update

paddle/fluid/operators/conv_transpose_op.cc

@@ -156,7 +156,7 @@ Parameters(strides, paddings) are two elements. These two elements represent hei
 and width, respectively.
 The input(X) size and output(Out) size may be different.
 
-Example:
+For an example:
   Input:
        Input shape: $(N, C_{in}, H_{in}, W_{in})$
        Filter shape: $(C_{in}, C_{out}, H_f, W_f)$

paddle/fluid/operators/crf_decoding_op.cc

@@ -53,17 +53,14 @@ sequence of observed tags.
 The output of this operator changes according to whether Input(Label) is given:
 
 1. Input(Label) is given:
-
    This happens in training. This operator is used to co-work with the chunk_eval
    operator.
-
    When Input(Label) is given, the crf_decoding operator returns a row vector
    with shape [N x 1] whose values are fixed to be 0, indicating an incorrect
    prediction, or 1 indicating a tag is correctly predicted. Such an output is the
    input to chunk_eval operator.
 
 2. Input(Label) is not given:
-
    This is the standard decoding process.
 
 The crf_decoding operator returns a row vector with shape [N x 1] whose values

paddle/fluid/operators/roi_pool_op.cc

@@ -149,7 +149,9 @@ The operator has three steps:
 
 1. Dividing each region proposal into equal-sized sections with
    the pooled_width and pooled_height
+
 2. Finding the largest value in each section
+
 3. Copying these max values to the output buffer
 
 ROI Pooling for Faster-RCNN. The link below is a further introduction: 

python/paddle/fluid/layers/io.py

@@ -109,8 +109,6 @@ class BlockGuardServ(BlockGuard):
 
 class ListenAndServ(object):
     """
-    ListenAndServ layer.
-
     ListenAndServ is used to create a rpc server bind and listen
     on specific TCP port, this server will run the sub-block when
     received variables from clients.
@@ -121,6 +119,9 @@ class ListenAndServ(object):
         fan_in(int): how many client are expected to report to this server, default: 1.
         optimizer_mode(bool): whether to run the server as a parameter server, default: True.
     
+    Returns:
+        None
+
     Examples:
         .. code-block:: python
 

python/paddle/fluid/layers/nn.py

@@ -806,7 +806,7 @@ def crf_decoding(input, param_attr, label=None):
         label(${label_type}): ${label_comment}
 
     Returns:
-        ${viterbi_path_comment}
+        Variable: ${viterbi_path_comment}
     """
     helper = LayerHelper('crf_decoding', **locals())
     transition = helper.get_parameter(param_attr.name)
@@ -828,7 +828,7 @@ def cos_sim(X, Y):
 
     Args:
         X (Variable): ${x_comment}
-        Y (Variable): ${x_comment}
+        Y (Variable): ${y_comment}
     
     Returns:
         Variable: the output of cosine(X, Y).
@@ -1036,9 +1036,9 @@ def chunk_eval(input,
         excluded_chunk_types (list): ${excluded_chunk_types_comment}
     
     Returns:
-        tuple: tuple containing: (precision, recall, f1_score,
-               num_infer_chunks, num_label_chunks,
-               num_correct_chunks)
+        tuple: tuple containing: precision, recall, f1_score,
+        num_infer_chunks, num_label_chunks,
+        num_correct_chunks
     """
     helper = LayerHelper("chunk_eval", **locals())
 
@@ -3050,8 +3050,6 @@ def nce(input,
 
 def transpose(x, perm, name=None):
     """
-    **transpose Layer**
-
     Permute the dimensions of `input` according to `perm`.
 
     The `i`-th dimension  of the returned tensor will correspond to the
@@ -3918,7 +3916,7 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
         spatial_scale (float): ${spatial_scale_comment} Default: 1.0
 
     Returns:
-        roi_pool (Variable): ${out_comment}.
+        Variable: ${out_comment}.
 
     Examples:
         .. code-block:: python