SquaredHinge

public class SquaredHinge

Computes the squared hinge loss between labels and predictions.

loss = square(maximum(1 - labels * predictions, 0))

labels values are expected to be -1 or 1. If binary (0 or 1) labels are provided, they will be converted to -1 or 1.

Standalone usage: 

    Operand<TFloat32> labels =
        tf.constant(new float[][] { {0., 1.}, {0., 0.} });
    Operand<TFloat32> predictions =
        tf.constant(new float[][] { {0.6f, 0.4f}, {0.4f, 0.6f} });
    SquaredHinge squaredHinge = new SquaredHinge(tf);
    Operand<TFloat32> result = squaredHinge.call(labels, predictions);
    // produces 1.86f

Calling with sample weight: 

    Operand<TFloat32> sampleWeight = tf.constant(new float[] {1.f, 0.f});
    Operand<TFloat32> result = squaredHinge.call(labels, predictions,
                                                  sampleWeight);
    // produces 0.73f

Using SUM reduction type: 

    SquaredHinge squaredHinge = new SquaredHinge(tf, Reduction.SUM);
    Operand<TFloat32> result = squaredHinge.call(labels, predictions);
    // produces 3.72f

Using NONE reduction type: 

    SquaredHinge squaredHinge = new SquaredHinge(tf, Reduction.NONE);
    Operand<TFloat32> result = squaredHinge.call(labels, predictions);
    // produces [1.46f, 2.26f]

Inherited Fields

Public Constructors

SquaredHinge(Ops tf)
Creates a Squared Hinge Loss using getSimpleName() as the loss name and a Loss Reduction of REDUCTION_DEFAULT
SquaredHinge(Ops tf, Reduction reduction)
Creates a Squared Hinge Loss using getSimpleName() as the loss name
SquaredHinge(Ops tf, String name, Reduction reduction)
Creates a Squared Hinge

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 SquaredHinge (Ops tf)

Creates a Squared Hinge Loss using getSimpleName() as the loss name and a Loss Reduction of REDUCTION_DEFAULT

Parameters
tf the TensorFlow Ops

public SquaredHinge (Ops tf, Reduction reduction)

Creates a Squared Hinge Loss using getSimpleName() as the loss name

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

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

Creates a Squared Hinge

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.

If run in Graph mode, the computation will throw TFInvalidArgumentException if the label values are not in the set [-1., 0., 1.]. In Eager Mode, this call will throw IllegalArgumentException, if the label values are not in the set [-1., 0., 1.].

Parameters
labels the truth values or labels, must be either -1, 0, or 1. Values are expected to be -1 or 1. If binary (0 or 1) labels are provided they will be converted to -1 or 1.
predictions the predictions, values must be in the range [0. to 1.] inclusive.
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
Throws
IllegalArgumentException if the predictions are outside the range [0.-1.].