Merge pull request #652 from nouiz/small

theano/gradient.py

@@ -256,10 +256,8 @@ def Rop(f, wrt, eval_points):
             eval_point = as_tensor_variable(eval_point)
 
         try:
-            wrt_dim = len(wrt_elem.type.broadcastable)
-            eval_dim = len(eval_point.type.broadcastable)
 
-            if wrt_dim != eval_dim:
+            if wrt_elem.type.ndim != eval_point.type.ndim:
                 raise ValueError('Element ' +
                                  str(i) +
                                  ' of wrt/eval_point have mismatched ' +
@@ -267,9 +265,9 @@ def Rop(f, wrt, eval_points):
                                  str(wrt_dim) +
                                  ' versus ' +
                                  str(eval_dim))
-        except:
-            # wrt_elem and eval_point can be non-tensor variable which do
-            # not have broadcastable flags
+        except AttributeError:
+            # wrt_elem and eval_point don't always have ndim like random type
+            # Tensor, Sparse and CudaNdArray have the ndim attribute
             pass
 
     seen_nodes = {}

theano/sparse/sandbox/sp2.py

@@ -113,6 +113,9 @@ class MulSDCSC(gof.Op):
         return gof.Apply(self, [a_data, a_indices, a_indptr, b],
                                [tensor.tensor(b.dtype, (False,))])
 
+    def c_code_cache_version(self):
+        return (1,)
+
     #def perform(self, node, (a_data, a_indices, a_indptr, b), (out,)):
     #    return NotImplementedError()
     def c_code(self, node, name, (_data, _indices, _indptr, _b,),
@@ -203,6 +206,9 @@ class MulSDCSR(gof.Op):
         return gof.Apply(self, [a_data, a_indices, a_indptr, b],
                                [tensor.tensor(b.dtype, (False,))])
 
+    def c_code_cache_version(self):
+        return (1,)
+
     #def perform(self, node, (a_data, a_indices, a_indptr, b), (out,)):
     #    return NotImplemented()
     def c_code(self, node, name, (_data, _indices, _indptr, _b,),
@@ -499,6 +505,9 @@ class MulSVCSR(gof.Op):
         return gof.Apply(self, [a_data, a_indices, a_indptr, b],
                                [tensor.tensor(b.dtype, (False,))])
 
+    def c_code_cache_version(self):
+        return (1,)
+
     def c_code(self, node, name, inputs, outputs, sub):
         _data, _indices, _indptr, _b, = inputs
         _zout, = outputs
@@ -660,6 +669,9 @@ class StrucutedAddSVCSR(gof.Op):
         return gof.Apply(self, [a_data, a_indices, a_indptr, b],
                                [tensor.tensor(b.dtype, (False,))])
 
+    def c_code_cache_version(self):
+        return (1,)
+
     def c_code(self, node, name, inputs, outputs, sub):
         _data, _indices, _indptr, _b, = inputs
         _zout, = outputs

theano/tensor/elemwise.py

@@ -1748,7 +1748,7 @@ class Prod(CAReduceDtype):
             return "Prod{%s}" % ", ".join(map(str, self.axis))
 
     def c_code_cache_version(self):
-        return ()
+        return (1,)
 
 
 class MulWithoutZeros(scalar.BinaryScalarOp):