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

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

See the guide: Tensor Transformations > Slicing and Joining

Creates a new tensor by applying sparse updates to individual

values or slices within a zero tensor of the given shape tensor according to indices. This operator is the inverse of the tf.gather_nd operator which extracts values or slices from a given tensor.

TODO(simister): Add a link to Variable.getitem documentation on slice syntax.

shape is a TensorShape with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into shape. 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 slices (if K < P) along the Kth dimension of shape.

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

[d_0, ..., d_{Q-2}, shape[K], ..., shape[P-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])
scatter = tf.scatter_nd(indices, updates, shape)
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])
scatter = tf.scatter_nd(indices, updates, shape)
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]]]

Args:

• 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 ref.
• updates: A Tensor. A Tensor. Must have the same type as tensor. A tensor of updated values to store in ref.
• shape: A Tensor. Must have the same type as indices. A vector. The shape of the resulting tensor.
• name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as updates. A new tensor with the given shape and updates applied according to the indices.

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