pytorch实现特殊的Module–Sqeuential三种写法
50 浏览量
2020-12-23
03:43:55
上传
评论
收藏 34KB PDF 举报
pytorch实现特殊的实现特殊的Module–Sqeuential三种写法三种写法
我就废话不多说了，直接上代码吧！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
大家多多支持软件开发网。
您可能感兴趣的文章您可能感兴趣的文章:在pytorch中为Module和Tensor指定GPU的例子用pytorch的nn.Module构造简单全链接层实例对Pytorch
中nn.ModuleList 和 nn.Sequential详解
资源评论
