PyTorch_12_神经网络-非线性激活

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))
print(input.shape)


dataset = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor())

dataloader = DataLoader(dataset, batch_size=64)


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

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


tudui = TuDui()
# output = tudui(input)
# print(output)

writer = SummaryWriter("logs")
step = 0

for data in dataloader:
    imgs, targets = data
    writer.add_images("input", imgs, global_step=step)
    output = tudui(imgs)
    writer.add_images("output", output, global_step=step)
    step = step + 1

writer.close()