Merge pull request lisa-lab#97 from DelightRun/master

replace time.clock() by time.time()

Author: nouiz

Diff for: code/DBN.py

@@ -2,7 +2,7 @@
 """
 import os
 import sys
-import time
+import timeit
 
 import numpy
 
@@ -327,7 +327,7 @@ def test_DBN(finetune_lr=0.1, pretraining_epochs=100,
                                                 k=k)
 
     print '... pre-training the model'
-    start_time = time.clock()
+    start_time = timeit.default_timer()
     ## Pre-train layer-wise
     for i in xrange(dbn.n_layers):
         # go through pretraining epochs
@@ -340,7 +340,7 @@ def test_DBN(finetune_lr=0.1, pretraining_epochs=100,
             print 'Pre-training layer %i, epoch %d, cost ' % (i, epoch),
             print numpy.mean(c)
 
-    end_time = time.clock()
+    end_time = timeit.default_timer()
     # end-snippet-2
     print >> sys.stderr, ('The pretraining code for file ' +
                           os.path.split(__file__)[1] +
@@ -372,7 +372,7 @@ def test_DBN(finetune_lr=0.1, pretraining_epochs=100,
 
     best_validation_loss = numpy.inf
     test_score = 0.
-    start_time = time.clock()
+    start_time = timeit.default_timer()
 
     done_looping = False
     epoch = 0
@@ -424,7 +424,7 @@ def test_DBN(finetune_lr=0.1, pretraining_epochs=100,
                 done_looping = True
                 break
 
-    end_time = time.clock()
+    end_time = timeit.default_timer()
     print(
         (
             'Optimization complete with best validation score of %f %%, '

Diff for: code/SdA.py

@@ -31,7 +31,7 @@
 """
 import os
 import sys
-import time
+import timeit
 
 import numpy
 
@@ -379,7 +379,7 @@ def test_SdA(finetune_lr=0.1, pretraining_epochs=15,
                                                 batch_size=batch_size)
 
     print '... pre-training the model'
-    start_time = time.clock()
+    start_time = timeit.default_timer()
     ## Pre-train layer-wise
     corruption_levels = [.1, .2, .3]
     for i in xrange(sda.n_layers):
@@ -394,7 +394,7 @@ def test_SdA(finetune_lr=0.1, pretraining_epochs=15,
             print 'Pre-training layer %i, epoch %d, cost ' % (i, epoch),
             print numpy.mean(c)
 
-    end_time = time.clock()
+    end_time = timeit.default_timer()
 
     print >> sys.stderr, ('The pretraining code for file ' +
                           os.path.split(__file__)[1] +
@@ -427,7 +427,7 @@ def test_SdA(finetune_lr=0.1, pretraining_epochs=15,
 
     best_validation_loss = numpy.inf
     test_score = 0.
-    start_time = time.clock()
+    start_time = timeit.default_timer()
 
     done_looping = False
     epoch = 0
@@ -471,7 +471,7 @@ def test_SdA(finetune_lr=0.1, pretraining_epochs=15,
                 done_looping = True
                 break
 
-    end_time = time.clock()
+    end_time = timeit.default_timer()
     print(
         (
             'Optimization complete with best validation score of %f %%, '

Diff for: code/cA.py

@@ -30,7 +30,7 @@
 """
 import os
 import sys
-import time
+import timeit
 
 import numpy
 
@@ -276,7 +276,7 @@ def test_cA(learning_rate=0.01, training_epochs=20,
         }
     )
 
-    start_time = time.clock()
+    start_time = timeit.default_timer()
 
     ############
     # TRAINING #
@@ -293,7 +293,7 @@ def test_cA(learning_rate=0.01, training_epochs=20,
         print 'Training epoch %d, reconstruction cost ' % epoch, numpy.mean(
             c_array[0]), ' jacobian norm ', numpy.mean(numpy.sqrt(c_array[1]))
 
-    end_time = time.clock()
+    end_time = timeit.default_timer()
 
     training_time = (end_time - start_time)
 

Diff for: code/convolutional_mlp.py

@@ -23,7 +23,7 @@
 """
 import os
 import sys
-import time
+import timeit
 
 import numpy
 
@@ -274,7 +274,7 @@ def evaluate_lenet5(learning_rate=0.1, n_epochs=200,
     best_validation_loss = numpy.inf
     best_iter = 0
     test_score = 0.
-    start_time = time.clock()
+    start_time = timeit.default_timer()
 
     epoch = 0
     done_looping = False
@@ -326,7 +326,7 @@ def evaluate_lenet5(learning_rate=0.1, n_epochs=200,
                 done_looping = True
                 break
 
-    end_time = time.clock()
+    end_time = timeit.default_timer()
     print('Optimization complete.')
     print('Best validation score of %f %% obtained at iteration %i, '
           'with test performance %f %%' %

Diff for: code/dA.py

@@ -32,7 +32,7 @@
 
 import os
 import sys
-import time
+import timeit
 
 import numpy
 
@@ -321,7 +321,7 @@ def test_dA(learning_rate=0.1, training_epochs=15,
         }
     )
 
-    start_time = time.clock()
+    start_time = timeit.default_timer()
 
     ############
     # TRAINING #
@@ -336,7 +336,7 @@ def test_dA(learning_rate=0.1, training_epochs=15,
 
         print 'Training epoch %d, cost ' % epoch, numpy.mean(c)
 
-    end_time = time.clock()
+    end_time = timeit.default_timer()
 
     training_time = (end_time - start_time)
 
@@ -379,7 +379,7 @@ def test_dA(learning_rate=0.1, training_epochs=15,
         }
     )
 
-    start_time = time.clock()
+    start_time = timeit.default_timer()
 
     ############
     # TRAINING #
@@ -394,7 +394,7 @@ def test_dA(learning_rate=0.1, training_epochs=15,
 
         print 'Training epoch %d, cost ' % epoch, numpy.mean(c)
 
-    end_time = time.clock()
+    end_time = timeit.default_timer()
 
     training_time = (end_time - start_time)
 

Diff for: code/logistic_cg.py

@@ -38,7 +38,7 @@
 
 import os
 import sys
-import time
+import timeit
 
 import numpy
 
@@ -275,7 +275,7 @@ def callback(theta_value):
     # using scipy conjugate gradient optimizer
     import scipy.optimize
     print ("Optimizing using scipy.optimize.fmin_cg...")
-    start_time = time.clock()
+    start_time = timeit.default_timer()
     best_w_b = scipy.optimize.fmin_cg(
         f=train_fn,
         x0=numpy.zeros((n_in + 1) * n_out, dtype=x.dtype),
@@ -284,7 +284,7 @@ def callback(theta_value):
         disp=0,
         maxiter=n_epochs
     )
-    end_time = time.clock()
+    end_time = timeit.default_timer()
     print(
         (
             'Optimization complete with best validation score of %f %%, with '

Diff for: code/logistic_sgd.py

@@ -38,7 +38,7 @@
 import gzip
 import os
 import sys
-import time
+import timeit
 
 import numpy
 
@@ -360,7 +360,7 @@ def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000,
 
     best_validation_loss = numpy.inf
     test_score = 0.
-    start_time = time.clock()
+    start_time = timeit.default_timer()
 
     done_looping = False
     epoch = 0
@@ -419,7 +419,7 @@ def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000,
                 done_looping = True
                 break
 
-    end_time = time.clock()
+    end_time = timeit.default_timer()
     print(
         (
             'Optimization complete with best validation score of %f %%,'

Diff for: code/lstm.py

@@ -543,7 +543,7 @@ def train_lstm(
 
     uidx = 0  # the number of update done
     estop = False  # early stop
-    start_time = time.clock()
+    start_time = time.time()
     try:
         for eidx in xrange(max_epochs):
             n_samples = 0
@@ -622,7 +622,7 @@ def train_lstm(
     except KeyboardInterrupt:
         print "Training interupted"
 
-    end_time = time.clock()
+    end_time = time.time()
     if best_p is not None:
         zipp(best_p, tparams)
     else:

Diff for: code/mlp.py

@@ -23,7 +23,7 @@
 
 import os
 import sys
-import time
+import timeit
 
 import numpy
 
@@ -336,7 +336,7 @@ def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000,
     best_validation_loss = numpy.inf
     best_iter = 0
     test_score = 0.
-    start_time = time.clock()
+    start_time = timeit.default_timer()
 
     epoch = 0
     done_looping = False
@@ -391,7 +391,7 @@ def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000,
                 done_looping = True
                 break
 
-    end_time = time.clock()
+    end_time = timeit.default_timer()
     print(('Optimization complete. Best validation score of %f %% '
            'obtained at iteration %i, with test performance %f %%') %
           (best_validation_loss * 100., best_iter + 1, test_score * 100.))

Diff for: code/rbm.py

@@ -4,7 +4,7 @@
 contain hidden variables. Restricted Boltzmann Machines further restrict BMs
 to those without visible-visible and hidden-hidden connections.
 """
-import time
+import timeit
 
 try:
     import PIL.Image as Image
@@ -428,7 +428,7 @@ def test_rbm(learning_rate=0.1, training_epochs=15,
     )
 
     plotting_time = 0.
-    start_time = time.clock()
+    start_time = timeit.default_timer()
 
     # go through training epochs
     for epoch in xrange(training_epochs):
@@ -441,7 +441,7 @@ def test_rbm(learning_rate=0.1, training_epochs=15,
         print 'Training epoch %d, cost is ' % epoch, numpy.mean(mean_cost)
 
         # Plot filters after each training epoch
-        plotting_start = time.clock()
+        plotting_start = timeit.default_timer()
         # Construct image from the weight matrix
         image = Image.fromarray(
             tile_raster_images(
@@ -452,10 +452,10 @@ def test_rbm(learning_rate=0.1, training_epochs=15,
             )
         )
         image.save('filters_at_epoch_%i.png' % epoch)
-        plotting_stop = time.clock()
+        plotting_stop = timeit.default_timer()
         plotting_time += (plotting_stop - plotting_start)
 
-    end_time = time.clock()
+    end_time = timeit.default_timer()
 
     pretraining_time = (end_time - start_time) - plotting_time
 

Diff for: code/rnnslu.py

@@ -8,7 +8,7 @@
 import stat
 import subprocess
 import sys
-import time
+import timeit
 
 import numpy
 
@@ -318,13 +318,13 @@ def main(param=None):
         shuffle([train_lex, train_ne, train_y], param['seed'])
 
         param['ce'] = e
-        tic = time.time()
+        tic = timeit.default_timer()
 
         for i, (x, y) in enumerate(zip(train_lex, train_y)):
             rnn.train(x, y, param['win'], param['clr'])
             print '[learning] epoch %i >> %2.2f%%' % (
                 e, (i + 1) * 100. / nsentences),
-            print 'completed in %.2f (sec) <<\r' % (time.time() - tic),
+            print 'completed in %.2f (sec) <<\r' % (timeit.default_timer() - tic),
             sys.stdout.flush()
 
         # evaluation // back into the real world : idx -> words