public void Backprop(Layer output)
{
Errors = new double[Length];
for (int k = 0; k < output.Length; k++)
{
for (int j = 0; j < Length; j++)
{
Errors[j] += output.Weights[k, j] * ActivationFunctions.TanhDerriv(output.Values[k]) * output.Errors[k];
}
}
}
public void Calculate(double[] input, bool output)
{
Values = new double[Length];
for (int k = 0; k < Length; k++)
{
for (int j = 0; j < InputLength; j++)
{
Values[k] += Weights[k, j] * input[j];
}
if (!output)
{
Values[k] += Biases[k];
Values[k] = ActivationFunctions.Tanh(Values[k]);
}
else
{
Values[k] = Values[k];
}
}
}
//Read a matrix from a file offset by two bytes of metadata
public static double[,] ReadNextImage()
{
//Singleton
if (ImageReaderRunning)
{
throw new Exception("Already accessing file");
}
//Read image
FileStream fs = File.OpenRead(ImagePath);
//Reset parameters and decrement NN hyperparameters upon new epoch (currently disabled)
if (!(ImageOffset < fs.Length))
{
ImageOffset = 16; LabelOffset = 8;
}
fs.Position = ImageOffset;
byte[] b = new byte[Resolution * Resolution];
try
{
fs.Read(b, 0, Resolution * Resolution);
}
catch (Exception ex) { Console.WriteLine("Reader exception: " + ex.ToString()); Console.ReadLine(); }
int[] array = Array.ConvertAll(b, Convert.ToInt32);
ImageOffset += Resolution * Resolution;
//Convert to 2d array
double[,] result = new double[Resolution, Resolution];
//Convert array to doubles and store in result
for (int i = 0; i < Resolution; i++)
{
for (int ii = 0; ii < Resolution; ii++)
{
result[i, ii] = (double)array[(Resolution * i) + ii];
}
}
//Normalize the result matrix
ActivationFunctions.Normalize(result, Resolution, Resolution);
fs.Close();
return(result);
}
