Better conv2d docstring

theano/tensor/nnet/conv.py

@@ -16,7 +16,7 @@ import numpy
 
 import theano
 from theano.tensor import (as_tensor_variable, blas, get_scalar_constant_value,
-                           patternbroadcast)
+                           patternbroadcast, NotScalarConstantError)
 from theano import OpenMPOp, config
 from theano.gof import Apply
 from theano.gof.python25 import any
@@ -56,13 +56,16 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
     :type subsample: tuple of len 2
     :param subsample: factor by which to subsample the output
 
-    :type image_shape: tuple of len 4 of int or Constant variable
+    :type image_shape: None, tuple/list of len 4 of int or Constant variable
     :param image_shape: (batch size, stack size, nb row, nb col)
-                        Optional, used for optimization.
-    :type filter_shape: tuple of len 4 of int or Constant variable
+                        Optional, used for optimization like loop unrolling
+                        You can put None for any element of the list
+                        to tell that this element is not constant.
+    :type filter_shape: None, tuple/list of len 4 of int or Constant variable
     :param filter_shape: (nb filters, stack size, nb row, nb col)
-                         Optional, used for optimization.
-
+                         Optional, used for optimization like loop unrolling
+                         You can put None for any element of the list
+                         to tell that this element is not constant.
     :param kwargs: kwargs are passed onto ConvOp.
                    Can be used to set the following:
                    unroll_batch, unroll_kern, unroll_patch,

theano/tensor/nnet/tests/test_conv.py

@@ -25,7 +25,18 @@ class TestConv2D(utt.InferShapeTester):
                  input=None, filters=None,
                  unroll_batch=None, unroll_kern=None, unroll_patch=None,
                  verify_grad=True, should_raise=False):
+        """
+        :param image_shape: The constant shape info passed to conv2d.
+        :param filter_shape: The constant shape info passed to conv2d.
+
+        :param N_image_shape: None(default to image_shape) or tuple of
+                              4 elements with the shape of the input image
 
+        :param N_filter_shape: None(default to filter_shape) or tuple
+                               of 4 elements with the shape of the
+                               input filter
+
+        """
         if N_image_shape is None:
             N_image_shape = [T.get_scalar_constant_value(T.
                 as_tensor_variable(x)) for x in image_shape]