Merge pull request #11424 from weixing02/fix_doc_err

fix errors

Author: weixing02

doc/v2/api/config/evaluators.rst

@@ -101,7 +101,7 @@ value_printer
     :noindex:
 
 Detection
-=====
+==========
 
 detection_map
 -------------

python/paddle/trainer_config_helpers/attrs.py

@@ -240,14 +240,15 @@ class ExtraLayerAttribute(object):
     :type error_clipping_threshold: float
     :param drop_rate: Dropout rate. Dropout will create a mask on layer output.
                       The dropout rate is the zero rate of this mask. The
-                      details of what dropout is please refer to `here
-                      <https://www.cs.toronto.edu/~hinton/absps/
-                      JMLRdropout.pdf>`_.
+                      details of what dropout is please refer to `JMLRdropout
+                      <https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf
+                      >`_.
     :type drop_rate: float
     :param device: device ID of layer. device=-1, use CPU. device>=0, use GPU.
-                   The details allocation in parallel_nn please refer to `here
-                   <http://www.paddlepaddle.org/doc/ui/cmd_argument/
-                   use_case.html#case-2-specify-layers-in-different-devices>`_.
+                   The details allocation in parallel_nn please refer to `use_case
+                   <https://github.com/PaddlePaddle/Paddle/blob/develop/doc/v2
+                   /howto/cmd_parameter/use_case_en.md#case-2-specify-layers-in
+                   -different-devices>`_.
     :type device: int
     """
 

python/paddle/trainer_config_helpers/layers.py

@@ -2556,7 +2556,7 @@ def img_conv_layer(input,
     the output will be obtained by concatenating the two results.
 
     The details of grouped convolution, please refer to:
-    `ImageNet Classification with Deep Convolutional Neural Networks
+    `ImageNet Classification With Deep Convolutional Neural Networks
     <http://www.cs.toronto.edu/~kriz/imagenet_classification_with_deep_convolutional.pdf>`_
     
     The example usage is: