Merge pull request lisa-lab#75 from nouiz/tests

lamblin · lamblin · commit bc7eb60fb786 · 2015-03-30T16:20:49.000-04:00
Add the lstm tutorial in the tests and speed test
diff --git a/code/lstm.py b/code/lstm.py
@@ -511,7 +511,7 @@ def train_lstm(
                 if numpy.mod(uidx, dispFreq) == 0:
                     print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost
 
-                if numpy.mod(uidx, saveFreq) == 0:
+                if saveto and numpy.mod(uidx, saveFreq) == 0:
                     print 'Saving...',
 
                     if best_p is not None:
@@ -571,10 +571,10 @@ def train_lstm(
     test_err = pred_error(f_pred, prepare_data, test, kf_test)
 
     print 'Train ', train_err, 'Valid ', valid_err, 'Test ', test_err
-
-    numpy.savez(saveto, train_err=train_err,
-                valid_err=valid_err, test_err=test_err,
-                history_errs=history_errs, **best_p)
+    if saveto:
+        numpy.savez(saveto, train_err=train_err,
+                    valid_err=valid_err, test_err=test_err,
+                    history_errs=history_errs, **best_p)
     print 'The code run for %d epochs, with %f sec/epochs' % (
         (eidx + 1), (end_time - start_time) / (1. * (eidx + 1)))
     print >> sys.stderr, ('Training took %.1fs' %
diff --git a/code/test.py b/code/test.py
@@ -12,6 +12,7 @@
 import rnnrbm
 import SdA
 import rnnslu
+import lstm
 
 
 def test_rnnslu():
@@ -61,13 +62,17 @@ def test_rnnrbm():
     rnnrbm.test_rnnrbm(num_epochs=1)
 
 
+def test_lstm():
+    lstm.train_lstm(max_epochs=1, test_size=1000, saveto='')
+
+
 def speed():
     """
     This fonction modify the configuration theano and don't restore it!
     """
 
     algo = ['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp',
-            'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm', 'rnnslu']
+            'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm', 'rnnslu', 'lstm']
     to_exec = [True] * len(algo)
 #    to_exec = [False] * len(algo)
 #    to_exec[-1] = True
@@ -82,9 +87,9 @@ 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, 50.8])
+                                       346.9, 381.9, 558.1, 186.3, 50.8, 113.6])
     expected_times_32 = numpy.asarray([8.1, 17.9, 42.5, 66.5, 71,
-                                       191.2, 226.8, 432.8, 176.2, 36.9])
+                                       191.2, 226.8, 432.8, 176.2, 36.9, 78.0])
 
     # Number with just 1 decimal are new value that are faster with
     # the Theano version 0.5rc2 Other number are older. They are not
@@ -104,8 +109,8 @@ def speed():
 #              1.35324519 1.7356905   1.12937868]
 
     expected_times_gpu = numpy.asarray([3.0, 7.55523491, 18.99226785,
-                                        5.8, 21.5,
-                                        11.8, 47.9, 290.1, 255.4, 72.4])
+                                        5.8, 20.5,
+                                        11.8, 47.9, 290.1, 255.4, 72.4, 17.0])
     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)
@@ -162,6 +167,8 @@ def do_tests():
              # 60 is recommended
              'savemodel': False}
         time_test(m, l, 9, rnnslu.main, param=s)
+        time_test(m, l, 10, lstm.train_lstm, max_epochs=1, test_size=1000,
+                  saveto='')
         return numpy.asarray(l)
 
     #test in float64 in FAST_RUN mode on the cpu
