【Pytorch】nn.Dropout的用法

1.nn.Dropout用法一

一句话总结：Dropout的是为了防止过拟合而设置

详解部分：
1.Dropout是为了防止过拟合而设置的
2.Dropout顾名思义有丢掉的意思
3.nn.Dropout(p = 0.3) # 表示每个神经元有0.3的可能性不被激活
4.Dropout只能用在训练部分而不能用在测试部分
5.Dropout一般用在全连接神经网络映射层之后，如代码的nn.Linear(20, 30)之后

代码部分：

class Dropout(nn.Module):
	def __init__(self):
		super(Dropout, self).__init__()
		self.linear = nn.Linear(20, 40)
		self.dropout = nn.Dropout(p = 0.3) # p=0.3表示下图（a）中的神经元有p = 0.3的概率不被激活

	def forward(self, inputs):
		out = self.linear(inputs)
		out = self.dropout(out)
		return out

net = Dropout()
# Dropout只能用在train而不能用在test	

2.nn.Dropout用法二

以代码为例

import torch
import torch.nn as nn

a = torch.randn(4, 4)
print(a)
"""
tensor([[ 1.2615, -0.6423, -0.4142,  1.2982],
        [ 0.2615,  1.3260, -1.1333, -1.6835],
        [ 0.0370, -1.0904,  0.5964, -0.1530],
        [ 1.1799, -0.3718,  1.7287, -1.5651]])
"""
dropout = nn.Dropout()
b = dropout(a)
print(b)
"""
tensor([[ 2.5230, -0.0000, -0.0000,  2.5964],
        [ 0.0000,  0.0000, -0.0000, -0.0000],
        [ 0.0000, -0.0000,  1.1928, -0.3060],
        [ 0.0000, -0.7436,  0.0000, -3.1303]])
"""

由以上代码可知Dropout还可以将部分tensor中的值置为0