www.pudn.com > 基于VC的神经网络开发程序包(源码).rar > MultiLayerNetwork.h

#ifndef _MULTILAYERNETWORK_H 
#define _MULTILAYERNETWORK_H 
 
 
#include "Layer.h" 
#include "InputLayer.h" 
#include "Network.h" 
#include "TrainingSet.h" 
 
namespace annie 
{ 
/** Abstraction of a multi-layer perceptron network. 
  * Basically, layers of SimpleNeurons constitute this network. 
  * Training is done using the backpropagation technique which uses 
  * the gradient descent method. 
  * 
  * The labels of the layers start from 0 (for the input layer) and then 
  * keep moving on. The labels of neurons in the layers is = 
  * *Layer::MAX_LAYER_SIZE +  
  * 
  * All neurons in the layers are allowed to have a bias. 
  * \todo The copy constructor 
  */ 
class MultiLayerNetwork : public Network 
{ 
protected: 
	/** Number of layers in the network. 
	  * If you derive your own network from this class, the onus of 
	  * keeping _nLayers consistent lies on you! 
	  */ 
	int _nLayers; 
 
	/// The layers 
	std::vector _layers; 
 
	/// The input layer 
	InputLayer *_inputLayer(); 
 
	/// The output layer 
	Layer *_outputLayer(); 
public: 
	/** Create a multi-layer network. 
	  * @param inputs The number of inputs taken in by the network 
	  */ 
	MultiLayerNetwork(int inputs); 
 
	/// Copy constructor, NOT YET IMPLEMENTED 
	MultiLayerNetwork(MultiLayerNetwork &srcNet); 
 
	/** Loads a multi-layer network from the given filename. 
	  * @param filename The name of the file from which to load the network 
	  * @throws Exception On any error 
	  */ 
	MultiLayerNetwork(const char *filename); 
 
	virtual ~MultiLayerNetwork(); 
 
	/// Adds a layer of the given size to the network. This new layer becomes the output layer 
	virtual void addLayer(int size); 
 
	/** Completely connects the given layer with the layer below it, i.e., all 
	  * neurons in the given layer will give their output to all the neurons in the 
	  * layer below. Weights are random. 
	  * @param srcLayer The index of the source layer 
	  * @throws Exception If the layer index provided is invalid 
	  */ 
	virtual void connectLayer(int srcLayer); 
 
	/** Connects two neurons, with random weight 
	  * @param srcLayer The index of the layer in which the source neuron lies 
	  * @param srcNrn The index of the source neuron in the source layer 
	  * @param destNrn The index of the destination neuron (will be in the layer with index srcLayer+1) 
	  * @throws Exception On any invalid argument values 
	  */ 
	virtual void connect(int srcLayer, int srcNrn, int destNrn); 
 
	/** Connects two neurons, with the weight provided. 
	  * @param srcLayer The index of the layer in which the source neuron lies 
	  * @param srcNrn The index of the source neuron in the source layer 
	  * @param destNrn The index of the destination neuron (will be in the layer with index srcLayer+1) 
	  * @param weight The weight of the link 
	  * @throws Exception On any invalid argument values 
	  */ 
	virtual void connect(int srcLayer, int srcNrn, int destNrn, real weight); 
 
	/** Sets the bias of the given neuron. 
	  * @param layer The index of the layer in which the neuron lies 
	  * @param nrn The index of the neuron in the layer 
	  * @param bias The bias to be given to the neuron 
	  */ 
	virtual void setBias(int layer, int nrn, real bias); 
 
	/// The number of layers in the network (does not count the input layer as a layer) 
	virtual int getLayerCount(); 
 
	/** Returns the output of the network for the given input. 
	  * @param input A vector of getDimension() reals 
	  * @return The corresponding output of the network 
	  */ 
	virtual VECTOR getOutput(VECTOR &input); 
 
	/** Wrapper function to allow getOutput() to work for an array 
	  * of real as input as well. 
	  * Does exactly the same thing as Network::getOutput(real*). 
	  */ 
	virtual VECTOR getOutput(real *input); 
 
	/** Trains the network with data from the given TrainingSet using the 
	  * backpropagation algorithm. 
	  * @param T The TrainingSet containing input/desired-output vector pairs 
	  * @param epochs The number of epochs to train the network. An epoch is a single 
	  *				iteration through all input/desired-output vector pairs in T. 
	  * @param learningRate The learning rate to be used for weight updation 
	  * @param momentum The momentum factor to be used during weight updation. 0 by default. 
	  */ 
	virtual void train(TrainingSet &T, int epochs, real learningRate, real momentum = 0.0); 
	 
	/** Trains the network with data from the given TrainingSet using the 
	  * backpropagation algorithm. 
	  * @param T The TrainingSet containing input/desired-output vector pairs 
	  * @param epochs The number of epochs to train the network. An epoch is a single 
	  *				iteration through all input/desired-output vector pairs in T. 
	  * @param learningRate The learning rate to be used for weight updation 
	  * @param momentum The momentum factor to be used during weight updation 
	  * @param verbose If true, prints the current epoch number onto stdout, if false 
	  *				no such status information is given. 
	  */ 
	virtual void train(TrainingSet &T, int epochs, real learningRate, real momentum, bool verbose); 
 
	/** Saves the network to the given filename. 
	  * The file is a simple text file, open it up in a text editor 
	  * to see the format, quite simple 
	  * @param filename The name of the file to save the network in. 
	  */ 
	virtual void save(const char *filename); 
 
	/** Sets the activation function used by the neurons in the provided layer. 
	  * @param layer The layer whose activation function is to be changed. 
	  *				 layer>0 (as input neurons don't have any activation function) 
	  *				 and layer