视频地址神经网络-非线性激活_哔哩哔哩_bilibili、
非线性变换主要目的是向网络当中引入非线性特征，非线性特征越多才能训练出符合各种曲线，符合更多特征的模型，如果都是直接表现的话泛化能力不好
import torch
import torchvision
from torch import nn
from torch.nn import ReLU, Sigmoid
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter# input = torch.tensor([[1, -0.5],
# [-1, 3]])
# input = torch.reshape(input, (-1, 1, 2, 2))dataset = torchvision.datasets.CIFAR10("CIFAR10", train=False, transform=torchvision.transforms.ToTensor(),download=True)dataloader = DataLoader(dataset, batch_size=64)class Tudui(nn.Module):def __init__(self):super(Tudui, self).__init__()self.relu1 = ReLU()# 若inplace为true 会直接对input进行操作，为false会在生成一个output进行变换，原来input不变，默认为falseself.sigmoid1 = Sigmoid()def forward(self, input):output = self.sigmoid1(input)return outputtudui = Tudui()
# output = tudui(input)
# print(output)
# # tensor([[[[1., 0.],
# # [0., 3.]]]]) 负数置为0
writer = SummaryWriter("logs_relu")
step = 0
for data in dataloader:imgs, targets= datawriter.add_images("input", imgs, global_step=step)output = tudui(imgs)writer.add_images("output", output, step)step += 1
writer.close()
)
