replace XTensor::IsIdentical with XTensor::IsSameShaped

source/network/XBackwardMath.cpp

@@ -96,7 +96,7 @@ void XMathGrad::GradMultiply(XTensor * node)
     XNoder::MakeGrad(a);
     XNoder::MakeGrad(b);
 
-    CheckNTErrors(XTensor::IsIdentical(a, b), "Wrong sized input tensors!");
+    CheckNTErrors(XTensor::IsSameShaped(a, b), "Wrong sized input tensors!");
     _Multiply(node->grad, b, a->grad, 1.0F);
     _Multiply(node->grad, a, b->grad, 1.0F);
 }

source/network/XBackwardShape.cpp

@@ -164,7 +164,7 @@ void XShapeGrad::GradMergeList(XTensor * node)
         smallsGrad.Add(tail->grad);
         
         if(i > 1){
-            CheckNTErrors(XTensor::IsIdentical(last, tail), 
+            CheckNTErrors(XTensor::IsSameShaped(last, tail), 
                          "Input tensors must be of the same size!");
         }
 

source/network/XNoder.cpp

@@ -29,7 +29,7 @@ void XNoder::MakeGrad(XTensor * node)
     if(node == NULL)
         return;
 
-    if(!XTensor::IsIdentical(node, node->grad)){
+    if(!XTensor::IsSameShaped(node, node->grad)){
         delete node->grad;
         node->grad = NewTensor(node);
         node->grad->SetZeroAll();

source/tensor/XTensor.cpp

@@ -370,7 +370,7 @@ judge whether the two matrices are in the same type and size
 >> b - anther tensor to compare with
 << return - whether the two input tensors are identical
 */
-bool XTensor::IsIdentical(const XTensor * a, const XTensor * b)
+bool XTensor::IsSameShaped(const XTensor * a, const XTensor * b)
 {
     if(a == NULL || b == NULL)
         return false;
@@ -402,9 +402,9 @@ judge whether the three matrices are in the same type and size
 >> c - a tensor again
 << return - whether the two input tensors are identical
 */
-bool XTensor::IsIdentical(XTensor * a, XTensor * b, XTensor * c)
+bool XTensor::IsSameShaped(XTensor * a, XTensor * b, XTensor * c)
 {
-    return IsIdentical(a, b) && IsIdentical(a, c);
+    return IsSameShaped(a, b) && IsSameShaped(a, c);
 }
 
 /* 

source/tensor/XTensor.h

@@ -207,11 +207,11 @@ public:
 
     /* judge whether the two matrices are in the same type and size */
     static
-    bool IsIdentical(const XTensor * a, const XTensor * b);
+    bool IsSameShaped(const XTensor * a, const XTensor * b);
 
     /* judge whether the three matrices are in the same type and size */
     static
-    bool IsIdentical(XTensor * a, XTensor * b, XTensor * c);
+    bool IsSameShaped(XTensor * a, XTensor * b, XTensor * c);
 
     /* set the size of each dimension */
     void SetDim(int * myDimSize);

source/tensor/core/arithmetic/Absolute.cpp

@@ -42,7 +42,7 @@ void _Absolute(const XTensor * a, XTensor * b)
 }
 #endif
 
-    CheckNTErrors((XTensor::IsIdentical(a, b)), "Input tensors should have the same type!");
+    CheckNTErrors((XTensor::IsSameShaped(a, b)), "Input tensors should have the same type!");
     CheckNTErrors((a->dataType == DEFAULT_DTYPE), "TODO!");
     DTYPE * d = (DTYPE*)a->data;
     DTYPE * db = (DTYPE*)b->data;

source/tensor/core/arithmetic/Absolute.cu

@@ -63,7 +63,7 @@ set each entry to its absolute value
 extern "C"
 void _CudaAbsolute(const XTensor * a, XTensor * b)
 {
-    CheckNTErrors((XTensor::IsIdentical(a, b)), "Input tensors should have the same type!");
+    CheckNTErrors((XTensor::IsSameShaped(a, b)), "Input tensors should have the same type!");
     CheckNTErrors((a->isSparse == false), "TODO!");
 
     int gridSize[3];

source/tensor/core/arithmetic/MatrixMULBatchedCPU.cpp

@@ -55,9 +55,9 @@ void _MatrixMULBatchedCPU(const XList * a, MATRIX_TRANS_TYPE transposedA,
         XTensor * ai = (XTensor*)a->GetItem(i);
         XTensor * bi = (XTensor*)b->GetItem(i);
         XTensor * ci = (XTensor*)c->GetItem(i);
-        if (!XTensor::IsIdentical(aim, ai) ||
-            !XTensor::IsIdentical(bim, bi) ||
-            !XTensor::IsIdentical(cim, ci))
+        if (!XTensor::IsSameShaped(aim, ai) ||
+            !XTensor::IsSameShaped(bim, bi) ||
+            !XTensor::IsSameShaped(cim, ci))
         {
             isUniform = false;
             break;

source/tensor/core/arithmetic/Negate.cpp

@@ -41,7 +41,7 @@ void _Negate(const XTensor * a, XTensor * b)
     }
 #endif
 
-    CheckNTErrors((XTensor::IsIdentical(a, b)), "Input tensors should have the same type!");
+    CheckNTErrors((XTensor::IsSameShaped(a, b)), "Input tensors should have the same type!");
     CheckNTErrors((a->dataType == DEFAULT_DTYPE), "TODO!");
     DTYPE * d = (DTYPE*)a->data;
     DTYPE * db = (DTYPE*)b->data;

source/tensor/core/arithmetic/Negate.cu

@@ -71,7 +71,7 @@ set each entry to its negtive value
 extern "C"
 void _CudaNegate(const XTensor * a, XTensor * b)
 {
-    CheckNTErrors((XTensor::IsIdentical(a, b)), "Input tensors should have the same type!");
+    CheckNTErrors((XTensor::IsSameShaped(a, b)), "Input tensors should have the same type!");
     CheckNTErrors((a->isSparse == false), "TODO!");
 
     int gridSize[3];

source/tensor/core/arithmetic/Sign.cpp

@@ -41,7 +41,7 @@ void _Sign(const XTensor * a, XTensor * b)
 }
 #endif
 
-    CheckNTErrors((XTensor::IsIdentical(a, b)), "Input tensors should have the same type!");
+    CheckNTErrors((XTensor::IsSameShaped(a, b)), "Input tensors should have the same type!");
     CheckNTErrors((a->dataType == DEFAULT_DTYPE), "TODO!");
     DTYPE * d = (DTYPE*)a->data;
     DTYPE * db = (DTYPE*)b->data;

source/tensor/core/arithmetic/Sign.cu

@@ -69,7 +69,7 @@ set each entry to its sign value
 extern "C"
 void _CudaSign(const XTensor * a, XTensor * b)
 {
-    CheckNTErrors((XTensor::IsIdentical(a, b)), "Input tensors should have the same type!");
+    CheckNTErrors((XTensor::IsSameShaped(a, b)), "Input tensors should have the same type!");
     CheckNTErrors((a->isSparse == false), "TODO!");
 
     int gridSize[3];

source/tensor/core/arithmetic/SumByColumnTV.cpp

@@ -40,7 +40,7 @@ where b is a vector.
 void _SumByColumnTV(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta)
 {
     CheckNTErrors((a && b && c), "Empty input tensors!");
-    CheckNTErrors((XTensor::IsIdentical(a, c)), "Unmatched tensors in addition!");
+    CheckNTErrors((XTensor::IsSameShaped(a, c)), "Unmatched tensors in addition!");
     CheckNTErrors((b->order == 2 && b->dimSizeRDI[0] == 1 && b->dimSizeRDI[1] == a->dimSizeRDI[1]),
         "Illegal input vector size!");
 

source/tensor/core/arithmetic/SumByColumnTV.cu

@@ -67,7 +67,7 @@ where b is a vector.
 void _CudaSumByColumnTV(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta)
 {
     CheckNTErrors((a && b && c), "Empty input tensors!");
-    CheckNTErrors((XTensor::IsIdentical(a, c)), "Unmatched tensors in addition!");
+    CheckNTErrors((XTensor::IsSameShaped(a, c)), "Unmatched tensors in addition!");
     CheckNTErrors((b->order == 2 && b->dimSizeRDI[0] == 1 && b->dimSizeRDI[1] == a->dimSizeRDI[1]),
         "Illegal input vector size!");
     CheckNTErrors((a->dataType == DEFAULT_DTYPE && b->dataType == DEFAULT_DTYPE &&

source/tensor/core/arithmetic/SumByColumnVT.cpp

@@ -40,7 +40,7 @@ where c and a are vectors, and b_col is a column in b.
 void _SumByColumnVT(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta)
 {
     CheckNTErrors((a && b && c), "Empty input tensors!");
-    CheckNTErrors((XTensor::IsIdentical(a, c)), "Unmatched tensors in addition!");
+    CheckNTErrors((XTensor::IsSameShaped(a, c)), "Unmatched tensors in addition!");
     CheckNTErrors((a->order == 2 && a->dimSizeRDI[0] == 1 && b->dimSizeRDI[1] == a->dimSizeRDI[1]),
         "Illegal input vector size!");
 

source/tensor/core/arithmetic/SumByColumnVT.cu

@@ -83,7 +83,7 @@ where c and a are vectors, and b_col is a column in b.
 void _CudaSumByColumnVT(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta)
 {
     CheckNTErrors((a && b && c), "Empty input tensors!");
-    CheckNTErrors((XTensor::IsIdentical(a, c)), "Unmatched tensors in addition!");
+    CheckNTErrors((XTensor::IsSameShaped(a, c)), "Unmatched tensors in addition!");
     CheckNTErrors((a->order == 2 && a->dimSizeRDI[0] == 1 && b->dimSizeRDI[1] == a->dimSizeRDI[1]),
         "Illegal input vector size!");
     CheckNTErrors((a->dataType == DEFAULT_DTYPE && b->dataType == DEFAULT_DTYPE &&

source/tensor/core/arithmetic/XTensorBLAS.cu

@@ -225,9 +225,9 @@ void _CudaBLASMatrixMULList(cublasHandle_t * handle,
         XTensor * ai = (XTensor*)a->GetItem(i);
         XTensor * bi = (XTensor*)b->GetItem(i);
         XTensor * ci = (XTensor*)c->GetItem(i);
-        if (!XTensor::IsIdentical(aim, ai) ||
-            !XTensor::IsIdentical(bim, bi) ||
-            !XTensor::IsIdentical(cim, ci))
+        if (!XTensor::IsSameShaped(aim, ai) ||
+            !XTensor::IsSameShaped(bim, bi) ||
+            !XTensor::IsSameShaped(cim, ci))
         {
             isUniform = false;
             break;

source/tensor/core/math/Log.cpp

@@ -42,7 +42,7 @@ void _Log(const XTensor * a, XTensor * b)
     }
 #endif
 
-    CheckNTErrors((XTensor::IsIdentical(a, b)), "Input tensors should have the same type!");
+    CheckNTErrors((XTensor::IsSameShaped(a, b)), "Input tensors should have the same type!");
     CheckNTErrors((a->dataType == DEFAULT_DTYPE), "TODO!");
     DTYPE * d = (DTYPE*)a->data;
     DTYPE * db = (DTYPE*)b->data;

source/tensor/core/math/Log.cu

@@ -63,7 +63,7 @@ set each entry to its log value
 extern "C"
 void _CudaLog(const XTensor * a, XTensor * b)
 {
-    CheckNTErrors((XTensor::IsIdentical(a, b)), "Input tensors should have the same type!");
+    CheckNTErrors((XTensor::IsSameShaped(a, b)), "Input tensors should have the same type!");
     CheckNTErrors((a->isSparse == false), "TODO!");
 
     int gridSize[3];

source/tensor/core/math/Normalize.cpp

@@ -45,9 +45,9 @@ where a and b are the scalar and bias respectively, and \epsilon is the adjustme
 void _Normalize(const XTensor * input, XTensor * output, int dim, const XTensor * mean, const XTensor * var, const XTensor * a, const XTensor * b, DTYPE epsilon)
 {
 	int dimRDI = input->order - dim - 1;
-    CheckNTErrors((XTensor::IsIdentical(input, output)), "Unmatched input tensors!");
-    CheckNTErrors((XTensor::IsIdentical(a, b)), "Unmatched input tensors");
-    CheckNTErrors((XTensor::IsIdentical(mean, var)), "Unmatched input tensors");
+    CheckNTErrors((XTensor::IsSameShaped(input, output)), "Unmatched input tensors!");
+    CheckNTErrors((XTensor::IsSameShaped(a, b)), "Unmatched input tensors");
+    CheckNTErrors((XTensor::IsSameShaped(mean, var)), "Unmatched input tensors");
     CheckNTErrors((input && output && mean && var && a && b), "Empty input tensors!");
     CheckNTErrors((dimRDI >= 0 && dimRDI < input->order), "Incorrect reduction dimension!");
     CheckNTErrors((dimRDI == a->order - 1), "Incorrect reduction dimension!");

source/tensor/core/math/Power.cu

@@ -103,7 +103,7 @@ void KernelPower(__half * a, __half * b, __half p, int size)
 extern "C"
 void _CudaPower(const XTensor * a, XTensor * b, DTYPE p)
 {
-    CheckNTErrors((XTensor::IsIdentical(a, b)), "Input tensors should have the same type!");
+    CheckNTErrors((XTensor::IsSameShaped(a, b)), "Input tensors should have the same type!");
     
     int gridSize[3];
     int blockSize[3];

source/tensor/core/movement/CopyInGrid.cpp

@@ -38,7 +38,7 @@ in the k-th grid
 */
 void _CopyInGrid(const XTensor * s, XTensor * t, int * index, int blockDim, int blockNumInGrid, bool isIndexOnDev)
 {
-    CheckNTErrors((XTensor::IsIdentical(s, t)), "Unmatched tensors!");
+    CheckNTErrors((XTensor::IsSameShaped(s, t)), "Unmatched tensors!");
 
     int blockDimRDI = s->order - blockDim - 1;
     int blockSize = 1;

source/tensor/core/reduce/ReduceSum.cpp

@@ -48,7 +48,7 @@ void _ReduceSum(const XTensor * input, XTensor * output, int dim, const XTensor
     CheckNTErrors((input->order == output->order + 1), "Incorrect tensor sizes!");
     CheckNTErrors((input->order > dim && dim >=0), "Illegal dimension to reduce!");
     CheckNTErrors((input->dataType == output->dataType), "Unmatched data types!");
-    CheckNTErrors((shift == NULL || XTensor::IsIdentical(output, shift)), "Incorrect shift tensor size!");
+    CheckNTErrors((shift == NULL || XTensor::IsSameShaped(output, shift)), "Incorrect shift tensor size!");
 
 	int dimRDI = input->order - dim - 1;
     for(int i = 0; i < input->order; i++){

source/tensor/core/shape/Concatenate.cpp

@@ -44,7 +44,7 @@ void _Concatenate(const XList * smalls, XTensor * big, int dim)
         XTensor * a = (XTensor*)smalls->GetItem(i - 1);
         XTensor * b = (XTensor*)smalls->GetItem(i);
         CheckNTErrors((a && b), "Empty input tensors!");
-        if (!XTensor::IsIdentical(a, b))
+        if (!XTensor::IsSameShaped(a, b))
             uniform = false;
     }
 
@@ -76,7 +76,7 @@ XTensor Concatenate(const XList &smalls, int dim)
         XTensor * a = (XTensor*)smalls.GetItem(i - 1);
         XTensor * b = (XTensor*)smalls.GetItem(i);
         CheckNTErrors((a && b), "Empty input tensors!");
-        if (!XTensor::IsIdentical(a, b))
+        if (!XTensor::IsSameShaped(a, b))
             uniform = false;
     }
     XTensor * tensor = (XTensor*)smalls.GetItem(0);
@@ -177,7 +177,7 @@ XTensor Concatenate(const XTensor &smallA, const XTensor &smallB, int dim)
         XTensor * a = (XTensor*)smalls.Get(i - 1);
         XTensor * b = (XTensor*)smalls.Get(i);
         CheckNTErrors((a && b), "Empty input tensors!");
-        if (!XTensor::IsIdentical(a, b))
+        if (!XTensor::IsSameShaped(a, b))
             uniform = false;
     }
     XTensor * tensor = (XTensor*)smalls.Get(0);

source/tensor/core/shape/Merge.cpp

@@ -356,7 +356,7 @@ merge two tensors into a big tensor (return a XTensor structure)
 */
 XTensor Merge(const XTensor &smallA, const XTensor &smallB, int whereToMerge)
 {
-    CheckNTErrors(XTensor::IsIdentical(&smallA, &smallB), 
+    CheckNTErrors(XTensor::IsSameShaped(&smallA, &smallB), 
                  "The two tensors must be of the same size!");
 
     int order = smallA.order;

source/tensor/core/sort/Sort.cpp

@@ -36,7 +36,7 @@ sort the tensor along a given dimension
 */
 void _Sort(const XTensor * a, XTensor * b, XTensor * index, int dim)
 {
-    CheckNTErrors((XTensor::IsIdentical(a, b)), "Input tensors should have the same type!");
+    CheckNTErrors((XTensor::IsSameShaped(a, b)), "Input tensors should have the same type!");
     CheckNTErrors((dim >= 0 && dim < a->order), "Incorrect dimension specified!");
     CheckNTErrors((a->order == index->order), "Unmatched input tensors!");
     CheckNTErrors((index->dataType == X_INT), "Wrong data type!");

source/tensor/function/HardTanH.cpp

@@ -106,7 +106,7 @@ void _HardTanHBackward(XTensor * gold, XTensor * y, XTensor * x,
                        XTensor * dedy, XTensor * dedx,
                        LOSS_FUNCTION_NAME lossName)
 {
-    CheckNTErrors((gold == NULL || XTensor::IsIdentical(gold, y)), 
+    CheckNTErrors((gold == NULL || XTensor::IsSameShaped(gold, y)), 
                    "The tensors must be of the same size!");
 
 #ifdef USE_CUDA

source/tensor/function/Identity.cpp

@@ -72,7 +72,7 @@ void _IdentityBackward(XTensor * gold, XTensor * y, XTensor * x,
                        XTensor * dedy, XTensor * dedx,
                        LOSS_FUNCTION_NAME lossName)
 {
-    CheckNTErrors((gold == NULL || XTensor::IsIdentical(gold, y)), 
+    CheckNTErrors((gold == NULL || XTensor::IsSameShaped(gold, y)), 
                   "The tensors must be of the same size!");
 
     if(x->dataType == DEFAULT_DTYPE && y->dataType == DEFAULT_DTYPE)

source/tensor/function/LogSoftmax.cpp

@@ -309,7 +309,7 @@ void _LogSoftmaxBackward(XTensor * gold, XTensor * y, XTensor * x,
                 }
             }
             else {
-                CheckNTErrors((XTensor::IsIdentical(gold, y)), "The tensors must be of the same size!");
+                CheckNTErrors((XTensor::IsSameShaped(gold, y)), "The tensors must be of the same size!");
                 for (int k = 0; k < blockNum; k++) {
                     gp = (DTYPE*)gold->data + k * blockSize;
                     op = (DTYPE*)y->data + k * blockSize;
@@ -363,7 +363,7 @@ void _LogSoftmaxBackward(XTensor * gold, XTensor * y, XTensor * x,
                 }
             }
             else {
-                CheckNTErrors((XTensor::IsIdentical(gold, y)), "The tensors must be of the same size!");
+                CheckNTErrors((XTensor::IsSameShaped(gold, y)), "The tensors must be of the same size!");
                 for (int k = 0; k < blockNum; k++) {
                     gp = (DTYPE*)gold->data + k * blockSize;
                     op = (DTYPE*)y->data + k * blockSize;

source/tensor/function/LogSoftmax.cu

@@ -409,7 +409,7 @@ void _CudaLogSoftmaxBackward(XTensor * gold, XTensor * y, XTensor * x,
                                                            dedx->dimSize[0], dedx->dimSize[1], gold->unitNumNonZero, lossName);
             }
             else {
-                CheckNTErrors((XTensor::IsIdentical(gold, y)), "The tensors must be of the same size!");
+                CheckNTErrors((XTensor::IsSameShaped(gold, y)), "The tensors must be of the same size!");
 
                 for (int k = 0; k < blockNum; k++) {
                     GDevs.GetCudaThread(x->devID, blockSize, cudaGridSize, cudaBlockSize);

source/tensor/function/Loss.cpp

@@ -48,7 +48,7 @@ DTYPE _LossCompute(XTensor * gold, XTensor * output, LOSS_FUNCTION_NAME LFName,
     DTYPE error = 0.0F;
     if (output->devID < 0) {
         CheckNTErrors((gLen >= 0 && gLen <= output->unitNum), "Illegal input length!");
-        CheckNTErrors((XTensor::IsIdentical(gold, output)), "The input tensors must be of the same size!");
+        CheckNTErrors((XTensor::IsSameShaped(gold, output)), "The input tensors must be of the same size!");
         CheckNTErrors((gold->dimSizeRDI[0] == 1 && output->dimSizeRDI[0] == 1), "TODO!");
         CheckNTErrors((gold->order > leadDim && leadDim >= 0), "Illegal leading dimension!");
         CheckNTErrors((gold->dataType == DEFAULT_DTYPE && output->dataType == DEFAULT_DTYPE),
@@ -206,7 +206,7 @@ DTYPE _LossComputeForLogScale(XTensor * gold, XTensor * output,
                              int leadDim, int gBeg, int gLen, int oBeg)
 {
     CheckNTErrors(gLen >= 0 && gLen <= output->unitNum, "Illegal input length!");
-    CheckNTErrors(XTensor::IsIdentical(gold, output), "The input tensors must be of the same size!");
+    CheckNTErrors(XTensor::IsSameShaped(gold, output), "The input tensors must be of the same size!");
     CheckNTErrors(gold->dimSizeRDI[0] == 1 && output->dimSizeRDI[0] == 1, "TODO!");
     CheckNTErrors(gold->order > leadDim && leadDim >= 0, "Illegal leading dimension!");
     CheckNTErrors(gold->dataType == DEFAULT_DTYPE && output->dataType == DEFAULT_DTYPE, "TODO!");
@@ -402,9 +402,10 @@ void _LossBackward(XTensor * dedy, XTensor * t, XTensor * y,
     
     if (y->devID < 0) {
         CheckNTErrors(tLen <= y->unitNum, "Illegal input length!");
-        CheckNTErrors(XTensor::IsIdentical(t, y)&& XTensor::IsIdentical(dedy, y),
+        CheckNTErrors(XTensor::IsSameShaped(t, y)&& XTensor::IsSameShaped(dedy, y),
                      "The input tensors must be of the same size!");
-        CheckNTErrors((dedy->devID == t->devID) && (dedy->devID == y->devID), "Tensor must be on the same device!");
+        CheckNTErrors((dedy->devID == t->devID) && (dedy->devID == y->devID),
+                     "Tensor must be on the same device!");
         CheckNTErrors(t->order > leadDim, "Illegal leading dimension!");
         CheckNTErrors(t->dataType == DEFAULT_DTYPE && y->dataType == DEFAULT_DTYPE, "TODO!");
 

source/tensor/function/Loss.cu

@@ -55,7 +55,7 @@ DTYPE _CudaLossCompute(XTensor * gold, XTensor * y, LOSS_FUNCTION_NAME LFName,
                       bool isLogOutput, int leadDim, int gBeg, int gLen, int yBeg)
 {
     CheckNTErrors((gLen >= 0 && gLen <= y->unitNum), "Illegal input length!");
-    CheckNTErrors((XTensor::IsIdentical(gold, y)), "The input tensors must be of the same size!");
+    CheckNTErrors((XTensor::IsSameShaped(gold, y)), "The input tensors must be of the same size!");
     CheckNTErrors((gold->dimSizeRDI[0] == 1 && y->dimSizeRDI[0] == 1), "TODO!");
     CheckNTErrors((gold->order > leadDim && leadDim >= 0), "Illegal leading dimension!");
     CheckNTErrors((gold->dataType == DEFAULT_DTYPE && y->dataType == DEFAULT_DTYPE),
@@ -333,9 +333,10 @@ void _CudaLossBackward(XTensor * dedy, XTensor * t, XTensor * y,
                       int leadDim, int tBeg, int tLen, int yBeg)
 {
     CheckNTErrors((tLen <= y->unitNum), "Illegal input length!");
-    CheckNTErrors((XTensor::IsIdentical(t, y)&& XTensor::IsIdentical(dedy, y)), 
+    CheckNTErrors((XTensor::IsSameShaped(t, y)&& XTensor::IsSameShaped(dedy, y)), 
                   "The input tensors must be of the same size!");
-    CheckNTErrors(((dedy->devID == t->devID) && (dedy->devID == y->devID)), "Tensor must be on the same device!");
+    CheckNTErrors(((dedy->devID == t->devID) && (dedy->devID == y->devID)), 
+                  "Tensor must be on the same device!");
     CheckNTErrors((t->order > leadDim), "Illegal leading dimension!");
     CheckNTErrors((t->dataType == DEFAULT_DTYPE && 
                    y->dataType == DEFAULT_DTYPE && 

source/tensor/function/Rectify.cpp

@@ -103,7 +103,7 @@ void _RectifyBackward(XTensor * gold, XTensor * y, XTensor * x,
                       XTensor * dedy, XTensor * dedx,
                       LOSS_FUNCTION_NAME lossName)
 {
-    CheckNTErrors((gold == NULL || XTensor::IsIdentical(gold, y)), 
+    CheckNTErrors((gold == NULL || XTensor::IsSameShaped(gold, y)), 
                   "The tensors must be of the same size!");
 
 #ifdef USE_CUDA

source/tensor/function/Sigmoid.cpp

@@ -94,7 +94,7 @@ void _SigmoidBackward(XTensor * gold, XTensor * y, XTensor * x,
                       XTensor * dedy, XTensor * dedx,
                       LOSS_FUNCTION_NAME lossName)
 {
-    CheckNTErrors((gold == NULL || XTensor::IsIdentical(gold, y)), 
+    CheckNTErrors((gold == NULL || XTensor::IsSameShaped(gold, y)), 
                   "The tensors must be of the same size!");
 
 #ifdef USE_CUDA

source/tensor/function/Softmax.cpp

@@ -230,7 +230,7 @@ void _SoftmaxBackward(XTensor * gold, XTensor * y, XTensor * x,
                 }
             }
             else{
-                CheckNTErrors((XTensor::IsIdentical(gold, y)), "The tensors must be of the same size!");
+                CheckNTErrors((XTensor::IsSameShaped(gold, y)), "The tensors must be of the same size!");
                 for(int k = 0; k < blockNum; k++){
                     gp = (DTYPE*)gold->data + k * blockSize;
                     op = (DTYPE*)y->data + k * blockSize;
@@ -269,7 +269,7 @@ void _SoftmaxBackward(XTensor * gold, XTensor * y, XTensor * x,
                 }
             }
             else{
-                CheckNTErrors((XTensor::IsIdentical(gold, y)), "The tensors must be of the same size!");
+                CheckNTErrors((XTensor::IsSameShaped(gold, y)), "The tensors must be of the same size!");
                 for(int k = 0; k < blockNum; k++){
                     gp = (DTYPE*)gold->data + k * blockSize;
                     op = (DTYPE*)y->data + k * blockSize;

source/tensor/function/Softmax.cu

@@ -167,7 +167,7 @@ void _CudaSoftmaxSumMax(const XTensor * x, XTensor * y, int leadDim, XTensor * s
 {
     CheckNTErrors((x->devID >= 0), "Forward computation of softmax must be run on GPUs.");
     CheckNTErrors((x->devID == y->devID), "Tensors used in softmax are not on the same GPU.");
-    CheckNTErrors((XTensor::IsIdentical(x, y)), "Input tensors must be of the same size!");
+    CheckNTErrors((XTensor::IsSameShaped(x, y)), "Input tensors must be of the same size!");
 
     int leadDimRDI = y->order - leadDim - 1;
     int dimensionSize = y->dimSizeRDI[leadDimRDI];