B站小土堆学习pytorch视频 非常棒的up主，讲的很详细明白

官网文档

https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity

常用1 ReLU

对输入做截断非线性处理，使模型泛化

>>> m = nn.ReLU()
>>> input = torch.randn(2)
>>> output = m(input)
An implementation of CReLU - https://arxiv.org/abs/1603.05201
>>> m = nn.ReLU()
>>> input = torch.randn(2).unsqueeze(0)
>>> output = torch.cat((m(input), m(-input)))

inplace

inplace=True 原位操作 改变变量本身的值
inplace=False 重新定义一个变量output 承接input-relu后的值，一般默认为False，保留输入数据

常用2 Sigmoid

>>> m = nn.Sigmoid()
>>> input = torch.randn(2)
>>> output = m(input)

弹幕：
激活层的作用是放大不同类别的得分差异
二分类输出层用sigmoid 隐藏层用relu
负值的来源：输入数据；卷积核；归一化；反向梯度下降导致负值；【不确定】
reshape(input, (-1,1,2,2))是将input这个22的张量转化为-1122的张量，其中-1表示张量元素个数除以其他维度大小的乘积，即“-1” == 22/(12*2) = 1

非线性变化主要目的：为我们的网络引入非线性特征 非线性越多才能训练不同的非线性曲线或者说特征，模型泛化能力才好。

代码

import torch
import torchvision.transforms
from torch import nn
from torch.nn import ReLU, Sigmoid
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets

test_set = datasets.CIFAR10('./dataset', train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(test_set, batch_size=64, drop_last=True)

class Activation(nn.Module):
    def __init__(self):
        super(Activation, self).__init__()
        self.relu1 = ReLU(inplace=False)
        self.sigmoid1 = Sigmoid()

    def forward(self, input):
        # output1 = self.relu1(input)
        output2 = self.sigmoid1(input)
        # return output1
        return output2

writer = SummaryWriter('logs')
step = 0
activate = Activation()
for data in dataloader:
    imgs, target = data
    writer.add_images("input", imgs, global_step=step)
    output = activate(imgs)
    # writer.add_images("output1", output, global_step=step)
    writer.add_images("output2", output, global_step=step)
    step += 1
writer.close()

logs