使用pytorch进行线性回归


x,y
3.3,1.7
4.4,2.76
5.5,2.09
6.71,3.19
6.93,1.694
4.168,1.573
9.779,3.366
6.182,2.596
7.59,2.53
2.167,1.221
7.042,2.827
10.791,3.465
5.313,1.65
7.997,2.904
3.1,1.3

以上是欲拟合数据

import torch
from torch import nn, optim
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

d = pd.read_csv("data.csv")
x_train = np.array(d.x[:],dtype=np.float32).reshape(15,1)

print(x_train)
y_train=np.array(d.y[:],dtype=np.float32).reshape(15,1)
print(y_train)

x_train = torch.from_numpy(x_train)

y_train = torch.from_numpy(y_train)


# 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


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
torch.save(model.state_dict(), './linear.pth')

　　使用pytorch进行线性回归