神经网络基本结构的使用（Non-linear Activations的使用）

作用：从神经网络中引入一些非线性的特征，非线性特征越多，才能模拟出越丰富的曲线 以RELU举例

CLASS torch.nn.ReLU(inplace=False)

实战：

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.shape)


class Peipei(nn.Module):
    def __init__(self) -> None:
        super(Peipei, self).__init__()
        self.relu1 = ReLU()

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


peipei = Peipei()
output = peipei(input)
print(output)

输出：

 tensor([[[[1., 0.],
          [0., 3.]]]])

sigmoid

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

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

class Peipei(nn.Module):
    def __init__(self) -> None:
        super(Peipei, self).__init__()
        self.sigmoid1 = Sigmoid()

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

peipei = Peipei()
writer = SummaryWriter("logs_sigmoid")
step = 0
# output = peipei(input)
# print(output)
for data in dataloader:
    imgs, target = data
    output = peipei(imgs)
    writer.add_images("input", imgs, step)
    writer.add_images("output", output, step)
    step = step + 1
writer.close()

输出：