现代卷积网络实战系列5：PyTorch从零构建GoogLeNet训练MNIST数据集

🌈🌈🌈现代卷积网络实战系列 总目录

本篇文章的代码运行界面均在Pycharm中进行
本篇文章配套的代码资源已经上传

1、MNIST数据集处理、加载、网络初始化、测试函数
2、训练函数、PyTorch构建LeNet网络
3、PyTorch从零构建AlexNet训练MNIST数据集
4、PyTorch从零构建VGGNet训练MNIST数据集
5、PyTorch从零构建GoogLeNet训练MNIST数据集
6、PyTorch从零构建ResNet训练MNIST数据集

12、GoogLeNet

2014年，GoogLeNet和VGG是当年ImageNet挑战赛(ILSVRC14)的双雄，GoogLeNet获得了第一名、VGG获得了第二名，这两类模型结构的共同特点是层次更深了。VGG继承了LeNet以及AlexNet的一些框架结构，而GoogLeNet则做了更加大胆的网络结构尝试，虽然深度有22层，但大小却比AlexNet和VGG小很多，GoogleNet参数为500万个，AlexNet参数个数是GoogleNet的12倍，VGGNet参数又是AlexNet的3倍，因此在内存或计算资源有限时，GoogleNet是比较好的选择；从模型结果来看，GoogLeNet的性能却更加优越。

GoogLeNet 网络是基于Inception 网络构建而成，融合了不同尺度的特征信息，是一种带有稀疏性和具备高性能的网络结构。
Inception 网络增加了网络的宽度，融合了不同小尺度的卷积与池化操作，可以达到更好的识别效果。将网络由深变宽，避免了网络太深出现梯度消失和模型退化的问题。GoogLeNet 网络还采取了全局均值池化策略，避免了全连接层参数过多但泛化能。

13、GoogLeNet网络架构

GoogLeNet( (pre_layers): Sequential(
(0): Conv2d(1, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True) ) (a3): Inception(
(b1): Sequential(
(0): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(b2): Sequential(
(0): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(96, 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=True)
)
(b3): Sequential(
(0): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
(6): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(inplace=True)
)
(b4): Sequential(
(0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1))
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
) ) (b3): Inception(
(b1): Sequential(
(0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(b2): Sequential(
(0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
(b3): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
(6): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(inplace=True)
)
(b4): Sequential(
(0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
) ) (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (a4): Inception(
(b1): Sequential(
(0): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(b2): Sequential(
(0): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
(b3): Sequential(
(0): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
(6): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(inplace=True)
)
(b4): Sequential(
(0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1))
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
) ) (b4): Inception(
(b1): Sequential(
(0): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(b2): Sequential(
(0): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
(b3): Sequential(
(0): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(24, 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=True)
(6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(inplace=True)
)
(b4): Sequential(
(0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
) ) (c4): Inception(
(b1): Sequential(
(0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(b2): Sequential(
(0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(128, 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=True)
)
(b3): Sequential(
(0): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(24, 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=True)
(6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(inplace=True)
)
(b4): Sequential(
(0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
) ) (d4): Inception(
(b1): Sequential(
(0): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(b2): Sequential(
(0): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
(b3): Sequential(
(0): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(32, 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=True)
(6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(inplace=True)
)
(b4): Sequential(
(0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
) ) (e4): Inception(
(b1): Sequential(
(0): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(b2): Sequential(
(0): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
(b3): Sequential(
(0): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(32, 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=True)
(6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(inplace=True)
)
(b4): Sequential(
(0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1))
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
) ) (a5): Inception(
(b1): Sequential(
(0): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(b2): Sequential(
(0): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
(b3): Sequential(
(0): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(32, 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=True)
(6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(inplace=True)
)
(b4): Sequential(
(0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1))
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
) ) (b5): Inception(
(b1): Sequential(
(0): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(b2): Sequential(
(0): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
(b3): Sequential(
(0): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(48, 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=True)
(6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(inplace=True)
)
(b4): Sequential(
(0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1))
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
) ) (avgpool): AvgPool2d(kernel_size=3, stride=1, padding=0) (linear1): Linear(in_features=25600, out_features=1024, bias=True)
(linear2): Linear(in_features=1024, out_features=10, bias=True) )

14、PyTorch构建Inception

class Inception(nn.Module):
    def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
        super(Inception, self).__init__()
        # 1x1 conv branch
        self.b1 = nn.Sequential(
            nn.Conv2d(in_planes, n1x1, kernel_size=1),
            nn.BatchNorm2d(n1x1),
            nn.ReLU(True),
        )

        # 1x1 conv -> 3x3 conv branch
        self.b2 = nn.Sequential(
            nn.Conv2d(in_planes, n3x3red, kernel_size=1),
            nn.BatchNorm2d(n3x3red),
            nn.ReLU(True),
            nn.Conv2d(n3x3red, n3x3, kernel_size=3, padding=1),
            nn.BatchNorm2d(n3x3),
            nn.ReLU(True),
        )

        # 1x1 conv -> 5x5 conv branch
        self.b3 = nn.Sequential(
            nn.Conv2d(in_planes, n5x5red, kernel_size=1),
            nn.BatchNorm2d(n5x5red),
            nn.ReLU(True),
            nn.Conv2d(n5x5red, n5x5, kernel_size=3, padding=1),
            nn.BatchNorm2d(n5x5),
            nn.ReLU(True),
            nn.Conv2d(n5x5, n5x5, kernel_size=3, padding=1),
            nn.BatchNorm2d(n5x5),
            nn.ReLU(True),
        )

        # 3x3 pool -> 1x1 conv branch
        self.b4 = nn.Sequential(
            nn.MaxPool2d(3, stride=1, padding=1),
            nn.Conv2d(in_planes, pool_planes, kernel_size=1),
            nn.BatchNorm2d(pool_planes),
            nn.ReLU(True),
        )

    def forward(self, x):
        y1 = self.b1(x)
        y2 = self.b2(x)
        y3 = self.b3(x)
        y4 = self.b4(x)
        return torch.cat([y1, y2, y3, y4], 1)
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15、PyTorch构建Inception

class GoogLeNet(nn.Module):
    def __init__(self, num_classes):
        super(GoogLeNet, self).__init__()
        self.pre_layers = nn.Sequential(
            nn.Conv2d(1, 192, kernel_size=3, padding=1),
            nn.BatchNorm2d(192),
            nn.ReLU(True),
        )

        self.a3 = Inception(192, 64, 96, 128, 16, 32, 32)
        self.b3 = Inception(256, 128, 128, 192, 32, 96, 64)

        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)

        self.a4 = Inception(480, 192, 96, 208, 16, 48, 64)
        self.b4 = Inception(512, 160, 112, 224, 24, 64, 64)
        self.c4 = Inception(512, 128, 128, 256, 24, 64, 64)
        self.d4 = Inception(512, 112, 144, 288, 32, 64, 64)
        self.e4 = Inception(528, 256, 160, 320, 32, 128, 128)

        self.a5 = Inception(832, 256, 160, 320, 32, 128, 128)
        self.b5 = Inception(832, 384, 192, 384, 48, 128, 128)

        self.avgpool = nn.AvgPool2d(3, stride=1)
        self.linear1 = nn.Linear(25600, 1024)
        self.linear2 = nn.Linear(1024, num_classes)

    def forward(self, x):
        out = self.pre_layers(x)
        out = self.a3(out)
        out = self.b3(out)
        out = self.maxpool(out)
        out = self.a4(out)
        out = self.b4(out)
        out = self.c4(out)
        out = self.d4(out)
        out = self.e4(out)
        out = self.maxpool(out)
        out = self.a5(out)
        out = self.b5(out)

        out = self.avgpool(out)
        out = out.view(out.size(0), -1)
        out = F.relu(self.linear1(out))
        out = self.linear2(out)
        return out
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