GradientDescentSharp 0.0.5

Warning, API change: Now after any nn learning method call Learn() method

var result = nn.Backwards(input, expected);
result.Learn(); //to learn last backpropagation
result.Unlearn(); //to unlearn last backpropagation

GradientDescentSharp

This little library allows to compute a approximate solution for some defined problem with error function, using gradient descent.

Simple example:

//first define a problem
var problem = (IDataAccess<double> x) =>
{
    var n = x[0];
    //we seek for such value, that n^n=5
    var needToMinimize = Math.Pow(n, n) - 5.0;
    return Math.Abs(needToMinimize);
};
//then define changing variables
var variables = new ArrayDataAccess<double>(1);
//set variables close to global minima
variables[0] = 1;

//define descent
var descent = new MineDescent(variables, problem)
{
    DescentRate = 0.1,             // how fast to descent, this value will be adjusted on the fly
    Theta = 1e-4,                  // what precision of found minima we need
    DescentRateDecreaseRate = 0.1, // how much decrease DescentRate when we hit a grow of error function
    Logger = new ConsoleLogger()   // logger for descent progress
};

//do 30 iterations
descent.Descent(30);

System.Console.WriteLine("For problem n^n=5");
System.Console.WriteLine($"Error is {problem(variables)}");
System.Console.WriteLine($"n={variables[0]}");
System.Console.WriteLine($"n^n={Math.Pow(variables[0], variables[0])}");

Output

--------------Mine descent began
Error is 3.8894657589454242
Changed by 0.11053424105457577
-------------
...
-------------
Error is 0.2503619082577506
Changed by 0.7496380917422432
-------------
Error is 0.66669577875009
Changed by 0.4163338704923394
Undo step. Decreasing descentRate.
-------------
...
-------------
Error is 0.00015378896740614323
Changed by 8.779536614600403E-05
--------------Mine done in 24 iterations
For problem n^n=5
Error is 0.00015378896740614323
n=2.1293900000000012
n^n=5.000153788967406

As you can see, when we hit a grow in error function, the descent undo step and decrease descentRate, so we are guaranteed to hit a local minima!

It still is very important to define a good error function and init variables.

Also I've a good working feed-forward neural network implementation here.

And it can learn from error function only, like a simplified reinforcement learning, also there is a way to do continuous learning, all with one simple class - without much hassle and abstraction layers.

Check out neural network playground examples.