Defined in generated file: python/ops/gen_array_ops.py Scatter updates into an existing tensor according to indices.

# Aliases:

• tf.compat.v1.tensor_scatter_nd_update
• tf.compat.v1.tensor_scatter_update
• tf.compat.v2.tensor_scatter_nd_update

 tf.tensor_scatter_nd_update(
    tensor,
    indices,
    updates,
    name=None
)

tf.scatter_ndThis operation creates a new tensor by applying sparse updates to the passed in tensor. This operation is very similar to , except that the updates are scattered onto an existing tensor (as opposed to a zero-tensor). If the memory for the existing tensor cannot be re-used, a copy is made and updated.

If indices contains duplicates, then their updates are accumulated (summed). WARNING: The order in which updates are applied is nondeterministic, so the output will be nondeterministic if indices contains duplicates -- because of some numerical approximation issues, numbers summed in different order may yield different results. 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])
    tensor = tf.ones([8], dtype=tf.int32)
    updated = tf.tensor_scatter_update(tensor, indices, updates)
    with tf.Session() as sess:
      print(sess.run(scatter))

The resulting tensor would look like this:

 [1, 11, 1, 10, 9, 1, 1, 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]]])
    tensor = tf.ones([4, 4, 4])
    updated = tf.tensor_scatter_update(tensor, indices, updates)
    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]],
 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
 [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]]

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:

• tensor: A Tensor. Tensor to copy/update.
• indices: A Tensor. Must be one of the following types: int32, int64. Index tensor.
• updates: A Tensor. Must have the same type as tensor. Updates to scatter into output.
• name: A name for the operation (optional).

# Returns:

A Tensor. Has the same type as tensor.