public void newNetworkFromFile(string fileName) {
_ann = new NeuralNetwork(fileName);
}
static void NetworkTraining(int inputSize, int width, int height, int itterations)
{
int outputSize = DataSample.labelSize;
string databasePath1 = @"C:/Users/Mikkel/Documents/MTA18434_SemesterProject/ML_Sound_Samples/Assets/Resources/SampleDatabase/Database.json";
string networkPath1 = Path.GetDirectoryName(Assembly.GetEntryAssembly().Location) + "/NetworkSave01.json";
if (File.Exists(networkPath1))
{
network = NeuralNetwork.LoadNetwork(networkPath1);
if (network.inputSize != inputSize)
{
throw new Exception();
}
if (network.hiddenDimension != width)
{
throw new Exception();
}
if (network.hiddenSize != height)
{
throw new Exception();
}
}
else
{
Console.WriteLine("Initializing NN");
network = new NeuralNetwork(inputSize, width, height, outputSize);
Console.WriteLine("NN Initialized");
}
if (File.Exists(databasePath1))
{
Console.WriteLine(File.Exists(databasePath1) ? "Database exists." : "File does not exist.");
SampleDatabase temp = null;
using (StreamReader r = new StreamReader("C:/Users/Mikkel/Documents/MTA18434_SemesterProject/ML_Sound_Samples/Assets/Resources/SampleDatabase/Database.json"))
{
using (JsonReader reader = new JsonTextReader(r))
{
JsonSerializer serializer = new JsonSerializer();
Console.WriteLine("Deserializing");
temp = serializer.Deserialize <SampleDatabase>(reader);
}
}
Console.WriteLine(temp.database[0].data.Length);
DataSample[] trainingSamples = new DataSample[10];
Random rand = new Random();
for (int i = 0; i < itterations; i++)
{
Console.WriteLine("Progress: " + i + " / " + itterations);
// pick 10 samples
for (int j = 0; j < 10; j++)
{
int num = rand.Next(0, temp.database.Length);
trainingSamples[j] = new DataSample(temp.database[num].data, temp.database[num].label);
//Console.WriteLine("Database sample " + temp.database[num].data[0] + " " + temp.database[num].label);
}
network.TrainNetwork(trainingSamples);
}
Console.WriteLine("Saving network");
network.SaveNetwork(networkPath1);
Console.WriteLine("Network saved");
}
else
{
Console.WriteLine(File.Exists(databasePath1) ? "File exists." : "File does not exist.");
throw new Exception();
}
}
protected void newNetwork(int[] nodes, double eta, double alpha) {
nodes[0] = _numInputs;
nodes[nodes.Length - 1] = _numOutputs;
//get a new ANN with learning rate eta and momentum alpha
_ann = new NeuralNetwork(nodes, eta, alpha);
}
public GradientWeightVector(NeuralNetwork network)
{
inputLayerWeights = new float[network.inputSize, network.hiddenSize];
hiddenLayerWeights = new float[network.hiddenDimension - 1, network.hiddenSize, network.hiddenSize];
outputLayerWeights = new float[network.outputSize, network.hiddenSize];
}
