神经网络构建

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.maxpool1 = MaxPool2d(kernel_size=3, ceil_mode=False)

    def forward(self, input):
        output = self.maxpool1(input)
        return output

Tensorboard 处理

writer = SummaryWriter("./logs_maxpool")
step = 0

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

Padding 层

将输入图像进行填充

非线性激活：

Relu:

inplace值：0，1影响

非线性激活代码试写Sigmoid：

import torch
import torchvision
from torch import nn
from torch.nn import  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 Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.Sigmoid1 = Sigmoid()

    def forward(self, input):
        output = self.Sigmoid1(input)
        return output

tudui = Tudui()

writer = SummaryWriter("./Sigmoidtest")
step = 0

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

writer.close()