038_实战Kaggle比赛图像分类CIFAR10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 实战Kaggle比赛图像分类CIFAR10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① 比赛的网址是 https://www.kaggle.com/c/cifar-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import math\n",
    "import os\n",
    "import shutil  # 用来进行文件操作\n",
    "import pandas as pd\n",
    "import torch \n",
    "import torchvision # 经典库但更新不是很频繁\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 我们提供包含前1000个训练图像和5个随即测试图像的数据集的小规模样本\n",
    "# cifar10_tiny是cifar10中每一个类把前面一千个训练图片拿出来，测试是每一个类挑五个图片\n",
    "d2l.DATA_HUB['cifar10_tiny'] = (d2l.DATA_URL + 'kaggle_cifar10_tiny.zip',\n",
    "                               '2068874e4b9a9f0fb07ebe0ad2b29754449ccacd')\n",
    "demo = True\n",
    "\n",
    "if demo:\n",
    "    data_dir = d2l.download_extract('cifar10_tiny')\n",
    "else:\n",
    "    data_dir = '../data/cifar-10'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': 'frog',\n",
       " '2': 'truck',\n",
       " '3': 'truck',\n",
       " '4': 'deer',\n",
       " '5': 'automobile',\n",
       " '6': 'automobile',\n",
       " '7': 'bird',\n",
       " '8': 'horse',\n",
       " '9': 'ship',\n",
       " '10': 'cat',\n",
       " '11': 'deer',\n",
       " '12': 'horse',\n",
       " '13': 'horse',\n",
       " '14': 'bird',\n",
       " '15': 'truck',\n",
       " '16': 'truck',\n",
       " '17': 'truck',\n",
       " '18': 'cat',\n",
       " '19': 'bird',\n",
       " '20': 'frog',\n",
       " '21': 'deer',\n",
       " '22': 'cat',\n",
       " '23': 'frog',\n",
       " '24': 'frog',\n",
       " '25': 'bird',\n",
       " '26': 'frog',\n",
       " '27': 'cat',\n",
       " '28': 'dog',\n",
       " '29': 'deer',\n",
       " '30': 'airplane',\n",
       " '31': 'airplane',\n",
       " '32': 'truck',\n",
       " '33': 'automobile',\n",
       " '34': 'cat',\n",
       " '35': 'deer',\n",
       " '36': 'airplane',\n",
       " '37': 'cat',\n",
       " '38': 'horse',\n",
       " '39': 'cat',\n",
       " '40': 'cat',\n",
       " '41': 'dog',\n",
       " '42': 'bird',\n",
       " '43': 'bird',\n",
       " '44': 'horse',\n",
       " '45': 'automobile',\n",
       " '46': 'automobile',\n",
       " '47': 'automobile',\n",
       " '48': 'bird',\n",
       " '49': 'bird',\n",
       " '50': 'airplane',\n",
       " '51': 'truck',\n",
       " '52': 'dog',\n",
       " '53': 'horse',\n",
       " '54': 'truck',\n",
       " '55': 'bird',\n",
       " '56': 'bird',\n",
       " '57': 'dog',\n",
       " '58': 'bird',\n",
       " '59': 'deer',\n",
       " '60': 'cat',\n",
       " '61': 'automobile',\n",
       " '62': 'automobile',\n",
       " '63': 'ship',\n",
       " '64': 'bird',\n",
       " '65': 'automobile',\n",
       " '66': 'automobile',\n",
       " '67': 'deer',\n",
       " '68': 'truck',\n",
       " '69': 'horse',\n",
       " '70': 'ship',\n",
       " '71': 'dog',\n",
       " '72': 'truck',\n",
       " '73': 'frog',\n",
       " '74': 'horse',\n",
       " '75': 'cat',\n",
       " '76': 'automobile',\n",
       " '77': 'truck',\n",
       " '78': 'airplane',\n",
       " '79': 'cat',\n",
       " '80': 'automobile',\n",
       " '81': 'cat',\n",
       " '82': 'dog',\n",
       " '83': 'deer',\n",
       " '84': 'dog',\n",
       " '85': 'horse',\n",
       " '86': 'horse',\n",
       " '87': 'deer',\n",
       " '88': 'horse',\n",
       " '89': 'truck',\n",
       " '90': 'deer',\n",
       " '91': 'bird',\n",
       " '92': 'cat',\n",
       " '93': 'ship',\n",
       " '94': 'airplane',\n",
       " '95': 'automobile',\n",
       " '96': 'frog',\n",
       " '97': 'automobile',\n",
       " '98': 'automobile',\n",
       " '99': 'deer',\n",
       " '100': 'automobile',\n",
       " '101': 'ship',\n",
       " '102': 'cat',\n",
       " '103': 'truck',\n",
       " '104': 'frog',\n",
       " '105': 'frog',\n",
       " '106': 'automobile',\n",
       " '107': 'ship',\n",
       " '108': 'dog',\n",
       " '109': 'bird',\n",
       " '110': 'truck',\n",
       " '111': 'truck',\n",
       " '112': 'ship',\n",
       " '113': 'automobile',\n",
       " '114': 'horse',\n",
       " '115': 'horse',\n",
       " '116': 'airplane',\n",
       " '117': 'airplane',\n",
       " '118': 'frog',\n",
       " '119': 'truck',\n",
       " '120': 'automobile',\n",
       " '121': 'bird',\n",
       " '122': 'bird',\n",
       " '123': 'truck',\n",
       " '124': 'bird',\n",
       " '125': 'frog',\n",
       " '126': 'frog',\n",
       " '127': 'automobile',\n",
       " '128': 'truck',\n",
       " '129': 'dog',\n",
       " '130': 'airplane',\n",
       " '131': 'deer',\n",
       " '132': 'horse',\n",
       " '133': 'frog',\n",
       " '134': 'horse',\n",
       " '135': 'automobile',\n",
       " '136': 'ship',\n",
       " '137': 'automobile',\n",
       " '138': 'automobile',\n",
       " '139': 'bird',\n",
       " '140': 'ship',\n",
       " '141': 'automobile',\n",
       " '142': 'cat',\n",
       " '143': 'cat',\n",
       " '144': 'frog',\n",
       " '145': 'bird',\n",
       " '146': 'deer',\n",
       " '147': 'truck',\n",
       " '148': 'truck',\n",
       " '149': 'dog',\n",
       " '150': 'deer',\n",
       " '151': 'cat',\n",
       " '152': 'frog',\n",
       " '153': 'horse',\n",
       " '154': 'deer',\n",
       " '155': 'frog',\n",
       " '156': 'ship',\n",
       " '157': 'dog',\n",
       " '158': 'dog',\n",
       " '159': 'deer',\n",
       " '160': 'cat',\n",
       " '161': 'automobile',\n",
       " '162': 'ship',\n",
       " '163': 'deer',\n",
       " '164': 'horse',\n",
       " '165': 'frog',\n",
       " '166': 'airplane',\n",
       " '167': 'truck',\n",
       " '168': 'dog',\n",
       " '169': 'automobile',\n",
       " '170': 'cat',\n",
       " '171': 'ship',\n",
       " '172': 'bird',\n",
       " '173': 'horse',\n",
       " '174': 'dog',\n",
       " '175': 'cat',\n",
       " '176': 'deer',\n",
       " '177': 'automobile',\n",
       " '178': 'dog',\n",
       " '179': 'horse',\n",
       " '180': 'airplane',\n",
       " '181': 'deer',\n",
       " '182': 'horse',\n",
       " '183': 'dog',\n",
       " '184': 'dog',\n",
       " '185': 'automobile',\n",
       " '186': 'airplane',\n",
       " '187': 'truck',\n",
       " '188': 'frog',\n",
       " '189': 'truck',\n",
       " '190': 'airplane',\n",
       " '191': 'ship',\n",
       " '192': 'horse',\n",
       " '193': 'ship',\n",
       " '194': 'ship',\n",
       " '195': 'bird',\n",
       " '196': 'dog',\n",
       " '197': 'bird',\n",
       " '198': 'cat',\n",
       " '199': 'dog',\n",
       " '200': 'airplane',\n",
       " '201': 'frog',\n",
       " '202': 'automobile',\n",
       " '203': 'truck',\n",
       " '204': 'cat',\n",
       " '205': 'frog',\n",
       " '206': 'truck',\n",
       " '207': 'automobile',\n",
       " '208': 'cat',\n",
       " '209': 'truck',\n",
       " '210': 'frog',\n",
       " '211': 'frog',\n",
       " '212': 'horse',\n",
       " '213': 'automobile',\n",
       " '214': 'airplane',\n",
       " '215': 'truck',\n",
       " '216': 'dog',\n",
       " '217': 'ship',\n",
       " '218': 'dog',\n",
       " '219': 'bird',\n",
       " '220': 'truck',\n",
       " '221': 'airplane',\n",
       " '222': 'ship',\n",
       " '223': 'ship',\n",
       " '224': 'airplane',\n",
       " '225': 'frog',\n",
       " '226': 'truck',\n",
       " '227': 'automobile',\n",
       " '228': 'automobile',\n",
       " '229': 'frog',\n",
       " '230': 'cat',\n",
       " '231': 'horse',\n",
       " '232': 'frog',\n",
       " '233': 'frog',\n",
       " '234': 'airplane',\n",
       " '235': 'frog',\n",
       " '236': 'frog',\n",
       " '237': 'automobile',\n",
       " '238': 'horse',\n",
       " '239': 'automobile',\n",
       " '240': 'dog',\n",
       " '241': 'ship',\n",
       " '242': 'cat',\n",
       " '243': 'frog',\n",
       " '244': 'frog',\n",
       " '245': 'ship',\n",
       " '246': 'frog',\n",
       " '247': 'ship',\n",
       " '248': 'deer',\n",
       " '249': 'frog',\n",
       " '250': 'frog',\n",
       " '251': 'automobile',\n",
       " '252': 'cat',\n",
       " '253': 'ship',\n",
       " '254': 'cat',\n",
       " '255': 'deer',\n",
       " '256': 'automobile',\n",
       " '257': 'horse',\n",
       " '258': 'automobile',\n",
       " '259': 'cat',\n",
       " '260': 'ship',\n",
       " '261': 'dog',\n",
       " '262': 'automobile',\n",
       " '263': 'automobile',\n",
       " '264': 'deer',\n",
       " '265': 'airplane',\n",
       " '266': 'truck',\n",
       " '267': 'cat',\n",
       " '268': 'horse',\n",
       " '269': 'deer',\n",
       " '270': 'truck',\n",
       " '271': 'truck',\n",
       " '272': 'bird',\n",
       " '273': 'deer',\n",
       " '274': 'truck',\n",
       " '275': 'truck',\n",
       " '276': 'automobile',\n",
       " '277': 'airplane',\n",
       " '278': 'dog',\n",
       " '279': 'truck',\n",
       " '280': 'airplane',\n",
       " '281': 'ship',\n",
       " '282': 'bird',\n",
       " '283': 'automobile',\n",
       " '284': 'bird',\n",
       " '285': 'airplane',\n",
       " '286': 'dog',\n",
       " '287': 'frog',\n",
       " '288': 'cat',\n",
       " '289': 'bird',\n",
       " '290': 'horse',\n",
       " '291': 'ship',\n",
       " '292': 'ship',\n",
       " '293': 'frog',\n",
       " '294': 'airplane',\n",
       " '295': 'horse',\n",
       " '296': 'truck',\n",
       " '297': 'deer',\n",
       " '298': 'dog',\n",
       " '299': 'frog',\n",
       " '300': 'deer',\n",
       " '301': 'bird',\n",
       " '302': 'automobile',\n",
       " '303': 'automobile',\n",
       " '304': 'bird',\n",
       " '305': 'automobile',\n",
       " '306': 'dog',\n",
       " '307': 'truck',\n",
       " '308': 'truck',\n",
       " '309': 'airplane',\n",
       " '310': 'ship',\n",
       " '311': 'deer',\n",
       " '312': 'automobile',\n",
       " '313': 'automobile',\n",
       " '314': 'frog',\n",
       " '315': 'cat',\n",
       " '316': 'cat',\n",
       " '317': 'truck',\n",
       " '318': 'airplane',\n",
       " '319': 'horse',\n",
       " '320': 'truck',\n",
       " '321': 'horse',\n",
       " '322': 'horse',\n",
       " '323': 'truck',\n",
       " '324': 'automobile',\n",
       " '325': 'dog',\n",
       " '326': 'automobile',\n",
       " '327': 'frog',\n",
       " '328': 'frog',\n",
       " '329': 'ship',\n",
       " '330': 'horse',\n",
       " '331': 'automobile',\n",
       " '332': 'cat',\n",
       " '333': 'airplane',\n",
       " '334': 'cat',\n",
       " '335': 'cat',\n",
       " '336': 'bird',\n",
       " '337': 'deer',\n",
       " '338': 'dog',\n",
       " '339': 'horse',\n",
       " '340': 'dog',\n",
       " '341': 'truck',\n",
       " '342': 'airplane',\n",
       " '343': 'cat',\n",
       " '344': 'deer',\n",
       " '345': 'airplane',\n",
       " '346': 'deer',\n",
       " '347': 'deer',\n",
       " '348': 'frog',\n",
       " '349': 'airplane',\n",
       " '350': 'airplane',\n",
       " '351': 'frog',\n",
       " '352': 'frog',\n",
       " '353': 'airplane',\n",
       " '354': 'ship',\n",
       " '355': 'automobile',\n",
       " '356': 'frog',\n",
       " '357': 'bird',\n",
       " '358': 'truck',\n",
       " '359': 'bird',\n",
       " '360': 'dog',\n",
       " '361': 'truck',\n",
       " '362': 'frog',\n",
       " '363': 'horse',\n",
       " '364': 'deer',\n",
       " '365': 'automobile',\n",
       " '366': 'ship',\n",
       " '367': 'horse',\n",
       " '368': 'cat',\n",
       " '369': 'frog',\n",
       " '370': 'truck',\n",
       " '371': 'cat',\n",
       " '372': 'airplane',\n",
       " '373': 'deer',\n",
       " '374': 'airplane',\n",
       " '375': 'dog',\n",
       " '376': 'automobile',\n",
       " '377': 'airplane',\n",
       " '378': 'cat',\n",
       " '379': 'deer',\n",
       " '380': 'ship',\n",
       " '381': 'dog',\n",
       " '382': 'deer',\n",
       " '383': 'horse',\n",
       " '384': 'bird',\n",
       " '385': 'cat',\n",
       " '386': 'truck',\n",
       " '387': 'horse',\n",
       " '388': 'frog',\n",
       " '389': 'horse',\n",
       " '390': 'automobile',\n",
       " '391': 'deer',\n",
       " '392': 'horse',\n",
       " '393': 'airplane',\n",
       " '394': 'automobile',\n",
       " '395': 'horse',\n",
       " '396': 'cat',\n",
       " '397': 'automobile',\n",
       " '398': 'ship',\n",
       " '399': 'deer',\n",
       " '400': 'deer',\n",
       " '401': 'bird',\n",
       " '402': 'airplane',\n",
       " '403': 'bird',\n",
       " '404': 'bird',\n",
       " '405': 'airplane',\n",
       " '406': 'airplane',\n",
       " '407': 'truck',\n",
       " '408': 'airplane',\n",
       " '409': 'truck',\n",
       " '410': 'frog',\n",
       " '411': 'ship',\n",
       " '412': 'bird',\n",
       " '413': 'horse',\n",
       " '414': 'horse',\n",
       " '415': 'deer',\n",
       " '416': 'airplane',\n",
       " '417': 'cat',\n",
       " '418': 'airplane',\n",
       " '419': 'ship',\n",
       " '420': 'truck',\n",
       " '421': 'deer',\n",
       " '422': 'bird',\n",
       " '423': 'horse',\n",
       " '424': 'bird',\n",
       " '425': 'dog',\n",
       " '426': 'bird',\n",
       " '427': 'dog',\n",
       " '428': 'automobile',\n",
       " '429': 'truck',\n",
       " '430': 'deer',\n",
       " '431': 'ship',\n",
       " '432': 'dog',\n",
       " '433': 'automobile',\n",
       " '434': 'horse',\n",
       " '435': 'deer',\n",
       " '436': 'deer',\n",
       " '437': 'airplane',\n",
       " '438': 'frog',\n",
       " '439': 'truck',\n",
       " '440': 'airplane',\n",
       " '441': 'horse',\n",
       " '442': 'ship',\n",
       " '443': 'ship',\n",
       " '444': 'truck',\n",
       " '445': 'truck',\n",
       " '446': 'cat',\n",
       " '447': 'cat',\n",
       " '448': 'deer',\n",
       " '449': 'airplane',\n",
       " '450': 'deer',\n",
       " '451': 'dog',\n",
       " '452': 'frog',\n",
       " '453': 'frog',\n",
       " '454': 'airplane',\n",
       " '455': 'automobile',\n",
       " '456': 'airplane',\n",
       " '457': 'ship',\n",
       " '458': 'airplane',\n",
       " '459': 'deer',\n",
       " '460': 'ship',\n",
       " '461': 'ship',\n",
       " '462': 'automobile',\n",
       " '463': 'dog',\n",
       " '464': 'bird',\n",
       " '465': 'frog',\n",
       " '466': 'ship',\n",
       " '467': 'automobile',\n",
       " '468': 'airplane',\n",
       " '469': 'airplane',\n",
       " '470': 'horse',\n",
       " '471': 'horse',\n",
       " '472': 'dog',\n",
       " '473': 'truck',\n",
       " '474': 'frog',\n",
       " '475': 'bird',\n",
       " '476': 'ship',\n",
       " '477': 'cat',\n",
       " '478': 'deer',\n",
       " '479': 'horse',\n",
       " '480': 'cat',\n",
       " '481': 'truck',\n",
       " '482': 'airplane',\n",
       " '483': 'automobile',\n",
       " '484': 'bird',\n",
       " '485': 'deer',\n",
       " '486': 'ship',\n",
       " '487': 'automobile',\n",
       " '488': 'ship',\n",
       " '489': 'frog',\n",
       " '490': 'deer',\n",
       " '491': 'deer',\n",
       " '492': 'dog',\n",
       " '493': 'horse',\n",
       " '494': 'automobile',\n",
       " '495': 'cat',\n",
       " '496': 'truck',\n",
       " '497': 'ship',\n",
       " '498': 'airplane',\n",
       " '499': 'automobile',\n",
       " '500': 'horse',\n",
       " '501': 'dog',\n",
       " '502': 'ship',\n",
       " '503': 'bird',\n",
       " '504': 'ship',\n",
       " '505': 'airplane',\n",
       " '506': 'deer',\n",
       " '507': 'automobile',\n",
       " '508': 'ship',\n",
       " '509': 'truck',\n",
       " '510': 'ship',\n",
       " '511': 'bird',\n",
       " '512': 'truck',\n",
       " '513': 'truck',\n",
       " '514': 'bird',\n",
       " '515': 'horse',\n",
       " '516': 'dog',\n",
       " '517': 'horse',\n",
       " '518': 'cat',\n",
       " '519': 'ship',\n",
       " '520': 'ship',\n",
       " '521': 'deer',\n",
       " '522': 'deer',\n",
       " '523': 'bird',\n",
       " '524': 'horse',\n",
       " '525': 'automobile',\n",
       " '526': 'frog',\n",
       " '527': 'deer',\n",
       " '528': 'airplane',\n",
       " '529': 'deer',\n",
       " '530': 'frog',\n",
       " '531': 'truck',\n",
       " '532': 'horse',\n",
       " '533': 'frog',\n",
       " '534': 'bird',\n",
       " '535': 'dog',\n",
       " '536': 'dog',\n",
       " '537': 'automobile',\n",
       " '538': 'horse',\n",
       " '539': 'bird',\n",
       " '540': 'bird',\n",
       " '541': 'bird',\n",
       " '542': 'truck',\n",
       " '543': 'dog',\n",
       " '544': 'deer',\n",
       " '545': 'bird',\n",
       " '546': 'horse',\n",
       " '547': 'ship',\n",
       " '548': 'automobile',\n",
       " '549': 'cat',\n",
       " '550': 'deer',\n",
       " '551': 'cat',\n",
       " '552': 'horse',\n",
       " '553': 'frog',\n",
       " '554': 'truck',\n",
       " '555': 'ship',\n",
       " '556': 'airplane',\n",
       " '557': 'frog',\n",
       " '558': 'airplane',\n",
       " '559': 'bird',\n",
       " '560': 'bird',\n",
       " '561': 'bird',\n",
       " '562': 'automobile',\n",
       " '563': 'ship',\n",
       " '564': 'deer',\n",
       " '565': 'airplane',\n",
       " '566': 'automobile',\n",
       " '567': 'ship',\n",
       " '568': 'ship',\n",
       " '569': 'automobile',\n",
       " '570': 'dog',\n",
       " '571': 'horse',\n",
       " '572': 'frog',\n",
       " '573': 'deer',\n",
       " '574': 'dog',\n",
       " '575': 'ship',\n",
       " '576': 'horse',\n",
       " '577': 'automobile',\n",
       " '578': 'truck',\n",
       " '579': 'automobile',\n",
       " '580': 'truck',\n",
       " '581': 'ship',\n",
       " '582': 'deer',\n",
       " '583': 'horse',\n",
       " '584': 'cat',\n",
       " '585': 'ship',\n",
       " '586': 'ship',\n",
       " '587': 'bird',\n",
       " '588': 'frog',\n",
       " '589': 'frog',\n",
       " '590': 'horse',\n",
       " '591': 'automobile',\n",
       " '592': 'frog',\n",
       " '593': 'ship',\n",
       " '594': 'automobile',\n",
       " '595': 'truck',\n",
       " '596': 'horse',\n",
       " '597': 'ship',\n",
       " '598': 'cat',\n",
       " '599': 'airplane',\n",
       " '600': 'automobile',\n",
       " '601': 'airplane',\n",
       " '602': 'ship',\n",
       " '603': 'ship',\n",
       " '604': 'cat',\n",
       " '605': 'airplane',\n",
       " '606': 'airplane',\n",
       " '607': 'automobile',\n",
       " '608': 'dog',\n",
       " '609': 'airplane',\n",
       " '610': 'ship',\n",
       " '611': 'ship',\n",
       " '612': 'horse',\n",
       " '613': 'truck',\n",
       " '614': 'truck',\n",
       " '615': 'airplane',\n",
       " '616': 'truck',\n",
       " '617': 'deer',\n",
       " '618': 'automobile',\n",
       " '619': 'cat',\n",
       " '620': 'frog',\n",
       " '621': 'frog',\n",
       " '622': 'deer',\n",
       " '623': 'deer',\n",
       " '624': 'horse',\n",
       " '625': 'dog',\n",
       " '626': 'frog',\n",
       " '627': 'airplane',\n",
       " '628': 'ship',\n",
       " '629': 'airplane',\n",
       " '630': 'cat',\n",
       " '631': 'bird',\n",
       " '632': 'ship',\n",
       " '633': 'deer',\n",
       " '634': 'frog',\n",
       " '635': 'truck',\n",
       " '636': 'truck',\n",
       " '637': 'horse',\n",
       " '638': 'airplane',\n",
       " '639': 'cat',\n",
       " '640': 'cat',\n",
       " '641': 'frog',\n",
       " '642': 'horse',\n",
       " '643': 'deer',\n",
       " '644': 'truck',\n",
       " '645': 'automobile',\n",
       " '646': 'frog',\n",
       " '647': 'bird',\n",
       " '648': 'horse',\n",
       " '649': 'bird',\n",
       " '650': 'bird',\n",
       " '651': 'airplane',\n",
       " '652': 'frog',\n",
       " '653': 'horse',\n",
       " '654': 'dog',\n",
       " '655': 'horse',\n",
       " '656': 'frog',\n",
       " '657': 'ship',\n",
       " '658': 'truck',\n",
       " '659': 'airplane',\n",
       " '660': 'truck',\n",
       " '661': 'deer',\n",
       " '662': 'deer',\n",
       " '663': 'horse',\n",
       " '664': 'airplane',\n",
       " '665': 'truck',\n",
       " '666': 'deer',\n",
       " '667': 'truck',\n",
       " '668': 'frog',\n",
       " '669': 'truck',\n",
       " '670': 'deer',\n",
       " '671': 'dog',\n",
       " '672': 'horse',\n",
       " '673': 'truck',\n",
       " '674': 'bird',\n",
       " '675': 'deer',\n",
       " '676': 'dog',\n",
       " '677': 'automobile',\n",
       " '678': 'deer',\n",
       " '679': 'cat',\n",
       " '680': 'truck',\n",
       " '681': 'frog',\n",
       " '682': 'dog',\n",
       " '683': 'frog',\n",
       " '684': 'truck',\n",
       " '685': 'cat',\n",
       " '686': 'cat',\n",
       " '687': 'dog',\n",
       " '688': 'airplane',\n",
       " '689': 'horse',\n",
       " '690': 'bird',\n",
       " '691': 'automobile',\n",
       " '692': 'cat',\n",
       " '693': 'frog',\n",
       " '694': 'deer',\n",
       " '695': 'airplane',\n",
       " '696': 'airplane',\n",
       " '697': 'bird',\n",
       " '698': 'dog',\n",
       " '699': 'airplane',\n",
       " '700': 'automobile',\n",
       " '701': 'airplane',\n",
       " '702': 'bird',\n",
       " '703': 'cat',\n",
       " '704': 'truck',\n",
       " '705': 'ship',\n",
       " '706': 'deer',\n",
       " '707': 'truck',\n",
       " '708': 'ship',\n",
       " '709': 'airplane',\n",
       " '710': 'bird',\n",
       " '711': 'frog',\n",
       " '712': 'deer',\n",
       " '713': 'deer',\n",
       " '714': 'airplane',\n",
       " '715': 'automobile',\n",
       " '716': 'ship',\n",
       " '717': 'ship',\n",
       " '718': 'cat',\n",
       " '719': 'frog',\n",
       " '720': 'truck',\n",
       " '721': 'frog',\n",
       " '722': 'frog',\n",
       " '723': 'horse',\n",
       " '724': 'ship',\n",
       " '725': 'bird',\n",
       " '726': 'deer',\n",
       " '727': 'dog',\n",
       " '728': 'horse',\n",
       " '729': 'frog',\n",
       " '730': 'dog',\n",
       " '731': 'cat',\n",
       " '732': 'airplane',\n",
       " '733': 'dog',\n",
       " '734': 'airplane',\n",
       " '735': 'dog',\n",
       " '736': 'airplane',\n",
       " '737': 'ship',\n",
       " '738': 'bird',\n",
       " '739': 'frog',\n",
       " '740': 'horse',\n",
       " '741': 'cat',\n",
       " '742': 'ship',\n",
       " '743': 'bird',\n",
       " '744': 'automobile',\n",
       " '745': 'horse',\n",
       " '746': 'frog',\n",
       " '747': 'horse',\n",
       " '748': 'automobile',\n",
       " '749': 'airplane',\n",
       " '750': 'truck',\n",
       " '751': 'dog',\n",
       " '752': 'dog',\n",
       " '753': 'airplane',\n",
       " '754': 'automobile',\n",
       " '755': 'horse',\n",
       " '756': 'frog',\n",
       " '757': 'truck',\n",
       " '758': 'airplane',\n",
       " '759': 'deer',\n",
       " '760': 'horse',\n",
       " '761': 'horse',\n",
       " '762': 'automobile',\n",
       " '763': 'dog',\n",
       " '764': 'truck',\n",
       " '765': 'deer',\n",
       " '766': 'airplane',\n",
       " '767': 'ship',\n",
       " '768': 'dog',\n",
       " '769': 'truck',\n",
       " '770': 'truck',\n",
       " '771': 'frog',\n",
       " '772': 'horse',\n",
       " '773': 'automobile',\n",
       " '774': 'ship',\n",
       " '775': 'cat',\n",
       " '776': 'bird',\n",
       " '777': 'cat',\n",
       " '778': 'ship',\n",
       " '779': 'bird',\n",
       " '780': 'bird',\n",
       " '781': 'deer',\n",
       " '782': 'frog',\n",
       " '783': 'airplane',\n",
       " '784': 'airplane',\n",
       " '785': 'dog',\n",
       " '786': 'cat',\n",
       " '787': 'ship',\n",
       " '788': 'bird',\n",
       " '789': 'cat',\n",
       " '790': 'horse',\n",
       " '791': 'bird',\n",
       " '792': 'truck',\n",
       " '793': 'cat',\n",
       " '794': 'ship',\n",
       " '795': 'horse',\n",
       " '796': 'ship',\n",
       " '797': 'bird',\n",
       " '798': 'horse',\n",
       " '799': 'truck',\n",
       " '800': 'airplane',\n",
       " '801': 'bird',\n",
       " '802': 'cat',\n",
       " '803': 'bird',\n",
       " '804': 'bird',\n",
       " '805': 'bird',\n",
       " '806': 'cat',\n",
       " '807': 'cat',\n",
       " '808': 'frog',\n",
       " '809': 'bird',\n",
       " '810': 'cat',\n",
       " '811': 'bird',\n",
       " '812': 'ship',\n",
       " '813': 'airplane',\n",
       " '814': 'dog',\n",
       " '815': 'dog',\n",
       " '816': 'automobile',\n",
       " '817': 'deer',\n",
       " '818': 'dog',\n",
       " '819': 'frog',\n",
       " '820': 'frog',\n",
       " '821': 'bird',\n",
       " '822': 'horse',\n",
       " '823': 'airplane',\n",
       " '824': 'automobile',\n",
       " '825': 'horse',\n",
       " '826': 'horse',\n",
       " '827': 'ship',\n",
       " '828': 'bird',\n",
       " '829': 'truck',\n",
       " '830': 'bird',\n",
       " '831': 'bird',\n",
       " '832': 'deer',\n",
       " '833': 'bird',\n",
       " '834': 'automobile',\n",
       " '835': 'automobile',\n",
       " '836': 'automobile',\n",
       " '837': 'frog',\n",
       " '838': 'frog',\n",
       " '839': 'frog',\n",
       " '840': 'dog',\n",
       " '841': 'automobile',\n",
       " '842': 'automobile',\n",
       " '843': 'horse',\n",
       " '844': 'airplane',\n",
       " '845': 'deer',\n",
       " '846': 'cat',\n",
       " '847': 'cat',\n",
       " '848': 'horse',\n",
       " '849': 'automobile',\n",
       " '850': 'bird',\n",
       " '851': 'cat',\n",
       " '852': 'dog',\n",
       " '853': 'dog',\n",
       " '854': 'dog',\n",
       " '855': 'frog',\n",
       " '856': 'automobile',\n",
       " '857': 'deer',\n",
       " '858': 'cat',\n",
       " '859': 'horse',\n",
       " '860': 'ship',\n",
       " '861': 'ship',\n",
       " '862': 'cat',\n",
       " '863': 'frog',\n",
       " '864': 'frog',\n",
       " '865': 'bird',\n",
       " '866': 'cat',\n",
       " '867': 'airplane',\n",
       " '868': 'truck',\n",
       " '869': 'deer',\n",
       " '870': 'cat',\n",
       " '871': 'ship',\n",
       " '872': 'airplane',\n",
       " '873': 'airplane',\n",
       " '874': 'automobile',\n",
       " '875': 'automobile',\n",
       " '876': 'dog',\n",
       " '877': 'deer',\n",
       " '878': 'truck',\n",
       " '879': 'cat',\n",
       " '880': 'automobile',\n",
       " '881': 'ship',\n",
       " '882': 'truck',\n",
       " '883': 'cat',\n",
       " '884': 'truck',\n",
       " '885': 'truck',\n",
       " '886': 'bird',\n",
       " '887': 'truck',\n",
       " '888': 'deer',\n",
       " '889': 'ship',\n",
       " '890': 'bird',\n",
       " '891': 'truck',\n",
       " '892': 'ship',\n",
       " '893': 'ship',\n",
       " '894': 'automobile',\n",
       " '895': 'dog',\n",
       " '896': 'cat',\n",
       " '897': 'frog',\n",
       " '898': 'ship',\n",
       " '899': 'horse',\n",
       " '900': 'frog',\n",
       " '901': 'truck',\n",
       " '902': 'ship',\n",
       " '903': 'airplane',\n",
       " '904': 'frog',\n",
       " '905': 'deer',\n",
       " '906': 'airplane',\n",
       " '907': 'airplane',\n",
       " '908': 'bird',\n",
       " '909': 'dog',\n",
       " '910': 'ship',\n",
       " '911': 'bird',\n",
       " '912': 'airplane',\n",
       " '913': 'bird',\n",
       " '914': 'horse',\n",
       " '915': 'frog',\n",
       " '916': 'truck',\n",
       " '917': 'horse',\n",
       " '918': 'automobile',\n",
       " '919': 'dog',\n",
       " '920': 'dog',\n",
       " '921': 'frog',\n",
       " '922': 'frog',\n",
       " '923': 'cat',\n",
       " '924': 'frog',\n",
       " '925': 'bird',\n",
       " '926': 'deer',\n",
       " '927': 'horse',\n",
       " '928': 'airplane',\n",
       " '929': 'dog',\n",
       " '930': 'frog',\n",
       " '931': 'deer',\n",
       " '932': 'frog',\n",
       " '933': 'dog',\n",
       " '934': 'bird',\n",
       " '935': 'deer',\n",
       " '936': 'frog',\n",
       " '937': 'automobile',\n",
       " '938': 'frog',\n",
       " '939': 'airplane',\n",
       " '940': 'deer',\n",
       " '941': 'airplane',\n",
       " '942': 'cat',\n",
       " '943': 'automobile',\n",
       " '944': 'ship',\n",
       " '945': 'dog',\n",
       " '946': 'deer',\n",
       " '947': 'deer',\n",
       " '948': 'automobile',\n",
       " '949': 'horse',\n",
       " '950': 'cat',\n",
       " '951': 'truck',\n",
       " '952': 'deer',\n",
       " '953': 'horse',\n",
       " '954': 'truck',\n",
       " '955': 'horse',\n",
       " '956': 'cat',\n",
       " '957': 'horse',\n",
       " '958': 'bird',\n",
       " '959': 'ship',\n",
       " '960': 'deer',\n",
       " '961': 'frog',\n",
       " '962': 'frog',\n",
       " '963': 'automobile',\n",
       " '964': 'bird',\n",
       " '965': 'truck',\n",
       " '966': 'airplane',\n",
       " '967': 'deer',\n",
       " '968': 'ship',\n",
       " '969': 'horse',\n",
       " '970': 'cat',\n",
       " '971': 'truck',\n",
       " '972': 'ship',\n",
       " '973': 'horse',\n",
       " '974': 'horse',\n",
       " '975': 'airplane',\n",
       " '976': 'bird',\n",
       " '977': 'deer',\n",
       " '978': 'automobile',\n",
       " '979': 'automobile',\n",
       " '980': 'deer',\n",
       " '981': 'automobile',\n",
       " '982': 'dog',\n",
       " '983': 'deer',\n",
       " '984': 'airplane',\n",
       " '985': 'dog',\n",
       " '986': 'frog',\n",
       " '987': 'bird',\n",
       " '988': 'ship',\n",
       " '989': 'dog',\n",
       " '990': 'airplane',\n",
       " '991': 'bird',\n",
       " '992': 'automobile',\n",
       " '993': 'cat',\n",
       " '994': 'dog',\n",
       " '995': 'horse',\n",
       " '996': 'cat',\n",
       " '997': 'dog',\n",
       " '998': 'automobile',\n",
       " '999': 'cat',\n",
       " '1000': 'dog'}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 整理数据集\n",
    "def read_csv_labels(fname):\n",
    "    \"\"\"读取 'fname' 来给标签字典返回一个文件名。\"\"\"\n",
    "    with open(fname, 'r') as f:\n",
    "        lines = f.readlines()[1:] # 一行一行读进来，每一行为列表中一个元素\n",
    "    tokens = [l.rstrip().split(',') for l in lines] # 遍历列表每一个元素，切分\n",
    "    return dict(((name, label) for name, label in tokens))\n",
    "\n",
    "labels = read_csv_labels(os.path.join(data_dir,'trainLabels.csv'))\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将验证集从原始的训练集中拆分出来\n",
    "# train文件夹下有所有train的图片，test文件夹下有所有test图片\n",
    "# 把train文件夹下所有类的图片创建一个类名文件夹，然后搬到对应文件夹下\n",
    "def copyfile(filename, target_dir):\n",
    "    \"\"\"将文件复制到目标目录\"\"\"\n",
    "    os.makedirs(target_dir, exist_ok=True)\n",
    "    shutil.copy(filename, target_dir)\n",
    "    \n",
    "def reorg_train_valid(data_dir, labels, valid_ratio):\n",
    "    n = collections.Counter(labels.values()).most_common()[-1][1]\n",
    "    n_valid_per_label = max(1,math.floor(n * valid_ratio))\n",
    "    label_count = {}\n",
    "    for train_file in os.listdir(os.path.join(data_dir,'train')):\n",
    "        label = labels[train_file.split('.')[0]]\n",
    "        fname = os.path.join(data_dir,'train',train_file)\n",
    "        copyfile(fname,os.path.join(data_dir,'train_valid_test','train_valid',label)) \n",
    "        if label not in label_count or label_count[label] < n_valid_per_label:                           \n",
    "            copyfile(fname,os.path.join(data_dir,'train_valid_test','valid',label))\n",
    "            label_count[label] = label_count.get(label,0) + 1\n",
    "        else:\n",
    "            copyfile(fname,os.path.join(data_dir,'train_valid_test','train',label))  \n",
    "    return n_valid_per_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 在预测期间整理测试集，以方便读取\n",
    "def reorg_test(data_dir):\n",
    "    for test_file in os.listdir(os.path.join(data_dir,'test')):\n",
    "        copyfile(os.path.join(data_dir,'test',test_file),\n",
    "                os.path.join(data_dir,'train_valid_test','test','unknown')) # unknown为 test文件夹里面的一个文件夹   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 调用前面定义的函数，前面只是定义函数，这里是调用\n",
    "def reorg_cifar10_data(data_dir,valid_ratio):\n",
    "    labels = read_csv_labels(os.path.join(data_dir,'trainLabels.csv'))\n",
    "    reorg_train_valid(data_dir,labels,valid_ratio)\n",
    "    reorg_test(data_dir)\n",
    "    \n",
    "batch_size = 32 if demo else 128\n",
    "valid_ratio = 0.1 # train 数据里面百分之九十用来训练，剩下百分之十用来验证  \n",
    "reorg_cifar10_data(data_dir, valid_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 图像增广\n",
    "transform_train = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.Resize(40),\n",
    "    torchvision.transforms.RandomResizedCrop(32,scale=(0.64,1.0),ratio=(1.0,1.0)),\n",
    "    torchvision.transforms.RandomHorizontalFlip(),\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize([0.4914,0.4822,0.4465],\n",
    "                                   [0.2023,0.1994,0.2010]) ])\n",
    "\n",
    "transform_test = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize([0.4914,0.4822,0.4465],\n",
    "                                    [0.2023,0.1994,0.2010]) ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取由原始图像组成的数据集\n",
    "train_ds, train_valid_ds = [\n",
    "    torchvision.datasets.ImageFolder(\n",
    "        os.path.join(data_dir,'train_valid_test',folder),\n",
    "        transform=transform_train) for folder in ['train','train_valid'] ]\n",
    "\n",
    "valid_ds, test_ds = [\n",
    "    torchvision.datasets.ImageFolder(\n",
    "        os.path.join(data_dir,'train_valid_test',folder),\n",
    "        transform=transform_test) for folder in ['valid','test'] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 指定上面定义的所有图像增广操作\n",
    "train_iter, train_valid_iter = [\n",
    "    torch.utils.data.DataLoader(dataset,batch_size,shuffle=True,drop_last=True)\n",
    "    for dataset in (train_ds, train_valid_ds) ]\n",
    "\n",
    "valid_iter = torch.utils.data.DataLoader(valid_ds,batch_size,shuffle=False,drop_last=True)    \n",
    "test_iter = torch.utils.data.DataLoader(test_ds,batch_size,shuffle=False,drop_last=False)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型\n",
    "def get_net():\n",
    "    num_classes = 10\n",
    "    net = d2l.resnet18(num_classes,3) # 3表示输入三通道，彩色图片\n",
    "    return net\n",
    "\n",
    "loss = nn.CrossEntropyLoss(reduction=\"none\") # reduction=\"none\" 表示不要把loss加起来sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练函数\n",
    "def train(net, train_iter, valid_iter, num_epoch, lr, wd, devices, lr_period, lr_decay):  # 每隔一段时间的lr_period把学习率lr_decay降低点     \n",
    "    trainer = torch.optim.SGD(net.parameters(),lr=lr,momentum=0.9,weight_decay=wd)\n",
    "    scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay)   # 使用StepLR调整lr\n",
    "    num_batches, timer = len(train_iter), d2l.Timer()\n",
    "    legend = ['train loss','train acc']\n",
    "    if valid_iter is not None:\n",
    "        legend.append('valid acc')\n",
    "    animator = d2l.Animator(xlabel='epoch',xlim=[1,num_epochs],legend=legend)   \n",
    "    net = nn.DataParallel(net,device_ids=devices).to(devices[0])\n",
    "    for epoch in range(num_epochs):\n",
    "        net.train()\n",
    "        metric = d2l.Accumulator(3)\n",
    "        for i,(features,labels) in enumerate(train_iter):\n",
    "            timer.start()\n",
    "            l, acc = d2l.train_batch_ch13(net,features,labels,loss,trainer,devices)    \n",
    "            metric.add(l,acc,labels.shape[0])\n",
    "            timer.stop()\n",
    "            if (i+1) % (num_batches // 5) == 0 or i == num_batches -1:\n",
    "                animator.add(epoch + (i + 1) / num_batches, (metric[0]/metric[2], metric[1]/metric[2],None))      \n",
    "        if valid_iter is not None:\n",
    "            valid_acc = d2l.evaluate_accuracy_gpu(net,valid_iter)\n",
    "            animator.add(epoch+1,(None,None,valid_acc))\n",
    "        scheduler.step()\n",
    "    measures = (f'train loss {metric[0] / metric[2]:.3f},'\n",
    "               f'train acc {metric[1] / metric[2]:.3f}')\n",
    "    if valid_iter is not None:\n",
    "        measures += f', valid acc {valid_acc:.3f}'\n",
    "    print(measures + f'\\n{metric[2] * num_epochs / timer.sum():.1f}'\n",
    "         f' examples/sec on {str(devices)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss 0.640,train acc 0.778, valid acc 0.453\n",
      "1627.9 examples/sec on [device(type='cuda', index=0)]\n"
     ]
    },
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   "source": [
    "# 训练和验证模型\n",
    "devices, num_epochs, lr, wd = d2l.try_all_gpus(), 20, 2e-4, 5e-4\n",
    "lr_period, lr_decay, net = 4, 0.9, get_net()   # 每隔4个epoch，学习率乘以0.9\n",
    "train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period, lr_decay)"
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  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "train loss 0.677,train acc 0.748\n",
      "1611.8 examples/sec on [device(type='cuda', index=0)]\n"
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   "source": [
    "# 对测试集进行分类并提交结果\n",
    "net, preds = get_net(), []\n",
    "train(net, train_valid_iter, None, num_epochs, lr, wd, devices, lr_period, lr_decay)    \n",
    "for X, _ in test_iter:\n",
    "    y_hat = net(X.to(devices[0]))\n",
    "    preds.extend(y_hat.argmax(dim=1).type(torch.int32).cpu().numpy())\n",
    "sorted_ids = list(range(1,len(test_ds)+1))\n",
    "sorted_ids.sort(key=lambda x: str(x))\n",
    "df = pd.DataFrame({'id':sorted_ids,'label':preds})\n",
    "df['label'] = df['label'].apply(lambda x: train_valid_ds.classes[x])\n",
    "df.to_csv('submission.csv',index=False)"
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