写一段WGAN代码，用于解决结构化数据的样本不均衡问题

以下是一个简单的 WGAN 代码示例，用于解决结构化数据的样本不均衡问题：

import tensorflow as tf
from tensorflow.keras import layers

# 定义生成器模型
def make_generator_model():
    model = tf.keras.Sequential()
    model.add(layers.Dense(256, use_bias=False, input_shape=(100,)))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Dense(512, use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Dense(1024, use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Dense(1, activation='tanh'))

    return model

# 定义判别器模型
def make_discriminator_model():
    model = tf.keras.Sequential()
    model.add(layers.Dense(1024, input_shape=(784,)))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(.3))

    model.add(layers.Dense(512))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(.3))

    model.add(layers.Dense(256))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(.3))

    model.add(layers.Dense(1))

    return model

# 定义 WGAN 模型
class WGAN(tf.keras.Model):
    def __init__(
        self,
        discriminator,
        generator,
        latent_dim,
        discriminator_extra_steps=3,
        gp_weight=10.,
    ):
        super(WGAN, self).__init__()
        self.discriminator = discriminator
        self.generator = generator
        self.latent_dim = latent_dim
        self.d_steps = discriminator_extra_steps
        self.gp_weight = gp_weight

    # 定义判别器损失函数
    def discriminator_loss(self, real, fake, interpolated):
        real_loss = tf.reduce_mean(real)
        fake_loss = tf.reduce_mean(fake)
        gradient_penalty = self.gradient_penalty(interpolated)
        return fake_loss - real_loss + gradient_penalty * self.gp_weight

    # 定义生成器损失函数
    def generator_loss(self, fake):
        return -tf.reduce_mean(fake)

    # 定义梯度惩罚函数
    def gradient_penalty(self, interpolated):
        with tf.GradientTape() as tape:
            tape.watch(interpolated)
            pred = self.discriminator(interpolated)
        gradients = tape.gradient(pred, interpolated)
        norm = tf.norm(tf.reshape(gradients, [tf.shape(gradients)[], -1]), axis=1)
        gp = tf.reduce_mean((norm - 1.) ** 2)
        return gp

    # 定义训练步骤
    @tf.function
    def train_step(self, real_data):
        # 生成随机噪声
        batch_size = tf.shape(real_data)[]
        noise = tf.random.normal([batch_size, self.latent_dim])

        # 训练判别器
        for i in range(self.d_steps):
            with tf.GradientTape() as tape:
                fake_data = self.generator(noise)
                interpolated = real_data + tf.random.uniform(
                    tf.shape(real_data), minval=., maxval=1.
                ) * (fake_data - real_data)
                real_pred = self.discriminator(real_data)
                fake_pred = self.discriminator(fake_data)
                disc_loss = self.discriminator_loss(real_pred, fake_pred, interpolated)
            grads = tape.gradient(disc_loss, self.discriminator.trainable_weights)
            self.discriminator.optimizer.apply_gradients(
                zip(grads, self.discriminator.trainable_weights)
            )

        # 训练生成器
        with tf.GradientTape() as tape:
            fake_data = self.generator(noise)
            fake_pred = self.discriminator(fake_data)
            gen_loss = self.generator_loss(fake_pred)
        grads = tape.gradient(gen_loss, self.generator.trainable_weights)
        self.generator.optimizer.apply_gradients(
            zip(grads, self.generator.trainable_weights)
        )

        return {"d_loss": disc_loss, "g_loss": gen_loss}

# 加载数据集
(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[], 784).astype("float32")
train_images = (train_images - 127.5) / 127.5  # 将像素值归一化到[-1, 1]之间

# 定义超参数
BUFFER_SIZE = 60000
BATCH_SIZE = 64
EPOCHS = 50
LATENT_DIM = 100

# 创建数据集
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)

# 创建模型
generator = make_generator_model()
discriminator = make_discriminator_model()

# 定义优化器
generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)

# 创建 WGAN 模型
wgan = WGAN(
    discriminator=discriminator,
    generator=generator,
    latent_dim=LATENT_DIM,
    discriminator_extra_steps=3,
    gp_weight=10.,
)

# 训练模型
for epoch in range(EPOCHS):
    for real_data in train_dataset:
        wgan.train_step(real_data)

    # 打印损失
    d_loss = wgan.trainable_variables["d_loss"].numpy()
    g_loss = wgan.trainable_variables["g_loss"].numpy()
    print(f"Epoch {epoch+1}, Discriminator loss: {d_loss}, Generator loss: {g_loss}")

注意：这只是一个简单的示例，实际应用中需要根据具体问题进行调整和优化。