ScatterNDUpdate#
Versioned name: ScatterNDUpdate-3
Category: Data movement
Short description: Creates a copy of the first input tensor with updated elements specified with second and third input tensors.
Detailed description: The operation produces a copy of data
tensor and updates its value to values specified
by updates
at specific index positions specified by indices
. The output shape is the same as the shape of data
.
indices
tensor must not have duplicate entries. In case of duplicate entries in indices
the result is undefined.
The last dimension of indices
can be at most the rank of data.shape
.
The last dimension of indices
corresponds to indices into elements if indices.shape[-1]
= data.shape.rank
or slices
if indices.shape[-1]
< data.shape.rank
. updates
is a tensor with shape indices.shape[:-1] + data.shape[indices.shape[-1]:]
Example 1 that shows update of four single elements in data
:
data = [1, 2, 3, 4, 5, 6, 7, 8]
indices = [[4], [3], [1], [7]]
updates = [9, 10, 11, 12]
output = [1, 11, 3, 10, 9, 6, 7, 12]
Example 2 that shows update of two slices of 4x4
shape in data
:
data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
indices = [[0], [2]]
updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
Attributes: ScatterNDUpdate does not have attributes.
Inputs:
1:
data
tensor of arbitrary rankr
>= 1 and of type T. Required.2:
indices
tensor with indices of arbitrary rankq
>= 1 and of type T_IND. All index valuesi_j
in index entry(i_0, i_1, ...,i_k)
(wherek = indices.shape[-1]
) must be within bounds[0, s_j - 1]
wheres_j = data.shape[j]
.k
must be at mostr
. Required.3:
updates
tensor of rankr - indices.shape[-1] + q - 1
of type T. If expectedupdates
rank is 0D it can be a tensor with single element. Required.
Outputs:
1: tensor with shape equal to
data
tensor of the type T.
Types
T: any numeric type.
T_IND:
int32
orint64
Example
<layer ... type="ScatterNDUpdate">
<input>
<port id="0">
<dim>1000</dim>
<dim>256</dim>
<dim>10</dim>
<dim>15</dim>
</port>
<port id="1">
<dim>25</dim>
<dim>125</dim>
<dim>3</dim>
</port>
<port id="2">
<dim>25</dim>
<dim>125</dim>
<dim>15</dim>
</port>
</input>
<output>
<port id="3">
<dim>1000</dim>
<dim>256</dim>
<dim>10</dim>
<dim>15</dim>
</port>
</output>
</layer>