# tf.scatter_min

tf.scatter_min(
ref,
indices,
use_locking=False,
name=None
)


Defined in generated file: tensorflow/python/ops/gen_state_ops.py.

See the guide: Variables > Sparse Variable Updates

Reduces sparse updates into a variable reference using the min operation.

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.