diff --git a/mindspore/nn/loss/loss.py b/mindspore/nn/loss/loss.py
index 629f351c95..1d022f79d5 100644
--- a/mindspore/nn/loss/loss.py
+++ b/mindspore/nn/loss/loss.py
@@ -238,17 +238,16 @@ class SoftmaxCrossEntropyWithLogits(_Loss):
     Measures the distribution error between the probabilities of the input (computed with softmax function) and the
     target where the classes are mutually exclusive (only one class is positive) using cross entropy loss.
 
-    Typical input into this function is unnormalized scores and target of each class.
-    Scores Tensor :math:`x` is of shape :math:`(N, C)` and target Tensor :math:`t` is a
-    Tensor of shape :math:`(N, C)` which contains one-hot labels of length :math:`C`.
+    Typical input into this function is unnormalized scores denoted as x whose shape is (N, C),
+    and the corresponding targets.
 
-    For each instance :math:`N_i`, the loss is given as:
+    For each instance :math:`x_i`, i ranges from 0 to N-1, the loss is given as:
 
     .. math::
-        \ell(x_i, t_i) = - \log\left(\frac{\exp(x_{t_i})}{\sum_j \exp(x_j)}\right)
-        =  -x_{t_i} + \log\left(\sum_j \exp(x_j)\right)
+        \ell(x_i, c) = - \log\left(\frac{\exp(x_i[c])}{\sum_j \exp(x_i[j])}\right)
+        =  -x_i[c] + \log\left(\sum_j \exp(x_i[j])\right)
 
-    where :math:`x_i` is a 1D score Tensor, :math:`t_i` is a scalar.
+    where :math:`x_i` is a 1D score Tensor, :math:`c` is the index of 1 in one-hot.
 
     Note:
         While the target classes are mutually exclusive, i.e., only one class is positive in the target, the predicted
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index 0139ea3132..8140fe3928 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -3487,8 +3487,8 @@ class NMSWithMask(PrimitiveWithInfer):
     Inputs:
         - **bboxes** (Tensor) - The shape of tensor is :math:`(N, 5)`. Input bounding boxes.
           `N` is the number of input bounding boxes. Every bounding box
-          contains 5 values, the first 4 values are the coordinates of bounding
-          box, and the last value is the score of this bounding box.
+          contains 5 values, the first 4 values are the coordinates(x0, y0, x1, y1) of bounding box which
+          represents the point of top-left and bottom-right, and the last value is the score of this bounding box.
           The data type must be float16 or float32.
 
     Outputs: