public void Sgd(List <DigitImageForStuding> trainingData, Parametrs param, List <DigitImageForEvaluating> testData)
{
var fileDiscryption = FileDescryption(param);
var fileNameResult = "Result with " + fileDiscryption + ".txt";
var fileNameNetwork = "Network with " + fileDiscryption + ".bin";
var nTest = 0;
if (testData != null)
{
nTest = testData.Count;
}
using (var file = new StreamWriter(_folderForResult + fileNameResult))
{
for (var i = 0; i < param.Epochs; i++)
{
trainingData.Shuffle();
List <List <DigitImageForStuding> > miniBatches = MakeMiniBatches(trainingData, param.MiniBatchSize);
// miniBatches.ForEach(x => UpdateMiniBatch(x, param.Eta, param.Beta));
foreach (List <DigitImageForStuding> miniBatch in miniBatches)
{
var tempPrevoiusWeights = _weights.ConvertAll(m => m.Clone()); //For momentum
var tempNet = _builder.Build(this);
UpdateMiniBatch(tempNet, miniBatch, param);
_updater.Update(this, tempNet);
_previousWeights = tempPrevoiusWeights; //For momentum
}
if (testData != null)
{
var currentEfficiency = Evaluate(testData);
if (_bestEfficiency < currentEfficiency)
{
SaveToBinary(_folderForNetwork + fileNameNetwork);
_bestEfficiency = currentEfficiency;
}
if (_dropoutOn)
{
_weights = _weights.Select(w => w * 0.5).ToList();
_biases = _biases.Select(b => b * 0.5).ToList();
}
var agregateCostValidationData = AgregateCost(testData.Select(x => x.Pixels).ToList(),
testData.Select(x => _helpers.VectorizedResult(x.Label)).ToList());
var agregateCostStudyData = AgregateCost(trainingData.Select(x => x.Pixels).ToList(),
trainingData.Select(x => x.Label).ToList());
Console.WriteLine("Epoch {0}: {1} / {2}", i, currentEfficiency, nTest);
Console.WriteLine("Cost on training set: {0}", agregateCostStudyData);
Console.WriteLine("Cost on validation set {0}", agregateCostValidationData);
file.WriteLine("Epoch {0}: {1} / {2}", i, currentEfficiency, nTest);
file.WriteLine("Cost on training set: {0}", agregateCostStudyData);
file.WriteLine("Cost on validation set {0}", agregateCostValidationData);
if (_dropoutOn)
{
_weights = _weights.Select(w => w * 2).ToList();
_biases = _biases.Select(b => b * 2).ToList();
}
}
else
{
Console.WriteLine("Epoch {0} complete", i);
file.WriteLine("Epoch {0} complete", i);
}
}
}
}
private void LateUpdate()
{
NetworkUpdater.Update();
}