Pytorch专题实战——线性回归(Linear Regression)

Vala ·

更新时间:2024-09-21

· 725 次阅读

文章目录1.计算流程2.Pytorch搭建线性回归模型2.1.导入必要模块2.2.构造训练数据2.3.测试数据及输入输出神经元个数2.4.搭建模型并实例化2.5.训练 1.计算流程

 1)设计模型： Design model (input, output, forward pass with different layers)   
 2) 构建损失函数与优化器：Construct loss and optimizer
 3) 循环：Training loop
      - Forward = compute prediction and loss
      - Backward = compute gradients
       - Update weights

2.Pytorch搭建线性回归模型 2.1.导入必要模块

import torch
import torch.nn as nn

2.2.构造训练数据

X = torch.tensor([[1],[2],[3],[4]], dtype=torch.float32)  
Y = torch.tensor([[2],[4],[6],[8]], dtype=torch.float32)
n_samples, n_features = X.shape  #4, 1(4个1维的样本)

2.3.测试数据及输入输出神经元个数

X_test = torch.tensor([5], dtype=torch.float32)
input_size = n_features
output_size = n_features

2.4.搭建模型并实例化

class LinearRegression(nn.Module):
    def __init__(self,input_dim, output_dim):
        super(LinearRegression,self).__init__()
        self.lin = nn.Linear(input_dim, output_dim)
    def forward(self, x):
        return self.lin(x)  
model = LinearRegression(input_size, output_size)

2.5.训练

print(f'Prediction before training:f(5)={model(X_test).item():.3f}')
learning_rate = 0.1
n_iters = 200
loss = nn.MSELoss()  #损失函数
optimizer = torch.optim.SGD(model.parameters(), learning_rate) #优化器
for epoch in range(n_iters):
    y_predicted = model(X)
    l = loss(Y, y_predicted)
    l.backward()
    optimizer.step()
    optimizer.zero_grad()
    if epoch%10 == 0:
        [w,b] = model.parameters()
        print('epoch',epoch+1,': w =',w.item(),'loss=',l)
print(f'Prediction after training:f(5)={model(X_test).item():.3f}')

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