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# Huber

public class Huber

Computes the Huber loss between labels and predictions.

For each value x in ``` error = labels - predictions ``` :

```     loss = 0.5 * x^2                  if |x| <= d
loss = 0.5 * d^2 + d * (|x| - d)  if |x| > d
```

where d is delta.

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} });
Huber huberLoss = new Huber(tf);
Operand<TFloat32> result = huberLoss.call(labels, predictions);
// produces 0.155
```

Calling with sample weight:

```    Operand<TFloat32> sampleWeight = tf.constant(new float[] {1.f, 0.f});
Operand<TFloat32> result = huberLoss.call(labels, predictions, sampleWeight);
// produces 0.09f
```

Using ``` SUM ``` reduction type:

```    Huber huberLoss = new Huber(tf, Reduction.SUM);
Operand<TFloat32> result = huberLoss.call(labels, predictions);
// produces 0.32f
```

Using ``` NONE ``` reduction type:

```    Huber huberLoss = new Huber(tf, Reduction.NONE);
Operand<TFloat32> result = huberLoss.call(labels, predictions);
// produces [0.18f, 0.13f]
```

### Constants

 float DELTA_DEFAULT

### Public Constructors

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

### Public Methods

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

## Constants

#### public static final float DELTA_DEFAULT

Constant Value: 1.0

## Public Constructors

#### public Huber (Ops tf)

Creates a Huber Loss using ``` getSimpleName() ``` as the loss name, ``` DELTA_DEFAULT ``` as the delta and a Loss Reduction of ``` REDUCTION_DEFAULT ```

##### Parameters
 tf the TensorFlow Ops

#### public Huber (Ops tf, String name)

Creates a Huber Loss using ``` DELTA_DEFAULT ``` as the delta and a Loss Reduction of ``` REDUCTION_DEFAULT ```

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

#### public Huber (Ops tf, Reduction reduction)

Creates a Huber Loss using ``` getSimpleName() ``` as the loss name and and ``` DELTA_DEFAULT ``` as the delta

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

#### public Huber (Ops tf, String name, Reduction reduction)

Creates a Huber Loss using ``` DELTA_DEFAULT ``` as the delta

##### Parameters
 tf the TensorFlow Ops the name of the loss, if null then ``` getSimpleName() ``` is used. Type of Reduction to apply to the loss.

#### public Huber (Ops tf, String name, float delta, Reduction reduction)

Creates a Huber Loss

##### Parameters
 tf the TensorFlow Ops the name of the loss, if null then ``` getSimpleName() ``` is used. the point where the Huber loss function changes from quadratic to linear. 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" }]