Add simple test to CTC wrapper tests

theano/tensor/nnet/tests/test_ctc.py

@@ -10,6 +10,13 @@ from theano.tensor.nnet.ctc import (ctc_enabled, ctc)
 
 
 class TestCTC(unittest.TestCase):
+    """
+    Test Baidu CTC wrapper implementation.
+
+    Expected values for costs and gradients are obtained through an external
+    C implementation, that uses the library directly.
+    """
+
     def setUp(self):
         if not ctc_enabled:
             self.skipTest('Optional library warp-ctc not available')
@@ -66,3 +73,23 @@ class TestCTC(unittest.TestCase):
         expected_gradients = np.asarray(grads, dtype=np.float32)
 
         self.run_ctc(activations, labels, activation_times, expected_costs, expected_gradients)
+
+    def simple_test(self):
+        activations = np.asarray([[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]],
+                                  [[0.6, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.5, 0.2, 0.1]]],
+                                 dtype=np.float32)
+
+        activation_times = np.asarray([2, 2], dtype=np.int32)
+
+        labels = np.asarray([[1, 2], [1, 2]], dtype=np.int32)
+
+        expected_costs = np.asarray([2.962858438, 3.053659201], dtype=np.float32)
+
+        grads = [[[0.177031219, -0.7081246376, 0.177031219, 0.177031219, 0.177031219],
+                  [0.177031219, -0.8229685426, 0.291875124, 0.177031219, 0.177031219]],
+                 [[0.291875124, 0.177031219, -0.8229685426, 0.177031219, 0.177031219],
+                  [0.1786672771, 0.1786672771, -0.7334594727, 0.1974578798, 0.1786672771]]]
+
+        expected_gradients = np.asarray(grads, dtype=np.float32)
+
+        self.run_ctc(activations, labels, activation_times, expected_costs, expected_gradients)