private void FindGradientAndUpdateWeights(
double[] layerGradient,
double learningRate,
int layerIndex,
ref double[][] prevWeight,
double[] inputResults = null,
double[] input = null,
double[] prevLayerGradient = null)
{
if (layerIndex == LayersCount - 1)
{
for (int i = 0; i < OutputLayer.NeuronsCount; ++i)
{
layerGradient[i] = (OutputLayer.OutputNonMatrix[i] - inputResults[i])
* OutputLayer.ActivateFunctionDerivative(OutputLayer.SumOutputNonMatrix[i]);
}
}
else
{
for (int i = 0; i < Layers[layerIndex].NeuronsCount; ++i)
{
layerGradient[i] = .0;
for (int j = 0; j < Layers[layerIndex + 1].NeuronsCount; ++j)
{
layerGradient[i] += prevLayerGradient[j] * prevWeight[i][j];
}
layerGradient[i] *= Layers[layerIndex].ActivateFunctionDerivative(Layers[layerIndex].SumOutputNonMatrix[i]);
}
}
prevWeight = Layers[layerIndex].Weights.DeepCopy();
if (layerIndex != 0)
{
for (int i = 0; i < Layers[layerIndex].WeightRowsCount; ++i)
{
for (int j = 0; j < Layers[layerIndex].WeightColumnsCount; ++j)
{
var gradientWeight = layerGradient[j] * Layers[layerIndex - 1].OutputNonMatrix[i];
Layers[layerIndex].Weights[i][j] -= learningRate * gradientWeight;
}
}
}
else
{
for (int i = 0; i < Layers[0].WeightRowsCount; ++i)
{
for (int j = 0; j < Layers[0].WeightColumnsCount; ++j)
{
var gradientWeight = layerGradient[j] * input[i];
Layers[0].Weights[i][j] -= learningRate * gradientWeight;
}
}
}
}
public double[] GetOutputLayerGradient(double[] inputResults)
{
var layerGradient = new double[OutputLayer.NeuronsCount];
for (int i = 0; i < OutputLayer.NeuronsCount; ++i)
{
layerGradient[i] = (OutputLayer.OutputNonMatrix[i] - inputResults[i])
* OutputLayer.ActivateFunctionDerivative(OutputLayer.SumOutputNonMatrix[i]);
}
return(layerGradient);
}
