tf.scatter_mul

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Multiplies sparse updates into a variable reference.

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

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.

Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions multiply.

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

ref A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. 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 multiply to ref.
use_locking An optional bool. Defaults to False. If True, the operation 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.