public frmMain()
{
TrainerManager tm = new TrainerManager();
DigitImageLoader loader = new DigitImageLoader();
data = loader.Load().OrderBy(x => rand.Next(int.MaxValue)).Take(500).ToList();
network = tm.GetTrainer(LearningSubject.Image, LearningObject.Network).Get <ICalculatableImageNetwork>();
InitializeComponent();
//imageList.Images.Add(Image.Fro)
CustomDigitImageProcessor d =
new CustomDigitImageProcessor(@"C:\Users\ostorc\Desktop\testData_whiteBlack.png", 2);
var img = d.Load();
SetMainText(img);
/*
* double succesRate = 0;
* foreach (var pair in data)
* {
* var di = pair;
* var data = network.GetNumber(pair);
* if (di.Label == data) succesRate += 1.0 / this.data.Count;
* }*/
}
/// <summary>
/// Load test data
/// </summary>
/// <returns>Load test data</returns>
public override List <KeyValuePair <double[], double[]> > GetTestData()
{
List <KeyValuePair <double[], double[]> > result = new List <KeyValuePair <double[], double[]> >();
DigitImageLoader loader = new DigitImageLoader();
var data = loader.Load();
foreach (DigitImage image in data)
{
var output = new double[NumberOfOutputs];
output[image.Label] = 1;
result.Add(new KeyValuePair <double[], double[]>(image.ToDoubleArray(), output));
}
return(result);
}
/// <summary>
/// Constructor for Image Network Trainer
/// </summary>
/// <param name="network">Network which should learn</param>
/// <param name="useCache">Shall be used cache if exists?</param>
public ImageNetworkTrainer(ILearnableNetwork network, bool useCache) : base(network, useCache)
{
DigitImageLoader loader = new DigitImageLoader(DigitImageLoader.DefaultImagesPathTest,
DigitImageLoader.DefaultLabelsPathTest);
var data = loader.Load();
Inputs = new double[data.Count][];
for (int i = 0; i < data.Count; i++)
{
Inputs[i] = data[i].ToDoubleArray();
}
Results = new double[data.Count][];
for (int i = 0; i < data.Count; i++)
{
Results[i] = new double[NumberOfOutputs];
Results[i][data[i].Label] = 1;
}
}
private async void frmMain_Shown(object sender, EventArgs e)
{
Tuple <ImageList, ListView> list =
await Task.Run(() =>
{
ImageList imgList = new ImageList();
ListView itmList = new ListView();
foreach (DigitImage image in data)
{
var guid = Guid.NewGuid().ToString();
imgList.Images.Add(guid, image.NumberImage);
itmList.Items.Add(image.Label.ToString(), imgList.Images.IndexOfKey(guid));
itmList.Items[itmList.Items.Count - 1].Tag = image;
}
return(new Tuple <ImageList, ListView>(imgList, itmList));
});
imageList = list.Item1;
foreach (ListViewItem itm in list.Item2.Items)
{
itm.Remove();
lvImages.Items.Add(itm);
}
//lvImages = list.Item2;
lvImages.LargeImageList = imageList;
lvImages.SmallImageList = imageList;
lvImages.Refresh();
lblTestSuccess.Text = await Task.Run(() =>
{
double succesRate = 0;
foreach (var pair in data)
{
var di = pair;
var d = network.GetNumber(pair);
if (di.Label == d)
{
succesRate += 1.0 / this.data.Count;
}
}
return(succesRate.ToString("p2", CultureInfo.CurrentCulture));
});
lblLearningSuccess.Text = await Task.Run(() =>
{
var data = new DigitImageLoader(DigitImageLoader.DefaultImagesPathTest, DigitImageLoader.DefaultLabelsPathTest).Load();
double succesRate = 0;
foreach (var pair in data)
{
var di = pair;
var d = network.GetNumber(pair);
if (di.Label == d)
{
succesRate += 1.0 / data.Count;
}
}
return(succesRate.ToString("p2", CultureInfo.CurrentCulture));
});
}
