def L(x_recon, x, y, z): if self.distributions['p_z'] == 'gaussian_marg': log_prior_z = tf.reduce_sum( utils.tf_gaussian_marg( z[1], z[2] ), 1 ) elif self.distributions['p_z'] == 'gaussian': log_prior_z = tf.reduce_sum( utils.tf_stdnormal_logpdf( z[0] ), 1 ) if self.distributions['p_y'] == 'uniform': y_prior = (1. / self.dim_y) * tf.ones_like( y ) log_prior_y = - tf.nn.softmax_cross_entropy_with_logits( labels=y_prior, logits=y ) if self.distributions['p_x'] == 'gaussian': log_lik = tf.reduce_sum( utils.tf_normal_logpdf( x, x_recon[0], x_recon[1] ), 1 ) if self.distributions['q_z'] == 'gaussian_marg': log_post_z = tf.reduce_sum( utils.tf_gaussian_ent( z[2] ), 1 ) elif self.distributions['q_z'] == 'gaussian': log_post_z = tf.reduce_sum( utils.tf_normal_logpdf( z[0], z[1], z[2] ), 1 ) _L = log_prior_y + log_lik + log_prior_z - log_post_z return _L
def L(x_recon, x, y, z): if self.distributions['p_z'] == 'gaussian_marg': log_prior_z = tf.reduce_sum( utils.tf_gaussian_marg( z[1], z[2] ), 1 ) elif self.distributions['p_z'] == 'gaussian': log_prior_z = tf.reduce_sum( utils.tf_stdnormal_logpdf( z[0] ), 1 ) if self.distributions['p_y'] == 'uniform': y_prior = (1. / self.dim_y) * tf.ones_like( y ) log_prior_y = - tf.nn.softmax_cross_entropy_with_logits( y_prior, y ) if self.distributions['p_x'] == 'gaussian': log_lik = tf.reduce_sum( utils.tf_normal_logpdf( x, x_recon[0], x_recon[1] ), 1 ) if self.distributions['q_z'] == 'gaussian_marg': log_post_z = tf.reduce_sum( utils.tf_gaussian_ent( z[2] ), 1 ) elif self.distributions['q_z'] == 'gaussian': log_post_z = tf.reduce_sum( utils.tf_normal_logpdf( z[0], z[1], z[2] ), 1 ) _L = log_prior_y + log_lik + log_prior_z - log_post_z return _L
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 L(x_recon, x, y, z):
if self.distributions['p_z'] == 'gaussian_marg':
log_prior_z = tf.reduce_sum(utils.tf_gaussian_marg(z[1], z[2]),
1)
elif self.distributions['p_z'] == 'gaussian':
log_prior_z = tf.reduce_sum(utils.tf_stdnormal_logpdf(z[0]), 1)
if self.distributions['p_y'] == 'uniform':
y_prior = (1. / self.dim_y) * tf.ones_like(y)
log_prior_y = -tf.nn.softmax_cross_entropy_with_logits(
y_prior, y)
if self.distributions['p_x'] == 'gaussian':
log_lik = tf.reduce_sum(
utils.tf_normal_logpdf(x, x_recon[0], x_recon[1]), 1)
if self.distributions['q_z'] == 'gaussian_marg':
log_post_z = tf.reduce_sum(utils.tf_gaussian_ent(z[2]), 1)
elif self.distributions['q_z'] == 'gaussian':
log_post_z = tf.reduce_sum(
utils.tf_normal_logpdf(z[0], z[1], z[2]), 1)
_L = log_prior_y + log_lik + log_prior_z - log_post_z
return _L
###########################
''' Labelled Datapoints '''
###########################
self.y_lab_logits, self.x_lab = self._generate_yx(
self.x_labelled_mu, self.x_labelled_lsgms)
self.z_lab, self.z_lab_mu, self.z_lab_lsgms = self._generate_zxy(
self.x_lab, self.y_lab)
self.x_recon_lab_mu, self.x_recon_lab_lsgms = self._generate_xzy(
self.z_lab, self.y_lab)
L_lab = L([self.x_recon_lab_mu, self.x_recon_lab_lsgms],
self.x_lab, self.y_lab,
[self.z_lab, self.z_lab_mu, self.z_lab_lsgms])
L_lab += -self.beta * tf.nn.softmax_cross_entropy_with_logits(
self.y_lab_logits, self.y_lab)
############################
''' Unabelled Datapoints '''
############################
def one_label_tensor(label):
indices = []
values = []
for i in range(self.num_ulab_batch):
indices += [[i, label]]
values += [1.]
_y_ulab = tf.sparse_tensor_to_dense(
tf.SparseTensor(indices=indices,
values=values,
shape=[self.num_ulab_batch, self.dim_y]),
0.0)
return _y_ulab
self.y_ulab_logits, self.x_ulab = self._generate_yx(
self.x_unlabelled_mu, self.x_unlabelled_lsgms, reuse=True)
for label in range(self.dim_y):
_y_ulab = one_label_tensor(label)
self.z_ulab, self.z_ulab_mu, self.z_ulab_lsgms = self._generate_zxy(
self.x_ulab, _y_ulab, reuse=True)
self.x_recon_ulab_mu, self.x_recon_ulab_lsgms = self._generate_xzy(
self.z_ulab, _y_ulab, reuse=True)
_L_ulab = tf.expand_dims(
L([self.x_recon_ulab_mu, self.x_recon_ulab_lsgms],
self.x_ulab, _y_ulab,
[self.z_ulab, self.z_ulab_mu, self.z_ulab_lsgms]), 1)
if label == 0:
L_ulab = tf.identity(_L_ulab)
else:
L_ulab = tf.concat(1, [L_ulab, _L_ulab])
self.y_ulab = self.y_ulab_logits.softmax_activation()
U = tf.reduce_sum(
tf.multiply(self.y_ulab,
tf.subscribe(L_ulab, tf.log(self.y_ulab))), 1)
def train(self,
x_labelled,
y,
x_unlabelled,
epochs,
x_valid,
y_valid,
print_every=1,
learning_rate=3e-4,
beta1=0.9,
beta2=0.999,
seed=31415,
stop_iter=100,
save_path=None,
load_path=None):
''' Session and Summary '''
if save_path is None:
self.save_path = 'checkpoints/model_GC_{}-{}-{}_{}.cpkt'.format(
self.num_lab, learning_rate, self.batch_size,
time.time())
else:
self.save_path = save_path
np.random.seed(seed)
tf.set_random_seed(seed)
with self.G.as_default():
self.optimiser = tf.train.AdamOptimizer(
learning_rate=learning_rate, beta1=beta1, beta2=beta2)
self.train_op = self.optimiser.minimize(self.cost)
init = tf.initialize_all_variables()
self._test_vars = None
_data_labelled = np.hstack([x_labelled, y])
_data_unlabelled = x_unlabelled
x_valid_mu, x_valid_lsgms = x_valid[:, :self.
dim_x], x_valid[:, self.
dim_x:2 *
self.dim_x]
with self.session as sess:
sess.run(init)
if load_path == 'default':
self.saver.restore(sess, self.save_path)
elif load_path is not None:
self.saver.restore(sess, load_path)
best_eval_accuracy = 0.
stop_counter = 0
for epoch in range(epochs):
''' Shuffle Data '''
np.random.shuffle(_data_labelled)
np.random.shuffle(_data_unlabelled)
''' Training '''
for x_l_mu, x_l_lsgms, y, x_u_mu, x_u_lsgms in utils.feed_numpy_semisupervised(
self.num_lab_batch, self.num_ulab_batch,
_data_labelled[:, :2 * self.dim_x],
_data_labelled[:, 2 * self.dim_x:],
_data_unlabelled):
training_result = sess.run(
[self.train_op, self.cost],
feed_dict={
self.x_labelled_mu: x_l_mu,
self.x_labelled_lsgms: x_l_lsgms,
self.y_lab: y,
self.x_unlabelled_mu: x_u_mu,
self.x_unlabelled_lsgms: x_u_lsgms
})
training_cost = training_result[1]
''' Evaluation '''
stop_counter += 1
if epoch % print_every == 0:
test_vars = tf.get_collection(
bookkeeper.GraphKeys.TEST_VARIABLES)
if test_vars:
if test_vars != self._test_vars:
self._test_vars = list(test_vars)
self._test_var_init_op = tf.initialize_variables(
test_vars)
self._test_var_init_op.run()
eval_accuracy, eval_cross_entropy = \
sess.run([self.eval_accuracy, self.eval_cross_entropy],
feed_dict={ self.x_labelled_mu: x_valid_mu,
self.x_labelled_lsgms: x_valid_lsgms,
self.y_lab: y_valid } )
if eval_accuracy > best_eval_accuracy:
best_eval_accuracy = eval_accuracy
self.saver.save(sess, self.save_path)
stop_counter = 0
utils.print_metrics(
epoch + 1, ['Training', 'cost', training_cost],
['Validation', 'accuracy', eval_accuracy], [
'Validation', 'cross-entropy',
eval_cross_entropy
])
if stop_counter >= stop_iter:
print('Stopping GC training')
print(
'No change in validation accuracy for {} iterations'
.format(stop_iter))
print('Best validation accuracy: {}'.format(
best_eval_accuracy))
print('Model saved in {}'.format(self.save_path))
break
########################
''' Prior on Weights '''
########################
L_weights = 0.
_weights = tf.trainable_variables()
for w in _weights:
L_weights += tf.reduce_sum(utils.tf_stdnormal_logpdf(w))
##################
''' Total Cost '''
##################
L_lab_tot = tf.reduce_sum(L_lab)
U_tot = tf.reduce_sum(U)
self.cost = ((L_lab_tot + U_tot) * self.num_batches +
L_weights) / (-self.num_batches * self.batch_size)
##################
''' Evaluation '''
##################
self.y_test_logits, _ = self._generate_yx(self.x_labelled_mu,
self.x_labelled_lsgms,
phase=pt.Phase.test,
reuse=True)
self.y_test_pred = self.y_test_logits.softmax(self.y_lab)
self.eval_accuracy = self.y_test_pred \
.softmax.evaluate_classifier(self.y_lab, phase=pt.Phase.test)
self.eval_cross_entropy = self.y_test_pred.loss
self.eval_precision, self.eval_recall = self.y_test_pred.softmax \
.evaluate_precision_recall(self.y_lab, phase=pt.Phase.test)