帮我写一个VAE算法对数据集进行增强生成的python代码

以下是一个简单的 VAE 算法的 Python 代码，用于数据集的增强生成：

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

# 定义 VAE 模型
class VAE(keras.Model):
    def __init__(self, latent_dim):
        super(VAE, self).__init__()
        self.latent_dim = latent_dim
        self.encoder = keras.Sequential(
            [
                layers.InputLayer(input_shape=(28, 28, 1)),
                layers.Conv2D(filters=32, kernel_size=3, strides=(2, 2), activation='relu'),
                layers.Conv2D(filters=64, kernel_size=3, strides=(2, 2), activation='relu'),
                layers.Flatten(),
                layers.Dense(latent_dim + latent_dim),
            ]
        )
        self.decoder = keras.Sequential(
            [
                layers.InputLayer(input_shape=(latent_dim,)),
                layers.Dense(units=7*7*32, activation=tf.nn.relu),
                layers.Reshape(target_shape=(7, 7, 32)),
                layers.Conv2DTranspose(filters=64, kernel_size=3, strides=(2, 2), padding='same', activation='relu'),
                layers.Conv2DTranspose(filters=32, kernel_size=3, strides=(2, 2), padding='same', activation='relu'),
                layers.Conv2DTranspose(filters=1, kernel_size=3, strides=(1, 1), padding='same'),
            ]
        )

    # 定义 VAE 的前向传播过程
    def call(self, x):
        encoded = self.encoder(x)
        mean, logvar = tf.split(encoded, num_or_size_splits=2, axis=1)
        eps = tf.random.normal(shape=mean.shape)
        z = eps * tf.exp(logvar * .5) + mean
        reconstructed = self.decoder(z)
        return reconstructed

# 定义损失函数
def vae_loss(x, reconstructed):
    x = tf.reshape(x, shape=(-1, 28*28))
    reconstructed = tf.reshape(reconstructed, shape=(-1, 28*28))
    cross_entropy = keras.losses.binary_crossentropy(x, reconstructed)
    kl_divergence = -.5 * tf.reduce_sum(1 + logvar - tf.square(mean) - tf.exp(logvar), axis=1)
    return tf.reduce_mean(cross_entropy + kl_divergence)

# 加载 MNIST 数据集
(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (-1, 28, 28, 1))
x_test = np.reshape(x_test, (-1, 28, 28, 1))

# 训练 VAE 模型
latent_dim = 2
vae = VAE(latent_dim)
vae.compile(optimizer=keras.optimizers.Adam(learning_rate=.001), loss=vae_loss)
vae.fit(x_train, x_train, epochs=10, batch_size=128)

# 生成新的样本
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 = vae.decoder(z_sample)
        digit = tf.reshape(x_decoded[], (digit_size, digit_size))
        figure[i * digit_size: (i + 1) * digit_size,
               j * digit_size: (j + 1) * digit_size] = digit.numpy()

# 显示生成的样本
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
plt.imshow(figure, cmap='Greys_r')
plt.axis('off')
plt.show()

希望这个代码能够帮助你进行数据集的增强生成。