KLDivergence

public class KLDivergence

Computes Kullback-Leibler divergence loss between labels and predictions.

loss = labels * log(labels / predictions)

Standalone usage: 

    Operand<TFloat32> labels =
        tf.constant(new float[][] { {0.f, 1.f}, {0.f, 0.f} });
    Operand<TFloat32> predictions =
        tf.constant(new float[][] { {0.6f, 0.4f}, {0.4f, 0.6f} });
    KLDivergence kld = new KLDivergence(tf);
    Operand<TFloat32> result = kld.call(labels, predictions);
    // produces 0.458

Calling with sample weight: 

    Operand<TFloat32> sampleWeight = tf.constant(new float[] {0.8f, 0.2f});
    Operand<TFloat32> result = kld.call(labels, predictions, sampleWeight);
    // produces 0.366f

Using SUM reduction type: 

    KLDivergence kld = new KLDivergence(tf, Reduction.SUM);
    Operand<TFloat32> result = kld.call(labels, predictions);
    // produces 0.916f

Using NONE reduction type: 

    KLDivergence kld = new KLDivergence(tf, Reduction.NONE);
    Operand<TFloat32> result = kld.call(labels, predictions);
    // produces [0.916f, -3.08e-06f]

See Also

Inherited Fields

Public Constructors

KLDivergence(Ops tf)
Creates a Kullback Leibler Divergence Loss using getSimpleName() as the loss name and a Loss Reduction of REDUCTION_DEFAULT
KLDivergence(Ops tf, Reduction reduction)
Creates a Kullback Leibler Divergence Loss Loss using getSimpleName() as the loss name
KLDivergence(Ops tf, String name, Reduction reduction)
Creates a Kullback Leibler Divergence Loss

Public Methods

<T extends TNumber> Operand<T>
call(Operand<? extends TNumber> labels, Operand<T> predictions, Operand<T> sampleWeights)
Generates an Operand that calculates the loss.

Inherited Methods

Public Constructors

public KLDivergence (Ops tf)

Creates a Kullback Leibler Divergence Loss using getSimpleName() as the loss name and a Loss Reduction of REDUCTION_DEFAULT

Parameters
tf the TensorFlow Ops

public KLDivergence (Ops tf, Reduction reduction)

Creates a Kullback Leibler Divergence Loss Loss using getSimpleName() as the loss name

Parameters
tf the TensorFlow Ops
reduction Type of Reduction to apply to the loss.

public KLDivergence (Ops tf, String name, Reduction reduction)

Creates a Kullback Leibler Divergence Loss

Parameters
tf the TensorFlow Ops
name the name of the loss
reduction Type of Reduction to apply to the loss.

Public Methods

public Operand<T> call (Operand<? extends TNumber> labels, Operand<T> predictions, Operand<T> sampleWeights)

Generates an Operand that calculates the loss.

Parameters
labels the truth values or labels
predictions the predictions
sampleWeights Optional sampleWeights acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If SampleWeights is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the SampleWeights vector. If the shape of SampleWeights is [batch_size, d0, .. dN-1] (or can be broadcast to this shape), then each loss element of predictions is scaled by the corresponding value of SampleWeights. (Note on dN-1: all loss functions reduce by 1 dimension, usually axis=-1.)
Returns
  • the loss