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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]
```

Public Constructors

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

Public Methods

 Operand ( Operand labels, Operand predictions, Operand sampleWeights) Generates an Operand that calculates the loss.

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 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 the name of the loss 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 the predictions 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
[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]