目录

神经网络-搭建小实战和Sequential的使用

版本1——未用Sequential 

版本2——用Sequential

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神经网络-搭建小实战和Sequential的使用

1. torch.nn.Sequential的官方文档地址，模块将按照它们在构造函数中传递的顺序添加。
2. 代码实现的是下图： 

版本1——未用Sequential 

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear


class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        # 3,32,32 ---> 32,32,32
        self.conv1 = Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2)
        # 32,32,32 ---> 32,16,16
        self.maxpool1 = MaxPool2d(kernel_size=2, stride=2)
        # 32,16,16 ---> 32,16,16
        self.conv2 = Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2)
        # 32,16,16 ---> 32,8,8
        self.maxpool2 = MaxPool2d(kernel_size=2, stride=2)
        # 32,8,8 ---> 64,8,8
        self.conv3 = Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=2)
        # 64,8,8 ---> 64,4,4
        self.maxpool3 = MaxPool2d(kernel_size=2, stride=2)
        # 64,4,4 ---> 1024
        self.flatten = Flatten()  # 因为start_dim默认为1，所以可不再另外设置
        # 1024 ---> 64
        self.linear1 = Linear(1024, 64)
        # 64 ---> 10
        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


model = Model()
print(model)

input = torch.ones((64, 3, 32, 32))
out = model(input)
print(out.shape)	# torch.Size([64, 10])

版本2——用Sequential

代码更简洁，而且会给每层自动从0开始编序。

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential


class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.model = Sequential(
            Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2),
            MaxPool2d(kernel_size=2, stride=2),
            Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2),
            MaxPool2d(kernel_size=2, stride=2),
            Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=2),
            MaxPool2d(kernel_size=2, stride=2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )

    def forward(self, x):
        return self.model(x)


model = Model()
print(model)

input = torch.ones((64, 3, 32, 32))
out = model(input)
print(out.shape)	# torch.Size([64, 10])

 在代码最末尾加上writer.add_gragh(model, input)就可看到模型计算图，可放大查看。

writer = SummaryWriter('./logs/Seq')
writer.add_graph(model, input)
writer.close()