ResourceScatterNdSub

Applies sparse subtraction to individual values or slices in a Variable.

`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 subtraction would look like this:

ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True)
 indices = tf.constant([[4], [3], [1], [7]])
 updates = tf.constant([9, 10, 11, 12])
 sub = tf.scatter_nd_sub(ref, indices, updates)
 with tf.Session() as sess:
   print sess.run(sub)
 

The resulting update to ref would look like this:

[1, -9, 3, -6, -4, 6, 7, -4]

See tf.scatter_nd for more details about how to make updates to slices.

Nested Classes

class

ResourceScatterNdSub.Options

Optional attributes for ResourceScatterNdSub

Constants

String

OP_NAME

The name of this op, as known by TensorFlow core engine

Public Methods

static ResourceScatterNdSub	create(Scope scope, Operand<?> ref, Operand<? extends TNumber> indices, Operand<? extends TType> updates, Options... options) Factory method to create a class wrapping a new ResourceScatterNdSub operation.
static ResourceScatterNdSub.Options	useLocking(Boolean useLocking)

Inherited Methods

From class org.tensorflow.op.RawOp

final boolean	equals(Object obj)
final int	hashCode()
Operation	op() Return this unit of computation as a single Operation.
final String	toString()

From class java.lang.Object

boolean	equals(Object arg0)
final Class<?>	getClass()
int	hashCode()
final void	notify()
final void	notifyAll()
String	toString()
final void	wait(long arg0, int arg1)
final void	wait(long arg0)
final void	wait()

From interface org.tensorflow.op.Op

abstract ExecutionEnvironment	env() Return the execution environment this op was created in.
abstract Operation	op() Return this unit of computation as a single Operation.