public static void CreateAndTrain(string location)
{
string imageFileName = @"C:\Users\user\source\Repos\NumberRecognitionNN\Neural Network\Neural Network\train-images.idx3-ubyte";
string labelFileName = @"C:\Users\user\source\Repos\NumberRecognitionNN\Neural Network\Neural Network\train-labels.idx1-ubyte";
byte[] imageFile = File.ReadAllBytes(imageFileName);
byte[] labelFile = File.ReadAllBytes(labelFileName);
NeuralNetwork network = new NeuralNetwork(new double[784], new int[] { 16, 10 });
int imagesIndex = 16;
int labelIndex = 8;
int percentDone = (int)(((double)imagesIndex / (double)imageFile.Length) * 100);
while (imagesIndex < imageFile.Length)
{
double[] imageFeed = new double[784];
for (int i = 0; i < 784; i++)
{
imageFeed[i] = (imageFile[imagesIndex]) / 256.0;
imagesIndex++;
}
network.Feed(imageFeed);
network.Backpropagation(NumberToDesiredOutput(labelFile[labelIndex]));
labelIndex++;
if (percentDone != (int)(((double)imagesIndex / (double)imageFile.Length) * 100))
{
percentDone = (int)(((double)imagesIndex / (double)imageFile.Length) * 100);
Console.Write(percentDone + "%\n");
}
}
network.Save(location);
#region printregion
/*
* int imagesIndex = 16;
* int labelIndex = 8;
*
* while (imagesIndex < 1000)
* {
* for (int i = 0; i < 28; i++)
* {
* for (int j = 0; j < 28; j++)
* {
* if (((double)imageFile[imagesIndex]) / 256.0 > 0.5)
* {
* Console.Write(1 + " ");
* }
* else
* {
* Console.Write(0 + " ");
* }
* imagesIndex++;
* }
* Console.Write("\n");
* }
*
*
* Console.Write("-----" + labelFile[labelIndex] + "-----");
* labelIndex++;
* Console.Write("\n");
* Console.Write("\n");
* }
*/
#endregion
}
