# tf.scatter_nd

tf.scatter_nd(
indices,
shape,
name=None
)


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

See the guide: Tensor Transformations > Slicing and Joining

Scatter updates into a new (initially zero) tensor according to indices.

Creates a new tensor by applying sparse updates to individual values or slices within a zero tensor of the given shape according to indices. This operator is the inverse of the tf.gather_nd operator which extracts values or slices from a given tensor.

WARNING: The order in which updates are applied is nondeterministic, so the output will be nondeterministic if indices contains duplicates.

indices is an integer tensor containing indices into a new tensor of shape shape. The last dimension of indices can be at most the rank of shape:

indices.shape[-1] <= shape.rank


The last dimension of indices corresponds to indices into elements (if indices.shape[-1] = shape.rank) or slices (if indices.shape[-1] < shape.rank) along dimension indices.shape[-1] of shape. updates is a tensor with shape

indices.shape[:-1] + shape[indices.shape[-1]:]


The simplest form of scatter is to insert individual elements in a tensor by index. For example, say we want to insert 4 scattered elements in a rank-1 tensor with 8 elements.

In Python, this scatter operation would look like this:

    indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
shape = tf.constant([8])
with tf.Session() as sess:
print(sess.run(scatter))


The resulting tensor would look like this:

[0, 11, 0, 10, 9, 0, 0, 12]


We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values.

In Python, this scatter operation would look like this:

    indices = tf.constant([[0], [2]])
updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]],
[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]]])
shape = tf.constant([4, 4, 4])
with tf.Session() as sess:
print(sess.run(scatter))


The resulting tensor would look like this:

[[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]


Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, the index is ignored.

#### Args:

• indices: A Tensor. Must be one of the following types: int32, int64. Index tensor.
• updates: A Tensor. Updates to scatter into output.
• shape: A Tensor. Must have the same type as indices. 1-D. The shape of the resulting tensor.
• name: A name for the operation (optional).

#### Returns:

A Tensor. Has the same type as updates.