pytorch实现boston房价预测

在PyTorch中实现波士顿房价预测通常会涉及到监督学习任务，特别是线性回归或神经网络模型。这里是一个简单的步骤概述：

1. 导入所需库：

import torch
import torch.nn as nn
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

2. 加载数据并预处理：

data = load_boston()
X = data.data
y = data.target

# 数据标准化
scaler = StandardScaler()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

3. 定义模型：

class BostonHousePricePredictor(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(BostonHousePricePredictor, self).__init__()
        self.linear = nn.Linear(input_dim, output_dim)

    def forward(self, x):
        return self.linear(x)

model = BostonHousePricePredictor(X_train.shape[1], 1)  # 假设我们要预测的是一个数值

4. 定义损失函数和优化器：

criterion = nn.MSELoss()  # 使用均方误差作为损失函数
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

5. 训练模型：

num_epochs = 100
for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    predictions = model(X_train)
    loss = criterion(predictions, y_train.view(-1, 1))  # 将标签转为列向量
    loss.backward()
    optimizer.step()

    if (epoch+1) % 10 == 0:
        print(f"Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}")

6. 验证和测试模型：

model.eval()
with torch.no_grad():
    y_pred_train = model(X_train).detach().numpy()
    y_pred_test = model(X_test).detach().numpy()