classificationLayer - (Not recommended) Classification output layer - MATLAB (original) (raw)

(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

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](#bu5lho8%5Fsep%5Fbu5lho8-coutputlayer) = classificationLayer creates a classification layer.

example

[layer](#bu5lho8%5Fsep%5Fbu5lho8-coutputlayer) = classificationLayer([Name,Value](#namevaluepairarguments)) sets the optional Name, ClassWeights, andClasses properties using one or more name-value pairs. For example, classificationLayer('Name','output') creates a classification layer with the name 'output'.

Examples

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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

Name-Value Arguments

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Specify optional pairs of arguments asName1=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'

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

The ClassificationOutputLayer object stores this property as a character vector.

Data Types: char | string

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 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

More About

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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]:

where N is the number of samples, K is the number of classes, wi is the weight for class i, tni is the indicator that sample n belongs to class i, and_yni_ is the output for sample_n_ for class i, which in this case, is the value from the softmax function. In other words, yni 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

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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).

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 ARMCompute 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

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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.