pytorch实现线性回归-蒲公英云

pytorch实现线性回归

安装依赖

pip install torch numpy matplotlib

编写代码

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from torch.autograd import Variable
# Hyper Parameters
input_size = 1
output_size = 1
num_epochs = 10000
learning_rate = 0.001
x_train = np.array([[2.3], [4.4], [3.7], [6.1], [7.3], [2.1], [5.6], [7.7], [8.7], [4.1],
                    [6.7], [6.1], [7.5], [2.1], [7.2],
                    [5.6], [5.7], [7.7], [3.1]], dtype=np.float32)
# xtrain生成矩阵数据
y_train = np.array([[3.7], [4.76], [4.], [7.1], [8.6], [3.5], [5.4], [7.6], [7.9], [5.3],
                    [7.3], [7.5], [8.5], [3.2], [8.7],
                    [6.4], [6.6], [7.9], [5.3]], dtype=np.float32)
plt.figure()
# 画图散点图
plt.scatter(x_train, y_train)
plt.xlabel('x_train')
# x轴名称
plt.ylabel('y_train')
# y轴名称
# 显示图片
plt.show()
# 线性回归模型
class LinearRegression(nn.Module):
    def __init__(self, input_size, output_size):
        super(LinearRegression, self).__init__()
        self.linear = nn.Linear(input_size, output_size)
    def forward(self, x):
        out = self.linear(x)
        return out
model = LinearRegression(input_size, output_size)
# 损失函数
criterion = nn.MSELoss()
# 优化器
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
# 训练模型
for epoch in range(num_epochs):
    # 张量数据
    inputs = Variable(torch.from_numpy(x_train))
    targets = Variable(torch.from_numpy(y_train))
    # 前向传播
    optimizer.zero_grad()
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    # 反向传播
    loss.backward()
    # 优化器
    optimizer.step()
    if (epoch + 1) % 5 == 0:
        print('Epoch [%d/%d], Loss: %.4f'
              % (epoch + 1, num_epochs, loss.data))
# 绘图
model.eval()
predicted = model(Variable(torch.from_numpy(x_train))).data.numpy()
plt.plot(x_train, y_train, 'ro')
plt.plot(x_train, predicted, label='predict')
plt.legend()
plt.show()