tf.scatter_update

tf.scatter_update(
    ref,
    indices,
    updates,
    use_locking=True,
    name=None
)

Defined in tensorflow/python/ops/state_ops.py.

See the guide: Variables > Sparse Variable Updates

Applies sparse updates to a variable reference.

This operation computes

    # Scalar indices
    ref[indices, ...] = updates[...]

    # Vector indices (for each i)
    ref[indices[i], ...] = updates[i, ...]

    # High rank indices (for each i, ..., j)
    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.

If values in ref is to be updated more than once, because there are duplicate entries in indices, the order at which the updates happen for each value is undefined.

Requires updates.shape = indices.shape + ref.shape[1:].

Args:

  • ref: A Variable.
  • 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 store in ref.
  • use_locking: An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  • name: A name for the operation (optional).

Returns:

Same as ref. Returned as a convenience for operations that want to use the updated values after the update is done.