Poisson

public class Poisson

Computes the Poisson loss between labels and predictions.

loss = predictions - labels * log(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[][] { {1.f, 1.f}, {0.f, 0.f} });
    Poisson poissonLoss = new Poisson(tf);
    Operand<TFloat32> result = poissonLoss.call(labels, predictions);
    // produces 0.5f

Calling with sample weight:

    Operand<TFloat32> sampleWeight = tf.constant(new float[] {0.8f, 0.2f});
    Operand<TFloat32> result = poissonLoss.call(labels, predictions, sampleWeight);
    // produces 0.4f

Using SUM reduction type:

    Poisson poissonLoss = new Poisson(tf, Reduction.SUM);
    Operand<TFloat32> result = poissonLoss.call(labels, predictions);
    // produces 0.999f

Using NONE reduction type:

    Poisson poissonLoss = new Poisson(tf, Reduction.NONE);
    Operand<TFloat32> result = poissonLoss.call(labels, predictions);
    // produces [0.999f, 0f]

Inherited Fields

From class org.tensorflow.framework.losses.Loss

public static final Reduction REDUCTION_DEFAULT

Public Constructors

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

From class org.tensorflow.framework.losses.Loss

abstract <T extends TNumber> Operand<T>	call(Operand<? extends TNumber> labels, Operand<T> predictions, Operand<T> sampleWeights) Generates an Operand that calculates the loss.
<T extends TNumber> Operand<T>	call(Operand<? extends TNumber> labels, Operand<T> predictions) Calculates the loss
Reduction	getReduction() Gets the loss reduction
Ops	getTF() Gets the TensorFlow Ops

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()

Public Constructors

public Poisson (Ops tf)

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

Parameters

tf	the TensorFlow Ops

public Poisson (Ops tf, String name)

Creates a Poisson Loss using a Loss Reduction of REDUCTION_DEFAULT

Parameters

tf	the TensorFlow Ops
name	the name of the loss, if null then `getSimpleName()` is used.

public Poisson (Ops tf, Reduction reduction)

Creates a Poisson Loss using getSimpleName() as the loss name

Parameters

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

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

Creates a Poisson Loss

Parameters

tf	the TensorFlow Ops
name	the name of the loss, if null then `getSimpleName()` is used.
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