public Network(int InputLayerSize, int[] HiddenLayerSizes, int OutputLayerSize, Function[] activationFuncs)
{
Random r = new Random();
//will create a network with InputLayerSize inputneurons, HiddenLayerSizes.count Hiddenlayers (Each with the size of the array at that position), and OutputLayerSize outputneurons
/*****************************
* InputNeurons
****************************/
InputLayer = new InputLayer(InputLayerSize, this);
/*****************************
* HiddenNeurons
****************************/
hiddenLayer = new HiddenLayer[HiddenLayerSizes.Length];
for (int i = 0; i < hiddenLayer.Length; i++) //for each layer
{
hiddenLayer[i] = new HiddenLayer(HiddenLayerSizes[i], this);
if (i == 0)
{
hiddenLayer[i].connectToInputLayer(InputLayer);//connect the hiddenlayer to the inputlayer
}
else
{
hiddenLayer[i].connectToInputLayer(hiddenLayer[i - 1]);//connect the hiddenlayer to the hiddenlayer before
}
hiddenLayer[i].ActivationFunction = activationFuncs[i];
}
/*****************************
* OutputNeurons
****************************/
outputLayer = new OutputLayer(OutputLayerSize, this);
outputLayer.connectToInputLayer(hiddenLayer[HiddenLayerSizes.Length - 1]);
outputLayer.ActivationFunction = activationFuncs[activationFuncs.Length - 1];
this.RandomizeAllWeights();
this.DatabaseID = db.getNetworkID();
}
