Fix doc formatting in pool.py.

theano/tensor/signal/pool.py

@@ -30,6 +30,7 @@ def max_pool_2d_same_size(input, patch_size):
     of non-overlapping patches of size (patch_size[0],patch_size[1]) to zero,
     keeping only the maximum values. The output has the same dimensions as
     the input.
+
     Parameters
     ----------
     input : 4-D theano tensor of input images
@@ -37,6 +38,7 @@ def max_pool_2d_same_size(input, patch_size):
     patch_size : tuple of length 2
         Size of the patch (patch height, patch width).
         (2,2) will retain only one non-zero value per patch of 4 values.
+
     """
     output = Pool(patch_size, True)(input)
     outs = MaxPoolGrad(patch_size, True)(input, output, output)
@@ -49,6 +51,7 @@ def pool_2d(input, ds, ignore_border=None, st=None, padding=(0, 0),
     Takes as input a N-D tensor, where N >= 2. It downscales the input image by
     the specified factor, by keeping only the maximum value of non-overlapping
     patches of size (ds[0],ds[1])
+
     Parameters
     ----------
     input : N-D theano tensor of input images
@@ -71,6 +74,7 @@ def pool_2d(input, ds, ignore_border=None, st=None, padding=(0, 0),
         Operation executed on each window. `max` and `sum` always exclude
         the padding in the computation. `average` gives you the choice to
         include or exclude it.
+
     """
     if input.ndim < 2:
         raise NotImplementedError('pool_2d requires a dimension >= 2')
@@ -121,15 +125,17 @@ class Pool(Op):
     For N-dimensional tensors, consider that the last two dimensions span
     images. This Op downsamples these images by taking the max, sum or average
     over different patch.
+
     The constructor takes the max, sum or average or different input patches.
+
     Parameters
     ----------
     ds : list or tuple of two ints
         Downsample factor over rows and column.
         ds indicates the pool region size.
     ignore_border : bool
-        If ds doesn't divide imgshape, do we include an extra row/col of partial
-        downsampling (False) or ignore it (True).
+        If ds doesn't divide imgshape, do we include an extra row/col
+        of partial downsampling (False) or ignore it (True).
     st : list or tuple of two ints or None
         Stride size, which is the number of shifts over rows/cols to get the
         next pool region. If st is None, it is considered equal to ds
@@ -141,6 +147,7 @@ class Pool(Op):
     mode : {'max', 'sum', 'average_inc_pad', 'average_exc_pad'}
         ('average_inc_pad' excludes the padding from the count,
         'average_exc_pad' include it)
+
     """
 
     __props__ = ('ds', 'ignore_border', 'st', 'padding', 'mode')
@@ -150,6 +157,7 @@ class Pool(Op):
         """
         Return the shape of the output from this op, for input of given
         shape and flags.
+
         Parameters
         ----------
         imgshape : tuple, list, or similar of integer or scalar Theano variable
@@ -168,12 +176,14 @@ class Pool(Op):
             (pad_h, pad_w), pad zeros to extend beyond four borders
             of the images, pad_h is the size of the top and bottom margins,
             and pad_w is the size of the left and right margins.
+
         Returns
         -------
         list
             The shape of the output from this op, for input of given shape.
             This will have the same length as imgshape, but with last two
             elements reduced as per the downsampling & ignore_border flags.
+
         """
         if len(imgshape) < 2:
             raise TypeError('imgshape must have at least two elements '
@@ -528,27 +538,36 @@ class PoolGrad(Op):
     def out_shape(imgshape, ds, ignore_border=False, st=None, padding=(0, 0)):
         """Return the shape of the output from this op, for input of given
         shape and flags.
-        :param imgshape: the shape of a tensor of images. The last two elements
-            are interpreted as the number of rows, and the number of cols.
-        :type imgshape: tuple, list, or similar of integer or
-            scalar Theano variable.
-        :param ds: downsample factor over rows and columns
-                   this parameter indicates the size of the pooling region
-        :type ds: list or tuple of two ints
-        :param st: the stride size. This is the distance between the pooling
-                   regions. If it's set to None, in which case it equlas ds.
-        :type st: list or tuple of two ints
-        :param ignore_border: if ds doesn't divide imgshape, do we include an
-            extra row/col of partial downsampling (False) or ignore it (True).
-        :type ignore_border: bool
-        :param padding: (pad_h, pad_w), pad zeros to extend beyond four borders
-            of the images, pad_h is the size of the top and bottom margins,
-            and pad_w is the size of the left and right margins.
-        :type padding: tuple of two ints
-        :rtype: list
-        :returns: the shape of the output from this op, for input of given
-            shape.  This will have the same length as imgshape, but with last
-            two elements reduced as per the downsampling & ignore_border flags.
+
+        Parameters
+        ----------
+        imgshape : tuple of integers or scalar Theano variables
+            the shape of a tensor of images. The last two elements are
+            interpreted as the number of rows, and the number of cols.
+        ds : tuple of two ints
+            downsample factor over rows and columns this parameter
+            indicates the size of the pooling region
+        st : tuple of two ints
+            the stride size. This is the distance between the pooling
+            regions. If it's set to None, in which case it equlas ds.
+        ignore_border : bool
+            if ds doesn't divide imgshape, do we include an extra
+            row/col of partial downsampling (False) or ignore it
+            (True).
+        padding : tuple of two ints
+            (pad_h, pad_w), pad zeros to extend beyond four borders of
+            the images, pad_h is the size of the top and bottom
+            margins, and pad_w is the size of the left and right
+            margins.
+
+        Returns
+        -------
+        list :
+            the shape of the output from this op, for input of given
+            shape.  This will have the same length as imgshape, but
+            with last two elements reduced as per the downsampling &
+            ignore_border flags.
+
         """
         if len(imgshape) < 2:
             raise TypeError('imgshape must have at least two elements '