Pytorch 中线性层作用与使用

线性层： 作用区域主要是 将 7 × 7 × 512 convert to 1 × × 4096

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

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

class Module(nn.Module):

    def __init__(self) -> None:
        super(Module, self).__init__()
        # 196608 : input nerve cell num, 10 : output nerve cell num
        self.linear = Linear(196608, 10)

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

model = Module()

for data in dataloader:
    imgs, targets = data
    print(imgs.shape)
    # convert to

    # output = torch.reshape(imgs, (1, 1, 1, -1)) # 转换为 1 × 1 拉长

    # flat : 展平，等同于上面一行的代码
    output = torch.flatten(imgs)
    print(output.shape)

    output = model(output)
    print(output.shape)