/// <summary>
/// Begin training, and continue until one or more termination conditions are reached.
/// </summary>
private void StartTraining()
{
IsTraining = true;
epochCount = 0;
evaluations.Clear();
initialiser.Initialise(Network);
gradients = new FeedForwardLayer.Gradient[Network.Layers.Count];
for (int i = 0; i < Network.Layers.Count; i++)
{
if (LayerIsDense(i))
{
gradients[i] = new FeedForwardLayer.Dense.Gradient(
(FeedForwardLayer.Dense)Network.Layers[i]); // Can safely be cast
}
if (LayerIsSparse(i))
{
gradients[i] = new FeedForwardLayer.Sparse.Gradient(
(FeedForwardLayer.Sparse)Network.Layers[i]); // Can safely be cast
}
}
while (!IsFinished())
{
if (epochCount % evaluationFrequency == 0)
{
evaluations.Add(EvaluateNetwork());
}
PerformEpoch();
}
IsTraining = false;
}
private void InitialiseNetwork(IComputationHandler handler, out int initialisedNumberCount, out int initialisedNDArrayCount)
{
Network.Initialise(handler);
initialisedNDArrayCount = 0;
initialisedNumberCount = 0;
RegistryResolver networkResolver = new RegistryResolver(Network.Registry.Get <IRegistry>("layers"));
List <string> orderedInitialiserIdentifiers = _initialisers.Keys.ToList();
orderedInitialiserIdentifiers.Sort(RegistryUtils.CompareIdentifierSpecificityAscending);
foreach (string identifier in orderedInitialiserIdentifiers)
{
object[] values = networkResolver.ResolveGet(identifier, new object[0]);
IInitialiser initialiser = _initialisers[identifier];
foreach (object value in values)
{
INDArray array = value as INDArray;
if (array != null)
{
initialiser.Initialise(array, handler, Sigma.Random);
initialisedNDArrayCount++;
}
else
{
INumber number = value as INumber;
if (number != null)
{
initialiser.Initialise(number, handler, Sigma.Random);
initialisedNumberCount++;
}
}
}
}
}
void Init(IInitialiser initialiser)
{
_store = initialiser.Initialise();
}
public static void Initalise()
{
_initialiser.Initialise();
}
