Backing out of the idea to add 'shape' attribute to TensorType. This is a

theano/tensor/basic.py

@@ -253,6 +253,11 @@ class TensorType(Type):
     When this is True, strict filtering rejects data containing NaN or Inf entries. (Used in `DebugMode`)
     """
 
+    use_shape = False
+    """
+    This should be removed (hardcoded to be False) after AISTATS09
+    """
+
     def __init__(self, dtype, broadcastable, name = None, shape=None):
         """Initialize self.dtype and self.broadcastable.
 
@@ -305,6 +310,7 @@ class TensorType(Type):
             if self.filter_checks_isfinite and (not numpy.all(numpy.isfinite(data))):
                 raise TypeError("non-finite elements not allowed")
 
+            if TensorType.use_shape:
                 for si, di in zip(self.shape, data.shape):
                     if not (si is None or si == di):
                         raise TypeError('%s requires ndarray with shape matching %s (got %s)'%(
@@ -347,9 +353,13 @@ class TensorType(Type):
 
     def __eq__(self, other):
         """Compare True iff other is the same kind of TensorType"""
+        if TensorType.use_shape:
             return type(self) == type(other) and other.dtype == self.dtype \
                     and other.broadcastable == self.broadcastable \
                     and other.shape == self.shape
+        else:
+            return type(self) == type(other) and other.dtype == self.dtype \
+                    and other.broadcastable == self.broadcastable
 
     @staticmethod
     def values_eq(a, b):
@@ -420,7 +430,10 @@ class TensorType(Type):
 
     def __hash__(self):
         """Hash equal for same kinds of TensorType"""
+        if TensorType.use_shape:
             return hashtype(self) ^ hash(self.dtype) ^ hash(self.broadcastable) ^ hash(self.shape)
+        else:
+            return hashtype(self) ^ hash(self.dtype) ^ hash(self.broadcastable)
 
     ndim = property(lambda self: len(self.broadcastable), doc = "number of dimensions")
     """Number of dimensions