p20神经网络-非线性激活

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

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

class Lixinyu(nn.Module):
    def __init__(self):
        super(Lixinyu, self).__init__()
        self.relu1 = ReLU()

    def forward(self, input):
        output = self.relu1(input)
        return output

lixinyu = Lixinyu()
output = lixinyu(input)
print(output)

D:anacondapython.exe C:/Users/ASUS/Desktop/tudui/nn_relu.py
torch.Size([1, 1, 2, 2])
tensor([[1., 0.],
        [0., 3.]])

Process finished with exit code 0

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

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


class Lixinyu(nn.Module):
    def __init__(self):
        super(Lixinyu, self).__init__()
        self.relu1 = ReLU()

    def forward(self, input):
        output = self.relu1(input)
        return output

lixinyu = Lixinyu()

step = 0
writer = SummaryWriter("p20")
for data in dataloader:
    imgs, targets = data
    writer.add_images("origin", imgs, step)
    output = lixinyu(imgs)
    writer.add_images("relu", output, step)
    step += 1

writer.close()

在这里插入代码片

引入非线性变换是为了使模型非线性模拟能力更强。