torch03：linear_regression

# coding=utf-8
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt


# 超参数的设置
input_size = 1
output_size = 1
num_epochs = 60
learning_rate = 0.001

# 样本和标签 （回归问题）
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)

# 定义线性回归模型
model = nn.Linear(input_size, output_size)

# 损失函数：选用适合的回归问题的损失函数，此处选用平房误差
criterion = nn.MSELoss()
# 优化算法
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  

# 开始训练模型
for epoch in range(num_epochs):
    # 转数组为tensor
    inputs = torch.from_numpy(x_train)
    targets = torch.from_numpy(y_train)

    # 前向传播
    outputs = model(inputs)
    # 计算loss
    loss = criterion(outputs, targets)
    
    # 后向传播、 调节权重
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    # 定义每五个opoch打印一次
    if (epoch+1) % 5 == 0:
        print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 画曲线
predicted = model(torch.from_numpy(x_train)).detach().numpy()
plt.plot(x_train, y_train, 'ro', label='Original data')
plt.plot(x_train, predicted, label='Fitted line')
plt.legend()
plt.show()

# 保存权重
torch.save(model.state_dict(), 'model.ckpt')