public static LeNetConfiguration FromCharacters(params char[] characters)
{
LeNetConfiguration result = new LeNetConfiguration(characters.Length);
Array.Copy(characters, result.Characters, characters.Length);
for (int i = 0; i < characters.Length; i++)
{
char character = characters[i];
if (characters.Count(item => character == item) > 1) throw new ArgumentException();
double[] definition = GenerateCharacterDefinition(character);
Array.Copy(definition, 0, result.ClassDefinitions, i * LeNetNetwork.OutputFeedForwardNeurons, definition.Length);
}
return result;
}
public static LeNetConfiguration FromCharacters(params char[] characters)
{
LeNetConfiguration result = new LeNetConfiguration(characters.Length);
Array.Copy(characters, result.Characters, characters.Length);
for (int i = 0; i < characters.Length; i++)
{
char character = characters[i];
if (characters.Count(item => character == item) > 1)
{
throw new ArgumentException();
}
double[] definition = GenerateCharacterDefinition(character);
Array.Copy(definition, 0, result.ClassDefinitions, i * LeNetNetwork.OutputFeedForwardNeurons, definition.Length);
}
return(result);
}
public void Initialise()
{
Console.WriteLine("Loading Training Data Set...");
trainingDataSet = DataSets.GetTrainingSet().Randomise(0);
Console.WriteLine("Loading Generalisation Data Set...");
generalisationDataSet = DataSets.GetGeneralisationSet().Randomise(1);
Console.WriteLine("Creating LeNet...");
LeNetConfiguration configuration = LeNetConfigurations.FromCharacters('0', '1', '2', '3', '4', '5', '6', '7', '8', '9');
Network = new LeNetNetwork(configuration);
Snapshot = new LeNetSnapshot(Network);
Network.LearningRate = 0.0005 / 16.0;
Network.Mu = 0.02;
bool isPreTraining = Network.IsPreTraining;
}
