Safer indexing. If we append more information, it will work correctly.

theano/sparse/opt.py

@@ -130,9 +130,9 @@ class StructuredDotCSC(gof.Op):
         if node.inputs[4].type.dtype in ('complex64', 'complex128'):
             raise NotImplementedError('Complex types are not supported for b')
 
-        typenum_z = node.outputs[0].type.dtype_specs()[-1]  # retrieve dtype number
-        typenum_a_val = node.inputs[0].type.dtype_specs()[-1]  # retrieve dtype number
-        typenum_b = node.inputs[4].type.dtype_specs()[-1]  # retrieve dtype number
+        typenum_z = node.outputs[0].type.dtype_specs()[2]  # retrieve dtype number
+        typenum_a_val = node.inputs[0].type.dtype_specs()[2]  # retrieve dtype number
+        typenum_b = node.inputs[4].type.dtype_specs()[2]  # retrieve dtype number
 
         rval = """
 
@@ -318,7 +318,7 @@ class StructuredDotCSR(gof.Op):
         @param sub: TODO, not too sure, something to do with weave probably
         """
         # retrieve dtype number
-        typenum_z = tensor.TensorType(self.dtype_out, []).dtype_specs()[-1]
+        typenum_z = tensor.TensorType(self.dtype_out, []).dtype_specs()[2]
         if node.inputs[0].type.dtype in ('complex64', 'complex128'):
             raise NotImplementedError('Complex types are not supported for a_val')
         if node.inputs[3].type.dtype in ('complex64', 'complex128'):
@@ -550,11 +550,11 @@ class UsmmCscDense(gof.Op):
             conv_type = "double"
             axpy = "daxpy_"
         # retrieve dtype numbers
-        typenum_alpha = node.inputs[0].type.dtype_specs()[-1]
-        typenum_x_val = node.inputs[1].type.dtype_specs()[-1]
-        typenum_y = node.inputs[5].type.dtype_specs()[-1]
-        typenum_z = node.inputs[6].type.dtype_specs()[-1]
-        typenum_zn = node.outputs[0].type.dtype_specs()[-1]
+        typenum_alpha = node.inputs[0].type.dtype_specs()[2]
+        typenum_x_val = node.inputs[1].type.dtype_specs()[2]
+        typenum_y = node.inputs[5].type.dtype_specs()[2]
+        typenum_z = node.inputs[6].type.dtype_specs()[2]
+        typenum_zn = node.outputs[0].type.dtype_specs()[2]
 
         inplace = int(self.inplace)
 
@@ -761,7 +761,7 @@ class CSMGradC(gof.Op):
     def c_code(self, node, name, (a_val, a_ind, a_ptr, a_dim,
                         b_val, b_ind, b_ptr, b_dim), (z,), sub):
         # retrieve dtype number
-        typenum_z = node.outputs[0].type.dtype_specs()[-1]
+        typenum_z = node.outputs[0].type.dtype_specs()[2]
         if node.inputs[0].type.dtype in ('complex64', 'complex128'):
             raise NotImplementedError('Complex types are not supported for a_val')
         if node.inputs[3].type.dtype in ('complex64', 'complex128'):
@@ -1558,15 +1558,15 @@ class SamplingDotCSR(gof.Op):
             cdot = "ddot_"
 
         # retrieve dtype number
-        typenum_x = node.inputs[0].type.dtype_specs()[-1]
-        typenum_y = node.inputs[1].type.dtype_specs()[-1]
-        typenum_p = node.inputs[2].type.dtype_specs()[-1]
+        typenum_x = node.inputs[0].type.dtype_specs()[2]
+        typenum_y = node.inputs[1].type.dtype_specs()[2]
+        typenum_p = node.inputs[2].type.dtype_specs()[2]
         typenum_zd = tensor.TensorType(node.outputs[0].dtype,
-                                       []).dtype_specs()[-1]
+                                       []).dtype_specs()[2]
         typenum_zi = tensor.TensorType(node.outputs[1].dtype,
-                                       []).dtype_specs()[-1]
+                                       []).dtype_specs()[2]
         typenum_zp = tensor.TensorType(node.outputs[2].dtype,
-                                       []).dtype_specs()[-1]
+                                       []).dtype_specs()[2]
 
         rval = """
         if (PyArray_NDIM(%(x)s) != 2) {