Tuesday, August 18, 2009
A Novel Generic Multi-Layer Neural Networks :: Part 1: The Neuron Class
This post presents a model that I created for a NN program that I developed to solve general numeric problems (prediction, estimation, interpolation, extrapolation, etc.).
public class Neuron
{
private double __lastOutput;
private double __lastError;
private double __lastBias;
private double[] __lastWeights;
private int __siblingCount;
private int __layerInputCount;
#region Properties
private Delegates.ActivationFunction _ActivationFunction;
public Delegates.ActivationFunction ActivationFunction
{ get { return _ActivationFunction; } }
private int _InputCount;
public int InputCount
{ get { return _InputCount; } }
private double _Bias;
public double Bias
{ get { return _Bias; } }
private double[] _Weights;
public double[] Weights
{ get { return _Weights; } }
#endregion
#region Constructor
public Neuron(Delegates.ActivationFunction activationFunction, int inputCount, double? initialBias, double[] initialWeights, int siblingCount)
{ _ActivationFunction = activationFunction;
_InputCount = inputCount;
__siblingCount = siblingCount;
__layerInputCount = _InputCount * __siblingCount;
__lastError = 0.0;
if (initialBias == null)
_Bias = GlobalModule.GetRandomValue();
else
_Bias = initialBias.Value;
__lastBias = _Bias;
if (initialWeights == null)
{
_Weights = GlobalModule.GetRandomValue(inputCount);
}
else if (initialWeights.Length == inputCount)
{
initialWeights.CopyTo(_Weights, 0);
initialWeights.CopyTo(__lastWeights, 0);
}
else
throw new Exception("Invalid number of initial weights!");
}
#endregion
#region Public Methods
public double ComputeOutput(double[] input)
{
double totalInput = _Bias;
for (int i = 0; i <>
totalInput += _Weights[i] * input[i];
__lastOutput = _ActivationFunction.Invoke(totalInput);
return __lastOutput;
}
public void ChangeWeights(double error) // it also modifies the bias
{
double roughSharePlus = Math.Abs(error) / __layerInputCount;
double roughShareMinus = -roughSharePlus;
double curBias = _Bias;
if (Math.Abs(error) > Math.Abs(__lastError)) // worse (weak result)
{
roughSharePlus = -roughSharePlus;
roughShareMinus = -roughShareMinus;
}
for (int i = 0; i <> _Weights[i])
{
__lastWeights[i] = _Weights[i];
_Weights[i] += roughShareMinus * GlobalModule.GetRandomValue();
}
} // next i
if(__lastBias <> curBias)
_Bias += roughShareMinus * GlobalModule.GetRandomValue() * (__lastBias - curBias);
__lastBias = curBias;
return;
}
public void ReinitializeWeights() // it also reinitialize the bias
{
_Weights = GlobalModule.GetRandomValue(_InputCount);
_Bias = GlobalModule.GetRandomValue();
}
#endregion
} // end of class Neuron
Sunday, August 9, 2009
Conceptual Design in Database Development
The conceptual design is the first phase in the database design process:
- Conceptual Design
- Logical Design
- Implementation (Adaption)
- Physical Design
The conceptual design phase is a process of "Analysis and Discovery" and its goal is to define the "Requirements" of a system (a company/business/organization/etc.).
There are four main tasks to do in this phase:
1. Documenting the entities and the relationships between entities
2. Documenting the business rules for data
3. Defining the scope of the system
4. Defining a plan for security of the system
Saturday, August 8, 2009
Could not start Telegram Desktop
