def apply_mask(x, scope=''):
"""Apply mask to a given weight tensor.
Args:
x: Input weight tensor
scope: The current variable scope. Defaults to "".
Returns:
Tensor representing masked_weights
"""
mask = pruning_utils.weight_mask_variable(x, scope)
threshold = pruning_utils.weight_threshold_variable(x, scope)
# Add masked_weights in the weights namescope so as to make it easier
# for the quantization library to add quant ops.
masked_weights = math_ops.multiply(mask, x, _MASKED_WEIGHT_NAME)
# Make sure the mask for a given variable are not added multiple times to the
# collection. This is particularly important when applying mask to RNN's
# weight variables
if mask not in ops.get_collection_ref(_MASK_COLLECTION):
ops.add_to_collection(_THRESHOLD_COLLECTION, threshold)
ops.add_to_collection(_MASK_COLLECTION, mask)
ops.add_to_collection(_MASKED_WEIGHT_COLLECTION, masked_weights)
ops.add_to_collection(_WEIGHT_COLLECTION, x)
return masked_weights
def apply_mask(x, scope=''):
"""Apply mask to a given weight tensor.
Args:
x: Input weight tensor
scope: The current variable scope. Defaults to "".
Returns:
Tensor representing masked_weights
"""
mask = pruning_utils.weight_mask_variable(x, scope)
threshold = pruning_utils.weight_threshold_variable(x, scope)
# Add masked_weights in the weights namescope so as to make it easier
# for the quantization library to add quant ops.
masked_weights = math_ops.multiply(mask, x, _MASKED_WEIGHT_NAME)
# Make sure the mask for a given variable are not added multiple times to the
# collection. This is particularly important when applying mask to RNN's
# weight variables
if mask not in ops.get_collection_ref(_MASK_COLLECTION):
ops.add_to_collection(_THRESHOLD_COLLECTION, threshold)
ops.add_to_collection(_MASK_COLLECTION, mask)
ops.add_to_collection(_MASKED_WEIGHT_COLLECTION, masked_weights)
ops.add_to_collection(_WEIGHT_COLLECTION, x)
return masked_weights