pytorch实现线性回归

￡神魔★判官ぃ 2023-02-15 12:54 73阅读 0赞

## 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()

## 运行代码 ##

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