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DeepLearning

  

Classify

  

train and use classifier for arbitrary data

 

Calling Sequence

Parameters

Options

Description

Compatibility

Calling Sequence

Classify( data, output )

Parameters

data

-

DataFrame, Matrix, or list of Matrices or Vectors

output

-

DataSeries, Matrix, or list

options

-

zero or more options as specified below

Options

• 

hidden_units=auto or list(integer)

  

Specifies the depth and number of interior nodes for the neural network underlying this classifier.

• 

num_classes=auto or posint

  

Specifies the number of distinct categories into which the data should be classified.

Description

• 

The Classify command accepts a set of training data which has been classified into a finite set of classes, trains a neural network model for this classification, and returns a classifier function which can be applied to arbitrary additional data.

training_set := Import("example/iris_training.csv", base=datadir);

(1)

test_set := Import("example/iris_test.csv", base=datadir);

(2)

classifier := DeepLearning:-Classify( training_set[1..4], training_set[5] );

classifier( test_set[1..4], test_set[5] );

new_sample := DataSeries([4.9,3.1,1.5,0.1], labels=["SepalLength","SepalWidth","PetalLength","PetalWidth"]);

new_sampleSepalLength4.9SepalWidth3.1PetalLength1.5PetalWidth0.1

(3)

classifier( new_sample );

classifier( new_sample, output = probabilities );

Compatibility

• 

The DeepLearning[Classify] command was introduced in Maple 2019.

• 

For more information on Maple 2019 changes, see Updates in Maple 2019.

See Also

DeepLearning

DeepLearning,DNNClassifier