class VariationalAutoencoder(object):
def __init__(self,
dim_x,
dim_z,
p_x='bernoulli',
q_z='gaussian_marg',
p_z='gaussian_marg',
hidden_layers_px=[600, 600],
hidden_layers_qz=[600, 600],
nonlin_px=tf.nn.softplus,
nonlin_qz=tf.nn.softplus,
l2_loss=0.0):
self.dim_x, self.dim_z = dim_x, dim_z
self.l2_loss = l2_loss
self.distributions = {'p_x': p_x, 'q_z': q_z, 'p_z': p_z}
''' Create Graph '''
self.G = tf.Graph()
with self.G.as_default():
self.x = tf.placeholder(tf.float32, [None, self.dim_x])
self.encoder = FullyConnected(dim_output=2 * self.dim_z,
hidden_layers=hidden_layers_qz,
nonlinearity=nonlin_qz)
self.decoder = FullyConnected(dim_output=self.dim_x,
hidden_layers=hidden_layers_px,
nonlinearity=nonlin_px)
self._objective()
self.saver = tf.train.Saver()
self.session = tf.Session()
def _draw_sample(self, mu, log_sigma_sq):
epsilon = tf.random_normal((tf.shape(mu)), 0, 1)
sample = tf.add(mu, tf.multiply(tf.exp(0.5 * log_sigma_sq), epsilon))
return sample
def _generate_zx(self, x, phase=pt.Phase.train, reuse=False):
with tf.variable_scope('encoder', reuse=reuse):
encoder_out = self.encoder.output(x, phase=phase)
z_mu, z_lsgms = encoder_out.split(split_dim=1, num_splits=2)
z_sample = self._draw_sample(z_mu, z_lsgms)
return z_sample, z_mu, z_lsgms
def _generate_xz(self, z, phase=pt.Phase.train, reuse=False):
with tf.variable_scope('decoder', reuse=reuse):
x_recon_logits = self.decoder.output(z, phase=phase)
x_recon = tf.nn.sigmoid(x_recon_logits)
return x_recon, x_recon_logits
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 train(self,
x,
x_valid,
epochs,
num_batches,
print_every=1,
learning_rate=3e-4,
beta1=0.9,
beta2=0.999,
seed=31415,
stop_iter=100,
save_path=None,
load_path=None,
draw_img=1):
self.num_examples = x.shape[0]
self.num_batches = num_batches
print("Num examples: {}".format(self.num_examples))
assert self.num_examples % self.num_batches == 0, '#Examples % #Batches != 0'
self.batch_size = self.num_examples // self.num_batches
''' Session and Summary '''
if save_path is None:
self.save_path = 'checkpoints/model_VAE_{}-{}_{}.cpkt'.format(
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.global_variables_initializer()
self._test_vars = None
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)
training_cost = 0.
best_eval_log_lik = -np.inf
stop_counter = 0
for epoch in range(epochs):
''' Shuffle Data '''
np.random.shuffle(x)
''' Training '''
for x_batch in utils.feed_numpy(self.batch_size, x):
training_result = sess.run([self.train_op, self.cost],
feed_dict={self.x: x_batch})
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_log_lik, x_recon_eval = \
sess.run( [self.eval_log_lik, self.x_recon_eval],
feed_dict = { self.x: x_valid } )
if eval_log_lik > best_eval_log_lik:
print('Saving')
best_eval_log_lik = eval_log_lik
self.saver.save(sess, self.save_path)
stop_counter = 0
utils.print_metrics(
epoch + 1, ['Training', 'cost', training_cost],
['Validation', 'log-likelihood', eval_log_lik])
if draw_img > 0 and epoch % draw_img == 0:
import matplotlib
matplotlib.use('Agg')
import pylab
import seaborn as sns
five_random = np.random.random_integers(
x_valid.shape[0], size=5)
x_sample = x_valid[five_random]
x_recon_sample = x_recon_eval[five_random]
sns.set_style('white')
f, axes = pylab.subplots(5, 2, figsize=(8, 12))
for i, row in enumerate(axes):
row[0].imshow(x_sample[i].reshape(28, 28),
vmin=0,
vmax=1)
im = row[1].imshow(x_recon_sample[i].reshape(
28, 28),
vmin=0,
vmax=1,
cmap=sns.light_palette(
(1.0, 0.4980, 0.0549),
input="rgb",
as_cmap=True))
pylab.setp([a.get_xticklabels() for a in row],
visible=False)
pylab.setp([a.get_yticklabels() for a in row],
visible=False)
f.subplots_adjust(left=0.0,
right=0.9,
bottom=0.0,
top=1.0)
cbar_ax = f.add_axes([0.9, 0.1, 0.04, 0.8])
f.colorbar(im, cax=cbar_ax, use_gridspec=True)
pylab.tight_layout()
pylab.savefig('img/recon-' + str(epoch) + '.png',
format='png')
pylab.clf()
pylab.close('all')
if stop_counter >= stop_iter:
print('Stopping VAE training')
print(
'No change in validation log-likelihood for {} iterations'
.format(stop_iter))
print('Best validation log-likelihood: {}'.format(
best_eval_log_lik))
print('Model saved in {}'.format(self.save_path))
break
def encode(self, x, sample=False):
if sample:
return self.session.run([self.z_sample, self.z_mu, self.z_lsgms],
feed_dict={self.x: x})
else:
return self.session.run([self.z_mu, self.z_lsgms],
feed_dict={self.x: x})
def decode(self, z):
return self.session.run([self.x_recon], feed_dict={self.z_sample: z})
class GenerativeClassifier( object ):
def __init__( self,
dim_x, dim_z, dim_y,
num_examples, num_lab, num_batches,
p_x = 'gaussian',
q_z = 'gaussian_marg',
p_z = 'gaussian_marg',
hidden_layers_px = [500],
hidden_layers_qz = [500],
hidden_layers_qy = [500],
nonlin_px = tf.nn.softplus,
nonlin_qz = tf.nn.softplus,
nonlin_qy = tf.nn.softplus,
alpha = 0.1,
l2_loss = 0.0 ):
self.dim_x, self.dim_z, self.dim_y = dim_x, dim_z, dim_y
self.distributions = { 'p_x': p_x,
'q_z': q_z,
'p_z': p_z,
'p_y': 'uniform' }
self.num_examples = num_examples
self.num_batches = num_batches
self.num_lab = num_lab
self.num_ulab = self.num_examples - num_lab
assert self.num_lab % self.num_batches == 0, '#Labelled % #Batches != 0'
assert self.num_ulab % self.num_batches == 0, '#Unlabelled % #Batches != 0'
assert self.num_examples % self.num_batches == 0, '#Examples % #Batches != 0'
self.batch_size = self.num_examples // self.num_batches
self.num_lab_batch = self.num_lab // self.num_batches
self.num_ulab_batch = self.num_ulab // self.num_batches
self.beta = alpha * ( float(self.batch_size) / self.num_lab_batch )
''' Create Graph '''
self.G = tf.Graph()
with self.G.as_default():
self.x_labelled_mu = tf.placeholder( tf.float32, [None, self.dim_x] )
self.x_labelled_lsgms = tf.placeholder( tf.float32, [None, self.dim_x] )
self.x_unlabelled_mu = tf.placeholder( tf.float32, [None, self.dim_x] )
self.x_unlabelled_lsgms = tf.placeholder( tf.float32, [None, self.dim_x] )
self.y_lab = tf.placeholder( tf.float32, [None, self.dim_y] )
self.classifier = FullyConnected( dim_output = self.dim_y,
hidden_layers = hidden_layers_qy,
nonlinearity = nonlin_qy,
l2loss = l2_loss )
self.encoder = FullyConnected( dim_output = 2 * self.dim_z,
hidden_layers = hidden_layers_qz,
nonlinearity = nonlin_qz,
l2loss = l2_loss )
self.decoder = FullyConnected( dim_output = 2 * self.dim_x,
hidden_layers = hidden_layers_px,
nonlinearity = nonlin_px,
l2loss = l2_loss )
self._objective()
self.saver = tf.train.Saver()
self.session = tf.Session()
def _draw_sample( self, mu, log_sigma_sq ):
epsilon = tf.random_normal( ( tf.shape( mu ) ), 0, 1 )
sample = tf.add( mu,
tf.mul(
tf.exp( 0.5 * log_sigma_sq ), epsilon ) )
return sample
def _generate_yx( self, x_mu, x_log_sigma_sq, phase = pt.Phase.train, reuse = False ):
x_sample = self._draw_sample( x_mu, x_log_sigma_sq )
with tf.variable_scope('classifier', reuse = reuse):
y_logits = self.classifier.output( x_sample, phase )
return y_logits, x_sample
def _generate_zxy( self, x, y, reuse = False ):
with tf.variable_scope('encoder', reuse = reuse):
encoder_out = self.encoder.output( tf.concat( 1, [x, y] ) )
z_mu, z_lsgms = encoder_out.split( split_dim = 1, num_splits = 2 )
z_sample = self._draw_sample( z_mu, z_lsgms )
return z_sample, z_mu, z_lsgms
def _generate_xzy( self, z, y, reuse = False ):
with tf.variable_scope('decoder', reuse = reuse):
decoder_out = self.decoder.output( tf.concat( 1, [z, y] ) )
x_recon_mu, x_recon_lsgms = decoder_out.split( split_dim = 1, num_splits = 2 )
return x_recon_mu, x_recon_lsgms
def _objective( self ):
###############
''' L(x,y) '''
###############
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.mul( self.y_ulab,
tf.sub( L_ulab,
tf.log( self.y_ulab ) ) ), 1 )
########################
''' 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 )
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
def predict_labels( self, x_test, y_test ):
test_vars = tf.get_collection(bookkeeper.GraphKeys.TEST_VARIABLES)
tf.initialize_variables(test_vars).run()
x_test_mu = x_test[:,:self.dim_x]
x_test_lsgms = x_test[:,self.dim_x:2*self.dim_x]
accuracy, cross_entropy, precision, recall = \
self.session.run( [self.eval_accuracy, self.eval_cross_entropy, self.eval_precision, self.eval_recall],
feed_dict = {self.x_labelled_mu: x_test_mu, self.x_labelled_lsgms: x_test_lsgms, self.y_lab: y_test} )
utils.print_metrics( 'X',
['Test', 'accuracy', accuracy],
['Test', 'cross-entropy', cross_entropy],
['Test', 'precision', precision],
['Test', 'recall', recall] )
class VariationalAutoencoder(object):
def __init__( self,
dim_x, dim_z,
p_x = 'bernoulli',
q_z = 'gaussian_marg',
p_z = 'gaussian_marg',
hidden_layers_px = [600, 600],
hidden_layers_qz = [600, 600],
nonlin_px = tf.nn.softplus,
nonlin_qz = tf.nn.softplus,
l2_loss = 0.0 ):
self.dim_x, self.dim_z = dim_x, dim_z
self.l2_loss = l2_loss
self.distributions = { 'p_x': p_x,
'q_z': q_z,
'p_z': p_z }
''' Create Graph '''
self.G = tf.Graph()
with self.G.as_default():
self.x = tf.placeholder( tf.float32, [None, self.dim_x] )
self.encoder = FullyConnected( dim_output = 2 * self.dim_z,
hidden_layers = hidden_layers_qz,
nonlinearity = nonlin_qz )
self.decoder = FullyConnected( dim_output = self.dim_x,
hidden_layers = hidden_layers_px,
nonlinearity = nonlin_px )
self._objective()
self.saver = tf.train.Saver()
self.session = tf.Session()
def _draw_sample( self, mu, log_sigma_sq ):
epsilon = tf.random_normal( ( tf.shape( mu ) ), 0, 1 )
sample = tf.add( mu,
tf.mul(
tf.exp( 0.5 * log_sigma_sq ), epsilon ) )
return sample
def _generate_zx( self, x, phase = pt.Phase.train, reuse = False ):
with tf.variable_scope('encoder', reuse = reuse):
encoder_out = self.encoder.output( x, phase = phase )
z_mu, z_lsgms = encoder_out.split( split_dim = 1, num_splits = 2 )
z_sample = self._draw_sample( z_mu, z_lsgms )
return z_sample, z_mu, z_lsgms
def _generate_xz( self, z, phase = pt.Phase.train, reuse = False ):
with tf.variable_scope('decoder', reuse = reuse):
x_recon_logits = self.decoder.output( z, phase = phase )
x_recon = tf.nn.sigmoid( x_recon_logits )
return x_recon, x_recon_logits
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 train( self, x, x_valid,
epochs, num_batches,
print_every = 1,
learning_rate = 3e-4,
beta1 = 0.9,
beta2 = 0.999,
seed = 31415,
stop_iter = 100,
save_path = None,
load_path = None,
draw_img = 1 ):
self.num_examples = x.shape[0]
self.num_batches = num_batches
assert self.num_examples % self.num_batches == 0, '#Examples % #Batches != 0'
self.batch_size = self.num_examples // self.num_batches
''' Session and Summary '''
if save_path is None:
self.save_path = 'checkpoints/model_VAE_{}-{}_{}.cpkt'.format(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
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 )
training_cost = 0.
best_eval_log_lik = - np.inf
stop_counter = 0
for epoch in range(epochs):
''' Shuffle Data '''
np.random.shuffle( x )
''' Training '''
for x_batch in utils.feed_numpy( self.batch_size, x ):
training_result = sess.run( [self.train_op, self.cost],
feed_dict = { self.x: x_batch } )
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_log_lik, x_recon_eval = \
sess.run( [self.eval_log_lik, self.x_recon_eval],
feed_dict = { self.x: x_valid } )
if eval_log_lik > best_eval_log_lik:
best_eval_log_lik = eval_log_lik
self.saver.save( sess, self.save_path )
stop_counter = 0
utils.print_metrics( epoch+1,
['Training', 'cost', training_cost],
['Validation', 'log-likelihood', eval_log_lik] )
if draw_img > 0 and epoch % draw_img == 0:
import matplotlib
matplotlib.use('Agg')
import pylab
import seaborn as sns
five_random = np.random.random_integers(x_valid.shape[0], size = 5)
x_sample = x_valid[five_random]
x_recon_sample = x_recon_eval[five_random]
sns.set_style('white')
f, axes = pylab.subplots(5, 2, figsize=(8,12))
for i,row in enumerate(axes):
row[0].imshow(x_sample[i].reshape(28, 28), vmin=0, vmax=1)
im = row[1].imshow(x_recon_sample[i].reshape(28, 28), vmin=0, vmax=1,
cmap=sns.light_palette((1.0, 0.4980, 0.0549), input="rgb", as_cmap=True))
pylab.setp([a.get_xticklabels() for a in row], visible=False)
pylab.setp([a.get_yticklabels() for a in row], visible=False)
f.subplots_adjust(left=0.0, right=0.9, bottom=0.0, top=1.0)
cbar_ax = f.add_axes([0.9, 0.1, 0.04, 0.8])
f.colorbar(im, cax=cbar_ax, use_gridspec=True)
pylab.tight_layout()
pylab.savefig('img/recon-'+str(epoch)+'.png', format='png')
pylab.clf()
pylab.close('all')
if stop_counter >= stop_iter:
print('Stopping VAE training')
print('No change in validation log-likelihood for {} iterations'.format(stop_iter))
print('Best validation log-likelihood: {}'.format(best_eval_log_lik))
print('Model saved in {}'.format(self.save_path))
break
def encode( self, x, sample = False ):
if sample:
return self.session.run( [self.z_sample, self.z_mu, self.z_lsgms], feed_dict = { self.x: x } )
else:
return self.session.run( [self.z_mu, self.z_lsgms], feed_dict = { self.x: x } )
def decode( self, z ):
return self.session.run( [self.x_recon],
feed_dict = { self.z_sample: z } )
class GenerativeClassifier(object):
def __init__(self,
dim_x,
dim_z,
dim_y,
num_examples,
num_lab,
num_batches,
p_x='gaussian',
q_z='gaussian_marg',
p_z='gaussian_marg',
hidden_layers_px=[500],
hidden_layers_qz=[500],
hidden_layers_qy=[500],
nonlin_px=tf.nn.softplus,
nonlin_qz=tf.nn.softplus,
nonlin_qy=tf.nn.softplus,
alpha=0.1,
l2_loss=0.0):
self.dim_x, self.dim_z, self.dim_y = dim_x, dim_z, dim_y
self.distributions = {
'p_x': p_x,
'q_z': q_z,
'p_z': p_z,
'p_y': 'uniform'
}
self.num_examples = num_examples
self.num_batches = num_batches
self.num_lab = num_lab
self.num_ulab = self.num_examples - num_lab
assert self.num_lab % self.num_batches == 0, '#Labelled % #Batches != 0'
assert self.num_ulab % self.num_batches == 0, '#Unlabelled % #Batches != 0'
assert self.num_examples % self.num_batches == 0, '#Examples % #Batches != 0'
self.batch_size = self.num_examples // self.num_batches
self.num_lab_batch = self.num_lab // self.num_batches
self.num_ulab_batch = self.num_ulab // self.num_batches
self.beta = alpha * (float(self.batch_size) / self.num_lab_batch)
''' Create Graph '''
self.G = tf.Graph()
with self.G.as_default():
self.x_labelled_mu = tf.placeholder(tf.float32, [None, self.dim_x])
self.x_labelled_lsgms = tf.placeholder(tf.float32,
[None, self.dim_x])
self.x_unlabelled_mu = tf.placeholder(tf.float32,
[None, self.dim_x])
self.x_unlabelled_lsgms = tf.placeholder(tf.float32,
[None, self.dim_x])
self.y_lab = tf.placeholder(tf.float32, [None, self.dim_y])
self.classifier = FullyConnected(dim_output=self.dim_y,
hidden_layers=hidden_layers_qy,
nonlinearity=nonlin_qy,
l2loss=l2_loss)
self.encoder = FullyConnected(dim_output=2 * self.dim_z,
hidden_layers=hidden_layers_qz,
nonlinearity=nonlin_qz,
l2loss=l2_loss)
self.decoder = FullyConnected(dim_output=2 * self.dim_x,
hidden_layers=hidden_layers_px,
nonlinearity=nonlin_px,
l2loss=l2_loss)
self._objective()
self.saver = tf.train.Saver()
self.session = tf.Session()
def _draw_sample(self, mu, log_sigma_sq):
epsilon = tf.random_normal((tf.shape(mu)), 0, 1)
sample = tf.add(mu, tf.mul(tf.exp(0.5 * log_sigma_sq), epsilon))
return sample
def _generate_yx(self,
x_mu,
x_log_sigma_sq,
phase=pt.Phase.train,
reuse=False):
x_sample = self._draw_sample(x_mu, x_log_sigma_sq)
with tf.variable_scope('classifier', reuse=reuse):
y_logits = self.classifier.output(x_sample, phase)
return y_logits, x_sample
def _generate_zxy(self, x, y, reuse=False):
with tf.variable_scope('encoder', reuse=reuse):
encoder_out = self.encoder.output(tf.concat(1, [x, y]))
z_mu, z_lsgms = encoder_out.split(split_dim=1, num_splits=2)
z_sample = self._draw_sample(z_mu, z_lsgms)
return z_sample, z_mu, z_lsgms
def _generate_xzy(self, z, y, reuse=False):
with tf.variable_scope('decoder', reuse=reuse):
decoder_out = self.decoder.output(tf.concat(1, [z, y]))
x_recon_mu, x_recon_lsgms = decoder_out.split(split_dim=1,
num_splits=2)
return x_recon_mu, x_recon_lsgms
def _objective(self):
''' L(x,y) '''
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.mul(self.y_ulab, tf.sub(L_ulab, tf.log(self.y_ulab))), 1)
''' 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)
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
def predict_labels(self, x_test, y_test):
test_vars = tf.get_collection(bookkeeper.GraphKeys.TEST_VARIABLES)
tf.initialize_variables(test_vars).run()
x_test_mu = x_test[:, :self.dim_x]
x_test_lsgms = x_test[:, self.dim_x:2 * self.dim_x]
accuracy, cross_entropy, precision, recall = \
self.session.run([self.eval_accuracy, self.eval_cross_entropy, self.eval_precision,
self.eval_recall],
feed_dict={
self.x_labelled_mu: x_test_mu, self.x_labelled_lsgms: x_test_lsgms,
self.y_lab: y_test})
utils.print_metrics('X', ['Test', 'accuracy', accuracy],
['Test', 'cross-entropy', cross_entropy],
['Test', 'precision', precision],
['Test', 'recall', recall])
class GenerativeClassifier(object):
def __init__(self,
dim_x1,
dim_x2,
dim_z,
dim_y,
num_examples,
num_lab,
num_batches,
p_x1='gaussian',
p_x2='gaussian',
q_z='gaussian_mixture_marg',
p_z='gaussian_marg',
hidden_layers_px1=[250],
hidden_layers_px2=[250],
hidden_layers_qz=[250],
hidden_layers_qy=[250],
nonlin_px1=tf.nn.softplus,
nonlin_px2=tf.nn.softplus,
nonlin_qz=tf.nn.softplus,
nonlin_qy=tf.nn.softplus,
beta=0.1,
l2_loss=0.0,
zeta_reg=1e6):
self.dim_x1, self.dim_x2, self.dim_z, self.dim_y = dim_x1, dim_x2, dim_z, dim_y
self.zeta_reg = zeta_reg
self.distributions = {
'p_x1': p_x1,
'p_x2': p_x2,
'q_z': q_z,
'p_z': p_z,
'p_y': 'uniform'
}
self.num_examples = num_examples
self.num_batches = num_batches
self.num_lab = num_lab
self.num_ulab = self.num_examples - num_lab
# assert self.num_lab % self.num_batches == 0, '#Labelled % #Batches != 0'
# assert self.num_ulab % self.num_batches == 0, '#Unlabelled % #Batches != 0'
# assert self.num_examples % self.num_batches == 0, '#Examples % #Batches != 0'
self.num_lab_batch = self.num_lab // self.num_batches
self.num_ulab_batch = self.num_ulab // self.num_batches
self.batch_size = self.num_examples // self.num_batches
# self.alpha = beta * ( float(self.batch_size) / self.num_lab_batch )
self.alpha = beta * float(self.batch_size)
''' Create Graph '''
self.G = tf.Graph()
with self.G.as_default():
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.01)
return tf.Variable(initial)
# zeta = weight_variable([2])
init_value = (np.sqrt(1.0 / 2)).astype('float32')
zeta = tf.Variable(tf.constant(init_value, shape=[2]))
# zeta = tf.constant(init_value, shape=[2])
zeta = zeta * zeta
self.zeta1 = zeta[0]
self.zeta2 = zeta[1]
self.x1_labelled_mu = tf.placeholder(tf.float32,
[None, self.dim_x1])
self.x1_labelled_lsgms = tf.placeholder(tf.float32,
[None, self.dim_x1])
self.x1_unlabelled_mu = tf.placeholder(tf.float32,
[None, self.dim_x1])
self.x1_unlabelled_lsgms = tf.placeholder(tf.float32,
[None, self.dim_x1])
self.x2_labelled_mu = tf.placeholder(tf.float32,
[None, self.dim_x2])
self.x2_labelled_lsgms = tf.placeholder(tf.float32,
[None, self.dim_x2])
self.x2_unlabelled_mu = tf.placeholder(tf.float32,
[None, self.dim_x2])
self.x2_unlabelled_lsgms = tf.placeholder(tf.float32,
[None, self.dim_x2])
self.y_lab = tf.placeholder(tf.float32, [None, self.dim_y])
self.classifier = FullyConnected(dim_output=self.dim_y,
hidden_layers=hidden_layers_qy,
nonlinearity=nonlin_qy,
l2loss=l2_loss)
self.encoder_1 = FullyConnected(dim_output=2 * self.dim_z,
hidden_layers=hidden_layers_qz,
nonlinearity=nonlin_qz,
l2loss=l2_loss)
self.encoder_2 = FullyConnected(dim_output=2 * self.dim_z,
hidden_layers=hidden_layers_qz,
nonlinearity=nonlin_qz,
l2loss=l2_loss)
self.decoder_1 = FullyConnected(dim_output=2 * self.dim_x1,
hidden_layers=hidden_layers_px1,
nonlinearity=nonlin_px1,
l2loss=l2_loss)
self.decoder_2 = FullyConnected(dim_output=2 * self.dim_x2,
hidden_layers=hidden_layers_px2,
nonlinearity=nonlin_px2,
l2loss=l2_loss)
self._objective()
self.saver = tf.train.Saver(write_version=tf.train.SaverDef.V1)
self.session = tf.Session()
def _draw_sample(self, mu, log_sigma_sq):
epsilon = tf.random_normal((tf.shape(mu)), 0, 1)
sample = tf.add(mu, tf.multiply(tf.exp(0.5 * log_sigma_sq), epsilon))
return sample
def _generate_yx1x2(self,
x1_mu,
x2_mu,
x1_log_sigma_sq,
x2_log_sigma_sq,
phase=pt.Phase.train,
reuse=False):
# x1_sample = self._draw_sample( x1_mu, x1_log_sigma_sq )
# x2_sample = self._draw_sample( x2_mu, x2_log_sigma_sq )
x1_sample = x1_mu
x2_sample = x2_mu
with tf.variable_scope('classifier', reuse=reuse):
y_logits = self.classifier.output(
tf.concat([x1_sample, x2_sample], 1), phase)
return y_logits, x1_sample, x2_sample
def _generate_zx1y(self, x1, y, reuse=False):
with tf.variable_scope('encoder_1', reuse=reuse):
encoder_out = self.encoder_1.output(tf.concat([x1, y], 1))
z1_mu, z1_lsgms = encoder_out.split(split_dim=1, num_splits=2)
z1_sample = self._draw_sample(z1_mu, z1_lsgms)
return z1_sample, z1_mu, z1_lsgms
def _generate_zx2y(self, x2, y, reuse=False):
with tf.variable_scope('encoder_2', reuse=reuse):
encoder_out = self.encoder_2.output(tf.concat([x2, y], 1))
z2_mu, z2_lsgms = encoder_out.split(split_dim=1, num_splits=2)
z2_sample = self._draw_sample(z2_mu, z2_lsgms)
return z2_sample, z2_mu, z2_lsgms
def _mixture_z(self, z1, z2, phase=pt.Phase.train, reuse=False):
z_sample = self.zeta1 * z1 + self.zeta2 * z2
return z_sample
def _generate_x1zy(self, z, y, reuse=False):
with tf.variable_scope('decoder_1', reuse=reuse):
decoder_out = self.decoder_1.output(tf.concat([z, y], 1))
x1_recon_mu, x1_recon_lsgms = decoder_out.split(split_dim=1,
num_splits=2)
# x1_recon_mu = tf.nn.sigmoid( x1_recon_mu )
# x1_recon_lsgms = tf.nn.sigmoid( x1_recon_lsgms )
return x1_recon_mu, x1_recon_lsgms
def _generate_x2zy(self, z, y, reuse=False):
with tf.variable_scope('decoder_2', reuse=reuse):
decoder_out = self.decoder_2.output(tf.concat([z, y], 1))
x2_recon_mu, x2_recon_lsgms = decoder_out.split(split_dim=1,
num_splits=2)
# x2_recon_mu = tf.nn.sigmoid( x2_recon_mu )
# x2_recon_lsgms = tf.nn.sigmoid( x2_recon_lsgms )
return x2_recon_mu, x2_recon_lsgms
def _objective(self):
###############
''' L(x,y) '''
###############
def L(x1_recon_z1, x1_recon_z2, x2_recon_z1, x2_recon_z2, x1, x2, y,
z1, z2, zeta1, zeta2):
if self.distributions['p_z'] == 'gaussian_marg':
log_prior_z1 = tf.reduce_sum(
utils.tf_gaussian_marg(z1[1], z1[2]), 1)
log_prior_z2 = tf.reduce_sum(
utils.tf_gaussian_marg(z2[1], z2[2]), 1)
log_prior_z = tf.add(tf.multiply(zeta1, log_prior_z1),
tf.multiply(zeta2, log_prior_z2))
elif self.distributions['p_z'] == 'gaussian':
log_prior_z1 = tf.reduce_sum(utils.tf_stdnormal_logpdf(z1[0]),
1)
log_prior_z2 = tf.reduce_sum(utils.tf_stdnormal_logpdf(z2[0]),
1)
log_prior_z = tf.add(tf.multiply(zeta1, log_prior_z1),
tf.multiply(zeta2, log_prior_z2))
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(
logits=y_prior, labels=y)
if self.distributions['p_x1'] == 'gaussian':
log_lik_x1_z1 = tf.reduce_sum(
utils.tf_normal_logpdf(x1, x1_recon_z1[0], x1_recon_z1[1]),
1)
log_lik_x1_z2 = tf.reduce_sum(
utils.tf_normal_logpdf(x1, x1_recon_z2[0], x1_recon_z2[1]),
1)
log_lik_x1 = zeta1 * log_lik_x1_z1 + zeta2 * log_lik_x1_z2
if self.distributions['p_x2'] == 'gaussian':
log_lik_x2_z1 = tf.reduce_sum(
utils.tf_normal_logpdf(x2, x2_recon_z1[0], x2_recon_z1[1]),
1)
log_lik_x2_z2 = tf.reduce_sum(
utils.tf_normal_logpdf(x2, x2_recon_z2[0], x2_recon_z2[1]),
1)
log_lik_x2 = zeta1 * log_lik_x2_z1 + zeta2 * log_lik_x2_z2
if self.distributions['q_z'] == 'gaussian_mixture_marg':
log_post_z = tf.reduce_sum(
utils.tf_gaussian_mixture_ent(z1[1], z2[1], z1[2], z2[2],
zeta1, zeta2), 1)
_L = log_lik_x1 + log_lik_x2 + log_prior_y + log_prior_z - log_post_z
return _L
###########################
''' Labelled Datapoints '''
###########################
self.y_lab_logits, self.x1_lab, self.x2_lab = self._generate_yx1x2(
self.x1_labelled_mu, self.x2_labelled_mu, self.x1_labelled_lsgms,
self.x2_labelled_lsgms)
self.z1_lab, self.z1_lab_mu, self.z1_lab_lsgms = self._generate_zx1y(
self.x1_lab, self.y_lab)
self.z2_lab, self.z2_lab_mu, self.z2_lab_lsgms = self._generate_zx2y(
self.x2_lab, self.y_lab)
self.x1_recon_lab_mu_z1, self.x1_recon_lab_lsgms_z1 = self._generate_x1zy(
self.z1_lab, self.y_lab)
self.x1_recon_lab_mu_z2, self.x1_recon_lab_lsgms_z2 = self._generate_x1zy(
self.z2_lab, self.y_lab, reuse=True)
self.x2_recon_lab_mu_z1, self.x2_recon_lab_lsgms_z1 = self._generate_x2zy(
self.z1_lab, self.y_lab)
self.x2_recon_lab_mu_z2, self.x2_recon_lab_lsgms_z2 = self._generate_x2zy(
self.z2_lab, self.y_lab, reuse=True)
L_lab = L([self.x1_recon_lab_mu_z1, self.x1_recon_lab_lsgms_z1],
[self.x1_recon_lab_mu_z2, self.x1_recon_lab_lsgms_z2],
[self.x2_recon_lab_mu_z1, self.x2_recon_lab_lsgms_z1],
[self.x2_recon_lab_mu_z2, self.x2_recon_lab_lsgms_z2],
self.x1_lab, self.x2_lab, self.y_lab,
[self.z1_lab, self.z1_lab_mu, self.z1_lab_lsgms],
[self.z2_lab, self.z2_lab_mu, self.z2_lab_lsgms], self.zeta1,
self.zeta2)
L_lab += -self.alpha * tf.nn.softmax_cross_entropy_with_logits(
logits=self.y_lab_logits, labels=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,
dense_shape=[self.num_ulab_batch, self.dim_y]),
0.0)
return _y_ulab
self.y_ulab_logits, self.x1_ulab, self.x2_ulab = self._generate_yx1x2(
self.x1_unlabelled_mu,
self.x2_unlabelled_mu,
self.x1_unlabelled_lsgms,
self.x2_unlabelled_lsgms,
reuse=True)
for label in range(self.dim_y):
_y_ulab = one_label_tensor(label)
self.z1_ulab, self.z1_ulab_mu, self.z1_ulab_lsgms = self._generate_zx1y(
self.x1_ulab, _y_ulab, reuse=True)
self.z2_ulab, self.z2_ulab_mu, self.z2_ulab_lsgms = self._generate_zx2y(
self.x2_ulab, _y_ulab, reuse=True)
self.x1_recon_ulab_mu_z1, self.x1_recon_ulab_lsgms_z1 = self._generate_x1zy(
self.z1_ulab, _y_ulab, reuse=True)
self.x1_recon_ulab_mu_z2, self.x1_recon_ulab_lsgms_z2 = self._generate_x1zy(
self.z2_ulab, _y_ulab, reuse=True)
self.x2_recon_ulab_mu_z1, self.x2_recon_ulab_lsgms_z1 = self._generate_x2zy(
self.z1_ulab, _y_ulab, reuse=True)
self.x2_recon_ulab_mu_z2, self.x2_recon_ulab_lsgms_z2 = self._generate_x2zy(
self.z2_ulab, _y_ulab, reuse=True)
_L_ulab = tf.expand_dims(
L([self.x1_recon_ulab_mu_z1, self.x1_recon_ulab_lsgms_z1],
[self.x1_recon_ulab_mu_z2, self.x1_recon_ulab_lsgms_z2],
[self.x2_recon_ulab_mu_z1, self.x2_recon_ulab_lsgms_z1],
[self.x2_recon_ulab_mu_z2, self.x2_recon_ulab_lsgms_z2],
self.x1_ulab, self.x2_ulab, _y_ulab,
[self.z1_ulab, self.z1_ulab_mu, self.z1_ulab_lsgms],
[self.z2_ulab, self.z2_ulab_mu, self.z2_ulab_lsgms],
self.zeta1, self.zeta2), 1)
if label == 0: L_ulab = tf.identity(_L_ulab)
else: L_ulab = tf.concat([L_ulab, _L_ulab], 1)
self.y_ulab = self.y_ulab_logits.softmax_activation()
U = tf.reduce_sum(
tf.multiply(self.y_ulab, tf.subtract(L_ulab, tf.log(self.y_ulab))),
1)
########################
''' 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)
zeta_loss = tf.pow(self.zeta1 + self.zeta2 - 1, 2)
self.cost = (
(L_lab_tot + U_tot - self.zeta_reg * zeta_loss) * self.num_batches
+ L_weights) / (-self.num_batches * self.batch_size)
##################
''' Evaluation '''
##################
self.y_test_logits, _, _ = self._generate_yx1x2(self.x1_labelled_mu,
self.x2_labelled_mu,
self.x1_labelled_lsgms,
self.x2_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 )
def train(self,
x1_labelled,
x2_labelled,
y,
x1_unlabelled,
x2_unlabelled,
epochs,
x1_valid,
x2_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 = 'models/model_GC_{}-{}-{}.cpkt'.format(
self.num_lab, learning_rate, self.batch_size)
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.global_variables_initializer()
self._test_vars = None
_data_labelled = np.hstack([x1_labelled, x2_labelled, y])
_data_unlabelled = np.hstack([x1_unlabelled, x2_unlabelled])
x1_valid_mu, x1_valid_lsgms = x1_valid[:, :self.
dim_x1], x1_valid[:, self.
dim_x1:2 *
self.dim_x1]
x2_valid_mu, x2_valid_lsgms = x2_valid[:, :self.
dim_x2], x2_valid[:, self.
dim_x2:2 *
self.dim_x2]
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 x1_l_mu, x2_l_mu, x1_l_lsgms, x2_l_lsgms, y, x1_u_mu, x2_u_mu, x1_u_lsgms, x2_u_lsgms in utils.feed_numpy_semisupervised_multiview(
self.num_lab_batch, self.num_ulab_batch,
_data_labelled[:, :2 * self.dim_x1],
_data_labelled[:, 2 * self.dim_x1:(2 * self.dim_x1 +
2 * self.dim_x2)],
_data_labelled[:,
(2 * self.dim_x1 + 2 * self.dim_x2):],
_data_unlabelled[:, :2 * self.dim_x1],
_data_unlabelled[:, 2 * self.dim_x1:]):
training_result = sess.run(
[self.train_op, self.cost, self.zeta1, self.zeta2],
feed_dict={
self.x1_labelled_mu: x1_l_mu,
self.x2_labelled_mu: x2_l_mu,
self.x1_labelled_lsgms: x1_l_lsgms,
self.x2_labelled_lsgms: x2_l_lsgms,
self.y_lab: y,
self.x1_unlabelled_mu: x1_u_mu,
self.x2_unlabelled_mu: x2_u_mu,
self.x1_unlabelled_lsgms: x1_u_lsgms,
self.x2_unlabelled_lsgms: x2_u_lsgms
})
training_cost = training_result[1]
zeta1 = training_result[2]
zeta2 = training_result[3]
''' 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.variables_initializer(
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.x1_labelled_mu: x1_valid_mu,
self.x2_labelled_mu: x2_valid_mu,
self.x1_labelled_lsgms: x1_valid_lsgms,
self.x2_labelled_lsgms: x2_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],
['Training', 'zeta1', zeta1],
['Training', 'zeta2', zeta2])
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
def predict_labels(self, x1_test, x2_test, y_test):
test_vars = tf.get_collection(bookkeeper.GraphKeys.TEST_VARIABLES)
tf.initialize_variables(test_vars).run()
x1_test_mu = x1_test[:, :self.dim_x1]
x1_test_lsgms = x1_test[:, self.dim_x1:2 * self.dim_x1]
x2_test_mu = x2_test[:, :self.dim_x2]
x2_test_lsgms = x2_test[:, self.dim_x2:2 * self.dim_x2]
accuracy, cross_entropy, precision, recall, y_test_pred= \
self.session.run( [self.eval_accuracy, self.eval_cross_entropy, self.eval_precision, self.eval_recall, self.y_test_pred],
feed_dict = {self.x1_labelled_mu: x1_test_mu,
self.x2_labelled_mu: x2_test_mu,
self.x1_labelled_lsgms: x1_test_lsgms,
self.x2_labelled_lsgms: x2_test_lsgms,
self.y_lab: y_test} )
utils.print_metrics('X', ['Test', 'accuracy', accuracy],
['Test', 'cross-entropy', cross_entropy],
['Test', 'precision', precision],
['Test', 'recall', recall])
return y_test_pred