assert_conv_shape resolves constants.

theano/tensor/nnet/abstract_conv.py

@@ -432,6 +432,9 @@ def check_conv_gradinputs_shape(image_shape, kernel_shape, output_shape,
 def assert_conv_shape(shape):
     """This function adds Assert nodes that check if shape is a valid convolution shape.
 
+    The first two dimensions should be larger than or equal to zero. The convolution
+    dimensions should be larger than zero.
+
     Parameters
     ----------
     shape: tuple of int (symbolic or numeric) corresponding to the input, output or
@@ -442,12 +445,23 @@ def assert_conv_shape(shape):
 
     Returns
     -------
-    Returns a tuple similar to the given `shape`, but with each element wrapped in
-    an `Assert` op that checks that dimension. The first two dimensions should be
-    larger than or equal to zero. The convolution dimensions should be larger than zero.
+    Returns a tuple similar to the given `shape`. For constant elements in `shape`,
+    the function checks the value and raises a `ValueError` if the dimension is invalid.
+    The elements that are not constant are wrapped in an `Assert` op that checks the
+    dimension at run time.
     """
     out_shape = []
     for i, n in enumerate(shape):
+        try:
+            const_n = get_scalar_constant_value(n)
+            if i < 2:
+                if const_n < 0:
+                    raise ValueError('The convolution would produce an invalid shape (dim[%d]: %d < 0).' % (i, const_n))
+            else:
+                if const_n <= 0:
+                    raise ValueError('The convolution would produce an invalid shape (dim[%d]: %d <= 0).' % (i, const_n))
+            out_shape.append(n)
+        except NotScalarConstantError:
             if i < 2:
                 assert_shp = Assert('The convolution would produce an invalid shape (dim[%d] < 0).' % i)
                 out_shape.append(assert_shp(n, theano.tensor.ge(n, 0)))