课程学习笔记，课程链接
Sequential 是一个时序容器。Modules 会以他们传入的顺序被添加到容器中。包含在 PyTorch 官网中 torch.nn 模块中的 Containers 中，在神经网络搭建的过程中如果使用 Sequential，代码更简洁。
搭建上述神经网络的具体代码如下。
import torchfrom torch import nnfrom torch.nn import Conv2d, MaxPool2d, Flatten, Linearclass Jiaolong(nn.Module): def __init__(self): super(Jiaolong, self).__init__() self.conv1 = Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2) self.maxpool1 = MaxPool2d(kernel_size=2) self.conv2 = Conv2d(in_channels=32, out_channels=32, kernel_size=5, padding=2) self.maxpool2 = MaxPool2d(kernel_size=2) self.conv3 = Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2) 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 xjiaolong = Jiaolong()print(jiaolong)input = torch.ones((64, 3, 32, 32)) # 指定数据创建的形状，都是1output = jiaolong(input)print(output.shape)
现以Sequential搭建上述一模一样的神经网络，并借助tensorboard显示计算图的具体信息。
import torchfrom torch import nnfrom torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequentialfrom torch.utils.tensorboard import SummaryWriterclass Jiaolong(nn.Module): def __init__(self): super(Jiaolong, self).__init__() self.model1 = Sequential( Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2), MaxPool2d(kernel_size=2), Conv2d(in_channels=32, out_channels=32, kernel_size=5, padding=2), MaxPool2d(kernel_size=2), Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2), MaxPool2d(kernel_size=2), Flatten(), Linear(1024, 64), Linear(64, 10) ) def forward(self, x): x = self.model1(x) return xjiaolong = Jiaolong()# print(jiaolong)input = torch.ones((64, 3, 32, 32)) # 指定数据创建的形状，都是1output = jiaolong(input)# print(output.shape)writer = SummaryWriter("logs")writer.add_graph(jiaolong, input) # 计算图writer.close()
在 Tensorboard 中查看计算图结果如下：