public void SaveConfigs(
DataProviderConfiguration dataProviderConfiguration,
GlobalTrainerConfiguration globalTrainerConfiguration,
Training1Parameters training1Parameters,
Training2Parameters training2Parameters)
{
}
public Training1Parameters GetTraining1Parameters()
{
var initialTraining1Parameters = new Training1Parameters
{
Momentum = Settings.Default.Training1Parameters_Momentum,
Decay = Settings.Default.Training1Parameters_Decay,
LearningRate = Settings.Default.Training1Parameters_LearningRate,
UnsupervisedEpochs = Settings.Default.Training1Parameters_UnsupervisedEpochs,
};
return(initialTraining1Parameters);
}
private static void SaveTraining1Parameters(Training1Parameters training1Parameters)
{
if (training1Parameters == null)
{
return;
}
Settings.Default.Training1Parameters_Momentum = training1Parameters.Momentum;
Settings.Default.Training1Parameters_Decay = training1Parameters.Decay;
Settings.Default.Training1Parameters_LearningRate = training1Parameters.LearningRate;
Settings.Default.Training1Parameters_UnsupervisedEpochs = training1Parameters.UnsupervisedEpochs;
}
public Training1Parameters GetTraining1Parameters()
{
var initialTraining1Parameters = new Training1Parameters
{
Momentum = 0.5,
Decay = 0.001,
LearningRate = 0.1,
UnsupervisedEpochs = 200,
};
return(initialTraining1Parameters);
}
public void SaveConfigs(
DataProviderConfiguration dataProviderConfiguration,
GlobalTrainerConfiguration globalTrainerConfiguration,
Training1Parameters training1Parameters,
Training2Parameters training2Parameters)
{
SaveDataProviderConfiguration(dataProviderConfiguration);
SaveGlobalTrainerConfiguration(globalTrainerConfiguration);
SaveTraining1Parameters(training1Parameters);
SaveTraining2Parameters(training2Parameters);
Settings.Default.Save();
}
public void RunTraining1(Training1Parameters parameters)
{
LogInfoUsingBothLoggers("Started unsupervised training.");
var teacher = new DeepBeliefNetworkLearning(NeuralNetwork)
{
Algorithm = (hiddenLayer, visibleLayer, i) => new ContrastiveDivergenceLearning(hiddenLayer, visibleLayer)
{
LearningRate = parameters.LearningRate,
Momentum = parameters.Momentum,
Decay = parameters.Decay,
}
};
var inputs = _configuration.InputsOutputsData.Inputs;
// Setup batches of input for learning.
var batchCount = Math.Max(1, inputs.Length / 100);
// Create mini-batches to speed learning.
var groups = Accord.Statistics.Tools.RandomGroups(inputs.Length, batchCount);
var batches = inputs.Subgroups(groups);
// Unsupervised learning on each hidden layer, except for the output layer.
var guiLogIntensity = GetGuiLogIntensity(parameters.UnsupervisedEpochs);
for (int layerIndex = 0; layerIndex < NeuralNetwork.Machines.Count - 1; layerIndex++)
{
teacher.LayerIndex = layerIndex;
var layerData = teacher.GetLayerInput(batches);
foreach (int i in Enumerable.Range(1, parameters.UnsupervisedEpochs))
{
var error = teacher.RunEpoch(layerData) / inputs.Length;
var message = $"Layer: {layerIndex} Epoch: {i}, Error: {error}";
LogCurrentEpochResult(message, guiLogIntensity, i, parameters.UnsupervisedEpochs);
if (_skipPhaseRequest.RequestedAndUnhandled)
{
LogPhaseSkippnigAndNotifyHandled(i, parameters.UnsupervisedEpochs);
break;
}
}
}
}