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

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：

 '''

 创建VGG块

 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

 '''

 def make_vgg_block(in_channel, out_channel, convs, pool=True):

     net = []

     # 不改变图片尺寸卷积

     net.append(nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1))

     net.append(nn.BatchNorm2d(out_channel))

     net.append(nn.ReLU(inplace=True))

     for i in range(convs - 1):

         # 不改变图片尺寸卷积

         net.append(nn.Conv2d(out_channel, out_channel, kernel_size=3, padding=1))

         net.append(nn.BatchNorm2d(out_channel))

         net.append(nn.ReLU(inplace=True))

     if pool:

         # 2*2最大池化，图片变为w/2 * h/2

         net.append(nn.MaxPool2d(2))

     return nn.Sequential(*net)

 # 定义网络模型

 class VGG19Net(nn.Module):

     def __init__(self):

         super(VGG19Net, self).__init__()

         net = []

         # 输入32*32，输出16*16

         net.append(make_vgg_block(3, 64, 2))

         # 输出8*8

         net.append(make_vgg_block(64, 128, 2))

         # 输出4*4

         net.append(make_vgg_block(128, 256, 4))

         # 输出2*2

         net.append(make_vgg_block(256, 512, 4))

         # 无池化层，输出保持2*2

         net.append(make_vgg_block(512, 512, 4, False))

         self.cnn = nn.Sequential(*net)

         self.fc = nn.Sequential(

             # 512个feature，每个feature 2*2

             nn.Linear(512*2*2, 256),

             nn.ReLU(),

             nn.Linear(256, 256),

             nn.ReLU(),

             nn.Linear(256, 10)

         )

     def forward(self, x):

         x = self.cnn(x)

         # x.size()[0]: batch size

         x = x.view(x.size()[0], -1)

         x = self.fc(x)

         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%。

深度学习识别CIFAR10：pytorch训练LeNet、AlexNet、VGG19实现及比较（三）的更多相关文章

深度学习识别CIFAR10：pytorch训练LeNet、AlexNet、VGG19实现及比较（二）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com AlexNet在2012年ImageNet图像分类任务竞赛中获得冠军.网络结构如下图所示: 对CIFA ...
深度学习识别CIFAR10：pytorch训练LeNet、AlexNet、VGG19实现及比较（一）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com 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. CIFA ...
MINIST深度学习识别：python全连接神经网络和pytorch LeNet CNN网络训练实现及比较（三）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com 在前两篇文章MINIST深度学习识别:python全连接神经网络和pytorch LeNet CNN网 ...
pytorch识别CIFAR10：训练ResNet-34（准确率80%）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com CNN的层数越多,能够提取到的特征越丰富,但是简单地增加卷积层数,训练时会导致梯度弥散或梯度爆炸. 何 ...
深度学习面试题12：LeNet(手写数字识别)
目录神经网络的卷积.池化.拉伸 LeNet网络结构 LeNet在MNIST数据集上应用参考资料 LeNet是卷积神经网络的祖师爷LeCun在1998年提出,用于解决手写数字识别的视觉任务.自那时起 ...
pytorch识别CIFAR10：训练ResNet-34（自定义transform，动态调整学习率，准确率提升到94.33%）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com 前面通过数据增强,ResNet-34残差网络识别CIFAR10,准确率达到了92.6. 这里对训练过程 ...
pytorch识别CIFAR10：训练ResNet-34（数据增强，准确率提升到92.6%）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com 在前一篇中的ResNet-34残差网络,经过减小卷积核训练准确率提升到85%. 这里对训练数据集做数据 ...
pytorch识别CIFAR10：训练ResNet-34（微调网络，准确率提升到85%）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com 在前一篇中的ResNet-34残差网络,经过训练准确率只达到80%. 这里对网络做点小修改,在最开始的 ...
用CNTK搞深度学习（二）训练基于RNN的自然语言模型 ( language model )
前一篇文章用 CNTK 搞深度学习 (一) 入门介绍了用CNTK构建简单前向神经网络的例子.现在假设读者已经懂得了使用CNTK的基本方法.现在我们做一个稍微复杂一点,也是自然语言挖掘中很火 ...

随机推荐

okhttputils【 Android 一个改善的okHttp封装库】使用（三）
版权声明:本文为HaiyuKing原创文章,转载请注明出处! 前言这一篇主要讲一下将OkHttpUtils运用到mvp模式中. 数据请求地址:http://www.wanandroid.com/to ...
jquery获取元素（父级的兄弟元素的子元素）
一.获取父级元素使用jquery获取父级元素: parent() 例如:$(this).parent('ul'); 二.获取同级元素使用jquery获取同级元素:siblings() 例如:$(t ...
JDK源码分析（2）之 Array 相关
在深入了解 Array 之前,一直以为 Array 比较简单,但是深入了解后才发现其实挺复杂的.所以我把重要的写在最前面,但凡遇到和语言本身相关的问题,都可以查阅 Java Language and ...
Notepad++替换SQL Server Select窗口列名的中括号的小技巧
条件:“查找模式”那选中“扩展” 一.简单替换查找目标(包括空格,各个SSMS版本可能不同): ]\r\n ,[ 替换为:, 二.替换为@ 查找目标(包括空格,各个SSMS版本可能不同): ]\r\ ...
2017-2018年Scrum状态调查报告
HOW SCRUM IS USED 在2017年的报告中,Scrum的应用范围在扩大,已经从其发源的IT部门扩展到了相距甚远的业务部门.2017-2018年度报告的其中一个主要目标就是关注更广泛的敏捷 ...
Java设计模式---Strategy策略模式
参考于 : 大话设计模式马士兵设计模式视频 1.场景介绍购物网站上有一个产品,有三个字段,档次,价格,重量. 有些同学喜欢轻的,有些手头紧,想要便宜的,有些喜欢档次高的. 那么我们为了提高网站用户 ...
【AO笔记】有关使用AO基于shp文件创建网络数据集的一个细节
前些日子,和群友交流时提及shp文件创建单一网络数据集的创建问题. 由于项目需求,用shp文件创建网络合适,所以不得不去找一些古老的代码资料... 以前的研究中,创建网络数据集是根据IDatasetC ...
Android为TV端助力外挂字幕（设置颜色，大小，位置，微调字幕）
前提摘要: 可以给电影加字幕,目前支持srt和ass格式, 功能摘要: 支持微调字幕,设置大小,颜色,位置 1 .字幕解析类 package com.hhzt.iptv.lvb_x.utils; ...
vs code配置flutter开发android
下载flutter_sdk压缩包,解压到指定目录,把sdk的bin目录添加到系统环境变量Path 设置中国临时镜像:添加两个系统变量 FLUTTER_STORAGE_BASE_URL=https:// ...
datatable动态列处理，重绘表格(敲黑板，划重点！！！我肝了一天半才彻底弄懂这个东西，TAT)
datatable动态列处理,重绘表格前言:至于动态列的绘画,我前面博客已经写过了,就是动态列的配置问题,不懂的去我博客看下,今天要写的呢,就是你已经写了一个动态列在datatable,现在你想重新 ...

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

深度学习识别CIFAR10：pytorch训练LeNet、AlexNet、VGG19实现及比较（三）的更多相关文章

随机推荐

热门专题