Multiple convolution neural networks approach for online handwriting recognition

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using ANN.Perceptron.ArchiveSerialization;
using ANN.Perceptron.Common;
using System.Drawing;
using System.Threading;
using System.Threading.Tasks;
using ANN.Perceptron.Connections;
using ANN.Perceptron.Neurons;
using ANN.Perceptron.Weights;
namespace ANN.Perceptron.Layers
{
    public class FullConnectedLayer: CommonLayer
    {
        public FullConnectedLayer(string sLabel, CommonLayer pPrev, int nNeurons)
        {
            label = sLabel;
            prevLayer = pPrev;
            weights = null;
            neurons = new Neuron[nNeurons]; //add a internal unkwown output
            neuronCount = nNeurons;
            featureMapSize = new Size(1,neuronCount);
            nFeatureMaps = 1;
            weightCount = 0;
            type = LayerTypes.FullyConnected;
            floatingPointWarning = false;
            ParallelOption = new ParallelOptions();
            ParallelOption.TaskScheduler = null;
            _maxDegreeOfParallelism = Environment.ProcessorCount;
            ParallelOption.MaxDegreeOfParallelism = _maxDegreeOfParallelism;
        }
        //create layer
        public override void Initialize()
        {
            floatingPointWarning = false;
            CreateLayer();
        }
        protected override void CreateLayer()
        {
            //  the final (output) layer:
            // This layer is a fully-connected layer .  Since it is fully-connected,
            int iNumWeight;
            var rdm = new Random();
            if (neuronCount > 0 || neurons != null)
            { //clear neurons
                neurons = null;
                neuronCount = 0;
            }
            if (weightCount > 0 || weights != null)
            {
                //clear weights;
                weights = null;
                weightCount = 0;
            }
            if (prevLayer != null)
            {
                neuronCount = nFeatureMaps * featureMapSize.Width * featureMapSize.Height;
                neurons = new Neuron[neuronCount];
                for (int ii = 0; ii < neuronCount; ii++)
                {
                    String lb = String.Format("Layer {0}, Neuron {1}", label, ii);
                    Neurons[ii] = new Neuron(lb);
                }
                rdm = new Random();
                weightCount = neuronCount * (prevLayer.NeuronCount + 1);
                weights = new Weight[weightCount];
                for (int ii = 0; ii < weightCount; ii++)
                {
                    String lb = String.Format("Layer {0}, Weight {1}", label, ii);
                    double initWeight = 0.05 * (2.0 * rdm.NextDouble() - 1.0);
                    weights[ii] = new Weight(lb, initWeight);
                }

                // Interconnections with previous layer: fully-connected

                iNumWeight = 0; // weights are not shared in this layer

                for (int jj = 0; jj < neuronCount; jj++)
                {
                    var n = Neurons[jj];
                    int connCount = prevLayer.NeuronCount + 1;
                    n.ConnectionCount = connCount;
                    n.Connections = new Connection[connCount];
                    n.AddConnection((uint)NNDefinations.ULONG_MAX, (uint)iNumWeight, 0); // bias weight
                    for (int ii = 0; ii < prevLayer.NeuronCount; ii++)
                    {
                        n.AddConnection((uint)ii, (uint)iNumWeight++, ii + 1);
                    }
                }
            }
            
        }
    }
}