pytorch（四）、完整小网络的搭建和sequential的使用

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前言

至此，神经网络的基础部分就基本结束了。

搭建小网络和sequential的使用

• 搭建一个完整的网络，网络结构如下：

一、 第一种形式如下：

import torch
from torch import nn

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

二、第二种方式，使用sequential

import torch
from torch import nn

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.model1 = nn.Sequential(
            nn.Conv2d(3, 32, 5, padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 32, 5, padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, 5, padding=2),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(1024, 64),
            nn.Linear(64, 10)
        )
    def forward(self,x):
        x = self.model1
        return x

model = Model()
print(model)