Subtracts sparse updates
from an existing tensor according to indices
.
tf.tensor_scatter_nd_sub(
tensor: Annotated[Any, TV_TensorScatterSub_T],
indices: Annotated[Any, TV_TensorScatterSub_Tindices],
updates: Annotated[Any, TV_TensorScatterSub_T],
name=None
) > Annotated[Any, TV_TensorScatterSub_T]
This operation creates a new tensor by subtracting sparse updates
from the
passed in tensor
.
This operation is very similar to tf.scatter_nd_sub
, except that the updates
are subtracted from an existing tensor (as opposed to a variable). If the memory
for the existing tensor cannot be reused, a copy is made and updated.
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 tensor_scatter_sub is to subtract individual elements from a tensor by index. For example, say we want to insert 4 scattered elements in a rank1 tensor with 8 elements.
In Python, this scatter subtract 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_nd_sub(tensor, indices, updates)
print(updated)
The resulting tensor would look like this:
[1, 10, 1, 9, 8, 1, 1, 11]
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 rank3 tensor with two matrices of new values.
In Python, this scatter add 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],dtype=tf.int32)
updated = tf.tensor_scatter_nd_sub(tensor, indices, updates)
print(updated)
The resulting tensor would look like this:
[[[4, 4, 4, 4], [5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[4, 4, 4, 4], [5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7]],
[[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.
Returns  

A Tensor . Has the same type as tensor .
