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')