p18 神经网络卷积层

import torch
import torchvision
from torch import nn
from torch.nn import Conv2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

#取出torchvision库中datasets模块的CIFAR10数据集
dataset = torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor(),download=False)

dataloader = DataLoader(dataset,batch_size=64)

class Tudui(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = Conv2d(in_channels=3,out_channels=6,kernel_size=3,stride=1,padding=0)

    def forward(self,x):
        x=self.conv1(x)
        return x

tudui = Tudui()
print(tudui)

writer = SummaryWriter("./logs")
step = 0
for data in dataloader:
    imgs,targets = data
    output = tudui(imgs)
    print(output.shape)
    print(imgs.shape)
    #输入torch.Size([64, 3, 32, 32])
    writer.add_images("input",imgs,step)
    #输出torch.Size([64, 6, 30, 30]) ->[xxx,3,30,30]
    output = torch.reshape(output,(-1,3,30,30))
    writer.add_images("output",output,step)
    step+=1