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()