machinelearning_notebook/demo_code/Linear_Regression.py

import torch as t
from torch import nn, optim
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt

# create numpy data
x_train = np.linspace(0, 10, 100)
y_train = 10*x_train + 4.5

# convert to tensor (need to change nx1, float32 dtype)
x_train = t.from_numpy(x_train.reshape(-1, 1).astype("float32"))
y_train = t.from_numpy(y_train.reshape(-1, 1).astype("float32"))


# Linear Regression Model
class LinearRegression(nn.Module):
    def __init__(self):
        super(LinearRegression, self).__init__()
        self.linear = nn.Linear(1, 1)  # input and output is 1 dimension

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

# create the model
model = LinearRegression()

# 定义loss和优化函数
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=1e-4)

# 开始训练
num_epochs = 1000
for epoch in range(num_epochs):
    inputs = Variable(x_train)
    target = Variable(y_train)

    # forward
    out = model(inputs)
    loss = criterion(out, target)

    # backward
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (epoch+1) % 20 == 0:
        print('Epoch[{}/{}], loss: {:.6f}'
              .format(epoch+1, num_epochs, loss.data[0]))

model.eval()
predict = model(Variable(x_train))
predict = predict.data.numpy()
plt.plot(x_train.numpy(), y_train.numpy(), 'ro', label='Original data')
plt.plot(x_train.numpy(), predict, label='Fitting Line')

# 显示图例
plt.legend() 
plt.show()

# 保存模型
t.save(model.state_dict(), './model_LinearRegression.pth')