## Define Custom Classification Output Layer

Tip

To construct a classification output layer with cross entropy loss for k mutually exclusive classes, use `classificationLayer`. If you want to use a different loss function for your classification problems, then you can define a custom classification output layer using this example as a guide.

This example shows how to define a custom classification output layer with the sum of squares error (SSE) loss and use it in a convolutional neural network.

To define a custom classification output layer, you can use the template provided in this example, which takes you through the following steps:

1. Name the layer – Give the layer a name so it can be used in MATLAB®.

2. Declare the layer properties – Specify the properties of the layer.

3. Create a constructor function (optional) – Specify how to construct the layer and initialize its properties. If you do not specify a constructor function, then the software initializes the properties with `''` at creation.

4. Create a forward loss function – Specify the loss between the predictions and the training targets.

5. Create a backward loss function (optional) – Specify the derivative of the loss with respect to the predictions. If you do not specify a backward loss function, then the forward loss function must support `dlarray` objects.

A classification SSE layer computes the sum of squares error loss for classification problems. SSE is an error measure between two continuous random variables. For predictions Y and training targets T, the SSE loss between Y and T is given by

`$L=\frac{1}{N}\sum _{n=1}^{N}\text{​}\sum _{i=1}^{K}\text{​}{\left({Y}_{ni}-{T}_{ni}\right)}^{2},$`

where N is the number of observations and K is the number of classes.

### Classification Output Layer Template

Copy the classification output layer template into a new file in MATLAB. This template outlines the structure of a classification output layer and includes the functions that define the layer behavior.

```classdef myClassificationLayer < nnet.layer.ClassificationLayer 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 ```

### Name the Layer

First, give the layer a name. In the first line of the class file, replace the existing name `myClassificationLayer` with `sseClassificationLayer`.

```classdef sseClassificationLayer < nnet.layer.ClassificationLayer ... end```

Next, rename the `myClassificationLayer` constructor function (the first function in the `methods` section) so that it has the same name as the layer.

``` methods function layer = sseClassificationLayer() ... end ... end```

#### Save the Layer

Save the layer class file in a new file named `sseClassificationLayer.m`. The file name must match the layer name. To use the layer, you must save the file in the current folder or in a folder on the MATLAB path.

### Declare Layer Properties

Declare the layer properties in the `properties` section.

By default, custom output layers have the following properties:

• `Name`Layer name, specified as a character vector or a string scalar. To include a layer in a layer graph, you must specify a nonempty unique layer name. If you train a series network with the layer and `Name` is set to `''`, then the software automatically assigns a name to the layer at training time.

• `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)`. The default value is `'auto'`.

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.

In this example, the layer does not require any additional properties, so you can remove the `properties` section.

### Create Constructor Function

Create the function that constructs the layer and initializes the layer properties. Specify any variables required to create the layer as inputs to the constructor function.

Specify the input argument `name` to assign to the `Name` property at creation. Add a comment to the top of the function that explains the syntax of the function.

``` function layer = sseClassificationLayer(name) % layer = sseClassificationLayer(name) creates a sum of squares % error classification layer and specifies the layer name. ... end```

#### Initialize Layer Properties

Replace the comment `% Layer constructor function goes here` with code that initializes the layer properties.

Give the layer a one-line description by setting the `Description` property of the layer. Set the `Name` property to the input argument `name`.

``` function layer = sseClassificationLayer(name) % layer = sseClassificationLayer(name) creates a sum of squares % error classification layer and specifies the layer name. % Set layer name. layer.Name = name; % Set layer description. layer.Description = 'Sum of squares error'; end```

### Create Forward Loss Function

Create a function named `forwardLoss` that returns the SSE loss between the predictions made by the network and the training targets. The syntax for `forwardLoss` is ```loss = forwardLoss(layer, Y, T)```, where `Y` is the output of the previous layer and `T` represents the training targets.

For classification problems, the dimensions of `T` depend on the type of problem.

2-D image classification1-by-1-by-K-by-N, where K is the number of classes and N is the number of observations.4
3-D image classification1-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 classificationK-by-N, where K is the number of classes and N is the number of observations.2
Sequence-to-sequence classificationK-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.

A classification SSE layer computes the sum of squares error loss for classification problems. SSE is an error measure between two continuous random variables. For predictions Y and training targets T, the SSE loss between Y and T is given by

`$L=\frac{1}{N}\sum _{n=1}^{N}\text{​}\sum _{i=1}^{K}\text{​}{\left({Y}_{ni}-{T}_{ni}\right)}^{2},$`

where N is the number of observations and K is the number of classes.

The inputs `Y` and `T` correspond to Y and T in the equation, respectively. The output `loss` corresponds to L. Add a comment to the top of the function that explains the syntaxes of the function.

``` function loss = forwardLoss(layer, Y, T) % loss = forwardLoss(layer, Y, T) returns the SSE loss between % the predictions Y and the training targets T. % Calculate sum of squares. sumSquares = sum((Y-T).^2); % Take mean over mini-batch. N = size(Y,4); loss = sum(sumSquares)/N; end```

Because the `forwardLoss` function only uses functions that support `dlarray` objects, defining the `backwardLoss` function is optional. For a list of functions that support `dlarray` objects, see List of Functions with dlarray Support.

### Completed Layer

View the completed classification output layer class file.

```classdef sseClassificationLayer < nnet.layer.ClassificationLayer % Example custom classification layer with sum of squares error loss. methods function layer = sseClassificationLayer(name) % layer = sseClassificationLayer(name) creates a sum of squares % error classification layer and specifies the layer name. % Set layer name. layer.Name = name; % Set layer description. layer.Description = 'Sum of squares error'; end function loss = forwardLoss(layer, Y, T) % loss = forwardLoss(layer, Y, T) returns the SSE loss between % the predictions Y and the training targets T. % Calculate sum of squares. sumSquares = sum((Y-T).^2); % Take mean over mini-batch. N = size(Y,4); loss = sum(sumSquares)/N; end end end```

### 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 CUDA® enabled NVIDIA® GPU with compute capability 3.0 or higher. For more information on working with GPUs in MATLAB, see GPU Computing in MATLAB (Parallel Computing Toolbox).

The MATLAB functions used in `forwardLoss` all support `dlarray` objects, so the layer is GPU compatible.

### Check Output Layer Validity

Check the layer validity of the custom classification output layer `sseClassificationLayer`.

Define a custom sum-of-squares error classification layer. To create this layer, save the file `sseClassificationLayer.m` in the current folder. Create an instance of the layer.

`layer = sseClassificationLayer('sse');`

Check the layer is valid using `checkLayer`. Specify the valid input size to be the size of a single observation of typical input to the layer. The layer expects a 1-by-1-by-K-by-N array inputs, where K is the number of classes, and N is the number of observations in the mini-batch.

```validInputSize = [1 1 10]; checkLayer(layer,validInputSize,'ObservationDimension',4);```
```Skipping GPU tests. No compatible GPU device found. Skipping code generation compatibility tests. To check validity of the layer for code generation, specify the 'CheckCodegenCompatibility' and 'ObservationDimension' options. Running nnet.checklayer.TestOutputLayerWithoutBackward ........ Done nnet.checklayer.TestOutputLayerWithoutBackward __________ Test Summary: 8 Passed, 0 Failed, 0 Incomplete, 2 Skipped. Time elapsed: 1.2646 seconds. ```

The test summary reports the number of passed, failed, incomplete, and skipped tests.

### Include Custom Classification Output Layer in Network

You can use a custom output layer in the same way as any other output layer in Deep Learning Toolbox. This section shows how to create and train a network for classification using the custom classification output layer that you created earlier.

`[XTrain,YTrain] = digitTrain4DArrayData;`

Define a custom sum-of-squares error classification layer. To create this layer, save the file `sseClassificationLayer.m` in the current folder. Create an instance of the layer. Create a layer array including the custom classification output layer `sseClassificationLayer`.

```layers = [ imageInputLayer([28 28 1]) convolution2dLayer(5,20) batchNormalizationLayer reluLayer fullyConnectedLayer(10) softmaxLayer sseClassificationLayer('sse')]```
```layers = 7x1 Layer array with layers: 1 '' Image Input 28x28x1 images with 'zerocenter' normalization 2 '' Convolution 20 5x5 convolutions with stride [1 1] and padding [0 0 0 0] 3 '' Batch Normalization Batch normalization 4 '' ReLU ReLU 5 '' Fully Connected 10 fully connected layer 6 '' Softmax softmax 7 'sse' Classification Output Sum of squares error ```

Set the training options and train the network.

```options = trainingOptions('sgdm'); net = trainNetwork(XTrain,YTrain,layers,options);```
```Training on single CPU. Initializing input data normalization. |========================================================================================| | Epoch | Iteration | Time Elapsed | Mini-batch | Mini-batch | Base Learning | | | | (hh:mm:ss) | Accuracy | Loss | Rate | |========================================================================================| | 1 | 1 | 00:00:00 | 9.38% | 0.9944 | 0.0100 | | 2 | 50 | 00:00:04 | 75.00% | 0.3561 | 0.0100 | | 3 | 100 | 00:00:07 | 92.97% | 0.1316 | 0.0100 | | 4 | 150 | 00:00:11 | 96.88% | 0.0915 | 0.0100 | | 6 | 200 | 00:00:15 | 95.31% | 0.0738 | 0.0100 | | 7 | 250 | 00:00:18 | 96.88% | 0.0485 | 0.0100 | | 8 | 300 | 00:00:22 | 99.22% | 0.0203 | 0.0100 | | 9 | 350 | 00:00:25 | 99.22% | 0.0264 | 0.0100 | | 11 | 400 | 00:00:30 | 100.00% | 0.0069 | 0.0100 | | 12 | 450 | 00:00:34 | 100.00% | 0.0045 | 0.0100 | | 13 | 500 | 00:00:40 | 100.00% | 0.0078 | 0.0100 | | 15 | 550 | 00:00:45 | 100.00% | 0.0059 | 0.0100 | | 16 | 600 | 00:00:49 | 100.00% | 0.0021 | 0.0100 | | 17 | 650 | 00:00:52 | 100.00% | 0.0040 | 0.0100 | | 18 | 700 | 00:00:55 | 100.00% | 0.0024 | 0.0100 | | 20 | 750 | 00:00:59 | 100.00% | 0.0028 | 0.0100 | | 21 | 800 | 00:01:02 | 100.00% | 0.0020 | 0.0100 | | 22 | 850 | 00:01:06 | 100.00% | 0.0017 | 0.0100 | | 24 | 900 | 00:01:09 | 100.00% | 0.0020 | 0.0100 | | 25 | 950 | 00:01:12 | 100.00% | 0.0013 | 0.0100 | | 26 | 1000 | 00:01:16 | 100.00% | 0.0012 | 0.0100 | | 27 | 1050 | 00:01:20 | 99.22% | 0.0104 | 0.0100 | | 29 | 1100 | 00:01:25 | 100.00% | 0.0013 | 0.0100 | | 30 | 1150 | 00:01:30 | 100.00% | 0.0012 | 0.0100 | | 30 | 1170 | 00:01:31 | 99.22% | 0.0077 | 0.0100 | |========================================================================================| ```

Evaluate the network performance by making predictions on new data and calculating the accuracy.

```[XTest,YTest] = digitTest4DArrayData; YPred = classify(net, XTest); accuracy = mean(YTest == YPred)```
```accuracy = 0.9844 ```