Deep Learning Tutorial buildbot Fail=0 Err=1 Ran=12 Skip=0 KnownFail=0 SpeedFailure=5
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li...@iro.umontreal.ca
May 17, 2013, 7:54:51 AM5/17/13
to theano-...@googlegroups.com
Summary of the output:
File "/part/01/Tmp/nightly_build/DeepLearningTutorials/code/mcrbm/test_mcrbm.py", line 93, in test_reproduce_ranzato_hinton_2010
Full output:
Fri May 17 02:13:09 EDT 2013
git version for Theano: b321e0790ac7296dd59f3b03219e483433d4bdab
git version: 3da5edbb800133e908e4f9b50373d2743b90f099
executing nosetests speed with mode=FAST_RUN
The code for file logistic_sgd.pyc ran for 10.2s
The code for file logistic_cg.pyc ran for 24.3s
The code for file mlp.pyc ran for 1.37m
The code for file convolutional_mlp.pyc ran for 1.26m
The no corruption code for file dA.pyc ran for 1.01m
The 30% corruption code for file dA.pyc ran for 1.00m
The pretraining code for file SdA.pyc ran for 3.06m
The training code for file SdA.pyc ran for 2.82m
The pretraining code for file DBN.pyc ran for 3.58m
The fine tuning code for file DBN.pyc ran for 2.82m
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
float64 times [ 10.23503613 24.36096525 82.25518036 75.70044208 121.29465938
353.82041597 385.26601672 552.4216826 193.32949686]
float64 expected [10.300000000000001, 23.699999999999999, 78.099999999999994, 73.700000000000003, 116.40000000000001, 346.89999999999998, 381.89999999999998, 558.10000000000002, 186.30000000000001]
float64 % expected/get [ 1.0063472 0.97286785 0.94948427 0.97357424 0.95964654 0.98044088
0.99126314 1.01027895 0.96363981]
The code for file logistic_sgd.pyc ran for 11.6s
The code for file logistic_cg.pyc ran for 29.8s
The code for file mlp.pyc ran for 0.78m
The code for file convolutional_mlp.pyc ran for 1.12m
The no corruption code for file dA.pyc ran for 0.59m
The 30% corruption code for file dA.pyc ran for 0.63m
The pretraining code for file SdA.pyc ran for 1.76m
The training code for file SdA.pyc ran for 1.52m
The pretraining code for file DBN.pyc ran for 2.36m
The fine tuning code for file DBN.pyc ran for 1.55m
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
float32 times [ 11.68477321 29.9400022 47.14251423 67.68978429 73.89255714
197.55145884 235.66201377 436.46544814 180.85114384]
float32 expected [11.6, 29.600000000000001, 47.200000000000003, 66.5, 71.0, 191.19999999999999, 226.80000000000001, 432.80000000000001, 176.19999999999999]
float32 % expected/get [ 0.99274498 0.98864388 1.0012194 0.98242299 0.96085455 0.96784909
0.96239524 0.99160197 0.97428192]
float64/float32 [ 0.87592938 0.81365943 1.74481955 1.11834367 1.64150036 1.79102912
1.63482443 1.26567105 1.06899792]
Duplicate the timing to have everything in one place
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
float64 times [ 10.23503613 24.36096525 82.25518036 75.70044208 121.29465938
353.82041597 385.26601672 552.4216826 193.32949686]
float64 expected [10.300000000000001, 23.699999999999999, 78.099999999999994, 73.700000000000003, 116.40000000000001, 346.89999999999998, 381.89999999999998, 558.10000000000002, 186.30000000000001]
float64 % expected/get [ 1.0063472 0.97286785 0.94948427 0.97357424 0.95964654 0.98044088
0.99126314 1.01027895 0.96363981]
float32 times [ 11.68477321 29.9400022 47.14251423 67.68978429 73.89255714
197.55145884 235.66201377 436.46544814 180.85114384]
float32 expected [11.6, 29.600000000000001, 47.200000000000003, 66.5, 71.0, 191.19999999999999, 226.80000000000001, 432.80000000000001, 176.19999999999999]
float32 % expected/get [ 0.99274498 0.98864388 1.0012194 0.98242299 0.96085455 0.96784909
0.96239524 0.99160197 0.97428192]
float64/float32 [ 0.87592938 0.81365943 1.74481955 1.11834367 1.64150036 1.79102912
1.63482443 1.26567105 1.06899792]
expected float64/float32 [ 0.88148908 0.79158311 1.65667872 1.08879059 1.57526014 1.75599817
1.62054119 1.27868083 1.03012896]
Using gpu device 0: GeForce GTX 285
The code for file logistic_sgd.pyc ran for 3.7s
The code for file logistic_cg.pyc ran for 10.4s
The code for file mlp.pyc ran for 0.49m
The code for file convolutional_mlp.pyc ran for 0.19m
The no corruption code for file dA.pyc ran for 0.20m
The 30% corruption code for file dA.pyc ran for 0.21m
The pretraining code for file SdA.pyc ran for 0.20m
The training code for file SdA.pyc ran for 0.12m
The pretraining code for file DBN.pyc ran for 0.82m
The fine tuning code for file DBN.pyc ran for 0.12m
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
gpu times [ 3.71791363 10.34460235 29.51879668 11.79640484 24.21770835
20.00711346 57.44709849 306.74637222 318.67248964]
gpu expected [3.0766348799999998, 7.5552349100000002, 18.992267850000001, 9.5999999999999996, 24.130070450000002, 20.399999999999999, 56.0, 302.60000000000002, 315.39999999999998]
gpu % expected/get [ 0.8275165 0.73035528 0.64339573 0.81380727 0.99638125 1.01963734
0.97480989 0.98648273 0.98973087]
float64/gpu [ 2.75289777 2.35494458 2.78653569 6.41724687 5.00851103
17.68473082 6.70644866 1.80090698 0.60667144]
Duplicate the timing to have everything in one place
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
float64 times [ 10.23503613 24.36096525 82.25518036 75.70044208 121.29465938
353.82041597 385.26601672 552.4216826 193.32949686]
float64 expected [10.300000000000001, 23.699999999999999, 78.099999999999994, 73.700000000000003, 116.40000000000001, 346.89999999999998, 381.89999999999998, 558.10000000000002, 186.30000000000001]
float64 % expected/get [ 1.0063472 0.97286785 0.94948427 0.97357424 0.95964654 0.98044088
0.99126314 1.01027895 0.96363981]
float32 times [ 11.68477321 29.9400022 47.14251423 67.68978429 73.89255714
197.55145884 235.66201377 436.46544814 180.85114384]
float32 expected [11.6, 29.600000000000001, 47.200000000000003, 66.5, 71.0, 191.19999999999999, 226.80000000000001, 432.80000000000001, 176.19999999999999]
float32 % expected/get [ 0.99274498 0.98864388 1.0012194 0.98242299 0.96085455 0.96784909
0.96239524 0.99160197 0.97428192]
gpu times [ 3.71791363 10.34460235 29.51879668 11.79640484 24.21770835
20.00711346 57.44709849 306.74637222 318.67248964]
gpu expected [3.0766348799999998, 7.5552349100000002, 18.992267850000001, 9.5999999999999996, 24.130070450000002, 20.399999999999999, 56.0, 302.60000000000002, 315.39999999999998]
gpu % expected/get [ 0.8275165 0.73035528 0.64339573 0.81380727 0.99638125 1.01963734
0.97480989 0.98648273 0.98973087]
float64/float32 [ 0.87592938 0.81365943 1.74481955 1.11834367 1.64150036 1.79102912
1.63482443 1.26567105 1.06899792]
expected float64/float32 [ 0.88148908 0.79158311 1.65667872 1.08879059 1.57526014 1.75599817
1.62054119 1.27868083 1.03012896]
float64/gpu [ 2.75289777 2.35494458 2.78653569 6.41724687 5.00851103
17.68473082 6.70644866 1.80090698 0.60667144]
expected float64/gpu [ 2.77037097 2.29104988 2.64577181 6.24766622 4.80640027
17.33883305 6.64785533 1.81941842 0.58461275]
float32/gpu [ 3.14283073 2.89426323 1.59703374 5.73817067 3.05117875 9.87406101
4.10224398 1.42288707 0.56751414]
expected float32/gpu [ 3.12002945 2.86139564 1.59898117 5.63731077 2.931739 9.55660098
3.94798007 1.41093763 0.55291877]
speed_failure_float64=1
speed_failure_float32=0
speed_failure_gpu=4
.
----------------------------------------------------------------------
Ran 1 test in 4072.323s
OK
executing nosetests with mode=FAST_RUN,floatX=float32
./part/01/Tmp/nightly_build/DeepLearningTutorials/code/mcrbm/test_mcrbm.py:68: UserWarning: The parameter 'updates' of theano.function() expects an OrderedDict, got <type 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7, or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.
updates=trainer.cd_updates())
EThe code for file logistic_sgd.pyc ran for 4.1s
.The code for file logistic_cg.pyc ran for 8.2s
.The code for file mlp.pyc ran for 0.15m
.The code for file convolutional_mlp.pyc ran for 0.22m
.The no corruption code for file dA.pyc ran for 0.29m
The 30% corruption code for file dA.pyc ran for 0.29m
.The pretraining code for file SdA.pyc ran for 1.69m
The training code for file SdA.pyc ran for 0.74m
.The pretraining code for file DBN.pyc ran for 2.29m
The fine tuning code for file DBN.pyc ran for 0.74m
...
======================================================================
ERROR: mcrbm.test_mcrbm.test_reproduce_ranzato_hinton_2010
----------------------------------------------------------------------
Traceback (most recent call last):
File "/opt/lisa/os/epd-7.1.2/lib/python2.7/site-packages/nose/case.py", line 187, in runTest
self.test(*self.arg)
File "/part/01/Tmp/nightly_build/DeepLearningTutorials/code/mcrbm/test_mcrbm.py", line 93, in test_reproduce_ranzato_hinton_2010
tile(imgs_fn(jj), "imgs_%06i.png" % jj)
File "/Tmp/nightly_build/Pylearn/pylearn/dataset_ops/image_patches.py", line 96, in save_filters_of_ranzato_hinton_2010
min_dynamic_range=min_dynamic_range)
File "/Tmp/nightly_build/Pylearn/pylearn/io/image_tiling.py", line 77, in tile_raster_images
raise NotImplementedError()
NotImplementedError:
-------------------- >> begin captured stdout << ---------------------
Learning...
--------------------- >> end captured stdout << ----------------------
----------------------------------------------------------------------
Ran 11 tests in 856.908s
FAILED (errors=1)
4494.04user 391.47system 1:22:16elapsed 98%CPU (0avgtext+0avgdata 1960144maxresident)k
357688inputs+2168outputs (851major+9011654minor)pagefaults 0swaps
File "/part/01/Tmp/nightly_build/DeepLearningTutorials/code/mcrbm/test_mcrbm.py", line 93, in test_reproduce_ranzato_hinton_2010
Full output:
Fri May 17 02:13:09 EDT 2013
git version for Theano: b321e0790ac7296dd59f3b03219e483433d4bdab
git version: 3da5edbb800133e908e4f9b50373d2743b90f099
executing nosetests speed with mode=FAST_RUN
The code for file logistic_sgd.pyc ran for 10.2s
The code for file logistic_cg.pyc ran for 24.3s
The code for file mlp.pyc ran for 1.37m
The code for file convolutional_mlp.pyc ran for 1.26m
The no corruption code for file dA.pyc ran for 1.01m
The 30% corruption code for file dA.pyc ran for 1.00m
The pretraining code for file SdA.pyc ran for 3.06m
The training code for file SdA.pyc ran for 2.82m
The pretraining code for file DBN.pyc ran for 3.58m
The fine tuning code for file DBN.pyc ran for 2.82m
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
float64 times [ 10.23503613 24.36096525 82.25518036 75.70044208 121.29465938
353.82041597 385.26601672 552.4216826 193.32949686]
float64 expected [10.300000000000001, 23.699999999999999, 78.099999999999994, 73.700000000000003, 116.40000000000001, 346.89999999999998, 381.89999999999998, 558.10000000000002, 186.30000000000001]
float64 % expected/get [ 1.0063472 0.97286785 0.94948427 0.97357424 0.95964654 0.98044088
0.99126314 1.01027895 0.96363981]
The code for file logistic_sgd.pyc ran for 11.6s
The code for file logistic_cg.pyc ran for 29.8s
The code for file mlp.pyc ran for 0.78m
The code for file convolutional_mlp.pyc ran for 1.12m
The no corruption code for file dA.pyc ran for 0.59m
The 30% corruption code for file dA.pyc ran for 0.63m
The pretraining code for file SdA.pyc ran for 1.76m
The training code for file SdA.pyc ran for 1.52m
The pretraining code for file DBN.pyc ran for 2.36m
The fine tuning code for file DBN.pyc ran for 1.55m
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
float32 times [ 11.68477321 29.9400022 47.14251423 67.68978429 73.89255714
197.55145884 235.66201377 436.46544814 180.85114384]
float32 expected [11.6, 29.600000000000001, 47.200000000000003, 66.5, 71.0, 191.19999999999999, 226.80000000000001, 432.80000000000001, 176.19999999999999]
float32 % expected/get [ 0.99274498 0.98864388 1.0012194 0.98242299 0.96085455 0.96784909
0.96239524 0.99160197 0.97428192]
float64/float32 [ 0.87592938 0.81365943 1.74481955 1.11834367 1.64150036 1.79102912
1.63482443 1.26567105 1.06899792]
Duplicate the timing to have everything in one place
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
float64 times [ 10.23503613 24.36096525 82.25518036 75.70044208 121.29465938
353.82041597 385.26601672 552.4216826 193.32949686]
float64 expected [10.300000000000001, 23.699999999999999, 78.099999999999994, 73.700000000000003, 116.40000000000001, 346.89999999999998, 381.89999999999998, 558.10000000000002, 186.30000000000001]
float64 % expected/get [ 1.0063472 0.97286785 0.94948427 0.97357424 0.95964654 0.98044088
0.99126314 1.01027895 0.96363981]
float32 times [ 11.68477321 29.9400022 47.14251423 67.68978429 73.89255714
197.55145884 235.66201377 436.46544814 180.85114384]
float32 expected [11.6, 29.600000000000001, 47.200000000000003, 66.5, 71.0, 191.19999999999999, 226.80000000000001, 432.80000000000001, 176.19999999999999]
float32 % expected/get [ 0.99274498 0.98864388 1.0012194 0.98242299 0.96085455 0.96784909
0.96239524 0.99160197 0.97428192]
float64/float32 [ 0.87592938 0.81365943 1.74481955 1.11834367 1.64150036 1.79102912
1.63482443 1.26567105 1.06899792]
expected float64/float32 [ 0.88148908 0.79158311 1.65667872 1.08879059 1.57526014 1.75599817
1.62054119 1.27868083 1.03012896]
Using gpu device 0: GeForce GTX 285
The code for file logistic_sgd.pyc ran for 3.7s
The code for file logistic_cg.pyc ran for 10.4s
The code for file mlp.pyc ran for 0.49m
The code for file convolutional_mlp.pyc ran for 0.19m
The no corruption code for file dA.pyc ran for 0.20m
The 30% corruption code for file dA.pyc ran for 0.21m
The pretraining code for file SdA.pyc ran for 0.20m
The training code for file SdA.pyc ran for 0.12m
The pretraining code for file DBN.pyc ran for 0.82m
The fine tuning code for file DBN.pyc ran for 0.12m
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
gpu times [ 3.71791363 10.34460235 29.51879668 11.79640484 24.21770835
20.00711346 57.44709849 306.74637222 318.67248964]
gpu expected [3.0766348799999998, 7.5552349100000002, 18.992267850000001, 9.5999999999999996, 24.130070450000002, 20.399999999999999, 56.0, 302.60000000000002, 315.39999999999998]
gpu % expected/get [ 0.8275165 0.73035528 0.64339573 0.81380727 0.99638125 1.01963734
0.97480989 0.98648273 0.98973087]
float64/gpu [ 2.75289777 2.35494458 2.78653569 6.41724687 5.00851103
17.68473082 6.70644866 1.80090698 0.60667144]
Duplicate the timing to have everything in one place
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
float64 times [ 10.23503613 24.36096525 82.25518036 75.70044208 121.29465938
353.82041597 385.26601672 552.4216826 193.32949686]
float64 expected [10.300000000000001, 23.699999999999999, 78.099999999999994, 73.700000000000003, 116.40000000000001, 346.89999999999998, 381.89999999999998, 558.10000000000002, 186.30000000000001]
float64 % expected/get [ 1.0063472 0.97286785 0.94948427 0.97357424 0.95964654 0.98044088
0.99126314 1.01027895 0.96363981]
float32 times [ 11.68477321 29.9400022 47.14251423 67.68978429 73.89255714
197.55145884 235.66201377 436.46544814 180.85114384]
float32 expected [11.6, 29.600000000000001, 47.200000000000003, 66.5, 71.0, 191.19999999999999, 226.80000000000001, 432.80000000000001, 176.19999999999999]
float32 % expected/get [ 0.99274498 0.98864388 1.0012194 0.98242299 0.96085455 0.96784909
0.96239524 0.99160197 0.97428192]
gpu times [ 3.71791363 10.34460235 29.51879668 11.79640484 24.21770835
20.00711346 57.44709849 306.74637222 318.67248964]
gpu expected [3.0766348799999998, 7.5552349100000002, 18.992267850000001, 9.5999999999999996, 24.130070450000002, 20.399999999999999, 56.0, 302.60000000000002, 315.39999999999998]
gpu % expected/get [ 0.8275165 0.73035528 0.64339573 0.81380727 0.99638125 1.01963734
0.97480989 0.98648273 0.98973087]
float64/float32 [ 0.87592938 0.81365943 1.74481955 1.11834367 1.64150036 1.79102912
1.63482443 1.26567105 1.06899792]
expected float64/float32 [ 0.88148908 0.79158311 1.65667872 1.08879059 1.57526014 1.75599817
1.62054119 1.27868083 1.03012896]
float64/gpu [ 2.75289777 2.35494458 2.78653569 6.41724687 5.00851103
17.68473082 6.70644866 1.80090698 0.60667144]
expected float64/gpu [ 2.77037097 2.29104988 2.64577181 6.24766622 4.80640027
17.33883305 6.64785533 1.81941842 0.58461275]
float32/gpu [ 3.14283073 2.89426323 1.59703374 5.73817067 3.05117875 9.87406101
4.10224398 1.42288707 0.56751414]
expected float32/gpu [ 3.12002945 2.86139564 1.59898117 5.63731077 2.931739 9.55660098
3.94798007 1.41093763 0.55291877]
speed_failure_float64=1
speed_failure_float32=0
speed_failure_gpu=4
.
----------------------------------------------------------------------
Ran 1 test in 4072.323s
OK
executing nosetests with mode=FAST_RUN,floatX=float32
./part/01/Tmp/nightly_build/DeepLearningTutorials/code/mcrbm/test_mcrbm.py:68: UserWarning: The parameter 'updates' of theano.function() expects an OrderedDict, got <type 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7, or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.
updates=trainer.cd_updates())
EThe code for file logistic_sgd.pyc ran for 4.1s
.The code for file logistic_cg.pyc ran for 8.2s
.The code for file mlp.pyc ran for 0.15m
.The code for file convolutional_mlp.pyc ran for 0.22m
.The no corruption code for file dA.pyc ran for 0.29m
The 30% corruption code for file dA.pyc ran for 0.29m
.The pretraining code for file SdA.pyc ran for 1.69m
The training code for file SdA.pyc ran for 0.74m
.The pretraining code for file DBN.pyc ran for 2.29m
The fine tuning code for file DBN.pyc ran for 0.74m
...
======================================================================
ERROR: mcrbm.test_mcrbm.test_reproduce_ranzato_hinton_2010
----------------------------------------------------------------------
Traceback (most recent call last):
File "/opt/lisa/os/epd-7.1.2/lib/python2.7/site-packages/nose/case.py", line 187, in runTest
self.test(*self.arg)
File "/part/01/Tmp/nightly_build/DeepLearningTutorials/code/mcrbm/test_mcrbm.py", line 93, in test_reproduce_ranzato_hinton_2010
tile(imgs_fn(jj), "imgs_%06i.png" % jj)
File "/Tmp/nightly_build/Pylearn/pylearn/dataset_ops/image_patches.py", line 96, in save_filters_of_ranzato_hinton_2010
min_dynamic_range=min_dynamic_range)
File "/Tmp/nightly_build/Pylearn/pylearn/io/image_tiling.py", line 77, in tile_raster_images
raise NotImplementedError()
NotImplementedError:
-------------------- >> begin captured stdout << ---------------------
Learning...
--------------------- >> end captured stdout << ----------------------
----------------------------------------------------------------------
Ran 11 tests in 856.908s
FAILED (errors=1)
4494.04user 391.47system 1:22:16elapsed 98%CPU (0avgtext+0avgdata 1960144maxresident)k
357688inputs+2168outputs (851major+9011654minor)pagefaults 0swaps
li...@iro.umontreal.ca
May 21, 2013, 4:51:58 AM5/21/13
to theano-...@googlegroups.com
Summary of the output:
File "/part/01/Tmp/nightly_build/DeepLearningTutorials/code/mcrbm/test_mcrbm.py", line 93, in test_reproduce_ranzato_hinton_2010
Full output:
Tue May 21 02:30:17 EDT 2013
File "/part/01/Tmp/nightly_build/DeepLearningTutorials/code/mcrbm/test_mcrbm.py", line 93, in test_reproduce_ranzato_hinton_2010
Full output:
git version for Theano: bf549f88a441f8beb3f8729ea03300a88508cf97
git version: 3da5edbb800133e908e4f9b50373d2743b90f099
executing nosetests speed with mode=FAST_RUN
The code for file logistic_sgd.pyc ran for 10.0s
executing nosetests speed with mode=FAST_RUN
The code for file logistic_cg.pyc ran for 23.5s
The code for file mlp.pyc ran for 1.27m
The code for file convolutional_mlp.pyc ran for 1.21m
The no corruption code for file dA.pyc ran for 0.96m
The 30% corruption code for file dA.pyc ran for 0.98m
The pretraining code for file SdA.pyc ran for 2.96m
The training code for file SdA.pyc ran for 2.86m
The pretraining code for file DBN.pyc ran for 3.54m
The fine tuning code for file DBN.pyc ran for 2.86m
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
float64 times [ 10.07351589 23.58783913 76.50742674 72.82545209 117.06590152
350.85699058 385.29646087 544.236233 186.61370802]
float64 expected [10.300000000000001, 23.699999999999999, 78.099999999999994, 73.700000000000003, 116.40000000000001, 346.89999999999998, 381.89999999999998, 558.10000000000002, 186.30000000000001]
float64 % expected/get [ 1.02248312 1.00475503 1.02081593 1.01200882 0.99431174 0.98872193
0.99118481 1.0254738 0.99831894]
The code for file logistic_sgd.pyc ran for 11.5s
The code for file logistic_cg.pyc ran for 29.3s
The code for file mlp.pyc ran for 0.77m
The code for file convolutional_mlp.pyc ran for 1.09m
The no corruption code for file dA.pyc ran for 0.58m
The 30% corruption code for file dA.pyc ran for 0.59m
The pretraining code for file SdA.pyc ran for 1.71m
The training code for file SdA.pyc ran for 1.50m
The pretraining code for file DBN.pyc ran for 2.34m
The fine tuning code for file DBN.pyc ran for 1.50m
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
float32 times [ 11.53647828 29.36764073 46.5161581 65.99822044 70.7238946
193.45776939 231.31574655 426.37927294 176.64749837]
float32 expected [11.6, 29.600000000000001, 47.200000000000003, 66.5, 71.0, 191.19999999999999, 226.80000000000001, 432.80000000000001, 176.19999999999999]
float32 % expected/get [ 1.00550616 1.00791208 1.01470117 1.00760293 1.00390399 0.98832939
0.980478 1.01505872 0.99746672]
float64/float32 [ 0.87318813 0.80319149 1.64474948 1.10344569 1.65525247 1.81361023
1.66567329 1.27641344 1.05641863]
Duplicate the timing to have everything in one place
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
350.85699058 385.29646087 544.236233 186.61370802]
float64 expected [10.300000000000001, 23.699999999999999, 78.099999999999994, 73.700000000000003, 116.40000000000001, 346.89999999999998, 381.89999999999998, 558.10000000000002, 186.30000000000001]
float64 % expected/get [ 1.02248312 1.00475503 1.02081593 1.01200882 0.99431174 0.98872193
0.99118481 1.0254738 0.99831894]
float32 times [ 11.53647828 29.36764073 46.5161581 65.99822044 70.7238946
193.45776939 231.31574655 426.37927294 176.64749837]
float32 expected [11.6, 29.600000000000001, 47.200000000000003, 66.5, 71.0, 191.19999999999999, 226.80000000000001, 432.80000000000001, 176.19999999999999]
float32 % expected/get [ 1.00550616 1.00791208 1.01470117 1.00760293 1.00390399 0.98832939
0.980478 1.01505872 0.99746672]
float64/float32 [ 0.87318813 0.80319149 1.64474948 1.10344569 1.65525247 1.81361023
1.66567329 1.27641344 1.05641863]
expected float64/float32 [ 0.89282013 0.80701069 1.67898647 1.11669678 1.64583696 1.79315621
1.65099007 1.30892854 1.05464273]
Using gpu device 0: GeForce GTX 285
The code for file logistic_sgd.pyc ran for 3.7s
The code for file logistic_cg.pyc ran for 10.2s
The code for file logistic_sgd.pyc ran for 3.7s
The code for file mlp.pyc ran for 0.48m
The code for file convolutional_mlp.pyc ran for 0.19m
The no corruption code for file dA.pyc ran for 0.19m
The 30% corruption code for file dA.pyc ran for 0.20m
The pretraining code for file SdA.pyc ran for 0.20m
The training code for file SdA.pyc ran for 0.12m
The pretraining code for file DBN.pyc ran for 0.82m
The fine tuning code for file DBN.pyc ran for 0.12m
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
gpu times [ 3.69237351 10.19157267 29.16963911 11.75662923 24.06727982
The training code for file SdA.pyc ran for 0.12m
The pretraining code for file DBN.pyc ran for 0.82m
The fine tuning code for file DBN.pyc ran for 0.12m
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
19.95286012 57.39508867 330.65728259 319.31312203]
gpu expected [3.0766348799999998, 7.5552349100000002, 18.992267850000001, 9.5999999999999996, 24.130070450000002, 20.399999999999999, 56.0, 302.60000000000002, 315.39999999999998]
gpu % expected/get [ 0.83324043 0.74132179 0.65109711 0.81656058 1.00260896 1.02240981
0.97569324 0.91514694 0.98774519]
float64/gpu [ 2.72819525 2.31444546 2.62284447 6.19441599 4.86411021
17.58429561 6.71305629 1.64592241 0.5844223 ]
Duplicate the timing to have everything in one place
['logistic_sgd', 'logistic_cg', 'mlp', 'convolutional_mlp', 'dA', 'SdA', 'DBN', 'rbm', 'rnnrbm']
350.85699058 385.29646087 544.236233 186.61370802]
float64 expected [10.300000000000001, 23.699999999999999, 78.099999999999994, 73.700000000000003, 116.40000000000001, 346.89999999999998, 381.89999999999998, 558.10000000000002, 186.30000000000001]
float64 % expected/get [ 1.02248312 1.00475503 1.02081593 1.01200882 0.99431174 0.98872193
0.99118481 1.0254738 0.99831894]
float32 times [ 11.53647828 29.36764073 46.5161581 65.99822044 70.7238946
193.45776939 231.31574655 426.37927294 176.64749837]
float32 expected [11.6, 29.600000000000001, 47.200000000000003, 66.5, 71.0, 191.19999999999999, 226.80000000000001, 432.80000000000001, 176.19999999999999]
float32 % expected/get [ 1.00550616 1.00791208 1.01470117 1.00760293 1.00390399 0.98832939
0.980478 1.01505872 0.99746672]
gpu times [ 3.69237351 10.19157267 29.16963911 11.75662923 24.06727982
19.95286012 57.39508867 330.65728259 319.31312203]
gpu expected [3.0766348799999998, 7.5552349100000002, 18.992267850000001, 9.5999999999999996, 24.130070450000002, 20.399999999999999, 56.0, 302.60000000000002, 315.39999999999998]
gpu % expected/get [ 0.83324043 0.74132179 0.65109711 0.81656058 1.00260896 1.02240981
0.97569324 0.91514694 0.98774519]
float64/float32 [ 0.87318813 0.80319149 1.64474948 1.10344569 1.65525247 1.81361023
1.66567329 1.27641344 1.05641863]
expected float64/float32 [ 0.89282013 0.80701069 1.67898647 1.11669678 1.64583696 1.79315621
1.65099007 1.30892854 1.05464273]
float64/gpu [ 2.72819525 2.31444546 2.62284447 6.19441599 4.86411021
17.58429561 6.71305629 1.64592241 0.5844223 ]
expected float64/gpu [ 2.7895336 2.32545072 2.67744142 6.26880363 4.83644188
17.38597865 6.65387943 1.68785032 0.58343985]
float32/gpu [ 3.12440717 2.88156124 1.59467719 5.61370263 2.93859111 9.69574128
4.03023589 1.28949004 0.5532109 ]
expected float32/gpu [ 3.14161066 2.90436039 1.61812081 5.6563832 2.95006335 9.5825861
3.95155762 1.30890811 0.55180946]
speed_failure_float64=0
speed_failure_float32=0
speed_failure_gpu=5
.
----------------------------------------------------------------------
Ran 1 test in 4030.887s
OK
executing nosetests with mode=FAST_RUN,floatX=float32
./part/01/Tmp/nightly_build/DeepLearningTutorials/code/mcrbm/test_mcrbm.py:68: UserWarning: The parameter 'updates' of theano.function() expects an OrderedDict, got <type 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7, or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.
updates=trainer.cd_updates())
.The code for file logistic_cg.pyc ran for 7.9s
.The code for file mlp.pyc ran for 0.15m
.The code for file convolutional_mlp.pyc ran for 0.22m
.The no corruption code for file dA.pyc ran for 0.29m
The 30% corruption code for file dA.pyc ran for 0.30m
.The code for file convolutional_mlp.pyc ran for 0.22m
.The no corruption code for file dA.pyc ran for 0.29m
.The pretraining code for file SdA.pyc ran for 1.72m
The training code for file SdA.pyc ran for 0.73m
.The pretraining code for file DBN.pyc ran for 2.27m
The fine tuning code for file DBN.pyc ran for 0.73m
...
======================================================================
ERROR: mcrbm.test_mcrbm.test_reproduce_ranzato_hinton_2010
----------------------------------------------------------------------
Traceback (most recent call last):
File "/opt/lisa/os/epd-7.1.2/lib/python2.7/site-packages/nose/case.py", line 187, in runTest
self.test(*self.arg)
File "/part/01/Tmp/nightly_build/DeepLearningTutorials/code/mcrbm/test_mcrbm.py", line 93, in test_reproduce_ranzato_hinton_2010
tile(imgs_fn(jj), "imgs_%06i.png" % jj)
File "/Tmp/nightly_build/Pylearn/pylearn/dataset_ops/image_patches.py", line 96, in save_filters_of_ranzato_hinton_2010
min_dynamic_range=min_dynamic_range)
File "/Tmp/nightly_build/Pylearn/pylearn/io/image_tiling.py", line 77, in tile_raster_images
raise NotImplementedError()
NotImplementedError:
-------------------- >> begin captured stdout << ---------------------
Learning...
--------------------- >> end captured stdout << ----------------------
----------------------------------------------------------------------
Ran 11 tests in 846.816s
======================================================================
ERROR: mcrbm.test_mcrbm.test_reproduce_ranzato_hinton_2010
----------------------------------------------------------------------
Traceback (most recent call last):
File "/opt/lisa/os/epd-7.1.2/lib/python2.7/site-packages/nose/case.py", line 187, in runTest
self.test(*self.arg)
File "/part/01/Tmp/nightly_build/DeepLearningTutorials/code/mcrbm/test_mcrbm.py", line 93, in test_reproduce_ranzato_hinton_2010
tile(imgs_fn(jj), "imgs_%06i.png" % jj)
File "/Tmp/nightly_build/Pylearn/pylearn/dataset_ops/image_patches.py", line 96, in save_filters_of_ranzato_hinton_2010
min_dynamic_range=min_dynamic_range)
File "/Tmp/nightly_build/Pylearn/pylearn/io/image_tiling.py", line 77, in tile_raster_images
raise NotImplementedError()
NotImplementedError:
-------------------- >> begin captured stdout << ---------------------
Learning...
--------------------- >> end captured stdout << ----------------------
----------------------------------------------------------------------
FAILED (errors=1)
4446.34user 387.50system 1:21:25elapsed 98%CPU (0avgtext+0avgdata 1960072maxresident)k
357208inputs+2176outputs (836major+9237229minor)pagefaults 0swaps
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