Fix setup of symbolic gradients in CTC test

theano/gpuarray/tests/test_ctc.py

@@ -26,7 +26,7 @@ class TestCTC(unittest.TestCase):
         # Compute CTC costs and gradients on the CPU to compare with GPU
         cpu_ctc_cost = ctc(t_activations, t_labels, t_activation_times)
         # Symbolic gradient of CTC cost
-        cpu_ctc_grad = T.grad(T.mean(t_cost), t_activations)
+        cpu_ctc_grad = T.grad(T.mean(cpu_ctc_cost), t_activations)
 
         # Compile CPU function without optimization
         cpu_train = theano.function([], [cpu_ctc_cost, cpu_ctc_grad], mode=mode_without_gpu)
@@ -34,7 +34,7 @@ class TestCTC(unittest.TestCase):
 
         gpu_ctc_cost = gpu_ctc(t_activations, t_labels, t_activation_times)
         # Symbolic gradient of CTC cost
-        gpu_ctc_grad = T.grad(T.mean(t_cost), t_activations)
+        gpu_ctc_grad = T.grad(T.mean(gpu_ctc_cost), t_activations)
         # Compile symbolic functions
         gpu_train = theano.function([], [gpu_ctc_cost, gpu_ctc_grad])
 
@@ -46,7 +46,7 @@ class TestCTC(unittest.TestCase):
         grad_from_gpu = np.asarray(gpu_grad)
 
         # Check that results are in conformance with expected values
-        utt.assert_allclose(expected_grads / gpu_ctc_cost.shape[0], grad_from_gpu)
+        utt.assert_allclose(expected_grads / cost_from_gpu.shape[0], grad_from_gpu)
         utt.assert_allclose(expected_costs, cost_from_gpu)
 
         # Compare values obtained from CPU and GPU implementations
@@ -87,13 +87,13 @@ class TestCTC(unittest.TestCase):
     def test_ctc(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='float32')
+                                 dtype=np.float32)
 
-        activation_times = np.asarray([2, 2], dtype='int32')
+        activation_times = np.asarray([2, 2], dtype=np.int32)
 
-        labels = np.asarray([[1, 2], [1, 2]], dtype='int32')
+        labels = np.asarray([[1, 2], [1, 2]], dtype=np.int32)
 
-        expected_costs = np.asarray([2.962858438, 3.053659201], dtype='float32')
+        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]],