代码语言：javascript复制
# 包import torchimport torch.nn as nnimport numpy as npimport matplotlib.pyplot as plt # 超参数设置input_size = 1output_size = 1num_epochs = 60learning_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: 7.7737Epoch [10/60], Loss: 3.2548Epoch [15/60], Loss: 1.4241Epoch [20/60], Loss: 0.6824Epoch [25/60], Loss: 0.3820Epoch [30/60], Loss: 0.2602Epoch [35/60], Loss: 0.2109Epoch [40/60], Loss: 0.1909Epoch [45/60], Loss: 0.1828Epoch [50/60], Loss: 0.1795Epoch [55/60], Loss: 0.1781Epoch [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()
