# huber

## Syntax

## Description

The Huber operation computes the Huber loss between network predictions and target values for regression tasks. When the `'TransitionPoint'`

option is 1, this is also known as *smooth L_{1} loss*.

The `huber`

function calculates the Huber loss using `dlarray`

data.
Using `dlarray`

objects makes working with high
dimensional data easier by allowing you to label the dimensions. For example, you can label
which dimensions correspond to spatial, time, channel, and batch dimensions using the
`"S"`

, `"T"`

, `"C"`

, and
`"B"`

labels, respectively. For unspecified and other dimensions, use the
`"U"`

label. For `dlarray`

object functions that operate
over particular dimensions, you can specify the dimension labels by formatting the
`dlarray`

object directly, or by using the `DataFormat`

option.

returns the Huber loss between the formatted `loss`

= huber(`Y`

,`targets`

)`dlarray`

object
`Y`

containing the predictions and the target values
`targets`

for regression tasks. The input `Y`

is a
formatted `dlarray`

. The output `loss`

is an unformatted
`dlarray`

scalar.

For unformatted input data, use the `'DataFormat'`

option.

also specifies the dimension format `loss`

= huber(___,'DataFormat',FMT)`FMT`

when `Y`

is not
a formatted `dlarray`

.

specifies options using one or more name-value pair arguments in addition to the input
arguments in previous syntaxes. For example,
`loss`

= huber(___,`Name,Value`

)`'NormalizationFactor','all-elements'`

specifies to normalize the loss by
dividing the reduced loss by the number of input elements.

## Examples

### Huber Loss

Create an array of predictions for 12 observations over 10 responses.

```
numResponses = 10;
numObservations = 12;
Y = rand(numResponses,numObservations);
dlY = dlarray(Y,'CB');
```

View the size and format of the predictions.

size(dlY)

`ans = `*1×2*
10 12

dims(dlY)

ans = 'CB'

Create an array of random targets.

targets = rand(numResponses,numObservations);

View the size of the targets.

size(targets)

`ans = `*1×2*
10 12

Compute the Huber loss between the predictions and the targets.

loss = huber(dlY,targets)

loss = 1x1 dlarray 0.7374

### Masked Huber Loss for Padded Sequences

Create arrays of predictions and targets for 12 sequences of varying lengths over 10 responses.

numResponses = 10; numObservations = 12; maxSequenceLength = 15; sequenceLengths = randi(maxSequenceLength,[1 numObservations]); Y = cell(numObservations,1); targets = cell(numObservations,1); for i = 1:numObservations Y{i} = rand(numResponses,sequenceLengths(i)); targets{i} = rand(numResponses,sequenceLengths(i)); end

View the cell arrays of predictions and targets.

Y

`Y=`*12×1 cell array*
{10x13 double}
{10x14 double}
{10x2 double}
{10x14 double}
{10x10 double}
{10x2 double}
{10x5 double}
{10x9 double}
{10x15 double}
{10x15 double}
{10x3 double}
{10x15 double}

targets

`targets=`*12×1 cell array*
{10x13 double}
{10x14 double}
{10x2 double}
{10x14 double}
{10x10 double}
{10x2 double}
{10x5 double}
{10x9 double}
{10x15 double}
{10x15 double}
{10x3 double}
{10x15 double}

Pad the prediction and target sequences in the second dimension using the `padsequences`

function and also return the corresponding mask.

[Y,mask] = padsequences(Y,2); targets = padsequences(targets,2);

Convert the padded sequences to `dlarray`

with format `'CTB'`

(channel, time, batch). Because formatted `dlarray`

objects automatically sort the dimensions, keep the dimensions of the targets and mask consistent by also converting them to a formatted `dlarray`

objects with the same formats.

dlY = dlarray(Y,'CTB'); targets = dlarray(targets,'CTB'); mask = dlarray(mask,'CTB');

View the sizes of the prediction scores, targets, and the mask.

size(dlY)

`ans = `*1×3*
10 12 15

size(targets)

`ans = `*1×3*
10 12 15

size(mask)

`ans = `*1×3*
10 12 15

Compute the Huber loss between the predictions and the targets. To prevent the loss values calculated from padding from contributing to the loss, set the `'Mask'`

option to the mask returned by the `padsequences`

function.

`loss = huber(dlY,targets,'Mask',mask)`

loss = 1x1 dlarray 8.1834

## Input Arguments

`Y`

— Predictions

`dlarray`

object | numeric array

Predictions, specified as a formatted or unformatted `dlarray`

object,
or a numeric array. When `Y`

is not a formatted
`dlarray`

, you must specify the dimension format using the
`DataFormat`

argument.

If `Y`

is a numeric array, `targets`

must be a
`dlarray`

object.

`targets`

— Target responses

`dlarray`

| numeric array

Target responses, specified as a formatted or unformatted `dlarray`

or a
numeric array.

The size of each dimension of `targets`

must match the size of the
corresponding dimension of `Y`

.

If `targets`

is a formatted `dlarray`

, then its format must
be the same as the format of `Y`

, or the same as
`DataFormat`

if `Y`

is
unformatted.

If `targets`

is an unformatted `dlarray`

or a numeric array,
then the function applies the format of `Y`

or the value of
`DataFormat`

to `targets`

.

**Tip**

Formatted `dlarray`

objects automatically permute the dimensions of the
underlying data to have the order `"S"`

(spatial), `"C"`

(channel), `"B"`

(batch), `"T"`

(time), then
`"U"`

(unspecified). To ensure that the dimensions of
`Y`

and `targets`

are consistent, when
`Y`

is a formatted `dlarray`

, also specify
`targets`

as a formatted `dlarray`

.

`weights`

— Weights

`dlarray`

| numeric array

Weights, specified as a `dlarray`

or a numeric array.

To specify response weights, specify a vector with a `'C'`

(channel) dimension with size matching the `'C'`

(channel) dimension of
the `Y`

. Specify the `'C'`

(channel) dimension of
the response weights by using a formatted `dlarray`

object or by using
the `'WeightsFormat'`

option.

To specify observation weights, specify a vector with a `"B"`

(batch)
dimension with size matching the `"B"`

(batch) dimension of
`Y`

. Specify the `"B"`

(batch) dimension of the
class weights by using a formatted `dlarray`

object or by using the
`WeightsFormat`

argument.

To specify weights for each element of the input independently, specify the weights as an
array of the same size as `Y`

. In this case, if
`weights`

is not a formatted `dlarray`

object, then
the function uses the same format as `Y`

. Alternatively, specify the
weights format using the `WeightsFormat`

argument.

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

*
Before R2021a, use commas to separate each name and value, and enclose*
`Name`

*in quotes.*

**Example: **`'NormalizationFactor','all-elements'`

specifies to normalize
the loss by dividing the reduced loss by the number of input elements

`TransitionPoint`

— Point where Huber loss transitions to a linear function

1 (default) | positive scalar

Point where Huber loss transitions from a quadratic function to a linear function,
specified as the comma-separated pair consisting of
`'TransitionPoint'`

and a positive scalar.

When `'TransitionPoint'`

is 1, this is also known as
*smooth L_{1}
loss*.

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

`Mask`

— Mask indicating which elements to include for loss computation

`dlarray`

| logical array | numeric array

Mask indicating which elements to include for loss computation, specified as a
`dlarray`

object, a logical array, or a numeric array with the same
size as `Y`

.

The function includes and excludes elements of the input data for loss computation when the corresponding value in the mask is 1 and 0, respectively.

If `Mask`

is a formatted `dlarray`

object, then its
format must match that of `Y`

. If `Mask`

is not a
formatted `dlarray`

object, then the function uses the same format as
`Y`

.

If you specify the `DataFormat`

argument, then the function also
uses the specified format for the mask.

The size of each dimension of `Mask`

must match the size of the
corresponding dimension in `Y`

. The default value is a logical array
of ones.

**Tip**

Formatted `dlarray`

objects automatically permute the dimensions of the
underlying data to have this order: `"S"`

(spatial), `"C"`

(channel), `"B"`

(batch), `"T"`

(time), and
`"U"`

(unspecified). For example, `dlarray`

objects
automatically permute the dimensions of data with format `"TSCSBS"`

to have
format `"SSSCBT"`

.

To ensure that the dimensions of `Y`

and the mask are consistent, when
`Y`

is a formatted `dlarray`

, also specify the mask as
a formatted `dlarray`

.

`Reduction`

— Loss value array reduction mode

`"sum"`

(default) | `"none"`

Loss value array reduction mode, specified as `"sum"`

or
`"none"`

.

If the `Reduction`

argument is `"sum"`

, then the function
sums all elements in the array of loss values. In this case, the output
`loss`

is a scalar.

If the `Reduction`

argument is `"none"`

, then the
function does not reduce the array of loss values. In this case, the output
`loss`

is an unformatted `dlarray`

object
of the same size as `Y`

.

`NormalizationFactor`

— Divisor for normalizing reduced loss

`"batch-size"`

(default) | `"all-elements"`

| `"mask-included"`

| `"none"`

Divisor for normalizing the reduced loss when `Reduction`

is
`"sum"`

, specified as one of the following:

`"batch-size"`

— Normalize the loss by dividing it by the number of observations in`Y`

.`"all-elements"`

— Normalize the loss by dividing it by the number of elements of`Y`

.`"mask-included"`

— Normalize the loss by dividing the loss values by the product of the number of observations and the number of included elements specified by the mask for each observation independently. To use this option, you must specify a mask using the`Mask`

option.`"none"`

— Do not normalize the loss.

`DataFormat`

— Description of data dimensions

character vector | string scalar

Description of the data dimensions, specified as a character vector or string scalar.

A data format is a string of characters, where each character describes the type of the corresponding data dimension.

The characters are:

`"S"`

— Spatial`"C"`

— Channel`"B"`

— Batch`"T"`

— Time`"U"`

— Unspecified

For example, consider an array containing a batch of sequences where the first, second,
and third dimensions correspond to channels, observations, and time steps, respectively. You
can specify that this array has the format `"CBT"`

(channel, batch,
time).

You can specify multiple dimensions labeled `"S"`

or `"U"`

.
You can use the labels `"C"`

, `"B"`

, and
`"T"`

once each, at most. The software ignores singleton trailing
`"U"`

dimensions after the second dimension.

If the input data is not a formatted `dlarray`

object, then you must
specify the `DataFormat`

option.

For more information, see Deep Learning Data Formats.

**Data Types: **`char`

| `string`

`WeightsFormat`

— Description of dimensions of weights

character vector | string scalar

Description of the dimensions of the weights, specified as a character vector or string scalar.

A data format is a string of characters, where each character describes the type of the corresponding data dimension.

The characters are:

`"S"`

— Spatial`"C"`

— Channel`"B"`

— Batch`"T"`

— Time`"U"`

— Unspecified

For example, consider an array containing a batch of sequences where the first, second,
and third dimensions correspond to channels, observations, and time steps, respectively. You
can specify that this array has the format `"CBT"`

(channel, batch,
time).

You can specify multiple dimensions labeled `"S"`

or `"U"`

.
You can use the labels `"C"`

, `"B"`

, and
`"T"`

once each, at most. The software ignores singleton trailing
`"U"`

dimensions after the second dimension.

If `weights`

is a numeric vector and
`Y`

has two or more nonsingleton
dimensions, then you must specify the
`WeightsFormat`

option.

If `weights`

is not a vector, or
`weights`

and
`Y`

are both vectors, then the
default value of `WeightsFormat`

is the same
as the format of `Y`

.

For more information, see Deep Learning Data Formats.

**Data Types: **`char`

| `string`

## Output Arguments

`loss`

— Huber loss

`dlarray`

Huber loss, returned as an unformatted `dlarray`

. The output
`loss`

is an unformatted `dlarray`

with the same
underlying data type as the input `Y`

.

The size of `loss`

depends on the `Reduction`

option.

## Algorithms

### Huber Loss

For each element *Y _{j}* of the input, the

`huber`

function computes the corresponding element-wise loss values
using the formula$${\text{loss}}_{j}=\{\begin{array}{cc}\frac{1}{2}{\left({Y}_{j}-{T}_{j}\right)}^{2}& \text{if}\left|{Y}_{j}-{T}_{j}\right|\le \delta \\ \delta \left|{Y}_{j}-{T}_{j}\right|-\frac{1}{2}{\delta}^{2}& \text{otherwise}\end{array},$$

where *T _{j}* is the corresponding
target value to the prediction

*Y*and $$\delta $$ is the transition point where the loss transitions from a quadratic function to a linear function.

_{j}When the transition point is 1, this is also known as *smooth
L_{1} loss*.

To reduce the loss values to a scalar, the function then reduces the element-wise loss using the formula

$$\text{loss}=\frac{1}{N}{\displaystyle \sum _{j}{m}_{j}{w}_{j}{\text{loss}}_{j},}$$

where *N* is the normalization factor,
*m _{j}* is the mask value for element

*j*, and

*w*is the weight value for element

_{j}*j*.

If you do not opt to reduce the loss, then the function applies the mask and the weights to the loss values directly:

$${\text{loss}}_{j}^{*}={m}_{j}{w}_{j}{\text{loss}}_{j}$$

### Deep Learning Array Formats

Most deep learning networks and functions operate on different dimensions of the input data in different ways.

For example, an LSTM operation iterates over the time dimension of the input data, and a batch normalization operation normalizes over the batch dimension of the input data.

To provide input data with labeled dimensions or input data with additional layout information, you can use *data formats*.

A data format is a string of characters, where each character describes the type of the corresponding data dimension.

The characters are:

`"S"`

— Spatial`"C"`

— Channel`"B"`

— Batch`"T"`

— Time`"U"`

— Unspecified

For example, consider an array containing a batch of sequences where the first, second,
and third dimensions correspond to channels, observations, and time steps, respectively. You
can specify that this array has the format `"CBT"`

(channel, batch,
time).

To create formatted input data, create a `dlarray`

object and specify the format using the second argument.

To provide additional layout information with unformatted data, specify the formats using the `DataFormat`

and `WeightsFormat`

arguments.

For more information, see Deep Learning Data Formats.

## Extended Capabilities

### GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The `huber`

function
supports GPU array input with these usage notes and limitations:

When at least one of the following input arguments is a

`gpuArray`

or a`dlarray`

with underlying data of type`gpuArray`

, this function runs on the GPU:`Y`

`targets`

`weights`

`'Mask'`

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

## Version History

**Introduced in R2021a**

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