Class SquaredHinge

java.lang.Object
org.tensorflow.framework.losses.SquaredHinge
All Implemented Interfaces:
Loss
public class SquaredHinge extends Object
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(Ops tf, 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(Ops tf, labels, predictions,
                                                 sampleWeight);
   // produces 0.73f

Using SUM reduction type: 

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

Using NONE reduction type: 

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

  • Field Details

    • REDUCTION_DEFAULT

      public static final Reduction REDUCTION_DEFAULT

    • reduction

      protected final Reduction reduction

  • Constructor Details

    • SquaredHinge

      public SquaredHinge()
      Creates a Squared Hinge AbstractLoss using Class.getSimpleName() as the loss name and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

    • SquaredHinge

      public SquaredHinge(Reduction reduction)
      Creates a Squared Hinge AbstractLoss using Class.getSimpleName() as the loss name
      Parameters:
      reduction - Type of Reduction to apply to the loss.

    • SquaredHinge

      public SquaredHinge(String name, Reduction reduction)
      Creates a Squared Hinge
      Parameters:
      name - the name of the loss
      reduction - Type of Reduction to apply to the loss.

  • Method Details

    • call

      public <T extends TNumber> Operand<T> call(Ops tf, 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.].

      Type Parameters:
      T - The data type of the predictions, sampleWeights and loss.
      Parameters:
      tf - the TensorFlow Ops
      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.].

    • call

      public <T extends TNumber> Operand<T> call(Ops tf, Operand<? extends TNumber> labels, Operand<T> predictions)
      Calculates the loss
      Type Parameters:
      T - The data type of the predictions and loss.
      Parameters:
      tf - the TensorFlow Ops
      labels - the truth values or labels
      predictions - the predictions
      Returns:
      the loss

    • getReduction

      public Reduction getReduction()
      Gets the loss reduction
      Returns:
      the loss reduction

    • getName

      public String getName()
      Gets the name for this loss
      Returns:
      the name for this loss