写一段python写VAE代码
时间: 2023-10-14 17:26:00 浏览: 49
好的,以下是一个简单的 Python VAE 代码示例:
```python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
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[0]
with tf.GradientTape() as tape:
z_mean, z_log_var = self.encoder(data)
z = self.reparameterize(z_mean, z_log_var)
reconstruction = self.decoder(z)
reconstruction_loss = tf.reduce_mean(tf.square(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 *= -0.5
total_loss = reconstruction_loss + kl_loss
grads = tape.gradient(total_loss, self.trainable_variables)
self.optimizer.apply_gradients(zip(grads, self.trainable_variables))
return {
"loss": total_loss,
"reconstruction_loss": reconstruction_loss,
"kl_loss": kl_loss,
}
def call(self, data):
z_mean, z_log_var = self.encoder(data)
z = self.reparameterize(z_mean, z_log_var)
reconstruction = self.decoder(z)
return reconstruction
def sample(self, eps=None):
if eps is None:
eps = tf.random.normal(shape=(100, latent_dim))
return self.decode(eps, apply_sigmoid=True)
def decode(self, z, apply_sigmoid=False):
logits = self.decoder(z)
if apply_sigmoid:
probs = tf.sigmoid(logits)
return probs
return logits
def reparameterize(self, z_mean, z_log_var):
eps = tf.random.normal(shape=z_mean.shape)
return eps * tf.exp(z_log_var * 0.5) + z_mean
epochs = 10
(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
mnist_digits = np.concatenate([x_train, x_test], axis=0)
mnist_digits = np.expand_dims(mnist_digits, -1).astype("float32") / 255
vae = VAE(encoder, decoder)
vae.compile(optimizer=keras.optimizers.Adam())
vae.fit(mnist_digits, epochs=epochs, batch_size=128)
```
这个代码示例是一个简单的 VAE 实现,使用 MNIST 数据集进行训练。如果您对 VAE 的实现和训练过程感兴趣,我可以为您提供更详细的说明。