Added doc on conv2d with openmp and small image/filter.

theano/tensor/nnet/conv.py

@@ -35,8 +35,7 @@ _logger=logging.getLogger("theano.tensor.nnet.conv")
 
 def conv2d(input, filters, image_shape=None, filter_shape=None,
                 border_mode='valid', subsample=(1,1), **kargs):
-    """
-    This function will build the symbolic graph for convolving a stack of input
+    """This function will build the symbolic graph for convolving a stack of input
     images with a set of filters. The implementation is modelled after
     Convolutional Neural Networks (CNN). It is simply a wrapper to the ConvOp but
     provides a much cleaner interface.
@@ -64,10 +63,23 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
                          Optional, used for optimization.
 
     :param kwargs: kwargs are passed onto ConvOp. Can be used to set the following:
-                   unroll_batch, unroll_kern, unroll_patch (see ConvOp doc)
+                   unroll_batch, unroll_kern, unroll_patch, openmp (see ConvOp doc)
+
+                   openmp: By default have the same value as
+                           config.openmp. For small image, filter,
+                           batch size, nkern and stack size, it can be
+                           faster to disable manually openmp. A fast and
+                           incomplete test show that with image size
+                           6x6, filter size 4x4, batch size==1,
+                           n kern==1 and stack size==1, it is faster
+                           to disable it in valid mode. But if we
+                           grow the batch size to 10, it is faster
+                           with openmp on a core 2 duo.
+
     :rtype: symbolic 4D tensor
     :return: set of feature maps generated by convolutional layer. Tensor is of shape
       (batch size, nb filters, output row, output col)
+
     """
 
     #accept Constant value for image_shape and filter_shape.