5. 线性层及其他层-Toy模板网

这篇具有很好参考价值的文章主要介绍了5. 线性层及其他层。希望对大家有所帮助。如果存在错误或未考虑完全的地方，请大家不吝赐教，您也可以点击"举报违法"按钮提交疑问。

5.1 神经网络结构

5. 线性层及其他层,动手学卷积神经网络,深度学习,pytorch,python

5.2 线性拉平

import torch
import torchvision
from torch import nn 
from torch.nn import ReLU
from torch.nn import Sigmoid
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

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

for data in dataloader:
    imgs, targets = data
    print(imgs.shape)
    output = torch.reshape(imgs,(1,1,1,-1))
    print(output.shape)

结果：

5. 线性层及其他层,动手学卷积神经网络,深度学习,pytorch,python

5.3 线性层

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

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

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()
writer = SummaryWriter("logs")
step = 0

for data in dataloader:
    imgs, targets = data
    print(imgs.shape)
    writer.add_images("input", imgs, step)
    output = torch.reshape(imgs,(1,1,1,-1)) # 方法一：拉平
    #output = torch.flatten(imgs)  # 方法二：拉平。展开为一维
    print(output.shape)
    output = tudui(output)
    print(output.shape)
    writer.add_images("output", output, step)
    step = step + 1

操作：

① 在 Anaconda 终端里面，激活py3.6.3环境，再输入 tensorboard --logdir=C:\Users\wangy\Desktop\03CV\logs 命令，将网址赋值浏览器的网址栏，回车，即可查看tensorboard显示日志情况。

5. 线性层及其他层,动手学卷积神经网络,深度学习,pytorch,python