MeanSquaredLogarithmicError

public class MeanSquaredLogarithmicError

Computes the mean squared logarithmic errors between labels and predictions.

loss = square(log(labels + 1.) - log(predictions + 1.))

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}, {1.f, 0.f} });
    MeanSquaredLogarithmicError msle = new MeanSquaredLogarithmicError(tf);
    Operand<TFloat32> result = msle.call(labels, predictions);
    // produces 0.240f

Calling with sample weight:

    Operand<TFloat32> sampleWeight = tf.constant(new float[] {0.7f, 0.3f});
    Operand<TFloat32> result = msle.call(labels, predictions, sampleWeight);
    // produces 0.120f

Using SUM reduction type:

    MeanSquaredLogarithmicError msle = new MeanSquaredLogarithmicError(tf, Reduction.SUM);
    Operand<TFloat32> result = msle.call(labels, predictions);
    // produces 0.480f

Using NONE reduction type:

    MeanSquaredLogarithmicError msle = new MeanSquaredLogarithmicError(tf, Reduction.NONE);
    Operand<TFloat32> result = msle.call(labels, predictions);
    // produces [0.240f, 0.240f]

Inherited Fields

From class org.tensorflow.framework.losses.Loss

public static final Reduction REDUCTION_DEFAULT

Public Constructors

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

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 MeanSquaredLogarithmicError (Ops tf)

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

Parameters

tf	the TensorFlow Ops

public MeanSquaredLogarithmicError (Ops tf, Reduction reduction)

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

Parameters

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

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

Creates a MeanSquaredError

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