Daten_NeuralNetwork

My object-oriented NN

The Main file is in neuralNet package

The main method is composed of the following:

1. Setting up the neuralNet

   • Datensatz training2 = new Datensatz("training2"):
     • via addDatenpunkt one can add a (new Datenpunkt(x, y, tester) which has two double values and a boolean label
   • int[] nodesInHiddenLayer = { 3,5 }:
     • is setting 3 nodes in first hidden layer and 5 nodes in second (not counting input/output layer)
   • two loops, j for epoch and i for going through the Datapoints

2. Sequence of Methods during each Datapoint processing

   • NeuralNet nn = new NeuralNet(nodesInHiddenLayer.length, nodesInHiddenLayer, training2):
     • The creating nn instance: Dataset training2 with point 1 is set for InputNeurons (x,y)
   • nn.allCompute(): Run Forward Propagation via recursive "Neuron" method compute() for neurons in output layer
   • nn.setLosses() :
     • this.lsfct = new LossFunction(NeuronLayer nl, Datenpunkt): instance will have first backpropagation errors for output neurons in a HashMap
     • The recursive backCompute(LossFunction ls) "Neuron" method: starts with instances of InputNeuron and will backpropagate the error component, saving it in a double for the relevant Neurons
   • nn.updateAllLayersWeights(): this will use the Neuron instance variable error to update the weights in its HashMap<Neuron, Double> neurolist of Neurons (as keys) acting as Input to the node
   • nn.nextPoint(): iterator for the Dataset of instance Datensatz (set within the nn instance) will point to the next element Datenpunkt as datapoint to continue the process in the next loop

This project stems from an uni exercise and is not fledged out in any sense. It is my first attempt to model an object-oriented neural network. Only one hidden layer is working so far in terms of predicive quality.