def _updated_mask(self, var, mask):
var, mask, alpha = self.session.run([var, mask, self.alpha])
threshold = self._threshold(var, alpha)
on_mask = util.abs(var) > self.on_factor * threshold
mask = util.logical_or(mask, on_mask)
off_mask = util.abs(var) > self.off_factor * threshold
return util.logical_and(mask, off_mask)
def _updated_mask(self, var, mask):
mask, gamma = self.session.run([mask, self.gamma])
if self.global_threshold:
threshold = self._global_threshold()
else:
if self.incremental:
gammas = gamma[util.nonzero(self.mask)]
threshold = self._threshold(gammas)
new_mask = gamma > threshold
if self.incremental:
return util.logical_and(mask, new_mask)
return new_mask
def _update(self):
# update positives mask and mean values
value = self.session.run(self.before)
# divide them into two groups
# mean = util.mean(value)
mean = 0.0
# find two central points
positives = value > mean
self.positives = positives
self.positives_mean = util.mean(value[util.where(positives)])
negatives = util.logical_and(util.logical_not(positives), value != 0)
self.negatives_mean = util.mean(value[util.where(negatives)])
if self.positives_mean.eval() == 0 or self.negatives_mean.eval() == 0:
log.warn(
'means are skewed, pos mean is {} and neg mean is {}'.format(
self.positives_mean.eval(), self.negatives_mean.eval()))
# update internal quantizer
self.quantizer.update()
for quantizer in self.parameter_quantizers.values():
quantizer.update()