PyTorch⑦---卷积神经网络_非线性激活

非线性激活

一般卷积层后面都要跟非线性激活

1、RELU函数

import torch
from torch import nn
from torch.nn import ReLU

input=torch.tensor([[1,-0.5],
                    [-1,3]])
input=torch.reshape(input,(-1,1,2,2))
print(input)

class Demo(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.relu1=ReLU()
    def forward(self,input):
        output=self.relu1(input)
        return output

demo=Demo()
output=demo(input)
print(output)

结果

2、sigmoid函数

import torch
import torchvision
from torch import nn
from torch.nn import Sigmoid
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset=torchvision.datasets.CIFAR10("dataset_CIFAR10",
                                     train=False,
                                     transform=torchvision.transforms.ToTensor())
dataloader=DataLoader(dataset,batch_size=64)

class Demo(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.sigmoid1=Sigmoid()
    def forward(self,input):
        output=self.sigmoid1(input)
        return output

demo=Demo()
writer=SummaryWriter("logs_sigmoid")
step=0
for data in dataloader:
    imgs,targets=data
    writer.add_images("input",imgs,step)
    output=demo(imgs)
    writer.add_images("output",output,step)
    step=step+1
writer.close()

结果