22_完整模型验证套路.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. GPU训练30轮次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前的设备为： cuda\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "训练数据集的长度：50000\n",
      "测试数据集的长度：10000\n",
      "-----第 1 轮训练开始-----\n",
      "总时间为：1.1507329940795898秒\n",
      "每轮时间：1.1507062911987305秒\n",
      "训练次数：100，Loss：2.2853338718414307\n",
      "总时间为：2.2994325160980225秒\n",
      "每轮时间：1.1486995220184326秒\n",
      "训练次数：200，Loss：2.285290479660034\n",
      "总时间为：3.446033477783203秒\n",
      "每轮时间：1.1466009616851807秒\n",
      "训练次数：300，Loss：2.2274346351623535\n",
      "总时间为：4.591372966766357秒\n",
      "每轮时间：1.1453394889831543秒\n",
      "训练次数：400，Loss：2.1635615825653076\n",
      "总时间为：5.735844135284424秒\n",
      "每轮时间：1.1444711685180664秒\n",
      "训练次数：500，Loss：2.1004605293273926\n",
      "总时间为：6.8807172775268555秒\n",
      "每轮时间：1.1448731422424316秒\n",
      "训练次数：600，Loss：2.03259539604187\n",
      "总时间为：8.02559781074524秒\n",
      "每轮时间：1.1448805332183838秒\n",
      "训练次数：700，Loss：1.9879624843597412\n",
      "整体测试集上的Loss：320.5063395500183\n",
      "在测试集上每个batch的平均Loss：2.051240573120117\n",
      "整体测试集上的分类正确率为：27.089998245239258%\n",
      "模型已保存\n",
      "-----第 2 轮训练开始-----\n",
      "总时间为：10.804321050643921秒\n",
      "每轮时间：2.7787232398986816秒\n",
      "训练次数：800，Loss：1.8766422271728516\n",
      "总时间为：11.948857545852661秒\n",
      "每轮时间：1.1445364952087402秒\n",
      "训练次数：900，Loss：1.883347749710083\n",
      "总时间为：13.092469215393066秒\n",
      "每轮时间：1.1436116695404053秒\n",
      "训练次数：1000，Loss：1.920778512954712\n",
      "总时间为：14.236363172531128秒\n",
      "每轮时间：1.1438939571380615秒\n",
      "训练次数：1100，Loss：1.9664976596832275\n",
      "总时间为：15.382010698318481秒\n",
      "每轮时间：1.1456475257873535秒\n",
      "训练次数：1200，Loss：1.7122691869735718\n",
      "总时间为：16.526482820510864秒\n",
      "每轮时间：1.1444721221923828秒\n",
      "训练次数：1300，Loss：1.656177282333374\n",
      "总时间为：17.670233726501465秒\n",
      "每轮时间：1.1437509059906006秒\n",
      "训练次数：1400，Loss：1.7311617136001587\n",
      "总时间为：18.880157709121704秒\n",
      "每轮时间：1.2099239826202393秒\n",
      "训练次数：1500，Loss：1.814982533454895\n",
      "整体测试集上的Loss：300.8184356689453\n",
      "在测试集上每个batch的平均Loss：1.92523798828125\n",
      "整体测试集上的分类正确率为：31.829998016357422%\n",
      "模型已保存\n",
      "-----第 3 轮训练开始-----\n",
      "总时间为：21.689326286315918秒\n",
      "每轮时间：2.809168577194214秒\n",
      "训练次数：1600，Loss：1.7265475988388062\n",
      "总时间为：22.840506315231323秒\n",
      "每轮时间：1.1511800289154053秒\n",
      "训练次数：1700，Loss：1.6192677021026611\n",
      "总时间为：23.989744901657104秒\n",
      "每轮时间：1.1492385864257812秒\n",
      "训练次数：1800，Loss：1.9247914552688599\n",
      "总时间为：25.13853907585144秒\n",
      "每轮时间：1.148794174194336秒\n",
      "训练次数：1900，Loss：1.7332711219787598\n",
      "总时间为：26.285487174987793秒\n",
      "每轮时间：1.1469480991363525秒\n",
      "训练次数：2000，Loss：1.9117506742477417\n",
      "总时间为：27.438769340515137秒\n",
      "每轮时间：1.1532821655273438秒\n",
      "训练次数：2100，Loss：1.5085296630859375\n",
      "总时间为：28.58175563812256秒\n",
      "每轮时间：1.1429862976074219秒\n",
      "训练次数：2200，Loss：1.48788583278656\n",
      "总时间为：29.727542877197266秒\n",
      "每轮时间：1.145787239074707秒\n",
      "训练次数：2300，Loss：1.793229103088379\n",
      "整体测试集上的Loss：265.4420747756958\n",
      "在测试集上每个batch的平均Loss：1.6988292785644532\n",
      "整体测试集上的分类正确率为：38.959999084472656%\n",
      "模型已保存\n",
      "-----第 4 轮训练开始-----\n",
      "总时间为：32.5024197101593秒\n",
      "每轮时间：2.774876832962036秒\n",
      "训练次数：2400，Loss：1.735345482826233\n",
      "总时间为：33.64506483078003秒\n",
      "每轮时间：1.1426451206207275秒\n",
      "训练次数：2500，Loss：1.3540042638778687\n",
      "总时间为：34.78791379928589秒\n",
      "每轮时间：1.1428489685058594秒\n",
      "训练次数：2600，Loss：1.5939345359802246\n",
      "总时间为：35.932435750961304秒\n",
      "每轮时间：1.144521951675415秒\n",
      "训练次数：2700，Loss：1.6396280527114868\n",
      "总时间为：37.075719594955444秒\n",
      "每轮时间：1.1432838439941406秒\n",
      "训练次数：2800，Loss：1.4929018020629883\n",
      "总时间为：38.2190420627594秒\n",
      "每轮时间：1.143322467803955秒\n",
      "训练次数：2900，Loss：1.6170893907546997\n",
      "总时间为：39.36195254325867秒\n",
      "每轮时间：1.1429104804992676秒\n",
      "训练次数：3000，Loss：1.3585351705551147\n",
      "总时间为：40.50460696220398秒\n",
      "每轮时间：1.1426544189453125秒\n",
      "训练次数：3100，Loss：1.5126011371612549\n",
      "整体测试集上的Loss：266.9037381410599\n",
      "在测试集上每个batch的平均Loss：1.7081839241027832\n",
      "整体测试集上的分类正确率为：38.459999084472656%\n",
      "模型已保存\n",
      "-----第 5 轮训练开始-----\n",
      "总时间为：43.28515028953552秒\n",
      "每轮时间：2.780543327331543秒\n",
      "训练次数：3200，Loss：1.3084392547607422\n",
      "总时间为：44.431050539016724秒\n",
      "每轮时间：1.1459002494812012秒\n",
      "训练次数：3300，Loss：1.48231041431427\n",
      "总时间为：45.5772967338562秒\n",
      "每轮时间：1.1462461948394775秒\n",
      "训练次数：3400，Loss：1.4865673780441284\n",
      "总时间为：46.72672414779663秒\n",
      "每轮时间：1.1494274139404297秒\n",
      "训练次数：3500，Loss：1.5571738481521606\n",
      "总时间为：47.874595403671265秒\n",
      "每轮时间：1.1478712558746338秒\n",
      "训练次数：3600，Loss：1.585862398147583\n",
      "总时间为：49.022743225097656秒\n",
      "每轮时间：1.1481478214263916秒\n",
      "训练次数：3700，Loss：1.383724570274353\n",
      "总时间为：50.17101263999939秒\n",
      "每轮时间：1.1482694149017334秒\n",
      "训练次数：3800，Loss：1.3168107271194458\n",
      "总时间为：51.31977701187134秒\n",
      "每轮时间：1.1487643718719482秒\n",
      "训练次数：3900，Loss：1.4248483180999756\n",
      "整体测试集上的Loss：254.62347280979156\n",
      "在测试集上每个batch的平均Loss：1.629590225982666\n",
      "整体测试集上的分类正确率为：41.560001373291016%\n",
      "模型已保存\n",
      "-----第 6 轮训练开始-----\n",
      "总时间为：54.09943962097168秒\n",
      "每轮时间：2.779662609100342秒\n",
      "训练次数：4000，Loss：1.4135210514068604\n",
      "总时间为：55.24248695373535秒\n",
      "每轮时间：1.1430473327636719秒\n",
      "训练次数：4100，Loss：1.4158220291137695\n",
      "总时间为：56.45201873779297秒\n",
      "每轮时间：1.2095317840576172秒\n",
      "训练次数：4200，Loss：1.517979383468628\n",
      "总时间为：57.603591203689575秒\n",
      "每轮时间：1.1515724658966064秒\n",
      "训练次数：4300，Loss：1.2307332754135132\n",
      "总时间为：58.74250841140747秒\n",
      "每轮时间：1.1389172077178955秒\n",
      "训练次数：4400，Loss：1.156707525253296\n",
      "总时间为：59.881744384765625秒\n",
      "每轮时间：1.1392359733581543秒\n",
      "训练次数：4500，Loss：1.3411213159561157\n",
      "总时间为：61.021029472351074秒\n",
      "每轮时间：1.1392850875854492秒\n",
      "训练次数：4600，Loss：1.4166814088821411\n",
      "整体测试集上的Loss：238.48150157928467\n",
      "在测试集上每个batch的平均Loss：1.526281610107422\n",
      "整体测试集上的分类正确率为：45.0099983215332%\n",
      "模型已保存\n",
      "-----第 7 轮训练开始-----\n",
      "总时间为：63.790196657180786秒\n",
      "每轮时间：2.769167184829712秒\n",
      "训练次数：4700，Loss：1.350705862045288\n",
      "总时间为：64.93077802658081秒\n",
      "每轮时间：1.1405813694000244秒\n",
      "训练次数：4800，Loss：1.5382401943206787\n",
      "总时间为：66.07175135612488秒\n",
      "每轮时间：1.1409733295440674秒\n",
      "训练次数：4900，Loss：1.3856297731399536\n",
      "总时间为：67.21188020706177秒\n",
      "每轮时间：1.1401288509368896秒\n",
      "训练次数：5000，Loss：1.3881943225860596\n",
      "总时间为：68.35453724861145秒\n",
      "每轮时间：1.1426570415496826秒\n",
      "训练次数：5100，Loss：1.022594928741455\n",
      "总时间为：69.495276927948秒\n",
      "每轮时间：1.1407396793365479秒\n",
      "训练次数：5200，Loss：1.3349088430404663\n",
      "总时间为：70.63544249534607秒\n",
      "每轮时间：1.1401655673980713秒\n",
      "训练次数：5300，Loss：1.2363134622573853\n",
      "总时间为：71.78689169883728秒\n",
      "每轮时间：1.151449203491211秒\n",
      "训练次数：5400，Loss：1.415278434753418\n",
      "整体测试集上的Loss：224.75647938251495\n",
      "在测试集上每个batch的平均Loss：1.4384414680480957\n",
      "整体测试集上的分类正确率为：48.5%\n",
      "模型已保存\n",
      "-----第 8 轮训练开始-----\n",
      "总时间为：74.59121012687683秒\n",
      "每轮时间：2.804318428039551秒\n",
      "训练次数：5500，Loss：1.2180050611495972\n",
      "总时间为：75.73377561569214秒\n",
      "每轮时间：1.1425654888153076秒\n",
      "训练次数：5600，Loss：1.2039563655853271\n",
      "总时间为：76.87600541114807秒\n",
      "每轮时间：1.1422297954559326秒\n",
      "训练次数：5700，Loss：1.1838489770889282\n",
      "总时间为：78.01869034767151秒\n",
      "每轮时间：1.1426849365234375秒\n",
      "训练次数：5800，Loss：1.2217626571655273\n",
      "总时间为：79.16077423095703秒\n",
      "每轮时间：1.1420838832855225秒\n",
      "训练次数：5900，Loss：1.3783268928527832\n",
      "总时间为：80.30567073822021秒\n",
      "每轮时间：1.1448965072631836秒\n",
      "训练次数：6000，Loss：1.5711983442306519\n",
      "总时间为：81.44886779785156秒\n",
      "每轮时间：1.1431970596313477秒\n",
      "训练次数：6100，Loss：1.0846683979034424\n",
      "总时间为：82.58977460861206秒\n",
      "每轮时间：1.140906810760498秒\n",
      "训练次数：6200，Loss：1.0762317180633545\n",
      "整体测试集上的Loss：213.20467841625214\n",
      "在测试集上每个batch的平均Loss：1.3645099418640136\n",
      "整体测试集上的分类正确率为：50.909996032714844%\n",
      "模型已保存\n",
      "-----第 9 轮训练开始-----\n",
      "总时间为：85.36891603469849秒\n",
      "每轮时间：2.779141426086426秒\n",
      "训练次数：6300，Loss：1.444697618484497\n",
      "总时间为：86.50989151000977秒\n",
      "每轮时间：1.1409754753112793秒\n",
      "训练次数：6400，Loss：1.1231311559677124\n",
      "总时间为：87.65134191513062秒\n",
      "每轮时间：1.1414504051208496秒\n",
      "训练次数：6500，Loss：1.5411744117736816\n",
      "总时间为：88.79314184188843秒\n",
      "每轮时间：1.1417999267578125秒\n",
      "训练次数：6600，Loss：1.0596672296524048\n",
      "总时间为：89.93515157699585秒\n",
      "每轮时间：1.1420097351074219秒\n",
      "训练次数：6700，Loss：1.1050515174865723\n",
      "总时间为：91.07884311676025秒\n",
      "每轮时间：1.1436915397644043秒\n",
      "训练次数：6800，Loss：1.1827858686447144\n",
      "总时间为：92.22320866584778秒\n",
      "每轮时间：1.1443655490875244秒\n",
      "训练次数：6900，Loss：1.1128523349761963\n",
      "总时间为：93.42996716499329秒\n",
      "每轮时间：1.2067584991455078秒\n",
      "训练次数：7000，Loss：0.9491467475891113\n",
      "整体测试集上的Loss：201.68711429834366\n",
      "在测试集上每个batch的平均Loss：1.2907975315093994\n",
      "整体测试集上的分类正确率为：54.060001373291016%\n",
      "模型已保存\n",
      "-----第 10 轮训练开始-----\n",
      "总时间为：96.20608258247375秒\n",
      "每轮时间：2.7761154174804688秒\n",
      "训练次数：7100，Loss：1.21222722530365\n",
      "总时间为：97.34974336624146秒\n",
      "每轮时间：1.1436607837677002秒\n",
      "训练次数：7200，Loss：0.9966534376144409\n",
      "总时间为：98.4934458732605秒\n",
      "每轮时间：1.143702507019043秒\n",
      "训练次数：7300，Loss：1.1393736600875854\n",
      "总时间为：99.6365225315094秒\n",
      "每轮时间：1.1430766582489014秒\n",
      "训练次数：7400，Loss：0.8192824721336365\n",
      "总时间为：100.77734303474426秒\n",
      "每轮时间：1.1408205032348633秒\n",
      "训练次数：7500，Loss：1.2027300596237183\n",
      "总时间为：101.92116641998291秒\n",
      "每轮时间：1.1438233852386475秒\n",
      "训练次数：7600，Loss：1.2878201007843018\n",
      "总时间为：103.06308126449585秒\n",
      "每轮时间：1.1419148445129395秒\n",
      "训练次数：7700，Loss：0.8417497277259827\n",
      "总时间为：104.20472073554993秒\n",
      "每轮时间：1.1416394710540771秒\n",
      "训练次数：7800，Loss：1.232582926750183\n",
      "整体测试集上的Loss：193.68442356586456\n",
      "在测试集上每个batch的平均Loss：1.2395803108215333\n",
      "整体测试集上的分类正确率为：56.48999786376953%\n",
      "模型已保存\n",
      "-----第 11 轮训练开始-----\n",
      "总时间为：106.97881531715393秒\n",
      "每轮时间：2.774094581604004秒\n",
      "训练次数：7900，Loss：1.4019293785095215\n",
      "总时间为：108.12071299552917秒\n",
      "每轮时间：1.1418976783752441秒\n",
      "训练次数：8000，Loss：1.151719331741333\n",
      "总时间为：109.26199197769165秒\n",
      "每轮时间：1.1412789821624756秒\n",
      "训练次数：8100，Loss：0.9768859148025513\n",
      "总时间为：110.40492939949036秒\n",
      "每轮时间：1.142937421798706秒\n",
      "训练次数：8200，Loss：1.2747836112976074\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总时间为：111.54706311225891秒\n",
      "每轮时间：1.1421337127685547秒\n",
      "训练次数：8300，Loss：1.2515535354614258\n",
      "总时间为：112.69000959396362秒\n",
      "每轮时间：1.142946481704712秒\n",
      "训练次数：8400，Loss：1.0433595180511475\n",
      "总时间为：113.83206939697266秒\n",
      "每轮时间：1.1420598030090332秒\n",
      "训练次数：8500，Loss：1.1961382627487183\n",
      "总时间为：114.9745421409607秒\n",
      "每轮时间：1.142472743988037秒\n",
      "训练次数：8600，Loss：0.9056577086448669\n",
      "整体测试集上的Loss：186.87713253498077\n",
      "在测试集上每个batch的平均Loss：1.196013648223877\n",
      "整体测试集上的分类正确率为：58.099998474121094%\n",
      "模型已保存\n",
      "-----第 12 轮训练开始-----\n",
      "总时间为：117.74911046028137秒\n",
      "每轮时间：2.7745683193206787秒\n",
      "训练次数：8700，Loss：1.1855067014694214\n",
      "总时间为：118.89421439170837秒\n",
      "每轮时间：1.145103931427002秒\n",
      "训练次数：8800，Loss：1.3576641082763672\n",
      "总时间为：120.03567481040955秒\n",
      "每轮时间：1.1414604187011719秒\n",
      "训练次数：8900，Loss：1.0233747959136963\n",
      "总时间为：121.17726540565491秒\n",
      "每轮时间：1.1415905952453613秒\n",
      "训练次数：9000，Loss：1.1333001852035522\n",
      "总时间为：122.31895041465759秒\n",
      "每轮时间：1.1416850090026855秒\n",
      "训练次数：9100，Loss：1.0926451683044434\n",
      "总时间为：123.46036314964294秒\n",
      "每轮时间：1.1414127349853516秒\n",
      "训练次数：9200，Loss：1.0366268157958984\n",
      "总时间为：124.60585689544678秒\n",
      "每轮时间：1.145493745803833秒\n",
      "训练次数：9300，Loss：1.0504931211471558\n",
      "整体测试集上的Loss：181.39308768510818\n",
      "在测试集上每个batch的平均Loss：1.1609157611846923\n",
      "整体测试集上的分类正确率为：59.27000045776367%\n",
      "模型已保存\n",
      "-----第 13 轮训练开始-----\n",
      "总时间为：127.40539717674255秒\n",
      "每轮时间：2.7995402812957764秒\n",
      "训练次数：9400，Loss：0.8929991722106934\n",
      "总时间为：128.54922008514404秒\n",
      "每轮时间：1.1438229084014893秒\n",
      "训练次数：9500，Loss：1.3111960887908936\n",
      "总时间为：129.6944375038147秒\n",
      "每轮时间：1.1452174186706543秒\n",
      "训练次数：9600，Loss：1.0539062023162842\n",
      "总时间为：130.90161299705505秒\n",
      "每轮时间：1.2071754932403564秒\n",
      "训练次数：9700，Loss：1.1375066041946411\n",
      "总时间为：132.04282188415527秒\n",
      "每轮时间：1.1412088871002197秒\n",
      "训练次数：9800，Loss：0.9122176170349121\n",
      "总时间为：133.18342089653015秒\n",
      "每轮时间：1.140599012374878秒\n",
      "训练次数：9900，Loss：1.0268447399139404\n",
      "总时间为：134.32395887374878秒\n",
      "每轮时间：1.140537977218628秒\n",
      "训练次数：10000，Loss：0.9435698390007019\n",
      "总时间为：135.46570444107056秒\n",
      "每轮时间：1.1417455673217773秒\n",
      "训练次数：10100，Loss：0.9541231393814087\n",
      "整体测试集上的Loss：176.64792704582214\n",
      "在测试集上每个batch的平均Loss：1.1305467330932617\n",
      "整体测试集上的分类正确率为：60.81999969482422%\n",
      "模型已保存\n",
      "-----第 14 轮训练开始-----\n",
      "总时间为：138.2394301891327秒\n",
      "每轮时间：2.773725748062134秒\n",
      "训练次数：10200，Loss：0.8106805086135864\n",
      "总时间为：139.38331818580627秒\n",
      "每轮时间：1.143887996673584秒\n",
      "训练次数：10300，Loss：0.9886364340782166\n",
      "总时间为：140.5260214805603秒\n",
      "每轮时间：1.1427032947540283秒\n",
      "训练次数：10400，Loss：1.1524239778518677\n",
      "总时间为：141.6675887107849秒\n",
      "每轮时间：1.1415672302246094秒\n",
      "训练次数：10500，Loss：0.8303748369216919\n",
      "总时间为：142.81058049201965秒\n",
      "每轮时间：1.1429917812347412秒\n",
      "训练次数：10600，Loss：0.9396215677261353\n",
      "总时间为：143.9535048007965秒\n",
      "每轮时间：1.1429243087768555秒\n",
      "训练次数：10700，Loss：0.7101913094520569\n",
      "总时间为：145.0950586795807秒\n",
      "每轮时间：1.1415538787841797秒\n",
      "训练次数：10800，Loss：0.8847861886024475\n",
      "总时间为：146.2379069328308秒\n",
      "每轮时间：1.142848253250122秒\n",
      "训练次数：10900，Loss：1.010374903678894\n",
      "整体测试集上的Loss：173.20271754264832\n",
      "在测试集上每个batch的平均Loss：1.1084973922729493\n",
      "整体测试集上的分类正确率为：61.599998474121094%\n",
      "模型已保存\n",
      "-----第 15 轮训练开始-----\n",
      "总时间为：149.0158920288086秒\n",
      "每轮时间：2.777985095977783秒\n",
      "训练次数：11000，Loss：1.0532711744308472\n",
      "总时间为：150.158273935318秒\n",
      "每轮时间：1.1423819065093994秒\n",
      "训练次数：11100，Loss：0.9046785235404968\n",
      "总时间为：151.30031609535217秒\n",
      "每轮时间：1.1420421600341797秒\n",
      "训练次数：11200，Loss：1.061505913734436\n",
      "总时间为：152.44341349601746秒\n",
      "每轮时间：1.1430974006652832秒\n",
      "训练次数：11300，Loss：1.1354142427444458\n",
      "总时间为：153.5847156047821秒\n",
      "每轮时间：1.1413021087646484秒\n",
      "训练次数：11400，Loss：0.8133754134178162\n",
      "总时间为：154.72617173194885秒\n",
      "每轮时间：1.141456127166748秒\n",
      "训练次数：11500，Loss：1.1118168830871582\n",
      "总时间为：155.86840176582336秒\n",
      "每轮时间：1.1422300338745117秒\n",
      "训练次数：11600，Loss：0.9180481433868408\n",
      "总时间为：157.01033735275269秒\n",
      "每轮时间：1.1419355869293213秒\n",
      "训练次数：11700，Loss：0.931682825088501\n",
      "整体测试集上的Loss：171.35405844449997\n",
      "在测试集上每个batch的平均Loss：1.0966659740447997\n",
      "整体测试集上的分类正确率为：62.269996643066406%\n",
      "模型已保存\n",
      "-----第 16 轮训练开始-----\n",
      "总时间为：159.7894172668457秒\n",
      "每轮时间：2.7790799140930176秒\n",
      "训练次数：11800，Loss：0.8194149732589722\n",
      "总时间为：160.93238759040833秒\n",
      "每轮时间：1.142970323562622秒\n",
      "训练次数：11900，Loss：1.0109199285507202\n",
      "总时间为：162.07383680343628秒\n",
      "每轮时间：1.141449213027954秒\n",
      "训练次数：12000，Loss：0.8484017252922058\n",
      "总时间为：163.21783351898193秒\n",
      "每轮时间：1.1439967155456543秒\n",
      "训练次数：12100，Loss：0.9236235618591309\n",
      "总时间为：164.35972118377686秒\n",
      "每轮时间：1.1418876647949219秒\n",
      "训练次数：12200，Loss：1.0500463247299194\n",
      "总时间为：165.501868724823秒\n",
      "每轮时间：1.1421475410461426秒\n",
      "训练次数：12300，Loss：0.9015226364135742\n",
      "总时间为：166.66055631637573秒\n",
      "每轮时间：1.1586875915527344秒\n",
      "训练次数：12400，Loss：0.9441960453987122\n",
      "总时间为：167.82704043388367秒\n",
      "每轮时间：1.1664841175079346秒\n",
      "训练次数：12500，Loss：0.742332398891449\n",
      "整体测试集上的Loss：170.13570177555084\n",
      "在测试集上每个batch的平均Loss：1.0888684913635254\n",
      "整体测试集上的分类正确率为：62.68999481201172%\n",
      "模型已保存\n",
      "-----第 17 轮训练开始-----\n",
      "总时间为：170.7242112159729秒\n",
      "每轮时间：2.8971707820892334秒\n",
      "训练次数：12600，Loss：0.8369194269180298\n",
      "总时间为：171.87416529655457秒\n",
      "每轮时间：1.149954080581665秒\n",
      "训练次数：12700，Loss：0.8984559774398804\n",
      "总时间为：173.02437734603882秒\n",
      "每轮时间：1.150212049484253秒\n",
      "训练次数：12800，Loss：0.7731732726097107\n",
      "总时间为：174.17376375198364秒\n",
      "每轮时间：1.1493864059448242秒\n",
      "训练次数：12900，Loss：1.0523988008499146\n",
      "总时间为：175.3232307434082秒\n",
      "每轮时间：1.1494669914245605秒\n",
      "训练次数：13000，Loss：0.9509157538414001\n",
      "总时间为：176.47110223770142秒\n",
      "每轮时间：1.147871494293213秒\n",
      "训练次数：13100，Loss：0.7265214920043945\n",
      "总时间为：177.62321591377258秒\n",
      "每轮时间：1.152113676071167秒\n",
      "训练次数：13200，Loss：0.8013477921485901\n",
      "整体测试集上的Loss：169.53949469327927\n",
      "在测试集上每个batch的平均Loss：1.0850527660369873\n",
      "整体测试集上的分类正确率为：62.80999755859375%\n",
      "模型已保存\n",
      "-----第 18 轮训练开始-----\n",
      "总时间为：180.44302320480347秒\n",
      "每轮时间：2.819807291030884秒\n",
      "训练次数：13300，Loss：0.9763777256011963\n",
      "总时间为：181.66989636421204秒\n",
      "每轮时间：1.2268731594085693秒\n",
      "训练次数：13400，Loss：0.7305389642715454\n",
      "总时间为：182.86049461364746秒\n",
      "每轮时间：1.1905982494354248秒\n",
      "训练次数：13500，Loss：0.8493726253509521\n",
      "总时间为：184.02996683120728秒\n",
      "每轮时间：1.1694722175598145秒\n",
      "训练次数：13600，Loss：1.327620029449463\n",
      "总时间为：185.22279334068298秒\n",
      "每轮时间：1.192826509475708秒\n",
      "训练次数：13700，Loss：0.7757801413536072\n",
      "总时间为：186.36467742919922秒\n",
      "每轮时间：1.1418840885162354秒\n",
      "训练次数：13800，Loss：1.0193597078323364\n",
      "总时间为：187.50773429870605秒\n",
      "每轮时间：1.143056869506836秒\n",
      "训练次数：13900，Loss：0.759716272354126\n",
      "总时间为：188.65262007713318秒\n",
      "每轮时间：1.144885778427124秒\n",
      "训练次数：14000，Loss：0.8337475061416626\n",
      "整体测试集上的Loss：168.62916314601898\n",
      "在测试集上每个batch的平均Loss：1.0792266441345215\n",
      "整体测试集上的分类正确率为：63.159996032714844%\n",
      "模型已保存\n",
      "-----第 19 轮训练开始-----\n",
      "总时间为：191.44544339179993秒\n",
      "每轮时间：2.792823314666748秒\n",
      "训练次数：14100，Loss：0.9511858224868774\n",
      "总时间为：192.59133958816528秒\n",
      "每轮时间：1.1458961963653564秒\n",
      "训练次数：14200，Loss：0.8840895891189575\n",
      "总时间为：193.73451447486877秒\n",
      "每轮时间：1.1431748867034912秒\n",
      "训练次数：14300，Loss：0.8891655802726746\n",
      "总时间为：194.87737584114075秒\n",
      "每轮时间：1.1428613662719727秒\n",
      "训练次数：14400，Loss：0.8868080377578735\n",
      "总时间为：196.02370762825012秒\n",
      "每轮时间：1.146331787109375秒\n",
      "训练次数：14500，Loss：0.9023066163063049\n",
      "总时间为：197.1663248538971秒\n",
      "每轮时间：1.1426172256469727秒\n",
      "训练次数：14600，Loss：1.0681746006011963\n",
      "总时间为：198.30916738510132秒\n",
      "每轮时间：1.1428425312042236秒\n",
      "训练次数：14700，Loss：0.7060369253158569\n",
      "总时间为：199.45040249824524秒\n",
      "每轮时间：1.141235113143921秒\n",
      "训练次数：14800，Loss：1.1015595197677612\n",
      "整体测试集上的Loss：168.41134345531464\n",
      "在测试集上每个batch的平均Loss：1.0778325981140138\n",
      "整体测试集上的分类正确率为：63.48999786376953%\n",
      "模型已保存\n",
      "-----第 20 轮训练开始-----\n",
      "总时间为：202.226717710495秒\n",
      "每轮时间：2.776315212249756秒\n",
      "训练次数：14900，Loss：0.6403122544288635\n",
      "总时间为：203.3679678440094秒\n",
      "每轮时间：1.1412501335144043秒\n",
      "训练次数：15000，Loss：0.8768005967140198\n",
      "总时间为：204.5094175338745秒\n",
      "每轮时间：1.1414496898651123秒\n",
      "训练次数：15100，Loss：0.7786276936531067\n",
      "总时间为：205.65164613723755秒\n",
      "每轮时间：1.142228603363037秒\n",
      "训练次数：15200，Loss：0.91826331615448\n",
      "总时间为：206.85818219184875秒\n",
      "每轮时间：1.206536054611206秒\n",
      "训练次数：15300，Loss：0.6496982574462891\n",
      "总时间为：207.99737572669983秒\n",
      "每轮时间：1.1391935348510742秒\n",
      "训练次数：15400，Loss：0.8772464990615845\n",
      "总时间为：209.13790822029114秒\n",
      "每轮时间：1.1405324935913086秒\n",
      "训练次数：15500，Loss：0.9106253385543823\n",
      "总时间为：210.27889037132263秒\n",
      "每轮时间：1.1409821510314941秒\n",
      "训练次数：15600，Loss：0.8666422963142395\n",
      "整体测试集上的Loss：169.11506420373917\n",
      "在测试集上每个batch的平均Loss：1.0823364109039306\n",
      "整体测试集上的分类正确率为：63.33000183105469%\n",
      "模型已保存\n",
      "-----第 21 轮训练开始-----\n",
      "总时间为：213.05868434906006秒\n",
      "每轮时间：2.7797939777374268秒\n",
      "训练次数：15700，Loss：0.7588158249855042\n",
      "总时间为：214.19985032081604秒\n",
      "每轮时间：1.1411659717559814秒\n",
      "训练次数：15800，Loss：0.8479087948799133\n",
      "总时间为：215.34013605117798秒\n",
      "每轮时间：1.1402857303619385秒\n",
      "训练次数：15900，Loss：0.8917092084884644\n",
      "总时间为：216.48668766021729秒\n",
      "每轮时间：1.1465516090393066秒\n",
      "训练次数：16000，Loss：0.7005805373191833\n",
      "总时间为：217.6292417049408秒\n",
      "每轮时间：1.1425540447235107秒\n",
      "训练次数：16100，Loss：0.6299080848693848\n",
      "总时间为：218.77247428894043秒\n",
      "每轮时间：1.1432325839996338秒\n",
      "训练次数：16200，Loss：0.8220117688179016\n",
      "总时间为：219.91516089439392秒\n",
      "每轮时间：1.1426866054534912秒\n",
      "训练次数：16300，Loss：0.6951096653938293\n",
      "总时间为：221.05559086799622秒\n",
      "每轮时间：1.140429973602295秒\n",
      "训练次数：16400，Loss：0.7811569571495056\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "整体测试集上的Loss：169.23971420526505\n",
      "在测试集上每个batch的平均Loss：1.0831341709136963\n",
      "整体测试集上的分类正确率为：63.599998474121094%\n",
      "模型已保存\n",
      "-----第 22 轮训练开始-----\n",
      "总时间为：223.82850980758667秒\n",
      "每轮时间：2.772918939590454秒\n",
      "训练次数：16500，Loss：0.975710391998291\n",
      "总时间为：224.9677815437317秒\n",
      "每轮时间：1.1392717361450195秒\n",
      "训练次数：16600，Loss：0.8183400630950928\n",
      "总时间为：226.1073670387268秒\n",
      "每轮时间：1.1395854949951172秒\n",
      "训练次数：16700，Loss：0.9378455877304077\n",
      "总时间为：227.24818634986877秒\n",
      "每轮时间：1.1408193111419678秒\n",
      "训练次数：16800，Loss：0.719350278377533\n",
      "总时间为：228.38605904579163秒\n",
      "每轮时间：1.1378726959228516秒\n",
      "训练次数：16900，Loss：0.6483535766601562\n",
      "总时间为：229.5261218547821秒\n",
      "每轮时间：1.1400628089904785秒\n",
      "训练次数：17000，Loss：1.061880350112915\n",
      "总时间为：230.6662015914917秒\n",
      "每轮时间：1.1400797367095947秒\n",
      "训练次数：17100，Loss：0.68842613697052\n",
      "总时间为：231.80604696273804秒\n",
      "每轮时间：1.139845371246338秒\n",
      "训练次数：17200，Loss：0.6699042320251465\n",
      "整体测试集上的Loss：170.0798933506012\n",
      "在测试集上每个batch的平均Loss：1.0885113174438477\n",
      "整体测试集上的分类正确率为：63.650001525878906%\n",
      "模型已保存\n",
      "-----第 23 轮训练开始-----\n",
      "总时间为：234.5755798816681秒\n",
      "每轮时间：2.7695329189300537秒\n",
      "训练次数：17300，Loss：0.946103036403656\n",
      "总时间为：235.71703481674194秒\n",
      "每轮时间：1.1414549350738525秒\n",
      "训练次数：17400，Loss：1.0596325397491455\n",
      "总时间为：236.85880970954895秒\n",
      "每轮时间：1.1417748928070068秒\n",
      "训练次数：17500，Loss：0.6733362078666687\n",
      "总时间为：238.00000643730164秒\n",
      "每轮时间：1.1411967277526855秒\n",
      "训练次数：17600，Loss：0.6578547954559326\n",
      "总时间为：239.13997340202332秒\n",
      "每轮时间：1.1399669647216797秒\n",
      "训练次数：17700，Loss：0.9028400182723999\n",
      "总时间为：240.28094816207886秒\n",
      "每轮时间：1.140974760055542秒\n",
      "训练次数：17800，Loss：0.8382620811462402\n",
      "总时间为：241.42213702201843秒\n",
      "每轮时间：1.1411888599395752秒\n",
      "训练次数：17900，Loss：0.8333138823509216\n",
      "整体测试集上的Loss：171.03719401359558\n",
      "在测试集上每个batch的平均Loss：1.0946380416870116\n",
      "整体测试集上的分类正确率为：63.720001220703125%\n",
      "模型已保存\n",
      "-----第 24 轮训练开始-----\n",
      "总时间为：244.2630033493042秒\n",
      "每轮时间：2.8408663272857666秒\n",
      "训练次数：18000，Loss：0.8636463284492493\n",
      "总时间为：245.4024577140808秒\n",
      "每轮时间：1.1394543647766113秒\n",
      "训练次数：18100，Loss：0.6116511225700378\n",
      "总时间为：246.54138112068176秒\n",
      "每轮时间：1.1389234066009521秒\n",
      "训练次数：18200，Loss：0.8219123482704163\n",
      "总时间为：247.68105483055115秒\n",
      "每轮时间：1.1396737098693848秒\n",
      "训练次数：18300，Loss：0.7129583358764648\n",
      "总时间为：248.82015299797058秒\n",
      "每轮时间：1.1390981674194336秒\n",
      "训练次数：18400，Loss：0.6003238558769226\n",
      "总时间为：249.96202611923218秒\n",
      "每轮时间：1.1418731212615967秒\n",
      "训练次数：18500，Loss：0.8028343915939331\n",
      "总时间为：251.1011562347412秒\n",
      "每轮时间：1.1391301155090332秒\n",
      "训练次数：18600，Loss：0.5355821251869202\n",
      "总时间为：252.24103045463562秒\n",
      "每轮时间：1.1398742198944092秒\n",
      "训练次数：18700，Loss：0.7491245269775391\n",
      "整体测试集上的Loss：173.2679580450058\n",
      "在测试集上每个batch的平均Loss：1.108914931488037\n",
      "整体测试集上的分类正确率为：63.63999938964844%\n",
      "模型已保存\n",
      "-----第 25 轮训练开始-----\n",
      "总时间为：255.01125931739807秒\n",
      "每轮时间：2.770228862762451秒\n",
      "训练次数：18800，Loss：0.7840339541435242\n",
      "总时间为：256.15041971206665秒\n",
      "每轮时间：1.139160394668579秒\n",
      "训练次数：18900，Loss：0.5224756002426147\n",
      "总时间为：257.28973364830017秒\n",
      "每轮时间：1.1393139362335205秒\n",
      "训练次数：19000，Loss：0.6754499673843384\n",
      "总时间为：258.42915749549866秒\n",
      "每轮时间：1.1394238471984863秒\n",
      "训练次数：19100，Loss：0.6541109085083008\n",
      "总时间为：259.5689890384674秒\n",
      "每轮时间：1.13983154296875秒\n",
      "训练次数：19200，Loss：0.5143263936042786\n",
      "总时间为：260.70950531959534秒\n",
      "每轮时间：1.1405162811279297秒\n",
      "训练次数：19300，Loss：0.663909912109375\n",
      "总时间为：261.84786081314087秒\n",
      "每轮时间：1.1383554935455322秒\n",
      "训练次数：19400，Loss：0.8378692865371704\n",
      "总时间为：262.98734998703003秒\n",
      "每轮时间：1.1394891738891602秒\n",
      "训练次数：19500，Loss：0.6194844245910645\n",
      "整体测试集上的Loss：174.5102099776268\n",
      "在测试集上每个batch的平均Loss：1.1168653438568115\n",
      "整体测试集上的分类正确率为：63.650001525878906%\n",
      "模型已保存\n",
      "-----第 26 轮训练开始-----\n",
      "总时间为：265.75934767723083秒\n",
      "每轮时间：2.7719976902008057秒\n",
      "训练次数：19600，Loss：0.8050453066825867\n",
      "总时间为：266.8999547958374秒\n",
      "每轮时间：1.1406071186065674秒\n",
      "训练次数：19700，Loss：0.6454012989997864\n",
      "总时间为：268.0399944782257秒\n",
      "每轮时间：1.1400396823883057秒\n",
      "训练次数：19800，Loss：0.8465774655342102\n",
      "总时间为：269.1790986061096秒\n",
      "每轮时间：1.1391041278839111秒\n",
      "训练次数：19900，Loss：0.8188177943229675\n",
      "总时间为：270.3214385509491秒\n",
      "每轮时间：1.1423399448394775秒\n",
      "训练次数：20000，Loss：0.8168498277664185\n",
      "总时间为：271.4628117084503秒\n",
      "每轮时间：1.1413731575012207秒\n",
      "训练次数：20100，Loss：0.754268229007721\n",
      "总时间为：272.6070330142975秒\n",
      "每轮时间：1.144221305847168秒\n",
      "训练次数：20200，Loss：0.921040952205658\n",
      "总时间为：273.7490828037262秒\n",
      "每轮时间：1.142049789428711秒\n",
      "训练次数：20300，Loss：0.5971534252166748\n",
      "整体测试集上的Loss：176.89736717939377\n",
      "在测试集上每个batch的平均Loss：1.13214314994812\n",
      "整体测试集上的分类正确率为：63.61000061035156%\n",
      "模型已保存\n",
      "-----第 27 轮训练开始-----\n",
      "总时间为：276.52873063087463秒\n",
      "每轮时间：2.7796478271484375秒\n",
      "训练次数：20400，Loss：0.6532500386238098\n",
      "总时间为：277.6721980571747秒\n",
      "每轮时间：1.1434674263000488秒\n",
      "训练次数：20500，Loss：0.630043089389801\n",
      "总时间为：278.81404519081116秒\n",
      "每轮时间：1.1418471336364746秒\n",
      "训练次数：20600，Loss：0.4778045415878296\n",
      "总时间为：279.9555308818817秒\n",
      "每轮时间：1.1414856910705566秒\n",
      "训练次数：20700，Loss：0.7116430997848511\n",
      "总时间为：281.0971710681915秒\n",
      "每轮时间：1.1416401863098145秒\n",
      "训练次数：20800，Loss：0.49174433946609497\n",
      "总时间为：282.29299235343933秒\n",
      "每轮时间：1.1958212852478027秒\n",
      "训练次数：20900，Loss：0.6567456722259521\n",
      "总时间为：283.43593168258667秒\n",
      "每轮时间：1.1429393291473389秒\n",
      "训练次数：21000，Loss：0.8423003554344177\n",
      "总时间为：284.5764718055725秒\n",
      "每轮时间：1.1405401229858398秒\n",
      "训练次数：21100，Loss：0.6456180214881897\n",
      "整体测试集上的Loss：177.97323352098465\n",
      "在测试集上每个batch的平均Loss：1.1390286945343018\n",
      "整体测试集上的分类正确率为：63.62000274658203%\n",
      "模型已保存\n",
      "-----第 28 轮训练开始-----\n",
      "总时间为：287.3506464958191秒\n",
      "每轮时间：2.774174690246582秒\n",
      "训练次数：21200，Loss：0.7784990668296814\n",
      "总时间为：288.49402618408203秒\n",
      "每轮时间：1.1433796882629395秒\n",
      "训练次数：21300，Loss：0.7132939696311951\n",
      "总时间为：289.6343252658844秒\n",
      "每轮时间：1.1402990818023682秒\n",
      "训练次数：21400，Loss：0.7270437479019165\n",
      "总时间为：290.77407217025757秒\n",
      "每轮时间：1.139746904373169秒\n",
      "训练次数：21500，Loss：0.5318655967712402\n",
      "总时间为：291.9148495197296秒\n",
      "每轮时间：1.140777349472046秒\n",
      "训练次数：21600，Loss：0.7217969298362732\n",
      "总时间为：293.0555374622345秒\n",
      "每轮时间：1.1406879425048828秒\n",
      "训练次数：21700，Loss：0.6728512644767761\n",
      "总时间为：294.20042657852173秒\n",
      "每轮时间：1.1448891162872314秒\n",
      "训练次数：21800，Loss：0.7484737634658813\n",
      "整体测试集上的Loss：180.65862202644348\n",
      "在测试集上每个batch的平均Loss：1.1562151809692383\n",
      "整体测试集上的分类正确率为：63.62000274658203%\n",
      "模型已保存\n",
      "-----第 29 轮训练开始-----\n",
      "总时间为：296.97589898109436秒\n",
      "每轮时间：2.775472402572632秒\n",
      "训练次数：21900，Loss：0.5330429077148438\n",
      "总时间为：298.11710119247437秒\n",
      "每轮时间：1.1412022113800049秒\n",
      "训练次数：22000，Loss：0.6437810063362122\n",
      "总时间为：299.25688076019287秒\n",
      "每轮时间：1.1397795677185059秒\n",
      "训练次数：22100，Loss：0.6562290191650391\n",
      "总时间为：300.39942836761475秒\n",
      "每轮时间：1.142547607421875秒\n",
      "训练次数：22200，Loss：0.47140902280807495\n",
      "总时间为：301.53977966308594秒\n",
      "每轮时间：1.1403512954711914秒\n",
      "训练次数：22300，Loss：0.5062679052352905\n",
      "总时间为：302.67986130714417秒\n",
      "每轮时间：1.1400816440582275秒\n",
      "训练次数：22400，Loss：0.7150157690048218\n",
      "总时间为：303.82129645347595秒\n",
      "每轮时间：1.141435146331787秒\n",
      "训练次数：22500，Loss：0.850351095199585\n",
      "总时间为：304.96291422843933秒\n",
      "每轮时间：1.141617774963379秒\n",
      "训练次数：22600，Loss：0.3967897295951843\n",
      "整体测试集上的Loss：180.1689465045929\n",
      "在测试集上每个batch的平均Loss：1.1530812576293945\n",
      "整体测试集上的分类正确率为：63.91999816894531%\n",
      "模型已保存\n",
      "-----第 30 轮训练开始-----\n",
      "总时间为：307.734961271286秒\n",
      "每轮时间：2.7720470428466797秒\n",
      "训练次数：22700，Loss：0.5834716558456421\n",
      "总时间为：308.8753740787506秒\n",
      "每轮时间：1.1404128074645996秒\n",
      "训练次数：22800，Loss：0.642602264881134\n",
      "总时间为：310.0158007144928秒\n",
      "每轮时间：1.1404266357421875秒\n",
      "训练次数：22900，Loss：0.6739823222160339\n",
      "总时间为：311.1573700904846秒\n",
      "每轮时间：1.1415693759918213秒\n",
      "训练次数：23000，Loss：0.4188717007637024\n",
      "总时间为：312.2992308139801秒\n",
      "每轮时间：1.1418607234954834秒\n",
      "训练次数：23100，Loss：0.5415220856666565\n",
      "总时间为：313.4412703514099秒\n",
      "每轮时间：1.1420395374298096秒\n",
      "训练次数：23200，Loss：0.7750859260559082\n",
      "总时间为：314.5849304199219秒\n",
      "每轮时间：1.143660068511963秒\n",
      "训练次数：23300，Loss：0.7833418846130371\n",
      "总时间为：315.7283344268799秒\n",
      "每轮时间：1.1434040069580078秒\n",
      "训练次数：23400，Loss：0.6360923051834106\n",
      "整体测试集上的Loss：178.3891492486\n",
      "在测试集上每个batch的平均Loss：1.1416905551910401\n",
      "整体测试集上的分类正确率为：64.55999755859375%\n",
      "模型已保存\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "import time\n",
    "\n",
    "# 定义训练的设备\n",
    "#device = torch.device(\"cpu\")\n",
    "device = torch.device(\"cuda\")   # 使用 GPU 方式一 \n",
    "#device = torch.device(\"cuda:0\") # 使用 GPU 方式二\n",
    "#device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(\"当前的设备为：\", device)\n",
    "\n",
    "# from model import * 相当于把 model中的所有内容写到这里，这里直接把 model 写在这里\n",
    "class Tudui(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Tudui, self).__init__()        \n",
    "        self.model1 = nn.Sequential(\n",
    "            nn.Conv2d(3,32,5,1,2),  # 输入通道3，输出通道32，卷积核尺寸5×5，步长1，填充2    \n",
    "            nn.MaxPool2d(2),\n",
    "            nn.Conv2d(32,32,5,1,2),\n",
    "            nn.MaxPool2d(2),\n",
    "            nn.Conv2d(32,64,5,1,2),\n",
    "            nn.MaxPool2d(2),\n",
    "            nn.Flatten(),  # 展平后变成 64*4*4 了\n",
    "            nn.Linear(64*4*4,64),\n",
    "            nn.Linear(64,10)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.model1(x)\n",
    "        return x\n",
    "\n",
    "# 准备数据集\n",
    "train_data = torchvision.datasets.CIFAR10(\"./dataset\",train=True,transform=torchvision.transforms.ToTensor(),download=True)       \n",
    "test_data = torchvision.datasets.CIFAR10(\"./dataset\",train=False,transform=torchvision.transforms.ToTensor(),download=True)       \n",
    "\n",
    "# length 长度\n",
    "train_data_size = len(train_data)\n",
    "test_data_size = len(test_data)\n",
    "# 如果train_data_size=10，则打印：训练数据集的长度为：10\n",
    "print(\"训练数据集的长度：{}\".format(train_data_size))\n",
    "print(\"测试数据集的长度：{}\".format(test_data_size))\n",
    "\n",
    "\n",
    "# 利用 Dataloader 来加载数据集\n",
    "train_dataloader = DataLoader(train_data, batch_size=64)        \n",
    "test_dataloader = DataLoader(test_data, batch_size=64)\n",
    "\n",
    "\n",
    "# 创建网络模型\n",
    "tudui = Tudui()\n",
    "tudui = tudui.to(device)  # 也可以不赋值，直接 tudui.to(device) \n",
    "\n",
    "# 损失函数\n",
    "loss_fn = nn.CrossEntropyLoss()  # 交叉熵，fn 是 function 的缩写\n",
    "loss_fn = loss_fn.to(device)  # 也可以不赋值，直接loss_fn.to(device)\n",
    "\n",
    "# 优化器\n",
    "learning_rate = 0.01  # 1e-2 就是 0.01 的意思\n",
    "optimizer = torch.optim.SGD(tudui.parameters(), lr=learning_rate)  # 随机梯度下降优化器  \n",
    "\n",
    "\n",
    "# 设置网络的一些参数\n",
    "# 记录训练的次数\n",
    "total_train_step = 0\n",
    "total_test_step = 0\n",
    "\n",
    "# 训练的轮次\n",
    "epoch = 30\n",
    "\n",
    "\n",
    "# 添加 tensorboard\n",
    "writer = SummaryWriter(\"logs\")\n",
    "\n",
    "start_time = time.time()\n",
    "last_time = time.time()\n",
    "\n",
    "for i in range(epoch):\n",
    "    print(\"-----第 {} 轮训练开始-----\".format(i+1))\n",
    "    \n",
    "    # 训练步骤开始\n",
    "    tudui.train()  # 当网络中有dropout层、batchnorm层时，tudui.train()会使得这些层能起作用\n",
    "    for data in train_dataloader:\n",
    "        imgs, targets = data\n",
    "        imgs = imgs.to(device)  # 也可以不赋值，直接 imgs.to(device)\n",
    "        targets = targets.to(device)  # 也可以不赋值，直接 targets.to(device)\n",
    "        outputs = tudui(imgs)\n",
    "        loss = loss_fn(outputs, targets) # 计算实际输出与目标输出的差距\n",
    "        \n",
    "        # 优化器对模型调优\n",
    "        optimizer.zero_grad()  # 梯度清零\n",
    "        loss.backward() # 反向传播，计算损失函数的梯度\n",
    "        optimizer.step()   # 根据梯度，对网络的参数进行调优\n",
    "        \n",
    "        total_train_step += 1\n",
    "        \n",
    "        if total_train_step % 100 == 0:\n",
    "            end_time = time.time()\n",
    "            print(f\"总时间为：{end_time - start_time}秒\") # 运行训练一百次后的时间间隔\n",
    "            print(f\"每轮时间：{end_time - last_time}秒\")\n",
    "            last_time = end_time\n",
    "            print(\"训练次数：{}，Loss：{}\".format(total_train_step, loss.item()))  # 方式二：获得loss值\n",
    "            writer.add_scalar(\"train_loss\", loss.item(), total_train_step)\n",
    "    \n",
    "    # 测试步骤开始\n",
    "    #（每一轮训练后都查看在测试数据集上的loss情况）\n",
    "    tudui.eval()  # 当网络中有dropout层、batchnorm层时，tudui.eval()会使得这些层不能起作用\n",
    "    total_test_loss = 0\n",
    "    total_accuracy = 0\n",
    "    with torch.no_grad():  # 没有梯度了\n",
    "        for data in test_dataloader:  # 测试数据集提取数据\n",
    "            imgs, targets = data\n",
    "            \n",
    "            imgs = imgs.to(device)  # 也可以不赋值，直接 imgs.to(device)\n",
    "            targets = targets.to(device)  # 也可以不赋值，直接 targets.to(device)\n",
    "            outputs = tudui(imgs)\n",
    "            \n",
    "            loss = loss_fn(outputs, targets)  # 仅data数据在网络模型上的损失\n",
    "            \n",
    "            total_test_loss += loss.item() # 所有loss\n",
    "            \n",
    "            accuracy = (outputs.argmax(1) == targets).sum()\n",
    "            total_accuracy += accuracy\n",
    "            \n",
    "    print(\"整体测试集上的Loss：{}\".format(total_test_loss))\n",
    "    print(\"在测试集上每个batch的平均Loss：{}\".format(total_test_loss * 64 / test_data_size))\n",
    "    print(\"整体测试集上的分类正确率为：{}%\".format(total_accuracy / test_data_size * 100))\n",
    "    writer.add_scalar(\"test_loss\", total_test_loss, total_test_step)\n",
    "    writer.add_scalar(\"test_accuracy\", total_accuracy / test_data_size, total_test_step)  \n",
    "    total_test_step += 1\n",
    "    \n",
    "    torch.save(tudui, \"./model/tudui_{}.pth\".format(i))  # 保存每一轮训练后的结果\n",
    "    # torch.save(tudui.state_dict(), \"./model/tudui_{}.pth\".format(i))  # 另一种保存方式\n",
    "    print(\"模型已保存\")\n",
    "        \n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 验证狗是否识别"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① 完整的模型验证(测试，demo)套路，利用已经训练好的模型，然后给它提供输入。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<PIL.Image.Image image mode=RGB size=592x354 at 0x7F9A21BE3AF0>\n",
      "torch.Size([3, 32, 32])\n",
      "Tudui(\n",
      "  (model1): Sequential(\n",
      "    (0): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (4): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (6): Flatten(start_dim=1, end_dim=-1)\n",
      "    (7): Linear(in_features=1024, out_features=64, bias=True)\n",
      "    (8): Linear(in_features=64, out_features=10, bias=True)\n",
      "  )\n",
      ")\n",
      "tensor([[-5.1236, -9.2203, -0.0977,  4.9035,  2.6987,  7.3353,  5.5696,  4.5287,\n",
      "         -7.8172, -3.0620]])\n",
      "tensor([5])\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "from PIL import Image\n",
    "from torch import nn\n",
    "import torch\n",
    "\n",
    "image_path = \"imgs/dog.png\"\n",
    "image = Image.open(image_path)  # PIL类型的Image\n",
    "image = image.convert(\"RGB\")  # 4通道的RGBA图片转为3通道的RGB图片\n",
    "print(image)\n",
    "\n",
    "transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32,32)),   \n",
    "                                            torchvision.transforms.ToTensor()])\n",
    "\n",
    "# 用transform将image变换为符合模型输入的3*32*32的输入\n",
    "image = transform(image)\n",
    "print(image.shape)\n",
    "\n",
    "\n",
    "class Tudui(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Tudui, self).__init__()        \n",
    "        self.model1 = nn.Sequential(\n",
    "            nn.Conv2d(3,32,5,1,2),\n",
    "            nn.MaxPool2d(2),\n",
    "            nn.Conv2d(32,32,5,1,2),\n",
    "            nn.MaxPool2d(2),\n",
    "            nn.Conv2d(32,64,5,1,2),\n",
    "            nn.MaxPool2d(2),\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(64*4*4,64),\n",
    "            nn.Linear(64,10)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.model1(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "model = torch.load(\"model/tudui_29.pth\", map_location=torch.device('cpu'))  # 需要将GPU上训练的pth文件映射到CPU上    \n",
    "print(model)\n",
    "\n",
    "image = torch.reshape(image,(1,3,32,32))  # 转为四维，符合网络输入需求\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():  # 不进行梯度计算，减少内存计算\n",
    "    output = model(image)\n",
    "\n",
    "print(output)\n",
    "print(output.argmax(1))  # 概率最大类别的输出"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![classes](./imgs/classes.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 验证飞机是否识别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<PIL.Image.Image image mode=RGB size=604x340 at 0x7F9A226039A0>\n",
      "torch.Size([3, 32, 32])\n",
      "Tudui(\n",
      "  (model1): Sequential(\n",
      "    (0): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (4): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (6): Flatten(start_dim=1, end_dim=-1)\n",
      "    (7): Linear(in_features=1024, out_features=64, bias=True)\n",
      "    (8): Linear(in_features=64, out_features=10, bias=True)\n",
      "  )\n",
      ")\n",
      "tensor([[ 7.5431,  3.2615,  2.3299, -2.0899, -0.3996, -3.8349, -8.4790,  1.5732,\n",
      "          4.1464, -2.7509]])\n",
      "tensor([0])\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "from PIL import Image\n",
    "from torch import nn\n",
    "import torch\n",
    "\n",
    "image_path = \"imgs/plane.png\"\n",
    "image = Image.open(image_path)  # PIL类型的Image\n",
    "image = image.convert(\"RGB\")  # 4通道的RGBA转为3通道的RGB图片\n",
    "print(image)\n",
    "\n",
    "transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32,32)),   \n",
    "                                            torchvision.transforms.ToTensor()])\n",
    "\n",
    "# 用transform将image变换为符合模型输入的3*32*32的输入\n",
    "image = transform(image)\n",
    "print(image.shape)\n",
    "\n",
    "class Tudui(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Tudui, self).__init__()        \n",
    "        self.model1 = nn.Sequential(\n",
    "            nn.Conv2d(3,32,5,1,2),\n",
    "            nn.MaxPool2d(2),\n",
    "            nn.Conv2d(32,32,5,1,2),\n",
    "            nn.MaxPool2d(2),\n",
    "            nn.Conv2d(32,64,5,1,2),\n",
    "            nn.MaxPool2d(2),\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(64*4*4,64),\n",
    "            nn.Linear(64,10)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.model1(x)\n",
    "        return x\n",
    "\n",
    "model = torch.load(\"model/tudui_29.pth\", map_location=torch.device('cpu'))  # 需要将GPU上训练的东西映射到CPU上    \n",
    "print(model)\n",
    "\n",
    "image = torch.reshape(image,(1,3,32,32))  # 转为四维，符合网络输入需求\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():  # 不进行梯度计算，减少内存计算\n",
    "    output = model(image)\n",
    "\n",
    "print(output)\n",
    "print(output.argmax(1))  # 概率最大类别的输出"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "350px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}