pytorch学习笔记（7）--线性层

（一）Liner Layers线性层

b 是偏移量bias

代码输入:

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=False)

dataloader = DataLoader(dataset, batch_size=64)

class Tudui(nn.Module):

    def __init__(self):

        super(Tudui, self).__init__()

        self.linear1 = Linear(196608, 10)

    def forward(self, input):

        output = self.linear1(input)

        return output

tudui = Tudui()

for data in dataloader:

    imgs, target = data

    print(imgs.shape)

    output = torch.reshape(imgs, (1, 1, 1, -1))

    print(output.shape)

    output = tudui(output)

    print(output.shape)

输出:

torch.Size([64, 3, 32, 32])

torch.Size([1, 1, 1, 196608])

torch.Size([1, 1, 1, 10])

改为 flatten 类似“平铺”:

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=False)

dataloader = DataLoader(dataset, batch_size=64)

class Tudui(nn.Module):

    def __init__(self):

        super(Tudui, self).__init__()

        self.linear1 = Linear(196608, 10)

    def forward(self, input):

        output = self.linear1(input)

        return output

tudui = Tudui()

for data in dataloader:

    imgs, target = data

    print(imgs.shape)

    # flatten

    output = torch.flatten(imgs)

    print(output.shape)

输出:

torch.Size([64, 3, 32, 32])

torch.Size([196608])

图形图像方面Module：

巴特西

pytorch学习笔记（7）--线性层

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