public static void Training()
{
reader = new Reader();
dataset = reader.ReadData();
lengthOfTrainigData = datadivision.GetPartOfData(70, dataset);
double[][] trainingData = datadivision.Take(dataset, lengthOfTrainigData); //creating a board with training data in the right size
for (int i = 0; i < SaveState; i++)
{
List <Network> ntw = new List <Network>();
var tasks = new Task[batchsize];
for (int j = 0; j < batchsize; j++)
{
//B/c ii may change and this code can't let that happen
int copyOfJ = j;
double[] row = reader.ReadNextRow(trainingData);
int correct = reader.ReadNextClassification(trainingData);
ntw.Add(new Network());
tasks[copyOfJ] = Task.Run(() => ntw[copyOfJ].Backprop(row, correct));
}
Task.WaitAll(tasks);
//Syncronously descend
foreach (Network nn in ntw)
{
nn.Descent();
//nn.Dispose();
}
//Updating the weights with the avg gradients
Network.Descent(batchsize);
UserValidation();
}
//Save weights and biases
NetworkParametersHandler.WriteWeightBias();
}
//Reset (re-initialize) weights and biases of the neural network
public static void reset()
{
Network nn = new Network();
nn.initialize();
NetworkParametersHandler.WriteWeightBias();
}
static void Main(string[] args)
{
NetworkParametersHandler.ReadWeightBias(); //Reading network parameters form file
//ProgramManager.reset();
if (trainMode)
{
while (!ProgramManager.finished)
{
ProgramManager.Training();
}
}
else
{
ProgramManager.Testing();
}
Console.WriteLine("Finished");
Console.ReadKey();
}
