/// <summary>
/// Train the network using specialized strategies.
/// </summary>
/// <returns>Whether the training was successful.</returns>
public bool Train(IControler controler, Progress progress)
{
TrainingConfig config = controler.TrainingConfiguration;
int patternCount = controler.PatternCount;
int epochs = config.AutoTrainingEpochs.Value;
int success = (int)(patternCount * config.AutoTrainingPercentSuccessful.Value);
int howmany = controler.CountSuccessfulPatterns();
if(howmany >= success)
return true;
int timeout = epochs;
while(timeout-- >= 0)
{
if(progress != null)
progress((int)(100 * (epochs - (double)timeout) / epochs));
for(int i=0;i<patternCount;i++)
{
controler.SelectShuffledPattern(i);
controler.NeuralNetwork.TrainCurrentPattern(true, false);
}
howmany = controler.CountSuccessfulPatterns();
if(howmany >= success)
return true;
}
// CLEAN UP:
//controler.CalculateCurrentNetwork();
return false;
}
/// <summary>
/// Train the network using specialized strategies.
/// </summary>
/// <returns>Whether the training was successful.</returns>
public bool Train(IControler controler, Progress progress)
{
TrainingConfig config = controler.TrainingConfiguration;
BasicConfig basic = config.Basic;
int patternCount = controler.PatternCount;
int epochs = config.AutoTrainingEpochs.Value;
int success = (int)(patternCount * config.AutoTrainingPercentSuccessful.Value);
int howmany = controler.CountSuccessfulPatterns();
double LRinitial = basic.LearningRate.Value;
double LRstep = LRinitial * 0.7 / epochs;
double LRcurrent = LRinitial;
double magrin;
if(howmany >= success)
return true;
int timeout = epochs;
while(timeout-- >= 0)
{
basic.LearningRate.Value = LRcurrent;
if(progress != null)
progress((int)(100 * (epochs - (double)timeout) / epochs));
for(int i=0;i<patternCount;i++)
{
controler.SelectShuffledPattern(i);
if(controler.CalculateCurrentNetwork(out magrin) != i)
{
controler.NeuralNetwork.TrainCurrentPattern(false, false);
howmany = controler.CountSuccessfulPatterns();
if(howmany >= success)
{
basic.LearningRate.Value = LRinitial;
return true;
}
}
else if(magrin < 0.5)
{
basic.LearningRate.Value = LRcurrent * 0.5;
controler.NeuralNetwork.TrainCurrentPattern(false, false);
basic.LearningRate.Value = LRcurrent;
}
}
LRcurrent -= LRstep;
}
// CLEAN UP:
//controler.CalculateCurrentNetwork();
basic.LearningRate.Value = LRinitial;
return false;
}
/// <summary>
/// Train the network using specialized strategies.
/// </summary>
/// <returns>Whether the training was successful.</returns>
public bool Train(IControler controler, Progress progress)
{
TrainingConfig config = controler.TrainingConfiguration;
BasicConfig basic = config.Basic;
int patternCount = controler.PatternCount;
int epochs = config.AutoTrainingEpochs.Value;
int success = (int)(patternCount * config.AutoTrainingPercentSuccessful.Value);
int howmany = controler.CountSuccessfulPatterns();
double LRinitial = basic.LearningRate.Value;
double LRstep = LRinitial * 0.7 / epochs;
double LRcurrent = LRinitial;
if (howmany >= success)
{
return(true);
}
int timeout = epochs;
while (timeout-- >= 0)
{
basic.LearningRate.Value = LRcurrent;
if (progress != null)
{
progress((int)(100 * (epochs - (double)timeout) / epochs));
}
for (int i = 0; i < patternCount; i++)
{
controler.SelectShuffledPattern(i);
controler.NeuralNetwork.TrainCurrentPattern(true, false);
}
howmany = controler.CountSuccessfulPatterns();
if (howmany >= success)
{
basic.LearningRate.Value = LRinitial;
return(true);
}
LRcurrent -= LRstep;
}
// CLEAN UP:
//controler.CalculateCurrentNetwork();
basic.LearningRate.Value = LRinitial;
return(false);
}
/// <summary>
/// Train the network using specialized strategies.
/// </summary>
/// <returns>Whether the training was successful.</returns>
public bool Train(IControler controler, Progress progress)
{
TrainingConfig config = controler.TrainingConfiguration;
int patternCount = controler.PatternCount;
int epochs = config.AutoTrainingEpochs.Value;
int success = (int)(patternCount * config.AutoTrainingPercentSuccessful.Value);
int howmany = controler.CountSuccessfulPatterns();
if (howmany >= success)
{
return(true);
}
int timeout = epochs;
while (timeout-- >= 0)
{
if (progress != null)
{
progress((int)(100 * (epochs - (double)timeout) / epochs));
}
for (int i = 0; i < patternCount; i++)
{
controler.SelectShuffledPattern(i);
if (controler.CalculateCurrentNetwork() != i)
{
controler.NeuralNetwork.TrainCurrentPattern(false, false);
howmany = controler.CountSuccessfulPatterns();
if (howmany >= success)
{
return(true);
}
}
}
}
// CLEAN UP:
//controler.CalculateCurrentNetwork();
return(false);
}
