trainSoftmaxLayer
Train a softmax layer for classification
Description
trains
a softmax layer, net
= trainSoftmaxLayer(X
,T
,Name,Value
)net
, with additional options
specified by one or more of the Name,Value
pair
arguments.
For example, you can specify the loss function.
Examples
Classify Using Softmax Layer
Load the sample data.
[X,T] = iris_dataset;
X
is a 4x150 matrix of four attributes of iris flowers: Sepal length, sepal width, petal length, petal width.
T
is a 3x150 matrix of associated class vectors defining which of the three classes each input is assigned to. Each row corresponds to a dummy variable representing one of the iris species (classes). In each column, a 1 in one of the three rows represents the class that particular sample (observation or example) belongs to. There is a zero in the rows for the other classes that the observation does not belong to.
Train a softmax layer using the sample data.
net = trainSoftmaxLayer(X,T);
Classify the observations into one of the three classes using the trained softmax layer.
Y = net(X);
Plot the confusion matrix using the targets and the classifications obtained from the softmax layer.
plotconfusion(T,Y);
Input Arguments
X
— Training data
m-by-n matrix
Training data, specified as an m-by-n matrix,
where m is the number of variables in training
data, and n is the number of observations (examples).
Hence, each column of X
represents a sample.
Data Types: single
| double
T
— Target data
k-by-n matrix
Target data, specified as a k-by-n matrix, where k is the number of classes, and n is the number of observations. Each row is a dummy variable representing a particular class. In other words, each column represents a sample, and all entries of a column are zero except for a single one in a row. This single entry indicates the class for that sample.
Data Types: single
| double
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: 'MaxEpochs',400,'ShowProgressWindow',false
specifies
the maximum number of iterations as 400 and hides the training window.
MaxEpochs
— Maximum number of training iterations
1000 (default) | positive integer value
Maximum number of training iterations, specified as the comma-separated
pair consisting of 'MaxEpochs'
and a positive integer
value.
Example: 'MaxEpochs',500
Data Types: single
| double
LossFunction
— Loss function for the softmax layer
'crossentropy'
(default) | 'mse'
Loss function for the softmax layer, specified as the comma-separated
pair consisting of 'LossFunction'
and either 'crossentropy'
or 'mse'
.
mse
stands for mean squared error function,
which is given by:
where n is
the number of training examples, and k is the number
of classes. is
the ijth entry of the target matrix, T
,
and is
the ith output from the autoencoder when the input
vector is xj.
The cross entropy function is given by:
Example: 'LossFunction','mse'
ShowProgressWindow
— Indicator to display the training window
true
(default) | false
Indicator to display the training window during training, specified
as the comma-separated pair consisting of 'ShowProgressWindow'
and
either true
or false
.
Example: 'ShowProgressWindow',false
Data Types: logical
TrainingAlgorithm
— Training algorithm
'trainscg'
(default)
Training algorithm used to train the softmax layer, specified as the comma-separated pair
consisting of
'TrainingAlgorithm'
and
'trainscg'
, which stands for
scaled conjugate gradient.
Example: 'TrainingAlgorithm','trainscg'
Output Arguments
net
— Softmax layer for classification
network
object
Softmax layer for classification, returned as a network
object.
The softmax layer, net
, is the same size as the
target T
.
Version History
Introduced in R2015b
See Also
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