Pytorch_linearRegression.py

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

# 超参数
input_size = 1
output_size = 1
num_epochs = 60
learning_rate = 0.001

# 训练数据集
x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168], 
                    [9.779], [6.182], [7.59], [2.167], [7.042], 
                    [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)

y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573], 
                    [3.366], [2.596], [2.53], [1.221], [2.827], 
                    [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)
                    
# 线性回归模型
model = nn.Linear(input_size, output_size)

# 损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    # 将numpy转为torch的张量
    inputs = torch.from_numpy(x_train)
    targets = torch.from_numpy(y_train)
    
    # forward 计算
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    
    # 反向传播和优化
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    # 打印信息
    running_loss += loss.item()
    if (epoch+1) % 5 == 0:
        print('Epoch [{}/{}], Loss[:.4f]'.format(epoch+1, num_epochs, running_loss))
        running_loss = 0.0
        
# 画图
predicted = model(torch.from_numpy(x_train)).detach().numpy() # 标准的取出预测值
plt.plot(x_train, y_train, 'ro', label='Original data')
plt.plot(x_train, predicted, label='Fitted line')
plt.legend()
plt.show()

# 保存模型参数
torch.save(model.state_dict(), 'model.ckpt')