Subtracts sparse `updates` from an existing tensor according to `indices`.
#### View aliases
**Compat aliases for migration**
See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.
[`tf.compat.v1.tensor_scatter_nd_sub`](https://www.tensorflow.org/api_docs/python/tf/tensor_scatter_nd_sub), [`tf.compat.v1.tensor_scatter_sub`](https://www.tensorflow.org/api_docs/python/tf/tensor_scatter_nd_sub)
tf.tensor_scatter_nd_sub(
tensor: Annotated[Any, TV_TensorScatterSub_T],
indices: Annotated[Any, TV_TensorScatterSub_Tindices],
updates: Annotated[Any, TV_TensorScatterSub_T],
bad_indices_policy: str = '',
name=None
) -> Annotated[Any, TV_TensorScatterSub_T]
### Used in the notebooks
| Used in the guide |
|---|
| - [Introduction to tensor slicing](https://www.tensorflow.org/guide/tensor_slicing) |
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 re-used, 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 rank-1 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
rank-3 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.
| ## 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. |
| `bad_indices_policy` | An optional `string`. Defaults to `""`. |
| `name` | A name for the operation (optional). |
| ## Returns ||
|---|---|
| A `Tensor`. Has the same type as `tensor`. ||