Refine api doc (#20037)

* refine doc, test=document_fix

* add API.spec,test=develop,test=document_fix

paddle/fluid/API.spec

@@ -204,7 +204,7 @@ paddle.fluid.layers.pad (ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], va
 paddle.fluid.layers.pad_constant_like (ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '95aa1972983f30fe9b5a3713e523e20f'))
 paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None)), ('document', '214f1dfbe95a628600bbe99e836319cf'))
 paddle.fluid.layers.roi_pool (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0)), ('document', '49368d724023a66b41b0071be41c0ba5'))
-paddle.fluid.layers.roi_align (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)), ('document', '9a7a3b88a4fae41d58d3ca9b10ba0591'))
+paddle.fluid.layers.roi_align (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)), ('document', 'dc2e2fa3d6e3d30de0a81e8ee70de733'))
 paddle.fluid.layers.dice_loss (ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,)), ('document', '7e8e4bf1f0f8612961ed113e8af8f0c5'))
 paddle.fluid.layers.image_resize (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1, 'NCHW')), ('document', 'd29d829607b5ff12924197a3ba296c89'))
 paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',)), ('document', 'bd97ebfe4bdf5110a5fcb8ecb626a447'))
@@ -232,7 +232,7 @@ paddle.fluid.layers.pow (ArgSpec(args=['x', 'factor', 'name'], varargs=None, key
 paddle.fluid.layers.stanh (ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.6666666666666666, 1.7159, None)), ('document', '1e1efad868714425da15c785dfb533a1'))
 paddle.fluid.layers.hard_sigmoid (ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None)), ('document', '607d79ca873bee40eed1c79a96611591'))
 paddle.fluid.layers.swish (ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'e0dc7bc66cba939033bc028d7a62c5f4'))
-paddle.fluid.layers.prelu (ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '2da40e447716338affebfe058d05d9a9'))
+paddle.fluid.layers.prelu (ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '1fadca6622c70bd33cc260817f4ff191'))
 paddle.fluid.layers.brelu (ArgSpec(args=['x', 't_min', 't_max', 'name'], varargs=None, keywords=None, defaults=(0.0, 24.0, None)), ('document', '49580538249a52c857fce75c94ad8af7'))
 paddle.fluid.layers.leaky_relu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(0.02, None)), ('document', '1eb3009c69060299ec87949ee0d4b9ae'))
 paddle.fluid.layers.soft_relu (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(40.0, None)), ('document', '6455afd2498b00198f53f83d63d6c6a4'))
@@ -271,11 +271,11 @@ paddle.fluid.layers.logical_or (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=
 paddle.fluid.layers.logical_xor (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '77ccf37b710c507dd97e03f08ce8bb29'))
 paddle.fluid.layers.logical_not (ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6e2fe8a322ec69811f6507d22acf8f9f'))
 paddle.fluid.layers.clip (ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0ce33756573c572da67302499455dbcd'))
-paddle.fluid.layers.clip_by_norm (ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '99a1b9012d9c4495efc89d69958c3be7'))
+paddle.fluid.layers.clip_by_norm (ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1fc6e217c7a6128df31b806c1a8067ff'))
 paddle.fluid.layers.mean (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '597257fb94d0597c404a6a5c91ab5258'))
 paddle.fluid.layers.mul (ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None)), ('document', '784b7e36cea88493f9e37a41b10fbf4d'))
 paddle.fluid.layers.sigmoid_cross_entropy_with_logits (ArgSpec(args=['x', 'label', 'ignore_index', 'name', 'normalize'], varargs=None, keywords=None, defaults=(-100, None, False)), ('document', '7637c974f2d749d359acae9062c4d96f'))
-paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '22df6542f3f9aa3f34c0c2dab5dc1d80'))
+paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '169882eb87fb693198e0153629134c22'))
 paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '26decdea9376b6b9a0d3432d82ca207b'))
 paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'f85b263b7b6698d000977529a28f202b'))
 paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '65c8362e48810b8226e311c5d046db51'))
@@ -412,30 +412,30 @@ paddle.fluid.layers.thresholded_relu (ArgSpec(args=['x', 'threshold'], varargs=N
 paddle.fluid.layers.prior_box (ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False)), ('document', '0fdf82762fd0a5acb2578a72771b5b44'))
 paddle.fluid.layers.density_prior_box (ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'flatten_to_2d', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, False, None)), ('document', '7a484a0da5e993a7734867a3dfa86571'))
 paddle.fluid.layers.multi_box_head (ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False)), ('document', 'fd58078fdfffd899b91f992ba224628f'))
-paddle.fluid.layers.bipartite_match (ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '080ce0d54d3f1950ad5a3a8e5ae529e9'))
+paddle.fluid.layers.bipartite_match (ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '409c351dee8a4a4ea02771dc691b49cb'))
 paddle.fluid.layers.target_assign (ArgSpec(args=['input', 'matched_indices', 'negative_indices', 'mismatch_value', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'e9685f32d21bec8c013626c0254502c5'))
 paddle.fluid.layers.detection_output (ArgSpec(args=['loc', 'scores', 'prior_box', 'prior_box_var', 'background_label', 'nms_threshold', 'nms_top_k', 'keep_top_k', 'score_threshold', 'nms_eta', 'return_index'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0, False)), ('document', '5485bcaceb0cde2695565a2ffd5bbd40'))
 paddle.fluid.layers.ssd_loss (ArgSpec(args=['location', 'confidence', 'gt_box', 'gt_label', 'prior_box', 'prior_box_var', 'background_label', 'overlap_threshold', 'neg_pos_ratio', 'neg_overlap', 'loc_loss_weight', 'conf_loss_weight', 'match_type', 'mining_type', 'normalize', 'sample_size'], varargs=None, keywords=None, defaults=(None, 0, 0.5, 3.0, 0.5, 1.0, 1.0, 'per_prediction', 'max_negative', True, None)), ('document', '14d1eeae0f41b6792be43c1c0be0589b'))
 paddle.fluid.layers.rpn_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'is_crowd', 'im_info', 'rpn_batch_size_per_im', 'rpn_straddle_thresh', 'rpn_fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.0, 0.5, 0.7, 0.3, True)), ('document', '651d98d51879dfa1bc1cd40391786a41'))
 paddle.fluid.layers.retinanet_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'gt_labels', 'is_crowd', 'im_info', 'num_classes', 'positive_overlap', 'negative_overlap'], varargs=None, keywords=None, defaults=(1, 0.5, 0.4)), ('document', 'fa1d1c9d5e0111684c0db705f86a2595'))
 paddle.fluid.layers.sigmoid_focal_loss (ArgSpec(args=['x', 'label', 'fg_num', 'gamma', 'alpha'], varargs=None, keywords=None, defaults=(2, 0.25)), ('document', 'aeac6aae100173b3fc7f102cf3023a3d'))
-paddle.fluid.layers.anchor_generator (ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None)), ('document', '0aaacaf9858b8270a8ab5b0aacdd94b7'))
+paddle.fluid.layers.anchor_generator (ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None)), ('document', 'd25e5e90f9a342764f32b5cd48657148'))
 paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,)), ('document', 'a82016342789ba9d85737e405f824ff1'))
 paddle.fluid.layers.generate_proposal_labels (ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random', 'is_cls_agnostic', 'is_cascade_rcnn'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True, False, False)), ('document', '69def376b42ef0681d0cc7f53a2dac4b'))
 paddle.fluid.layers.generate_proposals (ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None)), ('document', 'b7d707822b6af2a586bce608040235b1'))
 paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None), ('document', 'b319b10ddaf17fb4ddf03518685a17ef'))
 paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '72fca4a39ccf82d5c746ae62d1868a99'))
-paddle.fluid.layers.box_coder (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0)), ('document', '4c6225fc1a1c0b84955a8f0013008243'))
+paddle.fluid.layers.box_coder (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0)), ('document', '1d5144c3856673d05c29c752c7c8f821'))
 paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e308ce1661cb722b220a6f482f85b9e4'))
 paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gt_box', 'gt_label', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'gt_score', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', '400403175718d5a632402cdae88b01b8'))
 paddle.fluid.layers.yolo_box (ArgSpec(args=['x', 'img_size', 'anchors', 'class_num', 'conf_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ed56ff21536ca5c8ad418d0cfaf6a7b9'))
-paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9ddee76cb808db83768bf68010e39b2b'))
+paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '882c99ed2adad54f612a40275b881850'))
 paddle.fluid.layers.multiclass_nms (ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None)), ('document', 'f6e333d76922c6e564413b4d216c245c'))
 paddle.fluid.layers.multiclass_nms2 (ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'return_index', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, False, None)), ('document', 'be156186ee7a2ee56ab30b964acb15e5'))
 paddle.fluid.layers.retinanet_detection_output (ArgSpec(args=['bboxes', 'scores', 'anchors', 'im_info', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'nms_eta'], varargs=None, keywords=None, defaults=(0.05, 1000, 100, 0.3, 1.0)), ('document', '078d28607ce261a0cba2b965a79f6bb8'))
-paddle.fluid.layers.distribute_fpn_proposals (ArgSpec(args=['fpn_rois', 'min_level', 'max_level', 'refer_level', 'refer_scale', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6c023b9401214ae387a8b2d92638e5e4'))
-paddle.fluid.layers.box_decoder_and_assign (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'box_score', 'box_clip', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '3619a7847709f5868f5e929065947b38'))
-paddle.fluid.layers.collect_fpn_proposals (ArgSpec(args=['multi_rois', 'multi_scores', 'min_level', 'max_level', 'post_nms_top_n', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '80a75103e001ca1ba056fbbe0c6a19f3'))
+paddle.fluid.layers.distribute_fpn_proposals (ArgSpec(args=['fpn_rois', 'min_level', 'max_level', 'refer_level', 'refer_scale', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'be432c9b5f19ccba7aca38789ead29e4'))
+paddle.fluid.layers.box_decoder_and_assign (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'box_score', 'box_clip', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5203935538d06a6d47b8630ad80cb2b0'))
+paddle.fluid.layers.collect_fpn_proposals (ArgSpec(args=['multi_rois', 'multi_scores', 'min_level', 'max_level', 'post_nms_top_n', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '808fcca082e0040e2b77dbc53a0cf9d5'))
 paddle.fluid.layers.accuracy (ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None)), ('document', 'ef799022a6040597462ae2b3d2f1c407'))
 paddle.fluid.layers.auc (ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1)), ('document', '34b4575807f955f7e8698b8dead23858'))
 paddle.fluid.layers.exponential_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', 'eaf430c5a0380fb11bfe9a8922cd6295'))

paddle/fluid/operators/clip_by_norm_op.h

@@ -105,10 +105,11 @@ class ClipByNormOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
     AddInput("X",
-             "(Tensor) The input of clip_by_norm op."
+             "(Tensor) The input of clip_by_norm op and data type is float32."
              "The number of dimensions must be between [1, 9].");
     AddOutput("Out",
-              "(Tensor) The output of clip_by_norm op with shape as input(X)");
+              "(Tensor) The output of clip_by_norm op with shape as input(X)"
+              "The data type is float32.");
     AddAttr<float>("max_norm", "(float) The maximum norm value.");
     AddComment(R"DOC(
 ClipByNorm Operator.

paddle/fluid/operators/maxout_op.cc

@@ -25,11 +25,13 @@ class MaxOutOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput(
         "X",
-        "(Tensor) The input tensor of maxout operator. "
-        "The format of input tensor is NCHW. Where N is batch size, C is the "
-        "number of channels, H and W is the height and width of feature.");
+        "(Tensor) The input tensor of maxout operator with data type of "
+        "float32. The format of input tensor is NCHW. Where N is batch size,"
+        " C is the number of channels, H and W is the height and width of "
+        "feature.");
     AddOutput("Out",
               "(Tensor) The output tensor of maxout operator."
+              "The data type is float32."
               "The format of output tensor is also NCHW."
               "Where N is batch size, C is "
               "the number of channels, H and W is the height and "

paddle/fluid/operators/roi_align_op.cc

@@ -95,7 +95,7 @@ class ROIAlignOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput("X",
              "(Tensor), "
-             "The input of ROIAlignOp. "
+             "The input of ROIAlignOp. The data type is float32 or float64."
              "The format of input tensor is NCHW. Where N is batch size, "
              "C is the number of input channels, "
              "H is the height of the feature, and "
@@ -110,7 +110,8 @@ class ROIAlignOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("Out",
               "(Tensor), "
               "The output of ROIAlignOp is a 4-D tensor with shape "
-              "(num_rois, channels, pooled_h, pooled_w).");
+              "(num_rois, channels, pooled_h, pooled_w). The data type is "
+              "float32 or float64.");
     AddAttr<float>("spatial_scale",
                    "(float, default 1.0), "
                    "Multiplicative spatial scale factor "

python/paddle/fluid/layers/nn.py

@@ -8154,17 +8154,24 @@ def roi_align(input,
     Args:
         input (Variable): ${x_comment}
         rois (Variable): ROIs (Regions of Interest) to pool over.It should be
-                         a 2-D LoDTensor of shape (num_rois, 4), the lod level
-                         is 1. Given as [[x1, y1, x2, y2], ...], (x1, y1) is
-                         the top left coordinates, and (x2, y2) is the bottom
-                         right coordinates. 
-        pooled_height (integer): ${pooled_height_comment} Default: 1
-        pooled_width (integer): ${pooled_width_comment} Default: 1
-        spatial_scale (float): ${spatial_scale_comment} Default: 1.0
-        sampling_ratio(intger): ${sampling_ratio_comment} Default: -1
+            a 2-D LoDTensor of shape (num_rois, 4), the lod level is 1. The 
+            data type is float32 or float64. Given as [[x1, y1, x2, y2], ...], 
+            (x1, y1) is the top left coordinates, and (x2, y2) is the bottom
+            right coordinates. 
+        pooled_height (int32, optional): ${pooled_height_comment} Default: 1
+        pooled_width (int32, optional): ${pooled_width_comment} Default: 1
+        spatial_scale (float32, optional): ${spatial_scale_comment} Default: 1.0
+        sampling_ratio(int32, optional): ${sampling_ratio_comment} Default: -1
+        name(str, optional): For detailed information, please refer 
+            to :ref:`api_guide_Name`. Usually name is no need to set and 
+            None by default. 
 
     Returns:
-        Variable: ${out_comment}.
+        Variable:
+
+        Output: ${out_comment}.
+
+
     Examples:
         .. code-block:: python
 
@@ -10498,15 +10505,20 @@ def prelu(x, mode, param_attr=None, name=None):
         element: All elements do not share alpha. Each element has its own alpha.
 
     Args:
-        x (Variable): The input tensor.
-        mode (string): The mode for weight sharing. 
+        x (Variable): The input Tensor or LoDTensor with data type float32.
+        mode (str): The mode for weight sharing. 
         param_attr(ParamAttr|None): The parameter attribute for the learnable
-          weight (alpha), it can be create by ParamAttr.
-        name(str|None): A name for this layer(optional). If set None, the layer
-          will be named automatically.
+          weight (alpha), it can be create by ParamAttr. None by default.
+          For detailed information, please refer to :ref:`api_fluid_ParamAttr`.
+        name(str|None): For detailed information, please refer 
+          to :ref:`api_guide_Name`. Usually name is no need to set and 
+          None by default. 
 
     Returns:
-        Variable: The output tensor with the same shape as input.
+        Variable:
+
+        output(Variable): The tensor or LoDTensor with the same shape as input.
+        The data type is float32.
 
     Examples:
 
@@ -12561,11 +12573,16 @@ def clip_by_norm(x, max_norm, name=None):
     Args:
         x(${x_type}): ${x_comment}
         max_norm(${max_norm_type}): ${max_norm_comment}
-        name(basestring|None): Name of the output.
+        name(str, optional): For detailed information, please refer 
+            to :ref:`api_guide_Name`. Usually name is no need to set and 
+            None by default. 
 
     Returns:
+        Variable:
+
         out(${out_type}): ${out_comment}
 
+
     Examples:
         .. code-block:: python
 
@@ -12771,11 +12788,16 @@ def maxout(x, groups, name=None):
     Args:
         x(${x_type}): ${x_comment}
         groups(${groups_type}): ${groups_comment}
-        name(basestring|None): Name of the output.
+        name(str, optional): For detailed information, please refer 
+            to :ref:`api_guide_Name`. Usually name is no need to set and 
+            None by default.
 
     Returns:
+        Variable:
+
         out(${out_type}): ${out_comment}
 
+
     Examples:
         .. code-block:: python