Scatter
updates into a new tensor according to
indices.
Creates a new tensor by applying sparse
updates to individual values or
slices within a tensor (initially zero for numeric, empty for string) 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.
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:
python
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:
python
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]]]
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.
Arguments:
* scope: A Scope object
* indices: Index tensor.
* updates: Updates to scatter into output.
* shape: 1-D. The shape of the resulting tensor.
Returns:
*
Output: A new tensor with the given shape and updates applied according
to the indices.
