def merge(inputs, name=None):
"""Returns the value of an available element of `inputs`.
This op tests each of the tensors in `inputs` in turn to determine if any of
them is available. If it finds an available tensor, it returns it and its
index in `inputs`.
It is an error if more than one tensor in `inputs` is available. If no tensor
in `inputs` is available, the returned tensor and index are not set.
This op handles both `Tensor`s and `IndexedSlices`. If inputs has a mix of
`Tensor`s and `IndexedSlices`, all inputs are converted to IndexedSlices
before merging.
Args:
inputs: The input tensors, at most one of which is available.
name: A name for this operation (optional).
Returns:
A tuple containing the chosen input tensor and its index in `inputs`.
Raises:
ValueError: If inputs are IndexedSlices and some but not all have a
dense_shape property.
"""
with ops.op_scope(inputs, name, "Merge") as name:
inputs = [ops.convert_to_tensor_or_indexed_slices(inp) for inp in inputs]
if all([isinstance(inp, ops.Tensor) for inp in inputs]):
return gen_control_flow_ops._merge(inputs, name=name)
else:
inputs = math_ops._as_indexed_slices_list(inputs)
values, _ = gen_control_flow_ops._merge([inp.values for inp in inputs],
name=name)
indices, chosen_index = gen_control_flow_ops._merge(
[inp.indices for inp in inputs], name="indices")
if any(inp.dense_shape for inp in inputs):
if not all(inp.dense_shape for inp in inputs):
raise ValueError("Either all merged IndexedSlices must have a "
"dense_shape, or none must have a dense_shape.")
dense_shape, _ = gen_control_flow_ops._merge(
[inp.dense_shape for inp in inputs], name="dense_shape")
else:
dense_shape = None
return ops.IndexedSlices(values, indices, dense_shape), chosen_index
def _AccumulateNInitializedWithMerge(self, inputs):
return self._AccumulateNTemplate(
inputs,
init=array_ops.zeros_like(gen_control_flow_ops._merge(inputs)[0]),
shape=tensor_shape.vector(0),
validate_shape=False)
def _AccumulateNInitializedWithMerge(self, inputs):
return self._AccumulateNTemplate(
inputs,
init=array_ops.zeros_like(gen_control_flow_ops._merge(inputs)[0]),
shape=tensor_shape.vector(0),
validate_shape=False)
