static private void Train(double[][] Data, int input, int output)
{
//set parameters in layers
int numHidden = 10;
//create layers and construct them
List <NNlayers> Nlist = new List <NNlayers>();
NNlayers N1 = new NNlayers(NNlayers.Layers_family.Affine, input, numHidden);
NNlayers N2 = new NNlayers(NNlayers.Layers_family.BN, numHidden, numHidden);
NNlayers N3 = new NNlayers(NNlayers.Layers_family.Tanh, numHidden, numHidden);
NNlayers N4 = new NNlayers(NNlayers.Layers_family.Affine, numHidden, output);
Nlist.Add(N1);
Nlist.Add(N2);
Nlist.Add(N3);
Nlist.Add(N4);
//create a NN class
Ann = new ArtificialNeuralNetwork(Nlist.ToArray(), input, output);
Ann.PositiveLimit = 0.5;//default = 0.7
int maxEpochs = 100;
double learnRate = 0.05;
//create a error monitor backgroundworker
BackgroundWorker BGW = new BackgroundWorker();
BGW.DoWork += new DoWorkEventHandler(backgroundWorker_NN_DoWork);
BGW.RunWorkerAsync(maxEpochs);
//train
Ann.TrainModel(Data, maxEpochs, learnRate, 0);
double trainAcc = Ann.Accu_train;
Console.Write("\nFinal accuracy on train data = " +
trainAcc.ToString("F4"));
double testAcc = Ann.Accu_test;
Console.Write("\nFinal accuracy on test data = " +
testAcc.ToString("F4"));
Console.Write("\nTrain finish");
string site = System.DateTime.Now.ToString("yyMMddHHmm") + "_learnproj";
Directory.CreateDirectory(site);
Ann.Save_network(site, learnRate);
Ann.Save_H5files(site);
Console.Write("\nVariables have been save in " + site);
}
static private void Train(double[][] Data, int input, int output)
{
//set parameters in layers
int numHidden = 100;
int numHidden2 = 100;
int maps = 16;
int Deep1 = 20;
int Deep2 = 40;
int windows = 2;
//create layers and construct them
List <NNlayers> Nlist = new List <NNlayers>();
NNlayers C1 = new NNlayers(NNlayers.Layers_family.Convolution, input, Deep1, 3, false);
NNlayers C2 = new NNlayers(NNlayers.Layers_family.Maxpool, (input - 3 + 1), Deep1, 2);
int numCov = (int)Math.Ceiling((double)(input - 3 + 1) / 2);
NNlayers N1 = new NNlayers(NNlayers.Layers_family.BN, numCov * Deep1, numCov * Deep1);
NNlayers H2 = new NNlayers(NNlayers.Layers_family.ReLU, numCov * Deep1, numCov * Deep1);
NNlayers C3 = new NNlayers(NNlayers.Layers_family.Convolution, numCov, Deep2, 3, Deep1, false);
NNlayers C4 = new NNlayers(NNlayers.Layers_family.Meanpool, (numCov - 3 + 1), Deep2, 2);
int numCov2 = (int)Math.Ceiling((double)(numCov - 3 + 1) / 2) * Deep2;
//NNlayers N1 = new NNlayers(NNlayers.Layers_family.Affine, (int)Math.Ceiling((double)(input - mapsize + 1)/windows)* maps, numHidden);
NNlayers N2 = new NNlayers(NNlayers.Layers_family.BN, numCov2, numCov2);
//NNlayers N3 = new NNlayers(NNlayers.Layers_family.ReLU, numHidden, numHidden);
NNlayers H3 = new NNlayers(NNlayers.Layers_family.ReLU, numCov2, numCov2);
NNlayers N10 = new NNlayers(NNlayers.Layers_family.Affine, numCov2, output);
//NNlayers N11 = new NNlayers(NNlayers.Layers_family.Sigmoid, output, output);
Nlist.Add(C1);
Nlist.Add(C2);
Nlist.Add(N1);
Nlist.Add(H2);
Nlist.Add(C3);
Nlist.Add(C4);
//Nlist.Add(N1);
Nlist.Add(N2);
Nlist.Add(H3);
Nlist.Add(N10);
//Nlist.Add(N11);
//create a NN class
Ann = new ArtificialNeuralNetwork(Nlist.ToArray(), input, output);
Ann.PositiveLimit = 0.7;//default = 0.7
Ann.Batchsize = 100;
int maxEpochs = 4000;
double learnRate = 0.005;
//create a error monitor backgroundworker
BackgroundWorker BGW = new BackgroundWorker();
BGW.DoWork += new DoWorkEventHandler(backgroundWorker_NN_DoWork);
BGW.RunWorkerAsync(maxEpochs);
//train
Ann.TrainModel(Data, maxEpochs, learnRate, 0);
double trainAcc = Ann.Accu_train;
Console.Write("\nFinal accuracy on train data = " +
trainAcc.ToString("F4"));
double testAcc = Ann.Accu_test;
Console.Write("\nFinal accuracy on test data = " +
testAcc.ToString("F4"));
Console.Write("\nTrain finish");
string site = System.DateTime.Now.ToString("yyMMddHHmm") + "_learnproj";
Directory.CreateDirectory(site);
Ann.Save_network(site, learnRate);
Ann.Save_H5files(site);
Console.Write("\nVariables have been save in " + site);
}
