Classification layer
A classification layer computes the cross entropy loss for multi-class classification problems with mutually exclusive classes.
Create a classification layer using classificationLayer.
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). The default value is
'auto'.
Data Types: char | categorical | string | cell
OutputSize — Size of the output'auto' (default) | positive integerThis property is read-only.
Size of the output, specified as a positive integer. This value is
the number of labels in the data. Before the training, the output size
is set to 'auto'.
LossFunction — Loss function for training'crossentropyex'This property is read-only.
Loss function for training, specified as
'crossentropyex', which stands for
Cross Entropy Function for k Mutually
Exclusive Classes.
Name — Layer name'' (default) | character vector | string scalar
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
NumInputs — Number of inputsNumber of inputs of the layer. This layer accepts a single input only.
Data Types: double
InputNames — Input names{'in'} (default)Input names of the layer. This layer accepts a single input only.
Data Types: cell
NumOutputs — Number of outputsNumber of outputs of the layer. The layer has no outputs.
Data Types: double
OutputNames — Output names{} (default)Output names of the layer. The layer has no outputs.
Data Types: cell
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
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]:
where N is the number of samples, K is the number of classes, is the indicator that the ith sample belongs to the jth class, and 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.
ClassNames property will be removedNot recommended starting in R2018b
ClassNames will be removed. Use Classes instead. To
update your code, replace all instances of ClassNames with
Classes. There are some differences between the properties that
require additional updates to your code.
The ClassNames property of the output layer is a cell array of character
vectors. The Classes property is a categorical array. To use the value
of Classes with functions that require cell array input, convert the
classes using the cellstr function.
[1] Bishop, C. M. Pattern Recognition and Machine Learning. Springer, New York, NY, 2006.
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