def testSliceRegularizer(self):
concat_reg = concat_and_slice_regularizers.SlicingReferenceRegularizer(
lambda: self._reg1, 1, 2)
with self.cached_session():
self.assertAllEqual(self._alive_vec1[1:3],
concat_reg.alive_vector.eval())
self.assertAllClose(self._reg_vec1[1:3],
concat_reg.regularization_vector.eval(), 1e-5)
def _create_concat_regularizer(self, concat_op):
"""Creates an OpRegularizer for a concat op.
Args:
concat_op: A tf.Operation of type ConcatV2.
Returns:
An OpRegularizer for `concat_op`.
"""
# We omit the last input, because it's the concat dimension. Others are
# the tensors to be concatenated.
input_ops = [i.op for i in concat_op.inputs[:-1]]
regularizers_to_concat = [
self._get_regularizer(op) for op in input_ops
]
# If all inputs have no regularizer, so does the concat op.
if regularizers_to_concat == [None] * len(regularizers_to_concat):
return None
offset = 0
# Replace the regularizers_to_concat by SlicingReferenceRegularizer-s that
# slice the concatenated regularizer.
ops_to_concat = []
for r, op in zip(regularizers_to_concat, input_ops):
if r is None:
length = op.outputs[0].shape.as_list()[-1]
offset += length
ops_to_concat.append(self._ConstantOpReg(length))
else:
length = tf.shape(r.alive_vector)[0]
slice_ref = concat_and_slice_regularizers.SlicingReferenceRegularizer(
lambda: self._get_regularizer(concat_op), offset, length)
offset += length
self._replace_regularizer(r, slice_ref)
ops_to_concat.append(r)
# Create the concatenated regularizer itself.
return concat_and_slice_regularizers.ConcatRegularizer(ops_to_concat)