UnravelIndex

public final class UnravelIndex

Converts an array of flat indices into a tuple of coordinate arrays.

Example: 

y = tf.unravel_index(indices=[2, 5, 7], dims=[3, 3])
 # 'dims' represent a hypothetical (3, 3) tensor of indices:
 # [[0, 1, *2*],
 #  [3, 4, *5*],
 #  [6, *7*, 8]]
 # For each entry from 'indices', this operation returns
 # its coordinates (marked with '*'), such as
 # 2 ==> (0, 2)
 # 5 ==> (1, 2)
 # 7 ==> (2, 1)
 y ==> [[0, 1, 2], [2, 2, 1]]

Public Methods

Output<T>
asOutput()
Returns the symbolic handle of a tensor.
static <T extends Number> UnravelIndex<T>
create(Scope scope, Operand<T> indices, Operand<T> dims)
Factory method to create a class wrapping a new UnravelIndex operation.
Output<T>
output()
An 2-D (or 1-D if indices is 0-D) tensor where each row has the same shape as the indices array.

Inherited Methods

org.tensorflow.op.PrimitiveOp
final boolean
equals(Object obj)
final int
hashCode()
Operation
op()
Returns the underlying Operation
final String
toString()
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()
org.tensorflow.Operand
abstract Output<T>
asOutput()
Returns the symbolic handle of a tensor.

Public Methods

public Output<T> asOutput ()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static UnravelIndex<T> create (Scope scope, Operand<T> indices, Operand<T> dims)

Factory method to create a class wrapping a new UnravelIndex operation.

Parameters
scope current scope
indices An 0-D or 1-D `int` Tensor whose elements are indices into the flattened version of an array of dimensions dims.
dims An 1-D `int` Tensor. The shape of the array to use for unraveling indices.
Returns
  • a new instance of UnravelIndex

public Output<T> output ()

An 2-D (or 1-D if indices is 0-D) tensor where each row has the same shape as the indices array.