一维线性回归pytorch实现

训练代码：

import torch
import numpy as np

x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168], [9.799]])
y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573], [3.366]])

x_train = torch.from_numpy(x_train).float()
y_train = torch.from_numpy(y_train).float()

print('x_train:{}'.format(x_train))
print('y_train:{}'.format(y_train))

class LinearRegression(torch.nn.Module):
    def __init__(self):
        super(LinearRegression, self).__init__()
        self.linear = torch.nn.Linear(1, 1)          #输入输出均是一维的

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

model = LinearRegression() #定义模型

criterion = torch.nn.MSELoss() #定义损失函数
optimizer = torch.optim.SGD(model.parameters(), lr = 1e-2)      #优化器

num_epochs = 1000 				#优化1000次
for epoch in range(num_epochs):
    inputs_ = torch.autograd.Variable(x_train)
    target = torch.autograd.Variable(y_train)

    #forward
    out = model(inputs_) #由于继承了nn.module模块，该模块已经调用了forward函数，该语句等价于 out = model.forward(inputs_)
    loss = criterion(out, target)
    
    #backward
    optimizer.zero_grad()   #每次做反向传播前都要归零梯度
    loss.backward()
    optimizer.step()        #更新参数

    if (epoch+1) % 20 == 0: #每隔一段时间看下训练结果
        print('Epoch[{}/{}], loss: {:.6f}'.format(epoch+1, num_epochs, loss.data))

做完训练后，可以查看下预测结果：

import matplotlib.pyplot as plt

model.eval() #将模型变为预测模型
predict = model(torch.autograd.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')
plt.legend()
plt.show()