Fix Sphinx doc errors

docs/api/python/image/image.md

@@ -56,7 +56,7 @@ Iterators support loading image from binary `Record IO` and raw image files.
 
 We use helper function to initialize augmenters
 ```eval_rst
-    .. currentmodule:: mxnet
+.. currentmodule:: mxnet
 .. autosummary::
     :nosignatures:
 

docs/api/python/module/module.md

@@ -176,7 +176,7 @@ additional functionality. We summarize them in this section.
 .. autosummary::
     :nosignatures:
 
-    BucketModule.switch_bucket
+    BucketingModule.switch_bucket
 ```
 
 ### Class `SequentialModule`

docs/api/python/symbol/symbol.md

@@ -297,8 +297,8 @@ Composite multiple symbols into a new one by an operator.
     Symbol.take
     Symbol.one_hot
     Symbol.pick
-    Symbol.ravel_multi_index
-    Symbol.unravel_index
+    ravel_multi_index
+    unravel_index
 ```
 
 ### Get internal and output symbol
@@ -577,7 +577,7 @@ Composite multiple symbols into a new one by an operator.
     broadcast_logical_and
     broadcast_logical_or
     broadcast_logical_xor
-    broadcast_logical_not
+    logical_not
 ```
 
 ### Random sampling

python/mxnet/contrib/svrg_optimization/svrg_module.py

@@ -401,6 +401,7 @@ def fit(self, train_data, eval_data=None, eval_metric='acc',
             force_rebind=False, force_init=False, begin_epoch=0, num_epoch=None,
             validation_metric=None, monitor=None, sparse_row_id_fn=None):
         """Trains the module parameters.
+
         Parameters
         ----------
         train_data : DataIter

python/mxnet/rnn/rnn.py

@@ -35,7 +35,7 @@ def save_rnn_checkpoint(cells, prefix, epoch, symbol, arg_params, aux_params):
 
     Parameters
     ----------
-    cells : RNNCell or list of RNNCells
+    cells : mxnet.rnn.RNNCell or list of RNNCells
         The RNN cells used by this symbol.
     prefix : str
         Prefix of model name.
@@ -65,7 +65,7 @@ def load_rnn_checkpoint(cells, prefix, epoch):
 
     Parameters
     ----------
-    cells : RNNCell or list of RNNCells
+    cells : mxnet.rnn.RNNCell or list of RNNCells
         The RNN cells used by this symbol.
     prefix : str
         Prefix of model name.
@@ -100,7 +100,7 @@ def do_rnn_checkpoint(cells, prefix, period=1):
 
     Parameters
     ----------
-    cells : RNNCell or list of RNNCells
+    cells : mxnet.rnn.RNNCell or list of RNNCells
         The RNN cells used by this symbol.
     prefix : str
         The file prefix to checkpoint to

python/mxnet/rnn/rnn_cell.py

@@ -716,7 +716,7 @@ def unfuse(self):
 
         Returns
         -------
-        cell : SequentialRNNCell
+        cell : mxnet.rnn.SequentialRNNCell
             unfused cell that can be used for stepping, and can run on CPU.
         """
         stack = SequentialRNNCell()

python/mxnet/symbol/symbol.py

@@ -1347,7 +1347,7 @@ def simple_bind(self, ctx, grad_req='write', type_dict=None, stype_dict=None,
         shared_buffer : Dict of string to `NDArray`
             The dict mapping argument names to the `NDArray` that can be reused for initializing
             the current executor. This buffer will be checked for reuse if one argument name
-            of the current executor is not found in `shared_arg_names`. The `NDArray`s are
+            of the current executor is not found in `shared_arg_names`. The `NDArray` s are
             expected have default storage type.
 
         kwargs : Dict of str->shape

python/mxnet/symbol_doc.py

@@ -44,9 +44,6 @@
 - *Examples*: simple and short code snippet showing how to use this operator.
   It should show typical calling examples and behaviors (e.g. maps an input
   of what shape to an output of what shape).
-- *Regression Test*: longer test code for the operators. We normally do not
-  expect the users to read those, but they will be executed by `doctest` to
-  ensure the behavior of each operator does not change unintentionally.
 """
 from __future__ import absolute_import as _abs
 import re as _re
@@ -75,8 +72,6 @@ class ActivationDoc(SymbolDoc):
     >>> mlp
     <Symbol mlp>
 
-    Regression Test
-    ---------------
     ReLU activation
 
     >>> test_suites = [
@@ -107,8 +102,6 @@ class DropoutDoc(SymbolDoc):
     >>> data = Variable('data')
     >>> data_dp = Dropout(data=data, p=0.2)
 
-    Regression Test
-    ---------------
     >>> shape = (100, 100)  # take larger shapes to be more statistical stable
     >>> x = np.ones(shape)
     >>> op = Dropout(p=0.5, name='dp')
@@ -141,8 +134,6 @@ class EmbeddingDoc(SymbolDoc):
     >>> SymbolDoc.get_output_shape(op, letters=(seq_len, batch_size))
     {'embed_output': (10L, 64L, 16L)}
 
-    Regression Test
-    ---------------
     >>> vocab_size, embed_dim = (26, 16)
     >>> batch_size = 12
     >>> word_vecs = test_utils.random_arrays((vocab_size, embed_dim))
@@ -167,8 +158,6 @@ class FlattenDoc(SymbolDoc):
     >>> SymbolDoc.get_output_shape(flatten, data=(2, 3, 4, 5))
     {'flat_output': (2L, 60L)}
 
-    Regression Test
-    ---------------
     >>> test_dims = [(2, 3, 4, 5), (2, 3), (2,)]
     >>> op = Flatten(name='flat')
     >>> for dims in test_dims:
@@ -208,8 +197,6 @@ class FullyConnectedDoc(SymbolDoc):
     >>> net
     <Symbol pred>
 
-    Regression Test
-    ---------------
     >>> dim_in, dim_out = (3, 4)
     >>> x, w, b = test_utils.random_arrays((10, dim_in), (dim_out, dim_in), (dim_out,))
     >>> op = FullyConnected(num_hidden=dim_out, name='FC')

src/operator/contrib/adaptive_avg_pooling.cc

@@ -206,10 +206,10 @@ Applies a 2D adaptive average pooling over a 4D input with the shape of (NCHW).
 The pooling kernel and stride sizes are automatically chosen for desired output sizes.
 
 - If a single integer is provided for output_size, the output size is
-(N x C x output_size x output_size) for any input (NCHW).
+  (N x C x output_size x output_size) for any input (NCHW).
 
 - If a tuple of integers (height, width) are provided for output_size, the output size is
-(N x C x height x width) for any input (NCHW).
+  (N x C x height x width) for any input (NCHW).
 
 )code" ADD_FILELINE)
 .set_attr_parser(ParamParser<AdaptiveAvgPoolParam>)