pytorch实现bp神经网络预测波士顿房价

要实现bp神经网络预测波士顿房价，可以按照以下步骤进行：

1. 导入数据集并进行预处理，包括数据归一化、划分训练集和测试集等操作。
2. 定义神经网络模型，包括输入层、隐藏层和输出层，并选择合适的激活函数和损失函数。
3. 使用反向传播算法进行模型训练，并设置合适的学习率、迭代次数等参数。
4. 对模型进行评估，包括计算损失函数值、预测结果与真实值的比较等操作。

以下是一个简单的代码示例：

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

# 导入数据集并进行预处理
boston = load_boston()
X = boston.data
y = boston.target
scaler = StandardScaler()
X = scaler.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(13, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, 1)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.Adam(net.parameters(), lr=0.01)

# 训练模型
for epoch in range(1000):
    inputs = torch.autograd.Variable(torch.Tensor(X_train).float())
    labels = torch.autograd.Variable(torch.Tensor(y_train).float())

    optimizer.zero_grad()
    outputs = net(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()

    if epoch % 100 == 0:
        print('Epoch {}, Loss: {:.4f}'.format(epoch, loss.item()))

# 评估模型
with torch.no_grad():
    inputs = torch.autograd.Variable(torch.Tensor(X_test).float())
    labels = torch.autograd.Variable(torch.Tensor(y_test).float())
    outputs = net(inputs)
    loss = criterion(outputs, labels)
    print('Test Loss: {:.4f}'.format(loss.item()))