View source on GitHub |
Applies sparse subtraction to individual values or slices in a Variable.
tf.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 .
|