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前面几篇文章介绍了MINIST，对这种简单图片的识别，LeNet-5可以达到99%的识别率。
CIFAR10是另一个著名的深度学习图像分类识别数据集，比MINIST更复杂，而且是RGB彩色图片。
看看较简单的LeNet-5可以达到多少准确率。网络结构基本和前面MINIST代码中的差不多，主要是输入图片的通道数不同，代码如下：
1 # -*- coding:utf-8 -*- 2 3 u"""LeNet卷积神经网络训练学习CIFAR10""" 4 5 __author__ = 'zhengbiqing 460356155@qq.com' 6 7 8 import torch as t 9 import torchvision as tv 10 import torch.nn as nn 11 import torch.optim as optim 12 import torchvision.transforms as transforms 13 from torchvision.transforms import ToPILImage 14 import torch.backends.cudnn as cudnn 15 16 import datetime 17 import argparse 18 19 20 # 样本读取线程数 21 WORKERS = 4 22 23 # 网络参赛保存文件名 24 PARAS_FN = 'cifar_lenet_params.pkl' 25 26 # minist数据存放位置 27 ROOT = '/home/zbq/PycharmProjects/cifar' 28 29 # 目标函数 30 loss_func = nn.CrossEntropyLoss() 31 32 # 最优结果 33 best_acc = 0 34 35 36 # 定义网络模型 37 class LeNet(nn.Module): 38 def __init__(self): 39 super(LeNet, self).__init__() 40 41 # 卷积层 42 self.cnn = nn.Sequential( 43 # 卷积层1，3通道输入，6个卷积核，核大小5*5 44 # 经过该层图像大小变为32-5+1，28*28 45 # 经2*2最大池化，图像变为14*14 46 nn.Conv2d(3, 6, 5), 47 nn.ReLU(), 48 nn.MaxPool2d(2), 49 50 # 卷积层2，6输入通道，16个卷积核，核大小5*5 51 # 经过该层图像变为14-5+1，10*10 52 # 经2*2最大池化，图像变为5*5 53 nn.Conv2d(6, 16, 5), 54 nn.ReLU(), 55 nn.MaxPool2d(2) 56 ) 57 58 # 全连接层 59 self.fc = nn.Sequential( 60 # 16个feature，每个feature 5*5 61 nn.Linear(16 * 5 * 5, 120), 62 nn.ReLU(), 63 nn.Linear(120, 84), 64 nn.ReLU(), 65 nn.Linear(84, 10) 66 ) 67 68 def forward(self, x): 69 x = self.cnn(x) 70 71 # x.size()[0]: batch size 72 x = x.view(x.size()[0], -1) 73 x = self.fc(x) 74 75 return x 76 77 78 ''' 79 训练并测试网络 80 net：网络模型 81 train_data_load：训练数据集 82 optimizer：优化器 83 epoch：第几次训练迭代 84 log_interval：训练过程中损失函数值和准确率的打印频率 85 ''' 86 def net_train(net, train_data_load, optimizer, epoch, log_interval): 87 net.train() 88 89 begin = datetime.datetime.now() 90 91 # 样本总数 92 total = len(train_data_load.dataset) 93 94 # 样本批次训练的损失函数值的和 95 train_loss = 0 96 97 # 识别正确的样本数 98 ok = 0 99 100 for i, data in enumerate(train_data_load, 0): 101 img, label = data 102 img, label = img.cuda(), label.cuda() 103 104 optimizer.zero_grad() 105 106 outs = net(img) 107 loss = loss_func(outs, label) 108 loss.backward() 109 optimizer.step() 110 111 # 累加损失值和训练样本数 112 train_loss += loss.item() 113 # total += label.size(0) 114 115 _, predicted = t.max(outs.data, 1) 116 # 累加识别正确的样本数 117 ok += (predicted == label).sum() 118 119 if (i + 1) % log_interval == 0: 120 # 训练结果输出 121 122 # 损失函数均值 123 loss_mean = train_loss / (i + 1) 124 125 # 已训练的样本数 126 traind_total = (i + 1) * len(label) 127 128 # 准确度 129 acc = 100. * ok / traind_total 130 131 # 一个迭代的进度百分比 132 progress = 100. * traind_total / total 133 134 print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f} Acc: {:.6f}'.format( 135 epoch, traind_total, total, progress, loss_mean, acc)) 136 137 end = datetime.datetime.now() 138 print('one epoch spend: ', end - begin) 139 140 141 ''' 142 用测试集检查准确率 143 ''' 144 def net_test(net, test_data_load, epoch): 145 net.eval() 146 147 ok = 0 148 149 for i, data in enumerate(test_data_load): 150 img, label = data 151 img, label = img.cuda(), label.cuda() 152 153 outs = net(img) 154 _, pre = t.max(outs.data, 1) 155 ok += (pre == label).sum() 156 157 acc = ok.item() * 100. / (len(test_data_load.dataset)) 158 print('EPOCH:{}, ACC:{}\n'.format(epoch, acc)) 159 160 global best_acc 161 if acc > best_acc: 162 best_acc = acc 163 164 165 ''' 166 显示数据集中一个图片 167 ''' 168 def img_show(dataset, index): 169 classes = ('plane', 'car', 'bird', 'cat', 170 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') 171 172 show = ToPILImage() 173 174 data, label = dataset[index] 175 print('img is a ', classes[label]) 176 show((data + 1) / 2).resize((100, 100)).show() 177 178 179 def main(): 180 # 训练超参数设置，可通过命令行设置 181 parser = argparse.ArgumentParser(description='PyTorch CIFA10 LeNet Example') 182 parser.add_argument('--batch-size', type=int, default=64, metavar='N', 183 help='input batch size for training (default: 64)') 184 parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', 185 help='input batch size for testing (default: 1000)') 186 parser.add_argument('--epochs', type=int, default=20, metavar='N', 187 help='number of epochs to train (default: 20)') 188 parser.add_argument('--lr', type=float, default=0.01, metavar='LR', 189 help='learning rate (default: 0.01)') 190 parser.add_argument('--momentum', type=float, default=0.9, metavar='M', 191 help='SGD momentum (default: 0.9)') 192 parser.add_argument('--log-interval', type=int, default=100, metavar='N', 193 help='how many batches to wait before logging training status (default: 100)') 194 parser.add_argument('--no-train', action='store_true', default=False, 195 help='If train the Model') 196 parser.add_argument('--save-model', action='store_true', default=False, 197 help='For Saving the current Model') 198 args = parser.parse_args() 199 200 # 图像数值转换，ToTensor源码注释 201 """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor. 202 Converts a PIL Image or numpy.ndarray (H x W x C) in the range 203 [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]. 204 """ 205 # 归一化把[0.0, 1.0]变换为[-1,1], ([0, 1] - 0.5) / 0.5 = [-1, 1] 206 transform = tv.transforms.Compose([ 207 transforms.ToTensor(), 208 transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) 209 210 # 定义数据集 211 train_data = tv.datasets.CIFAR10(root=ROOT, train=True, download=True, transform=transform) 212 test_data = tv.datasets.CIFAR10(root=ROOT, train=False, download=False, transform=transform) 213 214 train_load = t.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True, num_workers=WORKERS) 215 test_load = t.utils.data.DataLoader(test_data, batch_size=args.test_batch_size, shuffle=False, num_workers=WORKERS) 216 217 net = LeNet().cuda() 218 print(net) 219 220 # 如果不训练，直接加载保存的网络参数进行测试集验证 221 if args.no_train: 222 net.load_state_dict(t.load(PARAS_FN)) 223 net_test(net, test_load, 0) 224 return 225 226 optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum) 227 228 start_time = datetime.datetime.now() 229 230 for epoch in range(1, args.epochs + 1): 231 net_train(net, train_load, optimizer, epoch, args.log_interval) 232 233 # 每个epoch结束后用测试集检查识别准确度 234 net_test(net, test_load, epoch) 235 236 end_time = datetime.datetime.now() 237 238 global best_acc 239 print('CIFAR10 pytorch LeNet Train: EPOCH:{}, BATCH_SZ:{}, LR:{}, ACC:{}'.format(args.epochs, args.batch_size, args.lr, best_acc)) 240 print('train spend time: ', end_time - start_time) 241 242 if args.save_model: 243 t.save(net.state_dict(), PARAS_FN) 244 245 246 if __name__ == '__main__': 247 main()
运行结果如下：
Files already downloaded and verified
LeNet(
(cnn): Sequential(
(0): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))
(1): ReLU()
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
(4): ReLU()
(5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(fc): Sequential(
(0): Linear(in_features=400, out_features=120, bias=True)
(1): ReLU()
(2): Linear(in_features=120, out_features=84, bias=True)
(3): ReLU()
(4): Linear(in_features=84, out_features=10, bias=True)
)
)
Train Epoch: 1 [6400/50000 (13%)] Loss: 2.297558 Acc: 10.000000
Train Epoch: 1 [12800/50000 (26%)] Loss: 2.219855 Acc: 16.000000
Train Epoch: 1 [19200/50000 (38%)] Loss: 2.117518 Acc: 20.000000
Train Epoch: 1 [25600/50000 (51%)] Loss: 2.030452 Acc: 23.000000
Train Epoch: 1 [32000/50000 (64%)] Loss: 1.956154 Acc: 26.000000
Train Epoch: 1 [38400/50000 (77%)] Loss: 1.894052 Acc: 29.000000
Train Epoch: 1 [44800/50000 (90%)] Loss: 1.845520 Acc: 31.000000
one epoch spend: 0:00:02.007186
EPOCH:1, ACC:43.86
Train Epoch: 2 [6400/50000 (13%)] Loss: 1.497962 Acc: 44.000000
Train Epoch: 2 [12800/50000 (26%)] Loss: 1.471271 Acc: 45.000000
Train Epoch: 2 [19200/50000 (38%)] Loss: 1.458853 Acc: 46.000000
Train Epoch: 2 [25600/50000 (51%)] Loss: 1.445787 Acc: 47.000000
Train Epoch: 2 [32000/50000 (64%)] Loss: 1.436431 Acc: 47.000000
Train Epoch: 2 [38400/50000 (77%)] Loss: 1.425798 Acc: 47.000000
Train Epoch: 2 [44800/50000 (90%)] Loss: 1.415501 Acc: 48.000000
one epoch spend: 0:00:01.879316
EPOCH:2, ACC:53.16
Train Epoch: 3 [6400/50000 (13%)] Loss: 1.288907 Acc: 52.000000
Train Epoch: 3 [12800/50000 (26%)] Loss: 1.293646 Acc: 53.000000
Train Epoch: 3 [19200/50000 (38%)] Loss: 1.284784 Acc: 53.000000
Train Epoch: 3 [25600/50000 (51%)] Loss: 1.281050 Acc: 53.000000
Train Epoch: 3 [32000/50000 (64%)] Loss: 1.281222 Acc: 53.000000
Train Epoch: 3 [38400/50000 (77%)] Loss: 1.269620 Acc: 54.000000
Train Epoch: 3 [44800/50000 (90%)] Loss: 1.262982 Acc: 54.000000
one epoch spend: 0:00:01.928787
EPOCH:3, ACC:54.31
Train Epoch: 4 [6400/50000 (13%)] Loss: 1.157912 Acc: 58.000000
Train Epoch: 4 [12800/50000 (26%)] Loss: 1.157038 Acc: 58.000000
Train Epoch: 4 [19200/50000 (38%)] Loss: 1.164880 Acc: 58.000000
Train Epoch: 4 [25600/50000 (51%)] Loss: 1.169460 Acc: 58.000000
Train Epoch: 4 [32000/50000 (64%)] Loss: 1.169655 Acc: 58.000000
Train Epoch: 4 [38400/50000 (77%)] Loss: 1.169239 Acc: 58.000000
Train Epoch: 4 [44800/50000 (90%)] Loss: 1.159252 Acc: 58.000000
one epoch spend: 0:00:01.928551
EPOCH:4, ACC:60.15
Train Epoch: 5 [6400/50000 (13%)] Loss: 1.081296 Acc: 61.000000
Train Epoch: 5 [12800/50000 (26%)] Loss: 1.073868 Acc: 61.000000
Train Epoch: 5 [19200/50000 (38%)] Loss: 1.086076 Acc: 61.000000
Train Epoch: 5 [25600/50000 (51%)] Loss: 1.088019 Acc: 61.000000
Train Epoch: 5 [32000/50000 (64%)] Loss: 1.083983 Acc: 61.000000
Train Epoch: 5 [38400/50000 (77%)] Loss: 1.088050 Acc: 61.000000
Train Epoch: 5 [44800/50000 (90%)] Loss: 1.087298 Acc: 61.000000
one epoch spend: 0:00:01.898825
EPOCH:5, ACC:59.84
Train Epoch: 6 [6400/50000 (13%)] Loss: 0.979352 Acc: 65.000000
Train Epoch: 6 [12800/50000 (26%)] Loss: 1.005338 Acc: 64.000000
Train Epoch: 6 [19200/50000 (38%)] Loss: 1.019300 Acc: 63.000000
Train Epoch: 6 [25600/50000 (51%)] Loss: 1.022704 Acc: 63.000000
Train Epoch: 6 [32000/50000 (64%)] Loss: 1.021217 Acc: 63.000000
Train Epoch: 6 [38400/50000 (77%)] Loss: 1.022035 Acc: 63.000000
Train Epoch: 6 [44800/50000 (90%)] Loss: 1.024987 Acc: 63.000000
one epoch spend: 0:00:01.926922
EPOCH:6, ACC:60.04
Train Epoch: 7 [6400/50000 (13%)] Loss: 0.952975 Acc: 66.000000
Train Epoch: 7 [12800/50000 (26%)] Loss: 0.965437 Acc: 65.000000
Train Epoch: 7 [19200/50000 (38%)] Loss: 0.964711 Acc: 65.000000
Train Epoch: 7 [25600/50000 (51%)] Loss: 0.962520 Acc: 65.000000
Train Epoch: 7 [32000/50000 (64%)] Loss: 0.964768 Acc: 65.000000
Train Epoch: 7 [38400/50000 (77%)] Loss: 0.966530 Acc: 65.000000
Train Epoch: 7 [44800/50000 (90%)] Loss: 0.971995 Acc: 65.000000
one epoch spend: 0:00:01.858537
EPOCH:7, ACC:62.63
Train Epoch: 8 [6400/50000 (13%)] Loss: 0.901441 Acc: 67.000000
Train Epoch: 8 [12800/50000 (26%)] Loss: 0.896776 Acc: 68.000000
Train Epoch: 8 [19200/50000 (38%)] Loss: 0.898365 Acc: 68.000000
Train Epoch: 8 [25600/50000 (51%)] Loss: 0.898383 Acc: 68.000000
Train Epoch: 8 [32000/50000 (64%)] Loss: 0.909455 Acc: 67.000000
Train Epoch: 8 [38400/50000 (77%)] Loss: 0.910068 Acc: 67.000000
Train Epoch: 8 [44800/50000 (90%)] Loss: 0.914733 Acc: 67.000000
one epoch spend: 0:00:01.849259
EPOCH:8, ACC:62.99
Train Epoch: 9 [6400/50000 (13%)] Loss: 0.842184 Acc: 69.000000
Train Epoch: 9 [12800/50000 (26%)] Loss: 0.853178 Acc: 69.000000
Train Epoch: 9 [19200/50000 (38%)] Loss: 0.863828 Acc: 69.000000
Train Epoch: 9 [25600/50000 (51%)] Loss: 0.868452 Acc: 69.000000
Train Epoch: 9 [32000/50000 (64%)] Loss: 0.870991 Acc: 69.000000
Train Epoch: 9 [38400/50000 (77%)] Loss: 0.874963 Acc: 69.000000
Train Epoch: 9 [44800/50000 (90%)] Loss: 0.878533 Acc: 68.000000
one epoch spend: 0:00:01.954615
EPOCH:9, ACC:62.5
Train Epoch: 10 [6400/50000 (13%)] Loss: 0.837819 Acc: 70.000000
Train Epoch: 10 [12800/50000 (26%)] Loss: 0.823905 Acc: 70.000000
Train Epoch: 10 [19200/50000 (38%)] Loss: 0.833733 Acc: 70.000000
Train Epoch: 10 [25600/50000 (51%)] Loss: 0.838861 Acc: 70.000000
Train Epoch: 10 [32000/50000 (64%)] Loss: 0.841117 Acc: 70.000000
Train Epoch: 10 [38400/50000 (77%)] Loss: 0.849762 Acc: 69.000000
Train Epoch: 10 [44800/50000 (90%)] Loss: 0.850071 Acc: 69.000000
one epoch spend: 0:00:01.812348
EPOCH:10, ACC:63.48
Train Epoch: 11 [6400/50000 (13%)] Loss: 0.781857 Acc: 72.000000
Train Epoch: 11 [12800/50000 (26%)] Loss: 0.773329 Acc: 72.000000
Train Epoch: 11 [19200/50000 (38%)] Loss: 0.785191 Acc: 72.000000
Train Epoch: 11 [25600/50000 (51%)] Loss: 0.797921 Acc: 71.000000
Train Epoch: 11 [32000/50000 (64%)] Loss: 0.802146 Acc: 71.000000
Train Epoch: 11 [38400/50000 (77%)] Loss: 0.804404 Acc: 71.000000
Train Epoch: 11 [44800/50000 (90%)] Loss: 0.805919 Acc: 71.000000
one epoch spend: 0:00:01.881838
EPOCH:11, ACC:63.72
Train Epoch: 12 [6400/50000 (13%)] Loss: 0.734165 Acc: 74.000000
Train Epoch: 12 [12800/50000 (26%)] Loss: 0.739923 Acc: 74.000000
Train Epoch: 12 [19200/50000 (38%)] Loss: 0.753080 Acc: 73.000000
Train Epoch: 12 [25600/50000 (51%)] Loss: 0.755026 Acc: 73.000000
Train Epoch: 12 [32000/50000 (64%)] Loss: 0.758760 Acc: 73.000000
Train Epoch: 12 [38400/50000 (77%)] Loss: 0.765208 Acc: 72.000000
Train Epoch: 12 [44800/50000 (90%)] Loss: 0.774539 Acc: 72.000000
one epoch spend: 0:00:01.856290
EPOCH:12, ACC:63.71
Train Epoch: 13 [6400/50000 (13%)] Loss: 0.709528 Acc: 75.000000
Train Epoch: 13 [12800/50000 (26%)] Loss: 0.713831 Acc: 74.000000
Train Epoch: 13 [19200/50000 (38%)] Loss: 0.720146 Acc: 74.000000
Train Epoch: 13 [25600/50000 (51%)] Loss: 0.723680 Acc: 74.000000
Train Epoch: 13 [32000/50000 (64%)] Loss: 0.730473 Acc: 73.000000
Train Epoch: 13 [38400/50000 (77%)] Loss: 0.742575 Acc: 73.000000
Train Epoch: 13 [44800/50000 (90%)] Loss: 0.744857 Acc: 73.000000
one epoch spend: 0:00:01.808256
EPOCH:13, ACC:61.71
Train Epoch: 14 [6400/50000 (13%)] Loss: 0.700821 Acc: 74.000000
Train Epoch: 14 [12800/50000 (26%)] Loss: 0.691082 Acc: 75.000000
Train Epoch: 14 [19200/50000 (38%)] Loss: 0.693119 Acc: 75.000000
Train Epoch: 14 [25600/50000 (51%)] Loss: 0.706147 Acc: 74.000000
Train Epoch: 14 [32000/50000 (64%)] Loss: 0.710033 Acc: 74.000000
Train Epoch: 14 [38400/50000 (77%)] Loss: 0.717097 Acc: 74.000000
Train Epoch: 14 [44800/50000 (90%)] Loss: 0.724987 Acc: 74.000000
one epoch spend: 0:00:01.797417
EPOCH:14, ACC:63.15
Train Epoch: 15 [6400/50000 (13%)] Loss: 0.624073 Acc: 77.000000
Train Epoch: 15 [12800/50000 (26%)] Loss: 0.637354 Acc: 77.000000
Train Epoch: 15 [19200/50000 (38%)] Loss: 0.646385 Acc: 76.000000
Train Epoch: 15 [25600/50000 (51%)] Loss: 0.662080 Acc: 76.000000
Train Epoch: 15 [32000/50000 (64%)] Loss: 0.668658 Acc: 76.000000
Train Epoch: 15 [38400/50000 (77%)] Loss: 0.679682 Acc: 75.000000
Train Epoch: 15 [44800/50000 (90%)] Loss: 0.688876 Acc: 75.000000
one epoch spend: 0:00:01.916400
EPOCH:15, ACC:62.81
Train Epoch: 16 [6400/50000 (13%)] Loss: 0.611007 Acc: 78.000000
Train Epoch: 16 [12800/50000 (26%)] Loss: 0.612629 Acc: 78.000000
Train Epoch: 16 [19200/50000 (38%)] Loss: 0.622980 Acc: 77.000000
Train Epoch: 16 [25600/50000 (51%)] Loss: 0.638267 Acc: 77.000000
Train Epoch: 16 [32000/50000 (64%)] Loss: 0.650756 Acc: 76.000000
Train Epoch: 16 [38400/50000 (77%)] Loss: 0.656675 Acc: 76.000000
Train Epoch: 16 [44800/50000 (90%)] Loss: 0.665181 Acc: 75.000000
one epoch spend: 0:00:01.878367
EPOCH:16, ACC:61.64
Train Epoch: 17 [6400/50000 (13%)] Loss: 0.591583 Acc: 78.000000
Train Epoch: 17 [12800/50000 (26%)] Loss: 0.601943 Acc: 78.000000
Train Epoch: 17 [19200/50000 (38%)] Loss: 0.612084 Acc: 78.000000
Train Epoch: 17 [25600/50000 (51%)] Loss: 0.619225 Acc: 77.000000
Train Epoch: 17 [32000/50000 (64%)] Loss: 0.633562 Acc: 77.000000
Train Epoch: 17 [38400/50000 (77%)] Loss: 0.641217 Acc: 77.000000
Train Epoch: 17 [44800/50000 (90%)] Loss: 0.648393 Acc: 76.000000
one epoch spend: 0:00:01.894760
EPOCH:17, ACC:61.44
Train Epoch: 18 [6400/50000 (13%)] Loss: 0.553651 Acc: 80.000000
Train Epoch: 18 [12800/50000 (26%)] Loss: 0.569668 Acc: 79.000000
Train Epoch: 18 [19200/50000 (38%)] Loss: 0.584057 Acc: 78.000000
Train Epoch: 18 [25600/50000 (51%)] Loss: 0.598776 Acc: 78.000000
Train Epoch: 18 [32000/50000 (64%)] Loss: 0.610767 Acc: 78.000000
Train Epoch: 18 [38400/50000 (77%)] Loss: 0.617563 Acc: 77.000000
Train Epoch: 18 [44800/50000 (90%)] Loss: 0.628669 Acc: 77.000000
one epoch spend: 0:00:01.925175
EPOCH:18, ACC:62.46
Train Epoch: 19 [6400/50000 (13%)] Loss: 0.554530 Acc: 79.000000
Train Epoch: 19 [12800/50000 (26%)] Loss: 0.574952 Acc: 78.000000
Train Epoch: 19 [19200/50000 (38%)] Loss: 0.576819 Acc: 79.000000
Train Epoch: 19 [25600/50000 (51%)] Loss: 0.584052 Acc: 78.000000
Train Epoch: 19 [32000/50000 (64%)] Loss: 0.590673 Acc: 78.000000
Train Epoch: 19 [38400/50000 (77%)] Loss: 0.599807 Acc: 78.000000
Train Epoch: 19 [44800/50000 (90%)] Loss: 0.607849 Acc: 78.000000
one epoch spend: 0:00:01.827582
EPOCH:19, ACC:62.16
Train Epoch: 20 [6400/50000 (13%)] Loss: 0.534505 Acc: 80.000000
Train Epoch: 20 [12800/50000 (26%)] Loss: 0.547133 Acc: 80.000000
Train Epoch: 20 [19200/50000 (38%)] Loss: 0.557482 Acc: 79.000000
Train Epoch: 20 [25600/50000 (51%)] Loss: 0.567949 Acc: 79.000000
Train Epoch: 20 [32000/50000 (64%)] Loss: 0.579047 Acc: 79.000000
Train Epoch: 20 [38400/50000 (77%)] Loss: 0.591825 Acc: 78.000000
Train Epoch: 20 [44800/50000 (90%)] Loss: 0.598099 Acc: 78.000000
one epoch spend: 0:00:01.846124
EPOCH:20, ACC:62.47
CIFAR10 pytorch LeNet Train: EPOCH:20, BATCH_SZ:64, LR:0.01, ACC:63.72
train spend time: 0:00:46.669295
Process finished with exit code 0
训练的lenet准确度在63%左右，远低于MINIST的99%，简单的LeNet对较复杂的图片准确度不高。
