VAE算法模型python代码怎么找

你可以在网上搜索相关的教程和代码示例，或者参考一些开源的VAE算法库，例如TensorFlow、PyTorch等。同时，你也可以阅读相关的论文和书籍，深入了解VAE算法的原理和实现方式。希望这些资源能够帮助你找到VAE算法模型的Python代码。

相关问题

帮我找一下VAE算法模型的python代码

以下是一个简单的VAE算法模型的Python代码：

import tensorflow as tf
from tensorflow.keras import layers

# 定义VAE模型
class VAE(tf.keras.Model):
    def __init__(self, latent_dim):
        super(VAE, self).__init__()
        self.latent_dim = latent_dim
        self.encoder = tf.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 = tf.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'),
        ])

    # 定义重参数化函数
    def reparameterize(self, mean, logvar):
        eps = tf.random.normal(shape=mean.shape)
        return eps * tf.exp(logvar * .5) + mean

    # 定义前向传播函数
    def call(self, inputs):
        mean, logvar = tf.split(self.encoder(inputs), num_or_size_splits=2, axis=1)
        z = self.reparameterize(mean, logvar)
        reconstructed = self.decoder(z)
        return reconstructed, mean, logvar

# 定义VAE损失函数
def vae_loss(reconstructed, inputs, mean, logvar):
    reconstruction_loss = tf.reduce_mean(tf.square(inputs - reconstructed))
    kl_loss = -.5 * tf.reduce_mean(1 + logvar - tf.square(mean) - tf.exp(logvar))
    return reconstruction_loss + kl_loss

希望这个代码能够帮到你！

帮我找一下VAE算法生成数据模型的python代码

以下是VAE算法生成数据模型的Python代码：

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

# 定义VAE模型
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)
encoder = keras.Model(encoder_inputs, [z_mean, z_log_var], name="encoder")

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)
decoder = keras.Model(latent_inputs, decoder_outputs, name="decoder")

class VAE(keras.Model):
    def __init__(self, encoder, decoder, **kwargs):
        super(VAE, self).__init__(**kwargs)
        self.encoder = encoder
        self.decoder = decoder

    def train_step(self, data):
        if isinstance(data, tuple):
            data = data[]
        with tf.GradientTape() as tape:
            z_mean, z_log_var = self.encoder(data)
            z = self.sampling((z_mean, z_log_var))
            reconstruction = self.decoder(z)
            reconstruction_loss = tf.reduce_mean(
                keras.losses.binary_crossentropy(data, reconstruction)
            )
            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 *= -.5
            total_loss = reconstruction_loss + kl_loss
        grads = tape.gradient(total_loss, self.trainable_weights)
        self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
        return {
            "loss": total_loss,
            "reconstruction_loss": reconstruction_loss,
            "kl_loss": kl_loss,
        }

    def call(self, inputs):
        z_mean, z_log_var = self.encoder(inputs)
        z = self.sampling((z_mean, z_log_var))
        return self.decoder(z)

    def sampling(self, args):
        z_mean, z_log_var = args
        batch = tf.shape(z_mean)[]
        dim = tf.shape(z_mean)[1]
        epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
        return z_mean + tf.exp(.5 * z_log_var) * epsilon

# 加载MNIST数据集
(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
mnist_digits = np.concatenate([x_train, x_test], axis=)
mnist_digits = np.expand_dims(mnist_digits, -1).astype("float32") / 255

# 训练VAE模型
vae = VAE(encoder, decoder)
vae.compile(optimizer=keras.optimizers.Adam())
vae.fit(mnist_digits, epochs=30, batch_size=128)

# 生成新的手写数字
n = 15
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.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

plt.figure(figsize=(10, 10))
plt.imshow(figure, cmap="Greys_r")
plt.show()