神经网络基础

1.nn.Module的基本使用

import torch
from torch import nn
 
class Tudui(nn.Module):
    def __init__(self):
         super(Tudui, self).__init__()
 
    def forward(self,input):
        output = input + 1
        return output
 
tudui = Tudui()
x = torch.tensor(1.0)
output = tudui(x)
print(output)

2. 卷积基本操作

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]])
#不满足尺寸要求 需要变换
input = torch.reshape(input, (1, 1, 5, 5))
kernel = torch.reshape(kernel, (1, 1, 3, 3))
 
print(input.shape)
print(kernel.shape)
 
output1 = F.conv2d(input, kernel, stride=1)
print(output1)
 
output2 = F.conv2d(input, kernel, stride=2)
print(output2)
 
output3 = F.conv2d(input, kernel, stride=1,padding=1)
print(output3)