Reduces sparse updates into a variable reference using the min
operation.
tf.raw_ops.ScatterMin(
ref, indices, updates, use_locking=False, name=None
)
This operation computes
# Scalar indices
ref[indices, ...] = min(ref[indices, ...], updates[...])
# Vector indices (for each i)
ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...])
# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = min(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 .
|