Fix perform of structureddot_grad with dense grad

theano/sparse/basic.py

@@ -1705,7 +1705,13 @@ class StructuredDotGradCSC(gof.Op):
             ind1 = a_indptr[j + 1]
             for i_idx in xrange(ind0, ind1):
                 i = a_indices[i_idx]
-                g_a_data[i_idx] = numpy.dot(g_ab[i], b[j].T)[0, 0]
+                # Depending on the type of g_ab and b (sparse or dense),
+                # the following dot product can result in a scalar or
+                # a (1, 1) sparse matrix.
+                dot_val = numpy.dot(g_ab[i], b[j].T)
+                if isinstance(dot_val, scipy.sparse.spmatrix):
+                    dot_val = dot_val[0, 0]
+                g_a_data[i_idx] = dot_val
         out[0] = g_a_data
 
     def c_code(self, node, name, (_indices, _indptr, _d, _g), (_zout, ), sub):
@@ -1820,7 +1826,13 @@ class StructuredDotGradCSR(gof.Op):
             for j_idx in xrange(ind0, ind1):
                 j = a_indices[j_idx]
                 # grad is dot product of i-th row of gradient with j-th row of b
-                g_a_data[j_idx] = numpy.dot(g_ab[i], b[j].T)[0, 0]
+                # Depending on the type of g_ab and b (sparse or dense),
+                # the following dot product can result in a scalar or
+                # a (1, 1) sparse matrix.
+                dot_val = numpy.dot(g_ab[i], b[j].T)
+                if isinstance(dot_val, scipy.sparse.spmatrix):
+                    dot_val = dot_val[0, 0]
+                g_a_data[j_idx] = dot_val
         out[0] = g_a_data
 
     def c_code(self, node, name, (_indices, _indptr, _d, _g), (_zout, ), sub):