1 神经网络的基本骨架

重写方法可通过将光标放在类中（如这里放在Tudui下一行），并在pycharm->code->generate->选择要重写的函数

import torch
from torch import nn

class Tudui(nn.Module):
    def __init__(self):
        super.__init__()  # 继承父类的初始化
        
    def forward(self, input):          # 将forward函数进行重写
        output = input + 1
        return output
    
tudui = Tudui()
x = torch.tensor(1.0)  # 创建一个值为 1.0 的tensor
output = tudui(x)
print(output)

2 卷积层原理

卷积运算包括输入图像和卷积核，按照设定的stride，
① 向右滑动stride格，到达边界后再向下滑动stride格
② 重复第①步，直到纵向也到达边界
具体运算逻辑如下：

（2）动态示例：

stride为1

stride为2

代码如下：

import torch
import torch.nn.functional as F

input = torch.tensor([[1, 2, 0, 3, 1],
                      [0, 1, 2, 3, 1],
                      [1, 2, 1, 0, 0],
                      [5, 2, 3, 1, 1],
                      [2, 1, 0, 1, 1]])

kernel = torch.tensor([[1, 2, 1],
                       [0, 1, 0],
                       [2, 1, 0]])

print(input.shape)  		# torch.Size([5, 5])
print(kernel.shape) 		# torch.Size([3, 3])
input = torch.reshape(input, (1,1,5,5)) 
kernel = torch.reshape(kernel, (1,1,3,3))
print(input.shape) 			# torch.Size([1, 1, 5, 5])
print(kernel.shape)			# torch.Size([1, 1, 3, 3])

output = F.conv2d(input, kernel, stride=1)
print(output)     # tensor([[[[10, 12, 12],
                  #			  [18, 16, 16],
         		  #		      [13,  9,  3]]]])

3 卷积实现

import torch
import torchvision
from torch import nn
from torch.nn import Conv2d
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)
class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.conv1 = Conv2d(in_channels=3,out_channels=6,kernel_size=3,stride=1,padding=0) # 彩色图像输入为3层，我们想让它的输出为6层，选3 * 3 的卷积                
    
    def forward(self,x):
        x = self.conv1(x)
        return x
    
tudui = Tudui()
for data in dataloader:
    imgs, targets = data
    output = tudui(imgs)
    print(imgs.shape)   # 输入为3通道32×32的64张图片
    print(output.shape) # 输出为6通道30×30的64张图片

4 最大池化

import torch
import torchvision
from torch import nn 
from torch.nn import MaxPool2d
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)

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.maxpool = MaxPool2d(kernel_size=3, ceil_mode=True)
        
    def forward(self, input):
        output = self.maxpool(input)
        return output

tudui = Tudui()  # 即调用forward()
writer = SummaryWriter("logs")
step = 0

for data in dataloader:
    imgs, targets = data
    writer.add_images("input", imgs, step)
    output = tudui(imgs)
    writer.add_images("output", output, step)
    step = step + 1

5 非线性激活

import torch
from torch import nn
from torch.nn import ReLU

input = torch.tensor([[1, -0.5],
                      [-1, 3]])
input = torch.reshape(input, (-1, 1, 2, 2))
print(input.shape)


class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.relu1 = ReLU()

    def forward(self, input):
        output = self.relu1(input)
        return output


tudui = Tudui()
output = tudui(input)
print(output)