【deep learning学习笔记】注释yusugomori的LR代码 --- LogisticRegression.h

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！