pytorch写对抗神经网络预测波士顿房价

以下是使用PyTorch实现对抗神经网络预测波士顿房价的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler

# 加载波士顿房价数据集并进行标准化处理
boston = load_boston()
X = boston.data
y = boston.target
scaler = StandardScaler()
X = scaler.fit_transform(X)
y = scaler.fit_transform(y.reshape(-1, 1)).flatten()

# 定义生成器和判别器
class Generator(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Generator, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)

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

class Discriminator(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Discriminator, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.sigmoid(self.fc2(x))
        return x

# 定义超参数
input_size = X.shape[1]
hidden_size = 10
output_size = 1
lr = 0.001
epochs = 1000

# 初始化生成器和判别器
G = Generator(input_size, hidden_size, output_size)
D = Discriminator(input_size, hidden_size, output_size)

# 定义损失函数和优化器
criterion = nn.BCELoss()
G_optimizer = optim.Adam(G.parameters(), lr=lr)
D_optimizer = optim.Adam(D.parameters(), lr=lr)

# 训练对抗神经网络
for epoch in range(epochs):
    # 训练判别器
    D_optimizer.zero_grad()
    real_data = torch.Tensor(X)
    fake_data = G(torch.randn(X.shape[0], input_size))
    D_real = D(real_data)
    D_fake = D(fake_data)
    D_loss = criterion(D_real, torch.ones(X.shape[0])) + criterion(D_fake, torch.zeros(X.shape[0]))
    D_loss.backward()
    D_optimizer.step()

    # 训练生成器
    G_optimizer.zero_grad()
    fake_data = G(torch.randn(X.shape[0], input_size))
    D_fake = D(fake_data)
    G_loss = criterion(D_fake, torch.ones(X.shape[0]))
    G_loss.backward()
    G_optimizer.step()

    if epoch % 100 == 0:
        print('epoch [{}/{}], loss_D: {:.4f}, loss_G: {:.4f}'.format(
            epoch, epochs, D_loss.item(), G_loss.item()))

# 使用生成器生成假数据并进行预测
fake_data = G(torch.randn(X.shape[0], input_size))
fake_data = scaler.inverse_transform(fake_data.detach().numpy())
y_pred = fake_data.flatten()
y_pred = scaler.inverse_transform(y_pred.reshape(-1, 1)).flatten()
mse = np.mean(np.square(y - y_pred))
print('MSE:', mse)

在这个示例中，我们定义了一个包含一个生成器和一个判别器的对抗神经网络，并使用PyTorch进行训练和预测。在训练过程中，我们使用BCELoss作为损失函数，使用Adam作为优化器。在预测过程中，我们使用生成器生成假数据，并使用逆标准化将预测值转换回原始单位。最终，我们计算预测值与真实值之间的均方误差（MSE）作为模型的性能指标。