如何在keras中实现生成对抗网络(GAN)的交叉验证？

from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, LeakyReLU, BatchNormalization
from keras.optimizers import Adam
from keras.datasets import mnist
import numpy as np

def build_generator():
    generator = Sequential()
    generator.add(Dense(256, input_dim=100))
    generator.add(LeakyReLU(alpha=0.01))
    generator.add(BatchNormalization(momentum=0.8))
    generator.add(Dense(512))
    generator.add(LeakyReLU(alpha=0.01))
    generator.add(BatchNormalization(momentum=0.8))
    generator.add(Dense(1024))
    generator.add(LeakyReLU(alpha=0.01))
    generator.add(BatchNormalization(momentum=0.8))
    generator.add(Dense(784, activation='tanh'))
    generator.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))
    return generator

def build_discriminator():
    discriminator = Sequential()
    discriminator.add(Dense(512, input_dim=784))
    discriminator.add(LeakyReLU(alpha=0.01))
    discriminator.add(Dense(256))
    discriminator.add(LeakyReLU(alpha=0.01))
    discriminator.add(Dense(1, activation='sigmoid'))
    discriminator.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))
    return discriminator

generator = build_generator()
discriminator = build_discriminator()

discriminator.trainable = False

gan_input = Input(shape=(100,))
gan_output = discriminator(generator(gan_input))

gan = Model(gan_input, gan_output)
gan.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))

def train(epochs, batch_size, save_interval):

    (X_train, _), (_, _) = mnist.load_data()

    X_train = X_train / 127.5 - 1.0
    X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1]*X_train.shape[2]))

    for epoch in range(epochs):
        # 随机选择真实图像样本
        idx = np.random.randint(0, X_train.shape[0], batch_size)
        real_images = X_train[idx]

        # 生成噪声作为输入
        noise = np.random.normal(0, 1, (batch_size, 100))

        # 通过生成器生成假图像
        generated_images = generator.predict(noise)

        # 训练判别器
        discriminator_loss_real = discriminator.train_on_batch(real_images, np.ones((batch_size, 1)))
        discriminator_loss_fake = discriminator.train_on_batch(generated_images, np.zeros((batch_size, 1)))
        discriminator_loss = 0.5 * np.add(discriminator_loss_real, discriminator_loss_fake)

        # 训练生成器
        noise = np.random.normal(0, 1, (batch_size, 100))
        generator_loss = gan.train_on_batch(noise, np.ones((batch_size, 1)))

        # 打印损失
        print('Epoch: %d, 生成器损失: %f, 判别器损失: %f' % (epoch+1, generator_loss, discriminator_loss))

        # 保存生成的图像
        if epoch % save_interval == 0:
            save_generated_images(epoch)

    # 保存最后一次的生成器和判别器模型
    generator.save('generator_model.h5')
    discriminator.save('discriminator_model.h5')

def save_generated_images(epoch):
    rows, cols = 5, 5
    noise = np.random.normal(0, 1, (rows * cols, 100))
    generated_images = generator.predict(noise)

    generated_images = 0.5 * generated_images + 0.5

    fig, axs = plt.subplots(rows, cols)
    idx = 0
    for i in range(rows):
        for j in range(cols):
            axs[i,j].imshow(generated_images[idx, :].reshape(28, 28), cmap='gray')
            axs[i,j].axis('off')
            idx += 1
    fig.savefig('generated_images_%d.png' % epoch)
    plt.close()

train(epochs=20000, batch_size=32, save_interval=1000)