//Stochastic Gradient Descent algortithm
public static void StochasticGradient(int batchsize, Brain brain)
{
//Take data
MNISTReader.DigitImage image;
List <MNISTReader.DigitImage> samples = TrainingImages.ToList();
//Shuffle it
Shuffle(samples);
//For each epoch:
var total = samples.Count;
while (samples.Count != 0)
{
Console.WriteLine("Percent Learning Sample: " + String.Format("{0:F5}", (total - samples.Count) / (1.0 * total)));
//Treat "batchsize" number of samples
for (int i = 0; i < batchsize; i++)
{
//For each sample
if (samples.Count > 0)
{
image = samples[0];
samples.RemoveAt(0);
Vector arrayvector = ConvertImageToVector(image);
//Input to the network
brain.SetInitialActivation(arrayvector);
//Forward propagation
brain.FeedForward();
//Backpropagation
Vector result = new Vector(10);
result.data[image.label] = 1;
brain.BackPropagation(result);
}
}
//Update the weights
brain.Update(batchsize);
}
}