utils.py

import prettytensor as pt
import tensorflow as tf
import numpy as np

logc = np.log(2.*np.pi)
c = - 0.5 * np.log(2*np.pi)

def tf_normal_logpdf(x, mu, log_sigma_sq):

	return ( - 0.5 * logc - log_sigma_sq / 2. - tf.div( tf.square( tf.sub( x, mu ) ), 2 * tf.exp( log_sigma_sq ) ) )

def tf_stdnormal_logpdf(x):

	return ( - 0.5 * ( logc + tf.square( x ) ) )

def tf_gaussian_ent(log_sigma_sq):

	return ( - 0.5 * ( logc + 1.0 + log_sigma_sq ) )

def tf_gaussian_marg(mu, log_sigma_sq):

	return ( - 0.5 * ( logc + ( tf.square( mu ) + tf.exp( log_sigma_sq ) ) ) )

def tf_binary_xentropy(x, y, const = 1e-10):

    return - ( x * tf.log ( tf.clip_by_value( y, const, 1.0 ) ) + \
             (1.0 - x) * tf.log( tf.clip_by_value( 1.0 - y, const, 1.0 ) ) )

def feed_numpy_semisupervised(num_lab_batch, num_ulab_batch, x_lab, y, x_ulab):

	size = x_lab.shape[0] + x_ulab.shape[0]
	batch_size = num_lab_batch + num_ulab_batch
	count = int(size / batch_size)

	dim = x_lab.shape[1]

	for i in xrange(count):
		start_lab = i * num_lab_batch
		end_lab = start_lab + num_lab_batch
		start_ulab = i * num_ulab_batch
		end_ulab = start_ulab + num_ulab_batch

		yield [	x_lab[start_lab:end_lab,:dim/2], x_lab[start_lab:end_lab,dim/2:dim], y[start_lab:end_lab],
				x_ulab[start_ulab:end_ulab,:dim/2], x_ulab[start_ulab:end_ulab,dim/2:dim] ]

def feed_numpy(batch_size, x):

	size = x.shape[0]
	count = int(size / batch_size)

	dim = x.shape[1]

	for i in xrange(count):
		start = i * batch_size
		end = start + batch_size

		yield x[start:end]

def print_metrics(epoch, *metrics):

	print(25*'-')
	for metric in metrics: 
		print('[{}] {} {}: {}'.format(epoch, metric[0],metric[1],metric[2]))
	print(25*'-')