def _get_weights(self, name, shape, dtype=None, initializer=None):
if initializer is None: initializer = self.initializer
else: initializer = initializers.get(initializer)
# Set default dtype if not specified
if dtype is None: dtype = hub.dtype
# Get regularizer if necessary
regularizer = None
if hub.use_global_regularizer:
regularizer = hub.get_global_regularizer()
# Get constraint if necessary
constraint = hub.get_global_constraint()
# Get weights
weights = tf.get_variable(name,
shape,
dtype=dtype,
initializer=initializer,
regularizer=regularizer,
constraint=constraint)
# If weight dropout is positive, dropout and return
if self.weight_dropout > 0:
return linker.dropout(weights, self.weight_dropout, rescale=True)
# If no mask is needed to be created, return weight variable directly
if not any(
[self.prune_is_on, self.being_etched, hub.force_to_use_pruner]):
return weights
# Register, context.pruner should be created in early model.build
assert context.pruner is not None
# Merged lottery logic into etch logic
if self.prune_is_on:
assert not self.being_etched
self.etch = 'lottery:prune_frac={}'.format(self.prune_frac)
# Register etch kernel to pruner
masked_weights = context.pruner.register_to_dense(weights, self.etch)
# if self.prune_is_on:
# masked_weights = context.pruner.register_to_dense(
# weights, self.prune_frac)
# else:
# # TODO
# assert self.being_etched
# mask = self._get_etched_surface(weights)
# masked_weights = context.pruner.register_with_mask(weights, mask)
# Return
assert isinstance(masked_weights, tf.Tensor)
return masked_weights
def dropout(input_, dropout_rate):
return linker.dropout(input_, dropout_rate)