new code for back propagation for fnnlm

source/network/Main.cpp

@@ -35,7 +35,6 @@ using namespace samplefnnlm;
 
 int main( int argc, const char ** argv )
 {   
-    
     if(argc > 1 && !strcmp(argv[1], "-test"))
         1;//Test();
     else if(argc > 1 && !strcmp(argv[1], "-fnnlm"))
@@ -47,6 +46,8 @@ int main( int argc, const char ** argv )
         fprintf(stderr, "Or run this program with \"-fnnlm\" for sample FNNLM!\n");
     }
 
+    return 0;
+
     XNet net;
     XTensor a;
     XTensor b;

source/network/XNet.cpp

@@ -143,7 +143,7 @@ void XNet::Backward(XList &roots, XList &golds, LOSS_FUNCTION_NAME loss)
 
     /* back-propagation from output to input */
     for(int i = nodes.count - 1; i >= 0; i--){
-        XTensor * node = (XTensor*)nodes.Get(i);
+        XTensor * node = (XTensor*)nodes.Get(i);;
         if(node->visitMark == NODE_FINISHED)
             continue;
 

source/sample/fnnlm/FNNLM.cpp

@@ -57,7 +57,7 @@ void LoadArgs(int argc, const char ** argv, FNNModel &model);
 void Init(FNNModel &model);
 void Check(FNNModel &model);
 void Copy(FNNModel &tgt, FNNModel &src);
-void Clear(FNNModel &model);
+void Clear(FNNModel &model, bool isNodeGrad);
 void InitModelTensor1D(XTensor &tensor, int num, FNNModel &model);
 void InitModelTensor2D(XTensor &tensor, int rowNum, int colNum, FNNModel &model);
 void Train(const char * train, bool isShuffled, FNNModel &model);
@@ -230,16 +230,37 @@ void Copy(FNNModel &tgt, FNNModel &src)
     }
 }
 
-/* reset model parameters */
-void Clear(FNNModel &model)
+/* 
+reset model parameters 
+>> model - the model whose parameter (gradient) is set to 0
+>> isNodeGrad - indicates whether the tensor node keeps the 
+                gradient information
+*/
+void Clear(FNNModel &model, bool isNodeGrad)
 {
+    if (isNodeGrad) {
+        if(model.embeddingW.grad != NULL)
+            model.embeddingW.grad->SetZeroAll();
+        for (int i = 0; i < MAX_HIDDEN_NUM; i++) {
+            if(model.hiddenW[i].grad != NULL)
+                model.hiddenW[i].grad->SetZeroAll();
+            if(model.hiddenB[i].grad != NULL)
+                model.hiddenB[i].grad->SetZeroAll();
+        }
+        if(model.outputW.grad != NULL)
+            model.outputW.grad->SetZeroAll();
+        if(model.outputB.grad != NULL)
+            model.outputB.grad->SetZeroAll();
+    }
+    else {
         model.embeddingW.SetZeroAll();
-    for(int i = 0; i < MAX_HIDDEN_NUM; i++){
+        for (int i = 0; i < MAX_HIDDEN_NUM; i++) {
             model.hiddenW[i].SetZeroAll();
             model.hiddenB[i].SetZeroAll();
         }
         model.outputW.SetZeroAll();
         model.outputB.SetZeroAll();
+    }
 }
 
 /* 
@@ -401,7 +422,7 @@ void Train(const char * train, bool isShuffled, FNNModel &model)
                 FNNNet net;
 
                 /* gradident = 0 */
-                Clear(grad);
+                Clear(grad, false);
 
                 /* forward computation */
                 Forward(inputs, output, model, net);
@@ -413,6 +434,9 @@ void Train(const char * train, bool isShuffled, FNNModel &model)
                 Update(model, grad, learningRate, false);
             }
             else{
+                /* gradient = 0 */
+                Clear(model, true);
+
                 /* forward + backward process */
                 ForwardAutoDiff(inputs, output, model);
 
@@ -507,6 +531,9 @@ void Update(FNNModel &model, FNNModel &grad, float epsilon, bool isNodeGrad)
         XTensor * para = (XTensor*)paraList.GetItem(i);
         XTensor * paraGrad = (XTensor*)gradList.GetItem(i);
 
+        //fprintf(stderr, "%d\n", i);
+        //paraGrad->Dump(stderr, "grad:", 10);
+
         /* the delta rule */
         _Sum(para, paraGrad, para, -epsilon);
     }
@@ -936,14 +963,16 @@ void ForwardAutoDiff(XTensor inputs[], XTensor &output, FNNModel &model)
 
     /* hidden layers */
     for(int i = 0; i < depth; i++){
-        b = Unsqueeze(model.hiddenB[i], 1, batchSize);
-        hidden = MMul(hidden, model.hiddenW) + b;
+        b = Unsqueeze(model.hiddenB[i], 0, batchSize);
+        hidden = MMul(hidden, model.hiddenW[i]) + b;
     }
 
-    b = Unsqueeze(model.outputB, 1, batchSize);
+    b = Unsqueeze(model.outputB, 0, batchSize);
 
     /* output layer */
     output = LogSoftmax(MMul(hidden, model.outputW) + b, 1);
+
+    //XLink::ShowNetwork(stderr, &output);
 }
 
 /* 

source/tensor/XTensor.cpp

@@ -1439,6 +1439,21 @@ void XTensor::Dump(FILE * file, const char * label, const int n, const int verbo
 }
 
 /* 
+dump data to a file
+>> tensor - tensor whose data is dumped
+>> file - where to domp the data
+>> label - label of the tensor
+>> n - number of items to dump
+>> verbose - verbose level
+*/
+void XTensor::Dump(const XTensor * tensor, FILE * file, const char * label, const int n, const int verbose)
+{
+    XTensor a(tensor->order, tensor->dimSize, tensor->dataType, tensor->denseRatio, tensor->devID, tensor->mem);
+    _CopyValues(tensor, &a);
+    a.Dump(file, label, n, verbose);
+}
+
+/* 
 read data from a file
 >> file - where to load the data
 >> label - label of the tensor
@@ -1687,13 +1702,13 @@ void InitTensor(XTensor * tensor,
 
         dims[0] = -abs(dims[0]);
 
-        tensor->Resize(myOrder, dims, myDataType, myDenseRatio);
-
-        if(myDevID == CURRENT_GPU)
+        if (myDevID == CURRENT_GPU)
             tensor->devID = XDevice::GetGPUDevice();
         else
             tensor->devID = myDevID;
 
+        tensor->Resize(myOrder, dims, myDataType, myDenseRatio);
+
         if(allocated)
             XTensor::AllocateData(tensor);
     }

source/tensor/XTensor.h

@@ -328,6 +328,10 @@ public:
     /* dump data to a file */
     void Dump(FILE * file, const char * label = NULL, const int n = -1, const int verbose = 0);
 
+    /* dump data to a file */
+    static
+    void Dump(const XTensor * tensor, FILE * file, const char * label = NULL, const int n = -1, const int verbose = 0);
+
     /* read data from a file */
     void Read(FILE * file, const char * label = NULL);