【PyTorch学习4】《PyTorch深度学习实践》——线性回归（Linear Regression）

news2024/11/17 23:42:14

一、实现框架

1、Prepare dataset
2、Design model using Class (inherit from nn.Module)
3、Construct loss and optimizer (using PyTorch API)
loss是为了计算损失，optimizer是为了优化参数
4、Training cycle (forward，backward，update)

二、程序实现

import torch

# prepare dataset
# x,y是矩阵，3行1列 也就是说总共有3个数据，每个数据只有1个特征
x_data = torch.tensor([[1.0], [2.0], [3.0]])
y_data = torch.tensor([[2.0], [4.0], [6.0]])

# design model using class
"""
our model class should be inherit from nn.Module, which is base class for all neural network modules.
member methods __init__() and forward() have to be implemented
class nn.linear contain two member Tensors: weight and bias
class nn.Linear has implemented the magic method __call__(),which enable the instance of the class can
be called just like a function.Normally the forward() will be called 
"""

class LinearModel(torch.nn.Module):
    def __init__(self):
        super(LinearModel, self).__init__()
        # (1,1)是指输入x和输出y的特征维度，这里数据集中的x和y的特征都是1维的
        # 该线性层需要学习的参数是w和b  获取w/b的方式分别是~linear.weight/linear.bias
        self.linear = torch.nn.Linear(1, 1)

    def forward(self, x):
        y_pred = self.linear(x)
        return y_pred

model = LinearModel()
print(model)

# construct loss and optimizer
# criterion = torch.nn.MSELoss(size_average = False)
criterion = torch.nn.MSELoss(reduction='sum') # 误差平方和
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# training cycle forward, backward, update
for epoch in range(100):
    y_pred = model(x_data)  # forward：predict
    loss = criterion(y_pred, y_data)  # forward: loss
    print(epoch, loss.item())

    optimizer.zero_grad()  # 梯度清零
    loss.backward()  # backward: autograd，自动计算梯度
    optimizer.step()  # update参数，即更新w和b的值

print('w = ', model.linear.weight.item()) #将w以数值打印，只能是一个数的张量可以用.item()
print('b = ', model.linear.bias.item())

x_test = torch.tensor([[4.0]])
y_test = model(x_test)
print('y_pred = ', y_test.data)

模型：
在这里插入图片描述
输出（部分截图）：

三、代码讲解

1.`self.linear = torch.nn.Linear(1, 1)`

torch.nn.Linear是一个类，（1,1）分别表示该线性层的输入和输出维度
来看下面这个例子（输入size是128x20，经过一个20x30的线性层，输出为128x30）：

import torch.nn as nn
import torch

m = nn.Linear(20, 30)

print(type(m))
print(type(nn.Linear(20, 30)))

input = torch.randn(128, 20)
output = m(input)
print(output.size())

在这里插入图片描述
更多理解看这里：https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear

2.`model(x_data)`

先讲一下torch.nn.Module，Base class for all neural network modules。Your models should also subclass this class.所有神经网络的基类，你的模型应该将其子类化。就是一般我们会继承这个类，主要完成初始化模型，然后前向传播等一些东西。
来看一段重要代码和输出：

import torch
x_data = torch.tensor([1.0])

class LinearModel(torch.nn.Module):
    def __init__(self):
        super(LinearModel, self).__init__()
        self.linear = torch.nn.Linear(1, 1, bias=False)

    def forward(self, x):
        n = x
        y_pred = self.linear(x)
        return y_pred,x

model = LinearModel()
print(type(model))
print(type(LinearModel()))

print(model.forward(x_data))
print(model(x_data))
print(model.__call__(x_data))

在这里插入图片描述
当你定义完LinearModel这个类后，你需要将其实例化为对象（当然对象也是类哈），才能进行调用。这个类需要一个参数，意思是，model = LinearModel()，model(x_data)才可以使用；而不可以直接 LinearModel(x_data)。
另外，model.forward(x_data)，model(x_data)，model.__call__(x_data)这三个输出一模一样，这和torch.nn.Module的内部封装有关系，具体可以看官方源代码：https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module