In this tutorial we present the software model of OpenNN. The whole process is carried out in the Unified Modeling Language (UML). The Unified Modeling Language (UML) is a general purpose visual modeling language that is used to specify, visualize, construct, and document the artifacts of a software system.
In order to construct a model for OpenNN, we follow a top-down development. This approach to the problem begins at the highest conceptual level and works down to the details. In this way, to create and evolve a conceptual class diagram, we iteratively model:
In colloquial terms a concept is an idea or a thing. In object-oriented modelling concepts are represented by means of classes. Therefore, a prime task is to identify the main concepts (or classes) of the problem domain. In UML class diagrams, classes are depicted as boxes.The next figure depicts a starting UML class diagram for the conceptual model of OpenNN.
Conceptual diagram for OpenNN.
Once identified the main concepts in the model it is necessary to aggregate the associations among them. An association is a relationship between two concepts which points some significant or interesting information.
In UML class diagrams, an association is shown as a line connecting two classes. The appropriate associations among the main concepts of OpenNN are next identified to be included to the UML class diagram of the system:
Classes are usually composed of another classes. The higher level classes manage the lower level ones. Regarding OpenNN, the concepts of DataSet, NeuralNetwork, TrainingStrategy, ModelSelection and TestingAnalysis, are quite high level structures. This means that this classes are composed by different elements.
In general the goal of OpenNN is to encapsulate basic concepts in elementary classes and then, create larger classes with broader concepts. For instance, referencing to NerualNetwork, PerceptronLayer, ProbabilisticLayer or another layer of neurons is a subclass enclose in Layer, this class represents the concept of a layer from any kind of neurons, and is necessary a set of layer to build a Layer.
DataSet is not composed of classes. It is a high level class where all methods are controlled by it.
The next uml diagram shows the most important classes of NeuralNetwork:
The following diagram shows the most relevant classes in TrainingStrategy:
In the picture below, the most important classes of ModelSelection are shown:
In object-oriented programming, some classes are designed only as a parent from which sub-classes may be derived, but which is not itself suitable for instantiation. This is said to be an abstract class, as opposed to a concrete class, which is suitable to be instantiated.
The derived class contains all the features of the base class, but may have new features added or redefine existing features. Associations between a base class an a derived class are of the kind is a.
The classes: Mean Square error, CrossEntropyError, MinkowskiError, NormalizedSquaredError, SumSquaredError, WeightedSquareError are derived from LossIndex, all of them have in common that they inherit the characteristics of the base class, however each of this classes also introduce new features such us its own definition of error.
Likewise GradientDescent, AdaptativeMomentEstimation, ConjugateGradient, QuasiNewtonMethod, LevenbergMarquadtAlgorithm, StochasticGradientDescent are a set of concrete classes that have inherited all the characteristics of the abstract class TrainingAlgotihm.
Like TrainingStrategy, ModelSelection is composed by 2 abstract classes InputSelectionAlgorithm and OrderSelectionAlgorithm, both have their own derived classes that inherit the features of each one.
InputSelectionAlgorithm is an abstract class composed by 3 concretes classes: GrowingInputs, GeneticAlgorithm, PruningInputs.
NeuronsSelection is an abstract class composed by a concrete class: IncrementalNeurons.
A member (or attribute) is a named value or relationship that exists for all or some instances of a class. A method (or operation) is a procedure associated with a class.
In UML class diagrams, classes are depicted as boxes with two sections: the top that lists the attributes of the class, and the bottom that lists the operations. The main members and methods of the different OpenNN classes are described throughout all this manual.
The most important attributes and methods of the DataSet class:
The most important attributes and methods of NeruralNetwork class are:
In the following chart the most important attributes of the training strategy class are shown:
The most important attributes and methods of the ModelSelection class are:
The next box shows the most important attributes and methods of TestingAnalisys class: