帮我找一下VAE算法模型的python代码
时间: 2023-04-01 14:01:10 浏览: 67
以下是一个简单的VAE算法模型的Python代码:
```python
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!