use MatrixMul to replace MatrixMul2D

source/sample/fnnlm/FNNLM.cpp

@@ -468,7 +468,7 @@ void Update(FNNModel &model, FNNModel &grad, float epsilon)
         XTensor * paraGrad = (XTensor*)gradList.GetItem(i);
 
         /* the delta rule */
-        Sum(para, paraGrad, para, -epsilon);
+        _Sum(para, paraGrad, para, -epsilon);
     }
 }
   
@@ -707,7 +707,7 @@ void Forward(XTensor inputs[], XTensor &output, FNNModel &model, FNNNet &net)
            s = s + b
            NOTE: the trick here is to extend b to a 2d tensor
                  to fit into the 2d representation in tensor summation */
-        Sum(&s, &b2D, &s);
+        _Sum(&s, &b2D, &s);
 
         /* pass the state through the hard tanh function:
            h = tanh(s) */
@@ -735,11 +735,13 @@ void Forward(XTensor inputs[], XTensor &output, FNNModel &model, FNNNet &net)
         InitTensor(&b2D, &s);
         Unsqueeze(&b, &b2D, 0, batchSize);
 
-        Sum(&s, &b2D, &s);
+        _Sum(&s, &b2D, &s);
 
         /* y = softmax(s) */
         LogSoftmax(&s, &y, 1);
     }
+    
+    
 }
 
 /*
@@ -855,7 +857,7 @@ void Backward(XTensor inputs[], XTensor &output, XTensor &gold, LOSS_FUNCTION_NA
         /* gradient of the embedding weight: dE/dw += x^T * dE/dy 
            NOTE that we accumulate dE/dw here because the matrix w
            is shared by several layers (or words) */
-        MatrixMul2D(&x, X_TRANS, dedy, X_NOTRANS, &dedw, 1.0F, 1.0F);
+        MatrixMul(&x, X_TRANS, dedy, X_NOTRANS, &dedw, 1.0F, 1.0F);
 
         delete dedy;
     }