【deep learning学习笔记】注释yusugomori的RBM代码 --- 头文件

百度了半天yusugomori，也不知道他是谁。不过这位老兄写了deep learning的代码，包括RBM、逻辑回归、DBN、autoencoder等，实现语言包括c、c++、java、python等。是学习的好材料。代码下载地址：https://github.com/yusugomori/DeepLearning。不过这位老兄不喜欢写注释，而且这些模型的原理、公式什么的，不了解的话就看不懂代码。我从给他写注释开始，边看资料、边理解它的代码、边给他写上注释。

工具包中RBM的实现包含了两个文件，RBM.h和RBM.cpp。RBM.h添加注释后，如下：

 class RBM
 {
 public:
     // the number of training sample
     int N;
     // the number of visiable node
     int n_visible;
     // the number of hidden node
     int n_hidden;
     // the weight connecting the visiable node and the hidden node
     double **W;
     // the bias of hidden node
     double *hbias;
     // the bias of visiable node
     double *vbias;

 public:
     // construct the RBM by input parameters
     RBM (int,       // N
         int,        // n_visible
         int,        // n_hidden
         double**,   // W
         double*,    // hbias
         double*     // vbias
         );
     // destructor, release all the memory of parameters
     ~RBM ();
     // CD-k algorithm to train RBM
      void contrastive_divergence (int*, // one input sample
          double,                        // the learning rate
          int                            // the k of CD-k, it is usually 1
          );

     // these the functions of Gibbs sample

     // sample the hidden node given the visiable node, 'sample' means calculating
     // 1. the output probability of the hidden node given the input of visiable node
     // and the weight of current RBM; 2. the 0-1 state of hidden node by a binomial
     // distribution given the calculated output probability of this hidden node
     void sample_h_given_v (int*,        // one input sample from visiable nodes -- input
         double*,                        // the output probability of hidden nodes -- output
         int*                            // the calculated 0-1 state of hidden node -- output
         );
     // sample the visiable node given the hidden node, 'sample' means calculating
     // 1. the output probability of the visiable node given the input of hidden node
     // and the weight of current RBM; 2. the 0-1 state of visiable node by a binomial
     // distribution given the calculated output probability of this visiable node
     void sample_v_given_h (int*,        // one input sample from hidden nodes -- input
         double*,                        // the output probability of visiable nodes -- output
         int*                            // the calculated 0-1 state of visiable node -- output
         );
     // 'propup' -- probability up. It's called by the 'sample_x_given_x' function and the reconstruct funciton
     //  To calculate the probability in 'upper' node given the input from 'lower' node in RBM
     // note: what is the 'up' and 'down'? the visiable node is below (down) the hidden node.
     // 'probability up' means calculating the probability of hidden node given the visiable node
     // return value: the output probability of the hidden node given the input of visiable node
     // and the weight of current RBM
     // the probability is : p (hi|v) = sigmod ( sum_j(vj * wij) + bi)
     double propup (int*,                // one input sample from visiable node -- input
         double*,                        // the weight W connecting one hidden node to all visible node -- input
         double                          // the bias for this hidden node -- input
         );
     // 'propdown' -- probability down. It's called by the 'sample_x_given_x' function and the reconstruct funciton
     //  To calculate the probability in 'lower' node given the input from 'upper' node in RBM
     // note: what is the 'up' and 'down'? the visiable node is below (down) the hidden node.
     // 'probability down' means calculating the probability of visiable node given the hidden node
     // return value: the output probability of the visiable node given the input of hidden node
     // and the weight of current RBM
     // the probability is : p (vi|h) = sigmod ( sum_j(hj * wij) + ci)
     double propdown (int*,              // one input sample from hidden node -- input
         int,                            // the index of visiable node in the W matrix -- input
         double                          // the bias for this visible node -- input
         );
     // 'gibbs_hvh' -- gibbs sample firstly from hidden node to visible node, then sample
     // from visiable node to hidden node. It is called by contrastive_divergence.
     void gibbs_hvh (int*,               // one input sample from hidden node, h0 -- input
         double*,                        // the output probability of visiable nodes -- output
         int*,                           // the calculated 0-1 state of visiable node -- output
         double*,                        // the output probability of reconstructed hidden node  h1 -- output
         int*                            // the calculated 0-1 state of reconstructed hidden node h1 -- output
         );
     // reconstruct the input visiable node by the trained RBM (so as to varify the RBM model)
     void reconstruct (int*,             // one input sample from visiable node
         double*                         // the reconstructed output by RBM model
         );
 };

主要添加了函数说明、参数说明、计算说明、调用关系等。