public void StartTraining()
{
_neuralNetwork.ViewModel = _viewModel;
TrainingCallBack TR = new TrainingCallBack(_neuralNetwork.Train);
res = TR.BeginInvoke(asyCallBack, TR);
}
private void buttonTrain_Click(object sender, EventArgs e)
{
SetBotones(false);
ResetManual.Reset();
TrainingCallBack TR = new TrainingCallBack(NeuralNet.Train);
res = TR.BeginInvoke(LlamadaAsincrona, TR);
DTStart = DateTime.Now;
timer1.Start();
try
{
t = 0;
hilo = new Thread(delegate()
{
ReconocimientoRedPatron patron__ = new ReconocimientoRedPatron(ReconocimientoRedPatronEnRed);
while (true)
{
for (int i = 0; i < ImgBits.Count; i++)
{
Bitmap bit = ImgBits[i];
try
{
this.Invoke(patron__, new object[] { ImgAbuscar, bit, t });
}
catch { }
System.Threading.Thread.Sleep(20);
}
}
});
hilo.Start();
}
catch { }
}
private void EntrenamientoCompleto(IAsyncResult result)
{
if (result.AsyncState is TrainingCallBack)
{
TrainingCallBack TR = (TrainingCallBack)result.AsyncState;
bool isSuccess = TR.EndInvoke(res);
SetBotones(true);
timer1.Stop();
t = 1;
}
}
private void cmd_trainNet_Click(object sender, EventArgs e)
{
UpdateState("Apmācības process sākts..\r\n");
SetButtons(false);
ManualReset.Reset();
TrainingCallBack TR = new TrainingCallBack(neuralNetwork.Train);
res = TR.BeginInvoke(asyCallBack, TR);
DTStart = DateTime.Now;
timer1.Start();
}
private void BtnTrain_Click(object sender, EventArgs e)
{
if (BtnTrain.Text == "Stop")
{
Manager.StopImageTraining = true;
BtnTrain.Text = "Start Training";
}
else
{
UpdateState("Began Training Process..\r\n");
//BtnTrain.Enabled = false;
BtnTrain.Text = "Stop";
ManualReset.Reset();
TrainingCallBack TR = new TrainingCallBack(Manager.DoTrainingCycle);
res = TR.BeginInvoke(asyCallBack, TR);
DTStart = DateTime.Now;
TimerTime.Start();
}
}
private void TraningCompleted(IAsyncResult result)
{
if (result.AsyncState is TrainingCallBack)
{
TrainingCallBack TR = (TrainingCallBack)result.AsyncState;
bool isSuccess = TR.EndInvoke(res);
if (isSuccess)
{
_viewModel.LogTextBox += "- Nauka sieci zakończona pomyśnie\r\n";
_viewModel.TrainingSuccess = true;
}
else
{
_viewModel.LogTextBox += "- Błąd - przekroczona maksymalna iteracja\r\n";
}
}
}
private void TraningCompleted(IAsyncResult result)
{
if (result.AsyncState is TrainingCallBack)
{
TrainingCallBack TR = (TrainingCallBack)result.AsyncState;
bool isSuccess = TR.EndInvoke(res);
if (isSuccess)
{
UpdateState("Completed Training Process Successfully.");
//
}
else
{
UpdateState("Training Process is Aborted or Exceed Maximum Iteration.");
//BtnTrain.Enabled = true;
}
TimerTime.Stop();
}
}
private void TrainingCompleted(IAsyncResult result)
{
if (result.AsyncState is TrainingCallBack)
{
TrainingCallBack TR = (TrainingCallBack)result.AsyncState;
bool isSuccess = TR.EndInvoke(res);
if (isSuccess)
{
UpdateState("Apmācība veiksmīgi pabeigta. \r\n");
}
else
{
UpdateState("Training Process is Aborted or Exceed Maximum Iteration\r\n");
}
SetButtons(true);
timer1.Stop();
}
}
private void TraningCompleted(IAsyncResult result)
{
if (result.AsyncState is TrainingCallBack)
{
TrainingCallBack TR = (TrainingCallBack)result.AsyncState;
bool isSuccess = TR.EndInvoke(res);
if (isSuccess)
{
UpdateState("Completed Training Process Successfully\r\n");
timer1.Stop();
// closeButton.Enabled = true;
//SaveFileDialog FD = new SaveFileDialog();
//FD.Filter = "Network File(*.net)|*.net";
//if (FD.ShowDialog() == DialogResult.OK)
//{
try
{
string savenet = positivePath + "\\network.net";
neuralNetwork.SaveNetwork(savenet);
UpdateProgressBar("completed");
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString());
}
//}
//FD.Dispose();
}
else
{
UpdateState("Training Process is Aborted or Exceed Maximum Iteration\r\n");
timer1.Stop();
}
}
}
private void TrainProcess_Load(object sender, EventArgs e)
{
InitializeSettings();
GenerateTrainingSet();
CreateNeuralNetwork();
asyCallBack = new AsyncCallback(TraningCompleted);
ManualReset = new ManualResetEvent(false);
progressBar1.Style = ProgressBarStyle.Marquee;
progressBar1.MarqueeAnimationSpeed = 10;
UpdateState("Began training process...\n");
ManualReset.Reset();
TrainingCallBack TR = new TrainingCallBack(neuralNetwork.Train);
res = TR.BeginInvoke(asyCallBack, TR);
DTStart = DateTime.Now;
timer1.Start();
}
