//static bool startFromZero = true;
static void Main(string[] args)
{
Stopwatch watch = new Stopwatch();
INetwork network = null;
try
{
network = AutoEncoderNetwork.GetAutoEncoder($"autoencoder_{nbInputOutput}_{nbHidden}_{bootleNeck}");
}
catch
{
network = new AutoEncoderNetwork(nbInputOutput, bootleNeck, nbHidden);
}
//if(startFromZero)
//else
var ds = GenerateRandomDataset(nbInputOutput, nbRow).ToArray();
watch.Start();
for (int i = 1; i <= nbIteration; i++)
{
Console.WriteLine($"iteration : {i}");
foreach (var(prediction, label) in network.Learning(ds, ds))
{
Console.WriteLine(
$"prediction : [{String.Join(", ", prediction.Select(x => Math.Round(x, 2)))}]" +
$"\nlabel : [{String.Join(", ", label.Select(x => Math.Round(x, 2)))}]\n");
}
Console.WriteLine();
if (i % sauvegarRate == 0)
{
new Thread(network.Sauvegarde).Start();
}
}
watch.Stop();
Console.WriteLine(watch.Elapsed);
if (nbIteration % sauvegarRate != 0)
{
network.Sauvegarde();
}
Console.Read();
}
private void button2_Click(object sender, EventArgs e)
{
this.Enabled = false;
int nbEntree = Convert.ToInt32(this.numericUpDown2.Value);
int nbBottleNeck = Convert.ToInt32(numericUpDown3.Value);
int nbHidden = Convert.ToInt32(numericUpDown4.Value);
Bitmap imgToDenoise = (Bitmap)Bitmap.FromFile(fileArrayPred[Convert.ToInt32(numericUpDown7.Value)]);
var bytes = Functions.GetAllPixelGrey(imgToDenoise).ToArray();
var img = Functions.NormalizeRow(bytes, 255, true);
INetwork network = null;
try
{
string reseauxName = textBox6.Text == "" ?
$"autoencoder_{nbEntree}_{nbHidden}_{nbBottleNeck}"
: textBox6.Text;
network = AutoEncoderNetwork.GetAutoEncoder(reseauxName);
}
catch
{
Console.WriteLine("error network doesn't exist");
}
var imgOut = network.Predict(img);
Functions.SaveImgGrey(Functions.UnNormalizeRow(img), @"output\displayedIn.jpg");
Functions.SaveImgGrey(bytes, @"output\displayedInOrigin.jpg");
Functions.SaveImgGrey(Functions.UnNormalizeRow(imgOut), @"output\displayedOut.jpg");
SetPictureBox(pictureBox1, @"output\displayedIn.jpg");
SetPictureBox(pictureBox2, @"output\displayedOut.jpg");
this.Enabled = true;
}
private void button1_Click(object sender, EventArgs e)
{
//const int sizeImage = 1024;
this.Enabled = false;
int nbEntree = Convert.ToInt32(this.numericUpDown2.Value);
int nbBottleNeck = Convert.ToInt32(numericUpDown3.Value);
int nbHidden = Convert.ToInt32(numericUpDown4.Value);
var bmpEntree = new List <Bitmap>();
foreach (var file in fileArrayTrain)
{
bmpEntree.Add((Bitmap)Bitmap.FromFile(file));
}
var bmpSortie = new List <Bitmap>();
for (int i = 0; i < 50; i++)
{
foreach (var file in fileArrayReel)
{
bmpSortie.Add((Bitmap)Bitmap.FromFile(file));
}
}
var bytesEntree = bmpEntree.Select(x => Functions.GetAllPixelGrey(x).ToArray());
var bytesSortie = bmpSortie.Select(x => Functions.GetAllPixelGrey(x).ToArray());
var dsEntree = Functions.NormalizeDS(bytesEntree, 255, true).ToArray();
var dsSortie = Functions.NormalizeDS(bytesSortie, 255, true).ToArray();
INetwork network = null;
try
{
string reseauxName = textBox1.Text == "" ?
$"autoencoder_{nbEntree}_{nbHidden}_{nbBottleNeck}"
: textBox1.Text;
network = AutoEncoderNetwork.GetAutoEncoder(reseauxName);
}
catch
{
network = new AutoEncoderNetwork(nbEntree, nbBottleNeck, nbHidden);
}
//int nbRow = Convert.ToInt32(numericUpDown5.Value);
int nbIteration = Convert.ToInt32(numericUpDown1.Value);
int sauvegardeRate = Convert.ToInt32(numericUpDown6.Value);
for (int i = 1; i <= nbIteration; i++)
{
IEnumerable <double> meanSquareErrors = network.Learning(dsEntree, dsSortie).Select(x => Functions.MatriceSquareDifference(x.Item1, x.Item2).Average());
double value = meanSquareErrors.Average();
//TextBoxWriteLine(
// $"prediction : [{String.Join(", ", prediction.Select(x => Math.Round(x, 2)))}]" +
// $"\nlabel : [{String.Join(", ", label.Select(x => Math.Round(x, 2)))}]\n");
Console.WriteLine();
if (i % sauvegardeRate == 0)
{
new Thread(network.Sauvegarde).Start();
}
if (i % 10 == 0)
{
textBox2.Text = i.ToString();
textBox7.Text = value.ToString();
this.textBox2.Refresh();
this.textBox4.Refresh();
textBox7.Refresh();
}
}
this.Enabled = true;
}
static void Main(string[] args)
{
//var bytes = File.ReadAllBytes(@"data\arrow.bin");
var bmpEntree = new List <Bitmap>();
for (int i = 0; i < 100; i++)
{
bmpEntree.Add((Bitmap)Bitmap.FromFile($"trainSet_15\\random_{i}.jpg"));
}
var bmpSortie = new List <Bitmap>();
for (int k = 0; k < 50; k++)
{
for (int i = 0; i < 1; i++)
{
bmpSortie.Add((Bitmap)Bitmap.FromFile($"img\\final_left.jpg"));
}
for (int i = 0; i < 1; i++)
{
bmpSortie.Add((Bitmap)Bitmap.FromFile($"img\\final_up.jpg"));
}
}
//var colo = bmpEntree.Select(x => Functions.GetAllPixelColo(x).ToArray()).ToArray();
//var red = colo.Select(x => toto().Concat(x.Select(y => y.R)).ToArray());
//var green = colo.Select(x => x.Select(y => y.G).ToArray());
//var blue = colo.Select(x => x.Select(y => y.B).ToArray());
//var binaryFile = red.Concat(green).Concat(blue).SelectMany(x => x).ToArray();
//File.WriteAllBytes("data\\arrow.bin", binaryFile);
//var bmpEntree = new List<Bitmap>() {
// (Bitmap)Bitmap.FromFile($"img\\left0.jpg"),
// (Bitmap)Bitmap.FromFile($"img\\left1.jpg"),
// (Bitmap)Bitmap.FromFile($"img\\left2.jpg"),
// (Bitmap)Bitmap.FromFile($"img\\up0.jpg"),
// (Bitmap)Bitmap.FromFile($"img\\up1.jpg"),
// (Bitmap)Bitmap.FromFile($"img\\up2.jpg"),
//};
//var bmpSortie = new List<Bitmap>()
//{
// (Bitmap) Bitmap.FromFile($"img\\final_left.jpg"),
// (Bitmap) Bitmap.FromFile($"img\\final_left.jpg"),
// (Bitmap) Bitmap.FromFile($"img\\final_left.jpg"),
// (Bitmap) Bitmap.FromFile($"img\\final_up.jpg"),
// (Bitmap) Bitmap.FromFile($"img\\final_up.jpg"),
// (Bitmap) Bitmap.FromFile($"img\\final_up.jpg"),
//};
//var bmpEntree = new List<Bitmap>() {
// //(Bitmap)Bitmap.FromFile($"img\\left1.jpg"),
// (Bitmap)Bitmap.FromFile($"img\\final_left.jpg"),
// (Bitmap)Bitmap.FromFile($"img\\final_up.jpg"),
//};
//var bmpSortie = new List<Bitmap>()
//{
// (Bitmap) Bitmap.FromFile($"img\\final_left.jpg"),
// (Bitmap) Bitmap.FromFile($"img\\final_up.jpg"),
//};
var bytesEntree = bmpEntree.Select(x => Functions.GetAllPixelGrey(x).ToArray());
//var sauvegarde = bytesEntree.Select(x => RandomBytes(x));
//int k = 0;
//for (int l = 0; l < 500 / 2; l++)
// foreach (var b in sauvegarde)
// Functions.SaveImgGrey(b.ToArray(), $"trainSet_15\\random_{(k++)}.jpg");
//k = 0;
//for (int l = 0; l < 125 / 2; l++)
// foreach (var b in sauvegarde)
// Functions.SaveImgGrey(b.ToArray(), $"testSet_15\\random_{(k++)}.jpg");
var bytesSortie = bmpSortie.Select(x => Functions.GetAllPixelGrey(x).ToArray());
//var bytesEntree = Functions.SplitBytes(bytes);
// Functions.SaveImgGrey(bytesEntree.First(), $"testSet\\random_{(k++)}.jpg");
Console.WriteLine(String.Join(", ", bytesSortie.First()));
Console.WriteLine(String.Join(", ", bytesEntree.First()));
var dsEntree = Functions.NormalizeDS(bytesEntree, 255, true).ToArray();
var dsSortie = Functions.NormalizeDS(bytesSortie, 255, true).ToArray();
//var dsEntree = GenerateRandomDataset(100, 2).ToArray();
//var dsSortie = dsEntree;
for (int i = 0; i < dsEntree.Length; i++)
{
Functions.SaveImgGrey(Functions.UnNormalizeRow(dsEntree[i]), @"output\original" + i + ".jpg");
}
Stopwatch watch = new Stopwatch();
INetwork network = null;
try
{
network = AutoEncoderNetwork.GetAutoEncoder(/*$"autoencoder_{nbInputOutput}_{nbHidden}_{bootleNeck}"*/);
}
catch
{
network = new AutoEncoderNetwork(nbInputOutput, bootleNeck, nbHidden);
}
watch.Start();
for (int i = 1; i <= nbIteration; i++)
{
IEnumerable <double> meanSquareErrors = network.Learning(dsEntree, dsSortie).Select(x => Functions.MatriceSquareDifference(x.Item1, x.Item2).Average());
foreach (var error in meanSquareErrors)
{
if (i % 10 == 0 || i % 11 == 0)
{
Console.WriteLine(error);
}
}
if (i % 10 == 0 || i % 11 == 0)
{
Console.WriteLine($"iteration : {i}");
}
if (i % sauvegarRate == 0)
{
new Thread(network.Sauvegarde).Start();
}
}
watch.Stop();
var predict = network.Predict(dsEntree);
int j = 0;
foreach (var pred in predict)
{
Functions.SaveImgGrey(Functions.UnNormalizeRow(pred), @"output\img" + j++ + ".jpg");
}
Console.WriteLine();
Console.WriteLine(string.Join(", ", Functions.UnNormalizeRow(predict.First())));
Console.WriteLine(string.Join(", ", predict.First().Select(x => Math.Round(x, 2))));
Console.WriteLine("");
Console.WriteLine(string.Join(", ", dsEntree.First().Select(x => Math.Round(x, 2))));
//Functions.SaveImgGrey(Functions.UnNormalizeRow(predict.First()), @"output\img_.jpg");
Console.WriteLine(watch.Elapsed);
if (nbIteration % sauvegarRate != 0)
{
network.Sauvegarde();
}
Console.Read();
}
