internal float[] Compute(ComputeDevice mathLib, float[] input, ref List <float[]> activations, ref List <float[]> zValues, bool flushMathlibWorkingCache)
{
var current = input;
bool applySigmoid = zValues == null;
PasstroughActivation passtroughActivation = applySigmoid ? null : new PasstroughActivation();
foreach (var layer in layers)
{
current = layer.Compute(mathLib, current, applySigmoid ? activationFunction : passtroughActivation);
if (zValues != null)
{
zValues.Add((float[])current.Clone());
for (int i = 0; i < current.Length; i++)
{
current[i] = activationFunction.Calculate(current[i]);
}
}
if (activations != null)
{
activations.Add(current);
}
}
if (flushMathlibWorkingCache)
{
mathLib.FlushWorkingCache();
}
return(current);
}
public void _EvaluateNetworkZValues(float[] input, Network network, ref List <float[]> z_values, ref List <float[]> activations)
{
Utils.Assert(activations.Count == 0);
Utils.Assert(z_values.Count == 0);
var passtrough_activation = new PasstroughActivation();
foreach (var layer in network.layers)
{
float[] layer_input = activations.Count == 0 ? input : activations.Last();
float[] z = CalculateLayer(layer.weightMx, layer.biases, layer_input, passtrough_activation);
float[] act = new float[z.Length];
for (int i = 0; i < z.Length; i++)
{
act[i] = layer.activationFunction.Calculate(z[i]);
}
z_values.Add(z);
activations.Add(act);
}
}
