public static void CNN_OverfittingTest()
{
CNN cnn = new CNN();
DigitImage[] digitImages = MNIST_Parser.ReadFromFile(DataSet.Testing, 100);
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
int iteration_count = 10;
for (int i = 0; i < iteration_count; i++)
{
for (int j = 0; j < digitImages.Length; ++j)
{
Matrix[] input = new Matrix[1];
input[0] = new Matrix(digitImages[j].pixels);
Matrix target = new Matrix(10, 1);
target[(int)digitImages[j].label, 0] = 1f;
cnn.Train(input, target);
double error = cnn.GetError();
int val = (int)((i - 0) / (double)(iteration_count - 1 - 0) * (100 - 0) + 0);
ProgressBar(val, i, iteration_count, error, stopwatch.ElapsedMilliseconds / 1000.0);
}
}
for (int j = 0; j < digitImages.Length; ++j)
{
Matrix[] input = new Matrix[1];
input[0] = new Matrix(digitImages[j].pixels);
Matrix output = cnn.Predict(input);
Console.WriteLine(output.ToString());
Console.WriteLine(digitImages[j].ToString());
Console.ReadLine();
}
}
private void Button_Next_Click(object sender, EventArgs e)
{
DigitImage[] digitImages = MNIST_Parser.ReadFromFile(DataSet.Training, 10000);
int idx = numPool[rnd.Next(0, numPool.Count)];
numPool.RemoveAt(idx);
input[0] = new Matrix(digitImages[idx].pixels);
Bitmap b = new Bitmap(28, 28);
for (int i = 0; i < b.Width; i++)
{
for (int j = 0; j < b.Height; j++)
{
b.SetPixel(i, j, Color.FromArgb((byte)(255 - input[0][j, i]), (byte)(255 - input[0][j, i]), (byte)(255 - input[0][j, i])));
}
}
bmp = new Bitmap(b, new Size(bmp.Width, bmp.Height));
panel1.Invalidate();
Predict();
}
public static void CNN_Training()
{
int trCount = 60000, tsCount = 10000;
double error = 0f, timeLimit = 0f;
int totalEpoch = 10;
bool predictionIsOn = true;
Random random = new Random();
DigitImage[] trainigDigitImagesDatas = MNIST_Parser.ReadFromFile(DataSet.Training, trCount);
DigitImage[] testingDigitImagesDatas = MNIST_Parser.ReadFromFile(DataSet.Testing, tsCount);
int training_count = trainigDigitImagesDatas.Length;
int dialogResult;
CNN cnn = CreateCNN(out dialogResult);
Matrix[] input, targets;
InitializeInputAndTarget(out input, out targets);
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
if (dialogResult == 0)
{
Console.WriteLine("System is getting trained...");
//if we never assigned this assign only once
if (cursorTopTraining == -1)
{
cursorTopTraining = Console.CursorTop;
}
for (int epoch = 0; epoch < totalEpoch; epoch++)
{
double lossSum = 0.0;
DigitImage[] digitImages = trainigDigitImagesDatas.OrderBy(image => random.Next(training_count)).ToArray();
for (int i = 0; i < training_count; i++)
{
for (int j = 0; j < 28; j++)
{
for (int k = 0; k < 28; k++)
{
input[0][j, k] = digitImages[i].pixels[j][k];
}
}
input[0].Normalize(0f, 255f, 0f, 1f);
cnn.Train(input, targets[digitImages[i].label]);
//if (stopwatch.ElapsedMilliseconds > timeLimit)
// {
// every 0.5 sec update error
//timeLimit += 500;
error = cnn.GetError();
lossSum += error;
//}
int val = Map(0, training_count * totalEpoch, 0, 100, training_count * epoch + i);
ProgressBar(val, training_count * epoch + i, training_count * totalEpoch, lossSum / (i + 1), stopwatch.ElapsedMilliseconds / 1000.0, cursorTopTraining);
}
CNN_Testing(testingDigitImagesDatas, predictionIsOn, cnn, epoch + 1);
}
Console.WriteLine("\nSystem has been trained.");
}
else
{
CNN_Testing(testingDigitImagesDatas, predictionIsOn, cnn, -1);
}
}
