## Define Custom Deep Learning Output Layers

**Tip**

This topic explains how to define custom deep learning output layers for your problems. For a list of built-in layers in Deep Learning Toolbox™, see List of Deep Learning Layers.

To learn how to define custom intermediate layers, see Define Custom Deep Learning Intermediate Layers.

If Deep Learning Toolbox does not provide the output layer that you require for your task, then you can define your own custom layer using this topic as a guide. After defining the custom layer, you can check that the layer is valid and GPU compatible, and outputs correctly defined gradients.

### Output Layer Architecture

At the end of a forward pass at training time, an output layer takes the predictions
(network outputs) *Y* of the previous layer and calculates the loss
*L* between these predictions and the training targets. The output
layer computes the derivatives of the loss *L* with respect to the
predictions *Y* and outputs (backward propagates) results to the
previous layer.

The following figure describes the flow of data through a convolutional neural network and an output layer.

### Output Layer Templates

To define a custom output layer, use one of these class definition templates. The templates give the structure of an output layer class definition. They outline:

The optional

`properties`

blocks for the layer properties. For more information, see Output Layer Properties.The layer constructor function.

The

`forwardLoss`

function. For more information, see Forward Loss Function.The optional

`backwardLoss`

function. For more information, see Backward Loss Function.

#### Classification Output Layer Template

This template outlines the structure of a classification output layer with a loss function. For an example showing how to define a classification output layer and specify a loss function, see Define Custom Classification Output Layer.

classdef myClassificationLayer < nnet.layer.ClassificationLayer % ... % & nnet.layer.Acceleratable % (Optional) properties % (Optional) Layer properties. % Layer properties go here. end methods function layer = myClassificationLayer() % (Optional) Create a myClassificationLayer. % Layer constructor function goes here. end function loss = forwardLoss(layer,Y,T) % Return the loss between the predictions Y and the training % targets T. % % Inputs: % layer - Output layer % Y – Predictions made by network % T – Training targets % % Output: % loss - Loss between Y and T % Layer forward loss function goes here. end function dLdY = backwardLoss(layer,Y,T) % (Optional) Backward propagate the derivative of the loss % function. % % Inputs: % layer - Output layer % Y – Predictions made by network % T – Training targets % % Output: % dLdY - Derivative of the loss with respect to the % predictions Y % Layer backward loss function goes here. end end end

#### Regression Output Layer Template

This template outlines the structure of a regression output layer with a loss function. For an example showing how to define a regression output layer and specify a loss function, see Define Custom Regression Output Layer.

classdef myRegressionLayer < nnet.layer.RegressionLayer % ... % & nnet.layer.Acceleratable % (Optional) properties % (Optional) Layer properties. % Layer properties go here. end methods function layer = myRegressionLayer() % (Optional) Create a myRegressionLayer. % Layer constructor function goes here. end function loss = forwardLoss(layer,Y,T) % Return the loss between the predictions Y and the training % targets T. % % Inputs: % layer - Output layer % Y – Predictions made by network % T – Training targets % % Output: % loss - Loss between Y and T % Layer forward loss function goes here. end function dLdY = backwardLoss(layer,Y,T) % (Optional) Backward propagate the derivative of the loss % function. % % Inputs: % layer - Output layer % Y – Predictions made by network % T – Training targets % % Output: % dLdY - Derivative of the loss with respect to the % predictions Y % Layer backward loss function goes here. end end end

### Custom Layer Acceleration

If you do not specify a backward function when you define a custom layer, then the software automatically determines the gradients using automatic differentiation.

When you train a network with a custom layer without a backward function, the software traces
each input `dlarray`

object of the custom layer forward function to determine
the computation graph used for automatic differentiation. This tracing process can take some
time and can end up recomputing the same trace. By optimizing, caching, and reusing the
traces, you can speed up gradient computation when training a network. The software can also
reuse these traces to speed up network predictions after training.

The trace depends on the size, format, and underlying data type of the layer inputs. That is, the layer triggers a new trace for inputs with a size, format, or underlying data type not contained in the cache. Any inputs differing only by value to a previously cached trace do not trigger a new trace.

To indicate that the custom layer supports acceleration, also inherit from the `nnet.layer.Acceleratable`

class when defining the custom layer. When a custom layer inherits from `nnet.layer.Acceleratable`

, the software automatically caches traces when passing data through a `dlnetwork`

object.

For example, to indicate that the custom layer `myLayer`

supports
acceleration, use this
syntax

classdef myLayer < nnet.layer.Layer & nnet.layer.Acceleratable ... end

#### Acceleration Considerations

Because of the nature of caching traces, not all functions support acceleration.

The caching process can cache values or code structures that you might expect to change or that depend on external factors. You must take care when accelerating custom layers that:

Generate random numbers.

Use

`if`

statements and`while`

loops with conditions that depend on the values of`dlarray`

objects.

Because the caching process requires extra computation, acceleration can lead to longer running code in some cases. This scenario can happen when the software spends time creating new caches that do not get reused often. For example, when you pass multiple mini-batches of different sequence lengths to the function, the software triggers a new trace for each unique sequence length.

When custom layer acceleration causes slowdown, you can disable acceleration by removing
the `Acceleratable`

mixin or by disabling acceleration of the
`dlnetwork`

object functions `predict`

and `forward`

by setting the `Acceleration`

option to
`"none"`

.

For more information about enabling acceleration support for custom layers, see Custom Layer Function Acceleration.

### Output Layer Properties

Declare the layer properties in the `properties`

section of the class
definition.

By default, custom output layers have the following properties:

`Name`

— Layer name, specified as a character vector or a string scalar. For`Layer`

array input, the`trainNetwork`

,`assembleNetwork`

,`layerGraph`

, and`dlnetwork`

functions automatically assign names to layers with the name`''`

.`Description`

— One-line description of the layer, specified as a character vector or a string scalar. This description appears when the layer is displayed in a`Layer`

array. If you do not specify a layer description, then the software displays`"Classification Output"`

or`"Regression Output"`

.`Type`

— Type of the layer, specified as a character vector or a string scalar. The value of`Type`

appears when the layer is displayed in a`Layer`

array. If you do not specify a layer type, then the software displays the layer class name.

Custom classification layers also have the following property:

`Classes`

— Classes of the output layer, specified as a categorical vector, string array, cell array of character vectors, or`"auto"`

. If`Classes`

is`"auto"`

, then the software automatically sets the classes at training time. If you specify the string array or cell array of character vectors`str`

, then the software sets the classes of the output layer to`categorical(str,str)`

.

Custom regression layers also have the following property:

`ResponseNames`

— Names of the responses, specified a cell array of character vectors or a string array. At training time, the software automatically sets the response names according to the training data. The default is`{}`

.

If the layer has no other properties, then you can omit the `properties`

section.

### Forward Loss Function

The output layer computes the loss `L`

between predictions and
targets using the forward loss function and computes the derivatives of the loss with
respect to the predictions using the backward loss function.

The syntax for `forwardLoss`

is ```
loss
= forwardLoss(layer,Y,T)
```

. The input `Y`

corresponds to the
predictions made by the network. These predictions are the output of the previous layer. The
input `T`

corresponds to the training targets. The output
`loss`

is the loss between `Y`

and `T`

according to the specified loss function. The output `loss`

must be
scalar.

### Backward Loss Function

The backward loss function computes the derivatives of the loss with respect to the
predictions. If the layer forward loss function supports `dlarray`

objects, then the software automatically determines the backward loss function using
automatic differentiation. The derivatives must be real-valued. For a list of functions
that support `dlarray`

objects, see List of Functions with dlarray Support. Alternatively, to
define a custom backward loss function, create a function named
`backwardLoss`

. For an example showing how to define a custom
backward loss function, see Specify Custom Output Layer Backward Loss Function.

The syntax for `backwardLoss`

is ```
dLdY
= backwardLoss(layer,Y,T)
```

. The input `Y`

contains the predictions
made by the network and `T`

contains the training targets. The output
`dLdY`

is the derivative of the loss with respect to the predictions
`Y`

. The output `dLdY`

must be the same size as the layer
input `Y`

.

For classification problems, the dimensions of `T`

depend on the type of
problem.

Classification Task | Input Size | Observation Dimension |
---|---|---|

2-D image classification | 1-by-1-by-K-by-N, where
K is the number of classes and
N is the number of observations | 4 |

3-D image classification | 1-by-1-by-1-by-K-by-N, where
K is the number of classes and
N is the number of observations | 5 |

Sequence-to-label classification | K-by-N, where
K is the number of classes and
N is the number of observations | 2 |

Sequence-to-sequence classification | K-by-N-by-S,
where K is the number of classes,
N is the number of observations, and
S is the sequence length | 2 |

The size of `Y`

depends on the output of the previous layer. To ensure that
`Y`

is the same size as `T`

, you must include a layer
that outputs the correct size before the output layer. For example, to ensure that
`Y`

is a 4-D array of prediction scores for *K*
classes, you can include a fully connected layer of size *K* followed by a
softmax layer before the output layer.

For regression problems, the dimensions of `T`

also depend on the type of
problem.

Regression Task | Input Size | Observation Dimension |
---|---|---|

2-D image regression | 1-by-1-by-R-by-N, where
R is the number of responses and
N is the number of observations | 4 |

2-D Image-to-image regression | h-by-w-by-c-by-N,
where h, w, and
c are the height, width, and number of channels
of the output, respectively, and N is the number of
observations | 4 |

3-D image regression | 1-by-1-by-1-by-R-by-N, where
R is the number of responses and
N is the number of observations | 5 |

3-D Image-to-image regression | h-by-w-by-d-by-c-by-N,
where h, w, d,
and c are the height, width, depth, and number of
channels of the output, respectively, and N is the
number of observations | 5 |

Sequence-to-one regression | R-by-N, where
R is the number of responses and
N is the number of observations | 2 |

Sequence-to-sequence regression | R-by-N-by-S,
where R is the number of responses,
N is the number of observations, and
S is the sequence length | 2 |

For example, if the network defines an image regression network with one response and has
mini-batches of size 50, then `T`

is a 4-D array of size
1-by-1-by-1-by-50.

The size of `Y`

depends on the output of the previous layer. To ensure
that `Y`

is the same size as `T`

, you must include a layer
that outputs the correct size before the output layer. For example, for image regression
with *R* responses, to ensure that `Y`

is a 4-D array of
the correct size, you can include a fully connected layer of size *R*
before the output layer.

The `forwardLoss`

and `backwardLoss`

functions have
the following output arguments.

Function | Output Argument | Description |
---|---|---|

`forwardLoss` | `loss` | Calculated loss between the predictions `Y` and the
true target `T` . |

`backwardLoss` | `dLdY` | Derivative of the loss with respect to the predictions
`Y` . |

The `backwardLoss`

function must output `dLdY`

with
the size expected by the previous layer and `dLdY`

must be the same
size as `Y`

.

### GPU Compatibility

If the layer forward functions fully support `dlarray`

objects, then the layer
is GPU compatible. Otherwise, to be GPU compatible, the layer functions must support inputs
and return outputs of type `gpuArray`

(Parallel Computing Toolbox).

Many MATLAB^{®} built-in functions support `gpuArray`

(Parallel Computing Toolbox) and `dlarray`

input arguments. For a list of
functions that support `dlarray`

objects, see List of Functions with dlarray Support. For a list of functions
that execute on a GPU, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
To use a GPU for deep
learning, you must also have a supported GPU device. For information on supported devices, see
GPU Computing Requirements (Parallel Computing Toolbox). For more information on working with GPUs in MATLAB, see GPU Computing in MATLAB (Parallel Computing Toolbox).

### Check Validity of Layer

If you create a custom deep learning layer, then you can use
the `checkLayer`

function
to check that the layer is valid. The function checks layers for validity, GPU compatibility,
correctly defined gradients, and code generation compatibility. To check that a layer is valid,
run the following
command:

checkLayer(layer,validInputSize)

`layer`

is an instance of the layer and `validInputSize`

is a vector or cell array
specifying the valid input sizes to the layer. To check with multiple observations, use the
`ObservationDimension`

option. To run the check for code generation compatibility,
set the `CheckCodegenCompatibility`

option to `1`

(true). For large input sizes, the gradient checks take longer to run.
To speed up the check, specify a smaller valid input size.For more information, see Check Custom Layer Validity.

## See Also

`checkLayer`

| `findPlaceholderLayers`

| `replaceLayer`

| `assembleNetwork`

| `PlaceholderLayer`