Prefer tf.ones and tf.zeros over tf.fill

[lgeiger]

This PR removes usage of tf.fill in favour of tf.ones or tf.zeros to simplify the code.

[lgeiger]

This could be further simplified, but would require changes to

tensorflow/tensorflow/python/ops/default_gradient.py

Lines 52 to 57 in 21f11eb

	def zeros_like(t):
	"""Like array_ops.zeros_like, but respects resource handles."""
	if t.dtype == dtypes.resource:
	return array_ops.zeros(*shape_and_dtype(t))
	else:
	return array_ops.zeros_like(t)

to accept keyword arguments like name. Let me know if you think that's worth doing.

[allenlavoie]

Does the array_ops.py ones_like work?

tensorflow/tensorflow/python/ops/array_ops.py

Line 3002 in 85c8b2a

def ones_like(tensor, dtype=None, name=None, optimize=True):

[lgeiger]

I don't think that's possible since judging from this check, y can be a tf.resource which is not supported by array_ops.ones_like.

[allenlavoie]

None of these (fill, array_ops.zeros, array_ops.zeros_like) support resource-dtype tensors. It looks like it's calling the same "ones" kernel right now, but having it call the standard API would be useful if we ever decide to have a "OnesLike" kernel like we do for zeros.

[lgeiger]

Not sure if I fully understand your comment. The current code works correctly for resource-dtype tensors since tf.shape can handle resource-dtype tensors, so array_ops.zeros never receives a resource-dtype tensors itself.

[allenlavoie]

Shape works fine, but neither constant_op.constant(1, dtype=dtypes.resource) nor array_ops.ones(shape=..., dtype=dtypes.resource) work. array_ops.ones_like just moves some of the boilerplate to a helper, the functionality is equivalent.

[allenlavoie]

Does the array_ops.py ones_like work?

tensorflow/tensorflow/python/ops/array_ops.py

Line 3002 in 85c8b2a

def ones_like(tensor, dtype=None, name=None, optimize=True):

[lgeiger]

@allenlavoie I had to update the PR to fix a pylint error on CI, do you mind approving it again?