public void Train(List <double[]> inputs, List <double[]> expectedOutputs, int batchlength, double dropOutRate = .27)
{
int i = 0;
int batchI = 0;
List <double[]> toPropagate, expectedToPropagate = new List <double[]>();
toPropagate = new List <double[]>();
double lastCost = 0;
do
{
if (toPropagate.Count >= batchlength)
{
NetworkValues gradients = BackPropagationBatch(toPropagate, expectedToPropagate, out double currentCost, dropOutRate);
ApplyGradients(gradients, Math.Abs(lastCost - currentCost));
lastCost = currentCost;
toPropagate = expectedToPropagate = new List <double[]>();
Console.WriteLine($"Epoch {batchI} finished!\tCurrent cost = {currentCost}");
batchI++;
}
else
{
toPropagate.Add(inputs[i]);
expectedToPropagate.Add(expectedOutputs[i]);
i++;
}
}while (i < inputs.Count);
if (toPropagate.Count > 0)
{
NetworkValues gradients = BackPropagationBatch(toPropagate, expectedToPropagate, out double currentCost, dropOutRate);
ApplyGradients(gradients, Math.Abs(lastCost - currentCost));
}
}
internal void ApplyGradients(NetworkValues gradients, double learningRate = 1.5)
{
for (int layerIndex = 0; layerIndex < layers.Count; layerIndex++)
{
for (int neuronIndex = 0; neuronIndex < layers[layerIndex].length; neuronIndex++)
{
for (int weigthIndex = 0; weigthIndex < layers[layerIndex].neurons[neuronIndex].Weigths.Count; weigthIndex++)
{
layers[layerIndex].neurons[neuronIndex].Weigths[weigthIndex]
-= gradients[layerIndex].neurons[neuronIndex].weigths[weigthIndex] * learningRate;
}
}
}
}
public NetworkValues BackPropagationBatch(List <double[]> inputs, List <double[]> expectedOutputs, out double averageCost, double dropOutRate = .27)
{
double currentCost;
averageCost = 0;
NetworkValues networkValues = BackPropagate(inputs[0], expectedOutputs[0], out currentCost);
averageCost += currentCost;
for (int i = 1; i < inputs.Count; i++)
{
networkValues += BackPropagate(inputs[i], expectedOutputs[i], out currentCost, dropOutRate);
averageCost += currentCost;
}
averageCost /= inputs.Count;
return(networkValues);
}
public void Train(double[] input, double[] expectedOutput, double dropOutRate, double lastCost, out double currentCost)
{
NetworkValues gradients = BackPropagate(input, expectedOutput, out currentCost, dropOutRate);
ApplyGradients(gradients, Math.Abs(lastCost - currentCost));
}
