【pytorch笔记】第八篇 线性层及其他层

1. 神经网络

① 神经网络训练的就是函数系数$K_k$与d。

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)

Output exceeds the size limit. Open the full output data in a text editor
Files already downloaded and verified
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
...
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([16, 3, 32, 32])
torch.Size([1, 1, 1, 49152])

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
    print(output.shape)
    output = tudui(output)
    print(output.shape)
    writer.add_images("output", output, step)
    step = step + 1

Output exceeds the size limit. Open the full output data in a text editor
Files already downloaded and verified
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([1, 1, 1, 10])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([1, 1, 1, 10])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([1, 1, 1, 10])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([1, 1, 1, 10])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([1, 1, 1, 10])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([1, 1, 1, 10])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([1, 1, 1, 10])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
torch.Size([1, 1, 1, 10])
...
torch.Size([1, 1, 1, 196608])
torch.Size([1, 1, 1, 10])
torch.Size([64, 3, 32, 32])
torch.Size([1, 1, 1, 196608])
Output exceeds the size limit. Open the full output data in a text editor
torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10])
...
torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10])

在 Anaconda 终端里面，激活py3.6.3环境，再输入 tensorboard --logdir=C:\Users\qj\CV\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。

4. 自定义层

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 MyModule(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.linear1 = Linear(196608,10)
        
    def forward(self, input):
        output = self.linear1(input)
        return output

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

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

Output exceeds the size limit. Open the full output data in a text editor
Files already downloaded and verified
torch.Size([64, 3, 32, 32])
torch.Size([196608])
torch.Size([10])
torch.Size([64, 3, 32, 32])
torch.Size([196608])
torch.Size([10])
torch.Size([64, 3, 32, 32])
torch.Size([196608])
torch.Size([10])
torch.Size([64, 3, 32, 32])
torch.Size([196608])
torch.Size([10])
torch.Size([64, 3, 32, 32])
torch.Size([196608])
torch.Size([10])
torch.Size([64, 3, 32, 32])
torch.Size([196608])
torch.Size([10])
torch.Size([64, 3, 32, 32])
torch.Size([196608])
torch.Size([10])
torch.Size([64, 3, 32, 32])
torch.Size([196608])
torch.Size([10])
...
torch.Size([64, 3, 32, 32])
torch.Size([196608])
torch.Size([10])
torch.Size([64, 3, 32, 32])
Output exceeds the size limit. Open the full output data in a text editor
torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608])
...
torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10])