Exemple #1
0
        public void Initialize(
            ActivationFunctions hiddenLayerActFunc = ActivationFunctions.Sigmoid,
            ActivationFunctions outPutLayerActFunc = ActivationFunctions.Sigmoid)
        {
            //assign Layer dependencies
            InputLayer.ChildLayer   = HiddenLayer;
            HiddenLayer.ParentLayer = InputLayer;
            HiddenLayer.ChildLayer  = OutputLayer;
            OutputLayer.ParentLayer = HiddenLayer;

            //set Activation Functions
            HiddenLayer.ActivationFunction = hiddenLayerActFunc;
            OutputLayer.ActivationFunction = outPutLayerActFunc;

            //Assign random values to the Weights and set the bias
            for (int i = 0; i < NrInputNeurons; i++)
            {
                InputLayer.NeuronBiasses[i] = 1;
                InputLayer.BiasWeights[i]   = Functions.RandomBias();
                for (int j = 0; j < InputLayer.ChildLayer.NeuronValues.Length; j++)
                {
                    InputLayer.SynapseWeights[i][j] = Functions.RandomBias();
                }
            }

            for (int i = 0; i < NrHiddenNeurons; i++)
            {
                HiddenLayer.NeuronBiasses[i] = 1;
                HiddenLayer.BiasWeights[i]   = Functions.RandomBias();
                for (int j = 0; j < HiddenLayer.ChildLayer.NeuronValues.Length; j++)
                {
                    HiddenLayer.SynapseWeights[i][j] = Functions.RandomBias();
                }
            }
        }
Exemple #2
0
 public Matrix ApplyDerActivation(Matrix input)
 {
     if (activationFunc == ActivationType.SIGMOID)
     {
         return(ActivationFunctions.DerSigmoid(input));
     }
     else if (activationFunc == ActivationType.TANH)
     {
         return(ActivationFunctions.DerTanh(input));
     }
     return(null);
 }
Exemple #3
0
        // создание матриц весов и входных и выходных сигналов
        void Create()
        {
            if (structure.inputs < 1)
            {
                throw new Exception("Create NeuralNetwork: inputs must be greater than zero");
            }

            if (structure.hiddens.Length == 0)
            {
                throw new Exception("Create NeuralNetwork: hiddens is null or zero");
            }

            for (int i = 0; i < structure.hiddens.Length; i++)
            {
                if (structure.hiddens[i] < 1)
                {
                    throw new Exception("Create NeuralNetwork: hiddens at " + i + " layer must be greater than zero");
                }
            }

            if (structure.outputs < 1)
            {
                throw new Exception("Create NeuralNetwork: outputs must be greater than zero");
            }

            layers  = new Matrix[1 + structure.hiddens.Length];
            inputs  = new Vector[1 + structure.hiddens.Length];
            outputs = new Vector[1 + structure.hiddens.Length];

            layers[0] = new Matrix(structure.hiddens[0], structure.inputs);

            for (int i = 0; i < structure.hiddens.Length - 1; i++)
            {
                layers[i + 1] = new Matrix(structure.hiddens[i + 1], structure.hiddens[i]);
            }

            layers[layers.Length - 1] = new Matrix(structure.outputs, structure.hiddens[structure.hiddens.Length - 1]);

            hiddensActivation = ActivationFunctions.GetFunction(structure.hiddensFunction);
            hiddensDerivative = ActivationFunctions.GetDerivative(structure.hiddensFunction);

            outputActivation = ActivationFunctions.GetFunction(structure.outputFunction);
            outputDerivative = ActivationFunctions.GetDerivative(structure.outputFunction);
        }