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Welcome to the ANN4j wiki!

This package provides Object oriented Neural Networks for making Explainable Networks. Object Oriented Network structure is helpful for observing each and every element the model. This package is developed for XAI research and development.

Features

• Observable implementation for Artificial Neural Networks (ANN)
• XAI method for relevance propagation
• Stochastic/batch gradient descent
• No hardcoded implementations lets researchers change the parameters as they want.
• Plug and play mnist type data. Other Data files can be handeled via extension

ANN4j - Creating observable object-oriented neural networks for better Explainable AI.

ANN4j is a java package that provides object oriented functionality to neural networks. It implements multilayer perceptrons in java by using Objects instead of matrix multiplications. Every neuron is treated as a seperate object. While this kind of implementation is highly inefficiant when compared to matrix multiplications, this implementation will help research in the fields of Explainable AI. Explainable AI aims at making the model interpretable. By pausing and observing the neural net at different stages, researchers can study neural networks more efficiantly. Indivisual observable interfaces are more easy to observe then matrices. Operations which are difficult to perform on matrices can be performed more easily using this technique.

Usage

import ann4j.*;

Setting parameters

• Setting the output file to be output.txt and enabling command line logging.

  	parameter.setOutputFile("output.txt", true);

• Setting the number of neurons in each layer.

  	parameter.setLayerArray(784, 32, 16, 16, 26);	

• Setting the training file to be emnist-letters-train.csv and the file type

  	parameter.setTrainingFileReader("emnist-letters-train.csv", "mnist");

• Setting the testing file

  	parameter.setTestingFileReader("emnist-letters-test.csv", "mnist");

• Setting the learning rate for weights

  	parameter.setLearningRate(1);

• Setting the learning rate for the bias to 1.

  	parameter.setBiasLearningRate(1);

• Setting the epsillion value for the relevance propagation algorithm.

  	parameter.setEpsillion(0);

• Setting the batch size

  	parameter.setBatchsize(10);

• Setting the rectification function.

  	parameter.setRectificationFunction("sigmoid");

Training the Model

• Creating a new instance of the Trainer class.

  	Trainer myTrainer = new Trainer();

• Training the network with 88800 samples for n epochs

  	myTrainer.train(m, n);

• Creating a new instance of the NeuronObserver class this class will observe the neurons and respond when every parameter is changed.

  	NeuronObserver myNeuronObserver = new NeuronObserver();

• Testing the network with 9990 samples.

  	myTrainer.test(9990);

• Adding the neuron at layer 1 and index 31 to be observed.

  	myNeuronObserver.addNeuronToBeObserved(1, 31);

Evaluating the model

• Training accuracy

  	myTrainer.getModelEvaluator().getTrainingAccuracy();	

• Testing accuracy

     	myTrainer.getModelEvaluator().getTrainingAccuracy();	

• Confusion Matrix

  	myTrainer.getModelEvaluator().printConfusionMatrix();	

XAI

• xai algorithm for relevance propagation.

  	myTrainer.relevancePropagate(2, 3);	

• xai algotithm for most significant input neurons

  	myTrainer.forwardPropagatewithExclusionInputLayerOnKSamples(2);

File rendering

ANN4j provides functionality to extend the InputFileReader to add file handling for various types of datasets apart from mnist type files.

Observable methods

In ANN4j, every neuron is an object of its own. Every Neuron can be observed by the NeuronObserver class when the values are updated. NeuronObserver class can be extended as per the requirement of the parameters to be observed. Neurons objects can also be obtined and observed independantly.

• Get a neuron object from a layer.

  	myTrainer.getLayerManager().getLayer(layerNum).getNeuron(neuronNum));

• Get activation of a neuron

  	neuron.getActivation();

• Get bias of the neuron

  	neuron.getBias();

• Get arraylist of the left or right connections of the neuron

  neuron.leftConnections;
  neuron.rightConnections;

• Get weight of a connection

  	connection.getWeight();

Examples

• Example code https://github.com/Aatmaj-Zephyr/ANN4j/blob/main/Main.java

• Sample output https://github.com/Aatmaj-Zephyr/ANN4j/blob/3721148ec24371bf095e1394fe39fc471f391466/output.txt

• Sample output https://github.com/Aatmaj-Zephyr/ANN4j/blob/ef0f34b505e6e6316f94b5a660b9ef651582667d/output.txt

Other resources

• More about Artificial Neural Networks https://www.3blue1brown.com/topics/neural-networks
• Relevance propagation example https://towardsdatascience.com/indepth-layer-wise-relevance-propagation-340f95deb1ea
• Rectification functions https://www.quora.com/What-is-the-purpose-of-rectifier-functions-in-neural-networks