/TensorFlow 2.4


Applies sparse addition to input using individual values or slices

from updates according to indices indices. The updates are non-aliasing: input is only modified in-place if no other operations will use it. Otherwise, a copy of input is made. This operation has a gradient with respect to both input and updates.

input is a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into input. It must be shape \([d_0, ..., d_{Q-2}, K]\) where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or (P-K)-dimensional slices (if K < P) along the Kth dimension of input.

updates is Tensor of rank Q-1+P-K with shape:

$$[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].$$

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that addition would look like this:

input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
with tf.Session() as sess:

The resulting value output would look like this:

[1, 13, 3, 14, 14, 6, 7, 20]

See tf.scatter_nd for more details about how to make updates to slices.

input A 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, bool. A Tensor.
indices A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into input.
updates A Tensor. Must have the same type as input. A Tensor. Must have the same type as ref. A tensor of updated values to add to input.
name A name for the operation (optional).
A Tensor. Has the same type as input.

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Code samples licensed under the Apache 2.0 License.