public void BatchTrain()
{
int trainCnt = 0;
Random rnd = new Random();
double [][] inputSet = new double[_trainSetSize][];
double[][] labelSet = new double[_trainSetSize][];
while (!stopCriteria())
{
for (int i = 0; i < _trainSetSize; i++)
{
int index = rnd.Next(_dataMgr.count - 1);
inputSet[i] = _dataMgr.GetInputData(index);
if (!_normalizeOuput)
{
labelSet[i] = _dataMgr.GetIntLabelData(index);
}
}
_network.BatchTrain(inputSet, labelSet);
Test();
trainCnt++;
Console.WriteLine("TRAINING TIME " + trainCnt + " with successrate " + _successRate + " and mse " + _mse);
}
}
public void Test()
{
int successTime = 0;
double mse = 0;
for (int i = 0; i < _testSetSize; i++)
{
double labelData = _dataMgr.GetDoubleLabelData(i);
double [] inputData = _dataMgr.GetNormalizedInputData(i);
double[] labelData2 = _dataMgr.GetIntLabelData(i);
_mlp.SetInput(inputData);
_mlp.Feedfowrad();
double maxIndex = _mlp.GetMaxOutputIndex();
if (labelData == maxIndex)
{
successTime++;
}
double[] outputData = _mlp.GetIntOutput();
mse += GetMSE(outputData, labelData2);
}
_mse = mse / (double)_testSetSize;
_successRate = (double)successTime / (double)_testSetSize;
Console.WriteLine("Test set - success rate : " + _successRate + " - mse : " + _mse);
}
public void OnlineTrain()
{
int trainCnt = 0;
Random rnd = new Random();
while (!stopCriteria(trainCnt))
{
Console.Write("\n");
for (int i = 0; i < _trainSetSize; i++)
{
int index = rnd.Next(_dataMgr.count - 1);
//Console.WriteLine(i);
//Console.Write("-");
if (!_normalizeOuput)
{
double[] input;
if (_normalizeInput)
{
input = _dataMgr.GetNormalizedInputData(index);
}
else
{
input = _dataMgr.GetInputData(index);
}
double[] label = _dataMgr.GetIntLabelData(index);
_network.OnlineTrain(input, label);
}
}
//Console.Write("\n");
Test();
trainCnt++;
if (trainCnt % 5 == 0)
{
_network.DumpToFile();
}
Console.WriteLine("TRAINING TIME " + trainCnt + " with successrate " + _successRate + " and mse " + _mse);
}
}