pytorch实现特殊的Module--Sqeuential三种写法

今天小编就为大家分享一篇pytorch实现特殊的Module--Sqeuential三种写法。具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

我就废话不多说了，直接上代码吧！

# -*- coding: utf-8 -*- #@Time :2019/7/1 13:34 #@Author :XiaoMa import torch as t from torch import nn #Sequential的三种写法 net1=nn.Sequential() net1.add_module('conv',nn.Conv2d(3,3,3)) #Conv2D(输入通道数，输出通道数，卷积核大小) net1.add_module('batchnorm',nn.Batchnorm2d(3)) #Batchnorm2d(特征数) net1.add_module('activation_layer',nn.ReLU()) net2=nn.Sequential(nn.Conv2d(3,3,3), nn.Batchnorm2d(3), nn.ReLU() ) from collections import OrderedDict net3=nn.Sequential(OrderedDict([ ('conv1',nn.Conv2d(3,3,3)), ('bh1',nn.Batchnorm2d(3)), ('al',nn.ReLU()) ])) print('net1',net1) print('net2',net2) print('net3',net3) #可根据名字或序号取出子module print(net1.conv,net2[0],net3.conv1)

输出结果：

net1 Sequential( (conv): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) (batchnorm): Batchnorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activation_layer): ReLU() ) net2 Sequential( (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) (1): Batchnorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() ) net3 Sequential( (conv1): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) (bh1): Batchnorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (al): ReLU() ) Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))

以上这篇pytorch实现特殊的Module--Sqeuential三种写法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持编程之家。