def scatter_max(ref, indices, updates, use_locking=False, name=None):
# pylint: disable=line-too-long
r"""Reduces sparse updates into a variable reference using the `max` operation.
This operation computes
# Scalar indices
ref[indices, ...] = max(ref[indices, ...], updates[...])
# Vector indices (for each i)
ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...])
# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...],
updates[i, ..., j, ...])
This operation outputs `ref` after the update is done.
This makes it easier to chain operations that need to use the reset value.
Duplicate entries are handled correctly: if multiple `indices` reference
the same location, their contributions combine.
Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape =
[]`.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/ScatterAdd.png"
alt>
</div>
Args:
ref: A mutable `Tensor`. Must be one of the following types: `half`,
`bfloat16`, `float32`, `float64`, `int32`, `int64`. Should be from a
`Variable` node.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A
tensor of indices into the first dimension of `ref`.
updates: A `Tensor`. Must have the same type as `ref`. A tensor of updated
values to reduce into `ref`.
use_locking: An optional `bool`. Defaults to `False`. If True, the update
will be protected by a lock; otherwise the behavior is undefined, but may
exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
if ref.dtype._is_ref_dtype:
return gen_state_ops.scatter_max(ref,
indices,
updates,
use_locking=use_locking,
name=name)
return ref._lazy_read(
gen_resource_variable_ops.resource_scatter_max( # pylint: disable=protected-access
ref.handle,
indices,
ops.convert_to_tensor(updates, ref.dtype),
name=name))
def scatter_max(ref, indices, updates, use_locking=False, name=None):
# pylint: disable=line-too-long
r"""Reduces sparse updates into a variable reference using the `max` operation.
This operation computes
# Scalar indices
ref[indices, ...] = max(ref[indices, ...], updates[...])
# Vector indices (for each i)
ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...])
# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...],
updates[i, ..., j, ...])
This operation outputs `ref` after the update is done.
This makes it easier to chain operations that need to use the reset value.
Duplicate entries are handled correctly: if multiple `indices` reference
the same location, their contributions combine.
Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape =
[]`.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/ScatterAdd.png"
alt>
</div>
Args:
ref: A mutable `Tensor`. Must be one of the following types: `half`,
`bfloat16`, `float32`, `float64`, `int32`, `int64`. Should be from a
`Variable` node.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A
tensor of indices into the first dimension of `ref`.
updates: A `Tensor`. Must have the same type as `ref`. A tensor of updated
values to reduce into `ref`.
use_locking: An optional `bool`. Defaults to `False`. If True, the update
will be protected by a lock; otherwise the behavior is undefined, but may
exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
return gen_state_ops.scatter_max(
ref=ref,
indices=indices,
updates=updates,
use_locking=use_locking,
name=name)