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PyTorch快速入门教程【小土堆】之非线性激活

article 2025/1/4 7:24:09

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false
        self.sigmoid1 = Sigmoid()

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


tudui = 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= data
    writer.add_images("input", imgs, global_step=step)
    output = tudui(imgs)
    writer.add_images("output", output, step)
    step += 1
writer.close()

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