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Reduces sparse updates into a variable reference using the max operation.
tf.compat.v1.scatter_max(
ref, indices, updates, use_locking=False, name=None
)
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 =
[].
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.
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