生成一个python的变分自编码器中输出均值和方差的代码

以下是一个简单的变分自编码器的代码，包括输出均值和方差的部分：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# 定义编码器
latent_dim = 2

encoder_inputs = keras.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)
z_mean = layers.Dense(latent_dim, name="z_mean")(x)
z_log_var = layers.Dense(latent_dim, name="z_log_var")(x)

# 定义采样函数
def sampling(args):
    z_mean, z_log_var = args
    epsilon = tf.keras.backend.random_normal(shape=(tf.keras.backend.shape(z_mean)[0], latent_dim),
                                          mean=0., stddev=1.)
    return z_mean + tf.keras.backend.exp(z_log_var) * epsilon

# 定义解码器
latent_inputs = keras.Input(shape=(latent_dim,))
x = layers.Dense(7 * 7 * 64, activation="relu")(latent_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)

# 定义整个变分自编码器模型
vae = keras.Model(encoder_inputs, decoder_outputs)

# 定义重构误差和KL散度
reconstruction_loss = tf.keras.losses.mse(encoder_inputs, decoder_outputs)
reconstruction_loss *= 28 * 28
kl_loss = 1 + z_log_var - tf.keras.backend.square(z_mean) - tf.keras.backend.exp(z_log_var)
kl_loss = tf.keras.backend.sum(kl_loss, axis=-1)
kl_loss *= -0.5
vae_loss = tf.keras.backend.mean(reconstruction_loss + kl_loss)
vae.add_loss(vae_loss)
vae.compile(optimizer="adam")

# 训练模型
(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
x_train = x_train.reshape(-1, 28, 28, 1) / 255.
x_test = x_test.reshape(-1, 28, 28, 1) / 255.
vae.fit(x_train, epochs=10, batch_size=128, validation_data=(x_test, None))

# 输出均值和方差
encoder = keras.Model(encoder_inputs, [z_mean, z_log_var])
mean, log_var = encoder.predict(x_test)