Applies sparse subtraction to individual values or slices in a Variable.
tf.compat.v1.scatter_nd_sub( ref, indices, updates, use_locking=False, name=None )
ref
is a Tensor
with rank P
and indices
is a Tensor
of rank Q
.
indices
must be integer tensor, containing indices into ref
. It must be shape [d_0, ..., d_{Q-2}, K]
where 0 < K <= P
.
The innermost dimension of indices
(with length K
) corresponds to indices into elements (if K = P
) or slices (if K < P
) along the K
th dimension of ref
.
updates
is Tensor
of rank Q-1+P-K
with shape:
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]
For example, say we want to subtract 4 scattered elements from a rank-1 tensor with 8 elements. In Python, that update would look like this:
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) op = tf.compat.v1.scatter_nd_sub(ref, indices, updates) with tf.compat.v1.Session() as sess: print sess.run(op)
The resulting update to ref would look like this:
[1, -9, 3, -6, -6, 6, 7, -4]
See tf.scatter_nd
for more details about how to make updates to slices.
Args | |
---|---|
ref | A mutable Tensor . Must be one of the following types: float32 , float64 , int32 , uint8 , int16 , int8 , complex64 , int64 , qint8 , quint8 , qint32 , bfloat16 , uint16 , complex128 , half , uint32 , uint64 . A mutable Tensor. Should be from a Variable node. |
indices | A Tensor . Must be one of the following types: int32 , int64 . A tensor of indices into ref. |
updates | A Tensor . Must have the same type as ref . A tensor of updated values to add to ref. |
use_locking | An optional bool . Defaults to False . An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. |
name | A name for the operation (optional). |
Returns | |
---|---|
A mutable Tensor . Has the same type as ref . |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/compat/v1/scatter_nd_sub