some small refines.

paddle/operators/mul_op.cc

@@ -81,18 +81,18 @@ class MulOpMaker : public framework::OpProtoAndCheckerMaker {
         "(int, default 1) "
         R"DOC(The mul_op can take tensors with more than two dimensions as its
               inputs. If the input `X` is a tensor with more than two
-              dimensions, `X` will be flatten into a two-dimensional matrix
-              first. The flatten rule is: the first `num_col_dims` will be
-              flatten to form the first dimension of the matrix (height of the
-              matrix), and the rest `rank(X) - num_col_dims` dimensions are
-             flattened to form the second dimension of the matrix (width of the
-             matrix). As a result, height of the flattened matrix is equal to
-             the product of `X`'s first `x_num_col_dims` dimensions' sizes,
-             and width of the flattened matrix is equal to the product of `X`'s
-             last `rank(x) - num_col_dims` dimensions' size.
-             For example, suppose `X` is a 6-dimensional tensor with the shape
-             [2, 3, 4, 5, 6], and `x_num_col_dims` = 3. Then, the flattened
-             matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
+              dimensions, `X` will be flattened into a two-dimensional matrix
+              first. The flattening rule is: the first `num_col_dims` will be
+              flattened to form the first dimension of the final matrix (height
+              of the matrix), and the rest `rank(X) - num_col_dims` dimensions
+              are flattened to form the second dimension of the final matrix (
+              width of the matrix). As a result, height of the flattened matrix
+              is equal to the product of `X`'s first `x_num_col_dims` dimensions'
+              sizes, and width of the flattened matrix is equal to the product
+              of `X`'s last `rank(x) - num_col_dims` dimensions' size.
+              For example, suppose `X` is a 6-dimensional tensor with the shape
+              [2, 3, 4, 5, 6], and `x_num_col_dims` = 3. Then, the flattened
+              matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
         )DOC")
         .SetDefault(1)
         .EqualGreaterThan(1);
@@ -102,14 +102,13 @@ class MulOpMaker : public framework::OpProtoAndCheckerMaker {
         R"DOC(The mul_op can take tensors with more than two dimensions as its
               inputs. If the input `Y` is a tensor with more than two
               dimensions, `Y` will be flatten into a two-dimensional matrix
-              first. The attribute `y_num_col_dims` is used to flatten `Y` into
-              a two-dimensional matrix. See the comments of `x_num_col_dims` for
-              more details.
+              first. The attribute `y_num_col_dims` determines how `Y` is
+              flattened. See comments of `x_num_col_dims` for more details.
         )DOC")
         .SetDefault(1)
         .EqualGreaterThan(1);
     AddComment(R"DOC(
-Mul Operator. 
+Mul Operator.
 
 This operator is used to perform matrix multiplication for input X and Y.
 

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

@@ -55,24 +55,27 @@ def fc(input,
        act: Activation to be applied to the output of the fully connected layer.
        name: Name/alias of the fully connected layer.
 
-    The fully connected can take multiple tensor as inputs. It creates a
-    variable (one for each input tensor) called weights which represents a
-    fully connected weight matrix from each input unit to each output unit.
-    The fully connected layer multiplies each input tensor with its coresponding
-    weight to produce an output Tensor. If multiple input tensors are given,
-    the results of multiple multiplications will be sumed up. If bias_attr is
-    not None, a biases variable will be created and added to the output.
-    Finally, if activation is not None, it will be applied to the output as well.
-
-    This process canbe formulated as follows:
+    The fully connected layer can take multiple tensors as its inputs. It
+    creates a variable (one for each input tensor) called weights for each input
+    tensor, which represents a fully connected weight matrix from each input
+    unit to each output unit. The fully connected layer multiplies each input
+    tensor with its coresponding weight to produce an output Tensor. If
+    multiple input tensors are given, the results of multiple multiplications
+    will be sumed up. If bias_attr is not None, a biases variable will be
+    created and added to the output. Finally, if activation is not None,
+    it will be applied to the output as well.
+
+    This process can be formulated as follows:
 
     .. math::
         Y = \sigma({\sum_{i=0}^{N-1}W_iX_i + b})
 
     where, :math:`N` is the number of input, :math:`X_i` is the input tensor,
-    :math`W` is the weights created by this layer, :math:`b` is the bias.
+    :math:`W` is the weights created by this layer, :math:`b` is the bias
+    created by this layer (if needed), :math:`\sigma` is the activation funtion.
 
     """
+
     helper = LayerHelper("fc", **locals())
 
     dtype = helper.input_dtype()