P12：Sequential的使用_(blocks): sequential(-优快云博客

本文链接：https://blog.youkuaiyun.com/The_friends/article/details/128677222

文章展示了如何使用PyTorch构建一个CIFAR10模型，该模型包含卷积层、最大池化层和全连接层。首先，通过逐层定义网络结构，然后改用Sequential模块简化网络定义。最后，利用tensorboard展示网络图。

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1、CIFAR10 model的结构：

2、定义网络：

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.conv1 = Conv2d(3, 32, 5, stride=1, padding=2)
        self.maxpool1 = MaxPool2d(kernel_size=2)
        self.conv2 = Conv2d(32, 32, kernel_size=5, padding=2, stride=1)
        self.maxpool2 = MaxPool2d(kernel_size=2)
        self.conv3 = Conv2d(32, 64, kernel_size=5, padding=2, stride=1)
        self.maxpool3 = MaxPool2d(kernel_size=2)
        self.flatten = Flatten()
        self.linear1 = Linear(1024, 64)
        self.linear2 = Linear(64, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.maxpool2(x)
        x = self.conv3(x)
        x = self.maxpool3(x)
        x = self.flatten(x)
        x = self.linear1(x)
        x = self.linear2(x)
        return x

运行结果如下：

3、检查网络：

input = torch.ones((64, 3, 32, 32))
output = tudui(input)
print(output.shape)

结果如下：

4、改用Sequential定义网络：

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()

        self.model1 = Sequential(
            Conv2d(3, 32, 5, padding=2, stride=1),
            MaxPool2d(kernel_size=2),
            Conv2d(32, 32, kernel_size=5, padding=2, stride=1),
            MaxPool2d(kernel_size=2),
            Conv2d(32, 64, kernel_size=5, padding=2, stride=1),
            MaxPool2d(kernel_size=2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )

    def forward(self, x):
        x = self.model1(x)
        return x

5、tensorboard显示网络情况：

writer = SummaryWriter('logs_sequential')
writer.add_graph(tudui, input)
writer.close()

结果如下：