ops.py

import tensorflow as tf 
import math
from six.moves import xrange

def lrelu(x, leak=0.2, name="lrelu"):
	return tf.maximum(x, leak*x)

def batch_normalization(logits, scale, offset, isCovNet = False, name="bn"):
	#exp_moving_avg = tf.train.ExponentialMovingAverage(0.9999, iteration)
	if isCovNet:
		mean, var = tf.nn.moments(logits, [0, 1, 2])
	else:
		mean, var = tf.nn.moments(logits, [0])
	#update_moving_avg = exp_moving_avg.apply([mean, var])
	#m = tf.cond(self.istest, lambda: exp_moving_avg.average(mean), lambda:mean)
	#v = tf.cond(self.istest, lambda: exp_moving_avg.average(var), lambda:var)
	output = tf.nn.batch_normalization(logits, mean, var, offset, scale, variance_epsilon=1e-5)
	return output

def get_conv_weights(weight_shape, sess, name="get_conv_weights"):
	return math.sqrt(2 / (9.0 * 64)) * sess.run(tf.truncated_normal(weight_shape))

def get_bn_weights(weight_shape, clip_b, sess, name="get_bn_weights"):
	weights = get_conv_weights(weight_shape, sess)
	return clipping(weights, clip_b)

def clipping(A, clip_b, name="clipping"):
	h, w = A.shape 
	for i in xrange(h):
		for j in xrange(w):
			if A[i,j] >= 0 and A[i,j] < clip_b:
				A[i,j] = clip_b
			elif A[i,j] > -clip_b and A[i,j] < 0:
				A[i,j] = -clip_b
	return A