PyTorch 基础篇（2）：线性回归（Linear Regression）

布满荆棘的人生 2024-02-05 12:40 14阅读 0赞

# 包 
        import torch 
        import torch.nn as nn 
        import numpy as np 
        import matplotlib.pyplot as plt

# 超参数设置 
        input_size = 1 
        output_size = 1 
        num_epochs = 60 
        learning_rate = 0.001 
          
        # Toy dataset 
        # 玩具资料：小数据集 
        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) 
          
        # 线性回归模型 
        model = nn.Linear(input_size, output_size) 
          
        # 损失函数和优化器 
        criterion = nn.MSELoss() 
        optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

# 训练模型 
        for epoch in range(num_epochs): 
            # 将Numpy数组转换为torch张量 
            inputs = torch.from_numpy(x_train) 
            targets = torch.from_numpy(y_train) 
          
            # 前向传播 
            outputs = model(inputs) 
            loss = criterion(outputs, targets) 
            
            # 反向传播和优化 
            optimizer.zero_grad() 
            loss.backward() 
            optimizer.step() 
            
            if (epoch 1) % 5 == 0: 
                print (‘Epoch [{}/{}], Loss: {:.4f}’.format(epoch 1, num_epochs, loss.item()))

Epoch [5/60], Loss: 7.7737 
        Epoch [10/60], Loss: 3.2548 
        Epoch [15/60], Loss: 1.4241 
        Epoch [20/60], Loss: 0.6824 
        Epoch [25/60], Loss: 0.3820 
        Epoch [30/60], Loss: 0.2602 
        Epoch [35/60], Loss: 0.2109 
        Epoch [40/60], Loss: 0.1909 
        Epoch [45/60], Loss: 0.1828 
        Epoch [50/60], Loss: 0.1795 
        Epoch [55/60], Loss: 0.1781 
        Epoch [60/60], Loss: 0.1776

# 绘制图形 
        # torch.from_numpy(x_train)将X_train转换为Tensor 
        # model()根据输入和模型，得到输出 
        # detach().numpy()预测结结果转换为numpy数组 
        predicted = model(torch.from_numpy(x_train)).detach().numpy() 
        plt.plot(x_train, y_train, ‘ro’, label=‘Original data’) 
        plt.plot(x_train, predicted, label=‘Fitted line’) 
        plt.legend() 
        plt.show()

![e149409471fdc71a06ae697ff106570b.png][]

[e149409471fdc71a06ae697ff106570b.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/05/7d6b9ac7672e4654868b94447f9e1a4c.png