tf.raw_ops.ScatterMin

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 = [].

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).

A mutable Tensor. Has the same type as ref.