private void backpropagation(){
        do {
            // Increment number of epochs
            currentEpoch++;
            for (DataTuple tuple : dataTuples) {
                // Set values of input neuron to current data tuple
                setInput(tuple);
                //Forward propagation by recursion
                for (Neuron n : outputNeurons) {
                    n.calculateOutput();
                }
                // Calculate error for each output neuron with their corresponding target value
                for (Neuron n : outputNeurons) {
                    // Calculate error for output neuron UP
                    if(n==outputUp)
                        outputUp.calculateError(tuple.normalizeMoveUp(tuple.DirectionChosen));
                    // Calculate error for output neuron RIGHT
                    else if(n==outputRight)
                        outputRight.calculateError(tuple.normalizeMoveRight(tuple.DirectionChosen));
                    // Calculate error for output neuron DOWN
                    else if(n==outputDown)
                        outputDown.calculateError(tuple.normalizeMoveDown(tuple.DirectionChosen));
                    // Calculate error for output neuron LEFT
                    else if(n==outputLeft)
                        outputLeft.calculateError(tuple.normalizeMoveLeft(tuple.DirectionChosen));
                    // Calculate error for output neuron NEUTRAL
                    else if(n==outputNeutral)
                        outputNeutral.calculateError(tuple.normalizeMoveNeutral(tuple.DirectionChosen));
                }
                // Calculate the error for the hidden layer
                for (Neuron n : HiddenNeurons) {
                        n.calculateError(1);
                }
                // Update the weights of the edges between neurons
                for (Edge e : edges) {
                    e.calculateWeights();
                }
                // Update the bias of the neurons
                for (Neuron n : network) {
                    n.calculateBias();
                }
            }
            // continue for x amount of epochs
        } while (currentEpoch<numberOfEpochs);
    }