一，代码

import torch
from torch.autograd import Variable
from torch import tensor, Tensor
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torch import nn
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt

x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168],
                   [9.779], [6.182], [7.59], [2.167], [7.042],
                   [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)
y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573],
                   [3.366], [2.596], [2.53], [1.221], [2.827],
                   [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)

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


class LinearRegression(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(1, 1)

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


if torch.cuda.is_available():
    model = LinearRegression().cuda()
else:
    model = LinearRegression()

criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)

num_epochs = 1000
for epoch in range(num_epochs):
    if torch.cuda.is_available():
        inputs = Variable(x_train).cuda()
        target = Variable(y_train).cuda()
    else:
        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.item()))

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')
plt.show()