SparseToDense

public final class SparseToDense

Converts a sparse representation into a dense tensor.

Builds an array `dense` with shape `output_shape` such that

# If sparse_indices is scalar
 dense[i] = (i == sparse_indices ? sparse_values : default_value)
 
 # If sparse_indices is a vector, then for each i
 dense[sparse_indices[i]] = sparse_values[i]
 
 # If sparse_indices is an n by d matrix, then for each i in [0, n)
 dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
 

All other values in `dense` are set to `default_value`. If `sparse_values` is a scalar, all sparse indices are set to this single value.

Indices should be sorted in lexicographic order, and indices must not contain any repeats. If `validate_indices` is true, these properties are checked during execution.

Nested Classes

class

SparseToDense.Options

Optional attributes for SparseToDense

Constants

String

OP_NAME

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

Public Methods

Output<U>	asOutput() Returns the symbolic handle of the tensor.
static <U extends TType, T extends TNumber> SparseToDense<U>	create(Scope scope, Operand<T> sparseIndices, Operand<T> outputShape, Operand<U> sparseValues, Operand<U> defaultValue, Options... options) Factory method to create a class wrapping a new SparseToDense operation.
Output<U>	dense() Dense output tensor of shape `output_shape`.
static SparseToDense.Options	validateIndices(Boolean validateIndices)

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.

From interface org.tensorflow.Operand

abstract Output<U extends TType>	asOutput() Returns the symbolic handle of the tensor.
abstract U extends TType	asTensor() Returns the tensor at this operand.
abstract Shape	shape() Returns the (possibly partially known) shape of the tensor referred to by the Output of this operand.
abstract Class<U extends TType>	type() Returns the tensor type of this operand

From interface org.tensorflow.ndarray.Shaped

abstract int	rank()
abstract Shape	shape()
abstract long	size() Computes and returns the total size of this container, in number of values.

Constants

public static final String OP_NAME

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

Constant Value: "SparseToDense"

Public Methods

public Output<U> asOutput ()

Returns the symbolic handle of the 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 SparseToDense<U> create (Scope scope, Operand<T> sparseIndices, Operand<T> outputShape, Operand<U> sparseValues, Operand<U> defaultValue, Options... options)

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

Parameters

scope	current scope
sparseIndices	0-D, 1-D, or 2-D. `sparse_indices[i]` contains the complete index where `sparse_values[i]` will be placed.
outputShape	1-D. Shape of the dense output tensor.
sparseValues	1-D. Values corresponding to each row of `sparse_indices`, or a scalar value to be used for all sparse indices.
defaultValue	Scalar value to set for indices not specified in `sparse_indices`.
options	carries optional attributes values

Returns

• a new instance of SparseToDense

public Output<U> dense ()

Dense output tensor of shape `output_shape`.

public static SparseToDense.Options validateIndices (Boolean validateIndices)

Parameters

validateIndices	If true, indices are checked to make sure they are sorted in lexicographic order and that there are no repeats.