def create_loss_optimizer(self):
print('[*] Defining Loss Functions and Optimizer...')
with tf.name_scope('reconstruct'):
self.reconstruction = losses.get_ell(self.x_batch_flat, self.x_recons_flat)
self.loss_reconstruction_m = tf.reduce_mean(self.reconstruction)
with tf.name_scope('prior_recons'):
self.prior_recons = losses.get_ell(self.sample_flat, self.sample_recons_flat)
self.prior_recons_m = tf.reduce_mean(self.prior_recons)
with tf.variable_scope("L2_loss", reuse=self.reuse):
tv = tf.trainable_variables()
self.L2_loss = tf.reduce_sum([ tf.nn.l2_loss(v) for v in tv if 'post_' in v.name])
with tf.variable_scope('ae_loss', reuse=self.reuse):
self.ae_loss = tf.add(tf.reduce_mean(self.reconstruction), self.l2*self.L2_loss, name='ae_loss') + self.prior_recons_m
with tf.variable_scope('bayae_loss', reuse=self.reuse):
if self.isConv:
self.bay_kl = -1 * losses.get_QP_kl(self.post_mean, self.post_var, \
tf.reshape(self.prior_mean, [self.MC_samples, self.batch_size, self.latent_dim]), tf.reshape(self.prior_var, [self.MC_samples, self.batch_size, self.latent_dim]) )
else:
self.bay_kl = -1 * losses.get_QP_kl(self.post_mean, self.post_var, \
self.prior_mean, self.prior_var)
self.bayae_loss = tf.add(tf.cast(self.num_batches, 'float32')*self.ae_loss, self.bay_kl, name='bayae_loss')
with tf.variable_scope("optimizer" ,reuse=self.reuse):
self.optimizer = tf.train.AdamOptimizer(self.lr)
self.train_step = self.optimizer.minimize(self.ae_loss, global_step=self.global_step_tensor)
self.losses = ['Total', 'AE', 'reconstruction', 'L2', 'prior_reconst', 'bayesian_KL']
def create_loss_optimizer(self):
print('[*] Defining Loss Functions and Optimizer...')
with tf.name_scope('reconstruct'):
self.reconstruction = losses.get_ell(self.x_batch_flat, self.x_recons_flat)
self.loss_reconstruction_m = tf.reduce_mean(self.reconstruction)
with tf.variable_scope('L2_loss', reuse=self.reuse):
tv = tf.trainable_variables()
self.L2_loss = tf.reduce_sum([ tf.nn.l2_loss(v) for v in tv ])
with tf.variable_scope('ae_loss', reuse=self.reuse):
self.ae_loss = tf.add(tf.reduce_mean(self.reconstruction), self.l2*self.L2_loss, name='ae_loss')
with tf.variable_scope('kl_loss', reuse=self.reuse):
self.kl_loss = losses.get_kl(self.encoder_mean, self.encoder_logvar)
self.kl_loss_m = tf.reduce_mean(self.kl_loss)
with tf.variable_scope('vae_loss', reuse=self.reuse):
self.vae_loss = tf.add(self.ae_loss, self.kl_loss_m)
with tf.variable_scope('annvae_loss', reuse=self.reuse):
c = losses.anneal(self.c_max, self.global_step_tensor, self.itr_thd)
regularizer = self.ann_gamma * tf.math.abs(self.kl_loss_m - c)
self.annvae_loss = tf.add(self.ae_loss, regularizer)
with tf.variable_scope("optimizer" ,reuse=self.reuse):
self.optimizer = tf.train.AdamOptimizer(self.lr)
self.train_step = self.optimizer.minimize(self.annvae_loss, global_step=self.global_step_tensor)
self.losses = ['AnnVAE','VAE', 'AE', 'reconstruction', 'L2']
def create_loss_optimizer(self):
print('[*] Defining Loss Functions and Optimizer...')
with tf.name_scope('reconstruct'):
self.reconstruction = losses.get_ell(self.x_batch_flat,
self.x_recons_flat)
self.loss_reconstruction_m = tf.reduce_mean(self.reconstruction)
with tf.variable_scope('L2_loss', reuse=self.reuse):
tv = tf.trainable_variables()
self.L2_loss = tf.reduce_sum([tf.nn.l2_loss(v) for v in tv])
with tf.variable_scope('ae_loss', reuse=self.reuse):
self.ae_loss = tf.add(tf.reduce_mean(self.reconstruction),
self.l2 * self.L2_loss,
name='ae_loss')
with tf.variable_scope('kl_loss', reuse=self.reuse):
self.kl_loss = losses.get_kl(self.encoder_mean,
self.encoder_logvar)
self.kl_loss_m = tf.reduce_mean(self.kl_loss)
with tf.variable_scope('vae_loss', reuse=self.reuse):
self.vae_loss = tf.add(self.ae_loss, self.kl_loss_m)
with tf.variable_scope('bvae_loss', reuse=self.reuse):
regularizer = tf.multiply(self.beta, self.kl_loss_m)
self.bvae_loss = tf.add(self.ae_loss, regularizer)
with tf.variable_scope('btcvae_loss', reuse=self.reuse):
"""
Based on Equation 4 with alpha = gamma = 1 of "Isolating Sources of Disentanglement in Variational
Autoencoders"
(https: // arxiv.org / pdf / 1802.04942).
If alpha = gamma = 1, Eq 4 can be
written as ELBO + (1 - beta) * TC.
"""
tc = tf.multiply(1-self.beta, self.total_correlation(self.latent_batch, self.encoder_mean, \
self.encoder_logvar))
regularizer = tf.add(self.kl_loss_m, tc)
self.btcvae_loss = tf.add(self.ae_loss, regularizer)
with tf.variable_scope("optimizer", reuse=self.reuse):
self.optimizer = tf.train.AdamOptimizer(self.lr)
self.train_step = self.optimizer.minimize(
self.btcvae_loss, global_step=self.global_step_tensor)
self.losses = [
'Beta-TCVAE', 'Beta-VAE', 'VAE', 'AE', 'reconstruction', 'L2'
]
def create_loss_optimizer(self):
print('[*] Defining Loss Functions and Optimizer...')
with tf.name_scope('reconstruct'):
self.reconstruction = losses.get_ell(self.x_batch_flat, self.x_recons_flat)
self.loss_reconstruction_m = tf.reduce_mean(self.reconstruction)
with tf.variable_scope("L2_loss", reuse=self.reuse):
tv = tf.trainable_variables()
self.L2_loss = tf.reduce_sum([ tf.nn.l2_loss(v) for v in tv ])
with tf.variable_scope('ae_loss', reuse=self.reuse):
self.ae_loss = tf.add(tf.reduce_mean(self.reconstruction), self.l2*self.L2_loss, name='ae_loss')
with tf.variable_scope("optimizer" ,reuse=self.reuse):
self.optimizer = tf.train.AdamOptimizer(self.lr)
self.train_step = self.optimizer.minimize(self.ae_loss, global_step=self.global_step_tensor)
self.losses = ['AE', 'reconstruction', 'L2']