public override void Update(int iteration, BaseLayer layer)
{
//If Decay rate is more than 0, the correct the learnng rate per iteration.
if (DecayRate > 0)
{
LearningRate = LearningRate * (1 / (1 + DecayRate * iteration));
}
//Loop through all the parameters in the layer
foreach (var p in layer.Parameters.ToList())
{
//Get the parameter name
string paramName = p.Key;
//Create a unique name to store in the dictionary
string varName = layer.Name + "_" + p.Key;
//Get the weight values
NDArray param = p.Value;
//Get the gradient/partial derivative values
NDArray grad = layer.Grads[paramName];
//If this is first time, initlalise all the moving average values with 0
if (!ms.ContainsKey(varName))
{
var ms_new = new NDArray(param.Shape);
ms_new.Fill(0);
ms[varName] = ms_new;
}
//If this is first time, initlalise all the moving average values with 0
if (!vs.ContainsKey(varName))
{
var vs_new = new NDArray(param.Shape);
vs_new.Fill(0);
vs[varName] = vs_new;
}
// Calculate the exponential moving average for Beta 1 against the gradient
ms[varName] = (Beta1 * ms[varName]) + (1 - Beta1) * grad;
//Calculate the exponential squared moving average for Beta 2 against the gradient
vs[varName] = (Beta2 * vs[varName]) + (1 - Beta2) * Square(grad);
//Correct the moving averages
var m_cap = ms[varName] / (1 - Math.Pow(Beta1, iteration));
var v_cap = vs[varName] / (1 - Math.Pow(Beta2, iteration));
//Update the weight of of the neurons
layer.Parameters[paramName] = param - (LearningRate * m_cap / (Sqrt(v_cap) + Epsilon));
}
}
static void Main(string[] args)
{
Operations K = new Operations();
//Load array to the tensor
NDArray a = new NDArray(3, 6);
a.Load(1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 8, 7, 6, 5, 4, 3, 2, 1);
a.Print("Load array");
//Transpose of the matrix
NDArray t = a.Transpose();
t.Print("Transpose");
//Create a tensor with all value 5
NDArray b = new NDArray(6, 3);
b.Fill(5);
b.Print("Constant 5");
//Create a tensor with all value 3
NDArray c = new NDArray(6, 3);
c.Fill(3);
c.Print("Constant 3");
// Subtract two tensor
b = b - c;
//Perform dot product
NDArray r = K.Dot(a, b);
r.Print("Dot product");
Console.ReadLine();
}