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：