private void button4_Click(object sender, EventArgs e)
{
if (trainingData.Count == 0)
{
var rnd = new Random();
for (int i = 0; i < 10000; i++)
{
float[] input = new float[5];
float[] output = new float[5];
float j = (float)rnd.NextDouble() * 5;
int jint = Math.Min(4, (int)Math.Floor(j));
input[jint] = 1.0f;
output[jint] = 1.0f;
trainingData.Add(new TrainingSuite.TrainingData(input, output));
}
}
var trainingSuite = new TrainingSuite(trainingData);
trainingSuite.config.miniBatchSize = 6;
trainingSuite.config.epochs = (int)numericUpDown6.Value;
trainingSuite.config.shuffleTrainingData = false;
trainingSuite.config.regularization = TrainingSuite.TrainingConfig.Regularization.None;
trainingSuite.config.costFunction = new CrossEntropyErrorFunction();
trainingPromise = solver.Train(trainingSuite, calculator);
progressBar1.Value = 0;
label2.Text = "Training...";
trainingBegin = DateTime.Now;
timer1.Start();
}
public TrainingDialog(TrainingPromise _promise)
{
InitializeComponent();
trainingPromise = _promise;
DispatcherTimer timer = new DispatcherTimer();
timer.Interval = TimeSpan.FromMilliseconds(updateInterval);
timer.Tick += timer_Tick;
timer.Start();
}
private void Trainingtimer_Tick(object sender, EventArgs e)
{
if (progressDialog != null && trainingPromise != null)
{
var timespan = (DateTime.Now - trainingStart);
string time = new TimeSpan(timespan.Hours, timespan.Minutes, timespan.Seconds).ToString();
progressDialog.UpdateResult(trainingPromise.GetTotalProgress(), trainingPromise.IsReady(), "Training... Epochs done: " + trainingPromise.GetEpochsDone(), time);
if (trainingPromise.IsReady())
{
trainingPromise = null;
progressDialog = null;
trainingtimer.Stop();
}
}
}
private void Button9_Click(object sender, EventArgs e)
{
System.IO.File.WriteAllText("D:\\nntmp\\log.txt", "BEGIN\n");
string imgFile = "";
string labelFile = "";
string testImgFile = "";
string testLabelFile = "";
openFileDialog1.Filter = "Image Training data (Image)|*.*";
openFileDialog1.Title = "Open Training images file";
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
imgFile = openFileDialog1.FileName;
}
else
{
return;
}
openFileDialog1.Filter = "Training data (Label)|*.*";
openFileDialog1.Title = "Open Training labels file";
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
labelFile = openFileDialog1.FileName;
}
else
{
return;
}
openFileDialog1.Filter = "Verification Image Training data (Image)|*.*";
openFileDialog1.Title = "Open Verification images file";
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
testImgFile = openFileDialog1.FileName;
}
else
{
return;
}
openFileDialog1.Filter = "Verification Training data (Label)|*.*";
openFileDialog1.Title = "Open Verification labels file";
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
testLabelFile = openFileDialog1.FileName;
}
else
{
return;
}
LoadingWindow wnd = new LoadingWindow();
wnd.Text = "Loading training data";
List <TrainingSuite.TrainingData> trainingData = new List <TrainingSuite.TrainingData>();
List <TrainingSuite.TrainingData> testData = new List <TrainingSuite.TrainingData>();
System.Threading.Thread thread = new System.Threading.Thread(() => {
LoadTestDataFromFiles(trainingData, labelFile, imgFile, (x) => { wnd.SetProgress(x); }, true);
LoadTestDataFromFiles(testData, testLabelFile, testImgFile, (x) => { wnd.SetProgress(x); }, false);
wnd.Finish();
});
thread.Start();
if (wnd.ShowDialog() != DialogResult.OK)
{
return;
}
int success = 0;
for (int i = 0; i < testData.Count; i++)
{
var output = network.Compute(testData[i].input, calculator);
int resultIdx = ClassifyOutput(output);
int expectedIdx = ClassifyOutput(testData[i].desiredOutput);
if (resultIdx == expectedIdx)
{
++success;
}
}
network.AttachDescription("Network (" + string.Join(",", network.GetLayerConfig()) + ") epoch: 0 (initial) Test success rate: [" + success + " of " + testData.Count + "]");
System.IO.File.WriteAllText("D:\\nntmp\\network_000000.json", network.ExportToJSON());
VisualizeNetworkSpecific(network, "D:\\nntmp\\network_000000_vis_", 0, ((float)success / testData.Count) * 100.0f);
var trainingSuite = new TrainingSuite(trainingData);
trainingSuite.config.miniBatchSize = (int)numMiniBatchSize.Value;
trainingSuite.config.learningRate = (float)numLearningRate.Value;
trainingSuite.config.regularizationLambda = (float)numLambda.Value;
trainingSuite.config.shuffleTrainingData = true;
if (comboRegularization.SelectedIndex == 0)
{
trainingSuite.config.regularization = TrainingSuite.TrainingConfig.Regularization.None;
}
else if (comboRegularization.SelectedIndex == 0)
{
trainingSuite.config.regularization = TrainingSuite.TrainingConfig.Regularization.L1;
}
else if (comboRegularization.SelectedIndex == 0)
{
trainingSuite.config.regularization = TrainingSuite.TrainingConfig.Regularization.L2;
}
if (comboCostFunction.SelectedIndex == 0)
{
trainingSuite.config.costFunction = new MeanSquaredErrorFunction();
}
else if (comboCostFunction.SelectedIndex == 1)
{
trainingSuite.config.costFunction = new CrossEntropyErrorFunction();
}
trainingSuite.config.epochs = 1;
LogDebug("Initial network saved " + DateTime.Now.ToString());
for (int epoch = 0; epoch < (int)numEpoch.Value; epoch++)
{
LogDebug("Starting epoch #" + (epoch + 1) + " " + DateTime.Now.ToString());
trainingPromise = network.Train(trainingSuite, calculator);
trainingPromise.Await();
LogDebug(" Training finished " + DateTime.Now.ToString());
success = 0;
for (int i = 0; i < testData.Count; i++)
{
var output = network.Compute(testData[i].input, calculator);
int resultIdx = ClassifyOutput(output);
int expectedIdx = ClassifyOutput(testData[i].desiredOutput);
if (resultIdx == expectedIdx)
{
++success;
}
}
LogDebug(" Verification finished Success rate: [" + success + " of " + testData.Count + "] " + DateTime.Now.ToString());
network.AttachDescription("Network (" + string.Join(",", network.GetLayerConfig()) + ") epoch: " + (epoch + 1) + " Test success rate: [" + success + " of " + testData.Count + "]");
System.IO.File.WriteAllText("D:\\nntmp\\network_" + (epoch + 1).ToString().PadLeft(6, '0') + ".json", network.ExportToJSON());
VisualizeNetworkSpecific(network, "D:\\nntmp\\network_" + (epoch + 1).ToString().PadLeft(6, '0') + "_vis_", epoch + 1, ((float)success / testData.Count) * 100.0f);
LogDebug(" Saving finished " + DateTime.Now.ToString());
}
}
private void button4_Click(object sender, EventArgs e)
{
string imgFile = "";
string labelFile = "";
openFileDialog1.Filter = "Image Training data (Image)|*.*";
openFileDialog1.Title = "Open Training images file";
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
imgFile = openFileDialog1.FileName;
}
else
{
return;
}
openFileDialog1.Filter = "Training data (Label)|*.*";
openFileDialog1.Title = "Open Training labels file";
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
labelFile = openFileDialog1.FileName;
}
else
{
return;
}
LoadingWindow wnd = new LoadingWindow();
wnd.Text = "Loading training data";
List <TrainingSuite.TrainingData> trainingData = new List <TrainingSuite.TrainingData>();
System.Threading.Thread thread = new System.Threading.Thread(() => {
LoadTestDataFromFiles(trainingData, labelFile, imgFile, (x) => { wnd.SetProgress(x); }, true);
wnd.Finish();
});
thread.Start();
if (wnd.ShowDialog() != DialogResult.OK)
{
return;
}
var trainingSuite = new TrainingSuite(trainingData);
trainingSuite.config.miniBatchSize = (int)numMiniBatchSize.Value;
trainingSuite.config.learningRate = (float)numLearningRate.Value;
trainingSuite.config.regularizationLambda = (float)numLambda.Value;
trainingSuite.config.shuffleTrainingData = checkShuffle.Checked;
if (comboRegularization.SelectedIndex == 0)
{
trainingSuite.config.regularization = TrainingSuite.TrainingConfig.Regularization.None;
}
else if (comboRegularization.SelectedIndex == 0)
{
trainingSuite.config.regularization = TrainingSuite.TrainingConfig.Regularization.L1;
}
else if (comboRegularization.SelectedIndex == 0)
{
trainingSuite.config.regularization = TrainingSuite.TrainingConfig.Regularization.L2;
}
if (comboCostFunction.SelectedIndex == 0)
{
trainingSuite.config.costFunction = new MeanSquaredErrorFunction();
}
else if (comboCostFunction.SelectedIndex == 1)
{
trainingSuite.config.costFunction = new CrossEntropyErrorFunction();
}
trainingSuite.config.epochs = (int)numEpoch.Value;
trainingStart = DateTime.Now;
trainingPromise = network.Train(trainingSuite, calculator);
trainingtimer.Start();
progressDialog = new TrainingWindow(trainingPromise);
progressDialog.ShowDialog();
}