卷积是如何计算的？

• 卷积操作简图：（类似于矩阵运算）
  
• 示例代码

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)
print(kernel.shape)
# 需要更改数据类型适应conv2d()函数
input = torch.reshape(input,(1,1,5,5))
kernel = torch.reshape(kernel,(1,1,3,3))
print(input.shape)
print(kernel.shape)
# stride参数
output = F.conv2d(input,kernel,stride=1)
# stride = 1 步长为1，移动矩阵kernel（卷积核）的步长为1
print(output)
output = F.conv2d(input,kernel,stride=2)
# stride = 2 步长为1，移动矩阵kernel（卷积核）的步长为2
print(output)
# padding参数：填充input周围，padding为几就填充几维
output = F.conv2d(input,kernel,stride=1,padding=1)
print(output)