Add speed test for rnnslu.

Author: nouiz

code/test.py

@@ -86,7 +86,7 @@ def speed():
     """
 
     algo = ['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp',
-            'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
+            'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm', 'rnnslu']
     to_exec = [True] * len(algo)
 #    to_exec = [False] * len(algo)
 #    to_exec[-1] = True
@@ -101,9 +101,9 @@ def speed():
     # 7.1-2 (python 2.7.2, mkl unknow). BLAS with only 1 thread.
 
     expected_times_64 = numpy.asarray([9.8, 22.5, 76.1, 73.7, 116.4,
-                                       346.9, 381.9, 558.1, 186.3])
+                                       346.9, 381.9, 558.1, 186.3, 50.8])
     expected_times_32 = numpy.asarray([8.1, 17.9, 42.5, 66.5, 71,
-                                       191.2, 226.8, 432.8, 176.2])
+                                       191.2, 226.8, 432.8, 176.2, 36.9])
 
     # Number with just 1 decimal are new value that are faster with
     # the Theano version 0.5rc2 Other number are older. They are not
@@ -124,8 +124,7 @@ def speed():
 
     expected_times_gpu = numpy.asarray([3.0, 7.55523491, 18.99226785,
                                         5.8, 21.5,
-                                        11.8, 47.9, 290.1, 315.4])
-
+                                        11.8, 47.9, 290.1, 315.4, 72.4])
     expected_times_64 = [s for idx, s in enumerate(expected_times_64)
                          if to_exec[idx]]
     expected_times_32 = [s for idx, s in enumerate(expected_times_32)
@@ -164,6 +163,24 @@ def do_tests():
         time_test(m, l, 7, rbm.test_rbm, training_epochs=1, batch_size=300,
                   n_chains=1, n_samples=1, output_folder='tmp_rbm_plots')
         time_test(m, l, 8, rnnrbm.test_rnnrbm, num_epochs=1)
+        s = {'fold': 3,
+             # 5 folds 0,1,2,3,4
+             'data': 'atis',
+             'lr': 0.0970806646812754,
+             'verbose': 1,
+             'decay': True,
+             # decay on the learning rate if improvement stops
+             'win': 7,
+             # number of words in the context window
+             'nhidden': 200,
+             # number of hidden units
+             'seed': 345,
+             'emb_dimension': 50,
+             # dimension of word embedding
+             'nepochs': 1,
+             # 60 is recommended
+             'savemodel': False}
+        time_test(m, l, 9, rnnslu.main, param=s)
         return numpy.asarray(l)
 
     #test in float64 in FAST_RUN mode on the cpu