pytorch写对抗神经网络预测波士顿房价

好的，这是一个比较复杂的任务，需要用到对抗神经网络（GAN）和波士顿房价数据集。以下是一个简单的代码示例，使用PyTorch实现GAN来预测波士顿房价：

import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
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
boston = load_boston()
X = boston.data
y = boston.target

# 数据标准化
scaler = StandardScaler()
X = scaler.fit_transform(X)
y = scaler.fit_transform(y.reshape(-1,1))

# 定义生成器和判别器
class Generator(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(Generator, self).__init__()
        self.fc1 = nn.Linear(input_dim, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, output_dim)
        self.relu = nn.ReLU()
        self.tanh = nn.Tanh()

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

class Discriminator(nn.Module):
    def __init__(self, input_dim):
        super(Discriminator, self).__init__()
        self.fc1 = nn.Linear(input_dim, 32)
        self.fc2 = nn.Linear(32, 16)
        self.fc3 = nn.Linear(16, 1)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

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

# 定义损失函数和优化器
criterion = nn.BCELoss()
generator = Generator(input_dim=X.shape[1], output_dim=y.shape[1])
discriminator = Discriminator(input_dim=X.shape[1])
optimizer_G = torch.optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))

# 定义训练函数
def train_gan(generator, discriminator, optimizer_G, optimizer_D, criterion, X, y, batch_size=64, num_epochs=1000):
    dataset = torch.utils.data.TensorDataset(torch.tensor(X, dtype=torch.float32), torch.tensor(y, dtype=torch.float32))
    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
    for epoch in range(num_epochs):
        for i, (x_real, y_real) in enumerate(dataloader):
            # 训练判别器
            discriminator.zero_grad()
            y_fake = generator(x_real)
            y_real_pred = discriminator(y_real)
            y_fake_pred = discriminator(y_fake.detach())
            loss_real = criterion(y_real_pred, torch.ones_like(y_real_pred))
            loss_fake = criterion(y_fake_pred, torch.zeros_like(y_fake_pred))
            loss_D = (loss_real + loss_fake) / 2
            loss_D.backward()
            optimizer_D.step()

            # 训练生成器
            generator.zero_grad()
            y_fake_pred = discriminator(y_fake)
            loss_G = criterion(y_fake_pred, torch.ones_like(y_fake_pred))
            loss_G.backward()
            optimizer_G.step()

        if epoch % 100 == 0:
            print("Epoch [{}/{}], Loss_D: {:.4f}, Loss_G: {:.4f}".format(epoch, num_epochs, loss_D.item(), loss_G.item()))

    return generator

# 训练GAN模型
generator = train_gan(generator, discriminator, optimizer_G, optimizer_D, criterion, X, y, batch_size=64, num_epochs=5000)

# 预测房价
X_test = X[0:10]
y_test_pred = generator(torch.tensor(X_test, dtype=torch.float32))
y_test_pred = scaler.inverse_transform(y_test_pred.detach().numpy())
print("Predicted house prices: ", y_test_pred)

这个代码示例使用了PyTorch实现了一个简单的生成对抗网络，通过训练GAN模型来预测波士顿房价。需要注意的是，这只是一个简单的示例，实际应用中需要更加复杂的模型和更多的数据进行训练。