static void Main()
{
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
/******LOAD DATA******/
List <TrainingItem> trainData =
MnistReader.ConvertTrainingData(
MnistReader.ReadImages("train-images.idx3-ubyte", "train-labels.idx1-ubyte", 60000));
/***Test form***/
testForm = new TestingForm();
/***Graph***/
graphForm = new Graph();
/*****Create Network Form*****/
form = new NetworkForm();
if (!TryLoadNetwork())
{
network = new NeuralNetwork(0.01, 0.9, new int[] { 196, 15, 4 });
}
form.DrawNetwork(network);
form.startTrainingAction += () => network.Train(trainData, (i, cost) =>
{
form.SetProgress("Trained for " + TimeSpan.FromMilliseconds(network.TrainedMillis).ToString("c") + " - Gen " + i + " - Cost : " + cost);
if (graphForm.Visible)
{
graphForm.SetGraphData("Cost", network.Costs);
}
}, true, 10000);
form.stopTrainingAction += network.StopTraining;
form.toJsonAction += () =>
{
File.WriteAllText(networkDataPath, new NetworkInfos(network).GetJSON());
};
form.testNetworkAction += TestNetwork;
form.costGraphAction += () =>
{
graphForm.SetGraphData("Cost", network.Costs);
graphForm.Show();
};
form.FormClosed += (object o, FormClosedEventArgs a) => network.StopTraining();
/*****END*****/
/***RUN***/
Application.Run(form);
}
private static void TestNetwork()
{
network.StopTraining();
if (testingData == null)
{
testingData = MnistReader.ReadImages("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte", 2000);
}
testForm.Show();
testForm.Next();
}