Add basic test for warp-ctc wrapper

theano/tensor/nnet/tests/test_ctc.py

+from __future__ import print_function
+
+import numpy as np
+import theano
+import theano.tensor as T
+
+from theano.tensor.nnet.ctc import ctc
+
+def softmax():
+    """ Compute softmax values of each set of scores in x. """
+    pass
+
+# Layout, from slowest to fastest changing dimension, is (time, batchSize, inputLayerSize)
+inputs = np.asarray([[[0, 0, 0, 0, 0, 0], [1, 2, 3, 4, 5, 0], [1, 2, 3, 4, 5, -6]],
+                     [[0, 0, 0, 0, 0, 0], [1, 2, 3, 4, 5, 5], [1, 2, 3, 4, 5, -11]],
+                     [[0, 0, 0, 0, 0, 0], [1, 2, 3, 4, 5, 10], [1, 2, 3, 4, 5, -16]]],
+                    dtype=np.float32)
+
+weights = np.asarray([[1, 0, 0, 0, 0],
+                      [0, 1, 0, 0, 0],
+                      [0, 0, 1, 0, 0],
+                      [0, 0, 0, 1, 0],
+                      [0, 0, 0, 0, 1],
+                      [1, 1, 1, 1, 1]], dtype=np.float32)
+
+activations = np.dot(inputs, weights)
+# Duration of each sequence
+activation_times = np.asarray([1, 3, 3], dtype=np.int32)
+
+print("Activations: {0}".format(activations))
+##print("Softmax outputs: {0}".format(softmax(activations)))
+
+# Labels for each sequence
+labels = np.asarray([[1, -1],
+                     [3,  3],
+                     [2,  3]], dtype=np.int32)
+
+# Create symbolic variables
+t_inputs = theano.shared(inputs, name="inputs")
+t_weights = theano.shared(weights, name="weights")
+t_activations = T.dot(t_inputs, t_weights)
+t_activaction_times = theano.shared(activation_times, "activation_times")
+t_labels = theano.shared(labels, "labels")
+
+# Symbolic CTC cost
+t_cost = ctc(t_activations, t_labels, t_activaction_times)
+# Symbolic gradient of CTC cost
+# - FIXME: eliminate need for slicing t_cost
+t_grad = T.grad(T.mean(t_cost[:1]), t_weights)
+
+# Compile symbolic functions
+ctc_func = theano.function([], [t_cost, t_grad])
+"""
+cost, grad = ctc_func()
+
+print("CTC costs: {0}".format(cost))
+print("Gradient of avg. CTC cost w.r.t. weights: {0}".format(np.asarray(grad)))
+
+# TODO: compare costs with softmax results (as in the warp-ctc tests)
+"""
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