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(); }