ConvolutionWeights.cs

﻿using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;
using System.Text;
using System.Threading.Tasks;

namespace AdvancedOCR
{
    class ConvolutionWeights : RectangularWeights
    {
        public ConvolutionWeights(int convolutionWidth, int convolutionHeight, int mapCount) : base(convolutionWidth, convolutionHeight, mapCount)
        {
            Weight = new double[convolutionWidth * convolutionHeight * mapCount];
            WeightStepSize = new double[convolutionWidth * convolutionHeight * mapCount];

            Randomise(Weight, convolutionHeight * convolutionWidth * mapCount);
            Array.Clear(WeightStepSize, 0, Size);
        }

        protected readonly double[] Weight;
        protected readonly double[] WeightStepSize;

        protected override void PropogateForward(RectangularStep downstream, int mapNumber)
        {
            RectangularStep upstream = downstream.Upstream[mapNumber];
            int index = 0;
            for (int y = 0; y < downstream.Height; y++)
            {
                for (int x = 0; x < downstream.Width; x++)
                {
                    downstream.WeightedInputs[index++] += PropogateForward(upstream, x, y, mapNumber);
                }
            }
        }

        protected double PropogateForward(RectangularStep upstream, int upstreamX, int upstreamY, int mapNumber)
        {
            Debug.Assert(upstreamX + Width <= upstream.Width);   // Check we are staying within the width limit of the step.
            Debug.Assert(upstreamY + Height <= upstream.Height);  // Check we are staying within the height limit of the step.

            double result = Bias;

            int upstreamIndex = (upstreamY * upstream.Width) + upstreamX;
            int weightIndex = mapNumber * Width * Height;
            for (int y = 0; y < Height; y++)
            {
                for (int x = 0; x < Width; x++)
                {
                    result += upstream.Output[upstreamIndex] * Weight[weightIndex];
                    upstreamIndex += 1;
                    weightIndex += 1;
                }
                upstreamIndex += upstream.Width - Width;
            }
            return result;
        }


        protected override void StartPreTrainingCore()
        {
            base.StartPreTrainingCore();
            Array.Clear(WeightStepSize, 0, Size);
        }

        protected override void PropogateUnitSecondDerivatives(RectangularStep downstream, int mapNumber)
        {
            RectangularStep upstream = downstream.Upstream[mapNumber];

            for (int y = 0; y < Height; y++)
            {
                for (int x = 0; x < Width; x++)
                {
                    PropogateUnitSecondDerivatives(upstream, downstream, x, y, mapNumber);
                }
            }
        }

        protected void PropogateUnitSecondDerivatives(RectangularStep upstream, RectangularStep downstream, int weightX, int weightY, int mapNumber)
        {
            double weight2ndDerivative = 0;

            int weightIndex = mapNumber * Width * Height + weightY * Width + weightX;
            double weight = Weight[weightIndex];

            int downstreamIndex = 0;
            int upstreamIndex = (weightY * upstream.Width) + weightX;

            // This loop here is equivalent to the sigma in equation 19 in Gradient-Based Learning Applied to Document Recognition.
            for (int y = 0; y < downstream.Height; y++)
            {
                for (int x = 0; x < downstream.Width; x++)
                {
                    double upstreamInput = upstream.Output[upstreamIndex];
                    double downstreamError2ndDerivative = downstream.ErrorDerivative[downstreamIndex]; // (d^2)E/(dAj)^2, where Aj is the sum of inputs to this downstream unit.

                    // Here we calculate (d^2)Ej/(dWij)^2 by multiplying the 2nd derivative of E with respect to the sum of inputs, Aj   
                    // by the state of Oi, the upstream unit, squared. Refer to Equation 25 in document.
                    // The summing happening here is described by equation 23.
                    weight2ndDerivative += downstreamError2ndDerivative * upstreamInput * upstreamInput;

                    // This is implementing the last sigma of Equation 27.
                    // This propogates error second derivatives back to previous layer, but will need to be multiplied by the second derivative
                    // of the activation function at the previous layer.
                    upstream.ErrorDerivative[upstreamIndex] += weight * weight * downstreamError2ndDerivative;

                    downstreamIndex += 1;
                    upstreamIndex += 1;
                }
                upstreamIndex += Width - 1; // Equal to: upstream.Width - downstream.Width;
            }

            WeightStepSize[weightIndex] += weight2ndDerivative;
        }

        protected override void EstimateBiasSecondDerivative(RectangularStep downstream)
        {
            for (int i = 0; i < downstream.Length; i++)
            {
                // Calculating the sum of: second derivatives of error with respect to the bias weight.
                // Note that the bias is implemented as an always-on Neuron with a (the same) weight to the outputs neurons.
                BiasStepSize += downstream.ErrorDerivative[i] * 1.0 * 1.0;
            }
        }

        protected override void CompletePreTrainingCore()
        {
            double sampleCount = (double)PreTrainingSamples;

            // Divide each of the 2nd derivative sums by the number of samples used to make the estimation, then  convert into step size.

            BiasStepSize = GlobalLearningRate / (GlobalMu + (BiasStepSize / sampleCount));

            double averageHkk = 0.0;
            for (int i = 0; i < Size; i++)
            {
                double hkk = WeightStepSize[i] / sampleCount;
                averageHkk += hkk;
                WeightStepSize[i] = GlobalLearningRate / (GlobalMu + hkk);
            }
            Debug.WriteLine("Average hkk: " + averageHkk);
        }

        protected override void PropogateError(RectangularStep downstream, int mapNumber)
        {
            RectangularStep upstream = downstream.Upstream[mapNumber];

            for (int y = 0; y < Height; y++)
            {
                for (int x = 0; x < Width; x++)
                {
                    PropogateError(upstream, downstream, x, y, mapNumber);
                }
            }
        }

        protected void PropogateError(RectangularStep upstream, RectangularStep downstream, int weightX, int weightY, int mapNumber)
        {
            int weightIndex = mapNumber * Width * Height + weightY * Width + weightX;
            double weight = Weight[weightIndex];

            int downstreamIndex = 0;
            double weightError = 0.0;
            int upstreamIndex = (weightY * upstream.Width) + weightX;
            double weightStepSize = WeightStepSize[weightIndex];

            // This loop here is equivalent to the sigma in equation 19 in Gradient-Based Learning Applied to Document Recognition.
            for (int y = 0; y < downstream.Height; y++)
            {
                for (int x = 0; x < downstream.Width; x++)
                {
                    double upstreamState = upstream.Output[upstreamIndex];
                    double downstreamErrorDerivative = downstream.ErrorDerivative[downstreamIndex]; 

                    // Calculate inputs error gradient by taking the sum, for all outputs of
                    // dEk/dAj multiplied by dAj/dOj (w/sum =dEj/dOj);
                    double inputError = downstreamErrorDerivative * weight;
                    upstream.ErrorDerivative[upstreamIndex] += inputError;

                    // Calculate the Weight's first derivative with respect to the error
                    double weightErrorGradient = downstreamErrorDerivative * upstreamState;
                    weightError += weightErrorGradient;

                    downstreamIndex += 1;
                    upstreamIndex += 1;
                }
                upstreamIndex += Width - 1; // Equal to: upstream.Width - downstream.Width;
            }
            double deltaWeight = weightError * weightStepSize;
            Weight[weightIndex] -= deltaWeight;
        }

        public override string ToString()
        {
            double averageWeight = Weight.Average(w => Math.Abs(w));
            return String.Format("AW:{0:0.00000}", averageWeight);
        }
    }
}