removed the expected time from the tutorial code. Now into test.py:speed

Author: nouiz

code/DBN.py

@@ -297,7 +297,7 @@ def test_DBN( finetune_lr = 0.1, pretraining_epochs = 10, \
             print 'Pre-training layer %i, epoch %d, cost '%(i,epoch),numpy.mean(c)
 
     end_time = time.clock()
-    print >> sys.stderr, ('The pretraining code for file '+os.path.split(__file__)[1]+' ran for %.2fm expected Xm our buildbot' % ((end_time-start_time)/60.))
+    print >> sys.stderr, ('The pretraining code for file '+os.path.split(__file__)[1]+' ran for %.2fm' % ((end_time-start_time)/60.))
 
     ########################
     # FINETUNING THE MODEL #
@@ -376,7 +376,7 @@ def test_DBN( finetune_lr = 0.1, pretraining_epochs = 10, \
     print(('Optimization complete with best validation score of %f %%,'
            'with test performance %f %%') %  
                  (best_validation_loss * 100., test_score*100.))
-    print >> sys.stderr, ('The fine tuning code for file '+os.path.split(__file__)[1]+' ran for %.2fm expected Xm our buildbot' % ((end_time-start_time)/60.))
+    print >> sys.stderr, ('The fine tuning code for file '+os.path.split(__file__)[1]+' ran for %.2fm' % ((end_time-start_time)/60.))
 
 
 

code/SdA.py

@@ -349,7 +349,7 @@ def test_SdA( finetune_lr = 0.1, pretraining_epochs = 15, \
 
     end_time = time.clock()
 
-    print >> sys.stderr, ('The pretraining code for file '+os.path.split(__file__)[1]+' ran for %.2fm expected 4.58m in our buildbot' % ((end_time-start_time)/60.))
+    print >> sys.stderr, ('The pretraining code for file '+os.path.split(__file__)[1]+' ran for %.2fm' % ((end_time-start_time)/60.))
 
     ########################
     # FINETUNING THE MODEL #
@@ -426,7 +426,7 @@ def test_SdA( finetune_lr = 0.1, pretraining_epochs = 15, \
     print(('Optimization complete with best validation score of %f %%,'
            'with test performance %f %%') %  
                  (best_validation_loss * 100., test_score*100.))
-    print >> sys.stderr, ('The training code for file '+os.path.split(__file__)[1]+' ran for %.2fm expected 3.91m in our buildbot' % ((end_time-start_time)/60.))
+    print >> sys.stderr, ('The training code for file '+os.path.split(__file__)[1]+' ran for %.2fm' % ((end_time-start_time)/60.))
 
 
 

code/convolutional_mlp.py

@@ -282,7 +282,7 @@ def evaluate_lenet5(learning_rate=0.1, n_epochs=200, dataset='../data/mnist.pkl.
     print('Best validation score of %f %% obtained at iteration %i,'\
           'with test performance %f %%' %  
           (best_validation_loss * 100., best_iter, test_score*100.))
-    print >> sys.stderr, ('The code for file '+os.path.split(__file__)[1]+' ran for %.2fm expected 2.51m in our buildbot' % ((end_time-start_time)/60.))
+    print >> sys.stderr, ('The code for file '+os.path.split(__file__)[1]+' ran for %.2fm' % ((end_time-start_time)/60.))
 
 if __name__ == '__main__':
     evaluate_lenet5()

code/dA.py

@@ -285,7 +285,7 @@ def test_dA( learning_rate = 0.1, training_epochs = 15, dataset ='../data/mnist.
 
     training_time = (end_time - start_time)
 
-    print >> sys.stderr, ('The no corruption code for file '+os.path.split(__file__)[1]+' ran for %.2fm expected 1.17m our buildbot' % ((training_time)/60.))
+    print >> sys.stderr, ('The no corruption code for file '+os.path.split(__file__)[1]+' ran for %.2fm' % ((training_time)/60.))
     image = PIL.Image.fromarray(tile_raster_images( X = da.W.value.T,
                  img_shape = (28,28),tile_shape = (10,10), 
                  tile_spacing=(1,1)))
@@ -327,7 +327,7 @@ def test_dA( learning_rate = 0.1, training_epochs = 15, dataset ='../data/mnist.
 
     training_time = (end_time - start_time)
 
-    print >> sys.stderr, ('The 30% corruption code for file '+os.path.split(__file__)[1]+' ran for %.2fm expected 1.18m our buildbot' % (training_time/60.))
+    print >> sys.stderr, ('The 30% corruption code for file '+os.path.split(__file__)[1]+' ran for %.2fm' % (training_time/60.))
 
     image = PIL.Image.fromarray(tile_raster_images( X = da.W.value.T,
                  img_shape = (28,28),tile_shape = (10,10), 

code/logistic_cg.py

@@ -302,7 +302,7 @@ def callback(theta_value):
           'test performance %f %%') % 
                (validation_scores[0]*100., validation_scores[1]*100.))
 
-    print >> sys.stderr, ('The code for file '+os.path.split(__file__)[1]+' ran for %.1fs expected 6.8s in our buildbot' % ((end_time-start_time)))
+    print >> sys.stderr, ('The code for file '+os.path.split(__file__)[1]+' ran for %.1fs' % ((end_time-start_time)))
 
 
 if __name__ == '__main__':

code/logistic_sgd.py

@@ -341,7 +341,7 @@ def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000, dataset='../data/m
            'with test performance %f %%') %  
                  (best_validation_loss * 100., test_score*100.))
     print 'The code run for %d epochs, with %f epochs/sec'%(epoch,1.*epoch/(end_time-start_time))
-    print >> sys.stderr, ('The code for file '+os.path.split(__file__)[1]+' ran for %.1fs expected 3.8s in our buildbot' % ((end_time-start_time)))
+    print >> sys.stderr, ('The code for file '+os.path.split(__file__)[1]+' ran for %.1fs' % ((end_time-start_time)))
 
 if __name__ == '__main__':
     sgd_optimization_mnist()

code/mlp.py

@@ -345,7 +345,7 @@ def test_mlp( learning_rate=0.01, L1_reg = 0.00, L2_reg = 0.0001, n_epochs=1000,
     print(('Optimization complete. Best validation score of %f %% '
            'obtained at iteration %i, with test performance %f %%') %  
                  (best_validation_loss * 100., best_iter, test_score*100.))
-    print >> sys.stderr, ('The code for file '+os.path.split(__file__)[1]+' ran for %.2fm expected 1.51m in our buildbot' % ((end_time-start_time)/60.))
+    print >> sys.stderr, ('The code for file '+os.path.split(__file__)[1]+' ran for %.2fm' % ((end_time-start_time)/60.))
 
 
 if __name__ == '__main__':