p21线性层及其它层介绍

正则化层

import torch
import torchvision
from torch import nn
from torch.nn import Linear
from torch.utils.data import DataLoader

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

dataloader = DataLoader(dataset, batch_size=64, drop_last=True)

class Lixinyu(nn.Module):
    def __init__(self):
        super(Lixinyu, self).__init__()
        self.linear1 = Linear(196608, 10)

    def forward(self, input):
        output = self.linear1(input)
        return output

lixinyu = Lixinyu()

for data in dataloader:
    imgs, targets = data
    print(imgs.shape)  # torch.Size([64, 3, 32, 32])
    output = torch.reshape(imgs, [1, 1, 1, -1]) #output = torch.flatten(imgs)这个比较好
    print(output.shape)  # torch.Size([1, 1, 1, 196608])
    output = lixinyu(output)
    print(output.shape)

现在我们已经能自己搭建网络模型了
但也可以使用pytorch自己的网络模型
图像方面torchvision

文字modle

语音model