/// <summary>
/// Calculates the value for this neuron
/// </summary>
/// <param name="prevVals">The values from the layer before the current one</param>
public void forward(float[] prevVals)
{
inputs = (float[])prevVals.Clone();
value = CpuFuncs.sigmoid(CpuFuncs.inner_product(weights, inputs));
if (isOutput) //linear if output
{
value = CpuFuncs.inner_product(weights, inputs);
}
}
/// <summary>
/// Calculates the gradient for this neuron.
/// This is only used in the backwards training phase.
/// </summary>
/// <param name="goal">Expected value of this neuron (gradient or goal value)</param>
/// <param name="nextWeight">Null if output neuron, otherwise equals to weights to this neuron from layer after it.</param>
public void calcGradient(float goal, float[] nextWeight)
{
float derivitive = CpuFuncs.sigmoid_prime(value);
if (isOutput)
{
gradient = (goal - value);
}
else
{
float sum = 0.0f;
for (int i = 0; i < nextWeight.Length; i++)
{
sum += goal * nextWeight[i];
}
gradient = derivitive * sum;
}
}