fix bugs from the gradient of Cast

theano/sparse/sandbox/sp2.py

@@ -34,8 +34,7 @@ class Cast(gof.op.Op):
     def make_node(self, x):
         x = as_sparse_variable(x)
         return gof.Apply(
-            self,
-            [x],
+            self, [x],
             [SparseType(dtype=self.out_type, format=x.format).make_variable()])
 
     def perform(self, node, (x, ), (out, )):
@@ -45,7 +44,7 @@ class Cast(gof.op.Op):
     def grad(self, inputs, outputs_gradients):
         if inputs[0].dtype in T.continuous_dtypes:
             gz = outputs_gradients[0]
-            return [Cast(self.out_type)(gz)]
+            return [Cast(inputs[0].dtype)(gz)]
         else:
             return [None]
 
@@ -56,7 +55,7 @@ class Cast(gof.op.Op):
         return self.__class__.__name__
 
 
-def astype(x, t):
+def cast(x, t):
     """Cast sparse variable `x` to the desired dtype `t`.
 
     This wrap the method astype from scipy.

theano/sparse/tests/test_sp2.py

@@ -66,12 +66,12 @@ class TestCast(utt.InferShapeTester):
 
     def test_cast(self):
         cast_csc = dict([
-            (x, [theano.function([x], S2.astype(x, t))
+            (x, [theano.function([x], S2.Cast(t)(x))
                  for t in self.compatible_types])
             for x in self.x_csc])
 
         cast_csr = dict([
-            (x, [theano.function([x], S2.astype(x, t))
+            (x, [theano.function([x], S2.Cast(t)(x))
                  for t in self.compatible_types])
             for x in self.x_csr])
 
@@ -90,7 +90,7 @@ class TestCast(utt.InferShapeTester):
             for t in self.compatible_types:
                 a = sp.csc_matrix(self.properties, dtype=x.dtype)
                 self._compile_and_check([x],
-                                        [S2.astype(x, t)],
+                                        [S2.Cast(t)(x)],
                                         [a],
                                         self.op_class)
 
@@ -98,25 +98,18 @@ class TestCast(utt.InferShapeTester):
             for t in self.compatible_types:
                 a = sp.csr_matrix(self.properties, dtype=x.dtype)
                 self._compile_and_check([x],
-                                        [S2.astype(x, t)],
+                                        [S2.Cast(t)(x)],
                                         [a],
                                         self.op_class)
 
     def test_grad(self):
-        x_csc = [S.csc_matrix(dtype=t) for t in T.float_dtypes]
-        x_csr = [S.csr_matrix(dtype=t) for t in T.float_dtypes]
-
-        # There is a problem with the grad
-        # TODO Find the problem
-        # for x in x_csc:
-        #     for t in T.float_dtypes:
-        #         a = sp.csc_matrix(self.properties, dtype=x.dtype)
-        #         verify_grad_sparse(S2.Cast(t), [a])
-
-        # for x in x_csr:
-        #     for t in T.float_dtypes:
-        #         a = sp.csr_matrix(self.properties, dtype=x.dtype)
-        #         verify_grad_sparse(S2.Cast(t), [a])
+        for dtype in T.float_dtypes:
+            a = sp.csc_matrix(self.properties, dtype=dtype)
+            verify_grad_sparse(S2.Cast('float64'), [a])
+
+        for dtype in T.float_dtypes:
+            a = sp.csr_matrix(self.properties, dtype=dtype)
+            verify_grad_sparse(S2.Cast('float64'), [a])
 
 
 class test_structured_add_s_v(unittest.TestCase):