帮我写一个VAE算法对数字数据集进行增强生成的python代码

好的，以下是VAE算法对数字数据集进行增强生成的Python代码：

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

# 加载数据集
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# 数据预处理
x_train = x_train.reshape(-1, 784).astype("float32") / 255.
x_test = x_test.reshape(-1, 784).astype("float32") / 255.

# 定义VAE模型
latent_dim = 2

encoder_inputs = keras.Input(shape=(784,))
x = layers.Dense(256, activation="relu")(encoder_inputs)
x = layers.Dense(128, 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)
z = layers.Lambda(sampling)([z_mean, z_log_var])
encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name="encoder")

latent_inputs = keras.Input(shape=(latent_dim,))
x = layers.Dense(128, activation="relu")(latent_inputs)
x = layers.Dense(256, activation="relu")(x)
decoder_outputs = layers.Dense(784, activation="sigmoid")(x)
decoder = keras.Model(latent_inputs, decoder_outputs, name="decoder")

outputs = decoder(encoder(encoder_inputs)[2])
vae = keras.Model(encoder_inputs, outputs, name="vae")

# 定义VAE损失函数
reconstruction_loss = keras.losses.binary_crossentropy(encoder_inputs, outputs)
reconstruction_loss *= 784
kl_loss = 1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)
kl_loss = tf.reduce_mean(kl_loss)
kl_loss *= -.5
vae_loss = tf.reduce_mean(reconstruction_loss + kl_loss)
vae.add_loss(vae_loss)
vae.compile(optimizer=keras.optimizers.Adam())

# 训练VAE模型
vae.fit(x_train, x_train, epochs=10, batch_size=128)

# 生成新的数字数据
n = 10
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * n))
grid_x = np.linspace(-4, 4, n)
grid_y = np.linspace(-4, 4, n)[::-1]
for i, yi in enumerate(grid_y):
    for j, xi in enumerate(grid_x):
        z_sample = np.array([[xi, yi]])
        x_decoded = decoder.predict(z_sample)
        digit = x_decoded[].reshape(digit_size, digit_size)
        figure[i * digit_size : (i + 1) * digit_size, j * digit_size : (j + 1) * digit_size] = digit

# 显示生成的数字数据
plt.figure(figsize=(10, 10))
plt.imshow(figure, cmap="Greys_r")
plt.show()

希望这个代码对你有帮助！