02intermediate.ipynb

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            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9416a1eed98d443795377b79db11e1a8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/21-10-4/zxing/blob/master/02intermediate.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1、CNN"
      ],
      "metadata": {
        "id": "HzV65Wo06xKc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pa0M16MT2ZO1"
      },
      "outputs": [],
      "source": [
        "import torch \n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "\n",
        "# Device configuration\n",
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Hyper parameters\n",
        "num_epochs = 5\n",
        "num_classes = 10\n",
        "batch_size = 100\n",
        "learning_rate = 0.001\n",
        "\n",
        "# MNIST dataset\n",
        "train_dataset = torchvision.datasets.MNIST(root='../../data/',\n",
        "                                           train=True, \n",
        "                                           transform=transforms.ToTensor(),\n",
        "                                           download=True)\n",
        "\n",
        "test_dataset = torchvision.datasets.MNIST(root='../../data/',\n",
        "                                          train=False, \n",
        "                                          transform=transforms.ToTensor())\n",
        "\n",
        "# Data loader\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
        "                                           batch_size=batch_size, \n",
        "                                           shuffle=True)\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n",
        "                                          batch_size=batch_size, \n",
        "                                          shuffle=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 可忽略\n",
        "img, label = train_dataset[0]\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjP31tNU38mk",
        "outputId": "15ffdba6-9205-47a4-d585-6a4cd131b45b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 28, 28])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 可忽略\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(transforms.ToPILImage()(img), interpolation=\"bicubic\")\n",
        "transforms.ToPILImage()(img).show() # Alternatively"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "9MX0OKF34LEs",
        "outputId": "5b05f6da-d22e-4bde-9f4c-3c0340480fae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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PXhefh7BMe8nQiD4E6uiXzrIatGWS1/p0L3ddJ9FW99zIvOnn9pgbHq5bPq3tsx0K4griLNPtXME5I/noMzfByH0bJp6FiWd+5tZNHN3MKOnK+rq39H3ZY1bCB0k4qUuGuye/+u2W6KUlBubqFhc6SEZLpVRdLijXr9cm0loWv9aK0DL5LZToeYDSm2OKb2HDgVjd1fvKCLF4puq5lMA5B+7TyF0aeTEfuJtHXpwPnM8D+d6jZzWiX7CjE36q6JyRWJCYmnjFlvB1o2Dz28dO9u+Lx4je43NvAyCLRT8MlJtAGT354JfJqxLMepc+XrntKNsm2h5LpC1kr6s73iy28xV1eSltOWdWubvmY4uhb3zk6M29/cqfeeYnnrkLz9XKUEOzxEbax+PLhxcAba6xayRSKQzkhVT2vfrK72WE2N32GkAhFwFGAApCrI5UlTk7YnHEouQilGIWfTWYljzLLYM/qcdpwadCbt6BNc7U1ZXf5Abm4pmy55QGzilwHwdOc+A8DcwXTzl59OTwJ7HjDP5cCeeKvxTcJTeyZyN7m3Vf4vVPjJ3s3wdvyrh3og8DdQzUw0i9CeSjJ9140k3r1+6TV73s9TDOljX2Xlzzfr8RHGcZb/FmqX27Da2k5bW5086yzF4Lg6YlSXbrZm79xHN3WUj+3J2tDCWRg/TY9pqcDxNpD9Efq6y1ckdFWbQhltJz/+jHCjPK1MpeoTr7eRWrdeeBcx64TwP3ceQcA+c5MCdPjI6SjfD2f6lNVMZaU2sVUlGm7FtyMOM1L++jVCEVt2T9Y3FMyTNnxxQ9c/TE2ZMnBxfFnRXXiB7uwd9XwqnizwV3LuiU0clm2k29JkEu1Kau8yln2WEn+/fHNkZfSmu6xOid6OUYyMdAunXEoxKPau2bByP7kmDz9XrEUtd4u/rWXeYL0qy3dwXnC6HHpN5i2KWs1RJkXgpeM6Hd9gTZqImjzkszSSf5rcwcdeIgmUMj+pakr8NjfSOu37aCtF79zJ4wUzHluIpSKAiuVgpKrH7JhN/nke/ige+mG17GgdPUkmTRUbJQs1LLSvaiSnaVnJToCxfvre99496DEb1W8wJStqRfzkpKjpyVEpUaFWZFJ7WR1ovgzuBP4E9bomfcJaGXhMzJrHoj/NaFB1h06z4BdrK/L1qZTJxbY/Q+x33wlBtPunXMzxzxVoi3NpSRb2z6ylzyTZytmMXWirjS+mqM2A9LWkELozOyH1xk0HxV1hrVWjsdhaB5SZCNalb7oHGpMT8k+UEqASNqJ6l7pKsmbyz+Yz/XTZvooz+nUmobXqEude25JehOebQE2XzDd9MN300H7qeBafLk6Cizg9S6ego2Oiq98lAprpJcZe55C7Ful0WEpmX0a2lHFsgCSa3PPgmu9bvbWGuLzS+r6x7ujej+lNBzQi8zMkWYI7VNu9WcV0XcTxivw072d0f78D606svRRBvq4Cmjue7xqEb0Z0J8BvFZJR8r5VBgzLihoM7ccOfM6niXcVpxWgjNFfdSlszy0G7HxVpbWWtL6n70WnZPmg1LljxxK3Nz2ROjZA5SCAKDCAETYHgTyfW1bXXX5NbX9oyW1ryyEmDGEavnVEa+yzd8G2/4Zjry3XTg7myZ8HyxGrdEQZJNm1E259IJ74BGerSNnrRee2AdsGkz65rtVgomUpGkiVXY0ZNw/tKO5rq7yyNEn2Mj+gONvE+MnezfB1cz4Z3wura4hs0s90FMPOFGiLcQn1fSs0K9zYTjzOEQl7LX1gX3mi3mFrPKXgqjpuaSdwu91qsPkgi9pNXI3bPeTgpKse+z/nwgt98rHCQTBAIQRAiij5Icrq15R6m9/CZXj3vsOdx2KKQquU1/5QqxWj/6fSt73aUDL+abheiX00A5eWRSm35rZJTSSNufVmQl/APFm+XfCEbyfjzSDaepHbGuhJ8rfiq4S8Vdmut+jsglWpy+JXpKixKuqcvWT+rCw072D0PvcW/NMrXPdAdpAgo08QQbs6zPEodnM8+PF74+XHgeLjwL06b5pJO4WWYqoX9vsdA9S76WtpSy3G6z4Y7t/TUjHigMzZIrZs0VCK+Rt8rd5eaa8Fe2qtYrt79Q2wWgtaGi5Lr93UKplVwrEWFGW5w+8jKP5r7PB15eGtHvA3pqsfMkSMSsccFCgGX4HRbRykeIDhuSl5Xca5sr13pziaUzTmfTnXM9EXdpApNzhGk21z3OG4vepKXLp0/OwU7298fVtNraCbd0v/km2DBAGaEeMuHGhjJ+frzn54eX/Gw48bU/c9SZo86Lhd6SealPdxJ3S93I/xiZoWfB2/1Nyasn3bZxuXvEbe+kLrUuybRc6zKJ/bqPrrbXdiJorZT23Jlsr9PScZnaDshV1s61cuBFso61F5eRy9ksurtvmfBz61rrZM91HT5p2E7rvSIN3R5r7r/d2kWjLqTXRnpN1vq6Kslaw4zO2cQu52iue4xmyVOi9vp675zbEn1vqvkCsBV8aO7jMjra6uMMhXFMPB8nfjae+P3DS/7AcMfX7sRzd2mJs/nK3X4XMj8sccGrZS77ef/ZSnA79Vdj827JO8nn2sluJLfJMKE8+L1+Po6K1koQO69Aew1hcd1Le85YITar3hNz93ngvremXjx6dkvJy59pzSud7CuJF1ydVr26uxC9NoJvyG397HarqUtX21BLl42WLhkdE8REjbFpxKfVkv8IiQ472T8ONlnWxZL0qbHWrqq+MLS21K/Cha/8ha/diZ/5l3zVmlkOEhmWTrRKaA0tbyLydZnr1VLYVbLsLRlyWIke6S52q4VXJWITYDO6dL7l9jzdc+j1+SCFQCE0C35F+vY6eSF8L7fZ0MldHLmfB6YptPr2prZ9qpYs633ojZz2x9+8ke3ba9/fknwhe16fYx1gaVNrpdqCiJyvNeVyvs649/i8N9D8CIkOO9nfHw+3edZmFR78Y7tL2cfXB7US2dFNS/nr2EpgliwzgrThtTeS+fsQ+fVZ8fY2mmvdiT7XytRIPlXHTO8r98zVUdBl9tvObw0zeovskgQkX5G+41LhUh2X6rkvo9XV08gpDVyiNbNoJ/t508QyWewseSWp/bFf//5WF3613tS6/n5txO5bZ3Lrems73JZ1Tykv5F4y7jFaIu5hfP4jIXnHTvYPwbJo0A7bTNI+QMWWAnQhg8f+793lHWgkb0Q39/e61g1vJ/eW0FeZ7zfgKmnWiH6plVjhvvpGxrAMp1xqIFbf4uzrj08PP6wDLy01fSvxZQ6szToWHuj6/NVc+HMOnOLAPHuYHG4S/GVtYgmngj8XGziJRkZ5eOHdYInl64OL8jJ+ur1fl7HUqxHVnnDrfe4PE3A/Urf9IXayfx/UAjj7UIhNMS2WoFTq4gY+EDTI1rSRsvVfp+KIxcYpM8KM41ATGbFRzuX16poItG+8clcfkHpL8tdZ8/IgvVYoxFoWol+qcCqeS/W8KAdOdeS+jAvhpxKW6bEtHmveua0TR5kW0vcwBcx9v68D92XkLh94mW345JKsTVXm1rV2AX+BcC74+4w/rz3odqF91aNa/n4P7/edcXW17NuvbSR1JXpdLgLrptZuxSnlsyE67GR/dzTi1WJ7wWutZhFKbSt825EKmteSjSRr/iipDWdkz3222PS+jBzLaEm5pldeEHJPzPHYwInhsYw3mFyLeQRvF0CzmHkl+rwh+os6cioj3+YjL8oNpzLwMh845YGpXahSdeRNbau35h51trZcNy3DNbc6c2iiET3p2EdNv81H7oqR/ZQGznOgzA43qc2Kt2YWdy5G9FNEp2iDJsv65O9B9v71dr0yrMTe1sM3a6KvSL557OdAdNjJ/n7oFn7553uzCCkvmdtOdG1dWDkq8+x4OQ/cDSPfuuOVomlBieKvhlBULM68GkZ5JOM9CGitremn6TNJeZTw3ap3ovcS2Fwrpw3Rv81Hvs1Hvsu3fJdv+C7dcJ9HXqaBSw7M2ZOaZBOsOnJ94ObGRW7czDM38cxNLUcxcxATkeh6b6cycpdv+C4d+TbecB8H5uhhVhOE6G2qU8FfGtFPk9W2U7fsb25WuZojf6xHffu9Uq9/Z/vcD0huv/J5EB12sr8/tq58aVs/SjCVkljQaMsLtK0qSpOSJs/9NPCbcFy65cBkiy8aFuvXy2+vWPWWnbcYv8X5tSytrubPGuEdjsI14R8jeqyFS0vGbYn+TX7GN+kZv47P+DYd+Xa+4WUauY/DMhmWi7QRU7FZcq2vHaW99Y30TTGmv5epBO7ygW/iLS/mG+7nYXHhdW5rluY2Kz5ldGpNLJfJsuHdve54W/PKQzLDOn76yPfqYxcENn3unwnRYSf790ct1KK2b7tKS8pZ8sZW/ZS21re2Dym2G+wipIvjMgy88NYlp01qKVbHyQ2LNlpvqnEPYuteb1/63MUGWiKZWxJ0SVNRtJqb3wn/NqLfV899DQvRfzc951fzV3wz3/J70611s00jl2ijpTk5SpGrNIJshne2QpO3Yb4i/o2bW+9+JVbHOQd+Mx/5bj5wngN5cri5N880FZi5IJPVuJmj1bfnlgV/i2V/BZvZ8lfUYx4m+TYXj/q6i8pnQHTYyf798DBh1hI2ONdKMfZh1Dmhs8fNvb0T3EUoZ0cMgTtna4ZqFebiOOdhsXzdtX8o2wQsse52cu2rGniuZ4qIxQuN8E5kid9fR/TYsu6X6rgrA98Wc91/Nz3nH05f87vzM351fs63lxtrW70YEYkKyRYeshkvrQpZK9FVLqFyHzIuZIYhM4bITUiLQs6gqS1dEObsuYvj0jFH13drnpG1qq6iELX3ofepsv6/+F7/y8c9gNdOpj32+M+E5B072d8HD6y71WGN8JLsQ6lzxk2Kn4R8ZlGoqc4xycB31bLzU/achmGxelv11Y5S9Urn7cbFZSb94u6ZnSO7k8XttCEYmmtfr934LdEvFU6N6Hflhm/zLb9OX/Gr+Sv+0fQVv3t+xq9PR16eDsynABere+sky1bV7TVp0b9rc/oleOJQiKFwGkbuhsQw2JjuGNIiapmrcJ4Dp8tA6rX1vik1tr70VJB+QS1W9rrqPf+A/+W7P/bzIvdD7GT/vnjMuveGipSt2ypm3JQpF4cfdV1q2Da3JPHMbUNozI5TDIvoYx9pfQwqhcGZauutM0mpS/CUFpc7Ck4riqnDUnssb+hNMwWIwKVqc98HXpTDEqP/en62EP3u5Q3x5YA0OSZ3NhJqXAdIuiu/LKrw9l6XXeiDowyVOBr5z0O2OX2/ejg5KWl2cHboxebHda6LTPOi1tobWj50yOQzJ+77YCf7+2Jr3Zsrb4m61js9J3RyuIvaEsK+v1sEUFIWUhLukzINnmHIV4oqW5Qqi6Rz0LIIRd4Pw1Wte+li0wlXS5sXL0stfts0c6nCVF0rr93yTXrGN+mW34u3/O7lmamp3h+ILwf0zhFett7008Pe9JU0XTRzWQPV9faGdn8U8uAooyMOlegLfc8bWSwDf1L8pYU+i2UvTZ7Zatw1W2L0xzA2+jlhJ/v7oFv3TnhpsXtWakpIdDAn3OSp541stEKfu5SEKa5EJQ6O1NYkqZZrRRWwntv2Pe9Niuo4Wgms17ktcZcJLVPvarSpt16Wo3etrXH6fQ28KAfuyoHv8pHfi5aM+83lhrvTSLxvRL9Twh2El5VwupZO7n3mwDoE1BZZ5FDbuG9f9NiWPY5KHqAGXXT3pGLZ967YeukXlYrEAqmV2Lryy7ZevuOdsJP9fbF151vLbM0FSQmSN+s+RdQrzgnBQRW19s0iaLZtIjnWFs8rOVRyX1e8vM7mvsLsC9NQmKInZndV5+7jscumE00UTGYKTK1lakQ/lcBdueGu3Czu+zfzLb93ueXF+cB8GpB7h78XwksY7irDnbWrunPBTa+2qy7a900quwRt1lzJQ7U1zocN6YOYBLba+9RkFt2fsCaaueLmssbrvbttaX399DPinxN2sn8IqmW+a1HIBdFm3ee4rHpS5/AqLKuaiyIZUhT0YNbMyL7dW94/zO13arurleIdeSxcotrOsebiBymL5NTQs/kFcpOcArPs1ovuljZVa5y54dt45JvpyIvLyPk8UE+OcFLCvRinexIAACAASURBVJhFf1kZ7qxd1Z2buGLMa185LDP9eKUGZ2QfHGVQ8qD4g5Cmvru9q+zK0hYo2bLv/lyXw03ZSm5pjdPtb7MT/ftiJ/vHwta6O0skyRxRZ6udbDTVY6OViibIEfJsbq5l6lnllGDdBgrm/Tuh+EqeHSUJUyt7uSYdPbpV1UYp4OCWmYKp3hTkiuh35cB36chv4pHfNAmo02UknzzurPh7aUqq2LTZKeNfzuilSTHFdO1Sty201ZkQpwSPBkcdHW50pGgtsK5b92HdUQeN7BH8VAln02GXuawlt/5aPwIN9s8RO9k/FI9Z9yhUsQ++bCbXpIJkh8ZKjkqK5rbmoSe1ZN3zvfkdYNGTL8GaTXJWUoVJBl5oxWtpSrMmQrksd3CQiUs33qUJOlqp7bgIO343Wy19vnjk7JaNJ8sShFPGnZIR/TRdK7RsQ5q+wNJ7pIlwMnskemT2lNGh0ZHnzYJKt77X3kTjWx+8O8e1NbbV1Zfk3I7vhZ3sHxO1ZebFhl9qU6G1/HvXOPNocmiy8c086rrmya1Wbn1OFi216iyrbWWvltVXz8VVXvjM6G9NVrrp1vUtLV/pZanbm6ijTbG93Ag73k0jlylQzh530WXazBJlLUa/JLPol5k6zatwQ7e07eJmG2gz4hRJwRqNUkDGjEaPRo+bzLVfdsq39yp5o/N2MdknaSKOr2iw7/he2Mn+MbCx7j0zX1URSVQ1sksbn9RSkOSRuVAHW0Bga5mlxeyPz6HbymVz+VMynfQeIyfnOfuRFz7bgkYXl2GTLlt1kAiwzo6XgVMeeJls28p5DsTJI7Nuhk/qdV/6bKFJjdGEFeNGQRWM6KqtySgb6XNBvFvairu0kwZnLr4TW2wpgtQmA9WakmSyC0uXf6pdQKKvPd4z8d8LbyW7iPxl4E8Bv6q1/tH2vZ8B/x3wh4G/C/yZWutvfrjT/IxQCzWDaO5G2cZi+0hs/8AHR537B16bCy+Pkr1Kb1YxT6BLJ1vm27LeKXjuh4Fv/Q2HNnE2NplpR6HoukDRViEb4S85cI6BOXpqbDLNrS99qXPPBY0tSZY2gg0PNNErrJI8rdFIaqEWZ6OoadOHEHxbsKEm5dUG9vqY8KLzNkfqNFlPfHu93YV/P7yLZf8rwF8C/urme/8u8LdqrX9RRP7d9vW/8/FP7zPC1rq3RhspxQhfC7UWSG7zgW8LIbsEtbYsXpNBvlJL7dtegyKp+fqbGN6NQhmV6RI4DYG7cOCuSVRvx2hVyqo4UwJz8W1c1ZGTQmw96U0z3RJmFc2NgEvnWlnHPetD8mXbzlIq4tQe46x0VpuUk/hWmuzJPJE15i9t1XHK16qtvQ9+z8K/N95K9lrr74jIH37w7T8N/Il2/78G/jeeOtk7eqMN2WJ2IrU4az5x5foDrwrerYsmZC3RLegy1a2cRe41bUcOghvBjS32Hx2XOXA/mGjjrZs5ucESdrWgtbRdaqYyE4sj9Y2o1SbYegVgkdZqyjvXqi6rjNMr7aqiRnQ1D4fm0dTSXHpnjUeo7cgTsRbipYuoK8S0pYh9QeIrGuy7C/+98b4x+x+stf6Ddv8fAn/wdQ8UkV8AvwA4cHzPl/tM8LCzbvOB79Nx4hxouv7AO7eQ+nVkxzvbJwfgzHX3UyVPLK53iUqcPVMyi33OtgXVNOkzg6RFIPJd8Ip+2zv9Dfq8t7JYeVEjvdgiRlxZ8hmL29+xVYbpBH8o5rgT/b3wwQm6WmsVef0u31rrL4FfAnwlP/vy/0sPCP/wA0/OZsn6B965zaIJfYXs3cUV700oQwQJzjrLoqyjoBEk2kDJlEz+aip+kZCK1eQsch+aaZN1XtvwjFRyy/qbDLbYRtmti/22972e9OtJ36w13aL3xz98njdJP+14L7wv2f+RiPyhWus/EJE/BPzqY57UZ4/lg/8GK1fKkrle9sbBK2Svoubm17rshLfylWslvL6XbNW6y1lNTaa4heilKln6dJwtmAhS8GIDOOoK+GLJwr7golcI3LrtRvpF6V3e/yOkB6skLMm81z7H5yf79GPH+5L9bwD/GvAX2+3/8NHO6EvCm0gPiyUXaV/rgw9/s36S7efSRDIkZqTvH0t1XXKYBJKQkiMWZc6rZc9XMtRlVYF1thk2hMwcCmXo66Rb7d+3Orhvyyu3nsg7v//1bwBYaAPXWchXfvfzU4L5seNdSm9/DUvG/VxE/h7w5zGS//ci8m8A/y/wZ37Ik/zs8ZD0mWbVNtYezMV9xI2vzlmTjnNIDiZZncu6iDBVG6zJkLOQszInxyV7y7g36ec+Kmt7222R5I0z5ZhDSFxCIQ21jaRit21JpfNqrb9OVxdcxc4/sybY3vo32H4vv3rR2In9g+FdsvF/9jU/+pMf+Vy+fLzBvZVO8vzgV6Rl6ttaor6MQkptktVGdLPsvCJb3WP3WDxRPKPEZUKuk/1ZmHgZRk5jJA3eyB1kEd2ofq0GiKpVElKy7bWlpfBreTvh3/Q32fGD491Tszs+Hh6OabYE1GNHb8G1ZFXfRtLFHGqTh+q7xAWJQp2VGD2X5Dmlgbl4puoX6w620OGgkVs/cetnjmHmMETcmCmHQj5Uysgyi14GI3sN3lz5XkFQsQuVPJJs2/Gjwk72T4mrJQbl1WOLPvHVatG2jKJLVtdVnHEW6CW46DmnwMtkrbHmzntKlWXX+0FN4upZmLgdIsMYYbTYfRGdGMyVL8FB8HZ4v3bBOXdN+B0/Suz/nU+NbuW31n75WSs5bZtZmqglW336ufexg5sEvSh5cpyngZdx4GUcuc+2ONGSdfZvD5IZJXHjZp6HabHuOmbKoZLHSh4hjSYpVUZHGTw1eHPlO9Fbhn4h/G7df5TYB2F+bHhYrwbW5ZGmvybF2k9NwTbjZsFNzibUBoyYF2UePC/DyDG0rLumdUGDtKy8Jo5uXqz7y2HkNCSmwZt81EgjvOJGxY0OZptkkxzsnMA6BTPWSFT6Zpo9Hv8xYSf7jxmt3x7c0kV2JWoZPToX3KXiQzFlmNDmxIOSg+fsA3fDaBtaXGTUhFI5umnRrevW/ehnbsPM3TAyj5k8OpOV6jJSg7XkyuTRGKyHvVYb9uE1hH/l/ez4VNjJ/hmgFlOWXSWrV1FLvSjeC8UH02r3rIT3jhQCd2FkcJmDi4uajZPCqDb2avcTz/zMyzBxHEfOYyCO3jTjDkI6QLpRdHYmRJHDA6K3W7WBF1m0tFgHZZra7fWb2y8Avy3sZP9c8FDUcraeem2dbd5p63brYhhCDRCD4+IH7toqpkEzvpEdWLbPHDRyozNHHzmGyMsxkQ6ZPKpZ9kZ4NyuavJX+Gm+lN9k4bSIT7mrbqTg2arAPLgDszTO/Lexk/7Fj68pvZa/UmYKLCuoE16SqTcq5NuVWoThH9pV7PxDajrlu3dVXa5PFtsP2uvttmLkZIpdhIB0c+caEInUGTdqWQxgxtS2+EFWbP28jqfQ599zI3IUp+wVgo7e1tMS+T51+xztjJ/tngnVstMlexdlUcFo3m2s170XGuWnamYV3RD/w0heCy826t3VSrXXWSSFoXmL3Y4icDpE0OfKNkicxRdxkgplUTxXBqaCqqHeL4IT0VcqL/HNZh4A2G3SM+LYBdmmh3RN7Pxh2sn8OeEz2ChDRFiebG62qeG1DLN4tve1m5c2df+EKQQte87I/7rm7tMGYdbXUs2HiNAbmgyfOSpql7XZrbrhY2FCC4INSJ0vcSczLTjZplYMudtGVZqQ0ye3cRC3ICG4zKbgT/ofATvbPDV32CixRB2bpRax33ZumXWgJuroMsyjJe85+4Ds1C++1EKTgKBzdDLAQ/lmYOI+By423PWxZTBGTJqbRLig+WHedzm3stks/L11+jeid/O1CUFOy3ENWiNIuBLD0C+8u/UfHTvbPBa/IXrVSFyyTc9JiZ+etBBe8UJpb31dQZQ3ca7GdclLa6mTbld4lrG7czK0LXMLE5eDJWTkXsVVTok2/3vrm86ikA/hJG9krmnzbz2YDO9rWN/Wll+buO5jVlmGyZvShDQrts+sfHTvZPzc8UMG50ql3pnzjvNrgSiNldaz75hxEN/DSVbzLNt66idt7ou7WT0zFZuJzUUpRJiCpt4x/G5RxB8FfIM3gZrfsgNPk1hHc2FY4xWKqsbODyS1JvavSXX+bGZZM/W7hPwp2sn9OeFT2aqNTHyPiTNfOCG8WvTiluObOeyF6x+QDd+7A6LItlnBpse5ObAf8V+FCqkou1l77QiqTVrLz1mAzKHmy/nnXFGk1ihE+NbHKBC5qW79ccJNDJ1uLpW7V1d8S3ur37jpp19//jvfGTvbPDa/ZIFtVr+rv4hrh21bVqn29lFn46D0XX3nhjegHl6z+jmXlg2Ru3URuC95FKk4Ld37kEgJpCKRRkUnJB7Wd7Z3waSW+pmoXgmi3/qK4Nh+PE1snrfKKZd/udKulj3DsNfkPwU72zxEbwtPEIx7W38WpCcuoEFxbK9Ulp1osn53n3o8MreFm2STTSnKjJp65CQAvhaG5/fdh4DRk5smTZkeebPW0TFtNvGvi9yGdEqol9domGKey7I8XaOXEtvr6qhtvQ/pek+9/ix3vhJ3snysa4V9bf2+KMq7JNJuAJObSN7JXD3MIvGzttNtk3ahpcef7ltig1nJ740dOIXIaA+c5MM+eFB1ldpRZrUQ3b4g/m5vvLm0+viUPbScW1hikLFWFRWU3tvs5L/X4R0m/E/6dsJP9c8br1k6lBNNKHHVi9Xf1LW7f9M8PjnMYuHNm3b2WZWVUJ/zRzYtm3Y2L3PrZrHsauB8GLslzmQNTNNLnpEb8qEgUdBLyLLhB8EMXsYSqzi5CIngwCy+y6MlXdUhbENG36bxC+p3w74yd7F8KautGy82dx/TrRAQ9N+vu1wGZEnrCzpFc5d6NeGfZeKXCYE/bXfuxa9bVyOQ8t27g7APnEDilgfMQOKfAJZloRkyOOFuNPk9m8cvQdtFvqwRqbb6IhRyu9diLqu2V60o4/ULWmnDoCcqd8O+MneyfO7bWnbxd5361NrrX371bR2BLEGoQSnBEH7hzBZFN33ojvHOFUczKH4gcVTm6mVgcpzJwbgspLtmIP2XPOQam5JmSY549cfLkwbL4tVcK1FZdVbFEYmhrrpyzbkCcWjjS6/GS1qUZrQlnJ/y7Yyf7l4JNdr7yyNpopzYw4xV/2qyJbjvSo3NMOnAnoFuJ58G+Dpt6PMChRrITjmUmescpD0zFc84DU3EL8c8pcIqB0zBwGQIxBKJ3qwvfcgn9NqjYVtd2vtshm6omlX1VqqtiF7md8G/FTvYvAT07D20UVq/753vCzjvcxVG7yEXrtCuNXEk9k1ZeNOuu/Wh980EytFvXVkAfdSZWxzN3YWqS1bYl1nPOgfs0ckqBu+HA3TBa6c4NJO0uvElSmyuvbRuNbz3+agnGXo/ve+/6hQyW1dA74d+OnexfCh5tp2398ypWf1dd6u8+dOtely2xiJIkcHnk6VXKsjJqKc9ROEgkVyWjFL0QqycjTMU2xU7B8zKPfBtvuPU3DC7znSvcu0qW0Eguy5opawCyspxXWUZ4dYnl16UaVqrrcTw74d+CnexfGt7WTjvNqLf4PSwutC5kqyiZa8KbhS+LlXdYg40T0NaEs0WuRtq5+mUP/DM38a2fGVwiuIzTwkupRB2I6lZX3m3OqXX9GekVaSutew7CQpSH7bY74V+HnexfEr5PO61rsbH6JVFmoYDAI4TvevMABSEjPNcLTgqOTJBkFwFZu9xyVaI6LjVw1Jmjm62WLwUvlgy864QXv1p5tRp8df2C1BpwuoXvhKc14cDjhG9nuxPesJP9S8O7tNOqWra7WfRldbLNy3JF+CrUdpQqpOJIgyN6R/FKrDNHnUDBSUQpDJJRTMG2oMzVcZDI2KbqHGVJAqrAC4FZIKmzc3K9p9/I7B1G/laaU1W0T/ptXXoeEr42Lfud8LCT/cvElvB9PPyBO880IyK4zm1ra2lPIFAFQUnFcylCKULMSiyOuTimwbfE3ET2Qkaba1+AbIRvVv5QlaEn9ihrSCBrqe9OK5MONlWnumbqnXX9VZUl7PDN8muz7jvh3w072b9UPMjQX7nzjeimbsNmmWQjfMXIXgTJSizCnIUXue2QS55Lq63/JJyJ1fG1Py0vrVpQCocmjNETe7odZBnWu9qGbF5o4awjyTXCO1mPFscHZ+frWtjx2CDNo4SHJx/D72T/kvG6dlpJ1FmWCTOFRf1VqkOKQwq2MLIIkiElR8rCy2R75KbkOR8Cl3FdC12qZdIzam59NVfeYaQ/aKRslxA1wgcpS5uuauXsRrJ6qnPrpF6P47vVX3IMdv6PE31P2m2xk/0pYDsd19Rtum5d3x67ULD2Q9HclGRz05+LQo7CJSo52VromB1z8aSqpKLEsK6XKihHma5c+iCJgwrPaReFgaWe78Wksn4jlZOMJK1E9Zumm16PB3Cr58L3JPwTxbvsZ//LwJ8CflVr/aPte/8+8G8Cv9se9u/VWv/mD3WSOz4AD6fjWv98ndefS1N8ddnuSw5IdqYkm01RVpNNsaWkpCjERviUlTk75uyYRr801sTqiM4xq+NWJ0K1OB4gkDlI5Cfuvln91rSjRnanhd9o4d6NzArJuaWl1mry1gpce66hZfFfS/hS2urrpy1o+S6W/a8Afwn4qw++/x/XWv/Dj35GOz4+HumfF6DOIH1XW61QKlor5Ipkb5JSyZlWfFsJrVnQJKQk5CyckpKSu07eFc9lCEw+8MxduOiZW5056IyjLhl7ABxLTO+kmHVvpTnviiXuXCCpB3VraU7q2nEn68dYtrPxV7fxWtDyCeKtZK+1/o6I/OEf/lR2/OCoDwhfSiO6bYuVWpFiq6ClrYWWHJBGeBdlsfCSLHmXUmBOynePJO/OQ+Arf+HkB57rhaNOHDRyEJO+6oM1FuKXpSVXpeLVGm9UKndaOFtb/Npiu1h4bfF707GXq5pCe1+FWpx5MS2ceYr4kJj9z4nIvwr8beDfrrX+5rEHicgvgF8AHDh+wMvt+CA8rL+TzbUlUoszC1+ykbttbnGpmFufPJp92wZjVl6ytFgecnLkTfLuHD33B1sV/dPxxMsw8rU/88xdFtLf6kyQBGDk14prLbi9F9+1WN5pQQTOai22rd7WknaykH9pEWBD+Fqp1d4H2d4z0rbsPDFX/n3J/p8BfwHzkP4C8B8B//pjD6y1/hL4JcBX8rOn9df9seHRDrv2/X60+F2yLXfQxcIb6SWDZkVzj+fNpZfUknezEqNjioHzIXCfBr4LB346HPhJOHHyI1/7E7FeuG2WPkjCyYyrZWm4CZqW9lwvlqn/VgsnrWQNbWqOpSnIknjO2n4bXHs/kvOyBfcpW/f3Inut9R/1+yLyXwD/40c7ox0/LBZr1ogu7cPPI259zstGF1dK2/BiyTvJZuWvkndRybOQZ+X+xjFPzcIfBu7jyN048tPhZKW6TdZe1WJ4J5HgksXvLXHnsO01XS7LSeVeKlED0TnT1eulOQGz6R4qbflkW0xRCuLSMg2IKE+N8e9FdhH5Q7XWf9C+/JeB//PjndKO3wre5tYDtXqL45vV18KarS8OzRVNugpMRrGdcLOQJyXeONLkmSbP6WCz7ZccSGUlumuxunXYJQLgtC5fq3S1W6vFB5dRLbzUytwSd1VsTHZtIlLrFUjeNtKkjKRMDX6x7pT05Gru71J6+2vAnwB+LiJ/D/jzwJ8QkX8Gc+P/LvBv/YDnuOOHwtbKPxyNpWXq6+rWSy7UMqzZ+uhNIjoqGhU3QboRdLYVz3kS8kGJN0qOjpSVWJRShdKC69BJvhyWpAs1Nfd+naX3bSGlk0JwhRfuwEWrZeqlraaq0hqCbLW0poKLAWKC5BfrjsqTK8O9Szb+zz7y7f/qBziXHZ8Kj8zCb+P4bXlOar3K1mv2lq2PlTSryUY30qfZxCZTFEpSztk2y9QqqFQGTRx15iCRW52BiQFrwBnovfT1+iLwoB7/rVTL1ANUt+QRNKotqZg9MmW7jdaVh3OrdYcnY+H3Drodhg3haVtVbbsqaxzfYngpZc3Wx4LM3lY/TQ4323pnNzXSHzfNOMVzqSBS8Vo4uMgzN/HcXYj1RK5WNx8oqGYONTGoWf5BMsOG9MuYLMbVEzZHH4sa4SO4WdsGGo9OHrwD7y2GzxmcW/fKPQHC72TfsaIRHlEj9jZx18tzrSwn2WJhjR6JwVz6yaGzbxtidNGNt5q8QFWSeC6u8sJnbsMNz/3EV/7CxQcK2pRw7EAg1EKoeePur9ZdWzNOP/VTFnIM5FlI7YLjJ8FdHGUM6ByRIVBLvs7MP5G++Z3sO67RCQ9XibtX3PqcWwIsWPIreuQQbInj7OyIrfuugNTW6qq2OvoSBl6MlqE/lYFLCcxtmEalEqg4sdbaIIVQrzP0neQApSq5Cik55llJk4UP7gJpVNyouNHB1GJ375FgZH9KffM72Xe8ikfq8RQ1t74UaimIc6123eL3Xt6KGVKwunzL3oOzp9UmY31Q0o3jMjcF2hy41HA1EWdEhyAY8Uk2SdfabaGTXEnFccmeyyGQjo48KfniLEk4YksoR4cOHmZvF6eUwJUn1Te/k33H43hAeGhuvWgryzUr71uHWvatS62gre3WEnq+Nb6o7XM/CHoR0uSYouecAuc8EKtrwpXrFJ5rwy3tUgEknBoRCyZyGasJadzngZfzyHkMTKOnjEoejex5FHJQdHC4IUDKlpkv1S5c8CT65ney73g7amlqL+3LRua+rAGgkpb5+GVOvnW3uaD4Vn93ExbHRyVFG5GdiiO2mfgtjOhC06vACJ/JGsmcyQiXEjj5gRfhwLfDDXfDyDwW8lCXHfJ5EFtOERzVmwafeG+hSPdQtvH7F2rdd7LveDs2RH+IWm3QBHS9CBRL3uFci+GL1eFjRZKVxshQitXdU7GR2NysNZg1d6wbXqkwNMJnMkUiUWaeuzN35cAzN/EsTIwhcR8KJVRbcdXWXPUtOBpsnbWtuHXYHK+85t19WdjJvuNVbD/8oovABa1TTUQaUdp6JufscX01U//VWtvFoM3JFzaHtHzf2mDzGFz/mUBoM6uFShTTrD9I5KgzNy5ycImDTzifyaFSfV0333iTt2p7rJfFE9Z9t7n/BbvyO9l3XEM2xKYr2mxI3iziQvhGdumW0juq77cb4chFSurVl1Te7jI7rPdda0VhWUU1SOYgMweNtnVWC6oVpFIXVdp2ND27ZV9cXw39RLCTfceKDdFfS/JuyXV73+Lg6jaED3aU0ZMHXVdEeygeqjO9uT6z7pbhl3VldHfhFaVsxCrNxa9Ld51r03HamnVUy1Y38+oCU/v6qCfium+xk32H4XVEb+2lVyT37tqKNwtfvZpIZLD7ZXDkUck3SrzpmXEoQ6WGiveZweVlLbRNu32/xFiu1/mE0jTuqW28vWvq9be5Gex5atjJvuNVom/c9E52vDeSe7+SPHgjeGijpsHZDnivy0roPArpINYrfxTyAfJYYcwMQ+LGR26cLZAYNoR3b7C8GWvozQilld8KQqptyKYIFLHeoJYj0AySKpKtA/Bqfr982c00HTvZnyoeS8I9Zs29R7w3kge/kjw46uApQW0rrFfyYHvX+xroHLDS16ENxtxCOlbKsRAOieMQeRYmbv20JNuG1lqztddlaaKpnb/WB18dGWGunksJxGKlvJLVSJ4byTNItgEeks3nkws1l6a826f7vmzS72R/SnhAcLt5EJt3ax78SvTgbRZ8S/LBUQZt8bhSBiEH2ZS6aPVtc93zwYienmX0NnJzM/P1eOHrcOG5a/p0EgliCyNhk4mHppoHuVZyhYgy47gUa8iZimcupnKbsy4lPkmYcGaszbLbbDspQclG+FIW3fwvmfA72b90vI7g/etuyftaqODXuHwIK8kHf03y0VlnWm9aCUIeaIk41mMwtz0fKuWmGNFvZ76+ufDTw4mfhBNfu6Y+K9H057gmekemkoGIEKsuktVT2wd/yYGYHDWtQzg2b2+El5itnTdnainWENQlq5o6D/DFxvM72b9UvEut/LEM+0OSby356FaSj0oemps+rlZ8JXldb8cCQ8HdJI7HiZ/cXPj5zUt+PtzzU3/iuTvzXM+tlFasTfZBzP6YC3+p1lN/KgPnPDAlT8yOmrr6LU02q6LRxnGtVdYs+1Oy6rCT/cvEw4Rbu/+KFd/WyXvi7W0kH1rSbbTb0jPsgdai2rLtQ6WGRvKhEIbEzTjz1WHiZ4d7fv/hJb9veMnP/Et+4k4mPimZINfxOnSLXsm1EjcufGyx+qUEpuKYsiclhdS08SKLdZdYmmVPNgTTrXqL2790qw472b88vKmEtiW590sC7nUkz2Nz2Q9CHrQRnM2AyeqmmwW3khqhoEPGh0wImTG0ZNww8ZPxzM+GE79/uOPnjejP9cxBkpGd3g//mBtvWfjuwl+KWfbuwk/ZkZMzq55kseqSKpraZF4uC8lrzivRv3CrDjvZvyw8QvTuom8tuSXdwhXJy+Cpo6OEHpP3w6x4GSAtI6Mt6TYawctYIFRkzHhvVjz4zM0QOfjEwVvW/XmY+Dqc+ak/8TN/v1p1mTlKIgjv5MIb4T3zJl6fs2dOjpIFsrQMvGXjNddFeJKUFsJT6jXRv2CrDjvZvxyIvL0hxnskhKvs+kLy7qoPfTRUrix5GbEY/VAXa157LD5mfEgMQ+IQrD/9xkdufOTgI7du5rYp0jx3F752J37Sjud65qiRQQqBd3Phe6x+qSZ6cc4Dl+xJ2VEXeWsrt5nefV9p1errbfbeym5Pg+iwk/3zxmtq5eLacIf31w0xPfE2BHPVD34Tj69W3Cy4ETwdmiU/bFz1oSBjJgyZYUyMIXITEjfBCH70Nphy62ZupHFB8wAAIABJREFU3MzRzRzVDkvG2YKI53rmVuISqztZ3fdeaou1EGslAnNVLt19L5aYs+RcsHg965qcK92qN/nr1khTc6G2uvpTiNO32Mn+ueFttfI3NcQMgTp66hiM4Id+bEh+6F1uG1f90Fz10ax4GBJDSBzHmYNP3IaZo5+59fMVwUdJHF1rmNG41NJvdeJWZg6SGCUTqItFz9SlvTVTF6LfF+VUPacycqoj92XklEfu08gpBS7JE6OD1NZMp3aU5sIvnXMPM/BPg+iwk/3zwbuQfFsrf9gQMwRKt+YHRzo48kHXVtZO8oNZcWuEMVddDpkwJoYxcTNEbkLkGGaehYmjn3nmZ27ddGXBR40cZLaptLbiqXfJBcktGVdaqc3eVm7qOKW57RmYqln0U/XclQMvyoFv85Hv8pHv8g0v0sh9HLnMgRwdEmVx4zVvCN8Jvu2LfwJJuS12sv/Y8bqRU3h9lv2Rhpgy2pFvzGVPfTilt7LebIh+U65IfjhEjkPkdpi5DTPP/MTzcDHBCD8tBN9uae3k7v3uXQc+SN/HXpqe3IoMxNYhVzCST9VxqZ77OnBXbvg2H/kmPePX8RnfzLd8Ox95MRvZy+Rwky2p0NZIo6kucllLLzw8OaLDTvYfL97QFNNj8kdJvrHmdVyJbm67kbxb83wwkqcbyDdrl5scMuEQGcfE7TjzfGzkDhNf+Ymv/JlnbuLojOi32l31ebHc2yUPphZb2pbWNr7a3lq36rERfC2t6ULy+zJyKuNi0b9Jt/x6esbvTbd8cz7y8jIynQMyOXQW3Axu7i2ytsyC3jHX3fgniJ3sP0a8SUDidZb8MZK3hpgrl/2wWvPer24kr9SbjD8kxkPkuJB84qvh/2/vW0Jsy87zvn+t/TrnVN3b98qmaRQRJcGzDOxgkoFMCJiERBMlExEPHJmYKIMYbPDAQpkIQkCExMGjQBsHpGDyAClYhAyiiDzwRFgSimRbxHaCTCzaet1763H2a+21/gz+tdZee59zqup2962qrrM/OJxn1V29u77zv7+/wWt5E7PpsnrZt7h6go8kH3XfU8utd8vmADCx4gYjyVvOI8nP3QpndoWzYY0XZo0XZoVn3RpnXYWzeoWmLsB1hmyrkNUiIa07IbwOzTRWVHGjZT9CLGS/b9jX/XbdyGkgeZ6BqynJXUkYItERrfnEZV85qJW466uyx2nZ41HZ4lHeepI3eJw1eKxrrFWHR7rFmkaXvYBD6fepB733URUWcVzV+ljZQghueLTkrXfXA8kv3AoXtsKFq3A2rHE+VHhhVuK2dxXO2xJ1W6BvcnCdQV9qZFtCVgNZzchahm4dqHexHx5+ycUkE39EWMh+n3ATAYl5hj1m2bVPwPlSWkUYSjVa8opgE5IPK598Ww0oqgHrqscmkLxo8TgXa/5YN3ic1dGSn6omkryKiTaOOu9B/jlAywqG+N9nfYOM4WlM3nKOc1ftteTnQ4nzfoWzvsJlV2LbFujaHK7JQI1CXish+hbILxn5lpHVDllrodrBL3W0gLNHm4kHFrLfD+xz2w9Z88J3vhX5mHzz9fLgrtvyCkuexuXlgGrlSV50eFw2eJy3eJQ1eJLXONEtXgvWXI1jqCnJc/IyUVHjfb+/Lt1vUkYzDG/JNbZcoHZSSnth17hwFZ4PG5wNKzzr1xOS112Ori1gGw1qNXSjkDWAbsSi51shenHpkG8H6HqA6szYD2+GMW5fEnQLbgVzFZZZbB4TcHNrnnS/HayXV5Qk4PwceZJ8Q2WR+7g8Tb499tZc3PUGJ7rFI9XgVDe+Jm5iXbwg5ze1XE3ysTHGj6Yy0M4y7MFlf2HXOBvWeO6t+fN+hbN+hbO2Qt0VaNsctsk8yQm6pRifZw0ja4C8ZuRbi6y20FsDagyo7YGu91Nufpz1SHGT/ewfAPBZAK9D2h3eZOZfI6KnAP49gA9CdrR/lJmfv7qjvsdxFcHD80MtrmlTTHDZy2xv8m1Yibs+VGOG3a4dULmYfDupumtJvlZdzLJvvKhEOpWWWvMAm4i9pZZcxCYoWvPalVJKs6sdl/1Zv8aLfoWzrsJFW6JtCpg2Axqx5Lol6JqQtYBugKxlIXvLyGpP9HaAqj3Jux5szGjV3XHG68DNLPsA4JeZ+WtEdArgq0T0RQA/B+BLzPxpIvoEgE8A+JVXd9T3KK4TjwBuVi+ftbiOJFeTDHt02Vc++VY56PWAsuqxLg0eVS1O8w5PyhqnWRvd9ce62SF5nEQjt9dlT2FSJRnA97FPy2gt56M1t6vYGHM2rHBmVnjerXHWS/Jt25ToW0m+KU/y0ZIDumHkzZiIyxoL1VroxoA6b9F7A+46uR+GUXqKH+bGl+twLdmZ+S0Ab/nHF0T0LQDvB/ARAH/Nf+wzAP47FrJPsS8WD89TAYnZfPnEZS/zqBJzI5d9ncTlSfLttOrwqGzxmi+jvZbXPvEm5TRJwL0cyUPUmxI8LaO1Xklm60ovMiHJt0tb4cJWeD6scWEqnJkKZ/0KF12Jy7YUl73OQI1GVnuiN2LJ85qhWyBvHLLGQbcOurVQrZFkXG9AvZlYdPYuvCjUHB/JA14qZieiDwL4CQBfBvC6/yIAgD+FuPn7fubjAD4OABXWb/ec7z1cJSBxFcnnWfbKN8XMLHnax5667G7F4MoiW0ny7aTq8Kjo8KSqY4b9abbFY13vkHyfu17sseLA6KZfZ8VbV+DCVdi6MpL8fKhwMVQ46ytcmAoXXYltJxl202ZAq6FrJbF5I0QPcbm47J7ojbjsYsk9yc0A7o3E6F6kYkL0I7XqwEuQnYhOAHwOwC8x83kqss/MTER7ryAzvwngTQB4RE8f/lV+uxtVrutjX6ldS16G5BvgKnHZ03r5o6rD46LFk7KezJE/1ttYKxeiD2OG/ZrEmwXvZNbnba2hGUaSbxUubYWzYSUkNxXOTYWtKXDZlWj6HF2bY+g00Gqodr/LnrXitusmlNQSl70LJJdEHExiyVMlmiMmOnBDshNRDiH6bzLz5/3L3yWiN5j5LSJ6A8D3XtUh7z1uqNo63Y1GyQiqPtjHPqzSFtek823FMmPu+9jVyg+qlCbWy18rGrllNZ7kWzzWDV7TWz9e2nprPnhrzjskT0UkHHMkeh9nyw8n3S5tFePxC1PhYihx3le47EvUfY6mK9B3GVyngU5FkuvWJ99aQLfemrdOYvPGQrcWurNTax5c9mEAm2E/yYGjJjpws2w8AfgNAN9i5l9N3voCgI8B+LS//61XcsL7ipsQfI+rvncvmta+nObXJVWZuOxrBbNSQvI1JT3siMk3VU1JflJ0vimm9XG5b4zx1lxmyMfx0sqX0TSAPJkn30d0k0yiBZK3nOHCraIVDyU06Xhb48xUuOgrnPfeVe9ymC4DtxrUKehWSWtrS/4e0B0jayXTrjsfl3dOsuztAOp9bN4ZwJjd2NxxlJwCcPQkD7iJZf8QgJ8F8E0i+rp/7ZMQkv8HIvp5AH8M4KOv5oj3DDdx0wPBVbDoaSlNLHxcfhh2ohUZuFCJyy5TacOaMKwht5WU0XglJC8rEXFcFwaPShlUCU0xj7I2ScA1sSlGiD5Nvu2LywPBAUys+U3r5Gemwlm3iiRv2xxDm0eSZw1Bd76PvU9JztAdS1+7J7nqLVQ3gDoL6pfY/O3iJtn43wYO7tT96Xf3OPcYB0i+dwJN0UjwLBOLnhJca8CvTQobVaIkVEU71nzYcNykQms/qFL1OEl62E/z1hO88YMqQvJRKGK3KSbtXw9IY/K0hz1k2Gsv8nhVnfzcVHjRrrDtCtSt1Mm5yaBahcy76jpx1XUHZB17sjuozskAS++gek9yM4CM74IzUkq70m1fiL6DpYPuJjgk5DjXXc+yXRc9bDj1Sw/h96CxDptUxp1otkj2oq28NV8z7MaB1xb5WjapTMpoRYPTTOrkp1oIHmrl+0gu6453h1TMNUMqxre2ppNoaVw+r5M3XYGu8f3rrUJWB6Ijdr3p4KonVlx3FqqXZQ7UC7kp1XqPopF2ScK9JBayXweiqTVPetV3BlPShYfBemcKLteyGzxXcJqE7H4nWliZFJct+J72Yc0Sn2/Emldrg9N1i8eVZNefFHUso4WR0/k0WqoGM7fk2g+lpBbc7imh9ZDkW5xGm02izUtosU4eWltrJaSOXW9jPJ61Tix7a6E6u2vFrSe5td5VdyPJmWN8vhD9ZljIfghzt92755HomZTI9qq15qOLLnvPFFin+8kp7kOTm6xOsiW8yKMQnVcWejNgte7waNXiSdXgabnFjxaXeOLXJh0qo+1riEkRxk3n7axpnTydKb+wMnJ6aUucD6s4iXZhSmz7YqdOHrvemgNdb6GE1kk8HqbToqseN7Yk1juKRs5IDhzlYMvLYiH7HFcptiZLDynPgbKQ6bMi922s4y40IbAScvutpkJ0jKT3ZOfMb1MpEt033/22WXWyF62s8b5Slis8ybd4qrd7E2/7auXAdNRUFGFGdz3NrIdut5BhTxthzocVLoYSl6bEZS8kn9TJO0/yjvywiid5WkJrxJLLbQC1gyTdPMlDCW1OcDhRhT1I8sWiX4uF7NdMoE22mwYhx0D0sgCvCrgqlMt03J4S9qAFyx3JnQm55TnL49yvS4oSzdIYc1p1OC06PC1r/Eh5iffl27hc4VQ1ydip9bexVp7PSmiOGRoEM9FgJ18jz/fOk4dmmDMjFvyir3BpCplC6/PdOnlHYwnND6noVpJvqcseS2hJQ0wsoVkrSbfUggNTgvvncr+Q/KY4LrIfILY8vKalNSV6kYOrAm6Vw1U5hk3mG19U3Jpi/RYVWyAS2uUAa0/unMEZA5kDFQ7ab1IpvUTzSd7H+fKnhZD8SbbFa3qLR6qNmutprbw40BATYMA7JbRtYsXn8+RnZjWJx+s+lxJal4G7pE7ej3Vy1U1Jrjue1slvWkK7zk1fSP7SOA6yX9UAA+yQG0r550mdPEygFbm36DnsOoNZZxjWCsOKYNZjK2vcgRYWHQZy5w6UOajcIctkXVKReZL7JQsneYdTL+w4j81PVeO736bWPDTEBJJP3HbmSPTQELP1Guznrjo4T542w7RNISQPdfLYBENQPST51kkZTXeMrHNQHU8z7J2RLPsha750vr1SPGyyXyfDPO9VD+QObvu8jObHTO0ql571tYbZaJg1wWx880vocisZHLanhEWHCbmLzKLKBpR6mG5RyTqRaNZdLKWlG1QqGrCm4VprbsGTzjcDoOXRbZ+Q3Cu2pvPk52GevMv31snFgieW3DfCZC1D9Q66dz7DPiujeYmoYM1Dpn3pfHv1eLhkP6TndtUwSiB3aIJJut6ky02PPeu+ldVsEqJvOM6Ro5puTymTHWhlNqDSBpU2OMl6rJQQfa36iTzzRnWxlHYoATePzYHdPva0vTUsWghEfz5s8NyM1vx5t/ZijiX6Ntupk6tuTLppT/TMW3DV+Y43Y30JbbeMFpphohVP3PaF6K8WD5PsN22CCe75rE89NsFkQvhQRrOlNMFI44uCWQNDQvRhLc0vemVRVj2qwmBTyPaUddaj0gM2maxHSsld0RAXLIwz5SY2xBwaO903rAIcjs3T1tZndoPnZoMXg1jz560sW7hsSzRNsVsnb2inGSZL4nHV+QGVfhA1133NMGkZzS9XXNz228PDI/uc6HPhxjybxOBhCGXSpx6bYaSExlnIsEuXW1SF8W77sHGx+aVcja2sJ0UX4+9N1u1Y77D3bGyCGfXXA8G1r5VfNZEGHLbm89j82XCCZ8MGz4fgsq/xol3hwkszm0b611W9v04uzTAhuz4jedrxZobdZphkg+pOGW0h+ivHwyM7MCX6vm63uXBjMohi89DGSrE+Pna5YbLd1K69JPPaQq+l+eWk6vA4yjHLUMqhFUnpgoVgwecdb6kVB3AwAXdVbP7CrfHCbnBmV/iBOcWzfpO47JVIM4fW1kYja2YkD3XyjkU0ohPhCNUNEpOHKbQQj89ddZeQPSH2hOTAQvRXjIdF9tmO8mjRfTsrFflUIKKSjjfnVxa7sJu88AQvEInuCqmTS5ebJOCC9FO5MtisOjyqRNstFXEMQynrmQUvyE6XHPqOt7n++r6RU2BMwAG7bnsdd6NVeGE3E2v+g+4kblO5aEs0dYmhSSSgDpF83traDlDBkvfjiOk8Hr+2GQZYSH5LeFhkD/DJuIlFjzrrObjKRYa5ShYdVrI5JVrvwpfPAtELllp53E/ukFcGK9/88qhs8bSsd/TdZCiln+xBC1tUcnJQ4J3e9X2Cji4hxKQTbmbNQxJuvgTxh2aDH3YbPO/WeNFUIujY5OAmg94qr70+lWaOI6fpkEon7jp1B6bQZkm3heD3Bw+O7KE5hoILH1Ra80zq41UuGfV1jmE1yj2Z1e5ucld6gvs6OQrJsGf5gLIYRJLZi0XsG0zZJEsPcxriLrT5osM5scPqYgBwiTzzfH1SOrgSFi5cuAoXdiT6D80G3+9P8Kzb4EW7kkx7XYqgY62R10qkmRvI6qTGSzOH5Fub1MnTuNwMXrX1mik0YInH7wkeDtmT6TQoirVy6FGOmYPc0zrHsB7LZ5JoS5Rgkt3kKKRGnucWRTGgygeschMVYU6zbkcRJiV5sOZhTbHy5E03mjoAYL+yOPz3zIgRlVxBsEwwUHBMByWanw0bPDMbPOvX+GG7wVlb4bIp0foliHrrSV77bSo1xx1pUbX1EMmvKaEtSbf7iYdD9gAfq1Oq2BoIX+ZwqwxDqJOvZ+UzP1bqKonF88KK5FNhsPLdbZu8wzozIhjht5rKLPnY/JIm3VS03gw90+TsebTpLtEHsUjLaRRfsyA4Vuih4VhNJtPC0MqZXfm6+Spm2s+aCk0jmXaqddx2mtWQ/Wg1e7KL/rrE5UMso1FnpnVya5OptFmmHViIfk/x8MgeW1xpIubovO56kH0ySZ3cbHxDzMbJ2mIvw7wqDDZFj9Oiw0nWYZP1scMtrC4O4o0b1cVdaGk8Hix4ILDl4J5TFIcIJBZCKzj/JTD+jIKD8p/P0LNG63K0XKB2BWpbTkZPw6bTi65A7XeXh2ULWU2yBLEBsi3PiD5ANb5/vTXTcdN5nXxPhn1x2+83HhTZ4zBLaJ7xVn0UcxxXJYloI03VYDYD8pXBJlGDSSWfTnSHte4mibe5Jd+3m3xcpkBwnuA9FAzrSF4Hhd4/H8mv/M8oWB4/33KGzuWorZD9wlTY2iKqt269emvcdBo64BqSaTS/1jivWZYt1A7ZdhgXLXT9/pj8qjo5sJD8nuNBkR2AuPAkGnBB90064HRsjBnKsE0lFXG0yFcGJ+txfvxJIZn1R95dX3vrnWbXN9RPymfzhJvMjo9xdpB4ajn3xM29lc7Rc+YJLTfHylt1ktecRueE6I3NsbUF6qHA1hTY9gXqPt9Rb80a6YDTTaIU0wB5LbPlWW2hm0E02Jteymi9mfauz5VhgCXL/h7EwyE7KV9y82U3RdLjno26b853wDmvCmMrybhz6aBXA1aVibXyJ0WDp8U2Jt6Crlt01ZM6eZp4A/Yn0+YKMK0rsHVFlHyqbYmOM7Qux+BUJLthcesHVuhsht5ptDZHM+Rohwx1V6AzGfo+g22z6Wx5UG/tZm2uPgkX3fbWa7B3fj/aXIN9qZM/CDwcss8QBl5YKd/+SokcVBCVkLIaCoc8t35YxWCdyfTZWkvXW6mMNMQoA00OmhwUjX/sjgmOCI4pZtPFFafors9lnuS+wKWtUNsCjS3Q2BydE0IPTmFgubdOyG6shnFKyD1k6LsMQ6+vnC0PGuwydpqU0/xseXTbr9Bg38muAwvB34N4GGTfJ0pBBFYEaLHyrEhEHzXgtIhIsAJYM0gzlHLItUWmHHJlkZOFAkdSWyb0PAovW1ZwJJlxDQc9aXqRRFvqrm9didblXs9NtNwubYntUKKxOeqhQGszdDaDsRqWCdYpWEcYrIZjgrUKw6BgBw3Xa6BPNqkkVlzmzBm69wIS/YHZ8i7pfjMmbjvdq8EesJD8PYuHQfZrwDS9Ib0lcCzWuLMZOp2jcwNqW4o7rSQ5psn5urlYdw2GTqy8TbLrPWu0XEgyzYXMeYGtLXE5FLg0pZDcu+Sd8US3CtYS2Ckwk4imWgW2BBgFGgiqDwRPNqm0MyveOSjDflDFj56m5bSQaQ9uu3Vi3ZdJtAeJoyB7KG8T+8cMkAXIAWQJPCgYo9H2OWpv3QHAsELtCpRqQKUMFFiInrjxGlMSBKKHJFvrxDVvbB4teD0UMeZu+xydyTAYIbkzCrAkNybAnxGOoAcI0Q082TFqr8exU9/D3lmo3o1NMUbq5KEp5sYa7MBC9AeCh0F25qmV9vrh5BhsJbmkLEMNDBXIMghhVKtgNWNQObaAt+wa27zAualQaYNMOWQk7j0A794LAaaxu3S1ORCM0xhYobcavRP3vBuy0YIPGoORGxs1WuyB5IvIeqK78UuJrD+3AVQ/3Yk2F3Q8JB6xV4N9XxJuIfqDw8MgewrnxyeZQc6BnIMaHJRxUL0SgmSQ+F3Bf0loOEcwRsEajabNcVFYieG1g1ZyC98nKumEC49Dp5tjycAzE4xVcE5Jsm3QsIMarbdRwEAgo6ANhOQDErJDPJBIdiRkl3g8TbpljawxjtNoycjpQQ32q0guF/GV/+9acHt4OGRn5wNyBbYOFCyZGUC9huoUtCZwBrBS8lEQyAnJrPEz7K2CyRkmc4BP3pFiyQEmJJ9rt8Z3mMAMsBPXm70LDktirQfvjhuCsoAyBDKAsvBkT8jtOJIdzn/GAtowlEn2ou1bY7xv5HS+ZMFft6Up5jjwcMjuESw6rAOGAWQ00GuoTEGH+jsD5BTUAFhfqgpz7OxLcqy1ZOsVy31gt7/foQOP98SQLxEeiUoWUN4Vp2FG7gFQA/vPJERPLXsgvhWiq16WH4bZcuoHUNvvqrYuc+ULPG6yn/0DAD4L4HXIn/SbzPxrRPQpAP8AwPf9Rz/JzP/5VR30JmDHIPItnT42JSNCkopCDMygIfOusIJt/eqlImxnkVo8h/IcEUAJ2a9BTAAy5N/j1FILWScuuWUhvGUhuuNI9uDGg1l+zp9fGQeyDuT3o00EJF5Wg30h+NHgJpZ9APDLzPw1IjoF8FUi+qJ/718y8z9/dcd7G2AHZgKsBQ0EJiVVNmYo794r46A7WdNki1FrzvlYXurwJKW6GNff9N+fEZ6n7nh4rCyDhkBu9mSXpGL4UpJ4QM4O59+zDLIWNLhpdn3fGmPfy7646QuAm+1nfwvAW/7xBRF9C8D7X/XBXhosppSditadITxldjFZR2YAdwMoz6ByDZ0nKrIZwWnffKNoUovnPRtW5qBAoplLHy1zJL4nrX9M1hPb+hCEPeHj7/PvOwacWHU4l2TXkxLaMCyqrQv24qVidiL6IICfAPBlAB8C8AtE9PcAfAVi/Z/v+ZmPA/g4AFRYv8Pj3gDswDY8lj9uKcF5S5hpUC/qspP96UoBmuRewYth0Oi+qxuY92QXWczlRaJ68vrbSHA3fsaXC9Pnk98bXksn0G6yxji5FguOF8Q3/CMgohMA/wPAP2XmzxPR6wB+ALFj/wTAG8z896/6HY/oKf8V+ul3eORrD+rvD0hJpyudgn48+aEZT/DYapv+vpdF8kWTPp9Y6fh4JGRMosXfMyNrILALn73BGuOF6EeDL/OXcM7P9v7R3siyE1EO4HMAfpOZPw8AzPzd5P1fB/Cf3oWzvnME/Tb2Lj0smFS0iLLPbUj2uo3bYnakrTzoJQg///JkYFLmimfc97p/vvMF7PYk1EJ2PfzcQvIF1+Am2XgC8BsAvsXMv5q8/oaP5wHg7wD43VdzxLeB+EfuXXpiEJMQb65TBwjxgd3n4de9G2dKCTs54/S9g56Wm72eaq/PXltIvmAfbmLZPwTgZwF8k4i+7l/7JICfIaIfh3Dh2wD+4Ss54TvBnPTAGExb7MbhNCX5qzuXu+KtGxJ13+9YSL7gCtwkG//b2F98utOa+kshJUFgPdH4BRCx88L9wULkBe8QD66D7sZYyLPgyHBLfuuCBQvuGgvZFyw4EixkX7DgSLCQfcGCI8FC9gULjgQL2RcsOBIsZF+w4EiwkH3BgiPBQvYFC44EC9kXLDgSLGRfsOBIsJB9wYIjwUL2BQuOBDeWpXpX/jGi7wP44+SlH4FIW91H3Nez3ddzAcvZ3i7ezbP9WWb+0X1v3CrZd/5xoq8w80/e2QGuwH092309F7Cc7e3its62uPELFhwJFrIvWHAkuGuyv3nH//5VuK9nu6/nApazvV3cytnuNGZfsGDB7eGuLfuCBQtuCQvZFyw4EtwJ2YnobxLR/yaiPyKiT9zFGQ6BiL5NRN8koq8T0Vfu+Cz/moi+R0S/m7z2lIi+SER/6O+f3KOzfYqIvuOv3deJ6MN3dLYPENF/I6LfJ6LfI6Jf9K/f6bW74ly3ct1uPWYnIg3gDwD8dQB/AuB3APwMM//+rR7kAIjo2wB+kpnvvAGDiP4qgEsAn2Xmv+hf+2cAnjHzp/0X5RNm/pV7crZPAbi86zXeRPQGZPdgXDMO4G8D+Dnc4bW74lwfxS1ct7uw7H8ZwB8x8/9l5h7AvwPwkTs4x70HM/9PAM9mL38EwGf8489A/lhuHQfOdi/AzG8x89f84wsAYc34nV67K851K7gLsr8fwP9Lnv8J7te+dwbwX4joq37d9H3D68mOvT8F8PpdHmYPfoGIvuHd/DsJMVLM1ozfm2s3OxdwC9dtSdDt4qeY+S8B+FsA/pF3V+8lWGKw+1Q7/VcA/gKAHwfwFoB/cZeH8WvGPwfgl5j5PH3vLq/dnnPdynW7C7J/B8AHkud/xr92L8DM3/H33wPwHyFhx33Cd33sF2LA793xeSKY+bvMbJnZAfh13OG127dmHPfg2h1af34b1+1J/4zzAAAA2klEQVQuyP47AH6MiP4cERUA/i6AL9zBOXZARBufOAERbQD8DdynVdSCLwD4mH/8MQC/dYdnmSAQyePO1ngfWjOOO752V60/Tz726q4bM9/6DcCHIRn5/wPgH9/FGQ6c688D+F/+9nt3fTYA/xbi1hlIbuPnAbwPwJcA/CGA/wrg6T06278B8E0A34AQ6407OttPQVz0bwD4ur99+K6v3RXnupXrtrTLLlhwJFgSdAsWHAkWsi9YcCRYyL5gwZFgIfuCBUeChewLFhwJFrIvWHAkWMi+YMGR4P8DfTvDjnHibA8AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convolutional neural network (two convolutional layers)\n",
        "class ConvNet(nn.Module):\n",
        "    def __init__(self, num_classes=10):\n",
        "        super(ConvNet, self).__init__()\n",
        "        self.layer1 = nn.Sequential(\n",
        "            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),\n",
        "            nn.BatchNorm2d(16),# batch normalization:使一个batch的feature map满足均值为0，方差为1的分布\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2))\n",
        "        self.layer2 = nn.Sequential(\n",
        "            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),\n",
        "            nn.BatchNorm2d(32),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2))\n",
        "        self.fc = nn.Linear(7*7*32, num_classes)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        out = self.layer1(x)\n",
        "        out = self.layer2(out)\n",
        "        out = out.reshape(out.size(0), -1)# 分类器前的处理\n",
        "        out = self.fc(out)\n",
        "        return out"
      ],
      "metadata": {
        "id": "4OqyNe5Q4jTc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = ConvNet(num_classes).to(device)\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(train_loader):\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "        \n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "        \n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        if (i+1) % 100 == 0:\n",
        "            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' \n",
        "                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))\n",
        "\n",
        "# Test the model\n",
        "model.eval()  # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)\n",
        "with torch.no_grad():\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for images, labels in test_loader:\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "        outputs = model(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))\n",
        "\n",
        "# Save the model checkpoint\n",
        "# torch.save(model.state_dict(), 'model.ckpt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54g-lHdK5dV6",
        "outputId": "b9377660-037f-4aa4-8ac3-28d29dc508d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/5], Step [100/600], Loss: 0.2061\n",
            "Epoch [1/5], Step [200/600], Loss: 0.0848\n",
            "Epoch [1/5], Step [300/600], Loss: 0.0766\n",
            "Epoch [1/5], Step [400/600], Loss: 0.0549\n",
            "Epoch [1/5], Step [500/600], Loss: 0.0787\n",
            "Epoch [1/5], Step [600/600], Loss: 0.0387\n",
            "Epoch [2/5], Step [100/600], Loss: 0.0351\n",
            "Epoch [2/5], Step [200/600], Loss: 0.1294\n",
            "Epoch [2/5], Step [300/600], Loss: 0.0208\n",
            "Epoch [2/5], Step [400/600], Loss: 0.0463\n",
            "Epoch [2/5], Step [500/600], Loss: 0.0119\n",
            "Epoch [2/5], Step [600/600], Loss: 0.0766\n",
            "Epoch [3/5], Step [100/600], Loss: 0.1463\n",
            "Epoch [3/5], Step [200/600], Loss: 0.0254\n",
            "Epoch [3/5], Step [300/600], Loss: 0.0087\n",
            "Epoch [3/5], Step [400/600], Loss: 0.0147\n",
            "Epoch [3/5], Step [500/600], Loss: 0.0087\n",
            "Epoch [3/5], Step [600/600], Loss: 0.0606\n",
            "Epoch [4/5], Step [100/600], Loss: 0.0234\n",
            "Epoch [4/5], Step [200/600], Loss: 0.0181\n",
            "Epoch [4/5], Step [300/600], Loss: 0.0349\n",
            "Epoch [4/5], Step [400/600], Loss: 0.0229\n",
            "Epoch [4/5], Step [500/600], Loss: 0.0059\n",
            "Epoch [4/5], Step [600/600], Loss: 0.0095\n",
            "Epoch [5/5], Step [100/600], Loss: 0.0900\n",
            "Epoch [5/5], Step [200/600], Loss: 0.0592\n",
            "Epoch [5/5], Step [300/600], Loss: 0.0069\n",
            "Epoch [5/5], Step [400/600], Loss: 0.0340\n",
            "Epoch [5/5], Step [500/600], Loss: 0.0057\n",
            "Epoch [5/5], Step [600/600], Loss: 0.0063\n",
            "Test Accuracy of the model on the 10000 test images: 98.86 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2、RNN"
      ],
      "metadata": {
        "id": "z4cS0utx6v4C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch \n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "\n",
        "# Device configuration\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Hyper-parameters\n",
        "sequence_length = 28\n",
        "input_size = 28\n",
        "hidden_size = 128\n",
        "num_layers = 2\n",
        "num_classes = 10\n",
        "batch_size = 100\n",
        "num_epochs = 2\n",
        "learning_rate = 0.01\n",
        "\n",
        "# MNIST dataset\n",
        "train_dataset = torchvision.datasets.MNIST(root='../../data/',\n",
        "                                           train=True, \n",
        "                                           transform=transforms.ToTensor(),\n",
        "                                           download=True)\n",
        "\n",
        "test_dataset = torchvision.datasets.MNIST(root='../../data/',\n",
        "                                          train=False, \n",
        "                                          transform=transforms.ToTensor())\n",
        "\n",
        "# Data loader\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
        "                                           batch_size=batch_size, \n",
        "                                           shuffle=True)\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n",
        "                                          batch_size=batch_size, \n",
        "                                          shuffle=False)\n",
        "\n",
        "# Recurrent neural network (many-to-one)\n",
        "class RNN(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, num_layers, num_classes):\n",
        "        super(RNN, self).__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "        self.num_layers = num_layers\n",
        "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",
        "        self.fc = nn.Linear(hidden_size, num_classes)\n",
        "    \n",
        "    def forward(self, x):\n",
        "        # Set initial hidden and cell states \n",
        "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) \n",
        "        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)\n",
        "        \n",
        "        # Forward propagate LSTM\n",
        "        out, _ = self.lstm(x, (h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size)\n",
        "        \n",
        "        # Decode the hidden state of the last time step\n",
        "        out = self.fc(out[:, -1, :])\n",
        "        return out\n",
        "\n",
        "model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)\n",
        "\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(train_loader):\n",
        "        images = images.reshape(-1, sequence_length, input_size).to(device)\n",
        "        labels = labels.to(device)\n",
        "        \n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "        \n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        if (i+1) % 100 == 0:\n",
        "            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' \n",
        "                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))\n",
        "\n",
        "# Test the model\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for images, labels in test_loader:\n",
        "        images = images.reshape(-1, sequence_length, input_size).to(device)\n",
        "        labels = labels.to(device)\n",
        "        outputs = model(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total)) \n",
        "\n",
        "# Save the model checkpoint\n",
        "# torch.save(model.state_dict(), 'model.ckpt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648,
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          ]
        },
        "id": "ty8x9Zqo5jhu",
        "outputId": "f731ce2f-7858-4fa2-e010-4585395dc952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../../data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2fa2bb16921e4beeaa061a54d8f8f54d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../../data/MNIST/raw/train-images-idx3-ubyte.gz to ../../data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../../data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "76d09e7109b44b178c39004286c247d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../../data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../../data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4cf5939d72224b528a5ddbadbb8ee09f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../../data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../../data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "964a2d679f5b47b79e247924fa94735d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../../data/MNIST/raw\n",
            "\n",
            "Epoch [1/2], Step [100/600], Loss: 0.6829\n",
            "Epoch [1/2], Step [200/600], Loss: 0.3405\n",
            "Epoch [1/2], Step [300/600], Loss: 0.1877\n",
            "Epoch [1/2], Step [400/600], Loss: 0.1180\n",
            "Epoch [1/2], Step [500/600], Loss: 0.1537\n",
            "Epoch [1/2], Step [600/600], Loss: 0.1342\n",
            "Epoch [2/2], Step [100/600], Loss: 0.1108\n",
            "Epoch [2/2], Step [200/600], Loss: 0.1295\n",
            "Epoch [2/2], Step [300/600], Loss: 0.0990\n",
            "Epoch [2/2], Step [400/600], Loss: 0.1648\n",
            "Epoch [2/2], Step [500/600], Loss: 0.0231\n",
            "Epoch [2/2], Step [600/600], Loss: 0.0467\n",
            "Test Accuracy of the model on the 10000 test images: 97.23 %\n"
          ]
        }
      ]
    }
  ]
}