public float[] Feed(float[] input, bool pushForward = true)
{
float[] output = new float[OutputSize];
for (int y = 0; y < OutputSize; y++)
{
int start = y * InputSize;
float z = Bias[y];
int w = start;
for (int x = 0; x < InputSize; x++, w++)
{
z += Weight[w] * input[x];
}
output[y] = FlatFunction(z);
}
if (pushForward && nextLayer != null)
{
return(nextLayer.Feed(output));
}
else
{
return(output);
}
}
public NeuralTrainLayer(NeuralNetworkLayer layer, float[] input)
{
float[] result = layer.Feed(input, false);
Input = input;
Output = result;
Layer = layer;
DCDB = new float[Output.Length];
DCDW = new float[input.Length * Output.Length];
}