public RBMTrainer(MnistDataMgr dataMgr, RBM rbm)
{
_dataMgr = dataMgr;
_rbm = rbm;
_rnd = new Random();
}
public LRBMTester(LRBM lrbm, MnistDataMgr dataMgr)
{
_lrbm = lrbm;
_dataMgr = dataMgr;
_testSetSize = _dataMgr.count;
}
public MnistMLPTrainer(MnistDataMgr mgr, MLP mlp, bool nInput = true, bool nOutput = false)
{
_dataMgr = mgr;
_normalizeInput = nInput;
_normalizeOuput = nOutput;
_network = mlp;
}
static void Main(string[] args)
{
string filename = "SAE-";
filename += DateTime.Now.Ticks;
filename += ".txt";
StreamWriter sw = new StreamWriter(filename);
Console.SetOut(sw);
sw.AutoFlush = true;
MnistDataMgr trainDataMgr = new MnistDataMgr("train.csv");
trainDataMgr.Load();
Console.WriteLine("Train data loaded");
MnistDataMgr testDataMgr = new MnistDataMgr("test.csv");
testDataMgr.Load();
Console.WriteLine("Test data loaded");
AutoEncoder autoEncoder = new AutoEncoder(0.8, trainDataMgr.inputNum, 100);
AutoEncoderTrainer autoEncoderTrainer = new AutoEncoderTrainer(autoEncoder, trainDataMgr);
autoEncoderTrainer.OnlineTrain();
sw.Flush();
sw.Close();
}
public LRBMTrainer(MnistDataMgr dataMgr, LRBM lrbm)
{
_dataMgr = dataMgr;
_lrbm = lrbm;
_rnd = new Random();
}
static void Main(string[] args)
{
//MLPWithBiasTest test = new MLPWithBiasTest();
//MLPTest test = new MLPTest();
//test.TestXOR();
//test.TestDigit();
//return;
/*
* CsvMgrTest test = new CsvMgrTest();
* test.LoadTest("test.csv");
*/
string filename = "MLP-";
filename += DateTime.Now.Ticks;
filename += ".txt";
StreamWriter sw = new StreamWriter(filename);
Console.SetOut(sw);
sw.AutoFlush = true;
MnistDataMgr trainDataMgr = new MnistDataMgr("train.csv");
trainDataMgr.Load();
Console.WriteLine("Train data loaded");
MnistDataMgr testDataMgr = new MnistDataMgr("test.csv");
testDataMgr.Load();
Console.WriteLine("Test data loaded");
MnistMLPTrainer trainer = new MnistMLPTrainer(trainDataMgr);
//int[] layerStruct = { trainDataMgr.inputNum, 100, 50, 20, 10 };
//int[] layerStruct = { trainDataMgr.inputNum, 150, 50, 10 };
//int[] layerStruct = { trainDataMgr.inputNum, 100, 50, 10 };
int[] layerStruct = { trainDataMgr.inputNum, 80, 30, 10 };
trainer.InitMLP(layerStruct, 0.8);
//trainer.InitMLP(200, 0.8);
trainer.OnlineTrain();
//trainer.BatchTrain();
//dataMgr.Dump("test2.csv");
MnistMLPTester tester = new MnistMLPTester(trainer.GetNetwork(), testDataMgr);
tester.Test();
trainer.GetNetwork().DumpToFile();
sw.Flush();
sw.Close();
return;
}
static void Main(string[] args)
{
string filename = "DBN-";
filename += DateTime.Now.Ticks;
filename += ".txt";
StreamWriter sw = new StreamWriter(filename);
Console.SetOut(sw);
sw.AutoFlush = true;
MnistDataMgr trainDataMgr = new MnistDataMgr("train.csv");
trainDataMgr.Load();
Console.WriteLine("Train data loaded");
MnistDataMgr testDataMgr = new MnistDataMgr("test.csv");
testDataMgr.Load();
Console.WriteLine("Test data loaded");
//int [] netStruct = {trainDataMgr.inputNum, 100, 50, 20};
//int[] netStruct = { trainDataMgr.inputNum, 200 };
int[] netStruct = { trainDataMgr.inputNum, 200 };
LRBM lrbm = new LRBM(1, netStruct);
LRBMTrainer lrbmTrainer = new LRBMTrainer(trainDataMgr, lrbm);
lrbmTrainer.BatchTrain();
//lrbmTrainer.OnlineTrain();
lrbm.DumpToFile();
//DeepBeliefNet dbn = new DeepBeliefNet(0.8, lrbm, 10);
DeepBeliefNet dbn = new DeepBeliefNet(0.8, lrbm, new int[] { 200, 10 });
dbn.DumpToFile();
dbn.GetMLP().DumpToFile();
//dbn.GetMLP().ClearBias();
//dbn.GetMLP()._trainType = MLP.TrainType.TRAIN_ONLY_OUTPUT_LAYER;
dbn.GetMLP()._trainType = MLP.TrainType.NORMAL_BP;
dbn.GetMLP().learningDepth = 2;
MnistMLPTrainer trainer = new MnistMLPTrainer(trainDataMgr, dbn.GetMLP());
trainer.OnlineTrain();
MnistMLPTester tester = new MnistMLPTester(dbn.GetMLP(), testDataMgr);
tester.Test();
sw.Flush();
sw.Close();
}
public MnistMLPTester(MLP mlp, MnistDataMgr dataMgr)
{
_mlp = mlp;
_dataMgr = dataMgr;
_testSetSize = _dataMgr.count;
_mse = 0;
_successRate = 0;
}
static void Main(string[] args)
{
string filename = "DBN-";
filename += DateTime.Now.Ticks;
filename += ".txt";
StreamWriter sw = new StreamWriter(filename);
Console.SetOut(sw);
sw.AutoFlush = true;
MnistDataMgr trainDataMgr = new MnistDataMgr("train.csv");
trainDataMgr.Load();
Console.WriteLine("Train data loaded");
MnistDataMgr testDataMgr = new MnistDataMgr("test.csv");
testDataMgr.Load();
Console.WriteLine("Test data loaded");
/*
* Stream stream2 = File.Open("lrbm-param.osl", FileMode.Open);
* BinaryFormatter bformatter2 = new BinaryFormatter();
*
* Console.WriteLine("Reading Employee Information");
* LRBM lrbm2 = (LRBM)bformatter2.Deserialize(stream2);
* stream2.Close();
*
* //lrbm2.DumpToFile();
*
*
*
* //dbn2.DumpToFile();
* //dbn2.GetMLP().DumpToFile();
*
* //dbn2.GetMLP()._trainType = MLP.TrainType.NORMAL_BP;
*
* //MnistMLPTrainer trainer = new MnistMLPTrainer(trainDataMgr, dbn2.GetMLP());
* //trainer.OnlineTrain();
*
* //MnistMLPTester tester = new MnistMLPTester(dbn2.GetMLP(), testDataMgr);
* //tester.Test();
*
* return;
*/
//int [] netStruct = {trainDataMgr.inputNum, 100, 50, 20};
int[] netStruct = { trainDataMgr.inputNum, 200 };
//int[] netStruct = { trainDataMgr.inputNum, 100 };
/*
* RBM rbm = new RBM(0.2, trainDataMgr.inputNum, 100);
*
* RBMTrainer rbmTrainer = new RBMTrainer(trainDataMgr, rbm);
* //rbmTrainer.OnlineTrain();
* rbmTrainer.BatchTrain();
*
* rbm.DumpToFile();
*
* rbm.DumpParamsToFile("rbmParams.osl");
*
* int testSize = 40;
*
* for (int t = 0; t < testSize; t++)
* {
* Random rnd1 = new Random();
* int fileIndex = rnd1.Next(trainDataMgr.count-1);
* double[] sample = trainDataMgr.GetNormalizedInputData(fileIndex);
*
* string wrFilename = "sample-input-" + fileIndex + ".csv";
*
* StreamWriter sampleWr = new StreamWriter(wrFilename);
* for (int i = 0; i < sample.Length; i++)
* {
* sampleWr.Write(sample[i] + ", ");
* }
*
* sampleWr.Flush();
* sampleWr.Close();
*
* rbm.Calc(sample);
* double[] sampleOut = rbm.GetVisible();
* double[] sampleOutProb = rbm.GetVisibleProb();
*
* wrFilename = "sample-prob-output-" + fileIndex + ".csv";
*
* StreamWriter outProbWr = new StreamWriter(wrFilename);
* for (int i = 0; i < sampleOutProb.Length; i++)
* {
* outProbWr.Write(sampleOutProb[i] + ", ");
* }
*
* outProbWr.Flush();
* outProbWr.Close();
*
* wrFilename = "sample-output-" + fileIndex + ".csv";
* StreamWriter outWr = new StreamWriter(wrFilename);
* for (int i = 0; i < sampleOut.Length; i++)
* {
* outWr.Write(sampleOut[i] + ", ");
* }
*
* outWr.Flush();
* outWr.Close();
* }
*
* return;
*
*/
LRBM lrbm = new LRBM(1, netStruct);
LRBMTrainer lrbmTrainer = new LRBMTrainer(trainDataMgr, lrbm);
lrbmTrainer.BatchTrain();
//lrbmTrainer.OnlineTrain();
lrbm.DumpToFile();
lrbm.DumpParamsToFile("lrbm-param.osl");
return;
DeepBeliefNet dbn = new DeepBeliefNet(0.8, lrbm, 10);
//DeepBeliefNet dbn = new DeepBeliefNet(0.8, lrbm, new int[]{50,10});
dbn.DumpToFile();
dbn.GetMLP().DumpToFile();
dbn.DumpParamsToFile("dbn-ser.osl");
return;
//dbn.GetMLP().DumpToFile();
dbn.GetMLP()._trainType = MLP.TrainType.TRAIN_ONLY_OUTPUT_LAYER;
//dbn.GetMLP()._trainType = MLP.TrainType.NORMAL_BP;
//MnistMLPTrainer trainer = new MnistMLPTrainer(trainDataMgr, dbn.GetMLP());
//trainer.OnlineTrain();
//MnistMLPTester tester = new MnistMLPTester(dbn.GetMLP(), testDataMgr);
//tester.Test();
//dbn.GetMLP().DumpToFile();
sw.Flush();
sw.Close();
}
public AutoEncoderTrainer(AutoEncoder encoder, MnistDataMgr mgr)
{
_autoEncoder = encoder;
_dataMgr = mgr;
}
public MnistMLPTrainer2(MnistDataMgr mgr, bool nInput = true, bool nOutput = true)
{
_dataMgr = mgr;
_normalizeInput = nInput;
_normalizeOuput = nOutput;
}
