Diff: SRNN.cpp

Revision:

7:92ea6cefc6a5

Parent:

6:e97ccc643bf1

--- a/SRNN.cpp	Thu Feb 19 13:52:45 2015 +0000
+++ b/SRNN.cpp	Thu Feb 19 19:15:04 2015 +0000
@@ -168,30 +168,6 @@
     int row_mid_out = dim_signal;
     int col_mid_out = num_mid_neuron + 1;
 
-    /*
-    // 行列のアロケート
-    // 係数行列の更新量
-    float* dWin_mid  = new float[row_in_mid * col_in_mid];
-    float* dWmid_out = new float[row_mid_out * col_mid_out];
-
-    // 前回の更新量:慣性項に用いる.
-    float* prevdWin_mid  = new float[row_in_mid * col_in_mid];
-    float* prevdWmid_out = new float[row_mid_out * col_mid_out];
-
-    // float* norm_sample   = new float[len_seqence * dim_signal]; // 正規化したサンプル信号; 実際の学習は正規化した信号を用います.
-
-    // 出力層の信号
-    float* out_signal = new float[dim_signal];
-
-    // 入力層->中間層の信号和
-    float* in_mid_net = new float[num_mid_neuron];
-    // 中間層->出力層の信号和.
-    float* mid_out_net = new float[dim_signal];
-
-    // 誤差信号
-    float* sigma = new float[dim_signal];
-    */
-
     // 係数行列の初期化
     for (int i=0; i < row_in_mid; i++)
         for (int j=0; j < col_in_mid; j++)
@@ -230,7 +206,7 @@
                         );
         }
 
-        printf("ite:%d err:%f \r\n", iteration, squareError);
+        // printf("ite:%d err:%f \r\n", iteration, squareError);
 
         // 系列の末尾に到達していたら,最初からリセットする.
         if (seq == len_seqence && !end_flag) {
@@ -424,16 +400,6 @@
 
     }
 
-    // delete [] dWin_mid;
-    // delete [] dWmid_out;
-    // delete [] prevdWin_mid;
-    // delete [] prevdWmid_out;
-    // delete [] norm_sample;
-    // delete [] out_signal;
-    // delete [] in_mid_net;
-    // delete [] mid_out_net;
-    // delete [] sigma;
-
     return squareError;
 }