神经网络的基本骨架—nn.Module使用

一、pytorch官网中torch.nn的相关简介

可以看到torch.nn中有许多模块：

二、Containers模块

1、MODULE（CLASS : torch.nn.Module）

import torch.nn as nn
import torch.nn.functional as Fclass Model(nn.Module):#nn.Module---所有神经网络模块的基类。def __init__(self): #初始化super(Model, self).__init__()self.conv1 = nn.Conv2d(1, 20, 5)self.conv2 = nn.Conv2d(20, 20, 5)def forward(self, x): #前向计算x = F.relu(self.conv1(x))return F.relu(self.conv2(x))

forward(*input)

Defines the computation performed at every call. Should be overridden by all subclasses.

2、搭建神经网络模型

import torch
import torch.nn as nn
import torch.nn.functional as F
# 定义自己的神经网络模板
class Lemon(nn.Module):def __init__(self) -> None:super().__init__()def forward(self,input):output = input + 1return output
# 创建神经网络
lemon = Lemon()
x = torch.tensor(1.0)
output = lemon(x)
print(output)

三、Convolution Layers

1. nn.Conv1d/nnCon2d

• input – input tensor of shape (minibatch,in_channels,iH,iW)输入

• weight – filters of shape (out_channels,groupsin_channels,kH,kW)权重/卷积核

• bias – optional bias tensor of shape (out_channels). Default: None偏置

• stride – the stride of the convolving kernel. Can be a single number or a tuple (sH, sW). Default: 1步进/长 SH和SW分别控制横向的步进和纵向的步进

• padding – implicit paddings on both sides of the input. Can be a single number or a tuple (padH, padW). Default: 0

• dilation – the spacing between kernel elements. Can be a single number or a tuple (dH, dW). Default: 1

• groups – split input into groups, in_channelsin_channels should be divisible by the number of groups. Default: 1

import torch
import torch.nn.functional as F
# 输入
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]])
# 卷积核
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])
#官方文档中输入input和卷积核weight需要四个参数——>input tensor of shape (minibatch,in_channels,iH,iW)
#所以可以使用reshape二参变四参
input = torch.reshape(input,(1,1,5,5)) #torch.Size([1, 1, 5, 5])
kernel = torch.reshape(kernel,(1,1,3,3)) #torch.Size([1, 1, 3, 3])
print(input.shape) #torch.Size([5, 5])
print(kernel.shape) #torch.Size([3, 3])output = F.conv2d(input,kernel,stride=1)
print(output)

一般来讲，输出的维度 = 输入的维度 - 卷积核大小/stride + 1

padding =1,为上下左右各填充一行，空的地方默认为0