CVAE python

CVAE (Conditional Variational Autoencoder) 是一种基于变分自编码器 (Variational Autoencoder) 的有条件生成模型。它可以用于生成具有特定条件的样本，例如在生成手写数字时，给定一个特定的数字作为条件，生成与该数字相对应的手写数字样本。

在 Python 中，你可以使用深度学习框架 TensorFlow 或 PyTorch 来实现 CVAE。以下是一个使用 TensorFlow 实现 CVAE 的简单示例：

import tensorflow as tf
from tensorflow import keras

# 定义 CVAE 模型
class CVAE(keras.Model):
    def __init__(self, latent_dim):
        super(CVAE, self).__init__()
        self.latent_dim = latent_dim
        self.encoder = keras.Sequential([
            keras.layers.Flatten(),
            keras.layers.Dense(256, activation='relu'),
            keras.layers.Dense(128, activation='relu'),
            # 编码器的输出为均值和方差
            keras.layers.Dense(latent_dim + latent_dim)
        ])
        self.decoder = keras.Sequential([
            keras.layers.Dense(128, activation='relu'),
            keras.layers.Dense(256, activation='relu'),
            keras.layers.Dense(784, activation='sigmoid'),
            keras.layers.Reshape((28, 28))
        ])

    # 采样函数，从潜在变量的分布中采样样本
    def sample(self, eps=None):
        if eps is None:
            eps = tf.random.normal(shape=(100, self.latent_dim))
        return self.decode(eps)

    # 编码器前向传播
    def encode(self, x):
        mean, logvar = tf.split(self.encoder(x), num_or_size_splits=2, axis=1)
        return mean, logvar

    # 解码器前向传播
    def decode(self, z):
        return self.decoder(z)

    # CVAE 的前向传播
    def call(self, x):
        mean, logvar = self.encode(x)
        eps = tf.random.normal(shape=mean.shape)
        z = eps * tf.exp(logvar * 0.5) + mean
        return self.decode(z)

# 创建 CVAE 模型实例
latent_dim = 10
cvae = CVAE(latent_dim)

# 定义损失函数和优化器
def loss_function(x, x_pred, mean, logvar):
    reconstruction_loss = keras.losses.binary_crossentropy(x, x_pred) * 28 * 28
    kl_loss = -0.5 * tf.reduce_sum(1 + logvar - tf.square(mean) - tf.exp(logvar), axis=-1)
    return tf.reduce_mean(reconstruction_loss + kl_loss)

optimizer = keras.optimizers.Adam(learning_rate=0.001)

# 定义训练步骤
@tf.function
def train_step(x):
    with tf.GradientTape() as tape:
        x_pred, mean, logvar = cvae(x)
        loss = loss_function(x, x_pred, mean, logvar)
    gradients = tape.gradient(loss, cvae.trainable_variables)
    optimizer.apply_gradients(zip(gradients, cvae.trainable_variables))

# 加载数据集并进行训练
(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

train_dataset = tf.data.Dataset.from_tensor_slices(x_train).shuffle(60000).batch(128)

epochs = 10

for epoch in range(epochs):
    for train_x in train_dataset:
        train_step(train_x)

# 生成样本
import matplotlib.pyplot as plt

n = 10  # 生成样本的数量
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * n))

for i in range(n):
    for j in range(n):
        z_sample = np.random.normal(size=(1, latent_dim))
        x_decoded = cvae.sample(z_sample)
        digit = x_decoded[0].numpy().reshape(digit_size, digit_size)
        figure[i * digit_size: (i + 1) * digit_size, j * digit_size: (j + 1) * digit_size] = digit

plt.figure(figsize=(10, 10))
plt.imshow(figure, cmap='Greys_r')
plt.axis('off')
plt.show()

这是一个简单的 CVAE 示例，使用 MNIST 数据集进行训练和生成手写数字样本。你可以根据实际需求对模型进行调整和改进。希望对你有帮助！