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-
- 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')
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