update doc

doc/library/tensor/nnet/conv.txt

@@ -31,11 +31,15 @@
     that will be replaced by an actual convolution implementation during
     the optimization phase.
 
+    Since the abstract Op does not have any implementation, it will prevent
+    computations in the un-optimized graph, and cause problems with DebugMode,
+    test values, and when compiling with optimizer=None.
+
     By default, if :ref:`cuDNN <libdoc_cuda_dnn>`
     is available, we will use it, otherwise we will fall back to using the
     gemm version (slower then cuDNN in most cases and uses more memory).
 
-    Both cuDNN and the gemm version can be disabled using the Theano flags
+    Either cuDNN and the gemm version can be disabled using the Theano flags
     ``optimizer_excluding=conv_dnn`` and ``optimizer_excluding=conv_gemm``,
     respectively. In this case, we will fall back to using the legacy
     convolution code, which is slower, but does not require extra memory.

theano/tensor/nnet/__init__.py

@@ -43,30 +43,33 @@ def conv2d(input, filters, input_shape=None, filter_shape=None,
     stack of 2D inputs with a set of 2D filters. The implementation is modelled
     after Convolutional Neural Networks (CNN).
 
-    :type input: symbolic 4D tensor
-    :param input: mini-batch of feature map stacks, of shape
+
+    Parameters
+    ----------
+    input: symbolic 4D tensor
+        Mini-batch of feature map stacks, of shape
         (batch size, input channels, input rows, input columns).
         See the optional parameter ``input_shape``.
 
-    :type filters: symbolic 4D tensor
-    :param filters: set of filters used in CNN layer of shape
+    filters: symbolic 4D tensor
+        Set of filters used in CNN layer of shape
         (output channels, input channels, filter rows, filter columns).
         See the optional parameter ``filter_shape``.
 
-    :type input_shape: None, tuple/list of len 4 of int or Constant variable
-    :param input_shape: The shape of the input parameter.
+    input_shape: None, tuple/list of len 4 of int or Constant variable
+        The shape of the input parameter.
         Optional, possibly used to choose an optimal implementation.
         You can give ``None`` for any element of the list to specify that this
         element is not known at compile time.
 
-    :type filter_shape: None, tuple/list of len 4 of int or Constant variable
-    :param filter_shape: The shape of the filters parameter.
+    filter_shape: None, tuple/list of len 4 of int or Constant variable
+        The shape of the filters parameter.
         Optional, possibly used to choose an optimal implementation.
         You can give ``None`` for any element of the list to specify that this
         element is not known at compile time.
 
-    :type border_mode: str, int or tuple of two int
-    :param border_mode: Either of the following:
+    border_mode: str, int or tuple of two int
+        Either of the following:
 
         ``'valid'``: apply filter wherever it completely overlaps with the
             input. Generates output of shape: input shape - filter shape + 1
@@ -81,31 +84,35 @@ def conv2d(input, filters, input_shape=None, filter_shape=None,
         ``(int1, int2)``: pad input with a symmetric border of ``int1`` rows
             and ``int2`` columns, then perform a valid convolution.
 
-    :type subsample: tuple of len 2
-    :param subsample: factor by which to subsample the output.
+    subsample: tuple of len 2
+        Factor by which to subsample the output.
         Also called strides elsewhere.
 
-    :type filter_flip: bool
-    :param filter_flip: If ``True``, will flip the filter rows and columns
+    filter_flip: bool
+        If ``True``, will flip the filter rows and columns
         before sliding them over the input. This operation is normally referred
         to as a convolution, and this is the default. If ``False``, the filters
         are not flipped and the operation is referred to as a cross-correlation.
 
-    :type image_shape: None, tuple/list of len 4 of int or Constant variable
-    :param image_shape: Deprecated alias for input_shape.
+    image_shape: None, tuple/list of len 4 of int or Constant variable
+        Deprecated alias for input_shape.
 
-    :param kwargs: Any other keyword arguments are accepted for backwards
+    kwargs: Any other keyword arguments are accepted for backwards
             compatibility, but will be ignored.
 
-    :rtype: symbolic 4D tensor
-    :return: set of feature maps generated by convolutional layer. Tensor is
+    Returns
+    -------
+    Symbolic 4D tensor
+        Set of feature maps generated by convolutional layer. Tensor is
         of shape (batch size, output channels, output rows, output columns)
 
-    :note: If CuDNN is available, it will be used on the
+    Notes
+    -----
+        If CuDNN is available, it will be used on the
         GPU. Otherwise, it is the *CorrMM* convolution that will be used
         "caffe style convolution".
 
-    :note: This is only supported in Theano 0.8 or the development
+        This is only supported in Theano 0.8 or the development
         version until it is released.
 
     """

theano/tensor/nnet/abstract_conv.py

@@ -124,22 +124,24 @@ class BaseAbstractConv2d(Op):
     Define an abstract convolution op that will be replaced with the
     appropriate implementation
 
-    :type imshp: None, tuple/list of len 4 of int or Constant variable
-    :param imshp: The shape of the input parameter.
+    Parameters
+    ----------
+     imshp: None, tuple/list of len 4 of int or Constant variable
+        The shape of the input parameter.
         Optional, possibly used to choose an optimal implementation.
         You can give ``None`` for any element of the list to specify that this
         element is not known at compile time.
         imshp is defined w.r.t the forward conv.
 
-    :type kshp: None, tuple/list of len 4 of int or Constant variable
-    :param kshp: The shape of the filters parameter.
+     kshp: None, tuple/list of len 4 of int or Constant variable
+        The shape of the filters parameter.
         Optional, possibly used to choose an optimal implementation.
         You can give ``None`` for any element of the list to specify that this
         element is not known at compile time.
         kshp is defined w.r.t the forward conv.
 
-    :type border_mode: str, int or tuple of two int
-    :param border_mode: Either of the following:
+     border_mode: str, int or tuple of two int
+        Either of the following:
 
         ``'valid'``: apply filter wherever it completely overlaps with the
             input. Generates output of shape: input shape - filter shape + 1
@@ -154,12 +156,12 @@ class BaseAbstractConv2d(Op):
         ``(int1, int2)``: pad input with a symmetric border of ``int1`` rows
             and ``int2`` columns, then perform a valid convolution.
 
-    :type subsample: tuple of len 2
-    :param subsample: factor by which to subsample the output.
+    subsample: tuple of len 2
+        Factor by which to subsample the output.
         Also called strides elsewhere.
 
-    :type filter_flip: bool
-    :param filter_flip: If ``True``, will flip the filter rows and columns
+    filter_flip: bool
+        If ``True``, will flip the filter rows and columns
         before sliding them over the input. This operation is normally referred
         to as a convolution, and this is the default. If ``False``, the filters
         are not flipped and the operation is referred to as a