public override void Update(AdaptationStrategy strategy)
{
Biases.Add(strategy.BiasesUpdate(_biasesGradientAccumulator));
Weights.Add(strategy.WeightsUpdate(_weightsGradientAccumulator));
_biasesGradientAccumulator.Zero();
_weightsGradientAccumulator.Zero();
}
private void CreateMatrix(int inputSize, int hiddenSize, int hiddenLayers, int outputSize)
{
// ??
for (int i = 0; i < hiddenLayers + 1; ++i)
{
int inSize = (i == 0) ? inputSize : hiddenSize;
int outSize = (i == hiddenLayers) ? outputSize : hiddenSize;
Weights.Add(new Matrix(inSize, outSize));
Biases.Add(new Matrix(1, outSize));
}
}
private void FindBiases()
{
foreach (var outputSynapse in Inputs.SelectMany(i => i.Outgoing))
{
Biases.Add(outputSynapse.To.BiasSynapse.From);
foreach (var targetOutput in outputSynapse.To.Outgoing)
{
Biases.Add(targetOutput.To.BiasSynapse.From);
}
}
}
public void Init(List <Data> trainData, List <Data> testData, int[] sizes)
{
TrainData = trainData;
TestData = testData;
Num_layers = sizes.Length;
Sizes = sizes;
for (int counter = 1; counter < Num_layers; counter++)
{
Biases.Add(np.random.randn(new int[] { Sizes[counter], 1 }));
}
foreach (var item in Sizes[..^ 1].Zip(Sizes[1..]))
public NNBrain(NNBrain other)
{
InputSize = other.InputSize;
OutputSize = other.OutputSize;
HiddenLayers = other.HiddenLayers;
HiddenSize = other.HiddenSize;
// ??
for (int i = 0; i < other.Weights.Count; ++i)
{
Matrix w = other.Weights[i].Copy();
Matrix b = other.Biases[i].Copy();
Weights.Add(w);
Biases.Add(b);
}
}
public void Init(int inputsAmount, int[] neuronsInHiddenLayersAmount, int outputsAmount,
Func <double, double>[] activationsFunctions)
{
Validate(neuronsInHiddenLayersAmount, activationsFunctions.Length, outputsAmount);
ActivationsFunctions = activationsFunctions;
InitHiddenLayers(inputsAmount, neuronsInHiddenLayersAmount);
var weightsLastHiddenLayerToOutputLayer =
new Matrix(neuronsInHiddenLayersAmount[neuronsInHiddenLayersAmount.Length - 1], outputsAmount);
Weights.Add(weightsLastHiddenLayerToOutputLayer);
Biases.Add(Random.Range(-1f, 1f));
GenerateWeightsValues();
}
public NeuralNetwork(ISource source, params int[] sizes)
{
_source = source;
Sizes = sizes.ToList();
NumLayers = sizes.Length;
for (var y = 1; y < sizes.Length; y++)
{
var newBiases = Vector <double> .Build.Random(sizes[y]);
Biases.Add(newBiases);
}
for (int x = 0, y = 1; y < sizes.Length; y++, x++)
{
var newWeights = Matrix <double> .Build.Random(sizes[x], sizes[y]);
Weights.Add(newWeights);
}
}
private void InitHiddenLayers(int inputsAmount, int[] neuronsInHiddenLayersAmount)
{
for (var neuronsCount = 0; neuronsCount < neuronsInHiddenLayersAmount.Length; neuronsCount++)
{
var hiddenLayer = new Matrix(1, neuronsInHiddenLayersAmount[neuronsCount]);
HiddenLayers.Add(hiddenLayer);
Biases.Add(Random.Range(-1f, 1f));
if (neuronsCount == 0)
{
var weightsInputToHiddenLayer1 = new Matrix(inputsAmount, neuronsInHiddenLayersAmount[neuronsCount]);
Weights.Add(weightsInputToHiddenLayer1);
}
else
{
var weightsHliToNextHl = new Matrix(neuronsInHiddenLayersAmount[neuronsCount - 1],
neuronsInHiddenLayersAmount[neuronsCount]);
Weights.Add(weightsHliToNextHl);
}
}
}
