vae-gan结构的python代码

我可以回答这个问题，以下是vae-gan结构的Python代码：

import tensorflow as tf
from tensorflow.keras import layers

# Encoder
encoder_inputs = layers.Input(shape=(28, 28, 1))
x = layers.Conv2D(32, 3, activation="relu", strides=2, padding="same")(encoder_inputs)
x = layers.Conv2D(64, 3, activation="relu", strides=2, padding="same")(x)
x = layers.Flatten()(x)
x = layers.Dense(16, activation="relu")(x)

# Latent space
z_mean = layers.Dense(2, name="z_mean")(x)
z_log_var = layers.Dense(2, name="z_log_var")(x)

# Reparameterization trick
def sampling(args):
    z_mean, z_log_var = args
    epsilon = tf.keras.backend.random_normal(shape=tf.shape(z_mean))
    return z_mean + tf.exp(0.5 * z_log_var) * epsilon

z = layers.Lambda(sampling, output_shape=(2,), name="z")([z_mean, z_log_var])

# Decoder
decoder_inputs = layers.Input(shape=(2,))
x = layers.Dense(7 * 7 * 64, activation="relu")(decoder_inputs)
x = layers.Reshape((7, 7, 64))(x)
x = layers.Conv2DTranspose(64, 3, activation="relu", strides=2, padding="same")(x)
x = layers.Conv2DTranspose(32, 3, activation="relu", strides=2, padding="same")(x)
decoder_outputs = layers.Conv2DTranspose(1, 3, activation="sigmoid", padding="same")(x)

# VAE model
vae = tf.keras.Model(encoder_inputs, decoder_outputs)

# VAE loss
reconstruction_loss = tf.keras.losses.binary_crossentropy(encoder_inputs, decoder_outputs)
reconstruction_loss *= 28 * 28
kl_loss = 1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)
kl_loss = tf.reduce_mean(kl_loss)
kl_loss *= -0.5
vae_loss = tf.reduce_mean(reconstruction_loss + kl_loss)
vae.add_loss(vae_loss)

# GAN model
discriminator_inputs = layers.Input(shape=(2,))
x = layers.Dense(256, activation="relu")(discriminator_inputs)
discriminator_outputs = layers.Dense(1, activation="sigmoid")(x)
discriminator = tf.keras.Model(discriminator_inputs, discriminator_outputs)

# GAN loss
discriminator.compile(loss="binary_crossentropy", optimizer=tf.keras.optimizers.Adam(lr=0.0003, decay=1e-5))
discriminator.trainable = False
gan_inputs = layers.Input(shape=(28, 28, 1))
gan_encoder_outputs = vae.encoder(gan_inputs)
gan_decoder_outputs = vae.decoder(gan_encoder_outputs)
gan_outputs = discriminator(gan_encoder_outputs)
gan = tf.keras.Model(gan_inputs, [gan_decoder_outputs, gan_outputs])
gan.compile(loss=["binary_crossentropy", "binary_crossentropy"], optimizer=tf.keras.optimizers.Adam(lr=0.0003, decay=1e-5))