CTCLossV2

public final class CTCLossV2

Calculates the CTC Loss (log probability) for each batch entry. Also calculates

the gradient. This class performs the softmax operation for you, so inputs should be e.g. linear projections of outputs by an LSTM.

Nested Classes

class

CTCLossV2.Options

Optional attributes for CTCLossV2

Constants

String

OP_NAME

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

Public Methods

static CTCLossV2	create(Scope scope, Operand<TFloat32> inputs, Operand<TInt64> labelsIndices, Operand<TInt32> labelsValues, Operand<TInt32> sequenceLength, Options... options) Factory method to create a class wrapping a new CTCLossV2 operation.
static CTCLossV2.Options	ctcMergeRepeated(Boolean ctcMergeRepeated)
Output<TFloat32>	gradient() The gradient of `loss`.
static CTCLossV2.Options	ignoreLongerOutputsThanInputs(Boolean ignoreLongerOutputsThanInputs)
Output<TFloat32>	loss() A vector (batch) containing log-probabilities.
static CTCLossV2.Options	preprocessCollapseRepeated(Boolean preprocessCollapseRepeated)

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.

Constants

public static final String OP_NAME

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

Constant Value: "CTCLossV2"

Public Methods

public static CTCLossV2 create (Scope scope, Operand<TFloat32> inputs, Operand<TInt64> labelsIndices, Operand<TInt32> labelsValues, Operand<TInt32> sequenceLength, Options... options)

Factory method to create a class wrapping a new CTCLossV2 operation.

Parameters

scope	current scope
inputs	3-D, shape: `(max_time x batch_size x num_classes)`, the logits. Default blank label is 0 rather num_classes - 1.
labelsIndices	The indices of a `SparseTensor`. `labels_indices(i, :) == [b, t]` means `labels_values(i)` stores the id for `(batch b, time t)`.
labelsValues	The values (labels) associated with the given batch and time.
sequenceLength	A vector containing sequence lengths (batch).
options	carries optional attributes values

Returns

• a new instance of CTCLossV2

public static CTCLossV2.Options ctcMergeRepeated (Boolean ctcMergeRepeated)

Parameters

ctcMergeRepeated	Scalar. If set to false, during CTC calculation repeated non-blank labels will not be merged and are interpreted as individual labels. This is a simplified version of CTC.

public Output<TFloat32> gradient ()

The gradient of `loss`. 3-D, shape: `(max_time x batch_size x num_classes)`.

public static CTCLossV2.Options ignoreLongerOutputsThanInputs (Boolean ignoreLongerOutputsThanInputs)

Parameters

ignoreLongerOutputsThanInputs	Scalar. If set to true, during CTC calculation, items that have longer output sequences than input sequences are skipped: they don't contribute to the loss term and have zero-gradient.

public Output<TFloat32> loss ()

A vector (batch) containing log-probabilities.

public static CTCLossV2.Options preprocessCollapseRepeated (Boolean preprocessCollapseRepeated)

Parameters

preprocessCollapseRepeated	Scalar, if true then repeated labels are collapsed prior to the CTC calculation.