def _objective( self ): ############ ''' Cost ''' ############ self.z_sample, self.z_mu, self.z_lsgms = self._generate_zx( self.x ) self.x_recon, self.x_recon_logits = self._generate_xz( self.z_sample ) if self.distributions['p_z'] == 'gaussian_marg': prior_z = tf.reduce_sum( utils.tf_gaussian_marg( self.z_mu, self.z_lsgms ), 1 ) if self.distributions['q_z'] == 'gaussian_marg': post_z = tf.reduce_sum( utils.tf_gaussian_ent( self.z_lsgms ), 1 ) if self.distributions['p_x'] == 'bernoulli': self.log_lik = - tf.reduce_sum( utils.tf_binary_xentropy( self.x, self.x_recon ), 1 ) l2 = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()]) self.cost = tf.reduce_mean( post_z - prior_z - self.log_lik ) + self.l2_loss * l2 ################## ''' Evaluation ''' ################## self.z_sample_eval, _, _ = self._generate_zx( self.x, phase = pt.Phase.test, reuse = True ) self.x_recon_eval, _ = self._generate_xz( self.z_sample_eval, phase = pt.Phase.test, reuse = True ) self.eval_log_lik = - tf.reduce_mean( tf.reduce_sum( utils.tf_binary_xentropy( self.x, self.x_recon_eval ), 1 ) )
def _objective( self ): ############ ''' Cost ''' ############ self.z_sample, self.z_mu, self.z_lsgms = self._generate_zx( self.x ) self.x_recon, self.x_recon_logits = self._generate_xz( self.z_sample ) if self.distributions['p_z'] == 'gaussian_marg': prior_z = tf.reduce_sum( utils.tf_gaussian_marg( self.z_mu, self.z_lsgms ), 1 ) if self.distributions['q_z'] == 'gaussian_marg': post_z = tf.reduce_sum( utils.tf_gaussian_ent( self.z_lsgms ), 1 ) if self.distributions['p_x'] == 'bernoulli': self.log_lik = - tf.reduce_sum( utils.tf_binary_xentropy( self.x, self.x_recon ), 1 ) l2 = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()]) self.cost = tf.reduce_mean( post_z - prior_z - self.log_lik ) + self.l2_loss * l2 ################## ''' Evaluation ''' ################## self.z_sample_eval, _, _ = self._generate_zx( self.x, phase = pt.Phase.test, reuse = True ) self.x_recon_eval, _ = self._generate_xz( self.z_sample_eval, phase = pt.Phase.test, reuse = True ) self.eval_log_lik = - tf.reduce_mean( tf.reduce_sum( utils.tf_binary_xentropy( self.x, self.x_recon_eval ), 1 ) )
def _objective(self):
############
''' Cost '''
############
self.z_sample, self.z_mu, self.z_lsgms = self._generate_zx(self.x)
self.x_hat = self._generate_xz(self.z_sample)
self.z_tau, _, _ = self._generate_zx(self.x_hat, reuse=True)
if self.distributions['p_z'] == 'gaussian_marg':
prior_z = tf.reduce_sum(
utils.tf_gaussian_marg(self.z_mu, self.z_lsgms), 1)
if self.distributions['q_z'] == 'gaussian_marg':
post_z = tf.reduce_sum(utils.tf_gaussian_ent(self.z_lsgms), 1)
if self.distributions['p_x'] == 'bernoulli':
self.log_lik = -tf.reduce_sum(
utils.tf_binary_xentropy(self.x, self.x_hat), 1)
l2 = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()])
latent_cost = -0.5 * tf.reduce_sum(
1 + self.z_lsgms - tf.square(self.z_mu) - tf.exp(self.z_lsgms),
axis=1)
latent_loss = tf.reduce_mean(latent_cost)
z_mean, z_var = tf.nn.moments(self.z_sample, axes=[0], keep_dims=True)
z_tau_mean, z_tau_var = tf.nn.moments(self.z_tau,
axes=[0],
keep_dims=True)
num = tf.reduce_mean(tf.multiply(tf.transpose(self.z_sample - z_mean),
(self.z_tau - z_tau_mean)),
axis=[0, 1])
den = tf.reduce_mean(tf.multiply(z_var, tf.transpose(z_tau_var)))
self.corr_loss = -num / (den + 1e-6)
self.mse_loss = tf.losses.mean_squared_error(labels=self.y,
predictions=self.x_hat)
# self.cost = tf.reduce_mean(post_z - prior_z) + self.corr_loss + self.mse_loss + self.l2_loss * l2
self.cost = self.mse_loss + latent_loss
##################
''' Evaluation '''
##################
self.z_sample_eval, _, _ = self._generate_zx(self.x, reuse=True)
self.x_hat_eval = self._generate_xz(self.z_sample_eval, reuse=True)
self.eval_log_lik = -tf.reduce_mean(
tf.reduce_sum(utils.tf_binary_xentropy(self.x, self.x_hat_eval),
1))
def _objective(self):
############
''' Cost '''
############
self.z_sample, self.z_mu, self.z_lsgms = self._generate_zx(self.x)
self.x_hat = self._generate_xz(self.z_sample)
self.z_tau, _, _ = self._generate_zx(self.x_hat, reuse=True)
# if self.distributions['p_z'] == 'gaussian_marg':
# prior_z = tf.reduce_sum(utils.tf_gaussian_marg(self.z_mu, self.z_lsgms), 1)
#
# if self.distributions['q_z'] == 'gaussian_marg':
# post_z = tf.reduce_sum(utils.tf_gaussian_ent(self.z_lsgms), 1)
#
# if self.distributions['p_x'] == 'bernoulli':
# self.log_lik = - tf.reduce_sum(utils.tf_binary_xentropy(self.x, self.x_hat), 1)
l2 = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()])
latent_cost = -0.5 * tf.reduce_sum(
1 + self.z_lsgms - tf.square(self.z_mu) - tf.exp(self.z_lsgms),
axis=1)
latent_loss = tf.reduce_mean(latent_cost)
z_mean, z_var = tf.nn.moments(self.z_sample, axes=[0], keep_dims=True)
z_tau_mean, z_tau_var = tf.nn.moments(self.z_tau,
axes=[0],
keep_dims=True)
num = tf.reduce_mean(tf.multiply((self.z_sample - z_mean),
(self.z_tau - z_tau_mean)),
axis=[0, 1])
den = tf.reduce_mean(tf.multiply(z_var, tf.transpose(z_tau_var)))
self.y_pred = self._generate_yz(self.z_sample) # _, T, C
# # TODO format code into metric
# eval_metric_ops = {
# "accuracy": tf.metrics.accuracy(labels=self.y_one_hot, predictions=self.predictions["classes"])
# }
#
# self.predictions = {
# "classes": tf.argmax(input=self.y_pred, axis=2),
# "class_target": tf.argmax(input=self.y_one_hot, axis=2),
# "probabilities": tf.nn.softmax(self.y_pred, name="softmax")
# }
_, self.accuracy = tf.metrics.accuracy(
labels=tf.argmax(self.y_one_hot, 2),
predictions=tf.argmax(self.y_pred, 2))
# classify
self.predict_loss = tf.losses.softmax_cross_entropy(
onehot_labels=self.y_one_hot, logits=self.y_pred)
self.corr_loss = -num / (den + 1e-6)
self.mse_loss = tf.losses.mean_squared_error(labels=self.x_con,
predictions=self.x_hat)
# self.cost = tf.reduce_mean(post_z - prior_z) + self.corr_loss + self.mse_loss + self.l2_loss * l2
self.cost = latent_loss + self.mse_loss + self.predict_loss + self.corr_loss
##################
''' Evaluation '''
##################
self.z_sample_eval, _, _ = self._generate_zx(self.x, reuse=True)
self.x_hat_eval = self._generate_xz(self.z_sample_eval, reuse=True)
self.eval_log_lik = -tf.reduce_mean(
tf.reduce_sum(utils.tf_binary_xentropy(self.x, self.x_hat_eval),
1))
