Res18.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Res18",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/WeinanZhi/-CS539-PlantSeedPrediction/blob/master/Res18.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IASORjihwhbA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "import timeit\n",
        "import torch\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import torchvision\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision import models\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import time\n",
        "import os\n",
        "import copy\n",
        "\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  print(\n",
        "      '\\n\\nThis error most likely means that this notebook is not '\n",
        "      'configured to use a GPU.  Change this in Notebook Settings via the '\n",
        "      'command palette (cmd/ctrl-shift-P) or the Edit menu.\\n\\n')\n",
        "  raise SystemError('GPU device not found')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gMWaUJW5wvP4",
        "colab_type": "code",
        "outputId": "03a0cc87-124f-4f06-f622-ad61b436ea34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_IZoaoCF1EmJ",
        "colab_type": "code",
        "outputId": "a6509251-4ca7-492e-abdd-7a89b28ec5c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "path =\"/content/drive/My Drive/课程作业/MachineLearning/MLproject/ProcessedData\"\n",
        "sampleX = torch.load(path+'/sample_train_X.pt')\n",
        "# raw_sampleX = torch.load(path+'/sample_train_X.pt').numpy()\n",
        "# sampleX = np.squeeze(raw_sampleX)\n",
        "sampleY = np.loadtxt(path+'/sample_train_Y.txt').astype(np.int64)\n",
        "sampleY = torch.from_numpy(sampleY)\n",
        "\n",
        "print(sampleX.size())\n",
        "print(sampleY.size())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([2371, 3, 224, 224])\n",
            "torch.Size([2371])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "peafrxQ_8EG8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8G_j5xvo5tX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "dataset = torch.utils.data.TensorDataset(sampleX, sampleY)\n",
        "\n",
        "data_size = int(0.25 * len(dataset))  #只取1/4的数据作为dataset\n",
        "dataset, _ = torch.utils.data.random_split(dataset, [data_size, len(dataset)-data_size])\n",
        "\n",
        "train_size = int(0.8 * len(dataset))\n",
        "test_size = len(dataset) - train_size\n",
        "train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])\n",
        "\n",
        "dataset_sizes={\n",
        "    \"Train\":train_size,\n",
        "    \"Test\":test_size\n",
        "}\n",
        "\n",
        "class_names = [\"Black grass\",\"Charlock\",\"Cleavers\",\"Common Chickweed\",\"Common wheat\",\"Fat Hen\",\"Loose Silky-bent\",\"Maize\",\"Scentless Mayweed\",\"Shepherds Purse\",\"Small-flowered Cranesbill\",\"Sugar beet\"]\n",
        "\n",
        "# 把 dataset 放入 DataLoader\n",
        "Train_loader = torch.utils.data.DataLoader(\n",
        "    dataset=train_dataset,      # torch TensorDataset format\n",
        "    batch_size=4,               # mini batch size\n",
        "    shuffle=True,               # 要不要打乱数据 (打乱比较好)\n",
        "    num_workers=4,              # 多线程来读数据\n",
        ")\n",
        "\n",
        "Test_loader = torch.utils.data.DataLoader(\n",
        "    dataset=test_dataset,      # torch TensorDataset format\n",
        "    batch_size=4,               # mini batch size\n",
        "    shuffle=False,               # 要不要打乱数据 (打乱比较好)\n",
        "    num_workers=4,              # 多线程来读数据\n",
        ")\n",
        "\n",
        "dataloaders={\n",
        "    \"Train\":Train_loader,\n",
        "    \"Test\":Test_loader\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ngi__saLkztn",
        "colab_type": "code",
        "outputId": "2541e136-afb8-41a0-a78d-24a39d4238eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "\n",
        "\n",
        "def imshow(inp, title=None):\n",
        "    \"\"\"Imshow for Tensor.\"\"\"\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    inp = std * inp + mean\n",
        "    inp = np.clip(inp, 0, 1)\n",
        "    plt.imshow(inp)\n",
        "    if title is not None:\n",
        "        plt.title(title)\n",
        "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
        "\n",
        "\n",
        "# Get a batch of training data\n",
        "inputs, classes = next(iter(Train_loader))\n",
        "\n",
        "# Make a grid from batch\n",
        "out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "imshow(out, title=[class_names[x] for x in classes])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LrP9zNMir1/zOsa7BaK5PagTnLZSMjLcbazBSxy5iRnrXLnJWhiHV5xMDIsjYnaqi5Wq6\n91gm9f/x+ugBRKQOUep9JRe0FBCDmLWETtXrJ0HAWFunU4/GVaAc0bAcTcpVmokx9Wq9naudj7M1\n/l9Dt2XsaqSIjH2sTbCqXn2m2eYmB3uHrM369XNVH5Lrc5zW5sIY2umEOCS0lHFI1yauToWAqfNi\nrMEYi7EWa+ocGGNADCUPrJYLSs7EflibO8U5hzHQ9xHvDNZIpffY/hENkhSypqv0ubovrk3fDVtP\n67iKx2qp46OQUyGrkguIUZx1GOfQXNt2PpBSIaYMqjhX5cuYFSMGtNS6V3e8ASMYEUopoIqiWOso\npWCMQfVorsZ1VY42j6l9l6McuroGSymIrD2PFnIpdW0jV+fjiO4YBcP160TW1sMRUcZ9aMVhxJBi\nvMpRZNwXwrU1LCLX9s1VJlLHbsRQtCDU8cYUCaHBOXv99la9eu9ReoILDU8+dfHimI19x3gxmP7j\nXJ+Fdy83ydTU+v7n7wG4+67T+rpzd6EqYBKKQSyQK/MwKqgm1FQmVWBkskcvmygIDmsMSmFQcEVQ\nk7BiKXm8y4AtQrGQSxnn2IAUiirGAMUgpZCpkyVWKVsOi0PUUBYZHSKV7oUwMj5F6bOBXBmRSKEo\nlFywVsgCRi2CksuAsY4MlDI+n1FEwYqCEaIqXmXtUDN1Uds6RuMUstBr3RCuSH1GUXZ0AcYQWsOJ\n0xM6o7gMoBhr8MFVZmktxlisNcSk7F9Z8OSjFxgI2K7DBscQV4i1aFaGYcUwRGIpiLGE0BK6jqJa\n30pUFCsGcR4jQiYRgifHzFPnH0fcFD/dQoxDgK4NrBaH5GGJGIPrJpw9dw8Xn3iCYeixVuiXhxgd\nDzij5Fzn1IpDFPAOrMVaSHGgFEXVUEQwCBQl5wi2YAHJGWMbnHGINRhniTmSYwQVxFXGIqpohs/4\n3M/h7T/z85V5RME4Q+Msq1wYoqIWnBVQGZl1PQRU66G/fWLCPR/5ep5+epd+2ZNTRimkEiGbeiA7\nQ3CeVcpoqkxz1k0RY7FO6EJLtzHl3ntO8PT5D7C3d8gjD32AzSmIcVBga2uHoSR+/VcfomvhtR9x\nho3pJt4J7XSDw8MDhpy4YM4ziMFIXXzWGAYGWm+hQM710E2acViMFDrvmeyd4PRdJ5ltnmJx4Qn2\nHv9t9pJhPsA995ygP5zjuw5vI2HacfLE3RyWKR/8rYfY2Joyc4ZC5rELK7rpBFtWLC4/gTWCuJZc\nQHNhMttgOT8glwSaCaElq8X6popvaSCnQkqJISt9yWQ1bJ44hZbMZDJliJk49BAjoAzLQ8pqiXeW\nopBUULWkoiyWK5qmpZl6utMeOwHGt3gohZx7rHc4MXjr66FhAmRluzlBayfsXd6lHyIYi+86prMZ\nBoirnlwK1hryEClaSP0Ka4TQBnJKOOdhFGwuXbzEKq44e+4etra2QOoBlXOuz14yBigI1gh3vfq/\n4hu/9buf9es8Xgym/y7gI6X+kMPjwH9PTUx5RmQtZApeuSZRimKxVTLBkErCFzAySvJFUFHECFoy\nWQporpIpBnIhW64y9DKeplak2rWMUIqSiuAoSBaiJKwxuDbANGEnln45sCrLeupvNrQ2UHYHpAjG\neUpKlGLIFJw1SMkjDyxYGSWUonV8AlYseZTGS1awFqN17pMBU8AWwFTp2jhDyhnjDKZAEiVnRbTg\njamSNJksFooyO9GhcWAya7j73Dk6azi88BQpJlIsDCWjLiM20bRTsA0Tb1nsr9g+sc1iUJZZGGJE\nrEfzSJPQYUMH/RL0miaQh4EgDjCIWEzWOvacWcT+quS3XK4Yxg08nXQMQ09BwRoQiybl/IcepQwr\nMgnbBrypEpn1Fusci0XCeQdFsOpAHKlkYklogRwzRapgULJijGAEWudx1pFXfZVwjZCGAScNZAEs\n1hmsAZXK9FfDCoCUtDJ2q/SlkItQSsZaS2Q8h8UiRcmqGFvLiwhiA8v5QO4T1lgGC2TFW0/SDMah\nJdPngsGgxlJKYRh6xFm8NGAFFzxNE9AiSIHWJoQZWxstWE/Jynvf8xDiLa85KdzbrjiUGcOgqB/I\n/UDciGhShAwaiEZImgjOYZwlr5SilkLGWTtK3IkgW/jtnVHjaXjs0cfJZgPKgo/86NeQ0kDq5xRW\nUISdrR2yOjY6wxs+8aN4+PyTRDWcOnMXH3zifdjlIcQDrNT9Z0uCWCipcLh7BayQNWMUclFMaMhA\nN5sy7Od6IBhLXM0R6zBS6BeHTCZT+lWPOFe12Jwp/QIblzgBV+UpijUslwsEi7eG5XwPdR0Tu81V\njU6glEQILWXUFpfDAmsCopFgHCn1SJhCUaQA5kjwGDVbI4gx5JJru7mQY2bZ95xqW3IpkOKoGRhW\n+7sMUtjbvcxkOiX40f4uUvcboKJYa67Tap4tXnCmr6pJRL6C+oZBC/wzVf3Pt7vvSJkpoylCc1Wo\nsomUqgBgRMlHZhOt02NVSLlg1ZCl4BBkVPuKgMkZi6BFRlUVVDMFxRQLFKZNy3KVyOMYwsmO3i6I\nOscOLVmFWHKVSrRnNURk5lBrkAS4jryK2HkmxhXO+asSfJaMuUrmXJk/iqTKRJyA5owVQ6xKLkYL\naFX7LJBShlJpk1CcCIJFtZBTREVIatGSQA2bOzMYDE0TaL2BNIBkRAvDYoH1LSYb1BsiA3E5MC8Z\na4WNnQ1YDuxd3CN0kyoNe0ceEtYIaoSOjmFYgWbQQhc8JdWNUVBwDh8Cy/m13KLQdswPr6BxBaos\ncqTrulGtDgz9QByWaEwYySiRPq1w3hPalhACfb/COw9SzSliLLZpkJLpYz0YNCur5QJjHaXUpdJO\nJ2zubGOAoelHIaEQSstyvqxCRsmoMdjQoJoo/YrWXcuw11wIjcfmQikJZ7Rqi8aTUgGJFFVUC5oc\nwSq9wmy6QS4FtZbcD9jxsLcFrPdgBUtbJXaUlDOUzJAydlyzy2WPKVcYTs9IMXKwd8Bj5/d51b0g\ndkK/ilgrbHYdXcjccyqj/QLvCz0ON/QMOTLPh6hYpGSMyTTqAItziZLrwUUB7wJihmoeIlBUufTI\neeY5MX3kKaI1rOKCnc2OUgxtaNmThvnuLpvbp/EibO609bWiTeA3f/kxZmfvZlguuXz5AmdPn0QX\nCyQr1jfkVapvf7SeWJQQWvrDFU3bMcRMFyrz75cDmID1Qh56Ss6Y0DBpO4xxLBaHeN+ynB/QhAYW\nB2i/xIcAZEzJWIHGwmRzRj8MuASHYjFNxreeXJaQI6Ft6SOUHCklYn2HNYGUEyKKFSFboZiq2UoS\nUoygcOmpp5lNJljvGWIPWhAgxUzW+v+Tjz9J6GB7c5thtQKEez/iNaRSaXGwd4m7zt0NKpQ+klPC\nWEPWUjXY5/HqnxfFpq/1xy9+6rYV11By5sivrKYyrwJorrY9LSAWNClqKlNEhUSV4jMFKZCop2Ii\no6VgREhay4wY0pHJ3UCOCSvCsixH6U9ptqa4TWWxGCgq9HEfr11Vq4xFdGTKJkHKZBpiSoSuAe9w\n+xD7jBFHUqWQsaVKlrnk2jHVDJW1HlqiVXovKWO8AakSYyk69lmZfynVIlBtrIliBIqppijV0e6p\nWK946/BGiId7aOyRrJQUCe0ELYIWIELMPTlnbBPAVDOT7xzdxJNHlVJsNQd5cWTNiBOEyoRzGhDr\nUHR8PhAczaRjOSxZLVfV1FMSoRGGNGCwGDGIVHsxCDkl4hAxBbrWYmyoawHBGEcuioillAGjUlXc\n4MkiVWJSQTPV7GQDQ4zkVH0YIgY72ryDN5SUyDmT+kRoA6vlAtAq+Y/0V7T6Q4Ai9XW0TpSVKLk+\nJhGLQynWjoe1jibKQgGssZy46wS7u8PYZpX4jEBGsQhGDKFpaZtAypmcFSjY5UDKqWqrktk/XLBc\nHOKsZ3E4hxDY2DpFGiLEgkjgzN13gQpPrfbZjruU+Bhm4x5KsQy6JJZYNU0TrpqIjWg1gaoZbf1a\nzatYxAxICcQ+c+nKHv0qc9fr72f30mWCWO554AG2Nrfo55foh4LYDT700ON83Me/FuM8pETKkfte\n95E8+v4PsL97EVC0FERN5VtFsL4FqXtCFIJz5LajKoGGIa4wrkEQci5Y50hxAIHgHXnoyZKYTWcs\nlj3OeaQUSoo0oa4j5wNGqqSvOWPyQLDCkBJdE7CtI6ceK1WYzHEFUv1QXjxaepCAMbZq7jmBq6bd\nOp5qUkUM3ltyztXnkRJCNcHmOGDE4LxntbhC0sTm1sZo6sk4b3A4igp7V3Y52N2jaTsEwYcwCiuK\nNUIqrzCm/1xw4dIVjIXNdsYTly7jGmHWeLrpjPnhilXssQrqKlM5XCUcFtVM8JZlhk6gzxmLJZFx\nAlCwYhExtBPLweHApKmTknKm68BLQNXSdkIfqyYRU90gwYJKxkSPJhhKItgGNKKSGKhS9zIOhNBh\nthpaHPsPPUXbNRzsXaEY2JjMuHLlEBscjbdM2kCJiaFkumCJsdCvMvPLsIwJ4yxnZ4WD5cDGbJP5\nPBOA3gzcvdMxZWB65i7e996LxJzJKLPWcbjsmUzPwHBIIwYdVhg1dUFax2TzBLlIlUhLopREQSlG\nWA4rItX55ryDrFc3o1JNJzCaWqxBTSKlTEoZaz3WVOYu4lnM6xufRatN0hjBOYsYiGlJkUyMgqch\nxwFNPUYV68AGh6pgfcAYg/WeHAfiMFSTnrWocUSU1lTHmh0dwBiL8Y5huWSYrwjtlKZpsMFXzShG\nrDeoFIL35NjTWiHnUXssC0QMzluMqdtDSDgTSNU6h7NCUoMrVVix1lGsQch4saCForXyyROneMOn\nfgIpwf58n8ViIC2XXNnbY2//KYa+EARm3hH7RAqFnIUQJmguLNMSWyCnAdt0xJJIOjCdtBzuXaGZ\ntBhx2GIopR7grTEs/AmGIcKFpxmA8OoTaD8HCVdNBWihmEJSWx2FRbHGolIwYlE6jFR785lTJ7n7\n/nP0yzk7J7Y5cfoszjoWB3scXjlAB8VI4P7X3MfuYaIdIqd2Olb7e1x84klYLviIB07zyPkL9It9\nPIJ3npwHshhssKRVxDnLwf4u1gWcb8gpQVGcd4Sm4bBfYcTi2oAbAtZYDIJYS44De7u72DbQlUwj\nShlNoM5ZtMTq/wotmiLkTLBSHdHB4KrdmEQhlrqnvG+IJeGMEMlYU/eFuIZh6EkhQtbqdAdS32O6\nZtTSFauZHIfqY9JU/XGSaVrP9um7MKaapEMT8M4xDHNA0Ji5/NRFmq5j6+QZZDyQMYWhj5T03H8R\n8xXD9KWB7ekGly5dYTJ1CMqqz8R8WJkGFuccJ8+d4rGHn6yScwYXOpIOtCHQxwEt4FqIUXCtrw+Y\nC7YJCInZpDr9iOC6jomDTR94ernCbWzQnvOkNNBOLXnIoB2ZjPpEifVwj7qg8YJBIReiKoIhp4iz\nDbms2Hj1CZaP72NDqAvAZmazjuVqRVHDkDOqmW7S4n2DW2UWukRtwY3aSxyUU1ubpATTzlLSACXw\n+NMDhczG/kXEgjOGEjMxZs6e2qEfUmU+zlPSUB3carFTC0EJzpIKaPEMhz0xRlaxsFgNNbrCeYwL\ndG1ARchFsU7JiwzGAwnrq7SeR2dZTANqqsRuoqClbio7+lByyXhvKMuMxoxrDKYoppTq3BZFAtUG\nbw1WLKHr6qbPinOebCOxUKM2LEymLXG1QjVhxygV6yxGDWXWYLwFa3EbgeRjtQXbBDZjNWOdYegX\nuE0d1WZDSYYyFCi29gP4bMEoRaoWUAbBOSWOUrIh4YrBWksScDlTMDgDTTOjncxwCJubG8i4VtLV\nQ1cRa2hDqD4JrY5ncR5nLavlksP5IaSBx97/GziBdljRtokGsClB6Xnog+c5d/8ZGqDlEOsMq+WA\nNjPOnjzBhXQFjAUnSK6mMZWEuIB3AzG6aoM+Wk9jQJMBnHHMzuzQzxfVHJcywQeGYcXeYw9DcDiJ\nLOLAXScf4N+97ZdYJpiEwIl77mKyPM+50xskHGfPnqXfv0IukEttp489cehprccWRXAglr6vEU/t\nNIBmlvMDrBPaSUMaIs7bGlWGUHLCNw3NJBD7JXHIeFvXUpFSDy+BfjUnbO5gm7q2cunBZXzTVE3I\n5upjKjUiCQy5VFOxCpTSV34iliCekiL7V3ax1lcNWDM6LBliz2xzE9t4JFiMwGrZ0/eJ/f19dk7M\ngIL3LTJGjxUt+GbKYn5Yzf1CjIwAACAASURBVHxiWcwXYHZpmnb0N0WG5Zzcr54zr33lMP1kmfcD\n2zsz2q7hcG9RTRke+gxUJYn9Kwec2N5kkQtpNUCBMJuRUyE4TwqRznq6qZCGiPWBIEJWoescqe9p\ngmU5MqVQFFzD5sSzeWqDZdwdVXtIfSZpqZEpBkzjSasBS/U5qFQnaqFg1aHGMOQlRQvBOJqTU+TQ\nkVPmzHbL7sFAFyZs7EzY25uT48BW07LIgHX4doLYiLETtCjT4PGhOia74Dmz0fHUwZLdgwVNE5h5\nSxoiUYVJV8PzrFFiEpx3FGw1e5RMzqk+Riik1NP3S3KEftnT95H5fGC+WDLd3MaFCbPtHbAGYy19\nilViXyY0FtQo3XTKsOpZ7F0h9ktQobqvq3lCx9AkESrj1nQ1bNRbj8ZMGwzkSEo9wYFYi7gxCqvU\n+/IQwQgpxcpwtZBywXnL0C+uRgzVDmsIIQLOWaaNR1qP7QrRLsAmDD1iBWMVSDg/YIzFj0F5JVu0\nBIbDAZvr9igOGl81iVVSxAqTNrDsM7lUU44RSKlgNbPSXIMHJGBEqp8aU810UiOLbBaSyOg8rn+M\nTnwxhuAr0/diadtA7Fd8cLWEkrBSwFgW/YoZhozSbgR29w7YnlT7tQ0NG7OCtlPEewYdsNbXA9xW\n56IzBmuVmGqQgxFPoXI4kVy1LetxLkC/oo9K17YMMbNcHLJ/4Um8A5wlOINrpmwGx9kzZxiGFWd2\nGrJZcXJymnZqiS5gWaClhjN658iqiPV4Y8n9iiIO68bD2ox+ODWQCuREmExIQ4+IJTQtqRxpaEo/\n9Mw2NphrxAyZUhTvR42iKNYHnBiySKWjcwg9jBGBiQwlgZUaBlxqKGwkjtpRdZ6K9VU71mpCunjp\nSTa2z9IaX01CwGQ2QexRWLCt2qj3LHavsFxc4czdp+q6LQVrXQ1FFaHpJiyWC8Q4cnX/sDw8pD/Y\nx1mLdUqMK4J/zm90fuUw/VNbO9VMr4mpCfQuEyY1WsHHSNKCD4E4RPpUJ8x2LRoV51s6D67pmDSO\nxcE+6gzaFLxz1TxAZTBTXxeJsR2pT0ynLTktaZyjv7LCnvXEOK+MylkkV7W90EKM+EmAIaKaqrRk\nwJkWLQolojZiTUvBYqaF6caMYDz7j1xhujnhYJ7oe5h0HdE7Vn3BNS2HMdG1gWQMYm2VJlxdeDsb\nnmWfefTSISVnHjh3mtWqJ8WIcZYuGKTA4VA4XEauXF7BLLDSJWiEUk0wpSi6P5CLkGJitVwxDJmM\nZTlfMptNmZ6+i6ZraWcTzBhT74sy29ignx+ShwTWEtqW1CcKiXh5iTWOYbXCeocxlpQL2ViGHGvo\naspYa0hpoAwFEQ8SCV1gEjbpuikqwnx+SOp7csnk5ZJghBwTUnINz9Q4hrnFGl5YIObqUG69r85U\nKdhGse1At2Gx3T7YJSE0DHEXlVAlNzLOBQx5dBiBHfMylnFFoP6Gd+dcZepxqNpAiSzmSjnKd6C+\nmD6rIs7iiiK5kEtmsjXFag3ZzVpt+Vz1QdRDUcWSoqIy2thHH5ZKwTqDJI+4RFwNDLkwlIJRZTJp\nmEw9T1zY48zd99Hm3eqMzZ5htcK5BkSZ7GwTLz+MSEMQRYzD1tMXqwusWIZiRxv2UXQZYDKZQkwr\nvMyY7x+wu79gPp+zvxg4vT0h9ytyqmHIp86cY+PEBjsn9gniUckEEZ66uI+bF564dAHfzsg5462t\nfi9qpN2km9DnCLY6G2K/wlmLIEgGJxYrhsY5louBotU8q8ZTxh2qRbG5HqzOWrw5irYHFzypFPAN\nQ8o4IOcBFYNtakCBE8tAoiRFxKGqDDFxlM8gY0w9VPrXfBfLkOCJJx/j1OkTbG9vEpwnNH5cGEpO\nGbGWEALCwD333Y+XggMcBouQrCDekHKkcR6wVeu2FiNCTAOCrwJBaPB2/QfMnh1eMUy/C5Y+R7Lx\nZGNopjOGPuIEprOOvb6nc4aYLd57ciw18sHkKn30PcFXiV6dx1pHtpnY95jgmQRLvyqocThj8Rmi\nyRiU7FsmzpJ2BvZSjRG+mogjpR5GuTIi+sjG1g7LwycpJYybd1XDAUtdIEUFLT3qBEtmMIG82dJH\ni/UWJJGz0DqHFsf+okesZ9J5lmRaA+IE74RFsuzHwubGlNXuCtHI4cECdZaNyRSskoaeg8WKaesh\nt+zv9yz250CqJheAUp2c/WpVk0pQYrGkmCl5hQ+Bk696gNA1VY3NiqParDVn0mrFZDIhh0xKiRwj\n/bCkm04oaZvVfA7LHnJ1tOcUydYjPlD6nsUwMOsCzoN4z3Rrk8nmDO8soelw1leJOwTi8pD5wS6a\nEl3TgngKjpQijXVoLqRSKCWQsdU5LkIsiULEb0TsZGCy0YAcYmy1mcayj7U178DbjlzAWY8WHRNo\nCmIcKSe62RQZg4+SZhgEZ8Cr0otHXZXSYso0wVMGRQRSTkydwQdDyXD50hUmk11wBmvCmPBU8xzi\nsCD4jiKRIkecpUaqBGoggQLOWuIQGXKsmkNo6brAEA+5cpjoNjbpD64w2WmQrKjpyPGQ4eAyJTl8\nPsC4qssUsRTNOKTmkLhQNTpNFGx1gEpCpPpmghFaP2Nmdnjk4AKLZc+r7j1D2NhCS2ZlWj7xkz8W\nlgecf+I8sniSfHhA7qacP/8ojz91wKvPbdCFkxQGpERsGRMAwwQdneqL+SGdsxQdqlaHQazB2jHR\nDK2+u8N9FMNiuUSMwwdPUqpzWCD2S/JqReM91tTItyCgKWJtAIWslj7WkNA4ZOxmh7iAaqzBAjli\nTA29TXm4yvQBjLGIKiTIosznS6xr2Jh2uOAJTSCYGkQhBqwzqLekmMgpc/e5+4Aar19SFeyOkiUx\nYGwNR84psRwKxoJpG5qmofGWVCKKIXTP/RcTXzFMHwuN9cz7zGJVwFUGGYfM/qBszmbEAiYtEZUx\n5r2QBSJ1UztTPelojQnf3N5kWQqtdeSYaKzUOHHncQ6KFJalEKwnbwmHaR84UnVNTf4Zow0Qj1hD\nkcxysaQJHX3MNeqoKFFKza6TUDdOLqgUNDSoJKZbO6RLA87AIEJRCMGx6CNWHCqQhoEuOCau0MeM\ntBM2cmGVlfl+T/AO3xm2Zi2XD3sO+oFFGnAlM+smGJTtrQmHiyWpCBlDN91kWCyr/QEBYxlSxArk\nAilXDaCbzdg4eaKqzqoYBUqhxIgxMoaHZlKMxBixztOEhmVeVnOJq3bYHCNq8hi6mMiZMaPSknLG\nNw5nx83hHN2kxbqAaj1k26ZB+yWtt9jW4r3B2jAGqHWUHNFS6Bc9y1hIxoFITdqyBd8k3CxDs6z2\nWSLFUpMgJFUmN4bzyvhcR/Y7kSPnWMTaFjv+HKxNShQD1qBFyKnadJ04vPc1hyIIthjiGJJbxsSt\np5+8wAfe9wH6VQ9SNU/nPJO2o511NJMZOyd22Ng6gTiHcR5nHLFkBjM6wr2h7/t6KIlhsdxjNjmN\nTYXLe7tsnT4JeSD4KVEiOSmDtewuYTnfp331zqiF+irKKBTjgIRrGtIqExUCdexWdDxIFWMdJ05s\nkQ8LWzvblLxLpjJSaTyQOXzqabZObjIcFnZ94ckndjl3d8vpu19Ft7PC9nOGGGkbhxdhVTIuNDUM\nN/YY68a8gPqzxVbMeJhTc2lE8dYQ44CqkrJisCCCsQ65Gg2kOLHV7yMZFUvXNlhT97HVqp0JhqHv\nMQgpFbyp66cYrQl2kiml2laM1LDuo8xwsVU+FxcgG9JyRdN4dk6doGvsaKoThDqWmguU8N5TysBq\nOWeMSQMxDDHinUWoayuVRJFRUBuFmX5YETyoa6nmU2EY1n/i+dnhFcP0u+mMg8M5OxuOtFIms4Zl\nsuRJIa96SoxsnDnDvC8wrJi0gRgzTRvAWFpXI3nQQgjNeGJC17Y1sahpSf2SzreQE613RAzSR1zX\nsOJyjZG1A85bxHXExZKasW6xUlBqLH3Oc5JkREFsS5iAFKWPsSqaGcTWLL4hLTDiaShIG7BUc1W0\nQp96XNPShZphOPGOrUldDGZe/RGHudB6QzCJVBLTrmFYDGw0lpQNs86TY2So8a0sE9hgyUmxOHLM\nNKEl5oGYCrEkvLfEoScPK7IWxFq6jQ3SaF80YqAkck4YS/Xm5boYRYSmbXG+Yeh7rKxwLqCt4MKK\nIZe6MUp12JZY7fzWWLwzzKaTGnvvLZMuELyr5qwiNe5aE62HZjbBSMba0SQqBmME1NeNP2mYL5TF\nAH1KlLYQZj3SVoZvXcFINTthAmUcgxU3hiRmjGtBMzkta6q9gpiAta4+c6qZmdYblkOCZClZsFZo\nrGExpKp+IzVZriS6IMRc47hbYyiaOVwO9MOKnCDrISXl6lMZImosrQvYpiYUiTiCEbq2wTUtvvW0\nbcPW1gxrqzj4UR/7SZQU6RdTJid7JpMZ+5evcPHpOUMx9HlF7jOL0mJDx3x1iPMBzaYye1tNCkig\nKBgpBGvICcRXBufU1ggeEYZVwlnPhQuXML7BGsP8cI9+sWB71jDZfA2He0v6GHnyoqM7sU1PZrvb\noMSePD3F4d5l8IGN2RaCVsl3WNGFhlgywTtIGWuq9inWXRWvC9VeX1KPsS3LxZLQTOiHgbDpsKWQ\nbUKKUoaMwZBiZhKaMfOlOvlLAe8tmgtoISbFB08uCWtCDUwg1vx+11A0jjZ6paYcCVpczZq2ynIx\npz9ccfbsWbqm+kOCrxFF3nuKJlQKpQg5DqQ84LwjpQFjDEUMafQvOLHE1YAYX/dpymixiDXklDFN\nIMcVxVSfT776s83PHq8Ypm+tZbKxhU0rQlhRjMMGsOpIIsRcuHD+PJIE0zoa8bjQYESYdBbtC5Pt\nHWIWvIfYRzSlunFSZIlig9C1jpwgpsRk2hEay2ANyXokZvIAGgeaacBJQIKMtkMw4pC8gpQoCi7M\nCLYlxQUxJqwEVGvdbDOl9DTekTUyTxfZbLaRocH6BqeZ5cpA7BHraKytr5AoSimFPkWaIsyawN7+\nEvWOSevoU2Jre0KOkZgGUqx23uAdOTcczAc2dxyN9zUDcIj4zteknJHx5pQwAq6bkEoCKcw2NklD\nj/MNzjmWhwum02nNyiwFJw6oGYauaQihqdEQyyXeNygO36zQ+RyLko3B2IzJ1ZHceMNk0uKbBu8c\nIVgab8fU+FxDLRHQhPOCkjGiuCbg24CxwnQyJaZIv1rRDxlMJs0PIUTypCdMBsRnMkM9IIwD02KM\nw9mAapUUvQlVO6AfM7pBy4CKrbH0UqNBTK7OsqJV+vTOYEx15qYhYaQGsxYMqhlToBShGcM2vW/4\nmE94A5+6dZpUqka1HBJFM/1ywdOPnSdb2L9wkX5xyHK+JA1VyuxjZMiJrrRr0qbBYaEJ5FRot09x\n4ZHHWC53iYcHiBiiWkzYYrVaQFoROkikykCt4EWJOWLdlFQGpNSEIahBANZaUhwZrtTXkXzofQ+x\nNd3C+oZ+vsBOD5jOttnZsmQG3v/u/8D0rnvxk4ZmWkNk9598ijJRggiTu8/Qi/Dk+ac5e7JhY+cU\nVy5dqNmxmrEasXhyyQiWxgcOhiWT2SaaE84Frlx+mhQTMR/QbZ0g2ZrM1a8WuKZFrIEyVDNdydXc\nOoyROKbgmgYtoyaXVnTBk30BozUbmVIZqkhNrsyruiaNqYl7FCgGJdF4XwUjF9g+MSUEN4byesQE\nXDdlurFBSpH9/d1qjy+FnCNalNnGJsvFgtB1TKYzFot9VCCnSNM0HB4sWA7g2wmhbWAQjANnPCn1\nWCzW/i5w5A65YF3D3qJw34kNDlZVbbd2fN9WUqxrCMFWO94yVskQYEhkCSyHWBM7UlXZc6kJTtla\nnMlQPKuhx9jm/+PuzX4ly7I0r98ez2Bmd3L38MjIGDuTyqQmqmipQXQD6gfU/yfiAdESEqIFLdQM\nJQTqAtSCqqSzhpwzY3T3O9hwhj2tfljHvXgOeAhhbxG68juYnbXXXuv7fh+pLuyNZa6G3XhF85W5\nu6fze5b1Qlmf8P6Zjj7CjlQTeZ3puoFKwjnouoG8zpS1gI1o7YrgoVldVjnT47AqFbSGsujCbl0z\nJgSCt5ALUbU2xBCZZz0IDCgXhgq1kc6JGiKPjye6PtIyOBcYnCOVxJobYhXuJKIHqelVo2+ag+3h\nr7UQux627hYDMQY1F7WKiMeFyLImvNUFWmtNHw5pmFpp8vYKDM4HnKhc0nvHusz4LurXl0xrQtdH\nfIyEEPDeb11ro9WkrkOpOK+jN2Sh5YZ3QaVqnXvX9eciinRIJ8RmTJfALLi+YGJFZMa5ATEG5waM\nVb6Q2YxshkZpCWd1fKAjhaY66m2uWuuKE1U+gRZyMeqEziVhxFMbrEUIHoxRaJh4oxgGDFjLbj9i\nWiXNF5xzOOvYBUcVYexu2PcDLgbsjwExWBGqFEppGGMouXKZV1wrPD58jgemy4zx8o4jEwcHOLKd\nFfxlG4Ot7J4P1OK5nI6IC4ClWYcDnPWUfMZZR/SeZCqyKjKiSMVRKMbhsYwuUE3cOuVM9JYuwuBX\nmgQ1FkrhNz/5KS8++3s8fP3AzcsXxOs9YjN+iFxef005nnj+7DkhDuRUGIYd0+WofKX1QprPDMNI\nTokQemXspJmaFuane+bjG4oIxns6d0dtGWs9DQW3ma1BWNdZC0qtVNQgp9p4IAby29ubbRgsuS00\nBtiMjU0ETMU6R6tVxRqg4g/rVCVmdH8TfMCZgPGesJnHfDdgQ0fKuvdqRY2kpej+EO/0ubEop8lA\nq0XHoRhSLjw+PlLE0YVAiJFlTu9uuLRKN+wU3/ItX9+Zol8wlMtMq5WH45lqAi7smNdKmSYIAWe9\njiSMpY8OyRUnhdR6gg0YVEIZOkNaVgTLezcHHo9nyrKCcVjvSZcF5wPTZSanpMwal9m/vMW4Dhcj\nuYzk+YiPB0LoMMUp70dUHzzGSE5n8goYt0E1K+Ig9D1mShgTWVtm9JH98AL71CNeMDbgTOOSCzkL\nxgccFWOFZdKi0nWGZV03lEKgC0KrHc2DNxGc4eb5DV9//Zquc0xr4cPbK5oUVmM26aNXFZI1BO+3\nMUSmurezR0fKb1keBesjgpDzqmqHlBCzsX9awTVDN46cTkcu5yctTCXjQ1RVQd9vHbQa44xzWLsw\n7jqc3wxiAAjWe+wmV2QzOeEClYqlqlY/BJzfluJGyGmllUrKiWoqyc+IP+MCVJ8pRnAMiPN43yNN\nXc8iRY19UrcxsXJljLNUSdCy6u2dBVEDjzOqngAwtmKrJZdGwav1vlm8V+fulTc8ZEsQUce3EZUG\nivA//Xf/PeuaqKK3mIYSJsllU8oIcRiJV9c8v7vDhsCzu1v6YSCEwGE/MIQdy+UbHurCeTpxfbXH\nOMM8XZiOM7kkWujpu5FD73Eu6AI+BGhZHdchqOPWGqILJKO68prUJe6cIedNMWY9iLrKpRjtuDE4\nG7C+MgzXuKHHpIKcHyAOdKPhl3/9C65f3GIfHlTjvrvj+OXPuf3+95lenfjBh+9TTmfs1pA0SdSl\n0llLaYllfqStiYmK7yLl8oRzA513DM/uaFQ1CB5fE1zADCN4Syppo4gaYoyIM9gq2yy/YGKnO0Jn\ncc4z14xrgtT0zm2dSqLYos3BRsR0Pm4jnu3wYHPnSUGqxUjQ0bELGONxzhNi/+7gTynRymakqo3W\nKn3Xs17OmOARKczzE4IurlMpTMcnbBxoZWHYDaRlwYioHNgbnA8ULTrfutZ+Z4p+WxWwVdeFaYKb\nl894enjD9d0znkrhdLoQomd3fc20rpSS6YNnGPc4a1lSI2KwYphPp62TNdw/TrpEbI7LtOJiZj+M\n1E0a5YNAXWnXmcvlgY3TxtjfYcxKWSfEH2gtqWmlGlKprHbWGaiJSn0MkdCPLNMr6pKgOapP7LqO\n0VzDG4/b7aBdoC4ksbS6Yk1g5O/s8JfUOFwFUqrc3uxIa6Z2Fhcc83lhH3ucCE+XGY/h7mbPsggx\njvzufgLTuL0OVGOxRtEFxmqHXlvBBc84dGqYWguuFh0rrYmu6zDWqP45Z4y1WGfpxn6bR6qsr4tq\njlFXr5rMRBqxV+ert4L1EZpSE521HJ/e0DehuYA03WEY22G2w9xhyTljSqblRW9ARigl46xC2VKa\nmdNKbpXVrCT3BJ3SPCsrzu3BRnVLtxlsxJoNZNYqmjNuqTXh3UDJszomUbgfDayNtFZZ8hlEFRIW\nw5ozQkDYvs5bRCy9h6Wpaa8ag9lGMdY2nr/4HqW/YVkL83xmnhdYdCGbRYF0IsIpPdEeHvni17+F\nDVNcmuCdYK3n6uqaH/3wQ0qBeU28+td/QWkWMYG725Eb13MulpQbNS9KFW2qa6+5gOuogo61aMqv\nQlkxvlNyZC3mnaMaU2nF4iTjfYcNlWXK1JLJNN48vmHII7E6aqmcz0/U/gWf/OhDTpdHzseFpU70\npxMxjnz5zZE5Z6Y5QZo3WFjlcH3L5fEBVypu3NN5hySVGRurpivvB2pOGAedH1jbTEMhfw6F9Llu\nAOfVF2JVnEAVQtDbnJWE973+fxwQdLT11sntdalvNpWQCjf0hqdSXsEajzUCaOPgrY7wnA8YdETY\njwOuC1yNO87HIyUnJGckpw3HAGk544yiXNpyRpxiOJZ1ZV2mje5rGcaedT7TSiYvCVszZgzE6FnX\nsyrBvuXrO1P0KzA9nbHWMe53TNNMjD2pCM4HYuzoOo+tQmcsw+GKbhgQY+m9wcfMfD5D6AA4n88M\n3U4VQLVhQyB0BWOqdpeiSgup0AXDulr8OJDagohXnkvsMLnQ6pkYRqwJTBnIIOL0QxlHmlhC57CS\nMA0EBwH62BP9gEsbH1saVSwpNcJYCb0adxZTGFZVEFwNkWAMuTWsC4Q+UM4TNVei98zLwuCNzvCB\nkhU1LLVgt0IfvOftndTZtwtQRSm7GHWkgKG1BVmMKjr+Hxzwtx21tYqrSDkxjj1u45ioXFbIKWOs\nI2XttPyGZ6irqGvTe3zQbj54z+V8VH34MOCsGmassB0yFSOZdDlj8kTwI2IqiMUaT6uFahqpLsz1\nwuoTqz0jRDU72QFntXuMrqPU01vJN9YNgEEItJZpbaFIpUrCOYMz3TYjdYhYjAm0tipuA7gsKgds\nNKpVuSut0qyjiKWWhti3/J22qYk8ty/v+OEf/kOaD5SaoUGpidKENC0cLxfmeWE+P/HVL36h7udc\nSKXQtUJtG0V169BrhVqEx3PhahepkilJONtKkcxxUmRB58M2CqucL2euTU+RhVYd2KDKLAEfd4jM\nWBzVbn8rVPZsfaDZRiDiRsvvfv0LHefkwnD7Mc5Z7aDPR6bTN7z38QfgYBjuyGFhH2751V/9FT/+\n4091jxIDf/F//IQ/+v0PENPAguSCdwqfq0D0Hc2gezVTCb7bxpReHdECvt9vnK4ZKUn9H8Zhe4dx\njtQKpgvUKak726gHw1lLWjNlQ2I748E0Uin01umzYAq1Vfq4U+5VKyAGZ5XpL1RKUaCik4J33Ts5\naTd0DLsR4wMihuPDAy2v2+FboVTEFB25mm2HhMPHgbwm5stFESNiwFl6GxT2VhIuRFKZscmC99Qq\nuPL/B/WOVVnc1f6adT3iXKfI2M1BiHXaRXqHT0aZG6Vg+khpDmNUWlgMvPj+x+R1YTpPII5g82aL\nt1Ara7uoFb5UjAeCw08dJU7K2sgreZnod9ckoOQLuRn6riMg4CK1VUq1mKCuSdMK5+kJZyLYRt85\nhAlnOu0kq4E0E52Bu5lTuyCtsIiFAiVErDf4GumWSG2W+/uF6Cpd10GrTEkXiiTtOMR4NHbAsCbo\nOuitwTdHqZlaC2lVw5TzHowQgid0A602rC36AFntKKVulEijkLMmVc0xxrOWxhC169kNex4ur0nr\nzFoyIrpHKEkRubYZJBUKBhdgzSu1Ci03LucTUivBX0Frm1s4I7lwev019fya/d6hoj2LM4ZUMilP\nFFu4yBPJLTRfqLYiJAwORGfmRhprekMXDwiyLUG1g30L3MJHSrngw2HrzN8qehT1u65PgEFEH49S\nBRcsTQTfhLKx8qNV+aDxTrvD2nCxo2UdCRzubvgf//k/w3hH30V2+wPDbqfjuqsbXj6/xsktJn7M\nH//xHyvMCwOm6ed+m9uTEz/7i/8VvM6dzwV+87snbpxh9/d2CPD4dAZJxBcf0xvPl199yQcvrxn3\nPWcRTIs476mmKbLCyHaDiuRpAROpokYpYxRK9tbfYW3k5QcveP20MLpRi2+Fmha+vn/io9//E+ba\nOOx31NRYL4oRef7yPeUHXRJxPPDpD58pHLEVPUCxHE8P3O1vdPG6FUlrIrhKiD3O97RaKGnGmEDo\nOuZlxe8czhqe7r/GFWhLhTCoabPfkdbKsiZCMBjvSMu0uWotay1gBWfQ4o8CCn0AQ687QWmbQ1lI\ndcU5h5WGD3HbiwSCOKw1DP3AmlfMMuNtJS06iaBmJceWQt3EANbo0l98x1Qq5pKZns6klBj2V8zr\nGR8dRiqmFfqgCp61JZY10QSC1dHvt319Z4r+uioRseZVeTqpUstEWyulNMbdSBd7pFVcsOAaj2++\nwbiI3x0Yukg/XJFroaQF6zr2Vz21ZJaSCWLo+46nh6fN6efBrbjcsFkowLwudE5HGqqTFUI4UKTR\n0plzutD1AzbwrhMOnSdPD5SqxbIf7+g6z7J8w9Ad8K8OOPGqxvGR6mZOnEiiIS6NhnGGVTLebs5N\na1TWWDtKmdhbHe+sOVGrJ9mG96rOydaQ14Jtif14RauVWnRRFLuAsTpKUXOL6qG7vlNdvDHkeeHy\neE/NGbvfUXLGhYBxDm893juwjlKE8/Gi2vVaNtRrIBijeGexTNNKyapRr5IprbCWhtTKOmcup5mb\nZ7fkBvM0Me5GrDPUNJPnhMmJYdzhXNHZLKrESS2zOmHiRDJnKjPOqcQO4/B2wDpHyo9b9xXVIU0l\ny6woFTKlrhgqzg74+QM3ewAAIABJREFUcNi0oMq8Ca6n1BWpSYuB9FD08QgeNTA1fX+KVIYAQwwc\nl4xpdkNOWFJLRO9pGMbhwP3jo6KVxWHcF++cv9IKpgrVGpwI1RiCGOXEWKGLKrd8dndD10dsW+iM\nsC4P/IN/94f85V/+kg8/eklNlaf713RDx+3tR7x8/wV/8qef8bDu+D//xT+DYUCYFS3QitIeW8GI\nB5MxbaNubpJZgx421eiye1kbqWbEep4/u0YEDlcHTN/TOc/V3Xv89G9+pofzfo8Jijk2tfDee1eU\nWrh68T6vv3nFuLuirk9KmxTD0+M965IpQ4U6g1UDm6eR1pkwHshpouHwrt/MVPktZB68pxsPiMBy\neqK77lVogFVRhXWb0kWRyKDCEJFVu/dSkM5hbFXuUAOd29fNyJYBv3lQFIuuwoYAzXF184xxGIje\naS4ChuP9PWmeaWUlpQVpG+KhVKwsOAwudsxrIdXA5Xjk+HACF7DnV/T9qMIpqXipkBs1K/pl2B2Y\nLmcF1PnuW9fa70zR7zqHNyqhck615qkapnmBWrAlINZRlwWcULNQrdA5wUqhrJXVBPUzlI4hAq3Q\n8kS0kSTC+y9fcl5WTWVqGTcG6pZkU4JQBdaWlcu+JMo60cUrYn/DnBPBO3KeGLtBY826jlpmDczA\nMPY3jF1PKkdC6NWhmhpJMoc+ciGx+nt1gDZVwBjrac2+S/drtZJYqGbFesEthrkYTEl0Q9S0JhE1\nCFlhrYZqHM9u9/hgOa2FrgeMkPKKs4ZhGLcUJv/ueum8ZV1kMwtFyrLiY4QQtqusyve0rhrqnGm5\naCyfQQ85UfiZALVU0rpsH3CVRlprKEXn/csiZCJzqdjgcUthOZ+BAajkZWGZTrj9qEY1aXRDz1oS\na12Z6pnZPKoyyljA6RxeAQi0munigVo3Zc6mSnImgmxXYRuhZcyGyG2lqjTURnKZtgi+ykZAJvgd\nAAVLK9AZsLTNca2KIytCaWrqMbC9iaoCWXMh5bJ1t4C1+C18JYnoclsvCNpRih7WrRRyAZsyYBiH\nyPWo3aJUAznxBz/+lKc3RyzCfndFqQmRzGeffYA3Cn/76Ed/wm9/9ZfqIBXRZ8vKxmRSZzhOcOKp\nYskUvGlUqdQmBAO2VNKlEF3l7vaOXFa80465iKGILjn3+x7vhC8+/yVRIvt95HDzMQ7L/VevEG9Y\nl4WuKakVgVYmxsNe83g2ZUptC2W9gMlcjvfs9neApeWsoTjOk0oGGmKsLrFTw7uB2ra5lehnV294\n0KruEKx1UBK7TompVbbm30NpWbMjjFHp7nbLddaprPldEJN+Prx0hKAKu5LVdKixh4W8XtRY1XQn\nlUuG2rZb5/bzVM/TqyOlBoSIWMd0eiKlxF14hkPZSHnV/AmRyrKuOj5qynb6tq/vTNGf60xwnral\ny+Aay5Q0NckY+i6yzgvD6JkXtfsb21NrxdeKcZ2GjORMGEamNJOmM4erK07HCUrhV7+6EH1jLZX9\n1Y45N6YmUC026kLQJV0UYUftup3dlkI9PgTWZWG/27GkGdpKXhaM9Mqvj56U1LEobaFIhx3ATIVU\noe2fWEXHEdYYcjOYXFTm2bTgiNW4RjGRiQvu4DHV4XLh+mrH4zGRmyHEXl15wavEDkXDVgRCACk4\nCyF4Yr/DWkuICvF6K9u0IrrY3e15+uobDne3hL4DUV2+taoQKCWT1gWjK1GWaWaapnfgKW8M67yQ\n0sJwNYCFp4d78sP2twTWJSM2kHOjRbDOsVxmcl6oeUc5TeQ14W6ucDGw5ImlXZBgObavaBEqGWzE\nmE5NVEbj/ozUbb9xwrlObfSoC7jJtJlkRmot9OEW0wpWdOkqxm15qpVaEjQL4kF6vNWi36zFi1D0\naoZHsxlybRSB4Cxs2I7BWaYGAcN+P6hLu7XNodxgyyRwTjNnnYEa9GvaRthyVpPUXFMgW7/ryenI\nvM48nlfa5695frOHdeLw7I4kDR96bOvITUc93TgQ9rf89Gf3fP8f7NTlWjPOerzzGtwj2gCsrWjB\nbBXjAmy+D6ic7+8Z4sjN7UtlyGBJq0ZYhn5E1onb/YCLlpu7Wz749DN+89NfcH13heSV/c0Vrz7/\nmtsPX3CIV9x//lOcd6T1zO1ht8VbqgEyhKhF1BZqVVDh5fhI5zv1kHQD5/MRt7vh5tn7PN3fY82B\nJZ0wvWUInpwTQ9dh9zvm8wnfROXBbaEbdspXqkrjNRiy6CgLp+l9Yj1iRPlANtLairdekdMt61gM\niDaAGHLWZfllmtUA16CkBQxanBFSSlweXvHBy+/TSEiprNMCLSiafNsR+W5PrhfGYWQ6viGdTzjr\n6LoBcDpxaBWsvpff9vWdKfrRGMqacV1HCxXXC2HfVOuOYTET/dBpqo048pqIYonjjto8EjpIC7ZV\nptOjLqDEcX7zhPEOZ3RJkovyR56eHrEh0MWdIhq2aD7nB7wzLGml2ADrE9H3GN+hbZyQW8bbypIS\nbRuBeONpplHlTBz2LE+WJgujOlA410aRRXG6oksph1XpFhu2YOskrBgw2m0ZB2dzwkZHnC9E6zFW\nM2iN90Rp+E6Xc1ISz24OSEtqovI6czRGcAGGw4iPHZIrec2s7aLd5tDRpR1f/OxXXH/vPV2Ye8u8\nap5uF5XH43xQ01DOLFV/zpIybV1AGsN+R0UYr0e6oaOsCQHSvPLm1aOir+dK9YK/utMbnVeSo/gO\nawPT5YyEjuomUj1TpbC6WZPObAfWbSYrdW9WmTcZ5ggbO8a7QClZF3FW9GGhbMtrZeMLeXO5u61r\n0u7VGI/1EVNHLJsBplZ8UIgc1tKhvhKPUQCYCIZC5yJLzYjRkZgYteO3zYeg/1U02LoUqtHtqRgh\nGKOO79bIgDFgW2GsjX43kMuR4Dv64PGxY14K+7sdfoiwJhCLGa75+c9/w7/zhz+AfObP//xfsuYK\nKK/IGV1ItpJxrkEICIW6HV5i9RaZSsbWwjgMpEWYl5l8u6GynaUfLK16Bm+Jfcf+ViDsWCrM5wsf\n/egT3nzziuth4OnpSHe9w5bG0+ULclo2kKUBF7a9hYaD1Lox6L1T4F4rCIEYVURgDRyG58zV8M03\n34BYXH/gaq9E2CqVfb+jLInQeYzbMz2daOeZw2HUIrwl4KVSVEZrHGZDgEQfEBpFqjagLRNip/sa\nUSeso+EEoh9Upl31tu4FypqxxnF9+x6vvvqNjplyYnp8hW0QY6Tb7XEhsH8WGafMelmppfDw+MSS\nGyWvLPOFdV11HFwSPkZqzQSxClqz3x62Bt+hok/XY7pE7mcqE9lqLqsYXfY5tyPJjAkNRktsEZc7\n6rxJCnMlRE9zI0taOdyO1No0gzJnSquknBmu9pweZgbrGA8D8+y5urni9euvGK87Zia64Zre91zO\nT1jZ0XwPFlKe0QzeSpVKotIzYEOPiZ6aXgFCmp9www0uC3Fx0AGxUpwGkRhNZsFKwRpHrgBpy8ON\nWKeyv2o3zC0G7wxPTLw33NFSxuJwQVjXjPcwXwqxR8FzrMSoBT/2PbHv6YeefhwRazf9+Ta+MQUf\nA9cffI/f/OznLL/9ktAHrp7dadJU9Li7SKnCNB/J60qrhlL096ylEH3ABQ+24kRvF8QOexgR00jz\nyrSc8FPkcH1gf7fj5tkNKt03BKtZs6WNPD58Ttvd0vwKQViYESvveDkWVIJKpdZJ1RNWi7NRGxzg\nEcnUlvT2hPJQnBs0m7hqfKQKLxKt2o0RVLHNkItwFa+Jov9uloqr2wHRGsUYZchUxTga2YJUWlUj\nkGvYJjzdn/jT//Afs8wXnh4eOD6eWE/HDQ+tLH2sOmFtFtIW5m5EUPMCnI8XTFmxxtL1HR99+FI9\nK1ti2C9/+QVV4P33n/PysON8fuRf/Ld/xu0H7/Pq1SP0HaYpKliM6LHTGs45vdls1gklM3lSTlgD\nwVpMcQz9yOnhDS4G0qpFrlwav/r5T/i9P/wxnoQRyxACtXqawHJZEALnqfDLn33FD3//E7qh4/Hz\nhy1cPIHTxTLWE31HWlZay+phaBA7TzOWfn+nObjo4ti6gEyKiU5ZVUA0DVKSdcVZx7DvVPDgPOfg\neXz1hmUuuH1H7Ebmy0Q2kFGlEyI4qz4WFyM1F6pUolNDo0o7g94SRP0Ku+Ggo5/SNBZVmqI8vCXE\njmG35/HrL7h9/oKbZ/82rRYlf3Y9sR/U0GgXanC0nLnadcwpU/IVp8cHvNdbQL9XMYlYSwxRDZXV\n6Hv3LV/fmaK/xjMtZHJb1R1J2LJswbmOIpki+gN7cSRfcV0hjgOXN5U66+w5hkDDKgIYURCXJpPT\nBc/58QFXEtUYTq9Xdrcfcnk640pDbMT7hfV8D87iO49k1Se3mtSavY1nSpuwzauZSBZarlAu9MN7\nIJGaK4M5MK9Zud3WUnLBuIhtniyzytY2nC1GFUoqDVTlihdPNRXThFQzxkYe65nbfs/l2HAeum5U\nk1ofmFdFKgyjfYdktW5LDRIhLSv9uFNEgvcqlSwaU9kwhL5XVHGBr758RR8DXefJqb4LYq5V9JYw\nTzhjGMZhG7FsIe7W6OzU6LISKs5D3ztMroy7wG4/4p2qqYxBdyN5xfcBAizliDNCbhPVvkXYglBp\nUjbFRQXjt++jTliHjoCci+pLQDbpqtlMNRVj4saSj7Q2AzOGa3UGA7RAdAcskS4qWtkZQ+eNprJ5\niy1CEtXmN3nLY93c0JuO3zhN3/rRpz/Ycm9FDYj1LexOWFMh5cx8PrEej5ynmeV04nI5sy5nUlGF\nVl7WzUTUIDpccxvBUaW93noul4mvfvtrUk56+zhd8E443Z+w7RnZnPVvURredaQ840LDEQCFtIko\nDE5KIgPRVo7nE7Ubmc4TaZ2pqSJNFSi//du/xcjMJ5+8R7rc880THG7vsNYSo47+bl9cEYeetTTo\nelyeaZvTOQ6qBLIh0jvP5fi4uWgjPjhiv0esQzZfgQkR4wLOFqQqrsR5gzOiWn/bGGNP10WCdTRr\n8NdXzE9H1qUyzYm0JEorpOhVvuoCImUbe+oy2+HwodO6YXVMU8pMdGpeNEZHot5Gisss86I6/6bG\nxP31Dd5+wONXP+fu2S2VyuV0pKWE6XcY46i10XcdBUNCcG1l6D2758+gNi7zTHO6IK6mMgw6nhy6\nnvnxxP8L9M53p+gnu6iRxJotELuHlgmu29yMohtykS0IIiCoK9M9m+ger2iLpZVEJ00DNoxoVNnG\nvQchmEI3DJTWOESrWGQLpj+wWovlqLz4eE2WzJqP5PmR0HdIbawCUtWRB2AC1HLGkIjhFtfdInlS\nwONDpLaMmIDLGbfvIGmkXWciSivXJQ0YDRwBXCs435HquqEBlHuTJXGqhVRnXozPqUUorWBCIGKp\nW8dhnOawemcYx5E47pSY2ZouY63Fdj01Z/quo1ZhyaqLPj0+YWLExZ7z6cL5WHl69QSwqVAaJa3c\nPb+l2++JQbvrJlvmr4sY7ygl4TfODwj7/Y4+OK7uDnQx0DvPMI7My/wOcGacZbjdk8IDVekC6h9w\nquQxdkPjbgEYtVYgancoWYu4KVQ0Ws5tRi23gcWciWrKYtlC3A2IR8qsMsFmcHIFdcS4jjUVDqAZ\nv2K2EZpFvBBEkO0w8kYLk1Too6PMmnx1ucz8V//ZfwpAjJa+3zEcDsrBv71lPx4Y+o6ruyvcy2dY\n1707n2qr6ih1gV/85M95evMlSxJ632NCQEoh18q+77lMCyUVZlN5dnOnvgkg1MQuRuY3R/wzNVI1\nU5Gy6qjCaMg8ol2+YUsMMwHTKp7A87sD0yQEErud49IaD8eZzhtSzhwOz/jiN2+4+uiH3H74Hj/5\nVz/le+9d8x//k3/E//zf/BlQKKmwXBJ+vMFcjlgTtvGXMM0XmJbtd04Y6xh2V3S+w/vAnObthqVG\nNoxhvDqoqfnhAWcrkcpAw8fA2Hu81+0TNtDve+rL9/nm/p41Vz24w4BE3eWUpigSJ6KUXvPWOS5Y\n07bRiuBtp+odP+LKgAiktCgmJHgeHx9pCFeHA1fXVxRj+OjDzyjnJ3zfcb07cJELpVQ644ixw4eA\nvfGcHu5J+RW2NebzA9dXO5bLmbQWzeN1Kt+U1pBlpq6NzW78rV7fmaIvW6dkTCNah6kV2RCLxhqk\nCGZTTnjroApLawRvENeQuwm+NPgMqUFaEzY4ovMMfiC3TF0zpQoYRTefEnhZGTrPklem3y7s37+i\njkdSvicON5QukpZZGSjW61xWHI2Cc3uVVdHw/R1DN9LSSTky6w5XYLCWdD7hr67prGF2Ri3opWhn\n35THEYD8dnZoHCDYVmlWO5kiBeMd1Rku9czYegbZqYSywTSdVZZXmyJjLBivaNcQOw0dl6b7i6Gn\ntUbsOqQ0RDKyKVyMQFoW+qhsHpqQWyEXlbWVdeVqPzDulYXvjN0M4RZcoIjgRdUsKgG0eGMYx4F4\nc6voBTSEfMFSW+NyuVBKo9IwvVMUsqs47wlOi7y29PqdZGOuGzdsHx6BVtCocWBDJDsTaKJ6bica\nll3aeQOJCcZESnrEcVC2UNnj7Q2xvyK4UaWYAGI2nDf0ChCliMF4TXUSEc1caMKSGi46Xv7gM87T\nwrxFeM6r8OayYB7u1ZZvDds2kYBT9gvbhaRVoncYp5yi73//QG2Kx/irv/hrDrcHxQm3xMeffI9+\nP3J59YavXh+Z04qUxtB5VvWDMT0abu7M1ly81eEXQOMJU816uLbGUi8aLm8skhupJF68uGMYdPZ+\ncxtZU+XEHpYT6fzI/vkHuG4kXU783u9/ysvne2R64B/+J/8e//Q//+dcXV2IneP89ERMBWcd87JQ\nNxz3W1mkaYV+2OPHQW+ctTCMB+VlGasub2vf5Vd30SIpE53Z2DcZj+BaJUQNfzfe8/y9O47TExI7\nStYGyzuraikfIIOpFrw6azun34eWt/2YbEt2z7rMDOMzRS0YSDlR1pXgLW2TdF7uH2nHB2zNiAjr\npLkFoeuxYQCx9N2O4/GB/e0NLnqd04vZmh/h6nrP+s0bpFTsFsBuXK9Nj1348LMffOta+50p+hXR\nwi+VGHpEigKWjGiGpBOii5r6VBPBQyuJZiJevI5ExkB6UraJC0HHF9OJ6jxiDEtrHK6vccPI+XRi\niIG5Gk7TSi2ZMfZYYMknQh9oa8aHkWV9wratBWuCtxGHpUmmSqHrDvjgyO3MEPfUZBhNZcoF5526\nZ0vG4/FO2eW1qYWblvEmbFI/sK7hHWpcsiDG/l2X2Sp+gzRNshByx250XFYwzm8STS18YoyGv5dK\nqWXTrRtaaTo/Fe3aa6uUTRFgnduklm7jjVugYr06AoWKjw4bVOtPU8a+oPkAJsRtB7OZvFrT9agx\n7K9uAFFiqehSuCS9amMhlVVhY6Zgg6e0RF4bPqC3B6OsEuf3quIxRg8qGzfpphpqsE0NTQiOqEoV\nke0gBVCFk3GOdXnUKZHJ2BZJs9ANAWciRt6y9sHYhrDlnObG4CyNDe+wvXGmWZy3NNGR2seffp/3\nXn7A+x/9W0zzhdM0c3k4cv/4QEqJep5IeaaW8o4R5LcRFEAuTcF4VdQnUatq16NnWRe8MdzcHqCc\nMJfMfoQPPnzOOjflyZeVYA14Qy2iON5qoBWygWAV8YszBAwpZZqBYHuM2ZqNYnH757SaiT4wzSvR\nC/vBUthR80xNhRY9pTb63uAQzvdPzIcej+XF3Z4vfv1rbp692PIIhCpVU6iKkFJmHA9AI/iI9xFj\n3UY+VVeyterI1uQqVboguvzGWNyWQhaielesVQcuxtFM01FicPgMEhytCmINVpRjv15WDVrf6YLf\nW4d1blOLOVor7/DfjkDn+00UoEods+VF5DJjEI73r0kP3zBEpX+mUgndqDRWY/E+8ubVa8QIp8cn\nFav4wJqmrelrdPuB7ilyWRKj2WNd5P0PP+Hpm2/odiOHm+tvXWu/M0VfSz5gwtYJ6YyWqhp45xpV\nEq3CtuekD4NqWNHnvhwy3XjLmG85nS+UplGEyTvSNOENxNApY75a3BAZsmGthf3tC9LlyMxJDV5L\n4rDbM47X0BLnkyoPvGuUulLKgiFDP+BjxNWqpqdL4+Puln/8p3v+iz974iyCdxZjBJsGhr0lT48E\nH/SX8IFadI4vFpxYMJ7UZtymGzbNYJtmn9ami8tK4eWH13zyvU/4v//mV6xZyYW1FmLUQBDvNTYv\nxAnVaaq5JeT41iNEao2UK+tp4vz4oJF1Jr697G8SPU0Pclh8hMPttcZFsj1YrYHTjGATI0Yq67RS\na6aP+nuqwga6cSTlskUDWlxzrHWluEJxiSRP4BzGOLzVfU5KE95VihSigSYZZ0aC78nlCZouZY0N\nWCs0C9HvlL++hWyLeXtjcGj0QMW5HlrAyhXLXJE58vj0hrXL9MNeF+ov9PcurfH8MLCWyrwkXIy6\nDxENGdeFrrxDP9zd3nL74n26bk9VARdGjOq8sVuDs2mtRdT3UAp5mrB9JC2JOSWWdeHV3/zveCOk\n04WPPn5OAz7/zW+hHfDe0loidpEDgb4z/NW//iXOGN67DpQkrPMTNj3HB6u3ISlUY7BWqDVhPMTN\nQFhaUaKpEdKSMQl17b5+xFMpvmGk0ZrF7/YsdeKr337FBx9HkB3BK6L4d7/9kk8//QSplZvnz/RA\nLIZcEyH2lJQgZZyxlDSDCEPfbY74QjWCtRYjmiz19taTcqKWto2iNBrRGYfbDuEYe7zz2ijaptLo\nWrm9u2X+8psNs2GouWGtYb1cOD/e4/yBeHC46HTDteE0MJYQImlZiXYg58yu26nHo7btmQg0qZtk\ne+X8eE/0jt3zl1zOR+bza73JiiC2o1RDrY2cFmqOTLUwzwlFvwlGDDE4rp7fMX/+OdP5wk0/cr5/\nw3R+4nuffAZSvnWt/c4UfY/K5opBE2w2HowaIhpGHLVZfLBUyTi/6Z6tJtLXVjT4mYmw+xBzeaJD\njSasE9F5Sq6kKtS0YnylriupWSyWtBxJTdSwIg1aYp4f9I3HKu41XaCoft06hzFC8HFDEi+czk+4\n08f89df3/Lg/MrUd0XbsrgacnCiMnBdHP6yU4pF62dx/PalUnelbNV2Zvsc3lW5ar+MLmsUDjkCW\nwt+8+g2X1LOs8P4nn1KXzPHxgfPpa8ZdJGct/mmaKLUSt1CJnBJVhFQrl+OFxzcP5CVpWpcLzMvM\n7f6KLJVmGtKMpgBRCV5n8c46nde7DVSlOCNVHawVFwe8RFqZCSEgpmGsJVVNa2q1AA1bLTVX1nKm\n+gVxOr+1oSPLhGuJUjUWM8YrWr3gbKTWC7Vc1EtgIBWhC23LMB1oYjULV48baj4T/J65nMB023ss\nRHOAucemQK2R8/0jp3qiG0dwlo8/+4xcGz54zvNKrmrdlypEZ1iKwaIMIWeFZjy2CV/88te8/OhH\niNX7wDYDI0ikGjBWsE0LS0MIso17dntdFl5tN7Naef3X/xtShbUKrIkYHB+8fInrdtw/LtQ88fwg\npHbh57/+Emk77vpEBKrVhfP8i9eMP37GVCeNFiSzrkIfO0pZMaZRyaoQaQbI1HPZ3gfItcO5jGQh\nFb0lut5x/fKW5cvX+H4k7K5UWJAWxCXCuEfWlePjvQLfdnsO3pDaqs9lUZRAXTdjoBvUqNWMLns3\nFpQxRrMCckajBAXThJrrO0icj5447NQHUYXQbeYsEaiVYITgHKUpMmTY0q1yTdxeX2PFIavANutX\n4YI6t0wzRDuSjpnB7mi5qvEwZxAh9prDbbLjq1/8mjKf8LcHvv7mc6QZrI06AiwVF4UyHxHnWOaJ\n9TLrXdGrACJ26g2AyrDvePnhh7z55g2XpyfyZdJa2fcoxOrbvb79NuD/45fyU8C0vyv4zQi18Y4H\no6TEpuaV7WuqVKrxFOMQKbrcTRMYw5xmlLivM/JxGDY5WIe1PalqB2itbAiGRnTK+OmGAaFwvnxJ\nWu8RJ9jQMDbRSiGgYz8hs6wPVFZaczA67OHA78539Ls9JnqezjPPrneM/UiZoqo7bMGGoNt5NEYv\nBE/c+CJ9cHSdV5OMVGqxLKmyJOGSMlRLNpnPj78h956//4/+I7736Wek1FgvF3Iq+oEzlpwLWLsx\nbFZSTqxroiTh8nBmOU2spahWG6Hre9W4m6aYYe+wzhCCo+97XWyW7bpt6ta1NkzTTGJpst3ABWss\n3iv0bbuTIQKh6zAIUgt5OW/vd6WZgvMjGmDhVUmx8eNqXVEoGgRzwNLpSSMOtyWnSUMf4M1VyzYW\nszZSJeHdCBScDUQboVioPU4GnPWErkecQayh63Vn4JzdrvONvlNpat1c2NEbvDEK9sJRBCqW2/fe\nB+9pRm+wmwuARtUusijDvwlYscpzMe4dYsBuUXm6x9KFv3eWX/7t1/R9VCJjqxhrOR7PvP7qFcvT\nxIurO57dRoV9rTNBEt6D768gWzBNbyRYgvVUSRhUnUKF1mZqudAHVXY10Qzm2Hkw2q1TDcaqoIJc\niFGdrpJX0rpSamGdJ169eSDsO25fPIdwyzyr5NkZu+3lNlgagpW6jaCK/t7W6YFtRFEgoN9fNGqw\n1roZlAw2RCVvCkzT5R1T3zqPSNN/H4t3GgjvrTB2nrFzXO1H9uOo6IysxtBS9QZXS6Nm2ZI2DaaY\ndzyompIaABGVnmJ4evPA6fUDtXmmeWZdFo0YrVWd2c5TcqLWlVqTsqtygU2eSdORnvcBazS5bBj2\nPH/ve3hvkdbIKb0z8X3b13em0xenC0yHEKyhUZRQZ9w2j9VRh3eBWhO5gcMot7wqfjdLxkgitxXr\nw7uT34nGs338h38ExjIOPWJgCIGH45F/9b/8S6ZW6Q7P+OEP/j73x19yP/1fDOOOki9My5m2ztrJ\nGraHLWLbCmUijp2OX6xjPS68/mLl9/7kP+AP/uA5/8N/+V9zSis//93Kskzkmnj/g4H3fq/DhSvS\nmiEWpGXlcTtoOaXTAAAgAElEQVTBSdUxgDSqRNpq1KIusCG5SHlVmZk/URqcHu6xQ6QgHJ+OrNOF\n3W6vXPah0wLUKk2gZliXlfmysJwuTEvCjAMG6EJU63hadPpds7LLncMHGA8HVTgYVQlVabpIFUXS\n1iqb9DPhBYK3hOD0gGyy6cHf1mOh1pUQPTNnpXhZR6kT3nulE5qAs/12sIAxHsRTpWzySxCT8H4A\nEuB0X4IucKuxm/pJZZNI3RLwEpSBuvS0dcBKB+hNp8pCzYalaidqjCVYAM+adOgl1lGr0eBqNG2p\n5EYfLQ14/sF71JQ0Kxm9TSqK1GDFKh9q6LAo58nzFguwFXqNcdJxjNXl/2WZ+dN//4+YLmdefPYZ\nv/jpz7gdR+7GHtdWrBiG4Mh15VUxSOypmH/D3bvF2pam51nP95/GYc65jvtYVd1V1e1229hYICMF\nbCQiOUKAgnJBFAkpUmIh5QbuyR23uQQJCSkXKPENByEkjjckCLcBWabtNh18SFcfq6t6V+3D2mut\neRhj/EcuvrHLLQJyuwJRiymV9t6z5p41as05vv//v+99n5fSIvlwZP5opnvXEsUgzdLaGlK+qses\nGFrS5LAcMzlZnMuQI7hGKwmDI/QD0jL3ty+oudDTWO6eEWOmtOc6SzDwvf/5Q01OyxP4kbOzczZ2\noSTNhU5EGhXxlnF7uRoA7XqCN1SrxV1SpqIHkLq6znOtxJhxXnk7vROyqKJKkRYGlrQuJHoioBYs\nFdc5pYSiu+spTzgsZWqU7k0bMFJKIYRAnGfsLLSl0F8+1tbMMmtIvfUcDwdub16w3M/Mp5lhuyMX\n/W9Z3+iHUbHh1pGmE9IMeY5MOWGMZ+hHuuYZnKHWlfXfDPOSWeYZ6wLXj5+SYuL1i0+pMSF9/7lr\n7U9N0VdJddaUIqsSO2fX/n1TfbiY9iepRwWK1cGR7zyxZUJSnMF+ylpVkiWliZQL237gSz/787gu\nrDhUUZnh8+ckhHnKLEthMz4kV+H14QNKusMZw+XFFYf7j4mxUdddDpzY7YTN2TvElQ44311z9xzs\n9gzv1WRlh6Affkka2bbA808n3vo5RSME50EMIegRti0TMVrmKFQM3laMM5RSUS6UVR78GuY8t0bK\nR/7+b/5dHjz4eWwQvLWauXk6ko6vNQzCKDc9ZtWWp1JIseCMpTlUcbRikgXogkdqxg+eoR80FcsK\nIRisKTrcWj+46hy1NkLoiKlATjgB2zQQ+k0mqrUeLxYjK72yFZoVZN3pxrqAUedoKRknvS58NWKN\nWtFbe5MNoNLX0HUgysuv63vHdNI8XAZV8hgLOePsQKt36oquHaSBuIyUBSgJ0xpGPFI9h9cHfFBz\nVmtNlSJFWEol2zWjVBymNapALLKCuAo+GHIs/Bd/5zdA9ORaraXzDu+Ebhy42F0yXJwxDhvGs3P6\nYSD4DmsdzYmCuayntkqqjVoadYpgLDFWPv4/PsSxsD8sXI8D1vRKZ6yZoQ8Yd85+CqvJ58Bu03F7\nf8vIBVL3pFJwxinCSCxSi0pQm1Cw1KOj1Mzr2xuCG+jG9pmUMc8T4ixd2NLqLbvdFoIhHj7m/nbi\n7ItfpNXG1eU5ca+O3P0Uubi44tHFJaVodGQFbj79EfPxoHp8I6ScME2UiIuoqqkWxLkVXqbehMN0\nousDrbPEnHFNqKUy3d8x7M5UrJAiMc1sdmfElIl5RozF+YGUinYLSsGFgdW2Bmk9jwqanrZEQrHU\npVCWyHzcMw4bliXSe09tidtXzxWJIsL5gyussxACJUaojbg/gPV0mxExsMyznnqKDqyn05EwOE7T\ngeA9aZooVUjNgglKyfX6eeyur3SDlcvnLrU/PUVfdDpu1hCEN8G/tcmaGGM0+UeMKk+Q1VquU/TB\n9aR2VLxta7h1WJREWdygZhYnWvBF2rrxUmORNbqA6LxTODyHqy9c4N3M4fQxQU9wpAKmzgwbXZRi\n3GNKRzyc8/I7ls53GKvDJ6zVYaWxWO8xNWO9xVS7atAb4mEbPNY5TtORWIWlGQ0wqYVSympO0tdK\nE10gWwMcoo0P5pR59ukf4NCMUrtq5sEgxrJ//RrfnVGTAglqA2OV6uiN8tTfDBeddVAWNmedJnd1\nA84A6+mDskBVyqa6RAp6WRnvlV5o9H5V8NXqPDboz8MglFrU3ZnzmmSln5GRsBqwAtQI6KCutbwC\nzvSo39b4xlXjiDUDpU4Uk0ECtUasA0SwYnF+Syt6E1Idpp5T4khbzGfpUcGvmOG25qa+GZaJKNa6\nVvVFuA6/wtmq0bYS4hCTkdZIKXJ/c0cpmWwdzmjs5WlZFK9wf+D589eqTKGypILzAecMVhyhE8bt\nOdvtGcPYY1ai65QKv/m13+HJ44dUKs4ZYhZenDJXm45eGlU0v+BunyjmhC2WGiN3eaLajnQbsbuO\nxqLcm1xx0ii2Qm6kagjWkO8z0+tXsNlx8/qOB2eaf2BDRyRhqyaciXfqo8jKEbq8cHzy8UseP3pE\nkUIYPf24ZXs54DtlA+WsyWjGOsazK/bPn9PvdhjTw9r6eaOLUo5+UXx3rZSciTFyfzjy4MElYgzT\ncc/28mqtF/AGmWC8oXMdlcaStf1mRMNWzJpP0FahRcwZGzRgRyW/hWA8klH/UGl03Ug8Tpz8PdTG\ndDitwSuaaFVzwQcobYGs914sld75z+6DrutUHixHvb9zZjkdCGFgGHtqTmAMgoPU6MaRcdtzPB4o\nCDZopnShfe5S+6cWfRH5j4G/CDxvrf3i+twV8J8B7wHfB/5Ka+21iAjwHwD/GnAC/npr7fd+oisp\nUQshMFeLE6OGl1ZJsdE7r/3tqiatJS940wMFcqNaQ+hGlXMtCy16whhgFjqC9gXfFGL0qCitqut0\n7Dmlk0oje083B374rRMvvmN56xdg+7ARI5jVBZfShO2Ujlg+eYfl0FGqxfpKN3b0/YAPno3v2OzO\nSO1+ddoKY9+4fD9ipBGcWSMNE/v7PXMSLKo3blKoppBrg1ix1q8jyaphJjliW17bJGVl/Gea27Md\nOkpcNCTECblGNudbSoGUCsu8kNGiGYJfvQeFrt9Qy4I3hfOLkbOLHYhaz0tecFr5qRT8GlZjUC55\npZFbIuakeAdEd61NbzCpVW9m57DeEuOsYDSBZrV3byWQ26SD2jThbEerWdcV0cWv1sKbhr0adjSq\nMmWNVKRWrAnU5rG2UxldncktrQYkB2WkxIE690h0KwfGrj3YhRAMzmsPH1RRVaYJ6XTXpYgspTFa\nqw7xBc1k9s7ivOfhO4/51//qXyVWzRlI8cjd/Yn9/Z7peGT/+p44H4kpYdtMXGZaEhZjWaIltzsN\n/XhZeLLVXN1tb3n0/hNsg1iFP/7wwKPLLbFGHj19h0WElmcMlvF6y6PH1+S0cPvqlmcfP+fiaos/\nRqbdQlNwJDjNgyUXRPzqDw1wf+Stt69UtfVgoKaFYryKAlYNUucsuXhyiWr+A3IynE4HPnqmLcuf\n+/L7hH5UI1ScoRux1pNbUtSzGA53B7qrK0Jwq9xSF9KSC+LWTV7VnneriQKkCq4fKWnifHfBOIyM\nYWDbjVhrGbcj01RJiyqV7k8zxXi1e9R1c1e0ltjQM4RGMZmWVc5cqMSWYckE6bDekeZIK4Xbl8/p\nu4ESE27sSVE3B41GavPKzVftfSnCKRb6TrBxwoeOLvR0/UhtE5iGkYxN92ADUjVM5jhXWjMs86xZ\nuqhwYhg78jJzMp9/HPuT7PT/DvAfAr/xY8/9TeDvt9b+loj8zfXP/y7wrwJfWf/5c8B/tP76E1xJ\nUGSCCINAlNVmHaBT4gpLzKoiMQUJupuuTZEN1IatAWtg2b3EnAy0EesMw2anBdO6FQurvWWawwfP\nxWZDjMqrORs32NzoO8fuSc94PdN17+Dca477Iy0azJBxRtiM1xwOlpg0KNsFx+78krHf0HUjPvSE\nEHBOtebjCMPVkXCWGLotYoTDYc+SZ3LW42SpcXV6FoxYbANrGxWzygEztjSSOIrojtg2jR4qBt0J\n5aZ9d+0UaxHuPCZV5nmi5omYMuPuYh2Iq7ehxlmNflKx3mrv1TqqCIgll7wGQnfAynKxAXJGu88F\nK4LBUSssOYE4vDHK45FGW08lczypwxelKbYGuU1Up8NEb71m464zq1rVUFUKeMtnfgX7WUjKjHNb\nSjkAE8Z4Sl1WA5SnlBkRTysjNW4xeUON2hJoFSQVUlrwVk9GGMOb7Om4oh3sWlSxgSpG9d5V83FM\na7Qm3JXGk82Ad47vfesfMnYd/WZkM2w4f/wQ84V3EBGctWs7pVFro9CwpVBWsqlKOiqn457v/N7X\nKDmTTwt20uAgGxuPh5G7/cx2N7DMkavHj0h5Ry4Lm+OeOE80GqHr2Zx3QKY0R2iWWG81a7mB+gxU\nymqawzQhGU9cKoaF0J9zd3ekOTWCuuCIsZBL43i4w5kR7zr80HM/HdhtN6R13vH6fs/jsUN0kk2t\ngAjeKw21tUo43zFNkWF3rpuA1lbcNEjWUJ44n6hoToNzFWlH4nIidIauD/TDqPJJEaw1xFkhgCkt\n+G7DlDKxGnW7m6TzMdPRjCMVzXwYtoGava4oVfOPpQg4jWdsoq1RvxofpVWW6YQxBlUtO2zrsIDz\nnlYNMnSc7u7Z3x7hYgQpiuIIjlDd+v3TMKNWPcZ4lnnBhoFTXPDGc7rfs9ltGYcR0KHwxx99iy/9\nwi/9RKX1Hym1f9oLWmtfE5H3/i9P/yXgz6+//7vA/4QW/b8E/EbT3sNvi8iFiDxtrT37U6+kaYxh\nbVmVBWqtVJu0MUjVYAMxGjvorNPjsNMtdxVFAdRaqSTc9iWUkS48xvqOUEXBYGsQdzOqMLHGIiGw\n22wIIWDF0Xc9IWxZ9o398zMefvmaU/su3meMaWu/FVI6EsYJYzd4a9mMI+e7M3of9L2cqkr6YYSW\n8ef32P5ECI6UZ2I60UrDSgNvaFlt5tKEpgBfqiicy5iyFiHFCbccEbyCxVpTPHB2eGtpeaatiUCt\nrTUdqFKwXuiHgSqzhskYgzTwvtNIv1Y/45B435GzkhXfBGOzumNLKXTDoG0aazBNkQvWeGQ1E+Up\nKa5XgqYOWf0cU4qrSkKVWTHOFFeodr1ea1ctvl0jSstKf9SCz7oIYQq1pTeCFy0YRlsk9bOwkkKR\nAnhaTlAclJ6SDWIcJU3klHWDYa16LpxBrNHADsAbS2tCbo0mCp/DQlgLlDWNVEQvCSH4QG6V3/6t\n/1UZR8asiOWCCRYvwmbYsNlt2Ww3mGFDt9myHTZavDqHc47gPWPnobPESfMVcoF4XKgCQ2/49vMT\nu+sHPL95hesa/facmmbCdsPgGkuqiA2cX15yev0a6UY4Tpgg1Bq1BYfDSGBOC1agnQxxsURnKVGY\n4j13S6PZE6FVznYb/ZkgVAkstbHpN9B5Hj0doFluXt4zR8ftqxvOdz21wXE68c6TUZPwVrS1MaqB\nefnJJ5RW2J3vVoKsBv1YabqTrmCMMuqtNM7OzvAWgnNYVE3mXaenFhHV9J8mjPXc391ppGWrusaJ\nw1ptjLpORQq5FdKS8WZkmWG7HRW3XArSCqYWYkyUqmN3kcawGVhm+RMj4xqkA+BCRy5oC9IZHCPz\nVFT8QKNKJQTHdDzSSiNmwRYwRSMVi0EzeVc8BDliWqFaWKao3+PP+fi8f/PxjxXyT4DH6+/fBn74\nY6/7aH3uTy36NaurzYuoE66oYscTNAWngQ2qYjBYXOcJg6frRkgzTRrTMlFKwlRDTHdYf6LaVzQe\nYtw1hULne6SJkgZ9QayjHzpyLYQw0Hcd0hoPHj/h/nhPu++4vPxVlg/f4vjx72PHD6kp8Ojy11im\nzN1dYjMKQzcQNhu2Z2fKLBcI1jNseuayx4zPIEwYaxRPHAuWjYaC26zyOA80R2xgYiM21bkE11Pr\nQmkWaxq5Fp1JUDAopM0YQ5FEpa4746LDb8vah1pUYmcM3ejwIYB1dP2oLZ9F2wL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My6Ta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\nDGOKsKBvGIYxRfwDpQn9mhiftiQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4uRTd-iyC1n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# training model\n",
        "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
        "    since = time.time()\n",
        "\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
        "        print('-' * 10)\n",
        "\n",
        "        # Each epoch has a training and validation phase\n",
        "        for phase in ['Train', 'Test']:\n",
        "            if phase == 'Train':\n",
        "                model.train()  # Set model to training mode\n",
        "            else:\n",
        "                model.eval()   # Set model to evaluate mode\n",
        "\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "\n",
        "            # Iterate over data.\n",
        "            for inputs, labels in dataloaders[phase]:\n",
        "\n",
        "                # zero the parameter gradients\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                # forward\n",
        "                # track history if only in train\n",
        "                with torch.set_grad_enabled(phase == 'Train'):\n",
        "                    outputs = model(inputs)\n",
        "                    _, preds = torch.max(outputs, 1)\n",
        "                    loss = criterion(outputs, labels)\n",
        "\n",
        "                    # backward + optimize only if in training phase\n",
        "                    if phase == 'Train':\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                # statistics\n",
        "                running_loss += loss.item() * inputs.size(0)\n",
        "                running_corrects += torch.sum(preds == labels.data)\n",
        "            if phase == 'Train':\n",
        "                scheduler.step()\n",
        "\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
        "\n",
        "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
        "                phase, epoch_loss, epoch_acc))\n",
        "\n",
        "            # deep copy the model\n",
        "            if phase == 'Test' and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "\n",
        "        print()\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60))\n",
        "    print('Best val Acc: {:4f}'.format(best_acc))\n",
        "\n",
        "    # load best model weights\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_O49A5xSzy4G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#可视化一些prediction\n",
        "def visualize_model(model, num_images=6):\n",
        "    was_training = model.training\n",
        "    model.eval()  #设置为evaluation模式\n",
        "    images_so_far = 0\n",
        "    fig = plt.figure()\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
        "\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "\n",
        "            for j in range(inputs.size()[0]):\n",
        "                images_so_far += 1\n",
        "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
        "                ax.axis('off')\n",
        "                ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n",
        "                imshow(inputs.cpu().data[j])\n",
        "\n",
        "                if images_so_far == num_images:\n",
        "                    model.train(mode=was_training)\n",
        "                    return\n",
        "        model.train(mode=was_training)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45KWSfQU0J3G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#Fine tuning\n",
        "\n",
        "\n",
        "model_ft = models.resnet18(pretrained=True)   #使用resnet18\n",
        "num_ftrs = model_ft.fc.in_features\n",
        "# Here the size of each output sample is set to 2.\n",
        "# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).\n",
        "model_ft.fc = nn.Linear(num_ftrs, 12)  #保留fc最后一层的in_feature=512,输出为12\n",
        "\n",
        "# model_ft.load_state_dict(torch.load('params.pkl'))  #加载训练好的模型\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Observe that all parameters are being optimized\n",
        "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
        "\n",
        "# Decay LR by a factor of 0.1 every 7 epochs, learning rate 每7个epoch缩小为0.1\n",
        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tF33zSeA1IsW",
        "colab_type": "code",
        "outputId": "7cb48bb0-892c-402f-da77-046aad38d8a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
        "                       num_epochs=25)\n",
        "torch.save(model_ft.state_dict(), 'params.pkl')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/24\n",
            "----------\n",
            "Train Loss: 2.0904 Acc: 0.2981\n",
            "Test Loss: 1.5789 Acc: 0.5546\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "Train Loss: 1.1641 Acc: 0.6300\n",
            "Test Loss: 1.1453 Acc: 0.6471\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "Train Loss: 0.6740 Acc: 0.7992\n",
            "Test Loss: 0.6210 Acc: 0.7815\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "Train Loss: 0.4340 Acc: 0.8605\n",
            "Test Loss: 0.6981 Acc: 0.7395\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "Train Loss: 0.3793 Acc: 0.8816\n",
            "Test Loss: 0.4750 Acc: 0.8403\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "Train Loss: 0.2331 Acc: 0.9387\n",
            "Test Loss: 0.5296 Acc: 0.7731\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "Train Loss: 0.2455 Acc: 0.9281\n",
            "Test Loss: 0.7238 Acc: 0.7899\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "Train Loss: 0.1694 Acc: 0.9535\n",
            "Test Loss: 0.6016 Acc: 0.8151\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "Train Loss: 0.1732 Acc: 0.9598\n",
            "Test Loss: 0.5465 Acc: 0.8235\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "Train Loss: 0.1393 Acc: 0.9577\n",
            "Test Loss: 0.5921 Acc: 0.7815\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "Train Loss: 0.1120 Acc: 0.9725\n",
            "Test Loss: 0.5255 Acc: 0.8319\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "Train Loss: 0.0725 Acc: 0.9873\n",
            "Test Loss: 0.4769 Acc: 0.8151\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "Train Loss: 0.0659 Acc: 0.9894\n",
            "Test Loss: 0.4814 Acc: 0.8403\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "Train Loss: 0.0640 Acc: 0.9894\n",
            "Test Loss: 0.5094 Acc: 0.8403\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "Train Loss: 0.1010 Acc: 0.9831\n",
            "Test Loss: 0.4625 Acc: 0.8235\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "Train Loss: 0.0674 Acc: 0.9873\n",
            "Test Loss: 0.4395 Acc: 0.8487\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "Train Loss: 0.0634 Acc: 0.9894\n",
            "Test Loss: 0.4249 Acc: 0.8151\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "Train Loss: 0.1709 Acc: 0.9577\n",
            "Test Loss: 0.4275 Acc: 0.8319\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "Train Loss: 0.0848 Acc: 0.9831\n",
            "Test Loss: 0.4647 Acc: 0.8067\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "Train Loss: 0.0593 Acc: 0.9937\n",
            "Test Loss: 0.4911 Acc: 0.7983\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "Train Loss: 0.0706 Acc: 0.9873\n",
            "Test Loss: 0.5176 Acc: 0.8235\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "Train Loss: 0.0710 Acc: 0.9894\n",
            "Test Loss: 0.5014 Acc: 0.8319\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "Train Loss: 0.0638 Acc: 0.9873\n",
            "Test Loss: 0.4516 Acc: 0.8487\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "Train Loss: 0.0757 Acc: 0.9831\n",
            "Test Loss: 0.4676 Acc: 0.8235\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "Train Loss: 0.0441 Acc: 0.9979\n",
            "Test Loss: 0.4771 Acc: 0.8067\n",
            "\n",
            "Training complete in 42m 10s\n",
            "Best val Acc: 0.848739\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}