深度学习——06 torch.nn.Sequential快速搭建神经网络

torch.nn.Sequential是一个Sequential容器，模块将按照构造函数中传递的顺序添加到模块中.

一、通常情况构建网络

class MyNet(nn.Module):
    def __init__(self):
        super(MyNet, self).__init__()
        self.conv1 = Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2)
        self.maxpool1 = MaxPool2d(2)
        self.conv2 = Conv2d(32, 32, 5, padding=2)
        self.maxpool2 = MaxPool2d(2)
        self.conv3 = Conv2d(32, 64, 5, padding=2)
        self.maxpool3 = MaxPool2d(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

打印一下该神经网络：

MyNet = MyNet()
print(MyNet)

二、使用Sequential构建网络

class MyNet(nn.Module):
    def __init__(self):
        super(MyNet, self).__init__()
        self.model1 = Sequential(
            Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10),
        )

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

打印一下该神经网络：

MyNet = MyNet()
print(MyNet)

方便操作

三、使用TensorBoard查看构建网络

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

writer = SummaryWriter("p13")
writer.add_graph(MyNet,input)
writer.close()