics.CategoricalAccuracy()])"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcxGQX9QnM4I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from PIL import Image"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dxc_6YKYnNY4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def preprocess(path):\n",
        "  image = Image.open(path)\n",
        "  image = image.resize((227, 227))\n",
        "  return np.array(image)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tE8C31TxnQDH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "889ed120-6fbc-4c9f-96fa-39743418921b"
      },
      "source": [
        "!git clone /~https://github.com/circulosmeos/gdown.pl.git"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'gdown.pl' already exists and is not an empty directory.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nj6SybWBnSSR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0248bcb2-7100-4495-947a-7eb968a1704d"
      },
      "source": [
        "!./gdown.pl/gdown.pl https://drive.google.com/file/d/0BxYys69jI14kb2o4ajJwQ3FOUm8/edit part1.tar.gz"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cannot open cookies file ‘gdown.cookie.temp’: No such file or directory\n",
            "--2020-07-09 14:44:51--  https://docs.google.com/uc?id=0BxYys69jI14kb2o4ajJwQ3FOUm8&export=download\n",
            "Resolving docs.google.com (docs.google.com)... 74.125.133.100, 74.125.133.113, 74.125.133.138, ...\n",
            "Connecting to docs.google.com (docs.google.com)|74.125.133.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/html]\n",
            "Saving to: ‘part1.tar.gz’\n",
            "\n",
            "     0K                                     31.1M=0s\n",
            "\n",
            "2020-07-09 14:44:52 (31.1 MB/s) - ‘part1.tar.gz’ saved [3233]\n",
            "\n",
            "--2020-07-09 14:44:52--  https://docs.google.com/uc?export=download&confirm=5Bs1&id=0BxYys69jI14kb2o4ajJwQ3FOUm8\n",
            "Resolving docs.google.com (docs.google.com)... 64.233.184.100, 64.233.184.102, 64.233.184.138, ...\n",
            "Connecting to docs.google.com (docs.google.com)|64.233.184.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-0g-74-docs.googleusercontent.com/docs/securesc/k1t4lq5s39l5jihu1n1o8sqk9007hdpt/h2vpemugm324ioqe1e49t2qa4musd47o/1594305825000/16869955760404710258/16079181027004516639Z/0BxYys69jI14kb2o4ajJwQ3FOUm8?e=download [following]\n",
            "--2020-07-09 14:44:52--  https://doc-0g-74-docs.googleusercontent.com/docs/securesc/k1t4lq5s39l5jihu1n1o8sqk9007hdpt/h2vpemugm324ioqe1e49t2qa4musd47o/1594305825000/16869955760404710258/16079181027004516639Z/0BxYys69jI14kb2o4ajJwQ3FOUm8?e=download\n",
            "Resolving doc-0g-74-docs.googleusercontent.com (doc-0g-74-docs.googleusercontent.com)... 66.102.1.132, 2a00:1450:400c:c06::84\n",
            "Connecting to doc-0g-74-docs.googleusercontent.com (doc-0g-74-docs.googleusercontent.com)|66.102.1.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://docs.google.com/nonceSigner?nonce=d768e8j7dlch0&continue=https://doc-0g-74-docs.googleusercontent.com/docs/securesc/k1t4lq5s39l5jihu1n1o8sqk9007hdpt/h2vpemugm324ioqe1e49t2qa4musd47o/1594305825000/16869955760404710258/16079181027004516639Z/0BxYys69jI14kb2o4ajJwQ3FOUm8?e%3Ddownload&hash=in0730sir3o7cs2ibi2j9qm2li7gi9cd [following]\n",
            "--2020-07-09 14:44:53--  https://docs.google.com/nonceSigner?nonce=d768e8j7dlch0&continue=https://doc-0g-74-docs.googleusercontent.com/docs/securesc/k1t4lq5s39l5jihu1n1o8sqk9007hdpt/h2vpemugm324ioqe1e49t2qa4musd47o/1594305825000/16869955760404710258/16079181027004516639Z/0BxYys69jI14kb2o4ajJwQ3FOUm8?e%3Ddownload&hash=in0730sir3o7cs2ibi2j9qm2li7gi9cd\n",
            "Connecting to docs.google.com (docs.google.com)|64.233.184.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://doc-0g-74-docs.googleusercontent.com/docs/securesc/k1t4lq5s39l5jihu1n1o8sqk9007hdpt/h2vpemugm324ioqe1e49t2qa4musd47o/1594305825000/16869955760404710258/16079181027004516639Z/0BxYys69jI14kb2o4ajJwQ3FOUm8?e=download&nonce=d768e8j7dlch0&user=16079181027004516639Z&hash=ct4k2n1cfoeps8vfsd8nbnpmnm3m7je9 [following]\n",
            "--2020-07-09 14:44:53--  https://doc-0g-74-docs.googleusercontent.com/docs/securesc/k1t4lq5s39l5jihu1n1o8sqk9007hdpt/h2vpemugm324ioqe1e49t2qa4musd47o/1594305825000/16869955760404710258/16079181027004516639Z/0BxYys69jI14kb2o4ajJwQ3FOUm8?e=download&nonce=d768e8j7dlch0&user=16079181027004516639Z&hash=ct4k2n1cfoeps8vfsd8nbnpmnm3m7je9\n",
            "Connecting to doc-0g-74-docs.googleusercontent.com (doc-0g-74-docs.googleusercontent.com)|66.102.1.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/gzip]\n",
            "Saving to: ‘part1.tar.gz’\n",
            "\n",
            "     0K ........ ........ ........ ........  115M\n",
            " 32768K ........ ........ ........ ........  123M\n",
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            "393216K ........ ........ ........ ........ 88.9M\n",
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            "786432K ........ ........ ........ ........  202M\n",
            "819200K ........ ........ ........ ........  141M\n",
            "851968K .                                    359M=5.7s\n",
            "\n",
            "2020-07-09 14:44:59 (147 MB/s) - ‘part1.tar.gz’ saved [873860912]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgwNz3HcnUDY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 860
        },
        "outputId": "7a0df75f-d497-4d1f-ed13-0b5f540bf7e8"
      },
      "source": [
        "!./gdown.pl/gdown.pl https://drive.google.com/file/d/0BxYys69jI14kNEt1SnNJN1Z2WWc/edit part2.tar.gz"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cannot open cookies file ‘gdown.cookie.temp’: No such file or directory\n",
            "--2020-07-09 14:45:02--  https://docs.google.com/uc?id=0BxYys69jI14kNEt1SnNJN1Z2WWc&export=download\n",
            "Resolving docs.google.com (docs.google.com)... 74.125.133.101, 74.125.133.139, 74.125.133.113, ...\n",
            "Connecting to docs.google.com (docs.google.com)|74.125.133.101|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/html]\n",
            "Saving to: ‘part2.tar.gz’\n",
            "\n",
            "     0K                                     8.02M=0s\n",
            "\n",
            "2020-07-09 14:45:02 (8.02 MB/s) - ‘part2.tar.gz’ saved [3233]\n",
            "\n",
            "--2020-07-09 14:45:02--  https://docs.google.com/uc?export=download&confirm=MYiW&id=0BxYys69jI14kNEt1SnNJN1Z2WWc\n",
            "Resolving docs.google.com (docs.google.com)... 64.233.184.100, 64.233.184.102, 64.233.184.138, ...\n",
            "Connecting to docs.google.com (docs.google.com)|64.233.184.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-08-80-docs.googleusercontent.com/docs/securesc/gretlekvkl5g9vhope75kdm5ppq3nkjv/g50ad53k0kpf21ftdgego8tl01b1iek6/1594305900000/16869955760404710258/01729482335468448710Z/0BxYys69jI14kNEt1SnNJN1Z2WWc?e=download [following]\n",
            "--2020-07-09 14:45:02--  https://doc-08-80-docs.googleusercontent.com/docs/securesc/gretlekvkl5g9vhope75kdm5ppq3nkjv/g50ad53k0kpf21ftdgego8tl01b1iek6/1594305900000/16869955760404710258/01729482335468448710Z/0BxYys69jI14kNEt1SnNJN1Z2WWc?e=download\n",
            "Resolving doc-08-80-docs.googleusercontent.com (doc-08-80-docs.googleusercontent.com)... 66.102.1.132, 2a00:1450:400c:c06::84\n",
            "Connecting to doc-08-80-docs.googleusercontent.com (doc-08-80-docs.googleusercontent.com)|66.102.1.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://docs.google.com/nonceSigner?nonce=k2ktk5gc8d1ok&continue=https://doc-08-80-docs.googleusercontent.com/docs/securesc/gretlekvkl5g9vhope75kdm5ppq3nkjv/g50ad53k0kpf21ftdgego8tl01b1iek6/1594305900000/16869955760404710258/01729482335468448710Z/0BxYys69jI14kNEt1SnNJN1Z2WWc?e%3Ddownload&hash=lqga3m6ntmu01tm48gg5h41f7gg6jkah [following]\n",
            "--2020-07-09 14:45:03--  https://docs.google.com/nonceSigner?nonce=k2ktk5gc8d1ok&continue=https://doc-08-80-docs.googleusercontent.com/docs/securesc/gretlekvkl5g9vhope75kdm5ppq3nkjv/g50ad53k0kpf21ftdgego8tl01b1iek6/1594305900000/16869955760404710258/01729482335468448710Z/0BxYys69jI14kNEt1SnNJN1Z2WWc?e%3Ddownload&hash=lqga3m6ntmu01tm48gg5h41f7gg6jkah\n",
            "Connecting to docs.google.com (docs.google.com)|64.233.184.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://doc-08-80-docs.googleusercontent.com/docs/securesc/gretlekvkl5g9vhope75kdm5ppq3nkjv/g50ad53k0kpf21ftdgego8tl01b1iek6/1594305900000/16869955760404710258/01729482335468448710Z/0BxYys69jI14kNEt1SnNJN1Z2WWc?e=download&nonce=k2ktk5gc8d1ok&user=01729482335468448710Z&hash=5schlj0j5sqkugukmko1doub3g2otebt [following]\n",
            "--2020-07-09 14:45:03--  https://doc-08-80-docs.googleusercontent.com/docs/securesc/gretlekvkl5g9vhope75kdm5ppq3nkjv/g50ad53k0kpf21ftdgego8tl01b1iek6/1594305900000/16869955760404710258/01729482335468448710Z/0BxYys69jI14kNEt1SnNJN1Z2WWc?e=download&nonce=k2ktk5gc8d1ok&user=01729482335468448710Z&hash=5schlj0j5sqkugukmko1doub3g2otebt\n",
            "Connecting to doc-08-80-docs.googleusercontent.com (doc-08-80-docs.googleusercontent.com)|66.102.1.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/gzip]\n",
            "Saving to: ‘part2.tar.gz’\n",
            "\n",
            "     0K ........ ........ ........ ........  103M\n",
            " 32768K ........ ........ ........ ........  243M\n",
            " 65536K ........ ........ ........ ........  108M\n",
            " 98304K ........ ........ ........ ........  195M\n",
            "131072K ........ ........ ........ ........  220M\n",
            "163840K ........ ........ ........ ........  248M\n",
            "196608K ........ ........ ........ ........  113M\n",
            "229376K ........ ........ ........ ........  210M\n",
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            "294912K ........ ........ ........ ........ 43.7M\n",
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            "393216K ........ ........ ........ ........ 86.2M\n",
            "425984K ........ ........ .....              160M=3.3s\n",
            "\n",
            "2020-07-09 14:45:06 (131 MB/s) - ‘part2.tar.gz’ saved [458577713]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gajwMmEOnWhR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "outputId": "9cd55987-7758-445f-b2d7-5dafdcf1675c"
      },
      "source": [
        "!./gdown.pl/gdown.pl https://drive.google.com/file/d/0BxYys69jI14kVkVTZHZHa21zUXM/edit part3.tar.gz"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cannot open cookies file ‘gdown.cookie.temp’: No such file or directory\n",
            "--2020-07-09 14:45:09--  https://docs.google.com/uc?id=0BxYys69jI14kVkVTZHZHa21zUXM&export=download\n",
            "Resolving docs.google.com (docs.google.com)... 74.125.133.101, 74.125.133.139, 74.125.133.113, ...\n",
            "Connecting to docs.google.com (docs.google.com)|74.125.133.101|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/html]\n",
            "Saving to: ‘part3.tar.gz’\n",
            "\n",
            "     0K                                     32.8M=0s\n",
            "\n",
            "2020-07-09 14:45:09 (32.8 MB/s) - ‘part3.tar.gz’ saved [3232]\n",
            "\n",
            "--2020-07-09 14:45:09--  https://docs.google.com/uc?export=download&confirm=sXTM&id=0BxYys69jI14kVkVTZHZHa21zUXM\n",
            "Resolving docs.google.com (docs.google.com)... 64.233.167.113, 64.233.167.138, 64.233.167.100, ...\n",
            "Connecting to docs.google.com (docs.google.com)|64.233.167.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-00-7o-docs.googleusercontent.com/docs/securesc/8hjuu82qfng5rdv52nnk6no9r8k5upmg/9r62emcamhr0psuqtuusg3ajhk7uirvd/1594305900000/16869955760404710258/06238962549222204611Z/0BxYys69jI14kVkVTZHZHa21zUXM?e=download [following]\n",
            "--2020-07-09 14:45:09--  https://doc-00-7o-docs.googleusercontent.com/docs/securesc/8hjuu82qfng5rdv52nnk6no9r8k5upmg/9r62emcamhr0psuqtuusg3ajhk7uirvd/1594305900000/16869955760404710258/06238962549222204611Z/0BxYys69jI14kVkVTZHZHa21zUXM?e=download\n",
            "Resolving doc-00-7o-docs.googleusercontent.com (doc-00-7o-docs.googleusercontent.com)... 66.102.1.132, 2a00:1450:400c:c06::84\n",
            "Connecting to doc-00-7o-docs.googleusercontent.com (doc-00-7o-docs.googleusercontent.com)|66.102.1.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://docs.google.com/nonceSigner?nonce=8i348bl0rd1du&continue=https://doc-00-7o-docs.googleusercontent.com/docs/securesc/8hjuu82qfng5rdv52nnk6no9r8k5upmg/9r62emcamhr0psuqtuusg3ajhk7uirvd/1594305900000/16869955760404710258/06238962549222204611Z/0BxYys69jI14kVkVTZHZHa21zUXM?e%3Ddownload&hash=82kurmcf43gtahp2nmf3stegevod256h [following]\n",
            "--2020-07-09 14:45:10--  https://docs.google.com/nonceSigner?nonce=8i348bl0rd1du&continue=https://doc-00-7o-docs.googleusercontent.com/docs/securesc/8hjuu82qfng5rdv52nnk6no9r8k5upmg/9r62emcamhr0psuqtuusg3ajhk7uirvd/1594305900000/16869955760404710258/06238962549222204611Z/0BxYys69jI14kVkVTZHZHa21zUXM?e%3Ddownload&hash=82kurmcf43gtahp2nmf3stegevod256h\n",
            "Connecting to docs.google.com (docs.google.com)|64.233.167.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://doc-00-7o-docs.googleusercontent.com/docs/securesc/8hjuu82qfng5rdv52nnk6no9r8k5upmg/9r62emcamhr0psuqtuusg3ajhk7uirvd/1594305900000/16869955760404710258/06238962549222204611Z/0BxYys69jI14kVkVTZHZHa21zUXM?e=download&nonce=8i348bl0rd1du&user=06238962549222204611Z&hash=2hhhb547kpnpas6fb000ve7nggqc07lv [following]\n",
            "--2020-07-09 14:45:10--  https://doc-00-7o-docs.googleusercontent.com/docs/securesc/8hjuu82qfng5rdv52nnk6no9r8k5upmg/9r62emcamhr0psuqtuusg3ajhk7uirvd/1594305900000/16869955760404710258/06238962549222204611Z/0BxYys69jI14kVkVTZHZHa21zUXM?e=download&nonce=8i348bl0rd1du&user=06238962549222204611Z&hash=2hhhb547kpnpas6fb000ve7nggqc07lv\n",
            "Connecting to doc-00-7o-docs.googleusercontent.com (doc-00-7o-docs.googleusercontent.com)|66.102.1.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/gzip]\n",
            "Saving to: ‘part3.tar.gz’\n",
            "\n",
            "     0K ........ ........ ........ ........  174M\n",
            " 32768K ........ ........ ......             301M=0.3s\n",
            "\n",
            "2020-07-09 14:45:10 (211 MB/s) - ‘part3.tar.gz’ saved [56967299]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6lqVluWsna3Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tarfile\n",
        "my_tar = tarfile.open('part1.tar.gz')\n",
        "my_tar.extractall('./part1') # specify which folder to extract to\n",
        "my_tar.close()"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmedLBSwndlT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_tar = tarfile.open('part2.tar.gz')\n",
        "my_tar.extractall('./part2') # specify which folder to extract to\n",
        "my_tar.close()"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1bBC3D_ngMg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_tar = tarfile.open('part3.tar.gz')\n",
        "my_tar.extractall('./part3') # specify which folder to extract to\n",
        "my_tar.close()"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5d9le1bnhAI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f28659ca-0271-44ab-b4c4-9b49b431b758"
      },
      "source": [
        "!ls part1/part1"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "3_1_4_20161223231741788.jpg    99_1_0_20170110182052119.jpg\n",
            "3_1_4_20170103205114064.jpg    99_1_2_20170110182418864.jpg\n",
            "3_1_4_20170103205816397.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IvtdNK_7nnI4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "9fa85d2b-fcef-4fcb-8202-92793785e8e6"
      },
      "source": [
        "!./gdown.pl/gdown.pl https://drive.google.com/file/d/1i1L3Yqwaio7YSOCj7ftgk8ZZchPG7dmH/edit train.csv"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cannot open cookies file ‘gdown.cookie.temp’: No such file or directory\n",
            "--2020-07-09 14:47:05--  https://docs.google.com/uc?id=1i1L3Yqwaio7YSOCj7ftgk8ZZchPG7dmH&export=download\n",
            "Resolving docs.google.com (docs.google.com)... 74.125.133.100, 74.125.133.113, 74.125.133.138, ...\n",
            "Connecting to docs.google.com (docs.google.com)|74.125.133.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-04-64-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/ksulmhp6n514gma43j67fm2m7gohcu6a/1594305975000/03161587497725415728/*/1i1L3Yqwaio7YSOCj7ftgk8ZZchPG7dmH?e=download [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2020-07-09 14:47:05--  https://doc-04-64-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/ksulmhp6n514gma43j67fm2m7gohcu6a/1594305975000/03161587497725415728/*/1i1L3Yqwaio7YSOCj7ftgk8ZZchPG7dmH?e=download\n",
            "Resolving doc-04-64-docs.googleusercontent.com (doc-04-64-docs.googleusercontent.com)... 66.102.1.132, 2a00:1450:400c:c06::84\n",
            "Connecting to doc-04-64-docs.googleusercontent.com (doc-04-64-docs.googleusercontent.com)|66.102.1.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/csv]\n",
            "Saving to: ‘train.csv’\n",
            "\n",
            "     0K ...                                  178M=0.02s\n",
            "\n",
            "2020-07-09 14:47:05 (178 MB/s) - ‘train.csv’ saved [3793020]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36aRcBYunpSL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "outputId": "05b2db54-f4af-49de-ef13-07f631982692"
      },
      "source": [
        "!./gdown.pl/gdown.pl https://drive.google.com/file/d/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86/edit dataset.zip"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cannot open cookies file ‘gdown.cookie.temp’: No such file or directory\n",
            "--2020-07-09 14:47:13--  https://docs.google.com/uc?id=1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86&export=download\n",
            "Resolving docs.google.com (docs.google.com)... 74.125.133.101, 74.125.133.139, 74.125.133.113, ...\n",
            "Connecting to docs.google.com (docs.google.com)|74.125.133.101|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/html]\n",
            "Saving to: ‘dataset.zip’\n",
            "\n",
            "     0K                                     37.4M=0s\n",
            "\n",
            "2020-07-09 14:47:13 (37.4 MB/s) - ‘dataset.zip’ saved [3271]\n",
            "\n",
            "--2020-07-09 14:47:13--  https://docs.google.com/uc?export=download&confirm=lrcf&id=1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86\n",
            "Resolving docs.google.com (docs.google.com)... 74.125.133.100, 74.125.133.139, 74.125.133.102, ...\n",
            "Connecting to docs.google.com (docs.google.com)|74.125.133.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-04-5c-docs.googleusercontent.com/docs/securesc/qsc6afjcs0v5f1nvli7qbelpaklak5q3/k8qrvs7t32bloi0ks5aa8s98h5ln109v/1594305975000/03161587497725415728/04235942565856941885Z/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86?e=download [following]\n",
            "--2020-07-09 14:47:14--  https://doc-04-5c-docs.googleusercontent.com/docs/securesc/qsc6afjcs0v5f1nvli7qbelpaklak5q3/k8qrvs7t32bloi0ks5aa8s98h5ln109v/1594305975000/03161587497725415728/04235942565856941885Z/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86?e=download\n",
            "Resolving doc-04-5c-docs.googleusercontent.com (doc-04-5c-docs.googleusercontent.com)... 66.102.1.132, 2a00:1450:400c:c06::84\n",
            "Connecting to doc-04-5c-docs.googleusercontent.com (doc-04-5c-docs.googleusercontent.com)|66.102.1.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://docs.google.com/nonceSigner?nonce=91h0951n6rqdq&continue=https://doc-04-5c-docs.googleusercontent.com/docs/securesc/qsc6afjcs0v5f1nvli7qbelpaklak5q3/k8qrvs7t32bloi0ks5aa8s98h5ln109v/1594305975000/03161587497725415728/04235942565856941885Z/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86?e%3Ddownload&hash=gv6tcpqs9prk8ej00ofpuse09fiit25g [following]\n",
            "--2020-07-09 14:47:14--  https://docs.google.com/nonceSigner?nonce=91h0951n6rqdq&continue=https://doc-04-5c-docs.googleusercontent.com/docs/securesc/qsc6afjcs0v5f1nvli7qbelpaklak5q3/k8qrvs7t32bloi0ks5aa8s98h5ln109v/1594305975000/03161587497725415728/04235942565856941885Z/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86?e%3Ddownload&hash=gv6tcpqs9prk8ej00ofpuse09fiit25g\n",
            "Connecting to docs.google.com (docs.google.com)|74.125.133.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://doc-04-5c-docs.googleusercontent.com/docs/securesc/qsc6afjcs0v5f1nvli7qbelpaklak5q3/k8qrvs7t32bloi0ks5aa8s98h5ln109v/1594305975000/03161587497725415728/04235942565856941885Z/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86?e=download&nonce=91h0951n6rqdq&user=04235942565856941885Z&hash=fjctdkl881c5tg806s7svp8mdfalv7uf [following]\n",
            "--2020-07-09 14:47:14--  https://doc-04-5c-docs.googleusercontent.com/docs/securesc/qsc6afjcs0v5f1nvli7qbelpaklak5q3/k8qrvs7t32bloi0ks5aa8s98h5ln109v/1594305975000/03161587497725415728/04235942565856941885Z/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86?e=download&nonce=91h0951n6rqdq&user=04235942565856941885Z&hash=fjctdkl881c5tg806s7svp8mdfalv7uf\n",
            "Connecting to doc-04-5c-docs.googleusercontent.com (doc-04-5c-docs.googleusercontent.com)|66.102.1.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/zip]\n",
            "Saving to: ‘dataset.zip’\n",
            "\n",
            "     0K ........ ........ ........ ........  166M\n",
            " 32768K ........ ........ ........ ........ 19.7M\n",
            " 65536K ........ ........ ........ ........  164M\n",
            " 98304K ........ ........ ........ ........  133M\n",
            "131072K ........ ........ ........ ........ 17.7M\n",
            "163840K ........ ........ ........ ........  162M\n",
            "196608K ........ ........ ........ ........ 22.0M\n",
            "229376K ........ ........ ........ ........  196M\n",
            "262144K ........ ........ ........ ........ 22.4M\n",
            "294912K ........ ........ ........ ........  112M\n",
            "327680K ........ ........ ........ ........  159M\n",
            "360448K ........ ........ ........ ........  207M\n",
            "393216K ........ ........ ........ ........ 22.7M\n",
            "425984K ........ ........ ........ ........  238M\n",
            "458752K ........ ........ ........ ........ 28.8M\n",
            "491520K ........ ........ ........ ........  156M\n",
            "524288K ........ ........ ........ ........ 19.9M\n",
            "557056K .......                              324M=12s\n",
            "\n",
            "2020-07-09 14:47:27 (44.3 MB/s) - ‘dataset.zip’ saved [578232678]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEjeYrTMnsOy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "2e2ee787-381e-4504-db80-d28775d96562"
      },
      "source": [
        "!unzip dataset.zip"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  dataset.zip\n",
            "replace train/1346.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQFZVzKRns8q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "d74c108a-4d0e-4fd1-d4cd-5c84e4b8154c"
      },
      "source": [
        "Image.open('part1/part1/3_1_3_20161219230521112.jpg')"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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RNJFGIzxs/OpcZsuC9T2meQHhHEO2EECE5i56oZeNvwZlrQvD0HvvnNNamyCy1jFIu7v24muX/of/7y+sr7bbK5ugg1Br770hpZVhdmWZg9g0CrWmoipdxc45EVEKldEG0WiINMVGb29uhdqUebG7P8zy3LIHAKWUtVZrE8exiHCDwxNaWx3drkRAAijM7MWBUO1sFAZZUX/+i1/48JOPPfnw/YDsylxrQlDee601ExHKEiRHvEscx+3eXY+NaI9SZBJEaFDdJjmTuUtrFBNZCEE1zu2YiTtu6BTRXb8lbUgbVBpILR+P/zS/XT5qavIKRUQaCREJEBG1npfuiHRduwY+2NjY2pvW/93/+3+kUCXdtbTbV8oAgDEaEcQ7RdBK4yQKhZ2tcrEVLBMdQIOkScUmaEXxye0TiQmn48lsNptOp3VdO/YmCpkFAK21SqlObwUA8jyPosgJe++dr70wIjYXjYjLzE8FRmkYjIvf/MxnhuOpiUJGcDyPIhQZAGBmRmAEWdg+UIRaoVagCEgLqgYbXD6iIlSklCLSQBqWUlCEiuZYviJYOCVQx5IzaNRgqYMy91ZNLAYLCH8eIC2LB4sYt7Eqx81js5pmDoAKCQEyCgEKIIRhPJtNmjdOZzmR6a+szWb5P/1n/3pqwQRJ0unVVqKkRXUdKF3kszAywPVsNi2KwgmjgHhWag6ZakWaVEAQ66AVRmu9PohMxmMB8N4zggCpQBvPcRwrpUCormtjTBRFIqK19mzBiwiT0ojakzB4rQ07T0RFVSqFgYLnXnj96e985yd+7JNBGDlboyADEYJ3DXi2jL/u0rT3uhIAAGBAfZdDwSOzydiwK+RoYRHpOLfgLtM65zcAANHinXOLu7CC9/wpRPfKrIFh8CjOPBZRsWdujDBVtVPKxGkniFq//i/+9XMvvhLEnY3NbRMkQNp7DpQG8VphXeS1Ldj5IGjwPm8tCaBSCoUVokLQSgVGBUahd4gk4p0Xx14UglZMKtIRCKZJWwWmKAqtdbfbnUxH2pBYcuxRgNErXEIK4oVJa1fXACRaFWB/7/f/4KnHHjmxuaoBnC2b2E8BitZieekj5nJDAYF77ubFoY47r+YMbmDaY7XJIzgLEXBeP8O737YEG2nxNXTkSps6yfuFHvOi6TFEEhqvBtD4Q5zjbiwAdVkZYxwLEQVRHEbJ17/1/Oc+/6UoXddap+1ONiuc81rrJI6GhwNtYDQ4SJL43LkzSqnLV9+dzXITBLVzc7hBhEBCrUKlNGCRz6IoQpbaWcvWAZHRoDSCqqrKex/qmIicr1lokTw1F8Xeiwcv3jGzF8/MyhhlQra182IQ3nz3zpee/dpPfvpTSahJGqvolDIA4oGBGfhYpgwAIk2SLO+TCuARxW2B0MLx0vGxUwUbOtoilZYFtLg4ixb/PYJYRISQj0SO2NjkYyI8/nc2xSEEACRqZLb880SESItzYZIWhR+Os3//G7/lQSVJEkVRURTec7uVZll2+9ZNAc/Tut/v9fv9qqrGs8x7H4WxF6b5fcQKyWhKAhNopRBcVdUitW2qm+LEYxjoIIh03Gq1RKTKC1JAQs65MAzquiYipZAZhVnESwNmEBExMxOpyovSwgChgi9/9Wvf/7GPntpaZ0CUuVsTER2YJcYIy+B5UQ9BUEfoBAA0WrVEHo7brUWay42qNMVVEUCgxc11LwR1XIFEpBFhE0rcc3wPrYf5+doAkKDSKli+RUQEQWsNinb39oMk/e3f/d0bt/d6qxtxHGpSCkEjuLqaDA6z6bgu8q3tjTiOGWHvcHD79u26siLoai9cg6+0wjCgQCOC31jpJ6GOQmPrkkBsXTeXkCQRkERh2Gm3kySpbVlmOQAoRVVVNbqCiMYYIrDWWmsFQSsFAM45QYhbLVBaaULC6zvDF156eTLLHUsYJ6CImysCaBIYrfUSUVLqKBA7/nzuopSa/9y9qjAPQY6OeSi3UMSjdB4XTJDmscnbjmS5YJscs4TH3dg9KGVjE6GJFQGbtA2ZIQiC2vrxZPbwo0/+7ue+8Ow3vqWjqHISBtpWdVl5W1pb1dPxOAzDtbXVXq93e+/OYDwqisI5x17ECSGSEo1kCJJAE/v1fqfTiiWi0WBAJGkc5s4JoHcMAFEU1XXdcEqb7FCYrXcifrkCKL5ZaGZGgdpWwowyXxMAZFIMUoH7/Jf+8OMf/cjWxsbhwZ3A6CiKwHMlPL9u5gboWC6diKCo5To3P002jcu6/zFDtICY50s8F+SyTg0g0Hw6chOnzJX67sjnSGQLgSwV8cirIS7/AhEBocaaIwKSFvEgXkTq2g0m015/xXn50pe/env3YH3rZDtuF66yzpZ5YStXZmUSRd1+v9ftXr169WB4SARBEASKoCE+MMchaaIoUOCqdpL0O2kaKI+6MsQIpOPCOg1IHhlVYAJrna1KQVBKgZK6duxqhehFBDwJNPXhea4FwNY3jAFmZkECQhL2nIbq0rW977z40trqJ5gBgcqyVErRoh62XIcmo51bl2Wau3xOArRkSd61esfvfiJcmCg4HjfKPVn5sYOPyKXHk+hjkpOFXI+ipobq0GgeIiOieCLywiyYF0UQBOub27/z+198650r7d4qkNFROt3bY8tlXjGLUri5uRkEwWg0yvMcgNm5wlYBqk7aCuPYVnWkXBiaXrszPhw8dN+5zdWVVhTauoqUjCbTSKnaerJMQKxCQaUiXVWV85bZCzN729zLyB48+4WbIVC+CR0ESCEDWS8sHlCToGNJorCo8s/+7u889siDJzZWERgXzJ6jO16ae+BeARxTAwXACISAi9jwfaDkhZuiJhnTOHcuSpZiQwZQC8IIwyJQXIr8uOYd+2haYJ7LYEQAgBAYGAGXsX5jLohofXv79u7e73/hC2XlgjgFHe4NhkRUi/PChGSCMIqiytrDw0MdqMDp2tUkEIe00opaQTQd+zSOVvsrcRxHbB8+f7bTbtV5xoYSTXWZB0lb60CGU8WIYVRbcYaAvWfnXN1UJoG94IItw9xQq5jnaaVGQCIUqsWLEKMgKRSxXhTA1WsHV6/dOHvyRJFPkjgqy/LI2IACuit6XMDitLjnl/f5UUDQ4IUC/ghTbN5zDM5d5tTzKGMekshStkvi4vy//hjtAI49CtFcme7OqRGRwKEAcEOYBRHPzEEUZ1nx1a9948qVd1lQGeO85EUdRlBUZVlVAYWi3GA0AWAWmU6nLLUh6KRR25gIOSZRAW2urJw+fRoAzm2sndne7LZbg71dIsrzLBu3TNpKW23r8WBaKh2gqyvvRYQa1MM5Ycey4B+yAIsHAfDMLH6Bu7MIiAIEQEKNiIoQiJM0tFn11a9+9cnHHooNFUURhqHlZTFlXs9aig1RLW+FIwcGR/7smEVFUkdasVx9IkKEOUYMACxyhE7OVW1+4lI/EFHuRsyOu7HlK3CXKRAG3XCP0LOAMLP1PjDmxq1bL7303cks13FXB5GtxITxaLI/mWbs2ANAqKbTaRiGSqkkSYS1ApsEJiSMFZ5Y6QQbqxurrdOnTwdB0O90t7e2kijc7HYQ5eBg4J0rKud16MXU9k7FoBmEHXvLzMjC7Ky1PE/8aXmfSUMaXpp/boIFAlCwgAGrqlYGEeHb33715Zdf/sGPfbQqsyiKbJ4v1uEugcE8pMclj3/xchNAyt0yE6UX63mUWyMiAqFe/rlLbYKlW7pbBo3MGO8xsnep9j3Kh4ggDXZKSJ4FYUGRcM4BwO7uroiEYVhWVodpYfPauSAIdKTZYZKmcZQGoZbMKQ3eAXiOw2Cj17lva/PC6bMRUq+brq6u9nq9JAqDIKjLotdtV1V18sRWVVXXd+4YZTZW1/YH03KcifdI2NhAQAZm5xwga2OY/V0yk3mophhdkxURNT0fnrm5qKquO7GqC/+Nb3zjg08+ERoaDoc6DAEAQS3XbSEJdU/JcBloNHrG7JbrLyKKNCDTouB89GkIWhb3Fy9Y1g0Dfa7bSjWUAhEB8ABqSbI8Xvr0INjUWZYh00KoVa3iKKirmUIWse12enM6AhVS2P3Cl7+wc1BWtVkxqQBAnRmXIxMIzrIqMEm7t2qLwk5np7vdjoGW6eTD3YvnzkShOXf+9MUHHhCEOOjGcRxFARIT4Wq3Qwg4nSCqM2fO5GW1tz+0PmsHPOGp9TlVoZ/OXFWKpgCVV4ETBlDW19KAhA2izuK999aWSqMijQQixBaYaQ7FescgoQbx797evb0/uHjuLHmrwRCBF9ZKVXXtvY9b7bpixKYTQQBZwJMIEjf12obBMq95QRPuI8/7uMQo1Xjbpi8NUR/FjSTzFp2lzixEJUdJ+3F7eLea4cILHtdOETFmXoYPjMltcXh4WJZlt7d27ebNw8ND73232w2CABCrqkJE731eFIoCJBmNh+vd7tp6n2x5cnuN6iyEemNjA4FPbJ9K03RlbVUqRK20UQpEa4rjCITFpczc6XTOnz0bxsmN23uKIAp0lvmqyLytRDwKkVYGxddS1/WywkzHb2paYn+Cclf3EJHWIUynVahhOBi9/tabp05ud5NWPssC0gDAzEEQiDQMqmZlcMGyQVjaRkV0ZLrUgg0iC6oH3HPg3Rjxvcc8jloG97QINhdSguO29P2cmYgQUW290UopCLSejIYiorW+du3a/v4+M/f6K1prz+yc895ba1kAvFUYgatiQ60oUJo3VrrTQbVyYrvdSuI43tjYMEHQanUoIuetiCiFuimgAKrAaBal1ObmJukgy8vxZOK9q8uiA6AVwCyvfO2RtTbGCIN4maNIDEANF7hZIxZBEPE4bymcw5LO2SQKqrJohcZ7+8J3XvrAY0+sPbhmqlopIgTnXANiOM9hGHvf3PuKmQWEGp0DtcgE5pHdkfVCRm5i7AVMwYQEIvI9ZdYIWhYUSVn0RBx3XUf6dPeNsBRYo9FKKWOU9yURKqWU4vF4vLe3t394AABaaxGx1jKzZV/ULg50WTtFsNLvRppsmZ3a3ljpdKrR7plTp4TdqVOnut0umUApFRBJabkpriJ6YYKmANgwpTGKgpV+N8umArbMZ5nyCn1V5sWs8tZSxMvcQwSbuG5eCOV5sRcWLWVzPGfhAiprA4PCqIPw7cuXX3/zrfvvf5C0ss5HcYDHaPjGGAAvjZaRiBDBPIlaknzes5Ik6OBIYk0uRrLstZUFj/wePZtnLd8r1f7ex9KcLsJ9bqTS/HGTyeTg4GA8HhORc84z13XdWGZpYl8AAt9JglZs2qE6sd4PlFcirTSOg3BzfSMM4zhOAxM1mJw2ZIxRSiEoVKRNAKSIlPdeE630uiv9bieJV7utWEEaUDs2rTiIQmWIFIImPAIAl+iROqrQ4nGL0nTC6MA59gJ5WRd5NZnJ8995aTgai0iDrpkgUEo1wGPT8EFESHIcpF0+JyJCjdTQTdWC5NEEMk38CdI0WRMtsavFQh/riXsvXVma1oH3WNnjt8LyleZzvLfCzjZ3ADMABEEQBDyZTLz3aRB476WBZNk3ns9aSwDsrS1mrX57q9+JFMxGg9hQQHjy3OmVlRXUurmJnatFPJFBVIwggI3KMTDpIAgCrbXWqrYVs9dEIyfjCbK3gJxXvmQBITCmZgEEaEgbanmzohIWzyJEMidINNSWZt2ck5CwqKSdmLfevvzyK2984iNPBUHgvCfnEFFrjSDCRyI/hkvNkf4jn0LHexuUYKNUx5UGmd+nB/49GrPwYI2dPC6u5ZctCg335oAA4DwDgHMOxAFzEAQMCFDmea5IB0HgEcqi8Au2szFUWzYAYmvwdRrqjW6bpGZbXrxw/uTW9pnz51rtduW8MlGjloAKCBlJ5mkgeQYPSgmbMBYRINra2grDEIFn3hvkqiy8twDgitqxhIFSSlkvxORh7nqYmYgUgygEz0tKCwB4Ee+9Uoa5JqNJSZK2ppPJF7/87BMP37ey0mPmuq6iOBARhQiG2Msyy150iSEi8hIHoSWcJAuiKgH45lG4aZcCENBzNH4hBlq0Bi/jwOP2Ut6vPeIoHjn2+vJRaVFo6rLyzgF7ramy3OCHy3S+qqogjry3URSpmWLbQOPQTsI0CuJQpUQqDC7ed35tdT0Mw7qurYd2N8mKPDQhKA+KQGuYR2HKQw2eS28NqKaGmSYtJLp569bmat9WeVkUcWiSslaUZ9ahjgrvKxTluBYmRt+A0A0exCKIIIyicN6nKw3mi9qUVd1JUg8Yxekrr73xzjvvrKx8yBjjvTXGeO+dk4A0ETV0CG6ocfMF42NGTS1FIUJzgJZJgLmRSUPLaEp62ABK3jceGI8dxxUOEVHd20y2KBDcFfkfL6oRURAEdV0jYhiaplfs4OAgDMMgCIwxs9mMiKy1WusgCIi0AAQKNAKBnNxc66YxgvvYxz7abreNMSKolEmSpCxLY0zNHCWpiWOlAx2EpA0p05A1wygmpcM4CaOkrG1dW9I6CcyJjfX1fjcyeiVtba50V5I4DailqaVVGqjYaEOsEQJFisAcFbzU8iZrJnc01xhFCSMcDkbtXn80nf3bf/tvneOmhNacHEXRvGym8fiT46UywbtQ/8aZEREqalzK3GYKoYA+Ok8aZtaycCbYJMvHYfv3sZ3/OweKOF93uq2AYHf39mw2G45nw+HwvvMX9wZZVTWwOlrnEFUYR0QUaLROdACdJI5DAwCnT57sdTpGB0opaDhbSjkW8dzurxRZZoWTVlpZX1WVMUaAtNa2qkBY6vpgMLxzZ6f2tWOYjMbWu/XeCrPcvrMXowTddDQt2q14UtZFzcSIRB5ZUHsh7/0cJ2Rm8czg5ajjqFl077jpxEla6duXr7/2xhtPfeCJII4UgXPO+VqroAnyFmrQIObYYBiygMkaQRwHko/bs4U4RR8T7zxqOB7yLasq79W55TlLlToezixfZJGyrAKSvdFhmqb7+/vT8TgIgq2tLaVenWW5AIhwVVWCJkFstVpTW3tnO6243+/HYbTW7Zw9e9oLJFEYx6k2oQ5DQQoQKY5m0xkAGGOqqprlpTFGoewd7o+Ho9lkcmfn1nQ0NnGgtXbetzvp1tZWVVXj8XSlrmldJrOpY9jo9Z3IYFZMi2JS1rNCzaz1LI6BlULkYzF1M8mF/KIMxgjeOUSsrV9b3Ti8dvX111+/cPF8kkSyYBkqpZxjAJnPGjhG0WlqCstq1xLdYuRjSON8aErT7KSPjR450qNGzxYAyPsL7L3R47F74UiiSiMwlmWWJMl4PCyKIsuyNE0591EUlQ4NUWFdA5I6Z6Mo2at2WwDWWm9dVVUbGxthnILnIExBG+vFlbVjTwQpShzH1lrnvQ5MGodFke3sjW7dvLm7c1shkQCgJEly8eJFbQIyGstJXdfZZFpulrPp+Nq1a3d298nXa71+qHUSqrioNWWUSV5bFLBzbN7DcacBogi99w2CKAiBMc65JEkOCd6+fKkoijSNm1i/qqqqqhRqAA+kjqnaEV8GAJh9w3K6e22PzRCY86/hrlj/rqVfyKypRdDdqva+Anvvh4hIbWtDxMyTWbbS77/x+utlWUZpPBjsG2OiCMums9Y5QZhOp2IiDeABytIhYrfbXdvcSqOoKsvasfiKiEIdBYEGAO/t3uF4dXUVxB/uD4syL/PCVkWk6cTG2nQ8OXXqVKvVQUWr6xus0HuvqlaRTTf6a0U2u3HN33fq1JmtrdFkXNWOwASUaiRvnXgWERTJBXgOCTVtgPPrCoKwrmvvPRFoTZp0URSzPCOCvb2DPM+d6xTi08aBITnnERTeVVe+l4PFy9lgTaZ0z2IuaB8auOl9g3tCjrnYFgYTFpjIPcJ6r9lcSrQJl+u61FGQZVkUqMuXL5dlWdd1lMLu7m7TBpnneZZljp0Xds5qURsbm+O9XSKoKltZd+nSpXaS2qoOw6jd6bRaLQZRChEFxL/y8qvf933f12qlYq0rC3AVsdXglaGT959vt7qD8YRMkBfZwWhUVVUvDG1dRxoV0kqnHaN31iYaBCmzfpSV0WgKAAzoPFdl6aGhYYkgzDFzRCBURgNAw0dWgOC5qoqDg71equu6HgzH3W47DE0ax1rr49oCLLLowZVl+LAA1t9HhHNBNmN8BJuZLku/1RxzLqM03Kx7hQRLX3W32h0LeI4MIzOHYbi3t9tptyejwxdf/E5Z1tsnTk8mEwAIgoCzuinK4LywhFVVtePIaLKWL1++nBCvttsr7W5Zlp1O7/S5s1tbW1Fsuq20121rEzzzzDNaq4ODA1vngUH0EJIxccTe79++fdNe39o+dWJr82A83t3dTZP2zuEdYDHEqdEKOFAq1nRiY80J59YPsyJIRhSEOpoKUJ7n6Oe3KhERYNMji4p4fmvycimMMXVdl9YFQT2dTmXeFynMvsiKMIxBQMQvxhaIyPxWaJbw3lX+HoeI6OOKco8klifB+9lP+B5Kdvy3IpLlWRAEly5d2t/diaLo1Kkzt3Z2QcVhGBpj5pQ0EWttVVVKERoBZ8MwpKI4PJzeunl7+8n1Vrdz5vx9IpImbUSsyroOjPfWe6FAa6U67bTQUOZTCjSxL7J8MhyJl+3NzZ2dmz//S798+fr1MIrJ6AT15vr6B5545MTaqsvHnThcWe+TQKi0NihK10JeGQrjwrrxbKazggGdc8BNxjbXDaU1IlprmR0wKMQkSeq6zjKXet+47aoqULjX66Vp6hwvlsUvVGz+Wc3LTVR5zFUdt5zzmMOjaEDdDEvDOWjlURBIE6IgM3gCQiWIBpCBNKD4ppLQ1KwJQIgBkRFYtCEScK4GkUBrz1zns53d4fVrV3vdVtpeW1lZv/rOlTAMN7e3D4YDrbkoR1WdC2rvvQABalNmkiZDgG7aevTciZ/48CMPnF4/ee5Uq7957dquiGJbUIwYGOvy1IQ1aqGQERBspNM6t9koE5ayjIL1M5+/vPPS1dHN9sWVH/2+3/n93zzcu/O0gw89ETz3nRdOn9w8c+Z0e217EMaddpvYcT7uIEPFkNegCm6bEVbiFKFGIEHxKF6AmKIg0sroSLmKyyLTWhOKgFcarQLWcN+DF+J2azYaTseTVhwFitA7EQSlQcA6FlIIygsQuYaaLcLIDcg1B0SQPYugkDRDeObhpegm9KGFmiNiU1sDOt6CeHQcxzfnrbANG9D5oxNAmv4iHQYr/U5VrI4Hh1xXObvRaPhDn/zkcDwdjkZZljUJoxOHiM18TAGonQMyXmhldbtwNHEQFv7lW28FcS8MolYS5NXUDme6bYJQ+1BYE1JI4Gp0M7Y5uDCOekn397/+tf/kr//X+IUvf+df/YugbZIoGihY7bT/0l/5y2+89lK/m168eCFIEhNEighcDZUWpZTCUKsoipKE0zStbu1HURxoXdc1IRpjREQ8YwCB0mEYOluB59p7RUBErRjW1tZEZGtj41ZVFnmeZVmF0G6367rm2jMSglJIDCjOAhHgUUy3xHjVop3sSPOQG1OqG5SPG/YNAxCSzOcb3CMtlHuNLgGiAkJFgCqApnVfEXnvnHXMjISRgofvv++3fvPlWzduPP7YIxcvXlxdXX335q3BYHRwOMzLsra2dNw081hrSUFe1SoOEczhqLhmRtf39q79wR8eWLYSlXnVS6OPfOCRZx59WKaHq2cv1KREERN4ryUMfKQNtTY2t9++emtle23j4Qd/BOiv/9f/18uXXpnNMgPwk//Jn3LOnT5z5oELZ4XAs7Tb7aosEbQjWlSCdRSYKArjOBZbM2GapoLCgMTs2CutWnHinGPnjTGCvixLJkiSRDxMJpOdnZ3t9bWzZ8/euHq1qio0uilihGFsgtB5FhCNAMGSojoXm5JFa/a9htEvPRSJCItr4oUmwxfwcuxoBLwU2PxMbxugC1iEHYtrWDENzR0WUYmz3E3Dgzs7vi7breTw8ND5+tVXX/3uq6+XVX04GmdFJaQQsZmH2IBDSkPtrGe8fPXm65dvfvZL33zh6p2/8Nf/2+ultM4+oNdO/v1f+jf/7F/9utLxeJBxWQeCBkkpFYZhkqbtbndtY63TaZ85dbK6cf3iA/f/ys/93AmT/NBD9/+Dv/M3P/mjP0Y6ePjhR5NWK03TXq/HzMyuWQfnanZ+SdwQ8RGw1IVmDhEDZLG1QVjptI3SaZyYQDXE5DRN4yitK0cE2TSfjGcH+wOt9cmTJ6MoiuM4z/OyKOq69HXlnXW28q5E9svCDBE1rX4aSd0L9C74dU3X1j0zWpaOkZnnFXEW0EzSsCcVsPB83IQFMEc3A7NGanoGiEgZ8t5XVVUN9vZ2bmyurWZxVDlLqG/cur1/MPBEWVl6hqgVk3HWWmavNLkKTARZWULQwkAfjmcl49/5b//ep/+Dn/4L/+e/8ZGP/NDNK28qgi+98Nqnnrl08Qd/OJ/kprcaRGEABGg5bpWzSTGentncPLWdXn/9df/apbNJ+9/9459PjEwP9wajYRQFpJVjl8SBZ1EatNauyNg675zz1vnautq6ylprgBWpkMCzU0hKQavTXu2veOG6rg2pbqsNAFmWWe+IqK5gWvvDw+Hqxvrt27udViJAOozW19ebuppIBYq0DpCoKdLMtQzV8WrWXCCLQL95rSGg6uPSwgVli1EajyeMiExMgkIsRMCETZZAAk3JAHletmhKAtLYUZGqqmazmSoLrfXZs2ffeffqdFBO8pyCcDjNstJbLzqMwjASrNh55yxp6MQw9RCHxFIXhWSzwygI7ztxoqPNQ2fv+zf/7H/VUBiARMOPfvrHXF0rR4gEZBR6QBWZwCHl2VTEhGkYsN8/GKRxK7SuHI7W4mht6yRAHadh0Iqr4YG1zjlnq1q8Z/beW+dq56ybH3VqVKfTCaKgzqegTWCCfruFwOw8CoRhGEWRc242mznngiBwAiaEz/zWb+d5/mOf/MTqow8zs2eIk1RIzSdPM3tmo5RSyiOCUDOHBo5lTe8JII8AJn3MDhybGya+6Spfos1zyr94QA2LsWgkgADNtQJAU9LUWjFDXVZZlmVZhrYM0nR/OLly7ab1/Po7N7Ka0USHuztogjhJHfu6rABEESgCFKhrMG0JNbYCdXLj7NmTJ/Kdm9mVK7/6P/1P/+V/8X8hqj/+A0/9hT//p7bOrt98550k7nkS9Lb0FtCJIpMEmlQct2aFNRGtb63FQai9b613gzicKkiiHvnSz2bM3Gqlh4cDJGBn2Xn2jq1ztbW28t464V4a9zstKxAbjcZYEY2YTcYHw9H6xpYxZjSaiEivt1JVxXA4NITOi9fqD7/81bfeeuuTP/yJH/mhT8RRM6wWmgmeRZk55xp5g1rkD3QULczHciIjN8O05liGiAdYNLbeJdtmsuZiPppaDIo5UsSmxgMISM3cOhCuK+ucQ8Q4jlGgruvG0F959+ZgMLhze+/KtXfDKNk6dUaj9uNpXlgTxHGajEajoiiT2ESB0oTEkCYwqcVCEbXj+89u37e17vavf/Mzv3bm9P3/6O/+30rON870wbjrO1eZvI0wE0eCdVPTJUCjCaFmpyMTRnEAxta+laS2mAXaxkmX2QKCiiLlqsFgEAZmMszQe3Y1W+d83ahYoxOxIY1grQ0DE8TRaFaw93VVOed2d3fLutJBFMdxXddesNXpldnY+dqjilrd2/uH//jn/uXv/O7v/52//beeePSRuiqYnWNu5l8B4ZxzDizNbEjC47JYAr5Lm0mIIEjLstnRqSKymB/fOEgFKMcORNSkFFLT0QwADa1jNpuNx+M7O7ezLEPEyWSSJMnq5pnK66jdj9r9qNNf3z41nMxeef3tSV6mrU6RV7PZjAECTeK9UQQanMB8PKqrVpLwoXNb92+vnV9rp1z0NW+2IuUqDd4o1ATNvA3rXRAE7DwxKi/oOSIE76KA4jQ0qZaAktVOrQDmLh/AVQAQBoEtK2HHzopndp6dr+rCe18UBRGFJgiMyvOZtVVRFAA8Go+runbsB6Ph/uEc9Z5OpwAQhqGOYhZkpKyyZ84/8KlP/xia8L/8K//NP//X/6awvHsw2j8cWi+kdRTFzvk8z+fKhc0IQwuw6MpcFGjuCQk1LnEqgKU9lWbIPGpcjlJqOj6lGQQ2h1oaFSYUAYlCA5xmWRYm4Wuvvbazs5O00vX19cs3buVebuzuX9/ZN0Hw2ttXssJWnqMoyvN8PB6z40AhsGgCo3TpxSkoMotiTT9Y73cunju9tbVRsTGq7TxygBIJBoAiZZ3Vzmpmaz2XrhfHk+HI59M4CbmehUGiANBoYwx768WrKOAjhE+EHXgn7MCzq2r2tralq2pvnbW+dpaZoyR27NfX1/ubmzXDYJzd2N29tbtfe9ZhECWxFx6OR2VZtlqtNI3DKDVBtL+7Z1lu7NzOivzEmfO9/uov/4t/1Wq1nnrycVTaBNHaan88Huf5LAzjBgxqisMLTvEcElvAhATQlGbUkT9roDMhZGZCs5w96IFRBAjomM7OW2bm3S5zkJGZSUEUB8PB6LOf/ez169fTduvwcLBy8ly3290/GAZhNB6Px5NZVvkwDKJWYK11zmpFYUMLUJqZSx+y0gAzBy4y0ebm9vrmWtBNvSMBQ6C00awYyWsnriywBZEichCHoc/yWKgo3XA0SlppvBpqRABBpZ14Fh+YwNcs7IBZvAfPwgyehR2IJwG2rjHpDdNSEIIkBlLtbr/TX3n10uWbt++M8jJM4mw8BUAAcXVlnRVCy3aS+RPbp/M8V8ZoEzLS25feeeONS6sr7Sznz/3BF69fv36wv7u9sb65sba1tbWxtlKqMmm3mjo4Inrv5w0Sy9We45JzBBER9WLIs+emYqqIxRHohm0InhBIBJkaa0hHvWgiLILNfG4ABbLS7b3x9lu//uu/fu3atWeeeSaIwrKs3nj31uDgMDRBp93auXUzqxgB4iiYjcfKBGFg1Jx2qYwylS1rCq0owIAFrIfOyqoDLLyDtG2dQggoCoUrxUqJoMPaltbWiAriWAEUw4OiruIwvHXndmztShi1210MFOnAeyy8VU1zgnjxnsUhewTWggDIiOLZWcvc7BwiIGQFup32rC5ff/EFFcSlSBAnw+kMtJ57PGGtVBCGSOKsa8oUYRJprdNWq9/vz8YjBVKGs2++/PZ3X377xHbaiqOVfjcOTeM4VaAafpiIF/EI821SmuoPeGDh5eBHRKbmFpN5fUCYGViY2YMsmyGcc8vEc+4VF16vwaiZuSiKN954bToaa62n0+nXv/71b37zuZ2dndiYMs9mo8GrL75YFLwSwg9835Ontze881VRNOzBpjMPlHZMJFBVRXObDaZjHUWFCIYhBpEEIYQhmoBUKESMxICo0YGLttZAYW1L1Yq3L5yjdtw/uTUoszujwxLqiqRWQFHAmhCFYH6p4hnEowChCHtx3lvXbPLS1MkQcVbXo7wEHfzkn/kPM+cYsXTeCljm0jtGECIL7MQyswmDqi67vU47SYsiu3P7tjHq3PkzVV2OC4kAnnnm0R/5oR8OjGHr2Pl2knQ6bYWYZVmez5jZGKMVNsI7cmJ8VzyhkWU+erxZJ1lwh2UuQgRG9My0nMIK86lwrACbUNU5VxTFxsbGjRsvvvzyywfDuh7WJ1bLdru9urJu06Au5aN//I9FUfSd77yQBCro96bbazduHwB71fQ1AxS1ZyHFldQlmFDYj4vZuJzVvIKkxYMWRaAMaGeE0Tt2EmuNti6L2Z07zJCNp9vrfTsb6SRq63S8c2t2uL9SnY9M7D0Hxmgy1ERN7AEYxQsLskcW8excLccHS5IWhElZtlbXCu+/9PWvZ2VZWCcU1MIewQkLUNNbywAgrEnEWVeVdV2D+DRtDQ/3r195p91Kn3nyIW+Lj33ko7acGWS29XQ4TALdXV9vOAdNKta4qmUjy9KrAahFiia6CVga0iPyokJKTWnay7Gptu95wopU8wXeORHJ8/z27dtxHP/Zn/r+aTY7efJUFEXr/T6BHB4ePvTAxd3d3f07N/PCdrs98+ADw/HEgwallDLWu7IqRSTkSgGgESKYVe7StXfuP79RFkWUxglqRA1aBQF5L7Vi6MXtGp3zAHUn7YYpfOMrz37+dz7z4EPnH3vqyXw6W+mshagS1NYzOge1ZQER32ACLMDMzQhqFPHWKUSjNEDVXKO1/uqNnXd3bsdp2wlMi6J0UthJ6SSMQq5rBhEAY0xodFNtTwIzGQ+ttcaoVhKNBsMiLz7x8Y+OhocGg04rqdDdd+akLYooMJrUbDYJgkChUppExNoKcT5FDVEpwAXqtAwS4V5O6jKEvGsE8l2tqwvzeIRzeWbnrG2129bauq6feuqpN956M8/zs2fPalcNh0Nf5+9efUdruv/8uUleeDA6K6MgtKAq770woWYPImAUaASHde2gYLhy7Yr1H/K1VQGjQQAEJxCwY5tzVUB19VvPv/r6Wz/8I3/snfHbRZbv3771pz79J+67cGp3cPDQuQu97TMJBj4rFYMCVEwV1Hft0+CZvXe15abtUwQAlh6hLMtuv/PurUkXs7x2WQkqxMqKCahJRrUK2FsvgorQIYBYW7XbqXOu227Xdd3v9/74T3z6wYsX/sk//J9/4GMfK8uy025n0xl6m40HwC5dX6uqynsRQqODe1pDG32/h4WgYd4EPu9HQyByjKJIETIoUsQOAXCOWAF6FlFaa0QU8CziGaxjRBkODj74xKOvv/KdW1feCLny3gZuJl5XeTYd7j/9gYdHo2GS4NVvvoVR59U33w1ayehwVlY2DIFQSGpEyQR0oqqaQEelL198ec/+hMawtGqArcgGSV0FHe6GbhI6VeTT8anzZ0yCvVanHee7uxfOPH3y5MlcuLt6JgzDIIydZcuVUsoSVq5q1xNvHTjr68oXlZSFLme6ntR16XxljR6JHrHJBGupd/cHu7jiTTAoACBAxcISoAfLDcUfrDVExIAO+p2+994ODirvz5w5NZmMlPgTGyuf+qGnn/3CF9YS6mobujxBZdJwktnrt66rSG+fPlNV1awupfbUMAhASGnn55wPD56w8XDc1L+OSFvNkyWtTkRQYInTL/0gNEzIhdo1h4gkSWKMSdP0U5/61JUrVzqdtvdeKWWMCYLg7NmzSqmTJ0/62o5H0/3dvVOnTonzWZFFUaS1FoSahRnYQ5F7QAIWBrl6Z+elN18zUWir2uV5BNCKDJAH8WBUvLJ68fx9T3/ow5tr65tr6x/9+Pc/+vBDKEAMAYAt8mo25boSW1bZpJxM6iyr67rJhxaIorO2alAb5xwAkjJewArs7h0OJjCcjHNbL+fqLEikAA2hHSBNkyRJmkS21WoJQqvdqa0vqnpz++SP/dgffeW7r332d3+30+lubZ04d+6ctf61N9948803r9+4tbd7UBTFYiTqorNUqSUIdbTBxVFWhXM+iBzPpo8bviVFdaGl0KTSTI0MAYDFszjvOIqiUJuHHnqoyPK9vd0wDMMwLDI3HB5Sv1PX9XA4GI3G62trKxunf+P3vlBX3IqisqrandTXzM3YBQIjoKIkn+ba6Jl133rjlR/5xIc2Oi0sCgjGnhybyIsl50AI2LnaGqUNUHk41Fp3o2gyHs/H72jrFHlhpRRqpZX4wrF17Gupa1/VbCvn3Jy7z1I4W3tf1pxV7vbhqGJw4AEAteKm8LQwWkRkjEmSuClmZlnWDPNptftV7WZ7e2EYTGbZV7/5zbdffz2MW4888eT69jaQKirbaffand6d3f39wbAosjCMtaa5nEBEnADMYfd78WKRxc5Pd6FWjQKRwLGdFXgpdTgSmyyVDFiaeVFaa2vt008/3czT3NnZCWLTbre11t7Lzs7tJElOnzr73HPPxUE4GE4JEIHrui7LspVESiGSMSoyTCDghSuAFy6/dePwAACMK6HKqB6zy9BAATIqa1fVXNvZaOyqWjNwUfppRpWFIq9Gw/He7nh3Z7p7e7x3uxoPpJh5762rbFWVZV6WeaNezGy9q6yd5kVZc8WwezAc5ZUHMFo3rTUiUrO37JfawOybbIyIut1uFAd5MWNSOkoZjYnTrKq/+dx3dm5P4lZ7Mi0m06yyLoxiHYSoTXelj4qKomB2SilSTTzumNn5ehFysDSkAxEGaYh69zTrL8Sw2IVynsodk+Zx0WJDJBEAAGOMc866Sms9m02ffvppZr58+XKe58YYa/14PK5rp1Xw4Q99aH114+q1vdV2emJrUxN4Wxujna9NGLTa/TRIQiAN7MWVBG/dzt/Z3dsbDF1eQDFRPgc3LcrxrCxqD9PRuC4rJRIHoY7jII5VELTiWDuvbG281d7V2XS0d2d4e2c23HdV7WrrXKNt3trKurqydZZlXsR6qVmyyl2+vjOrwC+IwLho6ENEUERGM0pjxKy1WT4tq5yI0jQVJB2EcasdJS1SgY7ilY3uaDL9rc9+8c1Ll/cODk+eOf3o40+8/fbbX//Gt4q8Kqvce4+LubQKcT5NqRkSvZCOY56n2CALXP9IAZEAhAi9x4aB7B0jInkRJYtuXRFBnHdXN59rrY2iQASTJGHngiA4eXL7zp07dV2vra0d7u9Zx8PBeDyZnTl3/5/+yZ+c5v4733391o2bQRBMijxOIlKm1U5D1Yao6fioJz6zyNMaPvvFZ584dXZFBTQeKrbsy9KpohIWrWbT1f5KbMLJ/l42mRqk2zs3r1x+ZzA4SNOUNA3Ho2k52zp54uFHH2qloWcrwgQCNJ/p1sS6jkVQld4NJvnO/vDmsASlolY3cPXSMQPOBxYTIQCS0XEQRnHQkFMBIApC0Xo8HiNKh3ReTgFJAIMoaHfdzu3dfqfrvf/2t5577vnvPvDA6QsXLgCAR7/wlPMZgrIgLQMAw3yysBduNsnT800Nl07LCwNgw8NDRG8ZEdgRE7NDFIXN7o0Nq+5IbM7VAAEieu82Nja+/e1vb29vN+bxzJlTUZpoHXR6fWWCK1dvnDh59vTp04ej2cEkG8wmSRIBYlmVQRxoqQ0pMmg0KCaL6EE++5XXP/2x65uPP8iuTowo8oaNsjidznQ2Hh3sfvelly+/+VYnTtfX1m69e302m33ko09vbGyESdSbjHNbbGxvrfTXPM+TGxEvC5SnCUkqa7PCHhyOr98Z3Li9JwAqjFXYClVWVRWSKI2yGKfS1Abquq7rUuler9cLgkC8G41GUdQjpayrRpOxczZUCkWKqjp54oQIfvOb37y9c6su4cGHTn3wgx+01koz+0LcESGOGZFRm4UW8dLiOWFhWXBS548MAOAZlMbGQhKxOPTI5IgbistyJ96lZ2QATpKkKoqm10E8O19XVTUej5nx0qVLJ06cmBbF+vb2eDwdjMbn70+2t09+8/kXna9nRdHttCfFNIgj5xxgXdUuaaVRZAzbMq+jJK1n2b/9gz88t7l2/3o7sLXKJKQ4qKgcDG6//ca3n3+eAC9cOL/RX3/4oYc2f3ItXVkBpUa7dw5Hw/7GRtROozTRcSAiXFvvna+tLYuyyFxVWGtr5yez2cF4dnt/b/9gMpnOWq20gCCvLRE7Xzdbz4o0uzFis51WHIfM3MxL6rY7RFDXNVHd7qSDQXV4eBgGOqvyjZUVBDw8PDy8szMbla0Uvu/7Ht3YXJ+Mx8xuY6Nb1zUp02gqLDrE4Ght4dieBoxIelk8a2Q2B0yERVCY0XkCYK2QWcQ3m/0gIrNHBK2053mjNC5gzSAI2Numsawsy8PhzBhTlLU2YavdX1k/ef3Gzm/+9u8JKB0Ebjpdabdr70Ug0DqIg7VWazSdjLPDMGqlHGaZK2feqOiL33nzoYvf/smPPxWv9VPGEOuwxo5Ct7Hy5/78nwtIs3Wudkmapmsr4OGdN970wpV3YZB4Ml5rpUyW56mwsxV765zLsqzKs6rMR6Ph7uHh7YPxcDRzwu1upyjEOwbmqi5xUconNe+Wd85FUYCIxpgyz8uyzGdZkiRxHE/GoyhcbyWJd1UzpmU0GnXSCJm3Vle21+nDH/pANp2UZfnIIw8xc6vdrq3N8sMoTsOosixJnIqI8x4RG2pXM4F8AUu5ozr1fE/rhZ1E9ohNHYAEPHDjzByhBpCmJsnsm3IMEdmqMkY3/HsQf/78ee/tbDb51V/7fLunPobff+78+cmNG2nSyav68tV3B4PxJC86nQ6ZYJJPC1cqpdI0FfCoRBkF5DVgAKoGZb04oF/49793Yq3Txgs9hrX+erfVAzIbW2kSJtbayWhaTvNRNt1/5dX9/f201SGjwzgKkjRMWzoMmcijFluKs2VRFPmsLMuqLMuqtN4XVc0Aa2sbUvq963eApZW2p3mtABjmA48AnVImTeN2u90QQKqqCsOwqqqqKKuqWl1dBZCiyD1bEXHOGaKtra0L505ng8Fat91Nk9Fo9IEnn9g+sbW3d4eIzp4///bbb09neRAElfPesTNO6wDFIdzVztQ8UQ2H53gqxou97gQR0SlEaEhwyjMjomFxKAbmeshNqANEFDbOzHtvq9KFoZnNZlVVfewHnvra1176zssvvXP9Vl6UpCPSJsvLWVkxAqASkSAIoiAEAFc5qzwgowJSSAChUuh1ISIUTHz5C7/67+/7S3/xmfPnB5M8koCVFpDC1lEYrq6vZXE+OBjmWZ72+0QUhGHcbieddhhHDqXBGMoic7YC7xSSCYLpdLx/OBwOh4PRWCUpBKG2ZafdcoYLQY50USELW+e1RkIlIq6uG5SyyWfCMMyyLAiCPK+n02kSxVk+tdZ6b0XECh8MB76u/qM/85M3r1wdDwcK3Hg8Lqvi7Nmz+/u7L333Ve992mkz0vhw2O12wzBuSuTzfHi+77RQs/+1kF4Aye6oyLaskwLIPFxkmJemWUQAGRDFz2dbNHEUM+R53rhlo3VRFHme93q9P/2nP3z6zLkvfuXZS1evmrg1y/ajOA2CiKLAF9VwPAKAtJN2u11lNBB6YNcwx5TSihEcCSkib8K6LneG9n/42V/6m3/+zz/1yCOjyaS1stIMWXGAdVWZJDp94fx6XmkddPr9OSNTqbrKZ7OZ9c5XeS8MfFWOs9l0Os2y7HAwOhyMplm2srYhJjiY5bPZhJ0NlBZArcOqGBGKBgiNIq2V0WEUtNPUe9/0HNd1necuDjQCIKimgBUEgYiq6xoE8qKo8+Jzn//Cn/+pn3rz1VevXnrz1u07q6v9a9dvDIeD9mq/0+0bY7K8EAQGrOu6oVTPxcECwMjimyy72cR36dLmE03YHS/eLDNwOAK0FicvNmaTBa6jNTUtWYPBYDgcMshzzz1/7sLFH/2xT2WVv3VnbBHf3RvsjcejWVbWtW8yDw8atVGBoYBV5BnZo0IixZ5Lh4XWDM5qinIP1wbV3/uHv/APf/XXr46nY4HJNJvmRe09hqYxg+2VXufkphfLrrZV4YppXUx9OcO6UM4ODw/39nZ3dnZu3rx548aN3f0D6zlpdUwYlHUVhmG/2yZwhny/FUWK262kk8ShAWBflyUBRkEYBLrTaiVR1O92W2knTcKqdojQarXCOKqqapZnRVVX1nkGITJBuHNn79d+/d+h0tOi1mG6vn2KgvDUufv6a2v9tbWirq+8ez1N2+1W1zMsGzLoCIn3wALsQbxmkKbm2fyyob+BMOiGGQQoHliBZyZPAF6O5q+BNFsFYZOfwWJnj5uH+2+88YbWenW1/+RTT5EJvv78d0oLFmFclCpQ0zw3SgfatJJYPM+3dmzGBOqwLL0ShlgAhAkQbRBEVEKgI2EYSgmkf/mL33j+6pUf/6Of+vRHnrCCs7LSpFA5URY8u9FIEZEwiEMRxbXiylV1NR1N93f3B4eH+4ejyXg0Gdd1HSVJq9UqbR2GYRjEJUsniUalVVy3QhW21rIsc85a75il00oI6XD/AJVO0zQM4iIfIWJgdKvViuMkiIKDw6Fjr5TRpIGddZ6N2tw6mXb7X/jDZ1e6rSvXbzz86CMmiNrtNGrHt27d+vwXvnTrxo756Z/+8Af6QRB455iZGvvW9F3P2aQ456TiopK5yAOORgMuU7cjcASViBDzYkOaOVAZRZFztbWita7renNz88yZU0EQfOuFl9+8fPXW/kHcCkJlxllZVj4MjBUAZ5mjZnZhsytOXZQzhb6UiNjXznsrCpBAKx8GOs8zDxCH3THVYde8cHX39Z/7X5//3MoP/uAPPvPMMysrK3Y6qZ1N46QoCra1QSH2Gjx7i1Xp8lk22B+Ph3du7ewdHjR4iA7Dbr/f7XbXkxhI7Q4ndV1HoWkjMqGOkkoHcWCIqKyqvCzW19crZ2/t3AFS3vtsVhweDphhbaXfjIC/dWunqOs4jhnEeadJIaEOosvvXrt9a4fruiiKwXDc7X+rrqtTp069den1V1955c5AtlfJOa6cF0Fp5gLC3JgJclM9n/fAwXuOxqUpONrqjJmBEZpRJ7iAtQSJZLEjAFdVpTU1yGYURefPn+9229/4xjfeeusSatPtds1wfDgrnJcgDr33SRQpYROGBOIbMq93k8nEdRIFhEDi2bNlAC9QV3kK6X3bZw9Gk0GZdbfX8mJUEYQRvfrO4M13fuNLX/7KT/z4px966CGN1ErjQFEahSDiihnXBdelLfLJeHhn59bLr7x+a+eOBzlx8vTJkydX1te6/R5q5bwMRkMiWOl2RemNMPVId/YPwATdbjdptw4Oh8NrozzPPUhVVah0XddFXrXbrXa70+1281k2Ho9NFKbCAlDkedJunT5x0tkarJcgJPZ5VdeOrc1+6zNfuHj/ieef//adw3K1Bz/88UcefeTx1dVVaFDAGpyvERYDQGDuhJokWqMsKFZsscGjAPV8j/j5wCdgQevJi1LAZIFISBMRoIhjItAogVYkpDQdZBkAtTurw2n55W++Wo9zE8cXzpwVB69ffqcmzGtLSisvhGaWVUGoSVFl2ZZ1mCaUHxpjOr0eElVZFYhnAGt9Gdlbs1uiJAoc5eOodoi0unIi5ztl5cY3B51XX8DAnW63mCV0UIfhxFaDupy5elJMR4PD4e7u4M6ed63+xn3nzt136sR2EgWhFm08QDnM9hJVZm58fmtjPe9YnyTpZo93Xx18fTLRF04+kgyyBx97Qq2mn/3Wd2wcB0B8eLhKkAStmmI06wpCU9W9We7C8E456yY9dFTvz3pRHIdh58S2Ss1z336OnV1vrzg7Xu+f3157YDt748f/yKe2VtbKvAx9HWmppai1JxSsbFB7qrx45xAqVDVKKPZ7z5VbHMzNlth3bbuMC94kEYjMPaVzznmOoqj0/uDg4NsvvjKdTn1eXDh96v77Lzz98e//mX/yT7LK1gdD67y1vtfrhSaw1lZVDcBazZFtRGzqW/NuZUXe+7Ksw9AgYlU556bz4XFEJg56/RTqfLXddWU95UntuR1GwzwTwlmeHY5Ho2xa5Vkat048sbl938MaKNU6QqG6EutItJhIhxuc4EZHMeqN0529O/vZ5M4DZ9MDPPHqd9+4AW8rk/zIH/vU86+9Lgy+mBYi1oOJokhHUBX54U5RWxfqoRKKVVa7Vowa8Pb+Lep2H7zwxDMf+dAffv0rnFVkYH17lRFevPxKoMNP/sCDTTRgy+qwqk6fP1s5VkYLsDSTkBEE5rtZNIees+aAj9rLRIAF54N9nIAGRABsOMqggEkROGbVjOUhRBKx1iIikSKioiiiuH7llVdGo9HHP/zhhx55rN1fyerixMmtrLSzshoMRkmSJlGolPK2mu/M4q2IaBMFQWCtnU6ntatVYMB750AAvPdKaQBwXiKj4jgGoKAdz8bD1SjYXl3lrAzTvqCtSVQSKjIrSq+0+0k7ARLrrYlCrz15AVtj7VUAAKEyEcXtds8UXsI0RYRIYxTq2WDf1+Unn/6hxy8++LO//Cutzd7nv/385z73ldVuh31xkLNpRxOKx7uHW6nRLis8Z0EUtKLJaNTqJrbOilm52QvbgbeDnUtfy6aX3nywF3VObqer3R/+0R/+yrPfuH7t5ic+8Qly3EtaEzMcT6ahCcoiA6WYGZkFhMEDgZf5FC5Z9lPLsaaxOXq1UKk5oHU0hBJRRFgxMQEhKEZshvrXdR3GiTHGO0HEw8PDJEkuXDhflNnwxuRf/tqvvbtbPPTQ2XYaet+uKjsaHIqIQkJE72rvWWuVpHEQmKIsvfhmvob3zaZSwCxEYowSEa01osqms84qfOSjz1S7d9phfKLTci5vtdLaOzAq0MFq0taAYRhiQIWvRJPK90mbmkIfGhW1QIeijI5iJbKepsVsGhiNjrX0DJm6rkcz2Fjb/L//vb/xG1/6ypef/aIrfbcD3bTt7XjsxBuIjQlQfDFW2njmw8OilyQwm7YJTm+mfUUt59oHt/vOPh4kmMbx2tZuVa6H+tR67+L5E3EYeajjOK6jgppIGQGExbMHAfSEwuIYhEWAGLzoI3b4HPxvhkQzLiCSBW9LgDU0E+SQBIVA8wKfZASjjPdlVVVJkqytreVZORgMHn/qA6QAvXzuc7/XbqeP9/sH42EQxkkUiPOVq7xlCgJtyDMhYhiaqqrqui6rqqmAiDRsImjSSe99oHQz64WdL0ROntp66rGHXxsPN9ZWVlotRZ0wDibZLI7TkPRq0kUr3tsgCkNsMXHLKNZBDlRShO2OBAEgRhojJdVs3EkQRYazrNXb6GyevrM3cAfvrp3c/vIr33rx+Rd6Hn786SfVrBrMZobM9aoQjShQl1U7TTbWV3az8WimZG92YbNzMjbnWklcZm40ffzcfVtr6xvrJ156+/KzX/pG6+xJeffWOkGvnQ7Ho1YQVVWVT2dhGA73DjA07JwQEnsAYfCMIuJlEd3ftT91g2k02TEAw3wTId/QxJtsAFmEmFCJeBLdCJURiqKIo6T2jpnX1tYuXb5a16KU6rTaX3r2K87Vjz/2ZOZcezAaT4vRaCcIgziOuaFYiwRBIADK0GyauTlxEBa4MwuDAFOzYSwgIwJLaAwiXvruzfZPBKdPn3r+xRd+6k9+uh3HKD7tdInIVU4ioyPdMkEQBFmWMXMWtFApUSowyoSxMkpESJgr6ytK074H6W/3pmW1t7/z2JOPr7X6f/Pv/j8qcWYf/swPPvpAt5MNijeK3Nd1O9LDauoC3Vk7FSod+HqVlOlvayX9CNcD6eWzVpGfP3M6NAryfHj7lj08fHRlHSDcf+G70gse+vATLgy6nTZ5qZyNomg8Hrf6XWBCo7nZmhxEwAv7eYoNfCSzxqUvjeTyUebzMRbPkVEafiQuszoWsdYmSUJCsyxPO/0wMltbq9evvvvZ0eHp82ejJE1a7RjRBHF/1V26fEWkbKUdACirSkSMMaiV9aIJREGotQkDQEVETiTPcxGJTOCtA5ZIG2UojiJg7ip46+WX/uL/6T/72Z/5B2+/e+3DTzxqCxeHgQljHzMqXVpbg60OJtVoenr7xLSzQSBas1FCyJpRGMsalOmqqFMjWrClnz7y8Q8efv6z/+pX/5cXvnZnbXXr+iuv/8kHzv/p++4fXH4zr7xJ9JlonVMzQmu2TpWmc/vd28Hg4P6VU2yD3nqnsqPxzcvnO+2z/b4R4SjMu+nnn39hb7d++tFHjDH9VvB2uX/j3bdPPfFB52rvwRgVRoa8BEo7kKYmADJvlUYRACZoGNdw17asx7bOXfbA3zX/r5mUtXzLMsKMoijLsqYu472PoujsmTM3buyPDgent0+vdVe3NjcVkattO0l7nQ4R8XJuNPvC1bM8G2eZcyIelMJAG60wCII4CAOlQ21CEwALO2sQY6O0cJnNfvRjjz98/vxv/+Zv/LW/8ddNGr/1zhVUBEBFUViUkqQMCVrJpC7uDA8cy0qSpmEQmAhUWIOug0TSHqRdZ5Ko06tR3ffUk4hoR8O33vjuc994ttDBjcuXP7Sy+p996COre6NHO+lpqh4I7TOd4EdPbH364Uc22b3+pd/31y9/oLtyMpfzhpLZMLX54+dPP3z65Gocba1vcBq/Ptx/x9fVevjueGC9rHY7sdE3r102CHVZMrukFesoTFoxs6urgliIPUqzGaygsOZmLxPRzT5CpNRcEgAA6q7hO80cLVTz7YMaITEjIGrVzKNwwuyd0qqBr7y3m5ubH/nI01/92osntk6+8eprDz76WBjFF89dqO2lw9Es0JoAy6KorVcKwzB23ntwaRpyUYSx6a3087x01iftuKoqAtSkxNrYmKJwCnykw73B6IOPPPjkww+e2toqy/zN19946vEnv/zFL2bT2af/2B/bHw58gB4RSFtEiIObB/sfTqOE86DTHpflDDBYXZtVVRSFh/t7T/zwD7/ymd+4ceXycPet3/zNf++J94aHeRa8euWF/+SDH/4L9z+webif7x9441YMJtVEWcYsHR6Or7/2Smfknnl4/YFId4PWoR3MXBHqeiNemR7srfc2xAQ393a+8sY7kxZyXvVDEaN2rt84eXZ1ey3KR+N2u51n07IsJ9lsY2PLGBOpiJm1VuK8Y1EN78Y7heToOI9YaKlAczQEFMwHgiyVj/x8F9b5EDOepwrUhCrzeYbek/D29ub3/8BTMi3FAzt/49rNV9988/SFC1fefiufZRqw9i42mhE6aWKicDqdzvJsvZMAUKhUuroyHE9CRU6gFUXAAuyqqmgbRdaOitGPPvPBD33oQ6e20lar9cwHPnRnf++Vl165774L999//y/96q/82Kc/pSITGH3u/IW3Xn3tgx95+vLbl6foVACiXLyeGsWvvv3a03/yxy89+xU7Oxg8/7mv/eFn9nf2nvvWd9qr6/c/+mSYGJmq/8/f/osP1XXw6isyGHWTeFSNg1C1O6mBsGA5k/Y+/cRTWTXa7nZaGECG43KSHd7p99utur3SWel1Vq7u7l+5ct3mQAbyGZS6Dk2oxdn9aUerajQJkrS07nB4GCdp6cq8zFAoDoyIRvboGXhBl5rnZ3PT1wy1a2amLrI0bIglTeegWuQATcQPgoDLeXOISAuxKaidhRrbndYHn3ryDz/zB31lXn319crbc6fP/MHv/8HupF7vpehYBDZWVipnoyAwxnTW1shszYa7YRx1Ou0wSuqyzLNpYEJiZat6NpsJgwNIAD7+1COf/iM/EhgdtKXX6620u088+NhwOHz10uuvvP7GR3/4h7716stnzp/Z2tqCKGDnMTI/+GOffPHFl378r/z45379V5585KIb7f3aL/73Zvzm8GBfg7myd+OTH//4P/hHv9heP3Hq7P2vvnXl+3/gB/+Lv/xfbV55deebL3bdLG5Hti47va6DCqxROqESesCr/RXBmH1N4oc8nEx2V2PzyJmz/TCKIfBFvb+738b4XCIDi9pbmOX5ZLIaBzYr4jvTUXf32nB0ezI4LPJHP/ABJhmOR8SQrK4TCsw31MTFcAFgJL3o8Gy82pyc2phEQAXU7DWuABFQzamrSEAKcL7ncjN4iVAcOxCvlG6yO2JZWe1/5KNPv/n25b3d23/6p3/6+e+8aOv6P/2pP/HWW5d2d3cP9wcto8labV1ACpWg51MntnZ2dvqnz7zzztW8qKIoHg0OgaUVJ61+b3A4Wu/GH3zssQ8++ViMvNHvpn2ldRDoMB9NOkn7+z/2iS89/7WDyWjn4ECnwdpK750XX/itz/xGNp1V1lkNo+nutb2rVX4rldkPPv1Y6PJHzp3urZyMTj38xsuXvUqDJB0MJ//pT/30gxfPuclAv/btaHwQdXp5VYdK68jMRnmru1061uCMWChrRqjFmwRZhkGkL5w+u5qkoQoAzHDvTlEUW51u0lu5M51OdOW9v3X9crS93dEmGJdmlKm2rCUpazKh9sAmMu0gRWm2N0NAmmMctJju+O//X391YQ/lnshiYevU8ZBEkxdUqJSgQjVv3iUQAQ/zwd3GMisdAKpJNtu/uvvaG69b9qfOnvud3/vc9slTf/tv/+03X3/zl37+lw729k+fPFPMMgAARdbaoiiGmJWF7a/2ZrPcO6zrOo6SbJa14vjU9vbWSveJRx9eSdN+J7147myn2y501Y3anNWdqOUdj6p89dyJ77z5Um+zj+CuvP3W3/1v/peNPnzx+a9Dt/ff/82/RVQ+cP+F9X5npdM5uX3CMaNOKEjGGZcVjwezEE01HJzqdntBsHPj6n3P/W7uVUGhAR/WWeC8gzCDyIu0yQa2gtoBxZUmE/kbt94eYHj/2fN+WnXb/dH+8OqNm5WghBGrYGRLr/XhbHbz5p3VJFyLWtu91atd1T930pxYu5qPw6217tqmJh1YjEElOiBgy15QmF1TmGSku/anPi45AMCjvUYUNmMsGrpds4n8YgtRFmlm6jZTkxFRhImANK31V9555e31rfUnnnry3Ws3CNz4cO/3fvu3YhNudrtuNA6cTeLI1XY6nZK1LcS6FbrKuqIKtLHsVze3AODi2bOH+wcXzp565gMfSDQFCnpJpIHJ1VESM3OQpE5Qx3FilK3sU4889uql13q91vPPPzcq4K/+V/8BtJKXPvOZ1Vbrhx75wJlz92EY1RRMLOtOL+qu7o/GQT+k6ezi2naxu2vUSpfJkE5nDirxs8ysxjowdVGEqA2EVFkVGicOoTZagzaacZaVItF2eyXCqAoRjL62dzuz1cmTJ613FTtlKBMHHRWeW89Gs8Fk0IqTNerhrLDj6cpqW3U6YRxo0HUxi1UMnpvWMkbwCKxJUFBQLwYbH20CuEzOFjpHiIigsBlgoYhIoVJIarmnEyIgEhCCn38OM3trW63QkwjK6bOnD4eDT/7ID73x+luhxrMnT7wVm1ip7PAgUgYBQmuxrp1zT370mTt37hwMhuPRpK48dVk8BNo8+uADN9+9dm5j477TJ5I42lpf21jpxmEwJvAMpbeBiWthFZg6K+pJqUqHnldWVn72H/21P/6f/8XP/dNf3rly4wMPPXbygWc8i0atmBNgO4GynrbDFDHwSopS0s5aO6zQObh9e+/61U0btJPQIY9GWWJawKo6HHTW+yBlLVRjCKINArIrM6fCra2oDQUzmb3DQyY4d/Z0N01ms2lC6Iu8KPKE9OrmytAEhzAeVflm2S7H05nyvZPrHJjJdKrJRE28NxcACgo3e0yTMk70smcAkAFh/gPHAg1cbNG5yL1RK1IGUM374FmAFoQtZhMoDRoRbV1ZGyZppAO1s7PT7/eee+65Dzz1xHgw/MrlK/s7d9b7PSOYjSbO2pPb23EcT6fTArnT6Xz84z/w7Fe/duvW7TRNr75z5ezpU48++rBmd/WdSxdOba70us0kdxQItfGCQas9nszSNEXH6/3epctvnt7crlR1/fr1MIn+5T/4HzmrHrxw8UNPPXXHXKinw5ZzKZdRNesGUk29s1ASt6IuaW1nkxIljoPJ9LCWEnwAgUCedaKk9qauJVxZhfEeKC9xZHUoDGRLxaquRLdXICshoKAV3Lp5W2m1vtqvJuNerEv0UytJQKVj5epOHOnNcG8wGu/u4lpvJlWrrgKtYxNoMpEn5ahpySQhRAZULA4REJUW8Hel1Iupm4goRILIhE0NsqERsNJKKd3MQm7CUERqUC5ENEFlEQBQUGld5Nafinafvfx079zW9n3Pctub8JU3X3F37nRB96ONzZVtCTubQcTToR/vray2fv3qG4888fgHHlh//VvFhUdPb3VX9M0rHzu39sjplYdP/+Affv4Lmi3Udbi2yklrqpArD2Kz2QDy2WzXg9AIjRcK+/39/eHP/MwvfOdrf/jPf/mf/dW/9NdWW2uTGW8E72ZKCqQhYhS0PDI12+yCLaXMRdc9gEDFB/v49isfOhzc3hj3Mo4pBDsp6lxIA4egERB0CCYfp00pXfKWDHohVbgeKjXeuTXbvfWhH/iwqw5bsbZOBE0UrdiyaAfR1DuH3O1FxtYHk1mogJ1w5tq6M6iz9tpaVdSmcGKFvNMgpBHA1cROQ+j10d6Qxw98v8mN99hMXGre3dTXZm/spkwjIidW1zuPPvLGtStnH33i4sMP/N7nf7vHOJzapBdKVX3goYf4MLvywstb/fTa/rWtrd5jjz78+5/7qgXeWF/thK37Tp+fPTm6eN+Fhx64//b+wdNPP32ws3tyc6vZK75kZyeFcI1gjWr2AeC6ri3jG6+8OsqGg/2dH/zB7/9zf+angIURFNIMnRMFiAY0ETSb+ABzSGGio3FexySrnWj65o3pYNby4IogR+VqVj7otHriuZ7mxIQmZmcc6f28XI1bs+lAVJcxNWgBYX/v8OL9j4Rxv+RgNJlEUeDZdpKwBWQL5/K6EhZUrbillBrVNjSAVcVV2W8l2XTUCVtAKNS4GgRoqDeiRQEsCOjH+FVHEpL3ExgsNghv7KRIE47MP0QW7Ojl+cbBiRPb747qL730ze0zW20BvTc4GwRPPfQwk3n7W8+vSXS+06vKiS+K82dPD1bD/+in/uh/+V/9lZ/7uZ//5le+2tGGAM6dOaU0bm1v7N66WeSz7a0NX1cqDnqdDmFc1ZmXSkWkAyOCtgbx0ErjU7L66qsvfe0PPl/WVRokVdSqyXsCpYwGrZCASEhYUDFILbWvOxQY0HB7sv/G9W7cGhZFL93OxNmIOxWMd4dhmtDKVl6WpmbOuTRKr/ZnuRVsg5YiaKex3b9yQ5Tur53cH3OrsxGFvavXL69utNHVCYnlqqOEAYu6Igq6xlhfDqcTU5Wz27eqlV5rY13YMXiriASl2TODwQACalCg39uYtlzxZlTnXWHlsT3rGtfWyAzvPk1E2PumBejw5u3b1++UhX/lW18LGDYBnzpzX48lomBY5i1j+iWc6nZKMXW9NRkcTiksnf9H//M/+PyXvvShR59KAtNKIhQR53r9flFmm5ubaZoKQJikubf9OJ2CmxSFq6xoCqMkiALtFdfWlfmpldV33r20efJEN07rsjAROV8rUUYDErGmmkQLQi1RmmaT0iNhXuppbnSQnjxxAFVrUq4+fPbK7ZtbnV5SwaSY7UwPHnr00dErl6WoRtZunF5v9/RkWuRZNi6m6eMP3HzjrVa3M6j92vn77hzup2nr0R/9I3u774xvXTfEYWjaCsmYw6IA8WHpUwFV5CrLY+/q6ZgjY+IWI9YkpEgxAAEhGhBNIRun/zfM4DI4wcU2YO975r02E9Ridw7QWj/68GOnT57fG07OPP7Is7/9u62St6I4neWmrLLheLuzsUJaTadGuwsnTs3quphWN+/c/ot/+S+/e/XqjXevPvgD58S61bV+u9t76bVXoij64IeeSJKktnaaZ2ErcTUzQ1XZSTHEqU7TdgChWD/c3a+y6eHBjhHppsl0eOAxCJKq1V0NA6V04EhXGoiIPBJJluWm3zVJtPPmW/7OzcPpyFZu9dyJ6XOv1PXs7f0bD/7A43D7MPNlHSh46iFz63Zuq7TX7j988Wu/+u8eSFbJxO2tFTh3Rp06YXM+rPIT673hzrWd6eFAVyfOnd0bDayDNGSVTaOAetoAi8HAFu5Ury/Z9Eynt9ZOr41HaatXMnskP98L1igAEjGgKy0a75o1DbRghi8L1seCflgawIYJeXxI+NH5MEe/mrbPcTbpdLu99Q0v/MjGRvnyW+U3X7qwfeLG9WurQm54mHY3A0WTytpIdda6k4N3Hnv04f29O2zrXq+LKP1+dzgcvnv7NhA8+uijcdqaTKe91RUCmFZFYgJFUeSTymfMzEVde2/LWhPpNJ5NA19V19690u9vxkknpjA0hUGDsWPlvUaFSjMoQohoUE+0sbvTvVY1XmnF+Z13x8UwQ/XwxQcfSeKDG7fKvNjLitNPfWDvldc3Hn5kd/it3cMRXb3x8T/5k2/8wVeU0ufWty+9dXXj/oeza3cGhwfgys3N/sGIX3/rzfufegTCdlGVLWZCEyoPwOh8TaaVpFES3qnd4dVrKxfve2T95Mh7CMISxQOTJ6UImNAzCjXTCO4SGBybaAb3RBzHZPa+o+gaPpeXuwavdldW372+U05ml1/6bj2anDp3yqx2JsVsM2m1hTpJYtlOBoebaWeltxqvrjz44INE9Oyzzw4Gg62trcuXr8xm+a1bt7z3J06d7nT7SDpM0vEkr2pHyozZWUQThd1Wt5+0umHYiaNuq9Vut9NOunlme/vUNiIO9/eHd+5Ilktl2VtBj+QNQgAQABAhkyjy3ZDUdBTPpmeVPlFyujN0UVwN8t1Lt9YeeGRCtHbmXCfuXXv7OiQt7HRPnbnw4tdffOPF1zYu3q9a3aC/+eK3Xt1aO722sp5NpsB2tHenGo8fPnuu2B9GXmEpPvcBhoZNKLptws7GVn9tM47aiQrvXHr3zmuXotz1KYwUoSJGYiQkPd96RgBIHcnseMixlAfJMet3XKUW45eWEmsaIJclUyJiBEa4tbO3v3/4xsuvmawe3tyJV9pnP/z4bjnr91fbSfr6u+/4RJ86daoazcI4vpZN3njjrQv3Pfh3f+XfnLtwcTKbfeBD3/f4E0/d98CDFx58OEzaNQuZIIxbUasFWoPWGEcSaDLGGIMCXNZcWRKIk7Dd7yqjgzjq9TpFPmsFkeRVLlw1LQECEUssRKStxhr9er9jimLy+hvtwQisdKdFd1p2emmYtIq3r47feOPM/We2ttbCSTa6dGVw+e3zD5zXtv70Rz6WeL+3e+vcqW24c6e6vTu6frO7vtnpdOH8xZWVlftOnXrg9LnRpas959eVSpFCMh5C0d1o9cwY1Tu7+29evvLuuzcPbtz59he+8qs/83OTa7cNIwgxNpN3CLUS3expjnPb2GzkK8dGpdJCItDQ/NVcbLwY6njcqGIzEK1hkjDP+3e9r6rq1Zdf73fb9XhmHT/x5OOVyNZjD2VvXh/uDMN+/8MP/BDkNQ0k1bHvrkwDJh29cemdp19+9U/8yT91/Z1rp89f7LY6rLQozaR0FCIGoLWCUKM0hBSpnSgCIqWUR++sA+BOp7u7f7PbbU8nbjfPwXNZllVVTfJypd9rkQqFIgkNmUpLqam0vhOGcPndXlHX2chaa+JQgUUsB8997fvOnJ7u3pihX41aUWY/8tDF/dvX8uzATiYl+nz/ljW0d0dknJ9ut+9cvZwPDqjbfu1rz8ZG4WxWTN+2+ThWirwHrbDdcUq9ce3Gpa+/spsXMYAijLtt0qotweGd4Stffe6D588w20BpjYqIPHgkAAJezG+8dzr4PAw5Fl8sHxUifY8ErnkbCDRKBiLe+432inNWPJ9a32qH4QzBtaPH/sgPffkf/fMLD1+48BOfgP3x8J/9fqp0cv9FkuARE3/ui1/A9NeefOqp++5/JIlaaafviLzWqDWARtKCDSVCiAStBaV0EDCHpa0yV2WTwlX1YLQ/y0aTIVV1PhkMPMCsyOOqYAksRlaFCowwQWC8ppycBwezfO/Ku7q2/fUVX84YfDb1U1d1KFSArU6npLoGCipi67ZPnjis8+6JjTqrB6PRw09/QDxbL4+tXhwejoJ+qx91R2z3xqPtdpioIOhrKktTS2Xh0nD/Wzd23ry+O8ihq+BEJ0lJ14yRxzaF1peHV24M7uzxRgcAqZkZbZSwb2ja+u4Ff39JSMOnPzZWZH7+sYG3xz+m+afpxjm5sm4VSyvd6q0oJKVUztA+fer8x5/2kYLVrjsY1KEpDkYnwiDDeH/vnT/903/u7AP3M9LmyobUXHkJk4SJlCaFWoCEl1t3QkLkvHdADiQXl7m6ctZ756tyvHdw/cY71pWA1FvbzOsqZ9sNWqIMBYEOIyQtSosmJRAIwTSb7dwmdjm6W9ODqJ3aSIftFSPR25de7oensjCYWddZ2cr2DsfjevW+83eu3Qgl4KgXtzdv3LoVpb2N9c4gm1qyu6PDtfMnvBuHkT482L3wwH2zg/2rOwc3dvbfHk13kOzmmh9mB6OiKxCQIpAwjFeDVmDxnUnGte10OtZBgrr2zoMwimOP2rw/X3++9txMfFw2Z/DSKuIRvefIoi5y6vn26c0rqQpmUDBKWRf9drS+upZrtLU78yM/fOnNl/cng9CQ7cWDO4Me4cXHn3iifSru9bwhy6LCmLRoMioIPQgTEmrwwiCexSMwA9dOGmaW0lESK0DsSCh6LU4PdtYunNwq6uyr3/rWYDB44NHTOolaURyQQmFA7xAUulRFKcUAGvb39J1DyHIbIteWPKIDU+KsyM+cvcCBmWTjNEl39wYb7X4FfOWVS9vb28p4xfrWlVv3XbzwwksvHh7cPr25actqcvPa+RM954rxncMkSTLv3zoY7Q1nVZiqzbQFBKUNoyiTfRVGiBiEOooCLdANQlNk7KxSqqxKpU0TVSiFAQb18b0h4W40ZKlMzYRGkiXhYD5GGo8Jafn2+W6lx9KEtBXXeRV2UgJBRWVZ1oHJyXTbiVrrFQLrF85fT79dn1m5kY04byftXmldp79OWlW1i3Wkg/D/V9d/B1ueX/eB2DnnG37pppdDx+nuyTMYYGaQI0GQhChGkZRWWntXXmlL2l1b1Jar7KotWyV5aXPtKpccytZaWtfumhRpikGkCAgMIBEIYDDA5NgznePL7934C99wjv/43dfdAOlbU13vvXnxnvs93xM+YVLVqkhRaRZopU+pLZoQaC5lrYCpDrEuq1AGF4DVxJWVqxtFanlx8e7BmBnSJM+NLHds3s8hIYgC1cQd7OK4Ki9f40vXzO7OAng1mayLThuoKonDLa5cb7DQxLI/HechHG3vts4IncmImF1V4+Fu1s/33nw9HR5OO3q4vT2w9vm1zdGly11FVicV45vvXdmrG+oNMlNE5ugrRVOasu73bWSIkRSDYgpRG+xinhUpGhQRBaiQGg4oSETI+AO63wCAMtfIf6DGv2/WdC8N8tzp/n6MH6j7mWVudAmIR9OjupkuL/dTELTEzIlNQSfD6Szp9La3b+5duTrp4MbZx3ubq9b2OlobDgzIUbIkYx8n41l3aaFiL62RDYjClmIqCiEKt7+MBrRAQVALGUJhjMJNjADc6Q3McLZze7tI+3h66HfL3auvHxzujp2beeenvqigNyq7R0cbJCl4Ho4MKgpoGBPlIZFq53rR7+SGuBquDNLYHIFW3ZyoGe9sbz28kBZYEZAvj/TaZsGSVLWq+cxgyQfvUI28H/lgB4upyerSx9IVaWoRqWx4dKg5EEdhcb5E6miC/qDIu/lUBAg5CrQyqd4FIU8/qNVOMhfclntn6AeutHlEWluvHzqXDwbvXu9GRJRq1QgEj0SMoowOzFt7+yv9hbW1DRL3zs5rJz7ylDGF6nTyaZAiUUaJUgLEzEWnqwAb78m0kmptcRRRhEACc1SKvYBzvpzN9g6HO3vVpCGB06dP1yGunjpxONx7+dVXLl/a1QqrYTmLB9msskej3Gq70j/y9bT2/c7S+mYvSxs1PJTxmDiQLYAbpzWUs9A02UIv+EYEDSPXjcrTqZ918o7fP+oQjn1F2UKsGqqrwjZp0N2kE0cNhMQ3aLqDuj5cPr0wEU8ReibBBHens5lzuuj0s5kNYiAYJd7XXml2sbOxaXLrJBhjpGGNlNoElcoYlVEa/tJxmQ8MmduOG/H4E1r4PrWxFPlLMfuhvhvmMDovwKlWvW7BEIFDf3Ghv3bComm2t2LVmMSGbsLGLuZZ4c0RQJZlDsAk1qCaz26IgKj1H8Nj7ROOMYIvHVH0FEJsnJ+W4/3DyeHUxXD9+vWAflIebe1u1bPy5Obi1q3D5z/wwVMfWOEbdwqipRMbsNL73uW394+GoVe89O5757N84KYL6G1iQVzFsdF5luaAKkSZsmRJGpqg06IEVoPBwcFwaXFl79LlqmMs0XRWmiQPWGZJLwwnurcqDlXaH9VR0k6jEIzhqq6qJgiqVGNIG/YGMbqSYsx6hQJBEKVofXP9zENnILfklTqoWSTNFHHsBHn/7m19rwuWuUU83rOJnKv2YJwP8iUgoBMCZgDUgq0eJyMIoSji+VCfDbCKUUVHgHo2wTTxlI0h7eV2oZ82l1565+UX337r3ac//Kn++vmFhYc0L2WqM/EUUr2M2pXc7w0cWAfkk7TGaK2GUOngMDbMIUbPQRRggaYkGdXOsHTI5pStLC0X/V4nzQ73R994/dV/+8YbZQrnugvPJotn3Wz4x9/bGD1eyuzNuFMe7AxiD3aOzDtbgVaf/sW/ffvyFbh4a1CWgL6mkLHKjjhmSpQSlpwsNUFEIkmGiKUjUqWfzrRkqLq1TKZ1iXxeOlAy2hTYjTTVC+kWhJjbhKMeew5Qo2IMWkIHHfKUdXesJ7kOA4m94IQibZ65q82tb12+E6WqZk09rLEcDcd5aZIhX40HGv6qEv/4Ivv/04S1X9K6xD8wDCGFAKAEW3KYOO+8j03TzzvCoZOl3cy64egrX/nKzSvvnzx5mpRBrTKT2zxPs4wUJEYPdQQBrz0DE5oEVPBBs5fomZkjRAZUGSmY1U05LSmMe6mW4Ej5Mgyb8kjH4GeTev/oqc3V997G63fl0aeTn/78p97W37EL6U4MN+/e/dTPfXqcu7ywq49k34Nvq2566kPPLh+Mp831JDLkFhW6JliMMbaOtkJE+GDV3ArhB0FErXWMkVnSNEXSEVkBMbDKsllTpwvdElkAiDQToyiSoOa+cdrk2tjUu3HlJRdRSTKeTr/zne9+98++dZTkgtI0I09V03AvJMUY9/Lmr6j171eM9wlpD84kYW6j0N5ocy0DABRCQhSMTMDErWhbvb68keSZtdqC10Bf+8bXtra2ltdPPPWhD3eWTqjOQFMuBAK+XR6UmTZkkBRGthoTLVYiSESjvGOHgop8jI0PzJLmRT867xqlwYXYoKNMrWSD29dupom68/71hxyeNvKFh5549pELs/Hej/wHvwS9M/ZPvrxx/ik5vF5Ws4vT/fVPPHN+88y13/yN9Oru0nAGzLFypRVAiBQhzt1wEPEBiEa76FWt8COR8j60iHdgRFINS0x0o7ACtonF6CACoxJUAIxCKBFBEZEkWuepDzPHHBTpNPeOL13buwUwSaYmTapyVgJEBh+bKcB0BpqO0anxBwM2v9sYeF6tHceMAfG++KYICooIKCGRqBAQBXzw3lGIiSIfZSGxKL6bJ69+5xuX33/79u3bT/zkT62cPh8wiTpFpUEBEVijtSbru8Yo1QoPS3RhGtkBQDUqW7nBum4UYGFSx240GiUuTNys4WY2G+X95YN65+2Dg2jN22+8tnPx+jJkH3j0PO2WX/7Xv7fy4cfh4bPgFxYeffzitZ1Okp8+dep6tXPohtXuFdrf2ixrSwTIrNBqCwAcAsR7qpX3td5AhGPEOR81CkIIgSMYnUhEUapCkMQcRKd63QAAgkAoSqNwu/rRCFqxVrYmoCyLM9OAd4SlZ8eqmyLW0jRelA4CooA0ACvVUER/f382N4XB+wGD4xn/vQnxA+8yzrcxggiAIBKRBQQwcnRNbOqMTJFnaZpxjGubK1de+XYCzk2PNk6eWFjbENtpIjFpa1MiUIQonoPk9RoqZGxExYZqL7WIBw7M7CbMIYoPZVntVzOJUWtNvrDWzGI1HE6/9Gd/en3n7lvXR/0+nO0NzmyewGGzdXT4qbMPddEzEzQOtCo2Tr37zqV1axeW1x750S++89638dat9aVesltCmLEmx8izqLidj7Vx+oE6WVo/HcQQYghBGDkCEJI2KBRRS66HGKeKO92sERYERAVKEC2J1zEyodaiTBQjmJhA5Bhrge2jYSNmY2Ozt7V/t2582USEqCAKVFEAIBD+MBiEZO5S/+CMCgBQjldrcuyr9cChbK2iIQb2PjY1u0Yzp1oViQXCQb94//vf2r3xvgkTX45Pbq53+otY9DDvU1IwKREBDhxi8F5HwaaSuqK6lHKkqomqZrO9A3c4nuwNy4OpODBklFLKks3U1taly5df+8qXfvd3fu3/PSiSlZXuqfMaCU6uLXz6E8+hbk4+svHYj318pWPU8ADu3IGFfr6y8uHPfUEXy1/+g69uv3JxfWnj5nuXx1t3oZyBpZAbsDZVNjdJYizMneLuCV9yS4mLIjGy954FEVGQlDKICGCDSEzsvi+lkzhsex4dQVghaK20aQ2TW7ldSrVoYk0NQA00quvxZDYo+ku9AQA0wEHAM7gAk8BT4DrIX82BbxMm39PeeWBYTFFAkbAIHdvlEEIEJCEAdk2sKiOSp1mRWBJovHOueeu1lz7w8PpCz1gVssyMy6pgYJ2BKJaggAhRAQNSJTscGwi1IQ++Qo6T6eziu1d39ifK5mUVJ7Np0cl7vXxn59bFt9/6zLMn7+ztvPTq5V/5p/+lq5vf/sPfP9lNH3/ioY1oR++//cy5zY9++HHI49p6x06G8t0X8MJzWSeB5cUTDz+S59l3v/od1W8WRqEbCEhBCBNXAxmrEic8rCe5Th/sf/j+ixWbpq5qh4hAits2CBWIElRHTT2FmFnF0RvSShGLAWRNBEojAGATCSxwIywTSIvcc2iEM2PFBxJOSRMAAoQ2IoaUkIopYKnbkyJ/5WUmACB87Oo6H1NFhLatJmQEAWDgOZwfIQYfG2eMLhKbWBOdnzTN669eZK7YTxa7m6c2l/q9oqpntQ8q14osBCLxHF3tXAzOJ0MIDbkmBufK0ju+dnfva9/+7vfeulKxnZW+9mUKsABgAALAuUfs537+J84+//iFDz/96suvXNu9+9M/9uMfP/XIq7/55efOPrL51BPXr7y3ffNGCrC5snb91bd2B79/4YPPmnxB52m+sPhk/tTw6Mp6JsbPpJ55EWtTH3kSSrEog5TH3A7qcM4uP85AhLVrmqZpbZ9duCfNTGj0/niXF/NaouWIDMYYY5SQ0gopsEIipSTUQXPJTidoet3Dcjqrq8QYpSTUdS9JciAGzeBdlAgMERAci+hjfZ55zKS9nB5YzqDMDXiZBQCQVUv3i5GFhLRSQMCsEH3jlUDR7SSKXN3UsxlEjibp9IuiSK0hV89Oba70V/v7UWaT8XJvI0SxSosL4jmGgIxcj42IK6flrIyB90az/+7/+9vv3BnVxkyaWHrXATjfWTylaKFpjKv6pnvhiacujQ9/+8/+fHY0Wn/kEZcXv/Vv/uDCIW8+sggHZQzUKxZW065ZWUtmrn7n7TopaO100Vsx60s0w06Gk/2XCLUnnaDCABQEFDUYqtikkszTCUalVLsrDq2wm6AguBAEkUHKuiJlwCZV8NPaoer5yMzOZgUjGGNYa02AGNkqg4YaBwGUFmO0qAhEoI0wxhg1SKYpABMIgxAACyhD6OG+HvH9CAGCyIOGd/fTY/sS4wjz/gtaXjNzQJEYhV0TXQAEUWS1MVoZY5Nup6tSt7QUuZlOx4Ned9DraOpd274xWNr0gUhpCp4EjEoEPHM2ORxqtE3tRmX5h3/8F+9cHh0K1OCLblaI6TT+1/9v/4e3v/Ll29978Zmzj1wfxxd+70uPffSZ6XiyN6oeOnnaWPvOxVv/0y/+bLl3ODzcS5e7cVibbufG999YfuaRzz7/7JU/+1ZyqrIfW4PMcrE0OqwXnv5Apc3Y3M5Hlak8cgQJKrpMsw/qXjnGAoqOBWeZXQxBGAijQO2cC4ERxgxX9vZGsUobr5PUpCrJsm63izZhhQQo3qEIKLBoLOskeqeBtdZpAsChcYBsgQ0HBQAQCWDuJyMSISLAD8dsHp5WFBzvf+TBsN6bPd4/kBw5MISohFvlRWNMkiSJtZIV3Uzc2ka1d6WbGaWUhPLkiXNXbl+uh9uLq2dc7TEG4RBjGB7sFp3B+MjVdS1AL715+b1Ld0+tryRHE0TV1aZA9fS5ky/87q8Xs8OVrjt1wmbTeOPKzWv72ysnTjzS6//Y009/+Q//4OOPrIJyjanWV1eHZXl0dLDVNNDrbJdD+sqXx1vDemu0PNiAR8/AYlpsbEq/aBBmqOPN3Xx/qOoGQ6AQUgwzmnsDK7iPc2GEGCXEKABkdGCumjpwREXNoH/t/bfqrubKaV2R1gGQlc7zon0+GSn6oCSYoBOtUkuNRq/IWtv4xnEkZpSoGA1A3foQz9nvEUARgpZjVgQQPrgJa2+549ni/eUKgGJmEZZj7xnkiFEkeEsqS9I0MdZaY5QyWmtdeYhWp8XSwd1rKulNjm7Pbt5YWDn98Obi9t2rS70BOFCk2HmLMbUKh1PjuGa5sbv1h3/y1UnlOp3uRx5/cnRnpy/y6InljYz01g7I5Pyp9el0aE3nxAyS4XA6rLeL5HSDz5Y2PfXIjYObj1x4mFRKewfF6qJ9+IzqFaNpDZ3sQ2cfv/X65bCzrVd7HjIZFLqzkJ7VTUBt8oi3vG901aQxmMCBjk1kWtWE+fRH5p5bIFapKKF2DYOQxm3Ci/t7fb3UjOueTU1SJN2u6fRAGxAgkUgBlQalWxkGhWi1QcNKWyYVEZUmDkET2Vaju33FzE2RGX7gnEVB/GEXoHv1ydxvSyQiceSWfUhEwBEic+SUVG6SLE1Ta7Q1pBUqJYRWFcrorLuSddfqGPO8N52Ot69d3Dj9xNHh/s1Lb3YWN5YXlspQjqbjUM/yo1rGo2lT/9GffqlS7rkf/eiL3/n+yI/TDNfSznjr9tpS/+zZE7la6qSq28neT+h0qtX1O9Vhs7Cx6W5M17ak2Ow/9A/+9nsvfrveLo3q6IcfmnzoYdXtFXcnJU4ODl3/zMn9w/3Zm/WJjz0vJSdpjxIT1hxHKEejyf7drEGP4BibEOYvaRKKsTUUAwAXQ+1d4JgoYhAXA2qlE3upLm9VdWw4zFx32aSdXtLpUZKRUsComJkYqSFUhoxFo1CyJCEnymasZ5gkNhFhVoqSdil2XGmIAAGSyH2vYzwW4fnLYUPEYx9PCOIF22sPJLJwpChasFt0MmUSbUhrIoVKK6OByGBHKQWm11nY3N++tD7oQmi2rr/f7y88fHL95Yt3vPdKQqJoMtmbHO2venXj5tUbo+H2ztGTzz1SDDIs4NtvXfrY4+t1Hla7i5Sk0ElHwypN8mkFZz7/yW6dYfPCYK2//OEPwtD5m980mw/Bykm9sHbr+tVHzj3c+4mfGOeMtrM+sLeGl0fNNiXlm++8Pjb44w+f6SqLY6+UMmnXDxbqXj5LdCjRIaGwiigigKyZlFKMc/lK733gyCBKtXxXsSbVxry1f3AAlDchcUI6JZ16ZQBQm1RHpBAII6Eh0q2Xm0LSpIJCpS1oI0qjVt57nWmrFYRWMx9QBASAEAV+2FN8nhLpB9qye58AAK0sMRBKEImBELs27Zi0k2apMvPKShNqDUoBYXSqrCMGhbYXKd3d28vJWYy7N6+sPpQtds3+9OhWVS32+001EW6uOP/KnWtHVb15eqmezN548+uPPvrQB37uqffffr0kH7rFYQyXwm6qg/eYsdG37lTj0NtciiFe/LOvZ0xrZxZ29m7u/D//W5eZcag7H7zgQzPQfYVZ6dzS8lnn7Lj2Zz72QepmXmFVVSwJ5FkEgk5ulheypUV0zrvoXSiauQ1IUCyCihUAt069c7i0ovnFT+LZ/8V7F4+mtVbDXrdrbMZkojKJTQQUiCBoICWkEJUhzWi01gEZUSljSZmIFBlijFpra62EipBaW7p2eMbygA/8vR30vTd+KGwPbkc5cowxepcZa63t9/oYAxFZbVCRaBJFASFyTIwFCdp28qLXOXvhzW+9d2LRPHb+/Le///LtvckHPvnjOPTX7+zcvX1tvLfdSfS/ffvd/Z3Z2VMnltZWb9/aDiPYvnTtuaceW11dvHXz/YXVNE3MkfO9RBH7WfTppfehN9g6GPaiOTlYvDs53C9sstjbnJT5YFBf6N6JR0VZ5IwBGp3ks1E0trN+4YKXzf3p0Z3h7sDBymKnDtMZNaQDpaktMtF2GnnmXHIvNzIdPyfSDhhjjD7OHy4EIKrr+v2tu9bD0bQKERQljQ+18yaTFOba3CCEoBQZIsVaW2W9iohoklTZRBAFQWBuSXM/zwnNlXUQ9QMwqfuTUAAganuyeK8qmUdUkasbo1TGCFHnJulSKk1UaRqVqkkRtWkXTQAtBDhESw171vlAnz515nNf/dK/tT/55MlHPvfmxddf/vofX1jbODea2tTudPG7r78EN+A//uufTor1f/dnXxtxvfzYQgT1e3/yrb//9/8nn/yRz/7pl3+7V2xESmqmYXmwtJK/D9OFDE8/1pvtj/vLhNPkkt+3ZnnCPnNwevX82Pt6f0p91VtbPPR3QzRsVLDaN0aUyTLl4+7B4a40NcZAzGE8GYVZs2BLNdhW9fI1jsSUakl0wwEkCJFHqVNTM7NEcTaCRVickHnzoJHa3AU/66Vpjs8kcq6abU448gEtLNaYMmWS5B7JQYCGjY7KrbO+afLxwPmaadcMxp3Yw+HadOcjLHsAdyir2CcSM6JZjAr5h3wsHjxk91PiA1gPAZljTplRIRpjtNathd2xHc4xTKG9q0MDQojS4vMuPPZo9D/xW3/w2x987snnnvvo9Xfefu3ltxaK7nA6mwmfe/wDH/trT29tj1544WWZuoVM9TtLo1ndWd/4xh98NYbRf/Ff/M8++lM/8+1f+63F7kKnZ37jN//7u7oHnW7HLMBiZ7K2ORpMA7Cj5Mbe1S988JmT5x/eOzqCsubajXZ2uhtLjU68a/02pVt0OqltZtOmnNTTaWhqijFWDQdWSiVZ2u/3p53Dqq6DK20kC5goIhVZWCKHsnZlnW7kgPqgHmfLm9997406MAOMZk3jYXdYzpJ8XHFvoVe7iAklxnqHCGCV1mmWKJhMVJIkDClkZFPRzrt6vjix1tpZCcdDjzDH4DyAu3owHz5w7OAHAtZ+fI7XJ6V0G7MW+P3g5XcsAc+UmOiDBkyNxeBKdo9++EMfme68+MK37ty58+wjT5x4bPNo/0iSJWOpVPil3/3Tt97bFYBzD5+p/ZS3DzZ7y5uD9Zt3bipT8EH5P/yTX2lY/YP/+lfcnWvJ6on/zT/636489cgf/eavPX3uIS3hD7/8B7/0n/4nnayH2dfWzz8cya5vnCEfq4PdGzu3MoO9zgqyIAv7yBIUiCWtbDKufTObiQsSfIyRgBKbdnv9yWJZlhy9WMY8CgKYgJEhT7IaGmCcNhUbM0YXjb98NAJMRWQ2i1t7R9NGOOk0QMOZ51wlGjUBASsQUAoRQ2BrM2NMCO0KtNX7ECD0HNMsy44scCBEmAt9AyL8oCfCcfa8d9r+ipgRqGN1RyIySuEcTNf+G5mBBOLxlzRVRcxK0XBSFon2oZ4wnHj4keeFX/r6t75564WUrE06xcbG1aODt65e4X3Ks6KRuLO1/ci5DeSoVVLvDz/y6FM+DK+/9sbheLe7sfGv//mv3LpzPctUPbzT3ODLF195+uzaq6++cunyO4mil77zXamCm7gsTUkbZszWTi/3+3fKQ7+308mLLE2Y0JXO1XWoK3aVuBDr4F3dFsMooJRK8wwvnArjcTkeTYfT2XiWOcmVIqKj6Whaz9I02Z8excSWGK5ev3hnBoVVShcjP9u6s729f7S3stbtLMaIVpRERuJU61TnHMTHyte1IWWSBENiLOcdKiL7JtXR+ypYa7tZoqqpQoNzeD6wgH7Q0uxebS9ynzLzQPAAYK6yysICqPWccvgDxacA3/tCliQxwQckMomNBigtJrNhYNncOPsjn85uXbluVXE4K7/89RdvzEApONdZ8Zb2DrYubKwJhtXlpSiFmP50Wq8vdLOsIixWTy2de+rx27fTm1fe+63/1z+33TRgfO3bf/reW++sKPz1/8s/J0kf33x8786dXt+lPgiQxTxbXs5rnbtotEKJ4L34GOqmmU19VZbjSVNVMTg1//NRoTJZ6k3TLdKlUycSJJo1PC7DbFbXtRXZu3wJtV1YXkg7RdnTly5erQA2MzwKTeWZAKQuAVmQgo86RFdW2gDZTBtSpFRW5Eo1oyZJEvK2Nl4nYLNEGc0ETQyUUZ4oqhiFBZFbU2WB+/3ZD6W1B7d8D863gjAjUpTEqHsxO06MLfD4PtFGAKBpDOFsNrNFMq5ntastQG6L8mAmTMsbpzHvv/zC967PgAA2lzYHOrt+tNuwgOVPfuaTccaXb47ZLnQWlhmnF84t705v3RkeuosXh3tbm/2lns088tH4aHr74Od+5K/1l9f+8M/+7Klnnn3ysQ/u7ox2RnvWTRe6C2V5iDOb5qbX7QAABM8AHLwv62o89VVZl1UMHqIEJUopIRRNYBRF7SQCos5SXXSSlUVhtt6PxsOzmwumDqpxRZr+2Cc/9OwXPvGt1166NdVvX7teX3MGgMqj0ExGQ8wK2zRjRJyB1soKAgAnGlOjbWoQO2WoGaYegiADYWwhNcCZQQsQMPIxcQmR7uOu/nIm/KGrrn1EYQagKGCSuXvuvYuQ4Z5huTo2yVOeu4N+CKF0jQNhhKasq8NJPJw2TTxo/PWdy3/+xnsVwKoq0Glphh0Ti5Xexvriay+99Ne/8DcOZuPdunC0Eqa7jCc/9rM/OXW71/dufei5nKZjty86Tba372ZanVs875rwqcc/VSyvZ53BctZLpvXOrTvDw91BlqXIXFcTCJooVUoTGqS6mo0ODmfTscQIHEUESSFpVBSgRZ1FJIXWcpo4pRyAj6E2nCxsNuMRlU1S+3o6ff3tN5ZOr3/2M5+4fvnSI+vmY09MkqS/uXaqXyhuJjUTKiBEzxij+BDjsXbO6tJybsVaa1OTeEhYTJKoJGPyKDFVkADUABEBEBQAwwM7zweffWnBwAAPcgPnQcV50gMAOvYUv/evIDAzylweEgVSkx3t7bMhpTE2nkPAOkjtxAXHvFuOlx89v3Tt+vsXt4g0VE6ZCYj70R/54vDgbr1Tbt/aAd9DXGRZrOt6Msv9SB9SMcWlR06ft4hXf+tLd+7ciXXVUfbqpaP3L76/dOrkhZ88W9+e0EJ/fe10Aun+jTsYrfKWJPrUldPZuK6IOZRlPSu9qzmE4BwAB2ENRlmDCqMIx1biQFGSYpJ4ERHBJLOUl/XMdgtr03p3b+PMif2j/TvD/UEG62mdQVwverOIa4uGISZpHoQRUSMxc+OD1saxzKpqPC23bt85vbGy2M91YjNWCQOQanzUSgFyQpwDzJDDseGBQtCh7cAeqOznMfirjpqIACLHaGDeYBKRItVqB8fI98IMyJoUEdWhMdYCR984N55xcJngcDSrJ9Od6aizsXr26UdPXr58++oWIShC1mCtevTJR668V6fdwdr60u3DqSYPEhSxtcYsL3Z9WO+pWVPbXvfc2YXDm+9S8GvaJEejjVGT8uH+X7yWPvlw8VgxdQcL3eXOue7B7v7IOWtz4TFKjMF718xGR6PhYdM0TVXF6BGREUip2I5XSYEih2itRasFSREiS4wCHDNtNYLEMh/0aoXQz09tngoJFte29/cOF5fXu5hOxruD1VMRvLVZaqxzNSES6cYzRIxpwkCBYHt7e+e2M1mqe73WqcdmHXBcVbNBkmsAjIAGANEgAIBuWYD3Gud7nj33sN4/cMgYGaK0gL/5J+G9z7mHPxCIbTHJzJNYZWir6YwQV9Le0dHBeDwCo7bGhxXK0w+fPzjY73ayTjc9Ohgt9zIuCluob7/0/Y3V3ubm8mAh19f2iqQaDJp8oDvFkd95beGhpYV+Ahy3X/3m9tsvZMsGK5yUk5rDzI+gBC0NSXnpypv9U6dYGoU66ZKOZjwccj2MIfi6YtdMR8Px0WF0LvqGQxQEUGSsJSJmCOyJGRItmgRRRJSAiqJEIoAQWQ0myRFIGTCouVesPLR5cOn1QFqAVjfWYdZ4jaBtZjMiowGBW3+/QEEANDPHmMZZOS3Hw3Kqfe3ECKoA1I7xDfgCjkU2BRWCILf3WUSci9weJ8Y51uP+BBnuzVFUC+t+oIkGFIg+CB6jsiXMk6eA0yrUpdQNNcG5Orqqrt3uwe4IwrnHHj0YH+pO75PPPnfrzcuXDm7VxgslS8urNul2uoPucveo2a/1flQ4nO1Pqv1+rzt7L1N7BSd6kNo4GsbVpY2NzdF0plHrafzwpz422jnynbx4eLE53Hnv9mtLRa+fdxJjRCSjZlhPg/O+blxVjoaHw6NDJayQlEaQuXMwRCZURmmTWBbRpEiQWIhFx/ZJAM8xBLFKMVIABKP2JtO1Xr84/fCV7fHw7l6++RAWRTQJ6tyjJdQRAktoMfQYGaLHGADAaq21nZRlOZuiyplbvywgYQM8ILjD88uLRCLeC6GISJDjQ9PeZ/ceOJcKaWdaBCT38OHtEZNj6VQAAGRs0T+RA3MFjcycqTyWrhxPpr45asqd8Wjz4TO62ynrytS+Kmc/9vnPGvkmM5ztLgadZKz3t/d6Kezu7VTkJnWltV4uYHs6fvvmVr64HAVSwIE11xr71dff+vBnPn3mkfP/+tf+zf/67/7nBy++eun2jc8MnltaOiV3tsqd/TgZJonNkqQaT8VLPZnOJiNf1vV0yq4xVidGAQASRpAYXF1WykQggsidNFOMKjJyi2pCABASbbT3TQQiIEKlFB4eHIZaHvrIZ19769rezo3RpE7W1yTJGVMJKYgGJQAEsUIMCkW1opfOE+osySehqoN3vvIBCMhHMMwJh34KugRpfzALA9xvsJhZBOM9Z+q/6oGI88wJ0JrOhhAUIVJ71TGLtBKRzMw+xBin1dQEqcuaqigihwfD/Wa8cGJNdFLFGIMQi4piFHWWBzNXa3bs+PD2nZiE8Wi3jOXOuOqvnr709qULp1ZuXXvv4x/96ObZp13Fs9HoOy99LxRpI1LH+NaNGyeffOLf/vGfbr3y3uqJjZdffKN3Yu3c2kOd/tm9re0rt26sbg4cQl9VlfflcNTUZagrDaKYOfjWMdBqwwAGyWhFyhBSEgCJQYIgsEAgAkIPgMw+BiaNghJRiZWGpWY4c4ptlmR5CG6QG4/CEUgrCJrBBggkSiJKBAwskYmU93WbzlxwTeAYtaKEBUVEc+ynSpXtAogQhH/IUzy2mPhjEnv7INT36J0PtgExSmvp0oZ5DvyLsTVZav+XczXVdWya2tc7o/1be9uz0BSd3vLSqta2Kps870yG0zzNXnnllW+9+Mblu7deufaOWVALawtZp7Px0DmvklNnzvc7/WefeXZ5/cSP//TP3zmoFtbOjRqzO5P/8p/+6lKWrPeKn/xP/u7pzY2D3b3PffLTZzdPv/nia8898VwC6ZV3ru7e3IfGrq+fu3XroHa2mU6b6bSZTMKsUjEmRltjiEUJCHNo6lg7dp6iGEELJCGKC+x8qF3wjXd1cE30jXOudW6TIG7W+NIZSZtJgLoihMIoaErNNfuphAqkfYFH56V2sXHBNdH7GF0EAFc3oXGIqJRqGZcuMKBCFhLppemDotFCoJ1zD4YNjiVC4H7T9gODRCKSdhDC7JxzqEB06x9yfP+xiEiIITjvPVblrG5Gtdse7UoDGyvrRafHnn2IlJhZ1VhrGx9Mkv7ML3zh2s72h5/uPXTq8ZXlU9NZVbnJL//S3/jm1799YuWUD83RcPsjH//wV77yvzx5anPn4OjSrYud5x/79Ec/4Y2Wm7ffuvTe+aefWPrUxz7byG4z7n38A0815a/+z3/52RPnP/LBZ31dnhrkV959c9gcVLOynEwIhBQYJEPkIfR73RhjiGxtmheFMQmgUqhiDBAhSmw9ahUSKmACxFQBYmCKFINEL9YmBweHi13pWGnQmTiL0wMfES1izMVJ8BwiS2AMgFFiQGbwdeN9BAEirWA+uA2BSWlEUSy5zTXMiAhFQ/QChP/iP/tZfOAhxxzcNnhyrP59L66oiEMkllzbQafITZJYkyWpiERuz1wUEfbBtePz6f4shjtHw9GseujkufWF9fFowoakSKa+SdPUII0Px3vj8QSZM/O5nzkTS3PxtWu7W3uT8dFyr7faWSoPxiuLC4urxTdf+NqHnv1IZPvqG2+de+rhiqfZLffos8/cleZaqDbPPtSvYVAKK3z19vs7413aOfzMmUc///xHv/udb4Wq6Sf54XgWY2yaCkWcq4N3IlJVVYsOztJ848TJtbU1Qt2ESEQsgooicdt3agIhjAhgNTJqhhRtFOEiOxLnc/PQGhy8/XZ99crJjUVZ6txxMemc6po18p2q9g078RU0FTofQpz6RkJsRiVKHPL0wI1d1FwVsUKqJsn4zkDJrOj/1qW9m4lF0ZkrxwR6bzopJ9OlhcV+twsCBBBCoHa3ooz3PjIoJIXaKt00jScRUkBcIUtTOuBciXdRCYOIxAAcYozRe+dcCOHuZHI0rkBnp85c6HQHe66eZoKpUKwQEETfLWe40pnEo+/90Z9//tknr/7F7E+//BfPP/HBh5TdWD6RJAo6mX307M7kcObGH3j49GmrZ2P/+SefFovfe/119dzzT/zEJ8zr7z6WZIeHw5dffvn5J58Ke5MnKkjvTJ7+xMcOQvV//MN/vZkOzqdLk53xtaRY31i8+Np3uqFZExiMXFpTVTknVHLj/eHwyrZZX1s4fVIvDSqrusJ1hMaYRsUYXcLOigOOhtPIqQ85ojHGQFnpMJrsD7F0dLSb6ETR8s6NWbq0NB023B3aOFYu2LqOPjgOnr0LUThWPjQmxEqSKt1wyRE3d7FssrpXz1ZDYqfgbWa7OBPnvCOTp8Hp119/XSOhQCfPrTbOOWNtWZaAmCQIAFoTMMboAzCpuT44MLT8zxBCVVUBAUQ0AqFwiHVTjkajyXBUVSUVaXdxkOcLWptpWTIxmZacgRAlhCDMoXGri0s/+1NfeHR5jbrRfPazj597Mk7GPD5E4sb76s5eUk11OVlWGd08jMEsrXQTk/rFk3oi1asXu+PZiVPLJ1fywaPuwpnzsOEuXnznI3/rl15/980PnD+3FHRX7OGVW65p8pDwzuGCmDw260VXVbN+XjgvYk0gMIjom/F4fOPl15vE6oUF3DxXIYQCIU+0UUhakEJsqhBtgiahxleVm6ap3jm8fTDbfXThRGQEpcvaoTbMdHQ0jEy5SqD2Urvog4fomH2MoeVohBCDACMIBmQkQRalSIgZAoL0rLWzxgtoiV6xHnR7Kyura6trhMo5D4CKzM7u/uLKshIWEc1AgAJRBBRBC0cQnLvvlq7kEDmG6BqOMQQXQpDoEZGUSntdladZp69sFjwCsNIKSZxvtLbGmrIslaLQ+J2dW09vbKx1exLGur/idg/8eGRjffrUJiTpwc5oNV/3w7iSGMqz3SpSNH5n+pReNKPoX3irSBPrNAOcmHlz/W4T/IbN6jvbD3eWisN6QfclwMNPPlPW1YvvbE0O9p44dbbeubnR6aWUxqkXoyOpSKCiN6QXit4SpftldXhr59Ldme0Puic3zKJupKmlMpYhUYHAI7tmTIERPDXu8Gjr7KMn68oHD/1O/8adrWJxdVwdeJFyUnMiWEWuHYcQQLyAY/FRVPAhRI7AjIwURRBFE2hNbCAgK+ElnaR1IwTIvtasOXBqE42EQoooSZLRZHo0mvSXlkmpGKNnb0gRCSkRFsJWNoTmKkk+Ol8H56XNjYCgDRndQlCU0bZImgiVcwTGKs0gMUZAqstGGex3e3f29wer/aPa//5v/H7yo588Nyjc0eTwYHJufROqcjy7nHQWlk0nmUw6Nbvx8HZ15+RzH9kdTrLK23EzUHAwm0ZjxtsHIcSE7BBkVJfRqr3h4aDo3twf5Xln4t1bd24MTq7fMQtLvey9m1c2iZvZeJESMqRtOpqVirRWyjJYZVby3kJe70/Lm/Xhwf7haHy3s7a2vLHW7Xe8+OFkFrSOda1YF8Zw0xxsbyM0A0qrydC5uFuOlLbDo3GwqQ8xz/r1rJY6svMcWAA8Yi3iGRLvQ2QWQqUYOAqLRM2iCURzVIIhLCmzAGAZGCBh0NK4w+2d0d5Bp9Oz1grQm2+/dXA0SrLUZg+lqQ2OAQQJAJjFI5t2loyIpIiIpG0pW6a6wnsFpAAEEmBxgkwKgFyM7AMbThJttHXjJtRQ5Hks60dPnX5Y6QGa7qyejiraGS51V3qYog9+0niMvva5Ue9s3X7u7/y8Pn9+9633D165+FiSG54sJVYC+0mpUKcZRZBFk41H4wvFSqxDk/QaoDzTw6Wlc489drluPvITn9+g+Mf/4v+RDnrGQb9bTEfTlW7ugieE3FqJMq0bUDBY6qZUH1R6d1Yd7l29snWt6PdXT24OVpbsoD+pXcfmUlb72/tdCc8//RSWYbcO+3vDTtHPs25q06u3d7J+z02nAsRBwAWIEgUDkQcJwhSZY9usc1QQYlQxQggSAysgiwhhWZtNgKEinybMXn/s2Q8h4mg4KcvKBw4sO1t7WyOQ196wWXrq1ClEZBESiTh3az3uuEkQBSJpJWhjjAwIkfkeu1ARIpaNB2VFKQnAPNdddc5FQUuklKpck6SqnySjstaR+oluwEiWp87rpkxApEM1qsSkwm7xxLo+ufrinUs3dm89s7HS3Np3ONOM2ksekRR7VwphYWx/sBD2h1olbNKJAp2rTVl4+4Xvpj/148//3E+/85u/vjAYDIqip1mXvt/Ng1KMIQTfYDTWWJ1qHxofjMSlzOZ5vljF4aR0TTi6duPWtWvYzc8+fB5tXR8crKWgUC2Rj+yvDGfd/qKwSorOjVt3kSXWzrkAWiEjRoYALBAFmDAiBm6bCIgogYU5KBfJe+UDI5BGjHFgi1NFCsJNnjIYncSQZZntdfZ91DYhkz52/sLo1cvb2244HC8vV4lWWmGMAYg0YpQ5ZkGOzeVFgAUZgQiRjG7J4cyBY4yBCJkhSgTGBEmRihxd9JkyWtvZrKTERNfcvb2zacxymuBoeuvK1VLRifWl1aUuTCbRh45JsQqTo/18wYJK9vf3h5Px2DcrPqa9FCtnrCJGjxiUNMReGhvYJijBNS42KP3F1TXOzer653/x71z+s29+70++9kTWp7IGz5QXoW5qqVVuFFKsnYtBFBnWKrBVPSMtCyxmeYLGlux3m+nOaPTWSy+dObneIdob7Z88udzppu9evVpWOs1TneSXb92uSh9CHCS5a2oyGhFJgAJwjNwOBIlCq1HFEFEiR/TRNME20URGINEUq2BDXE7yW+NDZzGg1ikAl6ViNhw6SY9M8sGnnpyUsyvXt6zSEIEJQBEAxSCkMM7XpQDQgotbITnVyrU/OEMhUK0viRdq7YIkim8cUEyMCT7MuDJJOgt1NzOgiEdlnvfSPNu4cJZXByW7nWrUV5yrhL3XDhbT7lTijW+88NjTj+oxh/fvZmJUGWczB2nqOTbCtuggysw3My1JB6jyKmJu7PjgIM2SUFbTrcN+ULw/TTv5xsISH+xD00BChLaOHkJIjbZG+5pDCF1QATSRAsUCoEUUalQqo3BioweZTRKbKvXQw4+un1y7Odx+c7tRqrt/MHVx2oTYKXra0rBstNWBhQg1CLfaQQwIqEQiiERgjgACHHUTk5oTJwYRFQZg74PSwQJUAYazhkOjT6wuVVVVN/Hpx57wQvvDCdoUPvKRM2d2VtbXtDaKkH3QpENsEp2CgnZY1e6Y8AeHk4gIrepLu5wRhBDnCpESQYSIBGIMAYEEqApOGxO8syI9Y6mu9imGhXya4vRwqBCT6LmOnXQZrAEfbW7ffffdE2c2wTk/q9Isx8aJLUoUyRPw3k8bmxrRxiWAed4MJzSqkuAtqRrl1taN6++/8x/+w3/46bff3P/mN3zhCAGsbsl+FpVSqBkwsmFgVEoggLB4BZBpYSRSCkh1Cb998d2G1Mc/85msv3j5YPQ7L37zO+9ckwwu9GdkTTEYLCyv14LMnA8GPgQCDrFpvBMXOEYRoVY8U5OKSBE9BwjO1C53MW+AFHiEwEJEKKAU5gY8QqGsHvQ6q8uLIAQ6kcr1ull3sLS0ugJvvFG6Rgg4ktZaESmdCHNsTZNFEFlaeoyIOp5GtgA8gvnKmwSIKbZiBgKt80k7bw0xKiJA8hwJotWUa50JXOJaDRb2ZqPlhb7sH2HkXn/Be6jZOx2268mJDz7++Ic/dGdnp788YAdUYw3iUxtREtA5g60RVewvL9snH4HrN7fefK+d8NSx+ewXf+x3OvqFP/l3H/+JH7n8tT/en00WlYLANknFOxQgjkpYGEKrm6FQmRw5RIiKRSIrI7lN+pQpof2D5sXvvmzS7rD2L27d3QMgTpu7dYQaYJIU2yZLO73uoNfH0Ghmg2KIW1KNBiwMZdq62YwCS2SBiByzAF2HHSFNekbgmR17QciyrA/5Annr+jrJMiJCpYNn5sDeB9dsnjh59uTGlZvXhSMHL8KuEat0YOZU3TtYIsIyHzkDqHnWFIZj2SwBwPlujgVa2EEQ4AjsXEysEaIQg5GYatUxOgvSpHbQ7Uzv3M5JUsEMNUQOQSpB6ncOptNPfv5T14b771+/mtZU6g4plMQuLC+NRiOqG6gDEXVSuv3+td547Kal9WiRvIve+euXrl09tfb3f/antr/2jSLv9nq9JAoIurKyxiALMIMQoAhBVBgBhQuAiNAACLJXDIaoY9WppYUQd3IV+70kLVK1BanCMXIFkJAKgPuzcDSbxP1JN9lLBHpEibDRQFo0Yk400OnAZDpOlIAWVASJUoVQH7EDGkEjotPiFROBUgpFfOUrKXWxsurqOoSAhEbAogj46fhwedALfn06q9pCsSrLhJABfQxz1BwitNj1Vupb4rHZXbvokfv2dvcVYCJLEOH2I4GjALXMQwuUE6Uxaptdv3htAOmpTrEUfSwPy9GojqZYXJ6UdSIIpX/1e98tjD21snH4/tV+2gcSY9MkjlCQCTyJAnVyYXU6KjusEp3XsxpB1haW333pxef+g7+Z9Vd//9d++xlRcDSLIKrTO9Y8iQAEEAUhIgAJAwQfARhBlBLSoJEhNibgZjfzU5WauJxjN9I6AibgwZUAFnRucwE+rMcTkBBhYFNyvgweG1AKkhQZVNYEj3XHhlTpzBijKVHUF1yIVGjVSAQkp3QJFGOU6KWp/QQamunu+ibHWE1ndTWrYugk/U6ns3ewb5PsoVObd7buMkNT1dggQmAWjcitUzLMscio6FjLkRCxvc6OgZAgIBFa3RfBORIhCqBSKsYoQqRRC2pmG0GHGJkeP/so1b68dbUiv9gtyolLTapnrgj+ZKc7eff6z33sc9/95rdmd3bPFF0OsZmVcX8vjCepTVSRRIiVeKh8YRKeVG7aZLZArQ5mzamTD116/87/9Sd/8YnAZxeXcyVQV6EqbZb6KAqR7gFjkAEEBUlNBQJK0IlHxVqjKNIRF7SaKpNGHDSRo5zr5UQ6sTZNAnnwnkWp3GSHvpwGnyjdM5kKIUKICEyKQGdoFlWxYqWTJFmaahIjkjP0QWVCTQyksYyRQnDQqMQuJdlmVTaE2iwsubpRqKzWcTLlGEhTmiadbprn+fhodzgc15ORuEBKaYEE8sgxMDMIEQoARgEQJGxtL1gEsIVhiYiwKAaIIAJRAbewSRQ2pOogzExCCGyQLAs4d2VvNjqqO6X/wPpynGxPY8DcKrG6JIoqsnrpa39xmqU5HC8CsauVsIq+KScSGkFBk4ASAYrCqEiBMjoHkx7VsxtcfuJv/syHf+4X4PW3vv8v/7tUax4dUMeC0QflLEtSK2yP0zuJIABBTFTloxNxhIJKkU4iEURkzxmYApOCtSOzmPfuutBNe4tmHGdNE0KR5C5Jdo5KR2A0knMGRAFEghgDQ+zYfC0vVnTIrE20EomKOSNKERMkQZWB5EiWMICQxuU0c50GoqFKmUmMMUlsb9BbXlZpNqurNLN5mlgFmxsrRaKJfaIwU9RJba61JVIiyEyRiRmEJYY2E7YI4iAchB1HxzEKRwmxdZ+HKMit0yvMdWKAmYklUzpBBbXHkyuDMychQhxXCRokqkVqBLE6WnVnfNA/uS6ZnTSVc3WeZoRRJxTQq4TQB5rO0oZTIGvtpG6CADAOh6Mro8ONH/8E/v2/9fr1V+H5Rz78y3/vqjuU1e6M4lGobScXEcUADMCMgBqEUJDAcGqCUTFFTgQSzzaI9WJt2k2yHBVVrgQKQWpbULGc5XGyZHizsCc6Sc/wnE8b6sLqLqgOqcxiZiHXakHrZasHqLqEmXDKnIKkBEYJYlTEGiBRlGpFAJq5h2oJ7YpCfTSbRc/dLI2h6S4spkbv3b2tJHBsjFKb6+vT4bAcjyQyMFpFDjSHGASEIZIgYGSeK96CCEI87tICRxEJQnKMBmeEe7mHmRG1AEiITGyUsoTg4za4bjU9UXQ1gyXyENHq2kfmRjTX2jz5uY/vW3P3q/ubScdzoxU1EmpfZWQ1gIoKvBgINQMjIRHHmHa6taoHH3zi0s03f+X3/ofPbJz+X/z835oNzB03yxIClZRlU4ghFmAAxGOmFyCCDX3glCgwxCgQJfHReuEAxEhNqIKP0DUTP4y9PnWCCk2OkBjjLPaEMoLIUCR6kBjlfcMBFSmDaUSLYkNIDZl5P0tGqUwpCwDMRtAL6IjELDFS5ARV32YMXudNZEHDrEiLFdPv9pvFo/279XCcW3PLTYFdmpqy9gFlVE7Jz/I873XzJnLp/KSpGBkSE8BFBAYioBbeaIIWgVpPFZu8sYJKCAKyYgRQrNIKGUKzUI4+1OttTKZa63B647ktfdDs1F0NpkgPuWgs5AbKGmq5q+ngmQuXnnv6y1/9asUcJqVOrAFtRVuxmVcSYdxRPqU0hL5jFnfEPu/adOzOjcrpK68//Df+809+6Ef+s49/Gq5e1ZOJzVLluVOLDQRQB+1AkYCKaDlCEmLR+JkZk8IYI3MEBAVR8yyJYkMTMY5Je11UPlkq1o+u7xZj0wuA6cBjQX7/yUG4tQW3IZc631zFAsvZBLyANNAV0Fk16Z1eq8eaCLViBhRUES0oJeyRPQlzg6mgY4pNj2wWpcoUEUCWJBCZ2smF0OLi0vrqxuLiijHJ4e5wNm1OnnjooYce7neWOvlC24jNsSEQWyMtRGn3vCyBpSX6MwCQiLrnZ/LAgwSC9wBgrUbEyWhsta6aumzq3gfPnTixsUJZKEsukqihKSvREDNlEt25fvD4tXr9xsTtHFhSqUprJRFERCJIUOwUewUM8++vsmS3meIgW1pY3PruG3f/5e/+41/4j/Sw2fnK1zfSXirkvSdrKt8IgmLQzCaCjoAAghxJ7qm23VPeeHCA0LY3ishqk2VZnmUm0+OmKl3ZMWkHzYXTCw24qETptF/oLAObJdZCgmBJKWKrSWvSpIzSLbNZIan5+J30sYyMSGwBIxpBs3eoyFU1WC0xxKbpdzLJeru3tkNdFfmg311kFj9xRbKgoVjqdI5Gw/39/SZ4sokyCjm62osmbhXvWhgKt9U/wj0PDWgJVCIICKCQtNaNm2XsE2Wr0SQvOpe3rv/59TfXbu3/xOJGR+mSm0SpFNWkqWqrGfPVQ3/r//NHz2z2Yf3UUq8Pt3b8Sh5AjAhD9IiBgJEFQVtbu6oh5MzsNdNB0T+bqO/8t7/B7x7F1954jvjcQlb6qmxcNEFbI+LaHgUkKlQBJAIL/SABpf3VYa6cTg9MzLWmIkus1Z3eyngEVhLtxuTdiYV1B0fb1cG4Uidth3CotAXhRPmUdKJFxePvBKIAVOvsLkBEhtiw0qQIBFgIBDRaEYrlzE3GXJVc1aGufFPXs9LXjoTqmdOULg6WrSokqG66uLF8+vTpU6vLK4sL/UG3ZwiDq2NwhPODhQLAAiACEZClHa0xR+Z4jOiaC2tEnk7HylA3z0fbe/20OBwPk9WlR598hBKKxIqQhJuqbCRyQsHQuJneOtwenFj54Kc/9uQHPzCcTSTRFJF4jsJEARMhiYACPgalVEHGlL6f5DW7N69c/NGf/sm/8ff+w97Gkk717OioUHYpybDxrZA4QwSIAMwQGUWQ47FKASK1Kr4A2HpkGmOISGLg6CV4AiYA37gjFypi0GKCDCQdoF3rZki4O57WrEUoRokBIZIWTDFoUvr4bGkgRaAACUWBGCBDaIk0ILKggCZlWUjcpJkeaKlRKiPeklSTUT2bgEQCaaZlqHw37Vg03IRB0T958uTK0mK/2+0UGaG4upLgDSmU1swEEOcCg8xBJFIUZo4SozBzYOYgzMzB+xgjosTgemlaj6c6zbaamdk76vQ7ZQaHk6MsQKrNJLqokJpQBtf7xJODf/IP4ANn7epSNDROyHpRx0h0FWPhYqcWzYCJmZRV5uSk7uKknkU3W0x7v/Cj1fmlcHZ5mmDe70LtE8fWA3phEIdSK/EoHiUKR5B5qriXHh+gVVqllVLMzCFy9BKZgH1V3p7WSbcADOKD9jBAc255ybEMQ5yJ1bpQjBzRRQKWXGqjSSFogkSR1aRJKYWtMBahWFQpadO+fiQCSIZEOsy4HhFXKpTip+wm5fSgng07qZZQV9Ojo72trVvXR4c7EppQl7PReDqd+qZhjiiMICDsXU3AKEACyCJy71wFOP7TW3JiK8rUYh/SxG5t3/2JH/388089HRvnAc3K0uilt5ujw4YiaAW1RwFOdONjjyy7sPmRp8VU/+zX/8WNwz3T7e1zaId1zOwhiohxbANj5Cq4otd14xnMGkOacpMsdGB6tB1m+804KvR1A7UDMUAqOh9FImAUiS1jHCJzeOD2ogf+a3HvWiMRICBDZJCoEIB9SLKAsXElJiaKoqpeQ+wC7Luw1USyuQ7IYhxqBO6zUyBKgATaCVb7NCIzIWhAg2AJLSlikcgkoGLUoRopgDgLmFjvfV3XWkAodvPiRjUcjstyOKxKn6e9Yt0e7t2+fHsnhGCtEYoxNhIbUjYyS2SRub+8iDBLGzZiYuCAEQU0Bo4BGDFGV3uy6dpC/5XvfHvjvTtnMNPdjl0tPjfYvHX7alKGQVZEX8+qOpLJVaI8plHBYYm18LCU3M7KOotSI0dmFpjfowSakQGEoCzLblH40kWKzKB3huHPXz37oc/sL53Y3KoSV4Ey0DQNi+0VDVQiqBi1l4gSEFkwCh7LkrYlFYgItvRmESIgBQqwCQ5jTBQ5QVX0dnfv9Fxz10lnaXMxyVam0xTgoIk3ZtPNhSUMMwZVKQLCvgQAhcAYW2l7FG5ZA4CKUNgIJqASxLpl10IUDppnpUkT76caC/AeXLOwsEAgr3zv++OjQ186P52CaPZVcDNEpReKxlXTcnI0Ptrb3wOtsjQR4BCjyFwADRiPa0hm1oyt5RXHtqdmZCZrbRAW5wYLg5VBd1F3vn3j+ovXJxdq88jy8hJRU5VRa5N20Dly0Ql3bb73F2+ufuwL//HHv/hHv/tbJyldUt0aGub2ChdGEGRBYRFgNElaNsGrWIVgQv78YPPWb/z7l536rO3i9hBYgZtBp0OgyhgDKZJgPGmPRBgJIsxdGPh4AiBzPQZCFA5MiBrJC3MIErxSZJW+e3f7H/+9/+iLP/3j/+q/+qfvfOfNp06ox3oLZ2n7VYYD78UaQWJUDTIL2uAkzY8PMmpA1f4YZIWoBTVIgmCQFHCYr1OEqml1tHsY61iPSsUqUfaNl1/906/80c2rV3xVdvJ00O/kmVEkpKJAMxwf7e3vh+iS1Fb1dDg6alzlfdNUlXMueo6eQwgigogxxtBeZcGH6Hz07VEez6YuhuiaRZMsKKNCqIMfluUXf+7n8yLtgDaTRiN5hKpyBWUqQuVD1inuvnflS//7//PuldvBQ2oyNYkxckvPmYNQYpAwZ7C7xgdmsgmDBFdPDo6M4Kc++ckiT5VSoLUEBpYo2A7YhBUzQCT2wEEgisicn4DtPwwxxuC8dxFg7q8SnFOI2lBoHEefi3nh29/6xp/8+//0V//ZI88+zbXLy/J8v9cBGM/c1viwIWFtAxMRaQQRJgANRCwc4oNUaQ5RA3ZMWuiEOLIPESQKa01FU83GrtZGuTC+ef3axXffLifjtdWlYrXT7w663a5NcmOSxsvh0bB0ddbJInCS2dNnT41nU5UmEVV0MYKKQQTnri7MQEQxCocIBKSJg2+aWiIanavUNrMRejeZTE8XRVmHSvjwaGKqgDlCZokQIscQAzIK5Yl58+aN3pOPf/S/+d/ByuBjv/iTr/yTX62aoFUUTRFBREyMlpVhjgKTZtbt9svDUeK4A8pZdQuGT3z6Q70f+9jWjSs+o0FZQ2IAFQSJyIDIgAEJEaIgC4CgYhRgaTnHfLyHQJKWLAn3vE+jQlKaLOISm/cvvnd3fPvxD33QdPK6mm0uLnz2zKnXX3jhrouH1WR5dWN/d2Y9a0gCNMACCgAFBRAEBQVii95QKAaUJbCkFMyXxgGQimJ5YWEzSwciaTWJ41GT2v65hx47sXl2ZXk9L3pp1un2F/Jen5JEp4ko2Tix0Rt0UXGv11nbWF1eXkQFInNqWmtbEEL03oco3K6yI/u6mtWzlh5AWk2m05V+/9NPPtONPD46DImiPL9x/fYyK41QpVTptmSjmjAikcCwKT/6d39uRw3/+b/5V7DUefYzn94LJTiWIDFGiayimCDkGTynJh1PZszQzEqZOlc3d92keP4CUHx/71ZjoaIQNdZ1zSGmyiBjFAgCDls4PUJkjMLBC3OLjWYOLRlh/pIEaJfIwILtrcC8oszOXvjZv/03r9y5/errr3U6hSI41R880V9aBDgchjqhqMgyArNv1ZFaQ9sHmsA4935BhaSRNCmF1JZ2AVDbbIFMqP3IkJx9aH1xaX1367ZvSqNBGSVoHGM9mQ2n0+msMmly9vxDeZ76bTeuhnVTJkVHadra2kJd6EwBJZEFBJglhgggAbRGFNeMq1GQkCeptTYieGAVeOud9x4Rsok+CPWJC+d2TbqsLcYw46ZmsKDFqkqJeK/LuLa0BKuL33/vzV///X/zI2cvfPCx8wfNbDntB4EorJiDQE1sWkcbJwhUGxUMLtlcuemaTnl3pGreiKovBM4JERsKkVWMAEzAFAUjR0FGAWZkJkTm2JbEbdpqRz+tUcJ9M3WAGKOLoaPhqQtLL7308vb1OycW+qGKW3tboW4eLrJYm4sTf2NvuzAL1lBTubBSzLt2BpL70n8taZ2IEEABWlKIGJgFRAlpMTrJ0hxhNpmK0mnRIWNdOa2bOkZHRjOxF2iETZafXjtF1ly5eX1n65bWCMi9XqfoDdL30+GstpSRMSwogZEASTMHL1Eadk0dfQArRmkCdACU2p27d58uOoUAWd1ovHzz5kx1DYI0NYhzIGxSUaA5ksRU0/jmztaffOOn/lf/6JFzjz5y9uHv/+P/anFlsWJharcF4hG8Iq0hjYS1pzy9drhri4wo9Fg/aRe3v/Ttkye+snbzqDusqXZNQt4oRonsCaKObDxTJIDoCSIIAOsWkQRtMfeDRrSoaA6IESTSmjTqRAuFcOfKTc2glGmksSApx2XmC/3e7uRg/6ChfsOsIotnNCL3vikCCQogoTADaFDSAhrmtCMUBGHUb1x+u56VdV0T4KPnL6yuLBTDoqxHtfPTctxwaKIrFhZWTpwo+v18qbhz7erd7Tt1U3VNCgBJZju93mBxsQrDunYQlDYWABQSIDNzHb2b1RiatEgpI4nRuRCtjj6sJOlG1rFHu7eHe7ONpQtPPHbx4rZDl4kZgKqRI0hkwSAKxEf/1LmHrn/vtdv/p//75qc/9vu//t/wuxc/oosmeEYmCRCZFTIBEyRBDOr9/f3zzz69fHLj4le+tiyZcTwajV/8td85F2K/nAIFZghCAKiFWuoicWyZMCLMEoCZBIFam+CIc9MOgXicGI9JlEqpBC2zHI0Oda7TKQFAQJhGt6qTOJ0UWvfQnV2EyyVUTT3zRN2OBCUkPF/azbt1BiCcG79IZGRRSmkyAp5FAEh/99XvWGVXlpaBZW+4vbhUmFwtLPednyRou2m3kZj2u93l7qSpdq9fvn3xPa0pz6y2Js/TNM+01hcuXLh5+8W7d3eZktW1zSTLRBhZmsbXDMb7FNFYLZqib9h7pw2D9IscygrKygc/9nW1uz/aP4xdEJIMyDKEiB5EQrSIMbiKw2g2kbvb9Wtv5C4OugudUROjeBEAQgEdIUYhFIgcHQvDyQ88A0sLvT/5Ljo5MF6ePLmyvKn29qI/guBQWU0IgblxRpFAjAie2CEEZAEAiShKGHAOOpA5rgWRheEB1p2IcIQQgy50GJfY2BnXZb+bKvbeL9rcQxjV9Yo1I4QDB6W4OniCHoOHv/Tg++4uggCGVKsLKCLCSn/vpbfSFE5srLuyGo/2lhe7iJJkpuhlBWW9pf6onB7OZnf3tvaHo0lVp1oleZqkpugWy4sLIYTd3d2i6O3v71+5MgQFadZZtBYQmaP3vgqcEFmjBMDF0MpmxxgX11b+zo//1O6/+h8V4NLK4jvlZBqG/bwnesQQhUE3Ykl5JS5EQLGKDpt6/fxDo6Xe5ZvXvvj0s92aRl/9ji1ytsQQEVEz6AgijIGMSRZy+/KX/0jqemXqozUHXbpVNOsDNdsvVcIJCkOMTWOFEmVEAiN74ojKI0Rpt9VtN63a7uxB7mSMEfWc/srM3vu6cTNXYyfpZmmI2F9cKd0k6XTczhTApplG9uQgo8QY5Rs/nE0MrMIPxawdmwrBsestglJqbv/NCAigtxPjHL91fbh3VHf3d/7lK3840PCBk4OnTi2eP7E6brJ//62X96az5dXuxkqx2k1VniiF6+urnV73aDp2UbqDNUn66fqZ6XvD4OBwOltYWXT1tK7HTVUO0m7aX6hBoYR6MkFFXmOCKd3a/u9/9b/+sbXFUgscqefXHttfG1xrptm1qGPjYzPV7A0gWKMSj6FU/gY1T33usZuJfP2t63f24Zd//gvff/vlp8uoBBWDIDVKDAIJNMp7YBPwRCMhCKdqonC5tquXZW14y82cc4ZxETwkAILoJSAis2YQAdHMSoSZmDCQno/dEOfkSRbGKCIcPYOgIhDyPsbImU6V9xH1BPzC5vpwFwzTRp6OmqZPppMu9GJYYTbSjFzQeXYAnXVXWUNGaa9CAFEoRkTFqIyuwEOmsUY1c0UDSSQnSlhpa6KxGlzodk05PdqZ1T0AOEI929TixpV74XuXRh5OncpWlj64sHJibbBSFJky+tqNa+++f9lkxUkoktwcDSeRQQBmVX14eCih0hTzNM3zPMzndw4RrbV15SN5COHsyROWvaBjwltbd67u3o6rg6hQCc4vexGKrANrYZS4LARXd7/4D//uZOw++/wndv78pY0RgyE51sWYw4qOn9g2YR4/y4xBAvH04JDnYBaaV33McrxVYbkHaL+PB7z37v3xo0irnQdz5jIStx9py/EYQqjL6kc++9mrL7482tpfzIq2zlSAmsAobcC3o6L7Pw5JJNzznGt/GSQBEKXQaEWeIgdBpZeWEhaUUUXa4CwiQwfhaCzT2exoON4dVbWHmuHubvWnX3vl+rXthy+c6/V6zOHtd9965+I1ZaAzeL+q+M72kYugAba3jyA0a8v9vEitoTjHCwoR1VWgxGRZNt0bnz/3yAklw/fe6na7AkmY1c6Vs72DkC4TkW4VMQJQBN1EyzHTKji8+/WXbHf5l57/yLUXL179d994nrtHukIBZgZmAIkg0JYKbbD4+Ek/1s6oY0jTFEU4zkM1FxF6IDb3jHJEBDgIEoi0ULrj4PE94jnN+eZtyCViFMYsybdv3ekuLZw4cWL71pYLwXMkIsWSCHSsDqoSie08GliEBJjnAm/zmUurHcatV4wxhhpoWzSdmhAFlhfstMHhpJ5VAAAJwKgc1yFev3XXMUSAmdNHW83W7rWvf/da0YFuN/XeTWcACPHgYFyCB8gVkELvxEfMs05RJEZB7V0IDZKNyGmaKqWOjsaLWWfr/SunO/bRhUXXNHvN9MT5h99543VFGE3kEIVZgAGJmRmIGSxSAmYlLb72e186O5ps39rKfGxCwzpSO0FowejcGkthO8kQAYnMwigihCKilGl91Jxzeh5XVkq14eUHRTbaGeYxgLMN27HSBhNpFsBWNVDmDC4BMknW1OFofyr95O6NO/2NE0W3E6eliwG1saQSRtQ6JkgsQCCMInNsdutk0I7KIrAgQmsGQ6AQNCkFIUigxGoF8eTG8mPnzjzz+In1Reh3YbmAgyMOILV3oKDo9sFkyyvLNi+CoVFDB1OeBVMzjGqYVMAAGiAyuiBJZjqdHihV1Y4Z2qmjMcZVNTNHBkL86R/74rK1y0l2uLXTAIxjfOmdt/e2d5ZsSi6QjxgYY7vLkYakNLBVj32ihr468fC5UWzSU6vP/+Jfu5l6CVEiI7BCvAdBv1d0tbzGVhQIBQgFWSCyhCghtkNRAryn7oUPytq08HVoSRDzIRUC4L26vM2Nxw5p7UCkCZEZNtaWxkfN8mChKIrJdNoE38TAzEokBUgAcmuIUJBJAO+5k0mbzKnN5yigEFFa7j1pAkUASvRoWBtjMFKnyPsPr68srk2Ohu7ooN4f3bp97cSp9dKM8+WT129v3dzbTwHApBJCHVv/r7aoAgAIAEpECxSohIhUEmPTeAYAa21T12mazvwEJC4vLG5fu56F0IuSdPqNMWoxO7m4hkVnMU3wyJGwAhEFAMAEDRCSUN4ZuSrkxfW9rWuTnRkBnH/cnukvXD1AZBJUrQOzCHLr1dbO+gWPER3UAlkIBBhQlCaFQAgRJMbQqjQAgoi0QrDt2yCgCEEwiiAgi7Tq3SggTO2L4xggQwASUKw1rnbL3Wy5v3T10hVggSRpogNUlmwEEN+SFUJrLTm/FIUAIzwg5TwvSoURmFobdWBRSj/37I+miTrY3TFoDZjN1bWmKOJiLzvbbN2+dfahjWR5tb/x0GBl8NWvfktpmPkIgBDb1wMSUhT2IARg0RBJBJiWdVk7jUFEafLG6slksrQySDArYz2dTv/4u3/4i2ceVrN6enTY2KW9EHdv3egA5aA0oBYkxFZMMM4hGFQ2MyQMZU0YnfPF+vLe+Mht3zkJ6b2X/Dxg7dFpC+Xjv5/m50SM0sCikFAbBmFmhcjMQNR+n/kI6QHlehAQZCUUoZXwhXhfLb0dFR7LqYj4yAYwT7Oj8fjqpcsquE5infda2DCnShGCDzEhjehddCgEMDdMomOYHs2BTggcUViBMQoVioTAqPU/+uV/du3yO7/zm/9jrjNtMkaiJHS7qQmzhU4qBs4vreXLC52+Pdq7evXKXVcpIqJ28BuDiBCqrN1KhCAcg9DRaCzRGZLFQa+bSFdn3W736OCQMsUAs2r6Cz/917PX3tZNKLTem5XmxIkwqa6++/65x54QQgHEOckXCEAJMEpXJ6FutFJLTI8tDQYbZ2+8/voaGIL7eam1KFNIcN8+keeYGABs0QIiIYQ2hXKIRDRnGLczw2MHqvaEISDBvCvidueJiMDH8r/3D4QS1b5hEuunvi7HqUmLtNi5s9frdqtqVigNAInSBnAWGkEkBIYfwggRErRC34jtqSYUIAKtCIE5Ro+OeoMTrrGIWSfrpSZNjS3yvE3x3U7az6xFn1J4/Nypn/nrXzixnlhDut1aQjQKrSYFEGNgjkli0zQtGzcp/fbeZOdgunc0bG/7xtfaGu89IabGvv/O2700SUA6Np3MqvNPP9NbWYkiiVKICIStmZdCtII6YsKIDeeikjJ0KtYHs02Vne0s9aICFnVctrcLBBExxswrwJbDKGyUds5prXF+f0g7cMK5Hdj8uaMHCn28Z3cUmZkRuH2/vSAVIERWbQJlbjvr9icSUWazo+FwMBisrq6PpxNQ1OpkogCEmKI2hAIREQWiamtO5ntTFQDgOD/HLbFBa221sYqUVf8/zIkRDjU043MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=145x185 at 0x7FAF499E6470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VA_K6NhynvgQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "7f4d0b7a-9ae5-4593-af4a-344645b6ccd9"
      },
      "source": [
        "!./gdown.pl/gdown.pl https://drive.google.com/file/d/1wOdja-ezstMEp81tX1a-EYkFebev4h7D/edit val.csv"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cannot open cookies file ‘gdown.cookie.temp’: No such file or directory\n",
            "--2020-07-09 14:48:55--  https://docs.google.com/uc?id=1wOdja-ezstMEp81tX1a-EYkFebev4h7D&export=download\n",
            "Resolving docs.google.com (docs.google.com)... 64.233.167.113, 64.233.167.138, 64.233.167.100, ...\n",
            "Connecting to docs.google.com (docs.google.com)|64.233.167.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-08-64-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/bivnuglkh0sd42g3l0gggkbcniuimlgg/1594306125000/03161587497725415728/*/1wOdja-ezstMEp81tX1a-EYkFebev4h7D?e=download [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2020-07-09 14:48:56--  https://doc-08-64-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/bivnuglkh0sd42g3l0gggkbcniuimlgg/1594306125000/03161587497725415728/*/1wOdja-ezstMEp81tX1a-EYkFebev4h7D?e=download\n",
            "Resolving doc-08-64-docs.googleusercontent.com (doc-08-64-docs.googleusercontent.com)... 66.102.1.132, 2a00:1450:400c:c06::84\n",
            "Connecting to doc-08-64-docs.googleusercontent.com (doc-08-64-docs.googleusercontent.com)|66.102.1.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 447631 (437K) [text/csv]\n",
            "Saving to: ‘val.csv’\n",
            "\n",
            "     0K                                    100%  133M=0.003s\n",
            "\n",
            "2020-07-09 14:48:56 (133 MB/s) - ‘val.csv’ saved [447631/447631]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0w52Oshn4zl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path = 'part1/part1/'\n",
        "input = []\n",
        "output = []\n",
        "for filename in os.listdir(path):\n",
        "  if filename.endswith(\".jpg\"): \n",
        "    if (filename.split('_')[2] == '3'):\n",
        "      actual_age = int(filename.split('_')[0])\n",
        "      if(actual_age>=0 and actual_age <=2):\n",
        "        output.append(0)\n",
        "      elif (actual_age>=3 and actual_age <= 9):\n",
        "        output.append(1)\n",
        "      elif (actual_age>9 and actual_age<=19):\n",
        "        output.append(2)\n",
        "      elif (actual_age>19 and actual_age<=29):\n",
        "        output.append(3)\n",
        "      elif (actual_age>29 and actual_age<=39):\n",
        "        output.append(4)\n",
        "      elif (actual_age>39 and actual_age<=49):\n",
        "        output.append(5)\n",
        "      elif (actual_age>49 and actual_age<=59):\n",
        "        output.append(6)\n",
        "      elif (actual_age>59 and actual_age<=69):\n",
        "        output.append(7)\n",
        "      else:\n",
        "        output.append(8)\n",
        "      input.append(preprocess(path+filename))\n",
        "    continue\n",
        "  else:\n",
        "    continue"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67t9q8-mn8SD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path = 'part2/part2/'\n",
        "for filename in os.listdir(path):\n",
        "  if filename.endswith(\".jpg\"): \n",
        "    if (filename.split('_')[2] == '3'):\n",
        "      actual_age = int(filename.split('_')[0])\n",
        "      if(actual_age>=0 and actual_age <=2):\n",
        "        output.append(0)\n",
        "      elif (actual_age>=3 and actual_age <= 9):\n",
        "        output.append(1)\n",
        "      elif (actual_age>9 and actual_age<=19):\n",
        "        output.append(2)\n",
        "      elif (actual_age>19 and actual_age<=29):\n",
        "        output.append(3)\n",
        "      elif (actual_age>29 and actual_age<=39):\n",
        "        output.append(4)\n",
        "      elif (actual_age>39 and actual_age<=49):\n",
        "        output.append(5)\n",
        "      elif (actual_age>49 and actual_age<=59):\n",
        "        output.append(6)\n",
        "      elif (actual_age>59 and actual_age<=69):\n",
        "        output.append(7)\n",
        "      else:\n",
        "        output.append(8)\n",
        "      input.append(preprocess(path+filename))\n",
        "    continue\n",
        "  else:\n",
        "    continue"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BItHUaltn-ow",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path = 'part3/part3/'\n",
        "for filename in os.listdir(path):\n",
        "  if filename.endswith(\".jpg\"): \n",
        "    if (filename.split('_')[2] == '3'):\n",
        "      actual_age = int(filename.split('_')[0])\n",
        "      if (actual_age>=0 and actual_age <=2):\n",
        "        output.append(0)\n",
        "      elif (actual_age>=3 and actual_age <= 9):\n",
        "        output.append(1)\n",
        "      elif (actual_age>9 and actual_age<=19):\n",
        "        output.append(2)\n",
        "      elif (actual_age>19 and actual_age<=29):\n",
        "        output.append(3)\n",
        "      elif (actual_age>29 and actual_age<=39):\n",
        "        output.append(4)\n",
        "      elif (actual_age>39 and actual_age<=49):\n",
        "        output.append(5)\n",
        "      elif (actual_age>49 and actual_age<=59):\n",
        "        output.append(6)\n",
        "      elif (actual_age>59 and actual_age<=69):\n",
        "        output.append(7)\n",
        "      else:\n",
        "        output.append(8)\n",
        "      input.append(preprocess(path+filename))\n",
        "    continue\n",
        "  else:\n",
        "    continue"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4o0D1MGr708",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "14f2c347-866b-4430-dd08-a03892a633be"
      },
      "source": [
        "dataset_train = pd.read_csv('train.csv')\n",
        "dataset_train.head()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>race</th>\n",
              "      <th>service_test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>train/1.jpg</td>\n",
              "      <td>50-59</td>\n",
              "      <td>Male</td>\n",
              "      <td>East Asian</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>train/2.jpg</td>\n",
              "      <td>30-39</td>\n",
              "      <td>Female</td>\n",
              "      <td>Indian</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>train/3.jpg</td>\n",
              "      <td>3-9</td>\n",
              "      <td>Female</td>\n",
              "      <td>Black</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>train/4.jpg</td>\n",
              "      <td>20-29</td>\n",
              "      <td>Female</td>\n",
              "      <td>Indian</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>train/5.jpg</td>\n",
              "      <td>20-29</td>\n",
              "      <td>Female</td>\n",
              "      <td>Indian</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          file    age  gender        race  service_test\n",
              "0  train/1.jpg  50-59    Male  East Asian          True\n",
              "1  train/2.jpg  30-39  Female      Indian         False\n",
              "2  train/3.jpg    3-9  Female       Black         False\n",
              "3  train/4.jpg  20-29  Female      Indian          True\n",
              "4  train/5.jpg  20-29  Female      Indian          True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppYATxuHoA5x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for ind in dataset_train.index: \n",
        "  if(dataset_train['race'][ind] != 'Indian'):\n",
        "    continue\n",
        "  path = dataset_train['file'][ind]\n",
        "  actual_age = dataset_train['age'][ind]\n",
        "  if (actual_age == '0-2'):\n",
        "    actual_age = 0\n",
        "  elif (actual_age == '3-9'):\n",
        "    actual_age = 1\n",
        "  elif (actual_age == '10-19'):\n",
        "    actual_age = 2\n",
        "  elif (actual_age == '20-29'):\n",
        "    actual_age = 3    \n",
        "  elif (actual_age == '30-39'):\n",
        "    actual_age = 4    \n",
        "  elif (actual_age == '40-49'):\n",
        "    actual_age = 5    \n",
        "  elif (actual_age == '50-59'):\n",
        "    actual_age = 6\n",
        "  elif (actual_age == '60-69'):\n",
        "    actual_age = 7\n",
        "  elif (actual_age == 'more than 70'):\n",
        "    actual_age = 8\n",
        "  input.append(preprocess(path))\n",
        "  output.append(actual_age)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FwLxRoXRoEYI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d28852ca-1324-44e0-b153-e06273cca1bb"
      },
      "source": [
        "print(len(input), len(output))\n",
        "input = np.asarray(input)\n",
        "output = np.asarray(output)\n",
        "b = np.zeros((output.size, output.max()+1))\n",
        "b[np.arange(output.size),output] = 1\n",
        "output = b"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "16346 16346\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cceESm2-oH4a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e628856c-873c-4451-fee2-ce8082bc3258"
      },
      "source": [
        "age.fit(input, output, batch_size=256, epochs=100, validation_split =0.20, shuffle=True)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 2.0408 - accuracy: 0.2514 - mae: 0.1858 - categorical_accuracy: 0.2514 - val_loss: 6.9529 - val_accuracy: 0.2520 - val_mae: 0.1664 - val_categorical_accuracy: 0.2520\n",
            "Epoch 2/100\n",
            "52/52 [==============================] - 13s 259ms/step - loss: 1.9656 - accuracy: 0.2756 - mae: 0.1842 - categorical_accuracy: 0.2756 - val_loss: 4.3254 - val_accuracy: 0.2520 - val_mae: 0.1675 - val_categorical_accuracy: 0.2520\n",
            "Epoch 3/100\n",
            "52/52 [==============================] - 14s 261ms/step - loss: 1.9386 - accuracy: 0.2755 - mae: 0.1833 - categorical_accuracy: 0.2755 - val_loss: 4.9105 - val_accuracy: 0.2520 - val_mae: 0.1667 - val_categorical_accuracy: 0.2520\n",
            "Epoch 4/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.9263 - accuracy: 0.2848 - mae: 0.1823 - categorical_accuracy: 0.2848 - val_loss: 2.8862 - val_accuracy: 0.2520 - val_mae: 0.1701 - val_categorical_accuracy: 0.2520\n",
            "Epoch 5/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.9197 - accuracy: 0.2816 - mae: 0.1823 - categorical_accuracy: 0.2816 - val_loss: 3.0798 - val_accuracy: 0.2520 - val_mae: 0.1709 - val_categorical_accuracy: 0.2520\n",
            "Epoch 6/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.9024 - accuracy: 0.2856 - mae: 0.1815 - categorical_accuracy: 0.2856 - val_loss: 3.1814 - val_accuracy: 0.2520 - val_mae: 0.1693 - val_categorical_accuracy: 0.2520\n",
            "Epoch 7/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.8908 - accuracy: 0.2901 - mae: 0.1808 - categorical_accuracy: 0.2901 - val_loss: 2.7185 - val_accuracy: 0.2520 - val_mae: 0.1776 - val_categorical_accuracy: 0.2520\n",
            "Epoch 8/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.8833 - accuracy: 0.2830 - mae: 0.1807 - categorical_accuracy: 0.2830 - val_loss: 2.9992 - val_accuracy: 0.2520 - val_mae: 0.1713 - val_categorical_accuracy: 0.2520\n",
            "Epoch 9/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.8759 - accuracy: 0.2845 - mae: 0.1799 - categorical_accuracy: 0.2845 - val_loss: 2.2853 - val_accuracy: 0.2514 - val_mae: 0.1810 - val_categorical_accuracy: 0.2514\n",
            "Epoch 10/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.8630 - accuracy: 0.2894 - mae: 0.1793 - categorical_accuracy: 0.2894 - val_loss: 2.1609 - val_accuracy: 0.2495 - val_mae: 0.1832 - val_categorical_accuracy: 0.2495\n",
            "Epoch 11/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.8537 - accuracy: 0.2948 - mae: 0.1790 - categorical_accuracy: 0.2948 - val_loss: 2.3936 - val_accuracy: 0.2382 - val_mae: 0.1814 - val_categorical_accuracy: 0.2382\n",
            "Epoch 12/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.8427 - accuracy: 0.2951 - mae: 0.1783 - categorical_accuracy: 0.2951 - val_loss: 2.5032 - val_accuracy: 0.2177 - val_mae: 0.1825 - val_categorical_accuracy: 0.2177\n",
            "Epoch 13/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.8311 - accuracy: 0.2973 - mae: 0.1776 - categorical_accuracy: 0.2973 - val_loss: 2.3807 - val_accuracy: 0.1336 - val_mae: 0.1886 - val_categorical_accuracy: 0.1336\n",
            "Epoch 14/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.8195 - accuracy: 0.3061 - mae: 0.1770 - categorical_accuracy: 0.3061 - val_loss: 2.0885 - val_accuracy: 0.2587 - val_mae: 0.1790 - val_categorical_accuracy: 0.2587\n",
            "Epoch 15/100\n",
            "52/52 [==============================] - 14s 265ms/step - loss: 1.8154 - accuracy: 0.3020 - mae: 0.1767 - categorical_accuracy: 0.3020 - val_loss: 2.3621 - val_accuracy: 0.1239 - val_mae: 0.1897 - val_categorical_accuracy: 0.1239\n",
            "Epoch 16/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.8047 - accuracy: 0.3103 - mae: 0.1763 - categorical_accuracy: 0.3103 - val_loss: 2.5665 - val_accuracy: 0.0688 - val_mae: 0.1950 - val_categorical_accuracy: 0.0688\n",
            "Epoch 17/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.7883 - accuracy: 0.3139 - mae: 0.1753 - categorical_accuracy: 0.3139 - val_loss: 2.2983 - val_accuracy: 0.0936 - val_mae: 0.1926 - val_categorical_accuracy: 0.0936\n",
            "Epoch 18/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.7884 - accuracy: 0.3132 - mae: 0.1756 - categorical_accuracy: 0.3132 - val_loss: 2.2842 - val_accuracy: 0.0728 - val_mae: 0.1935 - val_categorical_accuracy: 0.0728\n",
            "Epoch 19/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.7732 - accuracy: 0.3191 - mae: 0.1747 - categorical_accuracy: 0.3191 - val_loss: 1.9942 - val_accuracy: 0.2422 - val_mae: 0.1832 - val_categorical_accuracy: 0.2422\n",
            "Epoch 20/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.7552 - accuracy: 0.3257 - mae: 0.1734 - categorical_accuracy: 0.3257 - val_loss: 2.0691 - val_accuracy: 0.2547 - val_mae: 0.1819 - val_categorical_accuracy: 0.2547\n",
            "Epoch 21/100\n",
            "52/52 [==============================] - 14s 265ms/step - loss: 1.7468 - accuracy: 0.3318 - mae: 0.1727 - categorical_accuracy: 0.3318 - val_loss: 2.2970 - val_accuracy: 0.0722 - val_mae: 0.1952 - val_categorical_accuracy: 0.0722\n",
            "Epoch 22/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.7471 - accuracy: 0.3279 - mae: 0.1730 - categorical_accuracy: 0.3279 - val_loss: 2.0510 - val_accuracy: 0.2367 - val_mae: 0.1812 - val_categorical_accuracy: 0.2367\n",
            "Epoch 23/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.7351 - accuracy: 0.3332 - mae: 0.1719 - categorical_accuracy: 0.3332 - val_loss: 2.1213 - val_accuracy: 0.1627 - val_mae: 0.1892 - val_categorical_accuracy: 0.1627\n",
            "Epoch 24/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.7279 - accuracy: 0.3364 - mae: 0.1717 - categorical_accuracy: 0.3364 - val_loss: 1.9803 - val_accuracy: 0.2291 - val_mae: 0.1824 - val_categorical_accuracy: 0.2291\n",
            "Epoch 25/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.7148 - accuracy: 0.3389 - mae: 0.1708 - categorical_accuracy: 0.3389 - val_loss: 2.0735 - val_accuracy: 0.1829 - val_mae: 0.1873 - val_categorical_accuracy: 0.1829\n",
            "Epoch 26/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.7021 - accuracy: 0.3448 - mae: 0.1698 - categorical_accuracy: 0.3448 - val_loss: 2.2008 - val_accuracy: 0.1275 - val_mae: 0.1917 - val_categorical_accuracy: 0.1275\n",
            "Epoch 27/100\n",
            "52/52 [==============================] - 14s 265ms/step - loss: 1.6937 - accuracy: 0.3448 - mae: 0.1697 - categorical_accuracy: 0.3448 - val_loss: 1.7969 - val_accuracy: 0.2798 - val_mae: 0.1779 - val_categorical_accuracy: 0.2798\n",
            "Epoch 28/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.6796 - accuracy: 0.3480 - mae: 0.1687 - categorical_accuracy: 0.3480 - val_loss: 2.0106 - val_accuracy: 0.1847 - val_mae: 0.1883 - val_categorical_accuracy: 0.1847\n",
            "Epoch 29/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.6681 - accuracy: 0.3563 - mae: 0.1682 - categorical_accuracy: 0.3563 - val_loss: 1.9514 - val_accuracy: 0.2263 - val_mae: 0.1852 - val_categorical_accuracy: 0.2263\n",
            "Epoch 30/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.6633 - accuracy: 0.3585 - mae: 0.1678 - categorical_accuracy: 0.3585 - val_loss: 1.9016 - val_accuracy: 0.2820 - val_mae: 0.1845 - val_categorical_accuracy: 0.2820\n",
            "Epoch 31/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.6514 - accuracy: 0.3647 - mae: 0.1667 - categorical_accuracy: 0.3647 - val_loss: 2.0013 - val_accuracy: 0.2235 - val_mae: 0.1854 - val_categorical_accuracy: 0.2235\n",
            "Epoch 32/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.6428 - accuracy: 0.3662 - mae: 0.1665 - categorical_accuracy: 0.3662 - val_loss: 2.0787 - val_accuracy: 0.1459 - val_mae: 0.1919 - val_categorical_accuracy: 0.1459\n",
            "Epoch 33/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.6347 - accuracy: 0.3654 - mae: 0.1660 - categorical_accuracy: 0.3654 - val_loss: 1.9723 - val_accuracy: 0.2122 - val_mae: 0.1872 - val_categorical_accuracy: 0.2122\n",
            "Epoch 34/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.6187 - accuracy: 0.3705 - mae: 0.1650 - categorical_accuracy: 0.3705 - val_loss: 1.8463 - val_accuracy: 0.2682 - val_mae: 0.1811 - val_categorical_accuracy: 0.2682\n",
            "Epoch 35/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.6033 - accuracy: 0.3795 - mae: 0.1640 - categorical_accuracy: 0.3795 - val_loss: 1.7983 - val_accuracy: 0.2734 - val_mae: 0.1755 - val_categorical_accuracy: 0.2734\n",
            "Epoch 36/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.5967 - accuracy: 0.3772 - mae: 0.1635 - categorical_accuracy: 0.3772 - val_loss: 1.8084 - val_accuracy: 0.2872 - val_mae: 0.1779 - val_categorical_accuracy: 0.2872\n",
            "Epoch 37/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.5824 - accuracy: 0.3818 - mae: 0.1626 - categorical_accuracy: 0.3818 - val_loss: 1.7509 - val_accuracy: 0.3104 - val_mae: 0.1765 - val_categorical_accuracy: 0.3104\n",
            "Epoch 38/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.5744 - accuracy: 0.3901 - mae: 0.1620 - categorical_accuracy: 0.3901 - val_loss: 1.7904 - val_accuracy: 0.3086 - val_mae: 0.1811 - val_categorical_accuracy: 0.3086\n",
            "Epoch 39/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.5573 - accuracy: 0.3913 - mae: 0.1610 - categorical_accuracy: 0.3913 - val_loss: 1.8409 - val_accuracy: 0.2826 - val_mae: 0.1824 - val_categorical_accuracy: 0.2826\n",
            "Epoch 40/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.5431 - accuracy: 0.3974 - mae: 0.1601 - categorical_accuracy: 0.3974 - val_loss: 1.8336 - val_accuracy: 0.2859 - val_mae: 0.1840 - val_categorical_accuracy: 0.2859\n",
            "Epoch 41/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.5495 - accuracy: 0.3919 - mae: 0.1606 - categorical_accuracy: 0.3919 - val_loss: 1.8042 - val_accuracy: 0.2963 - val_mae: 0.1802 - val_categorical_accuracy: 0.2963\n",
            "Epoch 42/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.5229 - accuracy: 0.4068 - mae: 0.1589 - categorical_accuracy: 0.4068 - val_loss: 1.7653 - val_accuracy: 0.3300 - val_mae: 0.1798 - val_categorical_accuracy: 0.3300\n",
            "Epoch 43/100\n",
            "52/52 [==============================] - 14s 265ms/step - loss: 1.5248 - accuracy: 0.3991 - mae: 0.1585 - categorical_accuracy: 0.3991 - val_loss: 1.6798 - val_accuracy: 0.3333 - val_mae: 0.1729 - val_categorical_accuracy: 0.3333\n",
            "Epoch 44/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.5117 - accuracy: 0.4105 - mae: 0.1578 - categorical_accuracy: 0.4105 - val_loss: 1.9944 - val_accuracy: 0.2083 - val_mae: 0.1880 - val_categorical_accuracy: 0.2083\n",
            "Epoch 45/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.4967 - accuracy: 0.4157 - mae: 0.1569 - categorical_accuracy: 0.4157 - val_loss: 1.6617 - val_accuracy: 0.3456 - val_mae: 0.1730 - val_categorical_accuracy: 0.3456\n",
            "Epoch 46/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.4827 - accuracy: 0.4162 - mae: 0.1560 - categorical_accuracy: 0.4162 - val_loss: 1.8396 - val_accuracy: 0.2590 - val_mae: 0.1792 - val_categorical_accuracy: 0.2590\n",
            "Epoch 47/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.4665 - accuracy: 0.4205 - mae: 0.1550 - categorical_accuracy: 0.4205 - val_loss: 1.7594 - val_accuracy: 0.3116 - val_mae: 0.1747 - val_categorical_accuracy: 0.3116\n",
            "Epoch 48/100\n",
            "52/52 [==============================] - 14s 265ms/step - loss: 1.4581 - accuracy: 0.4247 - mae: 0.1542 - categorical_accuracy: 0.4247 - val_loss: 1.7144 - val_accuracy: 0.3615 - val_mae: 0.1775 - val_categorical_accuracy: 0.3615\n",
            "Epoch 49/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.4487 - accuracy: 0.4319 - mae: 0.1536 - categorical_accuracy: 0.4319 - val_loss: 1.7277 - val_accuracy: 0.3642 - val_mae: 0.1792 - val_categorical_accuracy: 0.3642\n",
            "Epoch 50/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.4456 - accuracy: 0.4261 - mae: 0.1535 - categorical_accuracy: 0.4261 - val_loss: 1.8517 - val_accuracy: 0.3284 - val_mae: 0.1848 - val_categorical_accuracy: 0.3284\n",
            "Epoch 51/100\n",
            "52/52 [==============================] - 14s 261ms/step - loss: 1.4222 - accuracy: 0.4360 - mae: 0.1523 - categorical_accuracy: 0.4360 - val_loss: 1.6480 - val_accuracy: 0.3266 - val_mae: 0.1685 - val_categorical_accuracy: 0.3266\n",
            "Epoch 52/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.4159 - accuracy: 0.4443 - mae: 0.1512 - categorical_accuracy: 0.4443 - val_loss: 1.6532 - val_accuracy: 0.3725 - val_mae: 0.1739 - val_categorical_accuracy: 0.3725\n",
            "Epoch 53/100\n",
            "52/52 [==============================] - 14s 261ms/step - loss: 1.4051 - accuracy: 0.4445 - mae: 0.1508 - categorical_accuracy: 0.4445 - val_loss: 1.6111 - val_accuracy: 0.3550 - val_mae: 0.1691 - val_categorical_accuracy: 0.3550\n",
            "Epoch 54/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.3887 - accuracy: 0.4470 - mae: 0.1496 - categorical_accuracy: 0.4470 - val_loss: 1.5809 - val_accuracy: 0.3590 - val_mae: 0.1673 - val_categorical_accuracy: 0.3590\n",
            "Epoch 55/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.3700 - accuracy: 0.4507 - mae: 0.1483 - categorical_accuracy: 0.4507 - val_loss: 1.5870 - val_accuracy: 0.3667 - val_mae: 0.1661 - val_categorical_accuracy: 0.3667\n",
            "Epoch 56/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.3626 - accuracy: 0.4585 - mae: 0.1477 - categorical_accuracy: 0.4585 - val_loss: 1.7865 - val_accuracy: 0.3061 - val_mae: 0.1743 - val_categorical_accuracy: 0.3061\n",
            "Epoch 57/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.3650 - accuracy: 0.4561 - mae: 0.1479 - categorical_accuracy: 0.4561 - val_loss: 1.6337 - val_accuracy: 0.3523 - val_mae: 0.1709 - val_categorical_accuracy: 0.3523\n",
            "Epoch 58/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.3298 - accuracy: 0.4693 - mae: 0.1458 - categorical_accuracy: 0.4693 - val_loss: 1.5819 - val_accuracy: 0.3651 - val_mae: 0.1660 - val_categorical_accuracy: 0.3651\n",
            "Epoch 59/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.3233 - accuracy: 0.4693 - mae: 0.1449 - categorical_accuracy: 0.4693 - val_loss: 1.6434 - val_accuracy: 0.3489 - val_mae: 0.1701 - val_categorical_accuracy: 0.3489\n",
            "Epoch 60/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.3116 - accuracy: 0.4748 - mae: 0.1442 - categorical_accuracy: 0.4748 - val_loss: 1.6814 - val_accuracy: 0.3324 - val_mae: 0.1727 - val_categorical_accuracy: 0.3324\n",
            "Epoch 61/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.3203 - accuracy: 0.4705 - mae: 0.1443 - categorical_accuracy: 0.4705 - val_loss: 1.7529 - val_accuracy: 0.3162 - val_mae: 0.1739 - val_categorical_accuracy: 0.3162\n",
            "Epoch 62/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.2949 - accuracy: 0.4811 - mae: 0.1432 - categorical_accuracy: 0.4811 - val_loss: 1.6026 - val_accuracy: 0.3639 - val_mae: 0.1663 - val_categorical_accuracy: 0.3639\n",
            "Epoch 63/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.2895 - accuracy: 0.4792 - mae: 0.1426 - categorical_accuracy: 0.4792 - val_loss: 1.7623 - val_accuracy: 0.3104 - val_mae: 0.1704 - val_categorical_accuracy: 0.3104\n",
            "Epoch 64/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.2686 - accuracy: 0.4929 - mae: 0.1409 - categorical_accuracy: 0.4929 - val_loss: 1.6195 - val_accuracy: 0.3569 - val_mae: 0.1664 - val_categorical_accuracy: 0.3569\n",
            "Epoch 65/100\n",
            "52/52 [==============================] - 14s 265ms/step - loss: 1.2670 - accuracy: 0.4917 - mae: 0.1408 - categorical_accuracy: 0.4917 - val_loss: 1.7799 - val_accuracy: 0.2771 - val_mae: 0.1698 - val_categorical_accuracy: 0.2771\n",
            "Epoch 66/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.2645 - accuracy: 0.4918 - mae: 0.1407 - categorical_accuracy: 0.4918 - val_loss: 1.5199 - val_accuracy: 0.4009 - val_mae: 0.1632 - val_categorical_accuracy: 0.4009\n",
            "Epoch 67/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.2186 - accuracy: 0.5086 - mae: 0.1375 - categorical_accuracy: 0.5086 - val_loss: 1.5610 - val_accuracy: 0.3979 - val_mae: 0.1675 - val_categorical_accuracy: 0.3979\n",
            "Epoch 68/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.2157 - accuracy: 0.5108 - mae: 0.1371 - categorical_accuracy: 0.5108 - val_loss: 1.7917 - val_accuracy: 0.2960 - val_mae: 0.1798 - val_categorical_accuracy: 0.2960\n",
            "Epoch 69/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.2399 - accuracy: 0.5039 - mae: 0.1391 - categorical_accuracy: 0.5039 - val_loss: 1.6117 - val_accuracy: 0.3541 - val_mae: 0.1650 - val_categorical_accuracy: 0.3541\n",
            "Epoch 70/100\n",
            "52/52 [==============================] - 14s 261ms/step - loss: 1.2055 - accuracy: 0.5173 - mae: 0.1359 - categorical_accuracy: 0.5173 - val_loss: 1.4611 - val_accuracy: 0.4205 - val_mae: 0.1599 - val_categorical_accuracy: 0.4205\n",
            "Epoch 71/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.1693 - accuracy: 0.5290 - mae: 0.1335 - categorical_accuracy: 0.5290 - val_loss: 1.6541 - val_accuracy: 0.3505 - val_mae: 0.1711 - val_categorical_accuracy: 0.3505\n",
            "Epoch 72/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.1801 - accuracy: 0.5254 - mae: 0.1340 - categorical_accuracy: 0.5254 - val_loss: 1.6150 - val_accuracy: 0.3462 - val_mae: 0.1670 - val_categorical_accuracy: 0.3462\n",
            "Epoch 73/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.1441 - accuracy: 0.5371 - mae: 0.1318 - categorical_accuracy: 0.5371 - val_loss: 1.5517 - val_accuracy: 0.3887 - val_mae: 0.1633 - val_categorical_accuracy: 0.3887\n",
            "Epoch 74/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.1358 - accuracy: 0.5393 - mae: 0.1306 - categorical_accuracy: 0.5393 - val_loss: 1.4568 - val_accuracy: 0.4235 - val_mae: 0.1589 - val_categorical_accuracy: 0.4235\n",
            "Epoch 75/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.1248 - accuracy: 0.5465 - mae: 0.1300 - categorical_accuracy: 0.5465 - val_loss: 1.4992 - val_accuracy: 0.3905 - val_mae: 0.1571 - val_categorical_accuracy: 0.3905\n",
            "Epoch 76/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.1074 - accuracy: 0.5553 - mae: 0.1281 - categorical_accuracy: 0.5553 - val_loss: 1.5650 - val_accuracy: 0.3694 - val_mae: 0.1619 - val_categorical_accuracy: 0.3694\n",
            "Epoch 77/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.0808 - accuracy: 0.5626 - mae: 0.1263 - categorical_accuracy: 0.5626 - val_loss: 1.5073 - val_accuracy: 0.3948 - val_mae: 0.1607 - val_categorical_accuracy: 0.3948\n",
            "Epoch 78/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.0816 - accuracy: 0.5645 - mae: 0.1260 - categorical_accuracy: 0.5645 - val_loss: 1.5190 - val_accuracy: 0.3777 - val_mae: 0.1609 - val_categorical_accuracy: 0.3777\n",
            "Epoch 79/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.0690 - accuracy: 0.5650 - mae: 0.1254 - categorical_accuracy: 0.5650 - val_loss: 1.7045 - val_accuracy: 0.3055 - val_mae: 0.1733 - val_categorical_accuracy: 0.3055\n",
            "Epoch 80/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 1.0511 - accuracy: 0.5716 - mae: 0.1244 - categorical_accuracy: 0.5716 - val_loss: 1.7542 - val_accuracy: 0.3235 - val_mae: 0.1627 - val_categorical_accuracy: 0.3235\n",
            "Epoch 81/100\n",
            "52/52 [==============================] - 14s 265ms/step - loss: 1.0559 - accuracy: 0.5696 - mae: 0.1241 - categorical_accuracy: 0.5696 - val_loss: 1.7278 - val_accuracy: 0.3495 - val_mae: 0.1599 - val_categorical_accuracy: 0.3495\n",
            "Epoch 82/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.0444 - accuracy: 0.5740 - mae: 0.1226 - categorical_accuracy: 0.5740 - val_loss: 1.5394 - val_accuracy: 0.3703 - val_mae: 0.1629 - val_categorical_accuracy: 0.3703\n",
            "Epoch 83/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 1.0133 - accuracy: 0.5916 - mae: 0.1205 - categorical_accuracy: 0.5916 - val_loss: 1.5844 - val_accuracy: 0.3560 - val_mae: 0.1645 - val_categorical_accuracy: 0.3560\n",
            "Epoch 84/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 1.0048 - accuracy: 0.5938 - mae: 0.1198 - categorical_accuracy: 0.5938 - val_loss: 1.4742 - val_accuracy: 0.4150 - val_mae: 0.1525 - val_categorical_accuracy: 0.4150\n",
            "Epoch 85/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 0.9920 - accuracy: 0.6035 - mae: 0.1187 - categorical_accuracy: 0.6035 - val_loss: 1.4470 - val_accuracy: 0.4092 - val_mae: 0.1576 - val_categorical_accuracy: 0.4092\n",
            "Epoch 86/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 0.9711 - accuracy: 0.6074 - mae: 0.1169 - categorical_accuracy: 0.6074 - val_loss: 1.5312 - val_accuracy: 0.3936 - val_mae: 0.1512 - val_categorical_accuracy: 0.3936\n",
            "Epoch 87/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 0.9767 - accuracy: 0.6059 - mae: 0.1167 - categorical_accuracy: 0.6059 - val_loss: 1.5731 - val_accuracy: 0.3557 - val_mae: 0.1628 - val_categorical_accuracy: 0.3557\n",
            "Epoch 88/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 0.9461 - accuracy: 0.6176 - mae: 0.1144 - categorical_accuracy: 0.6176 - val_loss: 1.6633 - val_accuracy: 0.3361 - val_mae: 0.1657 - val_categorical_accuracy: 0.3361\n",
            "Epoch 89/100\n",
            "52/52 [==============================] - 14s 265ms/step - loss: 0.9676 - accuracy: 0.6091 - mae: 0.1161 - categorical_accuracy: 0.6091 - val_loss: 1.6294 - val_accuracy: 0.3709 - val_mae: 0.1589 - val_categorical_accuracy: 0.3709\n",
            "Epoch 90/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 0.9353 - accuracy: 0.6188 - mae: 0.1136 - categorical_accuracy: 0.6188 - val_loss: 1.7832 - val_accuracy: 0.3416 - val_mae: 0.1557 - val_categorical_accuracy: 0.3416\n",
            "Epoch 91/100\n",
            "52/52 [==============================] - 14s 265ms/step - loss: 0.9186 - accuracy: 0.6314 - mae: 0.1116 - categorical_accuracy: 0.6314 - val_loss: 1.5835 - val_accuracy: 0.3765 - val_mae: 0.1547 - val_categorical_accuracy: 0.3765\n",
            "Epoch 92/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 0.8905 - accuracy: 0.6448 - mae: 0.1089 - categorical_accuracy: 0.6448 - val_loss: 1.7019 - val_accuracy: 0.3765 - val_mae: 0.1527 - val_categorical_accuracy: 0.3765\n",
            "Epoch 93/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 0.8785 - accuracy: 0.6482 - mae: 0.1080 - categorical_accuracy: 0.6482 - val_loss: 1.7340 - val_accuracy: 0.3107 - val_mae: 0.1677 - val_categorical_accuracy: 0.3107\n",
            "Epoch 94/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 0.9040 - accuracy: 0.6343 - mae: 0.1099 - categorical_accuracy: 0.6343 - val_loss: 1.4687 - val_accuracy: 0.3997 - val_mae: 0.1522 - val_categorical_accuracy: 0.3997\n",
            "Epoch 95/100\n",
            "52/52 [==============================] - 14s 263ms/step - loss: 0.8475 - accuracy: 0.6553 - mae: 0.1055 - categorical_accuracy: 0.6553 - val_loss: 1.5972 - val_accuracy: 0.3593 - val_mae: 0.1617 - val_categorical_accuracy: 0.3593\n",
            "Epoch 96/100\n",
            "52/52 [==============================] - 14s 262ms/step - loss: 0.8395 - accuracy: 0.6635 - mae: 0.1042 - categorical_accuracy: 0.6635 - val_loss: 1.8342 - val_accuracy: 0.2936 - val_mae: 0.1657 - val_categorical_accuracy: 0.2936\n",
            "Epoch 97/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 0.8384 - accuracy: 0.6624 - mae: 0.1040 - categorical_accuracy: 0.6624 - val_loss: 1.5980 - val_accuracy: 0.3838 - val_mae: 0.1529 - val_categorical_accuracy: 0.3838\n",
            "Epoch 98/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 0.8101 - accuracy: 0.6769 - mae: 0.1014 - categorical_accuracy: 0.6769 - val_loss: 1.6092 - val_accuracy: 0.3765 - val_mae: 0.1515 - val_categorical_accuracy: 0.3765\n",
            "Epoch 99/100\n",
            "52/52 [==============================] - 14s 264ms/step - loss: 0.7922 - accuracy: 0.6814 - mae: 0.0997 - categorical_accuracy: 0.6814 - val_loss: 2.0909 - val_accuracy: 0.3297 - val_mae: 0.1550 - val_categorical_accuracy: 0.3297\n",
            "Epoch 100/100\n",
            "52/52 [==============================] - 14s 265ms/step - loss: 0.8094 - accuracy: 0.6798 - mae: 0.1004 - categorical_accuracy: 0.6798 - val_loss: 1.5060 - val_accuracy: 0.3933 - val_mae: 0.1553 - val_categorical_accuracy: 0.3933\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7faf4a4f5358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2LzsBAJoJ2a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "age_classes = ['0-2', '3-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', 'more than 70']"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79KjKH_XoK9Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def age_predict(path):\n",
        "  l = []\n",
        "  l.append(preprocess(path))\n",
        "  l = np.asarray(l)\n",
        "  prediction = age.predict(l)[0]\n",
        "  return np.argmax(prediction)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryhPH0ouoNYd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a56bf58c-7f9a-4c15-e4e0-986e10f62d5e"
      },
      "source": [
        "age_predict('part1/part1/3_1_3_20161219230521112.jpg')"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umFkcmASoQdo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "b2093cf6-e7ce-4198-9882-2f3d0914fd45"
      },
      "source": [
        "validation = pd.read_csv('val.csv')\n",
        "validation.head()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>race</th>\n",
              "      <th>service_test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>val/1.jpg</td>\n",
              "      <td>3-9</td>\n",
              "      <td>Male</td>\n",
              "      <td>East Asian</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>val/2.jpg</td>\n",
              "      <td>50-59</td>\n",
              "      <td>Female</td>\n",
              "      <td>East Asian</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>val/3.jpg</td>\n",
              "      <td>30-39</td>\n",
              "      <td>Male</td>\n",
              "      <td>White</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>val/4.jpg</td>\n",
              "      <td>20-29</td>\n",
              "      <td>Female</td>\n",
              "      <td>Latino_Hispanic</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>val/5.jpg</td>\n",
              "      <td>20-29</td>\n",
              "      <td>Male</td>\n",
              "      <td>Southeast Asian</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        file    age  gender             race  service_test\n",
              "0  val/1.jpg    3-9    Male       East Asian         False\n",
              "1  val/2.jpg  50-59  Female       East Asian          True\n",
              "2  val/3.jpg  30-39    Male            White          True\n",
              "3  val/4.jpg  20-29  Female  Latino_Hispanic          True\n",
              "4  val/5.jpg  20-29    Male  Southeast Asian         False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_nE4PMxoWb4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "8d74f58b-f962-4fb3-b28c-be53807f242d"
      },
      "source": [
        "validation['race'].value_counts()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "White              2085\n",
              "Latino_Hispanic    1623\n",
              "Black              1556\n",
              "East Asian         1550\n",
              "Indian             1516\n",
              "Southeast Asian    1415\n",
              "Middle Eastern     1209\n",
              "Name: race, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WmXXV0KroZLj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "96063020-4ce5-4046-e593-b4cb128a7503"
      },
      "source": [
        "rows, cols = (9, 9) \n",
        "age_vs_age = [[0 for i in range(cols)] for j in range(rows)] \n",
        "for ind in validation.index: \n",
        "  if(validation['race'][ind] != 'Indian'):\n",
        "    continue\n",
        "  print(ind)\n",
        "  path = validation['file'][ind]\n",
        "  actual_age = validation['age'][ind]\n",
        "  if (actual_age == '0-2'):\n",
        "    actual_age = 0\n",
        "  elif (actual_age == '3-9'):\n",
        "    actual_age = 1\n",
        "  elif (actual_age == '10-19'):\n",
        "    actual_age = 2\n",
        "  elif (actual_age == '20-29'):\n",
        "    actual_age = 3    \n",
        "  elif (actual_age == '30-39'):\n",
        "    actual_age = 4    \n",
        "  elif (actual_age == '40-49'):\n",
        "    actual_age = 5    \n",
        "  elif (actual_age == '50-59'):\n",
        "    actual_age = 6\n",
        "  elif (actual_age == '60-69'):\n",
        "    actual_age = 7\n",
        "  elif (actual_age == 'more than 70'):\n",
        "    actual_age = 8\n",
        "  predicted_age = age_predict(path)\n",
        "  age_vs_age[actual_age][predicted_age]+=1\n",
        "\n",
        "    "
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnU24Qj4ocSw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "5bfd176e-2a5c-4885-9d8a-158c51cfcc18"
      },
      "source": [
        "for row in age_vs_age:\n",
        "  print(row)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[16, 1, 1, 0, 0, 0, 0, 0, 0]\n",
            "[29, 102, 17, 19, 8, 1, 7, 0, 0]\n",
            "[11, 44, 41, 65, 10, 9, 6, 0, 0]\n",
            "[8, 17, 24, 197, 68, 53, 15, 1, 0]\n",
            "[5, 9, 9, 121, 72, 94, 37, 1, 0]\n",
            "[4, 4, 2, 39, 26, 73, 51, 0, 0]\n",
            "[6, 1, 0, 14, 10, 33, 65, 1, 0]\n",
            "[0, 0, 0, 3, 4, 8, 28, 1, 0]\n",
            "[1, 0, 0, 2, 0, 2, 16, 2, 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_XVUGfy_5qc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "cc4d38ab-0d88-4ea1-961e-cefca55bec56"
      },
      "source": [
        "!pip install h5py"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (2.10.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from h5py) (1.12.0)\n",
            "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.6/dist-packages (from h5py) (1.18.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_GYFPerARI4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "02ab7bf3-d05e-496d-ced8-1c2d60ae5ee4"
      },
      "source": [
        "# serialize model to JSON\n",
        "model_json = age.to_json()\n",
        "with open(\"age.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "# serialize weights to HDF5\n",
        "age.save_weights(\"age.h5\")\n",
        "print(\"Saved model to disk\")"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saved model to disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92eIxJaTA7q4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 68,
      "outputs": []
    }
  ]
}