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import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from modules.callbacks import ImagesCallback
from modules.data_generator import DataGenerator
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
# Note : https://www.tensorflow.org/guide/keras/save_and_serialize#custom_objects
class Sampling(layers.Layer):
"""Uses (z_mean, z_log_var) to sample z, the vector encoding a digit."""
def call(self, inputs):
z_mean, z_log_var = inputs
batch = tf.shape(z_mean)[0]
dim = tf.shape(z_mean)[1]
epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
return z_mean + tf.exp(0.5 * z_log_var) * epsilon
class VAE(keras.Model):
def __init__(self, encoder=None, decoder=None, **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, z = self.encoder(data)
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 *= -0.5
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 reload(self,filename):
self.encoder = keras.models.load_model(f'{filename}-enc.h5', custom_objects={'Sampling': Sampling})
self.decoder = keras.models.load_model(f'{filename}-dec.h5')
def save(self,filename):
self.encoder.save(f'{filename}-enc.h5')
self.decoder.save(f'{filename}-dec.h5')