amend comments in cross_entropy_op

paddle/operators/cross_entropy_op.cc

@@ -114,15 +114,15 @@ class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
   CrossEntropyOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X",
-             "(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, "
-             "where N is the batch size and D is the number of classes. "
+             "(Tensor, default Tensor<float>), a 2-D tensor with shape [N x D],"
+             " where N is the batch size and D is the number of classes. "
              "This input is a probability computed by the previous operator, "
              "which is almost always the result of a softmax operator.");
     AddInput("Label",
              "(Tensor), the ground truth which is a 2-D tensor. When "
              "soft_label is set to false, Label is a Tensor<int64> with shape "
              "[N x 1]. When soft_label is set to true, Label is a "
-             "Tensor<float/double> with shape [N x K].");
+             "Tensor<float/double> with shape [N x D].");
     AddOutput("Y",
               "(Tensor, default Tensor<float>), a 2-D tensor with shape "
               "[N x 1]. The cross entropy loss.");

python/paddle/v2/fluid/layers/nn.py

@@ -365,47 +365,47 @@ def cross_entropy(input, label, **kwargs):
     both standard cross-entropy and soft-label cross-entropy loss computation.
 
     1) One-hot cross-entropy:
-	`soft_label = false`, `Label[i, 0]` indicates the class index for sample i:
+	`soft_label = False`, `Label[i, 0]` indicates the class index for sample i:
         
         .. math::
           
             Y[i] = -\log(X[i, Label[i]])
 
     2) Soft-label cross-entropy:
-	`soft_label = true`, `Label[i, j]` indicates the soft label of class j
+	`soft_label = True`, `Label[i, j]` indicates the soft label of class j
 	for sample i:
 
         .. math::
 
             Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}
 
-       Please make sure that in this case the summuation of each row of `label`
+       Please make sure that in this case the summation of each row of `label`
        equals one.
 
     3) One-hot cross-entropy with vecterized `label`:
 	 As a special case of 2), when each row of 'label' has only one
-	 non-zero element (equals 1), soft-label cross-entropy degenerates to a
-	 one-hot cross-entropy with one-hot label representation.
+	 non-zero element which is equal to 1, soft-label cross-entropy degenerates
+         to a one-hot cross-entropy with one-hot label representation.
     
     Args:
-        input (Variable|list):  a 2-D tensor with shape N x D, where N is the 
+        input (Variable|list):  a 2-D tensor with shape [N x D], where N is the 
             batch size and D is the number of classes. This input is a probability 
             computed by the previous operator, which is almost always the result
             of a softmax operator.
         label (Variable|list): the ground truth which is a 2-D tensor. When 
-              `soft_label` is set to `false`, `label` is a tensor<int64> with shape 
-              [N x 1]. When `soft_label` is set to `true`, `label` is a 
-              tensor<float/double> with shape [N x K].
+              `soft_label` is set to `False`, `label` is a tensor<int64> with shape 
+              [N x 1]. When `soft_label` is set to `True`, `label` is a 
+              tensor<float/double> with shape [N x D].
         soft_label (bool, via `**kwargs`): a flag indicating whether to interpretate
-              the given labels as soft labels, default `false`.
+              the given labels as soft labels, default `False`.
 
     Returns:
          A 2-D tensor with shape [N x 1], the cross entropy loss.
 
     Raises:
-        `ValueError`: 1) If the 1st dimension of `input` and `label` are not equal; 2) If \ 
-              `soft_label == true`, and the 2nd dimension of `input` and `label` are not \
-               equal; 3) If `soft_label == false`, and the 2nd dimension of `label` is not 1.
+        `ValueError`: 1) the 1st dimension of `input` and `label` are not equal; 2) when \ 
+              `soft_label == True`, and the 2nd dimension of `input` and `label` are not \
+               equal; 3) when `soft_label == False`, and the 2nd dimension of `label` is not 1.
 
     Examples:
         .. code-block:: python