classificationLayer

Classification output layer

A classification layer computes the cross entropy loss for multi-class classification problems 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, include a fully connected layer with output size K and a softmax layer before the classification layer.

Syntax

layer = classificationLayer
layer = classificationLayer(Name,Value)

Description

layer = classificationLayer creates a classification layer.

example

layer = classificationLayer(Name,Value) sets the optional Name and Classes properties using name-value pairs. For example, classificationLayer('Name','output') creates a classification layer with the name 'output'. Enclose each property name in single quotes.

Examples

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Create a classification layer with the name 'output'.

layer = classificationLayer('Name','output')
layer = 
  ClassificationOutputLayer with properties:

            Name: 'output'
         Classes: 'auto'
      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   ''   Convolution             20 5x5 convolutions with stride [1  1] and padding [0  0  0  0]
     3   ''   ReLU                    ReLU
     4   ''   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

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Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

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

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.

Data Types: char | string

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'.

Data Types: char | categorical | string | cell

Output Arguments

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Classification layer, returned as a ClassificationOutputLayer object.

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

More About

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Classification Layer

A classification layer computes the cross entropy loss for multi-class classification problems with mutually exclusive classes.

For typical classification networks, the classification layer must follow the 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]:

loss=i=1Nj=1Ktijlnyij,

where N is the number of samples, K is the number of classes, tij is the indicator that the ith sample belongs to the jth class, and yij is the output for sample i for class j, which in this case, is the value from the softmax function. That is, it is the probability that the network associates the ith input with class j.

References

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

Introduced in R2016a