Add test to check if gradient computations are disabled in CTC test

theano/tensor/nnet/tests/test_ctc.py

@@ -6,7 +6,7 @@ import numpy as np
 import theano
 import theano.tensor as T
 from theano.tests import unittest_tools as utt
-from theano.tensor.nnet.ctc import (ctc_enabled, ctc)
+from theano.tensor.nnet.ctc import (ctc_enabled, ctc, ConnectionistTemporalClassification)
 
 
 class TestCTC(unittest.TestCase):
@@ -38,6 +38,18 @@ class TestCTC(unittest.TestCase):
         utt.assert_allclose(expected_grads / cost.shape[0], grad)
         utt.assert_allclose(expected_costs, cost)
 
+        self.check_grads_disabled(t_activations, t_labels, t_activation_times)
+
+    def check_grads_disabled(self, activations, labels, input_length):
+        """
+        Check if optimization to disable gradients is working
+        """
+        ctc_cost = ctc(activations, labels, input_length)
+        ctc_function = theano.function([], [ctc_cost])
+        for node in ctc_function.maker.fgraph.apply_nodes:
+            if isinstance(node.op, ConnectionistTemporalClassification):
+                assert (node.op.compute_grad is False)
+
     # Test obtained from Torch tutorial at:
     # https://github.com/baidu-research/warp-ctc/blob/master/torch_binding/TUTORIAL.md
     def test_torch_case(self):