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 ANN.Perceptron.Neurons;
using System.Drawing;
using System.Threading;
using System.Threading.Tasks;
namespace ANN.Perceptron.Layers
{
    public class InputLayer:CommonLayer
    {
        public InputLayer(String sLabel, Size inputSize)
        {
            label = sLabel;
            prevLayer = null;
            weights = null;
            featureMapSize = inputSize;
            nFeatureMaps = 1;
            weightCount = 0;
            floatingPointWarning = false;
            ParallelOption = new ParallelOptions();
            ParallelOption.TaskScheduler = null;
            _maxDegreeOfParallelism = Environment.ProcessorCount;
            ParallelOption.MaxDegreeOfParallelism = _maxDegreeOfParallelism;
            type = LayerTypes.Input;
        }
        public override void Initialize()
        {
            floatingPointWarning = false;
            CreateLayer();
        }
        protected override void CreateLayer()
        { 
            
            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;
            }
         
            #region input layer type
            //
            //  the input layer.
            // Create neurons: exactly the same number of neurons as the input
            // vector of 29x29=841 pixels, and no weights/connections
            prevLayer = null;
            neuronCount = nFeatureMaps * featureMapSize.Width * featureMapSize.Height;
            neurons = new Neuron[NeuronCount];
            weightCount = 0;
            weights = null;
            for (int ii = 0; ii < neuronCount; ii++)
            {
                String lb = String.Format("Layer {0}, Neuron {0}", label, ii);
                neurons[ii] = new Neuron(lb);
            }
            #endregion
        }
    }
}