Removed shape attribute of TensorType.

theano/tensor/basic.py

@@ -238,20 +238,12 @@ def constant_or_value(x, rtype, name=None, ndim=None, dtype=None):
 
     try:
         if rtype is TensorConstant:
-            if 0:
-                # put the shape into the type
-
-                # This is disabled because if a tensor has shape, then the following fails:
-                # theano.lvector == as_tensor_variable([0,1]).type
-                # I think the solution is that we should implement something more like
-                # compatability instead of equality in our Type comparisons... but we're not
-                # there yet.
-                x_shape = x_.shape
-            else:
-                x_shape = None
-            return rtype(
-                    TensorType(dtype = x_.dtype, broadcastable = bcastable, shape=x_shape),
-                    x_.copy(), name=name)
+            rval = rtype(
+                    TensorType(dtype = x_.dtype, broadcastable = bcastable),
+                    x_.copy(),
+                    name=name)
+            rval.tag.shape = x_.shape
+            return rval
         else:
             # leave the shape out of the type
             return rtype(TensorType(dtype = x_.dtype, broadcastable = bcastable), x_, name=name)
@@ -320,7 +312,7 @@ class TensorType(Type):
     When this is True, strict filtering rejects data containing NaN or Inf entries. (Used in `DebugMode`)
     """
 
-    def __init__(self, dtype, broadcastable, name = None, shape=None):
+    def __init__(self, dtype, broadcastable, name = None):
         """Initialize self.dtype and self.broadcastable.
 
         :Parameters:
@@ -343,30 +335,7 @@ class TensorType(Type):
         self.dtype_specs() # error checking is done there
         self.name = name
         self.numpy_dtype = numpy.dtype(self.dtype)
-        if shape is None:
-          #backport self.shape = tuple((1 if b else None) for b in self.broadcastable)
-            l=[]
-            for b in self.broadcastable:
-              if b: l.append(1)
-              else: l.append(None)
-            self.shape = tuple(l)
-        else:
-            self.shape = tuple(shape)
-        if len(self.shape) != len(self.broadcastable):
-            raise ValueError('shape and broadcastable must have equal lengths', (self.shape,
-                self.broadcastable))
-
-    def __setstate__(self, dct):
-        self.__dict__.update(dct)
-
-        #add shape when unpickling  old pickled things
-        if 'shape' not in dct:
-            l=[]
-            for b in self.broadcastable:
-              if b: l.append(1)
-              else: l.append(None)
-            self.shape = tuple(l)
-            #backport  self.shape = tuple(1 if b else None for b in self.broadcastable)
+
     def filter(self, data, strict = False):
         """Convert `data` to something which can be associated to a `TensorVariable`.
 
@@ -523,8 +492,6 @@ class TensorType(Type):
     def __str__(self):
         if self.name:
             return self.name
-        elif not all(None == si for si in self.shape):
-            return 'TensorType{%s, %s}' % (self.dtype, self.shape)
         else:
             b = self.broadcastable
             named_broadcastable = {(): 'scalar',