继续看yusugomori的代码，看逻辑回归。在DBN（Deep Blief Network）中，下面几层是RBM，最上层就是LR了。关于回归、二类回归、以及逻辑回归，资料就是前面转的几篇。套路就是设定目标函数（softmax损失函数），对参数求偏导数，得出权重更新公式等。

LogisticRegression.h注释如下：

 

class LogisticRegression {public:  	int N;  		// number of input samples  	int n_in;		// number of input nodes  	int n_out;		// number of output nodes  	double **W;		// weights connecting the input nodes and the output nodes  	double *b;		// bias of the output nodes  	// allocate memory and initialize the parameters  	LogisticRegression(  			int, 	// N  			int, 	// n_in  			int		// n_out		  	);  	~LogisticRegression();  public:	// train the logistic regression model, update the value of W and b  	void train (	  		int*, 	// the input from input nodes in training set	  		int*, 	// the output from output nodes in training set	  		double	// the learning rate		  ); 	// calculate the softmax for a input vector 	// dSoftMax = exp(d_i - Max) / sum_i( exp(d_i - Max) )  	void softmax (	  		double*	// the calculated softmax probabiltiy -- input & output			  );	// do prediction by calculating the softmax probability from input  	void predict (	  		int*, 	// the input from input nodes in testing set	  		double*	// the calculated softmax probability			  );};

顺便提一句。从前RBM的那个注释，是在家用VS2008写的；现在这个，用CFree5.0，轻量级、编辑器操作贴心，赞一下！