skaae / experiment.py

python experiment.py
Using gpu device 0: GeForce GTX TITAN Black
/usr/local/lib/python2.7/dist-packages/Lasagne-0.1.dev-py2.7.egg/lasagne/init.py:86: UserWarning: The uniform initializer no longer uses Glorot et al.'s approach to determine the bounds, but defaults to the range (-0.01, 0.01) instead. Please use the new GlorotUniform initializer to get the old behavior. GlorotUniform is now the default for all layers.
  warnings.warn("The uniform initializer no longer uses Glorot et al.'s "
Loading data...
(168, 39)
/usr/local/lib/python2.7/dist-packages/Lasagne-0.1.dev-py2.7.egg/lasagne/layers/helper.py:69: UserWarning: get_all_layers() has been changed to return layers in topological order. The former implementation is still available as get_all_layers_old(), but will be removed before the first release of Lasagne. To ignore this warning, use `warnings.filterwarnings('ignore', '.*topo.*')`.
  warnings.warn("get_all_layers() has been changed to return layers in "
/usr/local/lib/python2.7/dist-packages/theano/scan_module/scan.py:1017: Warning: In the strict mode, all neccessary shared variables must be passed as a part of non_sequences
  'must be passed as a part of non_sequences', Warning)
Computing updates...
Compiling functions...
/usr/local/lib/python2.7/dist-packages/theano/scan_module/scan_perform_ext.py:133: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility
  from scan_perform.scan_perform import *
Training...
1.37378001213
1.32035112381
1.32456994057
1.32178497314
1.32462310791
1.32277989388
1.32382488251
1.32401013374
1.32540011406
1.32264709473
1.3265478611
1.32395792007
1.32552886009
1.32278013229
1.32727503777
1.3229329586
1.32568001747
1.32218718529
1.32564997673
1.32446789742
Epoch 0 took 1.32446789742, cost = 0.236172556877, error = 0.949728061149
1.32659506798
1.32414102554
1.3248398304
1.32406401634
1.3254160881
1.32578611374
1.32594680786
1.32349205017
1.32587790489
1.3234641552
1.32493591309
1.32370615005
1.32686805725
1.31945204735
1.32277989388
1.32013201714
1.32330608368
1.32456588745
1.32740783691
1.32403206825
Epoch 1 took 1.32403206825, cost = nan, error = 0.985447596649
1.32458901405
1.32266998291
1.32541108131
1.32362580299
1.32612109184
1.32395792007
1.32669401169
1.32302689552
1.32604598999
1.32323694229
1.3291079998
1.32212209702
1.32700800896
1.32311201096
1.33942484856
1.35423398018
1.32460212708
1.3217189312
1.32654309273
1.32117795944
Epoch 2 took 1.32117795944, cost = nan, error = 0.985447596649
1.32549214363
1.3265068531
1.33263301849
1.32968115807
1.33673191071
1.33021497726
1.33883690834
1.36176300049
1.34264016151
1.33736395836
1.33167695999
1.32827877998
1.33537817001
1.33314585686
1.35636711121
1.34423995018
1.3395011425
1.33643293381
1.33976006508
1.33662509918
Epoch 3 took 1.33662509918, cost = nan, error = 0.985447596649
1.333589077
1.3309340477
1.36852192879
1.33266496658
1.34567189217
1.33116602898
1.3335518837
1.33862900734
1.34646511078
1.33973097801
1.33759403229
1.3336250782
1.33578515053
1.33739089966
1.34413218498
1.34501600266
1.34341812134
1.33504104614
1.34449505806
1.34248209
Epoch 4 took 1.34248209, cost = nan, error = 0.985447596649
1.34506082535
1.33236694336
1.34146118164
1.33226299286
1.33261013031
1.34553003311
1.35776209831
1.33256912231
1.33536100388
1.33282804489
1.36304688454
1.36115098
1.39771103859
1.41714501381
1.35126900673
1.33921909332
1.35050702095
1.3311150074
1.33682894707
1.33464884758
Epoch 5 took 1.33464884758, cost = nan, error = 0.985447596649
1.34731292725
1.33311104774
1.33487915993
1.33093595505
1.36071395874
1.33625793457
1.33785796165
1.33547019958
1.33956599236
1.33442282677
1.35223317146
1.32734799385
1.33619403839
1.35089898109
1.33862018585
1.33473300934
1.34002017975
1.32798314095
1.33312296867
1.34791517258
Epoch 6 took 1.34791517258, cost = nan, error = 0.985447596649
1.34169697762
1.33289194107
1.34308600426
1.36367511749