ParseExampleDataset

public final class ParseExampleDataset

Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features.

Nested Classes

class

ParseExampleDataset.Options

Optional attributes for ParseExampleDataset

Constants

String

OP_NAME

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

Public Methods

Output<TType>	asOutput() Returns the symbolic handle of the tensor.
static ParseExampleDataset	create(Scope scope, Operand<?> inputDataset, Operand<TInt64> numParallelCalls, Iterable<Operand<?>> denseDefaults, List<String> sparseKeys, List<String> denseKeys, List<Class<? extends TType>> sparseTypes, List<Shape> denseShapes, List<Class<? extends TType>> outputTypes, List<Shape> outputShapes, List<Class<? extends TType>> raggedValueTypes, List<Class<? extends TNumber>> raggedSplitTypes, Options... options) Factory method to create a class wrapping a new ParseExampleDataset operation.
static ParseExampleDataset.Options	deterministic(String deterministic)
Output<?>	handle()
static ParseExampleDataset.Options	raggedKeys(List<String> raggedKeys)

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<TType>	asOutput() Returns the symbolic handle of the tensor.
abstract 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<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: "ParseExampleDatasetV2"

Public Methods

public Output<TType> 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 ParseExampleDataset create (Scope scope, Operand<?> inputDataset, Operand<TInt64> numParallelCalls, Iterable<Operand<?>> denseDefaults, List<String> sparseKeys, List<String> denseKeys, List<Class<? extends TType>> sparseTypes, List<Shape> denseShapes, List<Class<? extends TType>> outputTypes, List<Shape> outputShapes, List<Class<? extends TType>> raggedValueTypes, List<Class<? extends TNumber>> raggedSplitTypes, Options... options)

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

Parameters

scope	current scope
denseDefaults	A dict mapping string keys to `Tensor`s. The keys of the dict must match the dense_keys of the feature.
sparseKeys	A list of string keys in the examples features. The results for these keys will be returned as `SparseTensor` objects.
denseKeys	A list of Ndense string Tensors (scalars). The keys expected in the Examples features associated with dense values.
sparseTypes	A list of `DTypes` of the same length as `sparse_keys`. Only tf.float32 (`FloatList`), tf.int64 (`Int64List`), and tf.string (`BytesList`) are supported.
denseShapes	List of tuples with the same length as `dense_keys`. The shape of the data for each dense feature referenced by `dense_keys`. Required for any input tensors identified by `dense_keys`. Must be either fully defined, or may contain an unknown first dimension. An unknown first dimension means the feature is treated as having a variable number of blocks, and the output shape along this dimension is considered unknown at graph build time. Padding is applied for minibatch elements smaller than the maximum number of blocks for the given feature along this dimension.
outputTypes	The type list for the return values.
outputShapes	The list of shapes being produced.
options	carries optional attributes values

Returns

• a new instance of ParseExampleDataset

public static ParseExampleDataset.Options deterministic (String deterministic)

Parameters

deterministic	A string indicating the op-level determinism to use. Deterministic controls whether the dataset is allowed to return elements out of order if the next element to be returned isn't available, but a later element is. Options are "true", "false", and "default". "default" indicates that determinism should be decided by the `experimental_deterministic` parameter of tf.data.Options.

public Output<?> handle ()

public static ParseExampleDataset.Options raggedKeys (List<String> raggedKeys)