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  • 深度学习识别CIFAR10：pytorch训练LeNet、AlexNet、VGG19实现及比较（三）

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    VGGNet在2014年ImageNet图像分类任务竞赛中有出色的表现。网络结构如下图所示：

    同样的，对32*32的CIFAR10图片，网络结构做了微调：删除了最后一层最大池化，具体参见网络定义代码，这里采用VGG19，并加入了BN：

     1 '''
 2 创建VGG块
 3 参数分别为输入通道数，输出通道数，卷积层个数，是否做最大池化
 4 '''
 5 def make_vgg_block(in_channel, out_channel, convs, pool=True):
 6     net = []
 7 
 8     # 不改变图片尺寸卷积
 9     net.append(nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1))
10     net.append(nn.BatchNorm2d(out_channel))
11     net.append(nn.ReLU(inplace=True))
12 
13     for i in range(convs - 1):
14         # 不改变图片尺寸卷积
15         net.append(nn.Conv2d(out_channel, out_channel, kernel_size=3, padding=1))
16         net.append(nn.BatchNorm2d(out_channel))
17         net.append(nn.ReLU(inplace=True))
18 
19     if pool:
20         # 2*2最大池化，图片变为w/2 * h/2
21         net.append(nn.MaxPool2d(2))
22 
23     return nn.Sequential(*net)
24 
25 
26 # 定义网络模型
27 class VGG19Net(nn.Module):
28     def __init__(self):
29         super(VGG19Net, self).__init__()
30 
31         net = []
32 
33         # 输入32*32，输出16*16
34         net.append(make_vgg_block(3, 64, 2))
35 
36         # 输出8*8
37         net.append(make_vgg_block(64, 128, 2))
38 
39         # 输出4*4
40         net.append(make_vgg_block(128, 256, 4))
41 
42         # 输出2*2
43         net.append(make_vgg_block(256, 512, 4))
44 
45         # 无池化层，输出保持2*2
46         net.append(make_vgg_block(512, 512, 4, False))
47 
48         self.cnn = nn.Sequential(*net)
49 
50         self.fc = nn.Sequential(
51             # 512个feature，每个feature 2*2
52             nn.Linear(512*2*2, 256),
53             nn.ReLU(),
54 
55             nn.Linear(256, 256),
56             nn.ReLU(),
57 
58             nn.Linear(256, 10)
59         )
60 
61     def forward(self, x):
62         x = self.cnn(x)
63 
64         # x.size()[0]: batch size
65         x = x.view(x.size()[0], -1)
66         x = self.fc(x)
67 
68         return x

    其余代码同深度学习识别CIFAR10：pytorch训练LeNet、AlexNet、VGG19实现及比较（一）运行结果如下：

    Files already downloaded and verified
    VGG19Net(
      (cnn): Sequential(
        (0): Sequential(
          (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace)
          (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU(inplace)
          (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
        )
        (1): Sequential(
          (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace)
          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU(inplace)
          (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
        )
        (2): Sequential(
          (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace)
          (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU(inplace)
          (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (8): ReLU(inplace)
          (9): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (10): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (11): ReLU(inplace)
          (12): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
        )
        (3): Sequential(
          (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace)
          (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU(inplace)
          (6): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (7): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (8): ReLU(inplace)
          (9): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (10): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (11): ReLU(inplace)
          (12): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
        )
        (4): Sequential(
          (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace)
          (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (5): ReLU(inplace)
          (6): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (7): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (8): ReLU(inplace)
          (9): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (10): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (11): ReLU(inplace)
        )
      )
      (fc): Sequential(
        (0): Linear(in_features=2048, out_features=256, bias=True)
        (1): ReLU()
        (2): Linear(in_features=256, out_features=256, bias=True)
        (3): ReLU()
        (4): Linear(in_features=256, out_features=10, bias=True)
      )
    )
    Train Epoch: 1 [6400/50000 (13%)]    Loss: 1.991934  Acc: 22.000000
    Train Epoch: 1 [12800/50000 (26%)]    Loss: 1.851721  Acc: 27.000000
    Train Epoch: 1 [19200/50000 (38%)]    Loss: 1.765295  Acc: 31.000000
    Train Epoch: 1 [25600/50000 (51%)]    Loss: 1.708027  Acc: 33.000000
    Train Epoch: 1 [32000/50000 (64%)]    Loss: 1.652181  Acc: 36.000000
    Train Epoch: 1 [38400/50000 (77%)]    Loss: 1.597727  Acc: 38.000000
    Train Epoch: 1 [44800/50000 (90%)]    Loss: 1.552660  Acc: 41.000000
    one epoch spend:  0:01:08.269581
    EPOCH:1, ACC:55.08

    Train Epoch: 2 [6400/50000 (13%)]    Loss: 1.139670  Acc: 60.000000
    Train Epoch: 2 [12800/50000 (26%)]    Loss: 1.099960  Acc: 61.000000
    Train Epoch: 2 [19200/50000 (38%)]    Loss: 1.078881  Acc: 62.000000
    Train Epoch: 2 [25600/50000 (51%)]    Loss: 1.054403  Acc: 63.000000
    Train Epoch: 2 [32000/50000 (64%)]    Loss: 1.031371  Acc: 64.000000
    Train Epoch: 2 [38400/50000 (77%)]    Loss: 1.011668  Acc: 64.000000
    Train Epoch: 2 [44800/50000 (90%)]    Loss: 0.995242  Acc: 65.000000
    one epoch spend:  0:01:08.220392
    EPOCH:2, ACC:71.01

    Train Epoch: 3 [6400/50000 (13%)]    Loss: 0.823265  Acc: 71.000000
    Train Epoch: 3 [12800/50000 (26%)]    Loss: 0.799878  Acc: 73.000000
    Train Epoch: 3 [19200/50000 (38%)]    Loss: 0.791265  Acc: 73.000000
    Train Epoch: 3 [25600/50000 (51%)]    Loss: 0.790027  Acc: 73.000000
    Train Epoch: 3 [32000/50000 (64%)]    Loss: 0.777267  Acc: 73.000000
    Train Epoch: 3 [38400/50000 (77%)]    Loss: 0.771953  Acc: 74.000000
    Train Epoch: 3 [44800/50000 (90%)]    Loss: 0.766835  Acc: 74.000000
    one epoch spend:  0:01:08.485721
    EPOCH:3, ACC:69.48

    Train Epoch: 4 [6400/50000 (13%)]    Loss: 0.640418  Acc: 78.000000
    Train Epoch: 4 [12800/50000 (26%)]    Loss: 0.637256  Acc: 78.000000
    Train Epoch: 4 [19200/50000 (38%)]    Loss: 0.631245  Acc: 79.000000
    Train Epoch: 4 [25600/50000 (51%)]    Loss: 0.629215  Acc: 79.000000
    Train Epoch: 4 [32000/50000 (64%)]    Loss: 0.625925  Acc: 79.000000
    Train Epoch: 4 [38400/50000 (77%)]    Loss: 0.618307  Acc: 79.000000
    Train Epoch: 4 [44800/50000 (90%)]    Loss: 0.617456  Acc: 79.000000
    one epoch spend:  0:01:08.289673
    EPOCH:4, ACC:77.2

    Train Epoch: 5 [6400/50000 (13%)]    Loss: 0.537330  Acc: 82.000000
    Train Epoch: 5 [12800/50000 (26%)]    Loss: 0.529751  Acc: 82.000000
    Train Epoch: 5 [19200/50000 (38%)]    Loss: 0.529389  Acc: 82.000000
    Train Epoch: 5 [25600/50000 (51%)]    Loss: 0.528106  Acc: 82.000000
    Train Epoch: 5 [32000/50000 (64%)]    Loss: 0.526467  Acc: 82.000000
    Train Epoch: 5 [38400/50000 (77%)]    Loss: 0.525133  Acc: 82.000000
    Train Epoch: 5 [44800/50000 (90%)]    Loss: 0.521847  Acc: 82.000000
    one epoch spend:  0:01:08.272084
    EPOCH:5, ACC:78.26

    Train Epoch: 6 [6400/50000 (13%)]    Loss: 0.435377  Acc: 85.000000
    Train Epoch: 6 [12800/50000 (26%)]    Loss: 0.431456  Acc: 85.000000
    Train Epoch: 6 [19200/50000 (38%)]    Loss: 0.443582  Acc: 85.000000
    Train Epoch: 6 [25600/50000 (51%)]    Loss: 0.442819  Acc: 85.000000
    Train Epoch: 6 [32000/50000 (64%)]    Loss: 0.443313  Acc: 85.000000
    Train Epoch: 6 [38400/50000 (77%)]    Loss: 0.442025  Acc: 85.000000
    Train Epoch: 6 [44800/50000 (90%)]    Loss: 0.441722  Acc: 85.000000
    one epoch spend:  0:01:10.725170
    EPOCH:6, ACC:80.91

    Train Epoch: 7 [6400/50000 (13%)]    Loss: 0.350214  Acc: 88.000000
    Train Epoch: 7 [12800/50000 (26%)]    Loss: 0.351490  Acc: 88.000000
    Train Epoch: 7 [19200/50000 (38%)]    Loss: 0.361328  Acc: 88.000000
    Train Epoch: 7 [25600/50000 (51%)]    Loss: 0.362231  Acc: 87.000000
    Train Epoch: 7 [32000/50000 (64%)]    Loss: 0.364318  Acc: 87.000000
    Train Epoch: 7 [38400/50000 (77%)]    Loss: 0.367137  Acc: 87.000000
    Train Epoch: 7 [44800/50000 (90%)]    Loss: 0.375220  Acc: 87.000000
    one epoch spend:  0:01:09.395538
    EPOCH:7, ACC:80.55

    Train Epoch: 8 [6400/50000 (13%)]    Loss: 0.297754  Acc: 90.000000
    Train Epoch: 8 [12800/50000 (26%)]    Loss: 0.303383  Acc: 89.000000
    Train Epoch: 8 [19200/50000 (38%)]    Loss: 0.305170  Acc: 89.000000
    Train Epoch: 8 [25600/50000 (51%)]    Loss: 0.311823  Acc: 89.000000
    Train Epoch: 8 [32000/50000 (64%)]    Loss: 0.309851  Acc: 89.000000
    Train Epoch: 8 [38400/50000 (77%)]    Loss: 0.310422  Acc: 89.000000
    Train Epoch: 8 [44800/50000 (90%)]    Loss: 0.312672  Acc: 89.000000
    one epoch spend:  0:01:08.041167
    EPOCH:8, ACC:80.54

    Train Epoch: 9 [6400/50000 (13%)]    Loss: 0.277638  Acc: 90.000000
    Train Epoch: 9 [12800/50000 (26%)]    Loss: 0.276622  Acc: 90.000000
    Train Epoch: 9 [19200/50000 (38%)]    Loss: 0.276465  Acc: 90.000000
    Train Epoch: 9 [25600/50000 (51%)]    Loss: 0.278001  Acc: 90.000000
    Train Epoch: 9 [32000/50000 (64%)]    Loss: 0.277109  Acc: 90.000000
    Train Epoch: 9 [38400/50000 (77%)]    Loss: 0.277029  Acc: 90.000000
    Train Epoch: 9 [44800/50000 (90%)]    Loss: 0.275243  Acc: 90.000000
    one epoch spend:  0:01:08.143754
    EPOCH:9, ACC:83.53

    Train Epoch: 10 [6400/50000 (13%)]    Loss: 0.205785  Acc: 92.000000
    Train Epoch: 10 [12800/50000 (26%)]    Loss: 0.210659  Acc: 92.000000
    Train Epoch: 10 [19200/50000 (38%)]    Loss: 0.214871  Acc: 92.000000
    Train Epoch: 10 [25600/50000 (51%)]    Loss: 0.218910  Acc: 92.000000
    Train Epoch: 10 [32000/50000 (64%)]    Loss: 0.220843  Acc: 92.000000
    Train Epoch: 10 [38400/50000 (77%)]    Loss: 0.220417  Acc: 92.000000
    Train Epoch: 10 [44800/50000 (90%)]    Loss: 0.221100  Acc: 92.000000
    one epoch spend:  0:01:08.333929
    EPOCH:10, ACC:79.01

    Train Epoch: 11 [6400/50000 (13%)]    Loss: 0.186917  Acc: 93.000000
    Train Epoch: 11 [12800/50000 (26%)]    Loss: 0.183512  Acc: 93.000000
    Train Epoch: 11 [19200/50000 (38%)]    Loss: 0.182561  Acc: 93.000000
    Train Epoch: 11 [25600/50000 (51%)]    Loss: 0.186446  Acc: 93.000000
    Train Epoch: 11 [32000/50000 (64%)]    Loss: 0.187314  Acc: 93.000000
    Train Epoch: 11 [38400/50000 (77%)]    Loss: 0.185967  Acc: 93.000000
    Train Epoch: 11 [44800/50000 (90%)]    Loss: 0.189130  Acc: 93.000000
    one epoch spend:  0:01:10.476138
    EPOCH:11, ACC:81.57

    Train Epoch: 12 [6400/50000 (13%)]    Loss: 0.136427  Acc: 95.000000
    Train Epoch: 12 [12800/50000 (26%)]    Loss: 0.147904  Acc: 95.000000
    Train Epoch: 12 [19200/50000 (38%)]    Loss: 0.154502  Acc: 94.000000
    Train Epoch: 12 [25600/50000 (51%)]    Loss: 0.155767  Acc: 94.000000
    Train Epoch: 12 [32000/50000 (64%)]    Loss: 0.158346  Acc: 94.000000
    Train Epoch: 12 [38400/50000 (77%)]    Loss: 0.159562  Acc: 94.000000
    Train Epoch: 12 [44800/50000 (90%)]    Loss: 0.159924  Acc: 94.000000
    one epoch spend:  0:01:10.779635
    EPOCH:12, ACC:84.38

    Train Epoch: 13 [6400/50000 (13%)]    Loss: 0.110026  Acc: 96.000000
    Train Epoch: 13 [12800/50000 (26%)]    Loss: 0.113738  Acc: 96.000000
    Train Epoch: 13 [19200/50000 (38%)]    Loss: 0.117731  Acc: 96.000000
    Train Epoch: 13 [25600/50000 (51%)]    Loss: 0.123653  Acc: 95.000000
    Train Epoch: 13 [32000/50000 (64%)]    Loss: 0.127138  Acc: 95.000000
    Train Epoch: 13 [38400/50000 (77%)]    Loss: 0.128938  Acc: 95.000000
    Train Epoch: 13 [44800/50000 (90%)]    Loss: 0.131382  Acc: 95.000000
    one epoch spend:  0:01:09.020651
    EPOCH:13, ACC:83.46

    Train Epoch: 14 [6400/50000 (13%)]    Loss: 0.122690  Acc: 96.000000
    Train Epoch: 14 [12800/50000 (26%)]    Loss: 0.114584  Acc: 96.000000
    Train Epoch: 14 [19200/50000 (38%)]    Loss: 0.122652  Acc: 96.000000
    Train Epoch: 14 [25600/50000 (51%)]    Loss: 0.123031  Acc: 95.000000
    Train Epoch: 14 [32000/50000 (64%)]    Loss: 0.123427  Acc: 95.000000
    Train Epoch: 14 [38400/50000 (77%)]    Loss: 0.123146  Acc: 95.000000
    Train Epoch: 14 [44800/50000 (90%)]    Loss: 0.124063  Acc: 95.000000
    one epoch spend:  0:01:10.294790
    EPOCH:14, ACC:82.27

    Train Epoch: 15 [6400/50000 (13%)]    Loss: 0.087797  Acc: 97.000000
    Train Epoch: 15 [12800/50000 (26%)]    Loss: 0.086152  Acc: 97.000000
    Train Epoch: 15 [19200/50000 (38%)]    Loss: 0.088446  Acc: 97.000000
    Train Epoch: 15 [25600/50000 (51%)]    Loss: 0.093510  Acc: 96.000000
    Train Epoch: 15 [32000/50000 (64%)]    Loss: 0.092870  Acc: 96.000000
    Train Epoch: 15 [38400/50000 (77%)]    Loss: 0.092416  Acc: 96.000000
    Train Epoch: 15 [44800/50000 (90%)]    Loss: 0.095187  Acc: 96.000000
    one epoch spend:  0:01:10.375479
    EPOCH:15, ACC:82.73

    Train Epoch: 16 [6400/50000 (13%)]    Loss: 0.066554  Acc: 97.000000
    Train Epoch: 16 [12800/50000 (26%)]    Loss: 0.079139  Acc: 97.000000
    Train Epoch: 16 [19200/50000 (38%)]    Loss: 0.078223  Acc: 97.000000
    Train Epoch: 16 [25600/50000 (51%)]    Loss: 0.076825  Acc: 97.000000
    Train Epoch: 16 [32000/50000 (64%)]    Loss: 0.079679  Acc: 97.000000
    Train Epoch: 16 [38400/50000 (77%)]    Loss: 0.081081  Acc: 97.000000
    Train Epoch: 16 [44800/50000 (90%)]    Loss: 0.081967  Acc: 97.000000
    one epoch spend:  0:01:09.971818
    EPOCH:16, ACC:85.45

    Train Epoch: 17 [6400/50000 (13%)]    Loss: 0.061477  Acc: 98.000000
    Train Epoch: 17 [12800/50000 (26%)]    Loss: 0.066804  Acc: 97.000000
    Train Epoch: 17 [19200/50000 (38%)]    Loss: 0.069621  Acc: 97.000000
    Train Epoch: 17 [25600/50000 (51%)]    Loss: 0.068841  Acc: 97.000000
    Train Epoch: 17 [32000/50000 (64%)]    Loss: 0.069220  Acc: 97.000000
    Train Epoch: 17 [38400/50000 (77%)]    Loss: 0.071493  Acc: 97.000000
    Train Epoch: 17 [44800/50000 (90%)]    Loss: 0.070973  Acc: 97.000000
    one epoch spend:  0:01:10.599626
    EPOCH:17, ACC:83.02

    Train Epoch: 18 [6400/50000 (13%)]    Loss: 0.095195  Acc: 96.000000
    Train Epoch: 18 [12800/50000 (26%)]    Loss: 0.081690  Acc: 97.000000
    Train Epoch: 18 [19200/50000 (38%)]    Loss: 0.076400  Acc: 97.000000
    Train Epoch: 18 [25600/50000 (51%)]    Loss: 0.073249  Acc: 97.000000
    Train Epoch: 18 [32000/50000 (64%)]    Loss: 0.072114  Acc: 97.000000
    Train Epoch: 18 [38400/50000 (77%)]    Loss: 0.073739  Acc: 97.000000
    Train Epoch: 18 [44800/50000 (90%)]    Loss: 0.073761  Acc: 97.000000
    one epoch spend:  0:01:11.619880
    EPOCH:18, ACC:83.67

    Train Epoch: 19 [6400/50000 (13%)]    Loss: 0.049970  Acc: 98.000000
    Train Epoch: 19 [12800/50000 (26%)]    Loss: 0.051812  Acc: 98.000000
    Train Epoch: 19 [19200/50000 (38%)]    Loss: 0.053814  Acc: 98.000000
    Train Epoch: 19 [25600/50000 (51%)]    Loss: 0.054168  Acc: 98.000000
    Train Epoch: 19 [32000/50000 (64%)]    Loss: 0.054138  Acc: 98.000000
    Train Epoch: 19 [38400/50000 (77%)]    Loss: 0.055356  Acc: 98.000000
    Train Epoch: 19 [44800/50000 (90%)]    Loss: 0.055334  Acc: 98.000000
    one epoch spend:  0:01:10.397104
    EPOCH:19, ACC:84.23

    Train Epoch: 20 [6400/50000 (13%)]    Loss: 0.059795  Acc: 98.000000
    Train Epoch: 20 [12800/50000 (26%)]    Loss: 0.059780  Acc: 98.000000
    Train Epoch: 20 [19200/50000 (38%)]    Loss: 0.060332  Acc: 98.000000
    Train Epoch: 20 [25600/50000 (51%)]    Loss: 0.057949  Acc: 98.000000
    Train Epoch: 20 [32000/50000 (64%)]    Loss: 0.056517  Acc: 98.000000
    Train Epoch: 20 [38400/50000 (77%)]    Loss: 0.055322  Acc: 98.000000
    Train Epoch: 20 [44800/50000 (90%)]    Loss: 0.053375  Acc: 98.000000
    one epoch spend:  0:01:10.407573
    EPOCH:20, ACC:84.51

    CIFAR10 pytorch LeNet Train: EPOCH:20, BATCH_SZ:64, LR:0.01, ACC:85.45
    train spend time:  0:23:45.010363

    Process finished with exit code 0

    准确率达到85%，对比AlexNet的75%，提升了10%。
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  • 原文地址：https://www.cnblogs.com/zhengbiqing/p/10425811.html
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