www.pudn.com > neuroocr_src.zip > Layer.cs

// AForge Neural Net Library 
// 
// Copyright © Andrew Kirillov, 2005 
// andrew.kirillov@gmail.com 
// 
 
namespace AForge.NeuralNet 
{ 
	using System; 
 
	/// 
 
	/// Layer - represent a collection of neurons 
	/// 
 
	public class Layer 
	{ 
		protected int	inputsCount;	// inputs count of the layer 
		protected int	neuronsCount;	// neurons count in the layer 
		protected IActivationFunction	function;	// activation function of the layer 
		protected Neuron[]	neurons; 
		protected float[]	output; 
 
		// Inputs count property 
		public int InputsCount 
		{ 
			get { return inputsCount; } 
			set 
			{ 
				inputsCount = Math.Max(1, value); 
				InitLayer(); 
			} 
		} 
		// Neurons count property 
		public int NeuronsCount 
		{ 
			get { return neuronsCount; } 
			set 
			{ 
				neuronsCount = Math.Max(1, value); 
				InitLayer(); 
			} 
		} 
		// Activation function property 
		public IActivationFunction ActivationFunction 
		{ 
			get { return function; } 
			set 
			{ 
				function = value; 
 
				for (int i = 0; i < neuronsCount; i++) 
					neurons[i].ActivationFunction = value; 
			} 
		} 
		// Get neuron at the specified index 
		public Neuron this[int index] 
		{ 
			get { return (neurons[index]); } 
		} 
		// Get layer output 
		public float[] Output 
		{ 
			get { return output; } 
		} 
 
		 
		// Constructors 
		public Layer() 
			: this (1, 1, new SigmoidFunction()) 
		{ } 
		public Layer(int neuronsCount) 
			: this (neuronsCount, 1, new SigmoidFunction()) 
		{ } 
		public Layer(int neuronsCount, int inputsCount) 
			: this (neuronsCount, inputsCount, new SigmoidFunction()) 
		{ } 
		public Layer(int neuronsCount, int inputsCount, IActivationFunction function) 
		{ 
			this.inputsCount = Math.Max(1, inputsCount); 
			this.neuronsCount = Math.Max(1, neuronsCount); 
			this.function = function; 
 
			InitLayer(); 
		} 
 
		// Compute the output value of the layer 
		public float[] Compute(float[] input) 
		{ 
			// compute each neuron 
			for (int i = 0; i < neuronsCount; i++) 
				output[i] = neurons[i].Compute(input); 
 
			return output; 
		} 
 
		// Randomize the layer 
		public void Randomize() 
		{ 
			foreach (Neuron neuron in neurons) 
				neuron.Randomize(); 
		} 
 
 
		#region Private Members 
 
		// Initialize layer 
		private void InitLayer() 
		{ 
			// create collection of neurons 
			neurons = new Neuron[neuronsCount]; 
			// create each neuron 
			for (int i = 0; i < neuronsCount; i++) 
				neurons[i] = new Neuron(inputsCount, function); 
			// allocate output array 
			output = new float[neuronsCount]; 
		} 
 
		#endregion 
	} 
}