classificationLayer

(Not recommended) Classification output layer

classificationLayer is not recommended. Use the trainnet function and set the loss function to "crossentropy" instead. For more information, see Version History.

Syntax

layer = classificationLayer

layer = classificationLayer(Name,Value)

Description

A classification layer computes the cross-entropy loss for classification and weighted classification tasks with mutually exclusive classes.

The layer infers the number of classes from the output size of the previous layer. For example, to specify the number of classes K of the network, you can include a fully connected layer with output size K and a softmax layer before the classification layer.

layer = classificationLayer creates a classification layer.

layer = classificationLayer(Name,Value) sets the optional Name, ClassWeights, and Classes properties using one or more name-value pairs. For example, classificationLayer('Name','output') creates a classification layer with the name 'output'.

Examples

Create Classification Layer

Create a classification layer.

Create a classification layer with the name 'output'.

layer = classificationLayer('Name','output')

layer = 
  ClassificationOutputLayer with properties:

            Name: 'output'
         Classes: 'auto'
    ClassWeights: 'none'
      OutputSize: 'auto'

   Hyperparameters
    LossFunction: 'crossentropyex'

Include a classification output layer in a Layer array.

layers = [ ...
    imageInputLayer([28 28 1])
    convolution2dLayer(5,20)
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer]

layers = 
  7x1 Layer array with layers:

     1   ''   Image Input             28x28x1 images with 'zerocenter' normalization
     2   ''   2-D Convolution         20 5x5 convolutions with stride [1  1] and padding [0  0  0  0]
     3   ''   ReLU                    ReLU
     4   ''   2-D Max Pooling         2x2 max pooling with stride [2  2] and padding [0  0  0  0]
     5   ''   Fully Connected         10 fully connected layer
     6   ''   Softmax                 softmax
     7   ''   Classification Output   crossentropyex

Input Arguments

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: classificationLayer('Name','output') creates a classification layer with the name 'output'

Name — Layer name

"" (default) | character vector | string scalar

Layer name, specified as a character vector or a string scalar. For Layer array input, the trainNetwork function automatically assigns names to layers with the name "".

The ClassificationOutputLayer object stores this property as a character vector.

Data Types: char | string

ClassWeights — Class weights for weighted cross-entropy loss

'none' (default) | vector of positive numbers

Class weights for weighted cross-entropy loss, specified as a vector of positive numbers or 'none'.

For vector class weights, each element represents the weight for the corresponding class in the Classes property. To specify a vector of class weights, you must also specify the classes using the Classes option.

If the ClassWeights property is 'none', then the layer applies unweighted cross-entropy loss.

Classes — Classes of the output layer

"auto" (default) | categorical vector | string array | cell array of character vectors

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).

Data Types: char | categorical | string | cell

Output Arguments

layer — Classification layer

ClassificationOutputLayer object

Classification layer, returned as a ClassificationOutputLayer object.

For information on concatenating layers to construct convolutional neural network architecture, see Layer.

More About

Classification Layer

A classification layer computes the cross-entropy loss for classification and weighted classification tasks with mutually exclusive classes.

For typical classification networks, the classification layer usually follows a softmax layer. In the classification layer, trainNetwork takes the values from the softmax function and assigns each input to one of the K mutually exclusive classes using the cross entropy function for a 1-of-K coding scheme [1]:

$$\text{loss}=-\frac{1}{N}{\displaystyle \sum _{n=1}^N{\displaystyle \sum }_{i=1}^K}\text{}{w}_i{\text{t}}_{ni}\ln y_{ni},$$

where N is the number of samples, K is the number of classes, w_i is the weight for class i, t_ni is the indicator that sample n belongs to class i, and y_ni is the output for sample n for class i, which in this case, is the value from the softmax function. In other words, y_ni is the probability that the network associates observation n with class i.

References

[1] Bishop, C. M. Pattern Recognition and Machine Learning. Springer, New York, NY, 2006.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

The code generator represents characters in an 8-bit ASCII codeset that the locale setting determines. Therefore, the use of non-ASCII characters in class names, layer names, layer description, or network names might result in errors. For more information, see Encoding of Characters in Code Generation (MATLAB Coder).

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

• To generate CUDA^® or C++ code by using GPU Coder™, you must first construct and train a deep neural network. Once the network is trained and evaluated, you can configure the code generator to generate code and deploy the convolutional neural network on platforms that use NVIDIA^® or ARM^® GPU processors. For more information, see Deep Learning with GPU Coder (GPU Coder).

• For this layer, you can generate code that takes advantage of the NVIDIA CUDA deep neural network library (cuDNN), NVIDIA TensorRT™ high performance inference library, or the ARM Compute Library for Mali GPU.

• The code generator represents characters in an 8-bit ASCII codeset that the locale setting determines. Therefore, the use of non-ASCII characters in class names, layer names, layer description, or network names might result in errors. For more information, see Encoding of Characters in Code Generation (MATLAB Coder).

Version History

Introduced in R2016a

R2024a: Not recommended

Starting in R2024a, ClassificationOutputLayer objects are not recommended, use the trainnet and set the loss function to "crossentropy" instead.

There are no plans to remove support for ClassificationOutputLayer objects. However, the trainnet function has these advantages and is recommended instead:

• trainnet supports dlnetwork objects, which support a wider range of network architectures that you can create or import from external platforms.

• trainnet enables you to easily specify loss functions. You can select from built-in loss functions or specify a custom loss function.

• trainnet outputs a dlnetwork object, which is a unified data type that supports network building, prediction, built-in training, visualization, compression, verification, and custom training loops.

• trainnet is typically faster than trainNetwork.

This table shows some typical usages of the trainNetwork function with ClassificationOutputLayer objects and how to update your code to use the trainnet function instead.

Not Recommended	Recommended
net = trainNetwork(data,layers,options), where layers contains a ClassificationOutputLayer object.	net = trainnet(data,layers,"crossentropy",options); In this example, layers specifies same network without a ClassificationOutputLayer object.
net = trainNetwork(data,layers,options), where layers contains a ClassificationOutputLayer object with ClassWeights set to a numeric vector.	lossFcn = @(Y,T) crossentropy(Y,T,Weights=weights); net = trainnet(data,layers,"crossentropy",options); In this example, weights specifies the class weights and layers specifies same network without a ClassificationOutputLayer object.