# MeanAbsolutePercentageError

public class MeanAbsolutePercentageError

Computes the mean absolute percentage error between labels and predictions.

``` loss = 100 * abs(labels - predictions) / labels ```

Standalone usage:

```    Operand<TFloat32> labels =
tf.constant(new float[][] { {2.f, 1.f}, {2.f, 3.f} });
Operand<TFloat32> predictions =
tf.constant(new float[][] { {1.f, 1.f}, {1.f, 0.f} });
MeanAbsolutePercentageError mape = new MeanAbsolutePercentageError(tf);
Operand<TFloat32> result = mape.call(labels, predictions);
// produces 50f
```

Calling with sample weight:

```    Operand<TFloat32> sampleWeight = tf.constant(new float[] {0.7f, 0.3f});
Operand<TFloat32> result = mape.call(labels, predictions, sampleWeight);
// produces 20f
```

Using ``` SUM ``` reduction type:

```    MeanAbsolutePercentageError mape = new MeanAbsolutePercentageError(tf, Reduction.SUM);
Operand<TFloat32> result = mape.call(labels, predictions);
// produces 100.0f
```

Using ``` NONE ``` reduction type:

```    MeanAbsolutePercentageError mape = new MeanAbsolutePercentageError(tf, Reduction.NONE);
Operand<TFloat32> result = mape.call(labels, predictions);
// produces [25f, 75f]
```

### Public Constructors

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

### Public Methods

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

## Public Constructors

#### public MeanAbsolutePercentageError (Ops tf)

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

##### Parameters
 tf the TensorFlow Ops

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

Creates a MeanAbsolutePercentageError Loss using ``` getSimpleName() ``` as the loss name

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

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

Creates a MeanAbsolutePercentageError

##### 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" }]