update softmax layer comment

Author: JiayiFeng

python/paddle/fluid/layers/nn.py

@@ -1313,21 +1313,24 @@ def sequence_softmax(input, param_attr=None, bias_attr=None, use_cudnn=True):
 
 def softmax(input, param_attr=None, bias_attr=None, use_cudnn=True, name=None):
     """
-    The input of the softmax layer is a 2-D tensor with shape N x K (N is the
-    batch_size, K is the dimension of input feature). The output tensor has the
-    same shape as the input tensor.
+    The input of the softmax operator is a tensor of any rank. The output tensor 
+    has the same shape as the input.
 
-    For each row of the input tensor, the softmax operator squashes the
-    K-dimensional vector of arbitrary real values to a K-dimensional vector of real
-    values in the range [0, 1] that add up to 1.
+    The input tensor will first be logically flattened to a 2-D matrix. The matrix's 
+    second dimension(row length) is as same as the last dimension of the input 
+    tensor, and the first dimension(column length) is the product of all other 
+    dimensions of the input tensor. For each row of the matrix, the softmax operator 
+    squashes the K-dimensional(K is the width of the matrix, which is also the size 
+    of the input tensor's last dimension) vector of arbitrary real values to a 
+    K-dimensional vector of real values in the range [0, 1] that add up to 1.
 
     It computes the exponential of the given dimension and the sum of exponential
     values of all the other dimensions in the K-dimensional vector input.
     Then the ratio of the exponential of the given dimension and the sum of
     exponential values of all the other dimensions is the output of the softmax
     operator.
 
-    For each row :math:`i` and each column :math:`j` in Input(X), we have:
+    For each row :math:`i` and each column :math:`j` in the matrix, we have:
 
     .. math::