Theano_buildbot_dlt_speed - Build # 332 - Failure - Fail=3 Tot=24 Skip=0
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jenkins...@mila.quebec
Oct 16, 2018, 4:06:27 PM10/16/18
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Theano_buildbot_dlt_speed - Build # 332 - Failure -
Tot=24 Skip=0 Fail=3
See https://jenkins.mila.quebec/job/Theano_buildbot_dlt_speed/332/ to view the results.
Failed tests:
3 tests failed.
FAILED: test.test_rnnslu
Error Message:
[Errno 2] No such file or directory: '/home/jenkins/workspace/Theano_buildbot_dlt_speed/data/atis.fold3.pkl.gz'
-------------------- >> begin captured stdout << ---------------------
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
--------------------- >> end captured stdout << ----------------------
Stack Trace:
File "/miniconda/lib/python2.7/unittest/case.py", line 329, in run
testMethod()
File "/miniconda/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/test.py", line 79, in test_rnnslu
rnnslu.main(s)
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/rnnslu.py", line 280, in main
train_set, valid_set, test_set, dic = atisfold(param['fold'])
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/rnnslu.py", line 71, in atisfold
f = gzip.open(filename, 'rb')
File "/miniconda/lib/python2.7/gzip.py", line 34, in open
return GzipFile(filename, mode, compresslevel)
File "/miniconda/lib/python2.7/gzip.py", line 94, in __init__
fileobj = self.myfileobj = __builtin__.open(filename, mode or 'rb')
[Errno 2] No such file or directory: '/home/jenkins/workspace/Theano_buildbot_dlt_speed/data/atis.fold3.pkl.gz'
-------------------- >> begin captured stdout << ---------------------
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
--------------------- >> end captured stdout << ----------------------
FAILED: test.test_rnnslu
Error Message:
[Errno 2] No such file or directory: '/home/jenkins/workspace/Theano_buildbot_dlt_speed/data/atis.fold3.pkl.gz'
-------------------- >> begin captured stdout << ---------------------
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
--------------------- >> end captured stdout << ----------------------
Stack Trace:
File "/miniconda/lib/python2.7/unittest/case.py", line 329, in run
testMethod()
File "/miniconda/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/test.py", line 79, in test_rnnslu
rnnslu.main(s)
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/rnnslu.py", line 280, in main
train_set, valid_set, test_set, dic = atisfold(param['fold'])
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/rnnslu.py", line 71, in atisfold
f = gzip.open(filename, 'rb')
File "/miniconda/lib/python2.7/gzip.py", line 34, in open
return GzipFile(filename, mode, compresslevel)
File "/miniconda/lib/python2.7/gzip.py", line 94, in __init__
fileobj = self.myfileobj = __builtin__.open(filename, mode or 'rb')
[Errno 2] No such file or directory: '/home/jenkins/workspace/Theano_buildbot_dlt_speed/data/atis.fold3.pkl.gz'
-------------------- >> begin captured stdout << ---------------------
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
--------------------- >> end captured stdout << ----------------------
FAILED: test.speed
Error Message:
-------------------- >> begin captured stdout << ---------------------
logistic_sgd
... loading data
... building the model
... training the model
epoch 1, minibatch 83/83, validation error 12.458333 %
epoch 1, minibatch 83/83, test error of best model 12.375000 %
epoch 2, minibatch 83/83, validation error 11.010417 %
epoch 2, minibatch 83/83, test error of best model 10.958333 %
epoch 3, minibatch 83/83, validation error 10.312500 %
epoch 3, minibatch 83/83, test error of best model 10.312500 %
epoch 4, minibatch 83/83, validation error 9.875000 %
epoch 4, minibatch 83/83, test error of best model 9.833333 %
epoch 5, minibatch 83/83, validation error 9.562500 %
epoch 5, minibatch 83/83, test error of best model 9.479167 %
epoch 6, minibatch 83/83, validation error 9.322917 %
epoch 6, minibatch 83/83, test error of best model 9.291667 %
epoch 7, minibatch 83/83, validation error 9.187500 %
epoch 7, minibatch 83/83, test error of best model 9.000000 %
epoch 8, minibatch 83/83, validation error 8.989583 %
epoch 8, minibatch 83/83, test error of best model 8.958333 %
epoch 9, minibatch 83/83, validation error 8.937500 %
epoch 9, minibatch 83/83, test error of best model 8.812500 %
epoch 10, minibatch 83/83, validation error 8.750000 %
epoch 10, minibatch 83/83, test error of best model 8.666667 %
epoch 11, minibatch 83/83, validation error 8.666667 %
epoch 11, minibatch 83/83, test error of best model 8.520833 %
epoch 12, minibatch 83/83, validation error 8.583333 %
epoch 12, minibatch 83/83, test error of best model 8.416667 %
epoch 13, minibatch 83/83, validation error 8.489583 %
epoch 13, minibatch 83/83, test error of best model 8.291667 %
epoch 14, minibatch 83/83, validation error 8.427083 %
epoch 14, minibatch 83/83, test error of best model 8.281250 %
epoch 15, minibatch 83/83, validation error 8.354167 %
epoch 15, minibatch 83/83, test error of best model 8.270833 %
epoch 16, minibatch 83/83, validation error 8.302083 %
epoch 16, minibatch 83/83, test error of best model 8.239583 %
epoch 17, minibatch 83/83, validation error 8.250000 %
epoch 17, minibatch 83/83, test error of best model 8.177083 %
epoch 18, minibatch 83/83, validation error 8.229167 %
epoch 18, minibatch 83/83, test error of best model 8.062500 %
epoch 19, minibatch 83/83, validation error 8.260417 %
epoch 20, minibatch 83/83, validation error 8.260417 %
epoch 21, minibatch 83/83, validation error 8.208333 %
epoch 21, minibatch 83/83, test error of best model 7.947917 %
epoch 22, minibatch 83/83, validation error 8.187500 %
epoch 22, minibatch 83/83, test error of best model 7.927083 %
epoch 23, minibatch 83/83, validation error 8.156250 %
epoch 23, minibatch 83/83, test error of best model 7.958333 %
epoch 24, minibatch 83/83, validation error 8.114583 %
epoch 24, minibatch 83/83, test error of best model 7.947917 %
epoch 25, minibatch 83/83, validation error 8.093750 %
epoch 25, minibatch 83/83, test error of best model 7.947917 %
epoch 26, minibatch 83/83, validation error 8.104167 %
epoch 27, minibatch 83/83, validation error 8.104167 %
epoch 28, minibatch 83/83, validation error 8.052083 %
epoch 28, minibatch 83/83, test error of best model 7.843750 %
epoch 29, minibatch 83/83, validation error 8.052083 %
epoch 30, minibatch 83/83, validation error 8.031250 %
epoch 30, minibatch 83/83, test error of best model 7.843750 %
Optimization complete with best validation score of 8.031250 %,with test performance 7.843750 %
The code run for 30 epochs, with 5.214695 epochs/sec
logistic_cg
... loading data
... building the model
Optimizing using scipy.optimize.fmin_cg...
validation error 29.989583 %
validation error 24.656250 %
validation error 20.833333 %
validation error 16.979167 %
validation error 14.291667 %
validation error 14.239583 %
validation error 13.166667 %
validation error 12.281250 %
validation error 11.739583 %
validation error 11.531250 %
validation error 10.583333 %
validation error 10.406250 %
validation error 10.166667 %
validation error 10.208333 %
validation error 9.854167 %
validation error 9.718750 %
validation error 9.375000 %
validation error 8.968750 %
validation error 8.927083 %
validation error 8.760417 %
validation error 8.770833 %
validation error 8.625000 %
validation error 8.541667 %
validation error 8.250000 %
validation error 8.375000 %
validation error 8.395833 %
validation error 8.197917 %
validation error 8.031250 %
validation error 8.020833 %
validation error 7.968750 %
Optimization complete with best validation score of 7.968750 %, with test performance 7.958333 %
mlp
... loading data
... building the model
... training
epoch 1, minibatch 2500/2500, validation error 9.620000 %
epoch 1, minibatch 2500/2500, test error of best model 10.090000 %
epoch 2, minibatch 2500/2500, validation error 8.610000 %
epoch 2, minibatch 2500/2500, test error of best model 8.740000 %
epoch 3, minibatch 2500/2500, validation error 8.000000 %
epoch 3, minibatch 2500/2500, test error of best model 8.160000 %
epoch 4, minibatch 2500/2500, validation error 7.600000 %
epoch 4, minibatch 2500/2500, test error of best model 7.790000 %
epoch 5, minibatch 2500/2500, validation error 7.300000 %
epoch 5, minibatch 2500/2500, test error of best model 7.590000 %
Optimization complete. Best validation score of 7.300000 % obtained at iteration 12500, with test performance 7.590000 %
convolutional_mlp
... loading data
... building the model
... training
training @ iter = 0
epoch 1, minibatch 100/100, validation error 13.300000 %
epoch 1, minibatch 100/100, test error of best model 13.690000 %
training @ iter = 100
epoch 2, minibatch 100/100, validation error 9.840000 %
epoch 2, minibatch 100/100, test error of best model 10.200000 %
training @ iter = 200
epoch 3, minibatch 100/100, validation error 8.400000 %
epoch 3, minibatch 100/100, test error of best model 8.730000 %
training @ iter = 300
epoch 4, minibatch 100/100, validation error 7.340000 %
epoch 4, minibatch 100/100, test error of best model 7.620000 %
training @ iter = 400
epoch 5, minibatch 100/100, validation error 6.430000 %
epoch 5, minibatch 100/100, test error of best model 6.920000 %
Optimization complete.
Best validation score of 6.430000 % obtained at iteration 500, with test performance 6.920000 %
dA
... loading data
Training epoch 0, cost 63.2891694201
Training epoch 1, cost 55.7866565443
Training epoch 0, cost 81.7649857817
Training epoch 1, cost 73.4434466428
SdA
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost 194.516149
Pre-training layer 1, epoch 0, cost 695.487634
Pre-training layer 2, epoch 0, cost 529.058440
... getting the finetuning functions
... finetunning the model
epoch 1, minibatch 166/166, validation error 14.848485 %
epoch 1, minibatch 166/166, test error of best model 15.727273 %
epoch 2, minibatch 166/166, validation error 11.606061 %
epoch 2, minibatch 166/166, test error of best model 11.717172 %
Optimization complete with best validation score of 11.606061 %, on iteration 332, with test performance 11.717172 %
DBN
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost -165.613036644
Pre-training layer 1, epoch 0, cost -620.421300613
Pre-training layer 2, epoch 0, cost -134.389183501
... getting the finetuning functions
... finetuning the model
epoch 1, minibatch 166/166, validation error 28.181818 %
epoch 1, minibatch 166/166, test error of best model 30.090909 %
epoch 2, minibatch 166/166, validation error 20.262626 %
epoch 2, minibatch 166/166, test error of best model 21.434343 %
Optimization complete with best validation score of 20.262626 %, obtained at iteration 332, with test performance 21.434343 %
rbm
... loading data
Training epoch 0, cost is -51.6263725776
Training took 1.673142 minutes
... plotting sample 0
rnnrbm
Epoch 1/1
-15.0496314334
rnnslu
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
lstm
model options {'encoder': 'lstm', 'optimizer': <function adadelta at 0x7f8514f589b0>, 'validFreq': 370, 'lrate': 0.0001, 'batch_size': 16, 'decay_c': 0.0, 'patience': 10, 'reload_model': None, 'n_words': 10000, 'max_epochs': 1, 'dispFreq': 10, 'dataset': 'imdb', 'valid_batch_size': 64, 'use_dropout': True, 'dim_proj': 128, 'maxlen': 100, 'saveto': '', 'noise_std': 0.0, 'test_size': 1000, 'saveFreq': 1110}
Loading data
Building model
Optimization
1998 train examples
105 valid examples
1000 test examples
Epoch 0 Update 10 Cost 0.692138353384
Epoch 0 Update 20 Cost 0.690693189509
Epoch 0 Update 30 Cost 0.676380211732
Epoch 0 Update 40 Cost 0.672820589255
Epoch 0 Update 50 Cost 0.675394850171
Epoch 0 Update 60 Cost 0.698660008515
Epoch 0 Update 70 Cost 0.684332483641
Epoch 0 Update 80 Cost 0.714756376014
Epoch 0 Update 90 Cost 0.727102027393
Epoch 0 Update 100 Cost 0.672112529029
Epoch 0 Update 110 Cost 0.703670540776
Epoch 0 Update 120 Cost 0.758497490788
Seen 1998 samples
Train 0.422422422422 Valid 0.428571428571 Test 0.502
The code run for 1 epochs, with 17.961051 sec/epochs
logistic_sgd
... loading data
... building the model
... training the model
epoch 1, minibatch 83/83, validation error 12.458333 %
epoch 1, minibatch 83/83, test error of best model 12.375000 %
epoch 2, minibatch 83/83, validation error 11.010417 %
epoch 2, minibatch 83/83, test error of best model 10.958333 %
epoch 3, minibatch 83/83, validation error 10.312500 %
epoch 3, minibatch 83/83, test error of best model 10.312500 %
epoch 4, minibatch 83/83, validation error 9.875000 %
epoch 4, minibatch 83/83, test error of best model 9.833333 %
epoch 5, minibatch 83/83, validation error 9.562500 %
epoch 5, minibatch 83/83, test error of best model 9.479167 %
epoch 6, minibatch 83/83, validation error 9.322917 %
epoch 6, minibatch 83/83, test error of best model 9.291667 %
epoch 7, minibatch 83/83, validation error 9.187500 %
epoch 7, minibatch 83/83, test error of best model 9.000000 %
epoch 8, minibatch 83/83, validation error 8.989583 %
epoch 8, minibatch 83/83, test error of best model 8.958333 %
epoch 9, minibatch 83/83, validation error 8.937500 %
epoch 9, minibatch 83/83, test error of best model 8.812500 %
epoch 10, minibatch 83/83, validation error 8.750000 %
epoch 10, minibatch 83/83, test error of best model 8.666667 %
epoch 11, minibatch 83/83, validation error 8.666667 %
epoch 11, minibatch 83/83, test error of best model 8.520833 %
epoch 12, minibatch 83/83, validation error 8.583333 %
epoch 12, minibatch 83/83, test error of best model 8.416667 %
epoch 13, minibatch 83/83, validation error 8.489583 %
epoch 13, minibatch 83/83, test error of best model 8.291667 %
epoch 14, minibatch 83/83, validation error 8.427083 %
epoch 14, minibatch 83/83, test error of best model 8.281250 %
epoch 15, minibatch 83/83, validation error 8.354167 %
epoch 15, minibatch 83/83, test error of best model 8.270833 %
epoch 16, minibatch 83/83, validation error 8.302083 %
epoch 16, minibatch 83/83, test error of best model 8.239583 %
epoch 17, minibatch 83/83, validation error 8.250000 %
epoch 17, minibatch 83/83, test error of best model 8.177083 %
epoch 18, minibatch 83/83, validation error 8.229167 %
epoch 18, minibatch 83/83, test error of best model 8.062500 %
epoch 19, minibatch 83/83, validation error 8.260417 %
epoch 20, minibatch 83/83, validation error 8.260417 %
epoch 21, minibatch 83/83, validation error 8.208333 %
epoch 21, minibatch 83/83, test error of best model 7.947917 %
epoch 22, minibatch 83/83, validation error 8.187500 %
epoch 22, minibatch 83/83, test error of best model 7.927083 %
epoch 23, minibatch 83/83, validation error 8.156250 %
epoch 23, minibatch 83/83, test error of best model 7.958333 %
epoch 24, minibatch 83/83, validation error 8.114583 %
epoch 24, minibatch 83/83, test error of best model 7.947917 %
epoch 25, minibatch 83/83, validation error 8.093750 %
epoch 25, minibatch 83/83, test error of best model 7.947917 %
epoch 26, minibatch 83/83, validation error 8.104167 %
epoch 27, minibatch 83/83, validation error 8.104167 %
epoch 28, minibatch 83/83, validation error 8.052083 %
epoch 28, minibatch 83/83, test error of best model 7.843750 %
epoch 29, minibatch 83/83, validation error 8.052083 %
epoch 30, minibatch 83/83, validation error 8.031250 %
epoch 30, minibatch 83/83, test error of best model 7.843750 %
Optimization complete with best validation score of 8.031250 %,with test performance 7.843750 %
The code run for 30 epochs, with 7.328806 epochs/sec
logistic_cg
... loading data
... building the model
Optimizing using scipy.optimize.fmin_cg...
validation error 29.989583 %
validation error 24.656250 %
validation error 20.833333 %
validation error 16.979167 %
validation error 14.291667 %
validation error 14.239583 %
validation error 13.166667 %
validation error 12.281250 %
validation error 11.739583 %
validation error 11.531250 %
validation error 10.572917 %
validation error 10.406250 %
validation error 10.166667 %
validation error 10.208333 %
validation error 9.854167 %
validation error 9.718750 %
validation error 9.375000 %
validation error 8.968750 %
validation error 8.927083 %
validation error 8.760417 %
validation error 8.770833 %
validation error 8.625000 %
validation error 8.541667 %
validation error 8.250000 %
validation error 8.375000 %
validation error 8.395833 %
validation error 8.197917 %
validation error 8.031250 %
validation error 8.000000 %
validation error 7.968750 %
Optimization complete with best validation score of 7.968750 %, with test performance 7.989583 %
mlp
... loading data
... building the model
... training
epoch 1, minibatch 2500/2500, validation error 9.620000 %
epoch 1, minibatch 2500/2500, test error of best model 10.090000 %
epoch 2, minibatch 2500/2500, validation error 8.610000 %
epoch 2, minibatch 2500/2500, test error of best model 8.740000 %
epoch 3, minibatch 2500/2500, validation error 8.000000 %
epoch 3, minibatch 2500/2500, test error of best model 8.160000 %
epoch 4, minibatch 2500/2500, validation error 7.600000 %
epoch 4, minibatch 2500/2500, test error of best model 7.790000 %
epoch 5, minibatch 2500/2500, validation error 7.300000 %
epoch 5, minibatch 2500/2500, test error of best model 7.580000 %
Optimization complete. Best validation score of 7.300000 % obtained at iteration 12500, with test performance 7.580000 %
convolutional_mlp
... loading data
... building the model
... training
training @ iter = 0
epoch 1, minibatch 100/100, validation error 13.300000 %
epoch 1, minibatch 100/100, test error of best model 13.680000 %
training @ iter = 100
epoch 2, minibatch 100/100, validation error 9.830000 %
epoch 2, minibatch 100/100, test error of best model 10.200000 %
training @ iter = 200
epoch 3, minibatch 100/100, validation error 8.400000 %
epoch 3, minibatch 100/100, test error of best model 8.730000 %
training @ iter = 300
epoch 4, minibatch 100/100, validation error 7.350000 %
epoch 4, minibatch 100/100, test error of best model 7.620000 %
training @ iter = 400
epoch 5, minibatch 100/100, validation error 6.430000 %
epoch 5, minibatch 100/100, test error of best model 6.920000 %
Optimization complete.
Best validation score of 6.430000 % obtained at iteration 500, with test performance 6.920000 %
dA
... loading data
Training epoch 0, cost 63.2891692993
Training epoch 1, cost 55.7866566589
Training epoch 0, cost 81.7649857224
Training epoch 1, cost 73.4434465988
SdA
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost 194.516144
Pre-training layer 1, epoch 0, cost 695.487612
Pre-training layer 2, epoch 0, cost 529.058447
... getting the finetuning functions
... finetunning the model
epoch 1, minibatch 166/166, validation error 14.848485 %
epoch 1, minibatch 166/166, test error of best model 15.727273 %
epoch 2, minibatch 166/166, validation error 11.606061 %
epoch 2, minibatch 166/166, test error of best model 11.717172 %
Optimization complete with best validation score of 11.606061 %, on iteration 332, with test performance 11.717172 %
DBN
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost -165.613371424
Pre-training layer 1, epoch 0, cost -620.432120817
Pre-training layer 2, epoch 0, cost -134.449325286
... getting the finetuning functions
... finetuning the model
epoch 1, minibatch 166/166, validation error 28.131313 %
epoch 1, minibatch 166/166, test error of best model 30.010101 %
epoch 2, minibatch 166/166, validation error 20.303030 %
epoch 2, minibatch 166/166, test error of best model 21.393939 %
Optimization complete with best validation score of 20.303030 %, obtained at iteration 332, with test performance 21.393939 %
rbm
... loading data
Training epoch 0, cost is -52.7116
Training took 1.243071 minutes
... plotting sample 0
rnnrbm
Epoch 1/1
-15.0365748034
rnnslu
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
lstm
model options {'encoder': 'lstm', 'optimizer': <function adadelta at 0x7f8514f589b0>, 'validFreq': 370, 'lrate': 0.0001, 'batch_size': 16, 'decay_c': 0.0, 'patience': 10, 'reload_model': None, 'n_words': 10000, 'max_epochs': 1, 'dispFreq': 10, 'dataset': 'imdb', 'valid_batch_size': 64, 'use_dropout': True, 'dim_proj': 128, 'maxlen': 100, 'saveto': '', 'noise_std': 0.0, 'test_size': 1000, 'saveFreq': 1110}
Loading data
Building model
Optimization
1998 train examples
105 valid examples
1000 test examples
Epoch 0 Update 10 Cost 0.691160261631
Epoch 0 Update 20 Cost 0.683994412422
Epoch 0 Update 30 Cost 0.675427556038
Epoch 0 Update 40 Cost 0.712318658829
Epoch 0 Update 50 Cost 0.695389866829
Epoch 0 Update 60 Cost 0.675582230091
Epoch 0 Update 70 Cost 0.712137162685
Epoch 0 Update 80 Cost 0.698806881905
Epoch 0 Update 90 Cost 0.596462011337
Epoch 0 Update 100 Cost 0.669474124908
Epoch 0 Update 110 Cost 0.725587248802
Epoch 0 Update 120 Cost 0.73024970293
Seen 1998 samples
Train 0.422922922923 Valid 0.419047619048 Test 0.514
The code run for 1 epochs, with 13.161978 sec/epochs
logistic_sgd
... loading data
... building the model
... training the model
epoch 1, minibatch 83/83, validation error 12.458333 %
epoch 1, minibatch 83/83, test error of best model 12.375000 %
epoch 2, minibatch 83/83, validation error 11.010417 %
epoch 2, minibatch 83/83, test error of best model 10.958333 %
epoch 3, minibatch 83/83, validation error 10.312500 %
epoch 3, minibatch 83/83, test error of best model 10.312500 %
epoch 4, minibatch 83/83, validation error 9.875000 %
epoch 4, minibatch 83/83, test error of best model 9.833333 %
epoch 5, minibatch 83/83, validation error 9.562500 %
epoch 5, minibatch 83/83, test error of best model 9.479167 %
epoch 6, minibatch 83/83, validation error 9.322917 %
epoch 6, minibatch 83/83, test error of best model 9.291667 %
epoch 7, minibatch 83/83, validation error 9.187500 %
epoch 7, minibatch 83/83, test error of best model 9.000000 %
epoch 8, minibatch 83/83, validation error 8.989583 %
epoch 8, minibatch 83/83, test error of best model 8.958333 %
epoch 9, minibatch 83/83, validation error 8.937500 %
epoch 9, minibatch 83/83, test error of best model 8.812500 %
epoch 10, minibatch 83/83, validation error 8.750000 %
epoch 10, minibatch 83/83, test error of best model 8.666667 %
epoch 11, minibatch 83/83, validation error 8.666667 %
epoch 11, minibatch 83/83, test error of best model 8.520833 %
epoch 12, minibatch 83/83, validation error 8.583333 %
epoch 12, minibatch 83/83, test error of best model 8.416667 %
epoch 13, minibatch 83/83, validation error 8.489583 %
epoch 13, minibatch 83/83, test error of best model 8.291667 %
epoch 14, minibatch 83/83, validation error 8.427083 %
epoch 14, minibatch 83/83, test error of best model 8.281250 %
epoch 15, minibatch 83/83, validation error 8.354167 %
epoch 15, minibatch 83/83, test error of best model 8.270833 %
epoch 16, minibatch 83/83, validation error 8.302083 %
epoch 16, minibatch 83/83, test error of best model 8.239583 %
epoch 17, minibatch 83/83, validation error 8.250000 %
epoch 17, minibatch 83/83, test error of best model 8.177083 %
epoch 18, minibatch 83/83, validation error 8.229167 %
epoch 18, minibatch 83/83, test error of best model 8.062500 %
epoch 19, minibatch 83/83, validation error 8.260417 %
epoch 20, minibatch 83/83, validation error 8.260417 %
epoch 21, minibatch 83/83, validation error 8.208333 %
epoch 21, minibatch 83/83, test error of best model 7.947917 %
epoch 22, minibatch 83/83, validation error 8.187500 %
epoch 22, minibatch 83/83, test error of best model 7.927083 %
epoch 23, minibatch 83/83, validation error 8.156250 %
epoch 23, minibatch 83/83, test error of best model 7.958333 %
epoch 24, minibatch 83/83, validation error 8.114583 %
epoch 24, minibatch 83/83, test error of best model 7.947917 %
epoch 25, minibatch 83/83, validation error 8.093750 %
epoch 25, minibatch 83/83, test error of best model 7.947917 %
epoch 26, minibatch 83/83, validation error 8.104167 %
epoch 27, minibatch 83/83, validation error 8.104167 %
epoch 28, minibatch 83/83, validation error 8.052083 %
epoch 28, minibatch 83/83, test error of best model 7.843750 %
epoch 29, minibatch 83/83, validation error 8.052083 %
epoch 30, minibatch 83/83, validation error 8.031250 %
epoch 30, minibatch 83/83, test error of best model 7.843750 %
Optimization complete with best validation score of 8.031250 %,with test performance 7.843750 %
The code run for 30 epochs, with 31.844058 epochs/sec
logistic_cg
... loading data
... building the model
Optimizing using scipy.optimize.fmin_cg...
validation error 29.989583 %
validation error 24.656250 %
validation error 20.833333 %
validation error 16.979167 %
validation error 14.291667 %
validation error 14.239583 %
validation error 13.166667 %
validation error 12.281250 %
validation error 11.739583 %
validation error 11.531250 %
validation error 10.583333 %
validation error 10.406250 %
validation error 10.166667 %
validation error 10.208333 %
validation error 9.854167 %
validation error 9.718750 %
validation error 9.375000 %
validation error 8.968750 %
validation error 8.916667 %
validation error 8.760417 %
validation error 8.770833 %
validation error 8.625000 %
validation error 8.541667 %
validation error 8.250000 %
validation error 8.375000 %
validation error 8.395833 %
validation error 8.197917 %
validation error 8.031250 %
validation error 8.031250 %
validation error 7.968750 %
Optimization complete with best validation score of 7.968750 %, with test performance 7.968750 %
mlp
... loading data
... building the model
... training
epoch 1, minibatch 2500/2500, validation error 9.620000 %
epoch 1, minibatch 2500/2500, test error of best model 10.090000 %
epoch 2, minibatch 2500/2500, validation error 8.610000 %
epoch 2, minibatch 2500/2500, test error of best model 8.740000 %
epoch 3, minibatch 2500/2500, validation error 8.000000 %
epoch 3, minibatch 2500/2500, test error of best model 8.160000 %
epoch 4, minibatch 2500/2500, validation error 7.600000 %
epoch 4, minibatch 2500/2500, test error of best model 7.790000 %
epoch 5, minibatch 2500/2500, validation error 7.300000 %
epoch 5, minibatch 2500/2500, test error of best model 7.580000 %
Optimization complete. Best validation score of 7.300000 % obtained at iteration 12500, with test performance 7.580000 %
convolutional_mlp
... loading data
... building the model
... training
training @ iter = 0
epoch 1, minibatch 100/100, validation error 13.290000 %
epoch 1, minibatch 100/100, test error of best model 13.670000 %
training @ iter = 100
epoch 2, minibatch 100/100, validation error 9.830000 %
epoch 2, minibatch 100/100, test error of best model 10.200000 %
training @ iter = 200
epoch 3, minibatch 100/100, validation error 8.410000 %
epoch 3, minibatch 100/100, test error of best model 8.770000 %
training @ iter = 300
epoch 4, minibatch 100/100, validation error 7.330000 %
epoch 4, minibatch 100/100, test error of best model 7.620000 %
training @ iter = 400
epoch 5, minibatch 100/100, validation error 6.500000 %
epoch 5, minibatch 100/100, test error of best model 6.890000 %
Optimization complete.
Best validation score of 6.500000 % obtained at iteration 500, with test performance 6.890000 %
dA
... loading data
Training epoch 0, cost 63.2891690659
Training epoch 1, cost 55.7866565292
Training epoch 0, cost 81.7649854492
Training epoch 1, cost 73.4434465851
SdA
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost 194.516141
Pre-training layer 1, epoch 0, cost 695.487604
Pre-training layer 2, epoch 0, cost 529.058429
... getting the finetuning functions
... finetunning the model
epoch 1, minibatch 166/166, validation error 14.848485 %
epoch 1, minibatch 166/166, test error of best model 15.727273 %
epoch 2, minibatch 166/166, validation error 11.606061 %
epoch 2, minibatch 166/166, test error of best model 11.717172 %
Optimization complete with best validation score of 11.606061 %, on iteration 332, with test performance 11.717172 %
DBN
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost -165.613303219
Pre-training layer 1, epoch 0, cost -620.403747559
Pre-training layer 2, epoch 0, cost -134.217854373
... getting the finetuning functions
... finetuning the model
epoch 1, minibatch 166/166, validation error 28.222222 %
epoch 1, minibatch 166/166, test error of best model 30.191919 %
epoch 2, minibatch 166/166, validation error 20.292929 %
epoch 2, minibatch 166/166, test error of best model 21.434343 %
Optimization complete with best validation score of 20.292929 %, obtained at iteration 332, with test performance 21.434343 %
rbm
... loading data
Training epoch 0, cost is -51.6296
Training took 0.055968 minutes
... plotting sample 0
rnnrbm
Epoch 1/1
-15.0075673556
rnnslu
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
lstm
model options {'encoder': 'lstm', 'optimizer': <function adadelta at 0x7f8514f589b0>, 'validFreq': 370, 'lrate': 0.0001, 'batch_size': 16, 'decay_c': 0.0, 'patience': 10, 'reload_model': None, 'n_words': 10000, 'max_epochs': 1, 'dispFreq': 10, 'dataset': 'imdb', 'valid_batch_size': 64, 'use_dropout': True, 'dim_proj': 128, 'maxlen': 100, 'saveto': '', 'noise_std': 0.0, 'test_size': 1000, 'saveFreq': 1110}
Loading data
Building model
Optimization
1998 train examples
105 valid examples
1000 test examples
Epoch 0 Update 10 Cost 0.689358711243
Epoch 0 Update 20 Cost 0.710573196411
Epoch 0 Update 30 Cost 0.680263280869
Epoch 0 Update 40 Cost 0.667281806469
Epoch 0 Update 50 Cost 0.637676477432
Epoch 0 Update 60 Cost 0.685400247574
Epoch 0 Update 70 Cost 0.736423969269
Epoch 0 Update 80 Cost 0.641846060753
Epoch 0 Update 90 Cost 0.709488868713
Epoch 0 Update 100 Cost 0.627746403217
Epoch 0 Update 110 Cost 0.669853687286
Epoch 0 Update 120 Cost 0.734875559807
Seen 1998 samples
Train 0.423923923924 Valid 0.4 Test 0.524
The code run for 1 epochs, with 3.901582 sec/epochs
--------------------- >> end captured stdout << ----------------------
Stack Trace:
File "/miniconda/lib/python2.7/unittest/case.py", line 329, in run
testMethod()
File "/miniconda/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/test.py", line 239, in speed
assert not numpy.isnan(gpu_times).any()
-------------------- >> begin captured stdout << ---------------------
logistic_sgd
... loading data
... building the model
... training the model
epoch 1, minibatch 83/83, validation error 12.458333 %
epoch 1, minibatch 83/83, test error of best model 12.375000 %
epoch 2, minibatch 83/83, validation error 11.010417 %
epoch 2, minibatch 83/83, test error of best model 10.958333 %
epoch 3, minibatch 83/83, validation error 10.312500 %
epoch 3, minibatch 83/83, test error of best model 10.312500 %
epoch 4, minibatch 83/83, validation error 9.875000 %
epoch 4, minibatch 83/83, test error of best model 9.833333 %
epoch 5, minibatch 83/83, validation error 9.562500 %
epoch 5, minibatch 83/83, test error of best model 9.479167 %
epoch 6, minibatch 83/83, validation error 9.322917 %
epoch 6, minibatch 83/83, test error of best model 9.291667 %
epoch 7, minibatch 83/83, validation error 9.187500 %
epoch 7, minibatch 83/83, test error of best model 9.000000 %
epoch 8, minibatch 83/83, validation error 8.989583 %
epoch 8, minibatch 83/83, test error of best model 8.958333 %
epoch 9, minibatch 83/83, validation error 8.937500 %
epoch 9, minibatch 83/83, test error of best model 8.812500 %
epoch 10, minibatch 83/83, validation error 8.750000 %
epoch 10, minibatch 83/83, test error of best model 8.666667 %
epoch 11, minibatch 83/83, validation error 8.666667 %
epoch 11, minibatch 83/83, test error of best model 8.520833 %
epoch 12, minibatch 83/83, validation error 8.583333 %
epoch 12, minibatch 83/83, test error of best model 8.416667 %
epoch 13, minibatch 83/83, validation error 8.489583 %
epoch 13, minibatch 83/83, test error of best model 8.291667 %
epoch 14, minibatch 83/83, validation error 8.427083 %
epoch 14, minibatch 83/83, test error of best model 8.281250 %
epoch 15, minibatch 83/83, validation error 8.354167 %
epoch 15, minibatch 83/83, test error of best model 8.270833 %
epoch 16, minibatch 83/83, validation error 8.302083 %
epoch 16, minibatch 83/83, test error of best model 8.239583 %
epoch 17, minibatch 83/83, validation error 8.250000 %
epoch 17, minibatch 83/83, test error of best model 8.177083 %
epoch 18, minibatch 83/83, validation error 8.229167 %
epoch 18, minibatch 83/83, test error of best model 8.062500 %
epoch 19, minibatch 83/83, validation error 8.260417 %
epoch 20, minibatch 83/83, validation error 8.260417 %
epoch 21, minibatch 83/83, validation error 8.208333 %
epoch 21, minibatch 83/83, test error of best model 7.947917 %
epoch 22, minibatch 83/83, validation error 8.187500 %
epoch 22, minibatch 83/83, test error of best model 7.927083 %
epoch 23, minibatch 83/83, validation error 8.156250 %
epoch 23, minibatch 83/83, test error of best model 7.958333 %
epoch 24, minibatch 83/83, validation error 8.114583 %
epoch 24, minibatch 83/83, test error of best model 7.947917 %
epoch 25, minibatch 83/83, validation error 8.093750 %
epoch 25, minibatch 83/83, test error of best model 7.947917 %
epoch 26, minibatch 83/83, validation error 8.104167 %
epoch 27, minibatch 83/83, validation error 8.104167 %
epoch 28, minibatch 83/83, validation error 8.052083 %
epoch 28, minibatch 83/83, test error of best model 7.843750 %
epoch 29, minibatch 83/83, validation error 8.052083 %
epoch 30, minibatch 83/83, validation error 8.031250 %
epoch 30, minibatch 83/83, test error of best model 7.843750 %
Optimization complete with best validation score of 8.031250 %,with test performance 7.843750 %
The code run for 30 epochs, with 5.214695 epochs/sec
logistic_cg
... loading data
... building the model
Optimizing using scipy.optimize.fmin_cg...
validation error 29.989583 %
validation error 24.656250 %
validation error 20.833333 %
validation error 16.979167 %
validation error 14.291667 %
validation error 14.239583 %
validation error 13.166667 %
validation error 12.281250 %
validation error 11.739583 %
validation error 11.531250 %
validation error 10.583333 %
validation error 10.406250 %
validation error 10.166667 %
validation error 10.208333 %
validation error 9.854167 %
validation error 9.718750 %
validation error 9.375000 %
validation error 8.968750 %
validation error 8.927083 %
validation error 8.760417 %
validation error 8.770833 %
validation error 8.625000 %
validation error 8.541667 %
validation error 8.250000 %
validation error 8.375000 %
validation error 8.395833 %
validation error 8.197917 %
validation error 8.031250 %
validation error 8.020833 %
validation error 7.968750 %
Optimization complete with best validation score of 7.968750 %, with test performance 7.958333 %
mlp
... loading data
... building the model
... training
epoch 1, minibatch 2500/2500, validation error 9.620000 %
epoch 1, minibatch 2500/2500, test error of best model 10.090000 %
epoch 2, minibatch 2500/2500, validation error 8.610000 %
epoch 2, minibatch 2500/2500, test error of best model 8.740000 %
epoch 3, minibatch 2500/2500, validation error 8.000000 %
epoch 3, minibatch 2500/2500, test error of best model 8.160000 %
epoch 4, minibatch 2500/2500, validation error 7.600000 %
epoch 4, minibatch 2500/2500, test error of best model 7.790000 %
epoch 5, minibatch 2500/2500, validation error 7.300000 %
epoch 5, minibatch 2500/2500, test error of best model 7.590000 %
Optimization complete. Best validation score of 7.300000 % obtained at iteration 12500, with test performance 7.590000 %
convolutional_mlp
... loading data
... building the model
... training
training @ iter = 0
epoch 1, minibatch 100/100, validation error 13.300000 %
epoch 1, minibatch 100/100, test error of best model 13.690000 %
training @ iter = 100
epoch 2, minibatch 100/100, validation error 9.840000 %
epoch 2, minibatch 100/100, test error of best model 10.200000 %
training @ iter = 200
epoch 3, minibatch 100/100, validation error 8.400000 %
epoch 3, minibatch 100/100, test error of best model 8.730000 %
training @ iter = 300
epoch 4, minibatch 100/100, validation error 7.340000 %
epoch 4, minibatch 100/100, test error of best model 7.620000 %
training @ iter = 400
epoch 5, minibatch 100/100, validation error 6.430000 %
epoch 5, minibatch 100/100, test error of best model 6.920000 %
Optimization complete.
Best validation score of 6.430000 % obtained at iteration 500, with test performance 6.920000 %
dA
... loading data
Training epoch 0, cost 63.2891694201
Training epoch 1, cost 55.7866565443
Training epoch 0, cost 81.7649857817
Training epoch 1, cost 73.4434466428
SdA
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost 194.516149
Pre-training layer 1, epoch 0, cost 695.487634
Pre-training layer 2, epoch 0, cost 529.058440
... getting the finetuning functions
... finetunning the model
epoch 1, minibatch 166/166, validation error 14.848485 %
epoch 1, minibatch 166/166, test error of best model 15.727273 %
epoch 2, minibatch 166/166, validation error 11.606061 %
epoch 2, minibatch 166/166, test error of best model 11.717172 %
Optimization complete with best validation score of 11.606061 %, on iteration 332, with test performance 11.717172 %
DBN
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost -165.613036644
Pre-training layer 1, epoch 0, cost -620.421300613
Pre-training layer 2, epoch 0, cost -134.389183501
... getting the finetuning functions
... finetuning the model
epoch 1, minibatch 166/166, validation error 28.181818 %
epoch 1, minibatch 166/166, test error of best model 30.090909 %
epoch 2, minibatch 166/166, validation error 20.262626 %
epoch 2, minibatch 166/166, test error of best model 21.434343 %
Optimization complete with best validation score of 20.262626 %, obtained at iteration 332, with test performance 21.434343 %
rbm
... loading data
Training epoch 0, cost is -51.6263725776
Training took 1.673142 minutes
... plotting sample 0
rnnrbm
Epoch 1/1
-15.0496314334
rnnslu
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
lstm
model options {'encoder': 'lstm', 'optimizer': <function adadelta at 0x7f8514f589b0>, 'validFreq': 370, 'lrate': 0.0001, 'batch_size': 16, 'decay_c': 0.0, 'patience': 10, 'reload_model': None, 'n_words': 10000, 'max_epochs': 1, 'dispFreq': 10, 'dataset': 'imdb', 'valid_batch_size': 64, 'use_dropout': True, 'dim_proj': 128, 'maxlen': 100, 'saveto': '', 'noise_std': 0.0, 'test_size': 1000, 'saveFreq': 1110}
Loading data
Building model
Optimization
1998 train examples
105 valid examples
1000 test examples
Epoch 0 Update 10 Cost 0.692138353384
Epoch 0 Update 20 Cost 0.690693189509
Epoch 0 Update 30 Cost 0.676380211732
Epoch 0 Update 40 Cost 0.672820589255
Epoch 0 Update 50 Cost 0.675394850171
Epoch 0 Update 60 Cost 0.698660008515
Epoch 0 Update 70 Cost 0.684332483641
Epoch 0 Update 80 Cost 0.714756376014
Epoch 0 Update 90 Cost 0.727102027393
Epoch 0 Update 100 Cost 0.672112529029
Epoch 0 Update 110 Cost 0.703670540776
Epoch 0 Update 120 Cost 0.758497490788
Seen 1998 samples
Train 0.422422422422 Valid 0.428571428571 Test 0.502
The code run for 1 epochs, with 17.961051 sec/epochs
logistic_sgd
... loading data
... building the model
... training the model
epoch 1, minibatch 83/83, validation error 12.458333 %
epoch 1, minibatch 83/83, test error of best model 12.375000 %
epoch 2, minibatch 83/83, validation error 11.010417 %
epoch 2, minibatch 83/83, test error of best model 10.958333 %
epoch 3, minibatch 83/83, validation error 10.312500 %
epoch 3, minibatch 83/83, test error of best model 10.312500 %
epoch 4, minibatch 83/83, validation error 9.875000 %
epoch 4, minibatch 83/83, test error of best model 9.833333 %
epoch 5, minibatch 83/83, validation error 9.562500 %
epoch 5, minibatch 83/83, test error of best model 9.479167 %
epoch 6, minibatch 83/83, validation error 9.322917 %
epoch 6, minibatch 83/83, test error of best model 9.291667 %
epoch 7, minibatch 83/83, validation error 9.187500 %
epoch 7, minibatch 83/83, test error of best model 9.000000 %
epoch 8, minibatch 83/83, validation error 8.989583 %
epoch 8, minibatch 83/83, test error of best model 8.958333 %
epoch 9, minibatch 83/83, validation error 8.937500 %
epoch 9, minibatch 83/83, test error of best model 8.812500 %
epoch 10, minibatch 83/83, validation error 8.750000 %
epoch 10, minibatch 83/83, test error of best model 8.666667 %
epoch 11, minibatch 83/83, validation error 8.666667 %
epoch 11, minibatch 83/83, test error of best model 8.520833 %
epoch 12, minibatch 83/83, validation error 8.583333 %
epoch 12, minibatch 83/83, test error of best model 8.416667 %
epoch 13, minibatch 83/83, validation error 8.489583 %
epoch 13, minibatch 83/83, test error of best model 8.291667 %
epoch 14, minibatch 83/83, validation error 8.427083 %
epoch 14, minibatch 83/83, test error of best model 8.281250 %
epoch 15, minibatch 83/83, validation error 8.354167 %
epoch 15, minibatch 83/83, test error of best model 8.270833 %
epoch 16, minibatch 83/83, validation error 8.302083 %
epoch 16, minibatch 83/83, test error of best model 8.239583 %
epoch 17, minibatch 83/83, validation error 8.250000 %
epoch 17, minibatch 83/83, test error of best model 8.177083 %
epoch 18, minibatch 83/83, validation error 8.229167 %
epoch 18, minibatch 83/83, test error of best model 8.062500 %
epoch 19, minibatch 83/83, validation error 8.260417 %
epoch 20, minibatch 83/83, validation error 8.260417 %
epoch 21, minibatch 83/83, validation error 8.208333 %
epoch 21, minibatch 83/83, test error of best model 7.947917 %
epoch 22, minibatch 83/83, validation error 8.187500 %
epoch 22, minibatch 83/83, test error of best model 7.927083 %
epoch 23, minibatch 83/83, validation error 8.156250 %
epoch 23, minibatch 83/83, test error of best model 7.958333 %
epoch 24, minibatch 83/83, validation error 8.114583 %
epoch 24, minibatch 83/83, test error of best model 7.947917 %
epoch 25, minibatch 83/83, validation error 8.093750 %
epoch 25, minibatch 83/83, test error of best model 7.947917 %
epoch 26, minibatch 83/83, validation error 8.104167 %
epoch 27, minibatch 83/83, validation error 8.104167 %
epoch 28, minibatch 83/83, validation error 8.052083 %
epoch 28, minibatch 83/83, test error of best model 7.843750 %
epoch 29, minibatch 83/83, validation error 8.052083 %
epoch 30, minibatch 83/83, validation error 8.031250 %
epoch 30, minibatch 83/83, test error of best model 7.843750 %
Optimization complete with best validation score of 8.031250 %,with test performance 7.843750 %
The code run for 30 epochs, with 7.328806 epochs/sec
logistic_cg
... loading data
... building the model
Optimizing using scipy.optimize.fmin_cg...
validation error 29.989583 %
validation error 24.656250 %
validation error 20.833333 %
validation error 16.979167 %
validation error 14.291667 %
validation error 14.239583 %
validation error 13.166667 %
validation error 12.281250 %
validation error 11.739583 %
validation error 11.531250 %
validation error 10.572917 %
validation error 10.406250 %
validation error 10.166667 %
validation error 10.208333 %
validation error 9.854167 %
validation error 9.718750 %
validation error 9.375000 %
validation error 8.968750 %
validation error 8.927083 %
validation error 8.760417 %
validation error 8.770833 %
validation error 8.625000 %
validation error 8.541667 %
validation error 8.250000 %
validation error 8.375000 %
validation error 8.395833 %
validation error 8.197917 %
validation error 8.031250 %
validation error 8.000000 %
validation error 7.968750 %
Optimization complete with best validation score of 7.968750 %, with test performance 7.989583 %
mlp
... loading data
... building the model
... training
epoch 1, minibatch 2500/2500, validation error 9.620000 %
epoch 1, minibatch 2500/2500, test error of best model 10.090000 %
epoch 2, minibatch 2500/2500, validation error 8.610000 %
epoch 2, minibatch 2500/2500, test error of best model 8.740000 %
epoch 3, minibatch 2500/2500, validation error 8.000000 %
epoch 3, minibatch 2500/2500, test error of best model 8.160000 %
epoch 4, minibatch 2500/2500, validation error 7.600000 %
epoch 4, minibatch 2500/2500, test error of best model 7.790000 %
epoch 5, minibatch 2500/2500, validation error 7.300000 %
epoch 5, minibatch 2500/2500, test error of best model 7.580000 %
Optimization complete. Best validation score of 7.300000 % obtained at iteration 12500, with test performance 7.580000 %
convolutional_mlp
... loading data
... building the model
... training
training @ iter = 0
epoch 1, minibatch 100/100, validation error 13.300000 %
epoch 1, minibatch 100/100, test error of best model 13.680000 %
training @ iter = 100
epoch 2, minibatch 100/100, validation error 9.830000 %
epoch 2, minibatch 100/100, test error of best model 10.200000 %
training @ iter = 200
epoch 3, minibatch 100/100, validation error 8.400000 %
epoch 3, minibatch 100/100, test error of best model 8.730000 %
training @ iter = 300
epoch 4, minibatch 100/100, validation error 7.350000 %
epoch 4, minibatch 100/100, test error of best model 7.620000 %
training @ iter = 400
epoch 5, minibatch 100/100, validation error 6.430000 %
epoch 5, minibatch 100/100, test error of best model 6.920000 %
Optimization complete.
Best validation score of 6.430000 % obtained at iteration 500, with test performance 6.920000 %
dA
... loading data
Training epoch 0, cost 63.2891692993
Training epoch 1, cost 55.7866566589
Training epoch 0, cost 81.7649857224
Training epoch 1, cost 73.4434465988
SdA
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost 194.516144
Pre-training layer 1, epoch 0, cost 695.487612
Pre-training layer 2, epoch 0, cost 529.058447
... getting the finetuning functions
... finetunning the model
epoch 1, minibatch 166/166, validation error 14.848485 %
epoch 1, minibatch 166/166, test error of best model 15.727273 %
epoch 2, minibatch 166/166, validation error 11.606061 %
epoch 2, minibatch 166/166, test error of best model 11.717172 %
Optimization complete with best validation score of 11.606061 %, on iteration 332, with test performance 11.717172 %
DBN
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost -165.613371424
Pre-training layer 1, epoch 0, cost -620.432120817
Pre-training layer 2, epoch 0, cost -134.449325286
... getting the finetuning functions
... finetuning the model
epoch 1, minibatch 166/166, validation error 28.131313 %
epoch 1, minibatch 166/166, test error of best model 30.010101 %
epoch 2, minibatch 166/166, validation error 20.303030 %
epoch 2, minibatch 166/166, test error of best model 21.393939 %
Optimization complete with best validation score of 20.303030 %, obtained at iteration 332, with test performance 21.393939 %
rbm
... loading data
Training epoch 0, cost is -52.7116
Training took 1.243071 minutes
... plotting sample 0
rnnrbm
Epoch 1/1
-15.0365748034
rnnslu
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
lstm
model options {'encoder': 'lstm', 'optimizer': <function adadelta at 0x7f8514f589b0>, 'validFreq': 370, 'lrate': 0.0001, 'batch_size': 16, 'decay_c': 0.0, 'patience': 10, 'reload_model': None, 'n_words': 10000, 'max_epochs': 1, 'dispFreq': 10, 'dataset': 'imdb', 'valid_batch_size': 64, 'use_dropout': True, 'dim_proj': 128, 'maxlen': 100, 'saveto': '', 'noise_std': 0.0, 'test_size': 1000, 'saveFreq': 1110}
Loading data
Building model
Optimization
1998 train examples
105 valid examples
1000 test examples
Epoch 0 Update 10 Cost 0.691160261631
Epoch 0 Update 20 Cost 0.683994412422
Epoch 0 Update 30 Cost 0.675427556038
Epoch 0 Update 40 Cost 0.712318658829
Epoch 0 Update 50 Cost 0.695389866829
Epoch 0 Update 60 Cost 0.675582230091
Epoch 0 Update 70 Cost 0.712137162685
Epoch 0 Update 80 Cost 0.698806881905
Epoch 0 Update 90 Cost 0.596462011337
Epoch 0 Update 100 Cost 0.669474124908
Epoch 0 Update 110 Cost 0.725587248802
Epoch 0 Update 120 Cost 0.73024970293
Seen 1998 samples
Train 0.422922922923 Valid 0.419047619048 Test 0.514
The code run for 1 epochs, with 13.161978 sec/epochs
logistic_sgd
... loading data
... building the model
... training the model
epoch 1, minibatch 83/83, validation error 12.458333 %
epoch 1, minibatch 83/83, test error of best model 12.375000 %
epoch 2, minibatch 83/83, validation error 11.010417 %
epoch 2, minibatch 83/83, test error of best model 10.958333 %
epoch 3, minibatch 83/83, validation error 10.312500 %
epoch 3, minibatch 83/83, test error of best model 10.312500 %
epoch 4, minibatch 83/83, validation error 9.875000 %
epoch 4, minibatch 83/83, test error of best model 9.833333 %
epoch 5, minibatch 83/83, validation error 9.562500 %
epoch 5, minibatch 83/83, test error of best model 9.479167 %
epoch 6, minibatch 83/83, validation error 9.322917 %
epoch 6, minibatch 83/83, test error of best model 9.291667 %
epoch 7, minibatch 83/83, validation error 9.187500 %
epoch 7, minibatch 83/83, test error of best model 9.000000 %
epoch 8, minibatch 83/83, validation error 8.989583 %
epoch 8, minibatch 83/83, test error of best model 8.958333 %
epoch 9, minibatch 83/83, validation error 8.937500 %
epoch 9, minibatch 83/83, test error of best model 8.812500 %
epoch 10, minibatch 83/83, validation error 8.750000 %
epoch 10, minibatch 83/83, test error of best model 8.666667 %
epoch 11, minibatch 83/83, validation error 8.666667 %
epoch 11, minibatch 83/83, test error of best model 8.520833 %
epoch 12, minibatch 83/83, validation error 8.583333 %
epoch 12, minibatch 83/83, test error of best model 8.416667 %
epoch 13, minibatch 83/83, validation error 8.489583 %
epoch 13, minibatch 83/83, test error of best model 8.291667 %
epoch 14, minibatch 83/83, validation error 8.427083 %
epoch 14, minibatch 83/83, test error of best model 8.281250 %
epoch 15, minibatch 83/83, validation error 8.354167 %
epoch 15, minibatch 83/83, test error of best model 8.270833 %
epoch 16, minibatch 83/83, validation error 8.302083 %
epoch 16, minibatch 83/83, test error of best model 8.239583 %
epoch 17, minibatch 83/83, validation error 8.250000 %
epoch 17, minibatch 83/83, test error of best model 8.177083 %
epoch 18, minibatch 83/83, validation error 8.229167 %
epoch 18, minibatch 83/83, test error of best model 8.062500 %
epoch 19, minibatch 83/83, validation error 8.260417 %
epoch 20, minibatch 83/83, validation error 8.260417 %
epoch 21, minibatch 83/83, validation error 8.208333 %
epoch 21, minibatch 83/83, test error of best model 7.947917 %
epoch 22, minibatch 83/83, validation error 8.187500 %
epoch 22, minibatch 83/83, test error of best model 7.927083 %
epoch 23, minibatch 83/83, validation error 8.156250 %
epoch 23, minibatch 83/83, test error of best model 7.958333 %
epoch 24, minibatch 83/83, validation error 8.114583 %
epoch 24, minibatch 83/83, test error of best model 7.947917 %
epoch 25, minibatch 83/83, validation error 8.093750 %
epoch 25, minibatch 83/83, test error of best model 7.947917 %
epoch 26, minibatch 83/83, validation error 8.104167 %
epoch 27, minibatch 83/83, validation error 8.104167 %
epoch 28, minibatch 83/83, validation error 8.052083 %
epoch 28, minibatch 83/83, test error of best model 7.843750 %
epoch 29, minibatch 83/83, validation error 8.052083 %
epoch 30, minibatch 83/83, validation error 8.031250 %
epoch 30, minibatch 83/83, test error of best model 7.843750 %
Optimization complete with best validation score of 8.031250 %,with test performance 7.843750 %
The code run for 30 epochs, with 31.844058 epochs/sec
logistic_cg
... loading data
... building the model
Optimizing using scipy.optimize.fmin_cg...
validation error 29.989583 %
validation error 24.656250 %
validation error 20.833333 %
validation error 16.979167 %
validation error 14.291667 %
validation error 14.239583 %
validation error 13.166667 %
validation error 12.281250 %
validation error 11.739583 %
validation error 11.531250 %
validation error 10.583333 %
validation error 10.406250 %
validation error 10.166667 %
validation error 10.208333 %
validation error 9.854167 %
validation error 9.718750 %
validation error 9.375000 %
validation error 8.968750 %
validation error 8.916667 %
validation error 8.760417 %
validation error 8.770833 %
validation error 8.625000 %
validation error 8.541667 %
validation error 8.250000 %
validation error 8.375000 %
validation error 8.395833 %
validation error 8.197917 %
validation error 8.031250 %
validation error 8.031250 %
validation error 7.968750 %
Optimization complete with best validation score of 7.968750 %, with test performance 7.968750 %
mlp
... loading data
... building the model
... training
epoch 1, minibatch 2500/2500, validation error 9.620000 %
epoch 1, minibatch 2500/2500, test error of best model 10.090000 %
epoch 2, minibatch 2500/2500, validation error 8.610000 %
epoch 2, minibatch 2500/2500, test error of best model 8.740000 %
epoch 3, minibatch 2500/2500, validation error 8.000000 %
epoch 3, minibatch 2500/2500, test error of best model 8.160000 %
epoch 4, minibatch 2500/2500, validation error 7.600000 %
epoch 4, minibatch 2500/2500, test error of best model 7.790000 %
epoch 5, minibatch 2500/2500, validation error 7.300000 %
epoch 5, minibatch 2500/2500, test error of best model 7.580000 %
Optimization complete. Best validation score of 7.300000 % obtained at iteration 12500, with test performance 7.580000 %
convolutional_mlp
... loading data
... building the model
... training
training @ iter = 0
epoch 1, minibatch 100/100, validation error 13.290000 %
epoch 1, minibatch 100/100, test error of best model 13.670000 %
training @ iter = 100
epoch 2, minibatch 100/100, validation error 9.830000 %
epoch 2, minibatch 100/100, test error of best model 10.200000 %
training @ iter = 200
epoch 3, minibatch 100/100, validation error 8.410000 %
epoch 3, minibatch 100/100, test error of best model 8.770000 %
training @ iter = 300
epoch 4, minibatch 100/100, validation error 7.330000 %
epoch 4, minibatch 100/100, test error of best model 7.620000 %
training @ iter = 400
epoch 5, minibatch 100/100, validation error 6.500000 %
epoch 5, minibatch 100/100, test error of best model 6.890000 %
Optimization complete.
Best validation score of 6.500000 % obtained at iteration 500, with test performance 6.890000 %
dA
... loading data
Training epoch 0, cost 63.2891690659
Training epoch 1, cost 55.7866565292
Training epoch 0, cost 81.7649854492
Training epoch 1, cost 73.4434465851
SdA
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost 194.516141
Pre-training layer 1, epoch 0, cost 695.487604
Pre-training layer 2, epoch 0, cost 529.058429
... getting the finetuning functions
... finetunning the model
epoch 1, minibatch 166/166, validation error 14.848485 %
epoch 1, minibatch 166/166, test error of best model 15.727273 %
epoch 2, minibatch 166/166, validation error 11.606061 %
epoch 2, minibatch 166/166, test error of best model 11.717172 %
Optimization complete with best validation score of 11.606061 %, on iteration 332, with test performance 11.717172 %
DBN
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost -165.613303219
Pre-training layer 1, epoch 0, cost -620.403747559
Pre-training layer 2, epoch 0, cost -134.217854373
... getting the finetuning functions
... finetuning the model
epoch 1, minibatch 166/166, validation error 28.222222 %
epoch 1, minibatch 166/166, test error of best model 30.191919 %
epoch 2, minibatch 166/166, validation error 20.292929 %
epoch 2, minibatch 166/166, test error of best model 21.434343 %
Optimization complete with best validation score of 20.292929 %, obtained at iteration 332, with test performance 21.434343 %
rbm
... loading data
Training epoch 0, cost is -51.6296
Training took 0.055968 minutes
... plotting sample 0
rnnrbm
Epoch 1/1
-15.0075673556
rnnslu
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
lstm
model options {'encoder': 'lstm', 'optimizer': <function adadelta at 0x7f8514f589b0>, 'validFreq': 370, 'lrate': 0.0001, 'batch_size': 16, 'decay_c': 0.0, 'patience': 10, 'reload_model': None, 'n_words': 10000, 'max_epochs': 1, 'dispFreq': 10, 'dataset': 'imdb', 'valid_batch_size': 64, 'use_dropout': True, 'dim_proj': 128, 'maxlen': 100, 'saveto': '', 'noise_std': 0.0, 'test_size': 1000, 'saveFreq': 1110}
Loading data
Building model
Optimization
1998 train examples
105 valid examples
1000 test examples
Epoch 0 Update 10 Cost 0.689358711243
Epoch 0 Update 20 Cost 0.710573196411
Epoch 0 Update 30 Cost 0.680263280869
Epoch 0 Update 40 Cost 0.667281806469
Epoch 0 Update 50 Cost 0.637676477432
Epoch 0 Update 60 Cost 0.685400247574
Epoch 0 Update 70 Cost 0.736423969269
Epoch 0 Update 80 Cost 0.641846060753
Epoch 0 Update 90 Cost 0.709488868713
Epoch 0 Update 100 Cost 0.627746403217
Epoch 0 Update 110 Cost 0.669853687286
Epoch 0 Update 120 Cost 0.734875559807
Seen 1998 samples
Train 0.423923923924 Valid 0.4 Test 0.524
The code run for 1 epochs, with 3.901582 sec/epochs
--------------------- >> end captured stdout << ----------------------
Tot=24 Skip=0 Fail=3
See https://jenkins.mila.quebec/job/Theano_buildbot_dlt_speed/332/ to view the results.
Failed tests:
3 tests failed.
FAILED: test.test_rnnslu
Error Message:
[Errno 2] No such file or directory: '/home/jenkins/workspace/Theano_buildbot_dlt_speed/data/atis.fold3.pkl.gz'
-------------------- >> begin captured stdout << ---------------------
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
--------------------- >> end captured stdout << ----------------------
Stack Trace:
File "/miniconda/lib/python2.7/unittest/case.py", line 329, in run
testMethod()
File "/miniconda/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/test.py", line 79, in test_rnnslu
rnnslu.main(s)
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/rnnslu.py", line 280, in main
train_set, valid_set, test_set, dic = atisfold(param['fold'])
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/rnnslu.py", line 71, in atisfold
f = gzip.open(filename, 'rb')
File "/miniconda/lib/python2.7/gzip.py", line 34, in open
return GzipFile(filename, mode, compresslevel)
File "/miniconda/lib/python2.7/gzip.py", line 94, in __init__
fileobj = self.myfileobj = __builtin__.open(filename, mode or 'rb')
[Errno 2] No such file or directory: '/home/jenkins/workspace/Theano_buildbot_dlt_speed/data/atis.fold3.pkl.gz'
-------------------- >> begin captured stdout << ---------------------
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
--------------------- >> end captured stdout << ----------------------
FAILED: test.test_rnnslu
Error Message:
[Errno 2] No such file or directory: '/home/jenkins/workspace/Theano_buildbot_dlt_speed/data/atis.fold3.pkl.gz'
-------------------- >> begin captured stdout << ---------------------
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
--------------------- >> end captured stdout << ----------------------
Stack Trace:
File "/miniconda/lib/python2.7/unittest/case.py", line 329, in run
testMethod()
File "/miniconda/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/test.py", line 79, in test_rnnslu
rnnslu.main(s)
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/rnnslu.py", line 280, in main
train_set, valid_set, test_set, dic = atisfold(param['fold'])
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/rnnslu.py", line 71, in atisfold
f = gzip.open(filename, 'rb')
File "/miniconda/lib/python2.7/gzip.py", line 34, in open
return GzipFile(filename, mode, compresslevel)
File "/miniconda/lib/python2.7/gzip.py", line 94, in __init__
fileobj = self.myfileobj = __builtin__.open(filename, mode or 'rb')
[Errno 2] No such file or directory: '/home/jenkins/workspace/Theano_buildbot_dlt_speed/data/atis.fold3.pkl.gz'
-------------------- >> begin captured stdout << ---------------------
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
--------------------- >> end captured stdout << ----------------------
FAILED: test.speed
Error Message:
-------------------- >> begin captured stdout << ---------------------
logistic_sgd
... loading data
... building the model
... training the model
epoch 1, minibatch 83/83, validation error 12.458333 %
epoch 1, minibatch 83/83, test error of best model 12.375000 %
epoch 2, minibatch 83/83, validation error 11.010417 %
epoch 2, minibatch 83/83, test error of best model 10.958333 %
epoch 3, minibatch 83/83, validation error 10.312500 %
epoch 3, minibatch 83/83, test error of best model 10.312500 %
epoch 4, minibatch 83/83, validation error 9.875000 %
epoch 4, minibatch 83/83, test error of best model 9.833333 %
epoch 5, minibatch 83/83, validation error 9.562500 %
epoch 5, minibatch 83/83, test error of best model 9.479167 %
epoch 6, minibatch 83/83, validation error 9.322917 %
epoch 6, minibatch 83/83, test error of best model 9.291667 %
epoch 7, minibatch 83/83, validation error 9.187500 %
epoch 7, minibatch 83/83, test error of best model 9.000000 %
epoch 8, minibatch 83/83, validation error 8.989583 %
epoch 8, minibatch 83/83, test error of best model 8.958333 %
epoch 9, minibatch 83/83, validation error 8.937500 %
epoch 9, minibatch 83/83, test error of best model 8.812500 %
epoch 10, minibatch 83/83, validation error 8.750000 %
epoch 10, minibatch 83/83, test error of best model 8.666667 %
epoch 11, minibatch 83/83, validation error 8.666667 %
epoch 11, minibatch 83/83, test error of best model 8.520833 %
epoch 12, minibatch 83/83, validation error 8.583333 %
epoch 12, minibatch 83/83, test error of best model 8.416667 %
epoch 13, minibatch 83/83, validation error 8.489583 %
epoch 13, minibatch 83/83, test error of best model 8.291667 %
epoch 14, minibatch 83/83, validation error 8.427083 %
epoch 14, minibatch 83/83, test error of best model 8.281250 %
epoch 15, minibatch 83/83, validation error 8.354167 %
epoch 15, minibatch 83/83, test error of best model 8.270833 %
epoch 16, minibatch 83/83, validation error 8.302083 %
epoch 16, minibatch 83/83, test error of best model 8.239583 %
epoch 17, minibatch 83/83, validation error 8.250000 %
epoch 17, minibatch 83/83, test error of best model 8.177083 %
epoch 18, minibatch 83/83, validation error 8.229167 %
epoch 18, minibatch 83/83, test error of best model 8.062500 %
epoch 19, minibatch 83/83, validation error 8.260417 %
epoch 20, minibatch 83/83, validation error 8.260417 %
epoch 21, minibatch 83/83, validation error 8.208333 %
epoch 21, minibatch 83/83, test error of best model 7.947917 %
epoch 22, minibatch 83/83, validation error 8.187500 %
epoch 22, minibatch 83/83, test error of best model 7.927083 %
epoch 23, minibatch 83/83, validation error 8.156250 %
epoch 23, minibatch 83/83, test error of best model 7.958333 %
epoch 24, minibatch 83/83, validation error 8.114583 %
epoch 24, minibatch 83/83, test error of best model 7.947917 %
epoch 25, minibatch 83/83, validation error 8.093750 %
epoch 25, minibatch 83/83, test error of best model 7.947917 %
epoch 26, minibatch 83/83, validation error 8.104167 %
epoch 27, minibatch 83/83, validation error 8.104167 %
epoch 28, minibatch 83/83, validation error 8.052083 %
epoch 28, minibatch 83/83, test error of best model 7.843750 %
epoch 29, minibatch 83/83, validation error 8.052083 %
epoch 30, minibatch 83/83, validation error 8.031250 %
epoch 30, minibatch 83/83, test error of best model 7.843750 %
Optimization complete with best validation score of 8.031250 %,with test performance 7.843750 %
The code run for 30 epochs, with 5.214695 epochs/sec
logistic_cg
... loading data
... building the model
Optimizing using scipy.optimize.fmin_cg...
validation error 29.989583 %
validation error 24.656250 %
validation error 20.833333 %
validation error 16.979167 %
validation error 14.291667 %
validation error 14.239583 %
validation error 13.166667 %
validation error 12.281250 %
validation error 11.739583 %
validation error 11.531250 %
validation error 10.583333 %
validation error 10.406250 %
validation error 10.166667 %
validation error 10.208333 %
validation error 9.854167 %
validation error 9.718750 %
validation error 9.375000 %
validation error 8.968750 %
validation error 8.927083 %
validation error 8.760417 %
validation error 8.770833 %
validation error 8.625000 %
validation error 8.541667 %
validation error 8.250000 %
validation error 8.375000 %
validation error 8.395833 %
validation error 8.197917 %
validation error 8.031250 %
validation error 8.020833 %
validation error 7.968750 %
Optimization complete with best validation score of 7.968750 %, with test performance 7.958333 %
mlp
... loading data
... building the model
... training
epoch 1, minibatch 2500/2500, validation error 9.620000 %
epoch 1, minibatch 2500/2500, test error of best model 10.090000 %
epoch 2, minibatch 2500/2500, validation error 8.610000 %
epoch 2, minibatch 2500/2500, test error of best model 8.740000 %
epoch 3, minibatch 2500/2500, validation error 8.000000 %
epoch 3, minibatch 2500/2500, test error of best model 8.160000 %
epoch 4, minibatch 2500/2500, validation error 7.600000 %
epoch 4, minibatch 2500/2500, test error of best model 7.790000 %
epoch 5, minibatch 2500/2500, validation error 7.300000 %
epoch 5, minibatch 2500/2500, test error of best model 7.590000 %
Optimization complete. Best validation score of 7.300000 % obtained at iteration 12500, with test performance 7.590000 %
convolutional_mlp
... loading data
... building the model
... training
training @ iter = 0
epoch 1, minibatch 100/100, validation error 13.300000 %
epoch 1, minibatch 100/100, test error of best model 13.690000 %
training @ iter = 100
epoch 2, minibatch 100/100, validation error 9.840000 %
epoch 2, minibatch 100/100, test error of best model 10.200000 %
training @ iter = 200
epoch 3, minibatch 100/100, validation error 8.400000 %
epoch 3, minibatch 100/100, test error of best model 8.730000 %
training @ iter = 300
epoch 4, minibatch 100/100, validation error 7.340000 %
epoch 4, minibatch 100/100, test error of best model 7.620000 %
training @ iter = 400
epoch 5, minibatch 100/100, validation error 6.430000 %
epoch 5, minibatch 100/100, test error of best model 6.920000 %
Optimization complete.
Best validation score of 6.430000 % obtained at iteration 500, with test performance 6.920000 %
dA
... loading data
Training epoch 0, cost 63.2891694201
Training epoch 1, cost 55.7866565443
Training epoch 0, cost 81.7649857817
Training epoch 1, cost 73.4434466428
SdA
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost 194.516149
Pre-training layer 1, epoch 0, cost 695.487634
Pre-training layer 2, epoch 0, cost 529.058440
... getting the finetuning functions
... finetunning the model
epoch 1, minibatch 166/166, validation error 14.848485 %
epoch 1, minibatch 166/166, test error of best model 15.727273 %
epoch 2, minibatch 166/166, validation error 11.606061 %
epoch 2, minibatch 166/166, test error of best model 11.717172 %
Optimization complete with best validation score of 11.606061 %, on iteration 332, with test performance 11.717172 %
DBN
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost -165.613036644
Pre-training layer 1, epoch 0, cost -620.421300613
Pre-training layer 2, epoch 0, cost -134.389183501
... getting the finetuning functions
... finetuning the model
epoch 1, minibatch 166/166, validation error 28.181818 %
epoch 1, minibatch 166/166, test error of best model 30.090909 %
epoch 2, minibatch 166/166, validation error 20.262626 %
epoch 2, minibatch 166/166, test error of best model 21.434343 %
Optimization complete with best validation score of 20.262626 %, obtained at iteration 332, with test performance 21.434343 %
rbm
... loading data
Training epoch 0, cost is -51.6263725776
Training took 1.673142 minutes
... plotting sample 0
rnnrbm
Epoch 1/1
-15.0496314334
rnnslu
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
lstm
model options {'encoder': 'lstm', 'optimizer': <function adadelta at 0x7f8514f589b0>, 'validFreq': 370, 'lrate': 0.0001, 'batch_size': 16, 'decay_c': 0.0, 'patience': 10, 'reload_model': None, 'n_words': 10000, 'max_epochs': 1, 'dispFreq': 10, 'dataset': 'imdb', 'valid_batch_size': 64, 'use_dropout': True, 'dim_proj': 128, 'maxlen': 100, 'saveto': '', 'noise_std': 0.0, 'test_size': 1000, 'saveFreq': 1110}
Loading data
Building model
Optimization
1998 train examples
105 valid examples
1000 test examples
Epoch 0 Update 10 Cost 0.692138353384
Epoch 0 Update 20 Cost 0.690693189509
Epoch 0 Update 30 Cost 0.676380211732
Epoch 0 Update 40 Cost 0.672820589255
Epoch 0 Update 50 Cost 0.675394850171
Epoch 0 Update 60 Cost 0.698660008515
Epoch 0 Update 70 Cost 0.684332483641
Epoch 0 Update 80 Cost 0.714756376014
Epoch 0 Update 90 Cost 0.727102027393
Epoch 0 Update 100 Cost 0.672112529029
Epoch 0 Update 110 Cost 0.703670540776
Epoch 0 Update 120 Cost 0.758497490788
Seen 1998 samples
Train 0.422422422422 Valid 0.428571428571 Test 0.502
The code run for 1 epochs, with 17.961051 sec/epochs
logistic_sgd
... loading data
... building the model
... training the model
epoch 1, minibatch 83/83, validation error 12.458333 %
epoch 1, minibatch 83/83, test error of best model 12.375000 %
epoch 2, minibatch 83/83, validation error 11.010417 %
epoch 2, minibatch 83/83, test error of best model 10.958333 %
epoch 3, minibatch 83/83, validation error 10.312500 %
epoch 3, minibatch 83/83, test error of best model 10.312500 %
epoch 4, minibatch 83/83, validation error 9.875000 %
epoch 4, minibatch 83/83, test error of best model 9.833333 %
epoch 5, minibatch 83/83, validation error 9.562500 %
epoch 5, minibatch 83/83, test error of best model 9.479167 %
epoch 6, minibatch 83/83, validation error 9.322917 %
epoch 6, minibatch 83/83, test error of best model 9.291667 %
epoch 7, minibatch 83/83, validation error 9.187500 %
epoch 7, minibatch 83/83, test error of best model 9.000000 %
epoch 8, minibatch 83/83, validation error 8.989583 %
epoch 8, minibatch 83/83, test error of best model 8.958333 %
epoch 9, minibatch 83/83, validation error 8.937500 %
epoch 9, minibatch 83/83, test error of best model 8.812500 %
epoch 10, minibatch 83/83, validation error 8.750000 %
epoch 10, minibatch 83/83, test error of best model 8.666667 %
epoch 11, minibatch 83/83, validation error 8.666667 %
epoch 11, minibatch 83/83, test error of best model 8.520833 %
epoch 12, minibatch 83/83, validation error 8.583333 %
epoch 12, minibatch 83/83, test error of best model 8.416667 %
epoch 13, minibatch 83/83, validation error 8.489583 %
epoch 13, minibatch 83/83, test error of best model 8.291667 %
epoch 14, minibatch 83/83, validation error 8.427083 %
epoch 14, minibatch 83/83, test error of best model 8.281250 %
epoch 15, minibatch 83/83, validation error 8.354167 %
epoch 15, minibatch 83/83, test error of best model 8.270833 %
epoch 16, minibatch 83/83, validation error 8.302083 %
epoch 16, minibatch 83/83, test error of best model 8.239583 %
epoch 17, minibatch 83/83, validation error 8.250000 %
epoch 17, minibatch 83/83, test error of best model 8.177083 %
epoch 18, minibatch 83/83, validation error 8.229167 %
epoch 18, minibatch 83/83, test error of best model 8.062500 %
epoch 19, minibatch 83/83, validation error 8.260417 %
epoch 20, minibatch 83/83, validation error 8.260417 %
epoch 21, minibatch 83/83, validation error 8.208333 %
epoch 21, minibatch 83/83, test error of best model 7.947917 %
epoch 22, minibatch 83/83, validation error 8.187500 %
epoch 22, minibatch 83/83, test error of best model 7.927083 %
epoch 23, minibatch 83/83, validation error 8.156250 %
epoch 23, minibatch 83/83, test error of best model 7.958333 %
epoch 24, minibatch 83/83, validation error 8.114583 %
epoch 24, minibatch 83/83, test error of best model 7.947917 %
epoch 25, minibatch 83/83, validation error 8.093750 %
epoch 25, minibatch 83/83, test error of best model 7.947917 %
epoch 26, minibatch 83/83, validation error 8.104167 %
epoch 27, minibatch 83/83, validation error 8.104167 %
epoch 28, minibatch 83/83, validation error 8.052083 %
epoch 28, minibatch 83/83, test error of best model 7.843750 %
epoch 29, minibatch 83/83, validation error 8.052083 %
epoch 30, minibatch 83/83, validation error 8.031250 %
epoch 30, minibatch 83/83, test error of best model 7.843750 %
Optimization complete with best validation score of 8.031250 %,with test performance 7.843750 %
The code run for 30 epochs, with 7.328806 epochs/sec
logistic_cg
... loading data
... building the model
Optimizing using scipy.optimize.fmin_cg...
validation error 29.989583 %
validation error 24.656250 %
validation error 20.833333 %
validation error 16.979167 %
validation error 14.291667 %
validation error 14.239583 %
validation error 13.166667 %
validation error 12.281250 %
validation error 11.739583 %
validation error 11.531250 %
validation error 10.572917 %
validation error 10.406250 %
validation error 10.166667 %
validation error 10.208333 %
validation error 9.854167 %
validation error 9.718750 %
validation error 9.375000 %
validation error 8.968750 %
validation error 8.927083 %
validation error 8.760417 %
validation error 8.770833 %
validation error 8.625000 %
validation error 8.541667 %
validation error 8.250000 %
validation error 8.375000 %
validation error 8.395833 %
validation error 8.197917 %
validation error 8.031250 %
validation error 8.000000 %
validation error 7.968750 %
Optimization complete with best validation score of 7.968750 %, with test performance 7.989583 %
mlp
... loading data
... building the model
... training
epoch 1, minibatch 2500/2500, validation error 9.620000 %
epoch 1, minibatch 2500/2500, test error of best model 10.090000 %
epoch 2, minibatch 2500/2500, validation error 8.610000 %
epoch 2, minibatch 2500/2500, test error of best model 8.740000 %
epoch 3, minibatch 2500/2500, validation error 8.000000 %
epoch 3, minibatch 2500/2500, test error of best model 8.160000 %
epoch 4, minibatch 2500/2500, validation error 7.600000 %
epoch 4, minibatch 2500/2500, test error of best model 7.790000 %
epoch 5, minibatch 2500/2500, validation error 7.300000 %
epoch 5, minibatch 2500/2500, test error of best model 7.580000 %
Optimization complete. Best validation score of 7.300000 % obtained at iteration 12500, with test performance 7.580000 %
convolutional_mlp
... loading data
... building the model
... training
training @ iter = 0
epoch 1, minibatch 100/100, validation error 13.300000 %
epoch 1, minibatch 100/100, test error of best model 13.680000 %
training @ iter = 100
epoch 2, minibatch 100/100, validation error 9.830000 %
epoch 2, minibatch 100/100, test error of best model 10.200000 %
training @ iter = 200
epoch 3, minibatch 100/100, validation error 8.400000 %
epoch 3, minibatch 100/100, test error of best model 8.730000 %
training @ iter = 300
epoch 4, minibatch 100/100, validation error 7.350000 %
epoch 4, minibatch 100/100, test error of best model 7.620000 %
training @ iter = 400
epoch 5, minibatch 100/100, validation error 6.430000 %
epoch 5, minibatch 100/100, test error of best model 6.920000 %
Optimization complete.
Best validation score of 6.430000 % obtained at iteration 500, with test performance 6.920000 %
dA
... loading data
Training epoch 0, cost 63.2891692993
Training epoch 1, cost 55.7866566589
Training epoch 0, cost 81.7649857224
Training epoch 1, cost 73.4434465988
SdA
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost 194.516144
Pre-training layer 1, epoch 0, cost 695.487612
Pre-training layer 2, epoch 0, cost 529.058447
... getting the finetuning functions
... finetunning the model
epoch 1, minibatch 166/166, validation error 14.848485 %
epoch 1, minibatch 166/166, test error of best model 15.727273 %
epoch 2, minibatch 166/166, validation error 11.606061 %
epoch 2, minibatch 166/166, test error of best model 11.717172 %
Optimization complete with best validation score of 11.606061 %, on iteration 332, with test performance 11.717172 %
DBN
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost -165.613371424
Pre-training layer 1, epoch 0, cost -620.432120817
Pre-training layer 2, epoch 0, cost -134.449325286
... getting the finetuning functions
... finetuning the model
epoch 1, minibatch 166/166, validation error 28.131313 %
epoch 1, minibatch 166/166, test error of best model 30.010101 %
epoch 2, minibatch 166/166, validation error 20.303030 %
epoch 2, minibatch 166/166, test error of best model 21.393939 %
Optimization complete with best validation score of 20.303030 %, obtained at iteration 332, with test performance 21.393939 %
rbm
... loading data
Training epoch 0, cost is -52.7116
Training took 1.243071 minutes
... plotting sample 0
rnnrbm
Epoch 1/1
-15.0365748034
rnnslu
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
lstm
model options {'encoder': 'lstm', 'optimizer': <function adadelta at 0x7f8514f589b0>, 'validFreq': 370, 'lrate': 0.0001, 'batch_size': 16, 'decay_c': 0.0, 'patience': 10, 'reload_model': None, 'n_words': 10000, 'max_epochs': 1, 'dispFreq': 10, 'dataset': 'imdb', 'valid_batch_size': 64, 'use_dropout': True, 'dim_proj': 128, 'maxlen': 100, 'saveto': '', 'noise_std': 0.0, 'test_size': 1000, 'saveFreq': 1110}
Loading data
Building model
Optimization
1998 train examples
105 valid examples
1000 test examples
Epoch 0 Update 10 Cost 0.691160261631
Epoch 0 Update 20 Cost 0.683994412422
Epoch 0 Update 30 Cost 0.675427556038
Epoch 0 Update 40 Cost 0.712318658829
Epoch 0 Update 50 Cost 0.695389866829
Epoch 0 Update 60 Cost 0.675582230091
Epoch 0 Update 70 Cost 0.712137162685
Epoch 0 Update 80 Cost 0.698806881905
Epoch 0 Update 90 Cost 0.596462011337
Epoch 0 Update 100 Cost 0.669474124908
Epoch 0 Update 110 Cost 0.725587248802
Epoch 0 Update 120 Cost 0.73024970293
Seen 1998 samples
Train 0.422922922923 Valid 0.419047619048 Test 0.514
The code run for 1 epochs, with 13.161978 sec/epochs
logistic_sgd
... loading data
... building the model
... training the model
epoch 1, minibatch 83/83, validation error 12.458333 %
epoch 1, minibatch 83/83, test error of best model 12.375000 %
epoch 2, minibatch 83/83, validation error 11.010417 %
epoch 2, minibatch 83/83, test error of best model 10.958333 %
epoch 3, minibatch 83/83, validation error 10.312500 %
epoch 3, minibatch 83/83, test error of best model 10.312500 %
epoch 4, minibatch 83/83, validation error 9.875000 %
epoch 4, minibatch 83/83, test error of best model 9.833333 %
epoch 5, minibatch 83/83, validation error 9.562500 %
epoch 5, minibatch 83/83, test error of best model 9.479167 %
epoch 6, minibatch 83/83, validation error 9.322917 %
epoch 6, minibatch 83/83, test error of best model 9.291667 %
epoch 7, minibatch 83/83, validation error 9.187500 %
epoch 7, minibatch 83/83, test error of best model 9.000000 %
epoch 8, minibatch 83/83, validation error 8.989583 %
epoch 8, minibatch 83/83, test error of best model 8.958333 %
epoch 9, minibatch 83/83, validation error 8.937500 %
epoch 9, minibatch 83/83, test error of best model 8.812500 %
epoch 10, minibatch 83/83, validation error 8.750000 %
epoch 10, minibatch 83/83, test error of best model 8.666667 %
epoch 11, minibatch 83/83, validation error 8.666667 %
epoch 11, minibatch 83/83, test error of best model 8.520833 %
epoch 12, minibatch 83/83, validation error 8.583333 %
epoch 12, minibatch 83/83, test error of best model 8.416667 %
epoch 13, minibatch 83/83, validation error 8.489583 %
epoch 13, minibatch 83/83, test error of best model 8.291667 %
epoch 14, minibatch 83/83, validation error 8.427083 %
epoch 14, minibatch 83/83, test error of best model 8.281250 %
epoch 15, minibatch 83/83, validation error 8.354167 %
epoch 15, minibatch 83/83, test error of best model 8.270833 %
epoch 16, minibatch 83/83, validation error 8.302083 %
epoch 16, minibatch 83/83, test error of best model 8.239583 %
epoch 17, minibatch 83/83, validation error 8.250000 %
epoch 17, minibatch 83/83, test error of best model 8.177083 %
epoch 18, minibatch 83/83, validation error 8.229167 %
epoch 18, minibatch 83/83, test error of best model 8.062500 %
epoch 19, minibatch 83/83, validation error 8.260417 %
epoch 20, minibatch 83/83, validation error 8.260417 %
epoch 21, minibatch 83/83, validation error 8.208333 %
epoch 21, minibatch 83/83, test error of best model 7.947917 %
epoch 22, minibatch 83/83, validation error 8.187500 %
epoch 22, minibatch 83/83, test error of best model 7.927083 %
epoch 23, minibatch 83/83, validation error 8.156250 %
epoch 23, minibatch 83/83, test error of best model 7.958333 %
epoch 24, minibatch 83/83, validation error 8.114583 %
epoch 24, minibatch 83/83, test error of best model 7.947917 %
epoch 25, minibatch 83/83, validation error 8.093750 %
epoch 25, minibatch 83/83, test error of best model 7.947917 %
epoch 26, minibatch 83/83, validation error 8.104167 %
epoch 27, minibatch 83/83, validation error 8.104167 %
epoch 28, minibatch 83/83, validation error 8.052083 %
epoch 28, minibatch 83/83, test error of best model 7.843750 %
epoch 29, minibatch 83/83, validation error 8.052083 %
epoch 30, minibatch 83/83, validation error 8.031250 %
epoch 30, minibatch 83/83, test error of best model 7.843750 %
Optimization complete with best validation score of 8.031250 %,with test performance 7.843750 %
The code run for 30 epochs, with 31.844058 epochs/sec
logistic_cg
... loading data
... building the model
Optimizing using scipy.optimize.fmin_cg...
validation error 29.989583 %
validation error 24.656250 %
validation error 20.833333 %
validation error 16.979167 %
validation error 14.291667 %
validation error 14.239583 %
validation error 13.166667 %
validation error 12.281250 %
validation error 11.739583 %
validation error 11.531250 %
validation error 10.583333 %
validation error 10.406250 %
validation error 10.166667 %
validation error 10.208333 %
validation error 9.854167 %
validation error 9.718750 %
validation error 9.375000 %
validation error 8.968750 %
validation error 8.916667 %
validation error 8.760417 %
validation error 8.770833 %
validation error 8.625000 %
validation error 8.541667 %
validation error 8.250000 %
validation error 8.375000 %
validation error 8.395833 %
validation error 8.197917 %
validation error 8.031250 %
validation error 8.031250 %
validation error 7.968750 %
Optimization complete with best validation score of 7.968750 %, with test performance 7.968750 %
mlp
... loading data
... building the model
... training
epoch 1, minibatch 2500/2500, validation error 9.620000 %
epoch 1, minibatch 2500/2500, test error of best model 10.090000 %
epoch 2, minibatch 2500/2500, validation error 8.610000 %
epoch 2, minibatch 2500/2500, test error of best model 8.740000 %
epoch 3, minibatch 2500/2500, validation error 8.000000 %
epoch 3, minibatch 2500/2500, test error of best model 8.160000 %
epoch 4, minibatch 2500/2500, validation error 7.600000 %
epoch 4, minibatch 2500/2500, test error of best model 7.790000 %
epoch 5, minibatch 2500/2500, validation error 7.300000 %
epoch 5, minibatch 2500/2500, test error of best model 7.580000 %
Optimization complete. Best validation score of 7.300000 % obtained at iteration 12500, with test performance 7.580000 %
convolutional_mlp
... loading data
... building the model
... training
training @ iter = 0
epoch 1, minibatch 100/100, validation error 13.290000 %
epoch 1, minibatch 100/100, test error of best model 13.670000 %
training @ iter = 100
epoch 2, minibatch 100/100, validation error 9.830000 %
epoch 2, minibatch 100/100, test error of best model 10.200000 %
training @ iter = 200
epoch 3, minibatch 100/100, validation error 8.410000 %
epoch 3, minibatch 100/100, test error of best model 8.770000 %
training @ iter = 300
epoch 4, minibatch 100/100, validation error 7.330000 %
epoch 4, minibatch 100/100, test error of best model 7.620000 %
training @ iter = 400
epoch 5, minibatch 100/100, validation error 6.500000 %
epoch 5, minibatch 100/100, test error of best model 6.890000 %
Optimization complete.
Best validation score of 6.500000 % obtained at iteration 500, with test performance 6.890000 %
dA
... loading data
Training epoch 0, cost 63.2891690659
Training epoch 1, cost 55.7866565292
Training epoch 0, cost 81.7649854492
Training epoch 1, cost 73.4434465851
SdA
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost 194.516141
Pre-training layer 1, epoch 0, cost 695.487604
Pre-training layer 2, epoch 0, cost 529.058429
... getting the finetuning functions
... finetunning the model
epoch 1, minibatch 166/166, validation error 14.848485 %
epoch 1, minibatch 166/166, test error of best model 15.727273 %
epoch 2, minibatch 166/166, validation error 11.606061 %
epoch 2, minibatch 166/166, test error of best model 11.717172 %
Optimization complete with best validation score of 11.606061 %, on iteration 332, with test performance 11.717172 %
DBN
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost -165.613303219
Pre-training layer 1, epoch 0, cost -620.403747559
Pre-training layer 2, epoch 0, cost -134.217854373
... getting the finetuning functions
... finetuning the model
epoch 1, minibatch 166/166, validation error 28.222222 %
epoch 1, minibatch 166/166, test error of best model 30.191919 %
epoch 2, minibatch 166/166, validation error 20.292929 %
epoch 2, minibatch 166/166, test error of best model 21.434343 %
Optimization complete with best validation score of 20.292929 %, obtained at iteration 332, with test performance 21.434343 %
rbm
... loading data
Training epoch 0, cost is -51.6296
Training took 0.055968 minutes
... plotting sample 0
rnnrbm
Epoch 1/1
-15.0075673556
rnnslu
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
lstm
model options {'encoder': 'lstm', 'optimizer': <function adadelta at 0x7f8514f589b0>, 'validFreq': 370, 'lrate': 0.0001, 'batch_size': 16, 'decay_c': 0.0, 'patience': 10, 'reload_model': None, 'n_words': 10000, 'max_epochs': 1, 'dispFreq': 10, 'dataset': 'imdb', 'valid_batch_size': 64, 'use_dropout': True, 'dim_proj': 128, 'maxlen': 100, 'saveto': '', 'noise_std': 0.0, 'test_size': 1000, 'saveFreq': 1110}
Loading data
Building model
Optimization
1998 train examples
105 valid examples
1000 test examples
Epoch 0 Update 10 Cost 0.689358711243
Epoch 0 Update 20 Cost 0.710573196411
Epoch 0 Update 30 Cost 0.680263280869
Epoch 0 Update 40 Cost 0.667281806469
Epoch 0 Update 50 Cost 0.637676477432
Epoch 0 Update 60 Cost 0.685400247574
Epoch 0 Update 70 Cost 0.736423969269
Epoch 0 Update 80 Cost 0.641846060753
Epoch 0 Update 90 Cost 0.709488868713
Epoch 0 Update 100 Cost 0.627746403217
Epoch 0 Update 110 Cost 0.669853687286
Epoch 0 Update 120 Cost 0.734875559807
Seen 1998 samples
Train 0.423923923924 Valid 0.4 Test 0.524
The code run for 1 epochs, with 3.901582 sec/epochs
--------------------- >> end captured stdout << ----------------------
Stack Trace:
File "/miniconda/lib/python2.7/unittest/case.py", line 329, in run
testMethod()
File "/miniconda/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/home/jenkins/workspace/Theano_buildbot_dlt_speed/code/test.py", line 239, in speed
assert not numpy.isnan(gpu_times).any()
-------------------- >> begin captured stdout << ---------------------
logistic_sgd
... loading data
... building the model
... training the model
epoch 1, minibatch 83/83, validation error 12.458333 %
epoch 1, minibatch 83/83, test error of best model 12.375000 %
epoch 2, minibatch 83/83, validation error 11.010417 %
epoch 2, minibatch 83/83, test error of best model 10.958333 %
epoch 3, minibatch 83/83, validation error 10.312500 %
epoch 3, minibatch 83/83, test error of best model 10.312500 %
epoch 4, minibatch 83/83, validation error 9.875000 %
epoch 4, minibatch 83/83, test error of best model 9.833333 %
epoch 5, minibatch 83/83, validation error 9.562500 %
epoch 5, minibatch 83/83, test error of best model 9.479167 %
epoch 6, minibatch 83/83, validation error 9.322917 %
epoch 6, minibatch 83/83, test error of best model 9.291667 %
epoch 7, minibatch 83/83, validation error 9.187500 %
epoch 7, minibatch 83/83, test error of best model 9.000000 %
epoch 8, minibatch 83/83, validation error 8.989583 %
epoch 8, minibatch 83/83, test error of best model 8.958333 %
epoch 9, minibatch 83/83, validation error 8.937500 %
epoch 9, minibatch 83/83, test error of best model 8.812500 %
epoch 10, minibatch 83/83, validation error 8.750000 %
epoch 10, minibatch 83/83, test error of best model 8.666667 %
epoch 11, minibatch 83/83, validation error 8.666667 %
epoch 11, minibatch 83/83, test error of best model 8.520833 %
epoch 12, minibatch 83/83, validation error 8.583333 %
epoch 12, minibatch 83/83, test error of best model 8.416667 %
epoch 13, minibatch 83/83, validation error 8.489583 %
epoch 13, minibatch 83/83, test error of best model 8.291667 %
epoch 14, minibatch 83/83, validation error 8.427083 %
epoch 14, minibatch 83/83, test error of best model 8.281250 %
epoch 15, minibatch 83/83, validation error 8.354167 %
epoch 15, minibatch 83/83, test error of best model 8.270833 %
epoch 16, minibatch 83/83, validation error 8.302083 %
epoch 16, minibatch 83/83, test error of best model 8.239583 %
epoch 17, minibatch 83/83, validation error 8.250000 %
epoch 17, minibatch 83/83, test error of best model 8.177083 %
epoch 18, minibatch 83/83, validation error 8.229167 %
epoch 18, minibatch 83/83, test error of best model 8.062500 %
epoch 19, minibatch 83/83, validation error 8.260417 %
epoch 20, minibatch 83/83, validation error 8.260417 %
epoch 21, minibatch 83/83, validation error 8.208333 %
epoch 21, minibatch 83/83, test error of best model 7.947917 %
epoch 22, minibatch 83/83, validation error 8.187500 %
epoch 22, minibatch 83/83, test error of best model 7.927083 %
epoch 23, minibatch 83/83, validation error 8.156250 %
epoch 23, minibatch 83/83, test error of best model 7.958333 %
epoch 24, minibatch 83/83, validation error 8.114583 %
epoch 24, minibatch 83/83, test error of best model 7.947917 %
epoch 25, minibatch 83/83, validation error 8.093750 %
epoch 25, minibatch 83/83, test error of best model 7.947917 %
epoch 26, minibatch 83/83, validation error 8.104167 %
epoch 27, minibatch 83/83, validation error 8.104167 %
epoch 28, minibatch 83/83, validation error 8.052083 %
epoch 28, minibatch 83/83, test error of best model 7.843750 %
epoch 29, minibatch 83/83, validation error 8.052083 %
epoch 30, minibatch 83/83, validation error 8.031250 %
epoch 30, minibatch 83/83, test error of best model 7.843750 %
Optimization complete with best validation score of 8.031250 %,with test performance 7.843750 %
The code run for 30 epochs, with 5.214695 epochs/sec
logistic_cg
... loading data
... building the model
Optimizing using scipy.optimize.fmin_cg...
validation error 29.989583 %
validation error 24.656250 %
validation error 20.833333 %
validation error 16.979167 %
validation error 14.291667 %
validation error 14.239583 %
validation error 13.166667 %
validation error 12.281250 %
validation error 11.739583 %
validation error 11.531250 %
validation error 10.583333 %
validation error 10.406250 %
validation error 10.166667 %
validation error 10.208333 %
validation error 9.854167 %
validation error 9.718750 %
validation error 9.375000 %
validation error 8.968750 %
validation error 8.927083 %
validation error 8.760417 %
validation error 8.770833 %
validation error 8.625000 %
validation error 8.541667 %
validation error 8.250000 %
validation error 8.375000 %
validation error 8.395833 %
validation error 8.197917 %
validation error 8.031250 %
validation error 8.020833 %
validation error 7.968750 %
Optimization complete with best validation score of 7.968750 %, with test performance 7.958333 %
mlp
... loading data
... building the model
... training
epoch 1, minibatch 2500/2500, validation error 9.620000 %
epoch 1, minibatch 2500/2500, test error of best model 10.090000 %
epoch 2, minibatch 2500/2500, validation error 8.610000 %
epoch 2, minibatch 2500/2500, test error of best model 8.740000 %
epoch 3, minibatch 2500/2500, validation error 8.000000 %
epoch 3, minibatch 2500/2500, test error of best model 8.160000 %
epoch 4, minibatch 2500/2500, validation error 7.600000 %
epoch 4, minibatch 2500/2500, test error of best model 7.790000 %
epoch 5, minibatch 2500/2500, validation error 7.300000 %
epoch 5, minibatch 2500/2500, test error of best model 7.590000 %
Optimization complete. Best validation score of 7.300000 % obtained at iteration 12500, with test performance 7.590000 %
convolutional_mlp
... loading data
... building the model
... training
training @ iter = 0
epoch 1, minibatch 100/100, validation error 13.300000 %
epoch 1, minibatch 100/100, test error of best model 13.690000 %
training @ iter = 100
epoch 2, minibatch 100/100, validation error 9.840000 %
epoch 2, minibatch 100/100, test error of best model 10.200000 %
training @ iter = 200
epoch 3, minibatch 100/100, validation error 8.400000 %
epoch 3, minibatch 100/100, test error of best model 8.730000 %
training @ iter = 300
epoch 4, minibatch 100/100, validation error 7.340000 %
epoch 4, minibatch 100/100, test error of best model 7.620000 %
training @ iter = 400
epoch 5, minibatch 100/100, validation error 6.430000 %
epoch 5, minibatch 100/100, test error of best model 6.920000 %
Optimization complete.
Best validation score of 6.430000 % obtained at iteration 500, with test performance 6.920000 %
dA
... loading data
Training epoch 0, cost 63.2891694201
Training epoch 1, cost 55.7866565443
Training epoch 0, cost 81.7649857817
Training epoch 1, cost 73.4434466428
SdA
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost 194.516149
Pre-training layer 1, epoch 0, cost 695.487634
Pre-training layer 2, epoch 0, cost 529.058440
... getting the finetuning functions
... finetunning the model
epoch 1, minibatch 166/166, validation error 14.848485 %
epoch 1, minibatch 166/166, test error of best model 15.727273 %
epoch 2, minibatch 166/166, validation error 11.606061 %
epoch 2, minibatch 166/166, test error of best model 11.717172 %
Optimization complete with best validation score of 11.606061 %, on iteration 332, with test performance 11.717172 %
DBN
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost -165.613036644
Pre-training layer 1, epoch 0, cost -620.421300613
Pre-training layer 2, epoch 0, cost -134.389183501
... getting the finetuning functions
... finetuning the model
epoch 1, minibatch 166/166, validation error 28.181818 %
epoch 1, minibatch 166/166, test error of best model 30.090909 %
epoch 2, minibatch 166/166, validation error 20.262626 %
epoch 2, minibatch 166/166, test error of best model 21.434343 %
Optimization complete with best validation score of 20.262626 %, obtained at iteration 332, with test performance 21.434343 %
rbm
... loading data
Training epoch 0, cost is -51.6263725776
Training took 1.673142 minutes
... plotting sample 0
rnnrbm
Epoch 1/1
-15.0496314334
rnnslu
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
lstm
model options {'encoder': 'lstm', 'optimizer': <function adadelta at 0x7f8514f589b0>, 'validFreq': 370, 'lrate': 0.0001, 'batch_size': 16, 'decay_c': 0.0, 'patience': 10, 'reload_model': None, 'n_words': 10000, 'max_epochs': 1, 'dispFreq': 10, 'dataset': 'imdb', 'valid_batch_size': 64, 'use_dropout': True, 'dim_proj': 128, 'maxlen': 100, 'saveto': '', 'noise_std': 0.0, 'test_size': 1000, 'saveFreq': 1110}
Loading data
Building model
Optimization
1998 train examples
105 valid examples
1000 test examples
Epoch 0 Update 10 Cost 0.692138353384
Epoch 0 Update 20 Cost 0.690693189509
Epoch 0 Update 30 Cost 0.676380211732
Epoch 0 Update 40 Cost 0.672820589255
Epoch 0 Update 50 Cost 0.675394850171
Epoch 0 Update 60 Cost 0.698660008515
Epoch 0 Update 70 Cost 0.684332483641
Epoch 0 Update 80 Cost 0.714756376014
Epoch 0 Update 90 Cost 0.727102027393
Epoch 0 Update 100 Cost 0.672112529029
Epoch 0 Update 110 Cost 0.703670540776
Epoch 0 Update 120 Cost 0.758497490788
Seen 1998 samples
Train 0.422422422422 Valid 0.428571428571 Test 0.502
The code run for 1 epochs, with 17.961051 sec/epochs
logistic_sgd
... loading data
... building the model
... training the model
epoch 1, minibatch 83/83, validation error 12.458333 %
epoch 1, minibatch 83/83, test error of best model 12.375000 %
epoch 2, minibatch 83/83, validation error 11.010417 %
epoch 2, minibatch 83/83, test error of best model 10.958333 %
epoch 3, minibatch 83/83, validation error 10.312500 %
epoch 3, minibatch 83/83, test error of best model 10.312500 %
epoch 4, minibatch 83/83, validation error 9.875000 %
epoch 4, minibatch 83/83, test error of best model 9.833333 %
epoch 5, minibatch 83/83, validation error 9.562500 %
epoch 5, minibatch 83/83, test error of best model 9.479167 %
epoch 6, minibatch 83/83, validation error 9.322917 %
epoch 6, minibatch 83/83, test error of best model 9.291667 %
epoch 7, minibatch 83/83, validation error 9.187500 %
epoch 7, minibatch 83/83, test error of best model 9.000000 %
epoch 8, minibatch 83/83, validation error 8.989583 %
epoch 8, minibatch 83/83, test error of best model 8.958333 %
epoch 9, minibatch 83/83, validation error 8.937500 %
epoch 9, minibatch 83/83, test error of best model 8.812500 %
epoch 10, minibatch 83/83, validation error 8.750000 %
epoch 10, minibatch 83/83, test error of best model 8.666667 %
epoch 11, minibatch 83/83, validation error 8.666667 %
epoch 11, minibatch 83/83, test error of best model 8.520833 %
epoch 12, minibatch 83/83, validation error 8.583333 %
epoch 12, minibatch 83/83, test error of best model 8.416667 %
epoch 13, minibatch 83/83, validation error 8.489583 %
epoch 13, minibatch 83/83, test error of best model 8.291667 %
epoch 14, minibatch 83/83, validation error 8.427083 %
epoch 14, minibatch 83/83, test error of best model 8.281250 %
epoch 15, minibatch 83/83, validation error 8.354167 %
epoch 15, minibatch 83/83, test error of best model 8.270833 %
epoch 16, minibatch 83/83, validation error 8.302083 %
epoch 16, minibatch 83/83, test error of best model 8.239583 %
epoch 17, minibatch 83/83, validation error 8.250000 %
epoch 17, minibatch 83/83, test error of best model 8.177083 %
epoch 18, minibatch 83/83, validation error 8.229167 %
epoch 18, minibatch 83/83, test error of best model 8.062500 %
epoch 19, minibatch 83/83, validation error 8.260417 %
epoch 20, minibatch 83/83, validation error 8.260417 %
epoch 21, minibatch 83/83, validation error 8.208333 %
epoch 21, minibatch 83/83, test error of best model 7.947917 %
epoch 22, minibatch 83/83, validation error 8.187500 %
epoch 22, minibatch 83/83, test error of best model 7.927083 %
epoch 23, minibatch 83/83, validation error 8.156250 %
epoch 23, minibatch 83/83, test error of best model 7.958333 %
epoch 24, minibatch 83/83, validation error 8.114583 %
epoch 24, minibatch 83/83, test error of best model 7.947917 %
epoch 25, minibatch 83/83, validation error 8.093750 %
epoch 25, minibatch 83/83, test error of best model 7.947917 %
epoch 26, minibatch 83/83, validation error 8.104167 %
epoch 27, minibatch 83/83, validation error 8.104167 %
epoch 28, minibatch 83/83, validation error 8.052083 %
epoch 28, minibatch 83/83, test error of best model 7.843750 %
epoch 29, minibatch 83/83, validation error 8.052083 %
epoch 30, minibatch 83/83, validation error 8.031250 %
epoch 30, minibatch 83/83, test error of best model 7.843750 %
Optimization complete with best validation score of 8.031250 %,with test performance 7.843750 %
The code run for 30 epochs, with 7.328806 epochs/sec
logistic_cg
... loading data
... building the model
Optimizing using scipy.optimize.fmin_cg...
validation error 29.989583 %
validation error 24.656250 %
validation error 20.833333 %
validation error 16.979167 %
validation error 14.291667 %
validation error 14.239583 %
validation error 13.166667 %
validation error 12.281250 %
validation error 11.739583 %
validation error 11.531250 %
validation error 10.572917 %
validation error 10.406250 %
validation error 10.166667 %
validation error 10.208333 %
validation error 9.854167 %
validation error 9.718750 %
validation error 9.375000 %
validation error 8.968750 %
validation error 8.927083 %
validation error 8.760417 %
validation error 8.770833 %
validation error 8.625000 %
validation error 8.541667 %
validation error 8.250000 %
validation error 8.375000 %
validation error 8.395833 %
validation error 8.197917 %
validation error 8.031250 %
validation error 8.000000 %
validation error 7.968750 %
Optimization complete with best validation score of 7.968750 %, with test performance 7.989583 %
mlp
... loading data
... building the model
... training
epoch 1, minibatch 2500/2500, validation error 9.620000 %
epoch 1, minibatch 2500/2500, test error of best model 10.090000 %
epoch 2, minibatch 2500/2500, validation error 8.610000 %
epoch 2, minibatch 2500/2500, test error of best model 8.740000 %
epoch 3, minibatch 2500/2500, validation error 8.000000 %
epoch 3, minibatch 2500/2500, test error of best model 8.160000 %
epoch 4, minibatch 2500/2500, validation error 7.600000 %
epoch 4, minibatch 2500/2500, test error of best model 7.790000 %
epoch 5, minibatch 2500/2500, validation error 7.300000 %
epoch 5, minibatch 2500/2500, test error of best model 7.580000 %
Optimization complete. Best validation score of 7.300000 % obtained at iteration 12500, with test performance 7.580000 %
convolutional_mlp
... loading data
... building the model
... training
training @ iter = 0
epoch 1, minibatch 100/100, validation error 13.300000 %
epoch 1, minibatch 100/100, test error of best model 13.680000 %
training @ iter = 100
epoch 2, minibatch 100/100, validation error 9.830000 %
epoch 2, minibatch 100/100, test error of best model 10.200000 %
training @ iter = 200
epoch 3, minibatch 100/100, validation error 8.400000 %
epoch 3, minibatch 100/100, test error of best model 8.730000 %
training @ iter = 300
epoch 4, minibatch 100/100, validation error 7.350000 %
epoch 4, minibatch 100/100, test error of best model 7.620000 %
training @ iter = 400
epoch 5, minibatch 100/100, validation error 6.430000 %
epoch 5, minibatch 100/100, test error of best model 6.920000 %
Optimization complete.
Best validation score of 6.430000 % obtained at iteration 500, with test performance 6.920000 %
dA
... loading data
Training epoch 0, cost 63.2891692993
Training epoch 1, cost 55.7866566589
Training epoch 0, cost 81.7649857224
Training epoch 1, cost 73.4434465988
SdA
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost 194.516144
Pre-training layer 1, epoch 0, cost 695.487612
Pre-training layer 2, epoch 0, cost 529.058447
... getting the finetuning functions
... finetunning the model
epoch 1, minibatch 166/166, validation error 14.848485 %
epoch 1, minibatch 166/166, test error of best model 15.727273 %
epoch 2, minibatch 166/166, validation error 11.606061 %
epoch 2, minibatch 166/166, test error of best model 11.717172 %
Optimization complete with best validation score of 11.606061 %, on iteration 332, with test performance 11.717172 %
DBN
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost -165.613371424
Pre-training layer 1, epoch 0, cost -620.432120817
Pre-training layer 2, epoch 0, cost -134.449325286
... getting the finetuning functions
... finetuning the model
epoch 1, minibatch 166/166, validation error 28.131313 %
epoch 1, minibatch 166/166, test error of best model 30.010101 %
epoch 2, minibatch 166/166, validation error 20.303030 %
epoch 2, minibatch 166/166, test error of best model 21.393939 %
Optimization complete with best validation score of 20.303030 %, obtained at iteration 332, with test performance 21.393939 %
rbm
... loading data
Training epoch 0, cost is -52.7116
Training took 1.243071 minutes
... plotting sample 0
rnnrbm
Epoch 1/1
-15.0365748034
rnnslu
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
lstm
model options {'encoder': 'lstm', 'optimizer': <function adadelta at 0x7f8514f589b0>, 'validFreq': 370, 'lrate': 0.0001, 'batch_size': 16, 'decay_c': 0.0, 'patience': 10, 'reload_model': None, 'n_words': 10000, 'max_epochs': 1, 'dispFreq': 10, 'dataset': 'imdb', 'valid_batch_size': 64, 'use_dropout': True, 'dim_proj': 128, 'maxlen': 100, 'saveto': '', 'noise_std': 0.0, 'test_size': 1000, 'saveFreq': 1110}
Loading data
Building model
Optimization
1998 train examples
105 valid examples
1000 test examples
Epoch 0 Update 10 Cost 0.691160261631
Epoch 0 Update 20 Cost 0.683994412422
Epoch 0 Update 30 Cost 0.675427556038
Epoch 0 Update 40 Cost 0.712318658829
Epoch 0 Update 50 Cost 0.695389866829
Epoch 0 Update 60 Cost 0.675582230091
Epoch 0 Update 70 Cost 0.712137162685
Epoch 0 Update 80 Cost 0.698806881905
Epoch 0 Update 90 Cost 0.596462011337
Epoch 0 Update 100 Cost 0.669474124908
Epoch 0 Update 110 Cost 0.725587248802
Epoch 0 Update 120 Cost 0.73024970293
Seen 1998 samples
Train 0.422922922923 Valid 0.419047619048 Test 0.514
The code run for 1 epochs, with 13.161978 sec/epochs
logistic_sgd
... loading data
... building the model
... training the model
epoch 1, minibatch 83/83, validation error 12.458333 %
epoch 1, minibatch 83/83, test error of best model 12.375000 %
epoch 2, minibatch 83/83, validation error 11.010417 %
epoch 2, minibatch 83/83, test error of best model 10.958333 %
epoch 3, minibatch 83/83, validation error 10.312500 %
epoch 3, minibatch 83/83, test error of best model 10.312500 %
epoch 4, minibatch 83/83, validation error 9.875000 %
epoch 4, minibatch 83/83, test error of best model 9.833333 %
epoch 5, minibatch 83/83, validation error 9.562500 %
epoch 5, minibatch 83/83, test error of best model 9.479167 %
epoch 6, minibatch 83/83, validation error 9.322917 %
epoch 6, minibatch 83/83, test error of best model 9.291667 %
epoch 7, minibatch 83/83, validation error 9.187500 %
epoch 7, minibatch 83/83, test error of best model 9.000000 %
epoch 8, minibatch 83/83, validation error 8.989583 %
epoch 8, minibatch 83/83, test error of best model 8.958333 %
epoch 9, minibatch 83/83, validation error 8.937500 %
epoch 9, minibatch 83/83, test error of best model 8.812500 %
epoch 10, minibatch 83/83, validation error 8.750000 %
epoch 10, minibatch 83/83, test error of best model 8.666667 %
epoch 11, minibatch 83/83, validation error 8.666667 %
epoch 11, minibatch 83/83, test error of best model 8.520833 %
epoch 12, minibatch 83/83, validation error 8.583333 %
epoch 12, minibatch 83/83, test error of best model 8.416667 %
epoch 13, minibatch 83/83, validation error 8.489583 %
epoch 13, minibatch 83/83, test error of best model 8.291667 %
epoch 14, minibatch 83/83, validation error 8.427083 %
epoch 14, minibatch 83/83, test error of best model 8.281250 %
epoch 15, minibatch 83/83, validation error 8.354167 %
epoch 15, minibatch 83/83, test error of best model 8.270833 %
epoch 16, minibatch 83/83, validation error 8.302083 %
epoch 16, minibatch 83/83, test error of best model 8.239583 %
epoch 17, minibatch 83/83, validation error 8.250000 %
epoch 17, minibatch 83/83, test error of best model 8.177083 %
epoch 18, minibatch 83/83, validation error 8.229167 %
epoch 18, minibatch 83/83, test error of best model 8.062500 %
epoch 19, minibatch 83/83, validation error 8.260417 %
epoch 20, minibatch 83/83, validation error 8.260417 %
epoch 21, minibatch 83/83, validation error 8.208333 %
epoch 21, minibatch 83/83, test error of best model 7.947917 %
epoch 22, minibatch 83/83, validation error 8.187500 %
epoch 22, minibatch 83/83, test error of best model 7.927083 %
epoch 23, minibatch 83/83, validation error 8.156250 %
epoch 23, minibatch 83/83, test error of best model 7.958333 %
epoch 24, minibatch 83/83, validation error 8.114583 %
epoch 24, minibatch 83/83, test error of best model 7.947917 %
epoch 25, minibatch 83/83, validation error 8.093750 %
epoch 25, minibatch 83/83, test error of best model 7.947917 %
epoch 26, minibatch 83/83, validation error 8.104167 %
epoch 27, minibatch 83/83, validation error 8.104167 %
epoch 28, minibatch 83/83, validation error 8.052083 %
epoch 28, minibatch 83/83, test error of best model 7.843750 %
epoch 29, minibatch 83/83, validation error 8.052083 %
epoch 30, minibatch 83/83, validation error 8.031250 %
epoch 30, minibatch 83/83, test error of best model 7.843750 %
Optimization complete with best validation score of 8.031250 %,with test performance 7.843750 %
The code run for 30 epochs, with 31.844058 epochs/sec
logistic_cg
... loading data
... building the model
Optimizing using scipy.optimize.fmin_cg...
validation error 29.989583 %
validation error 24.656250 %
validation error 20.833333 %
validation error 16.979167 %
validation error 14.291667 %
validation error 14.239583 %
validation error 13.166667 %
validation error 12.281250 %
validation error 11.739583 %
validation error 11.531250 %
validation error 10.583333 %
validation error 10.406250 %
validation error 10.166667 %
validation error 10.208333 %
validation error 9.854167 %
validation error 9.718750 %
validation error 9.375000 %
validation error 8.968750 %
validation error 8.916667 %
validation error 8.760417 %
validation error 8.770833 %
validation error 8.625000 %
validation error 8.541667 %
validation error 8.250000 %
validation error 8.375000 %
validation error 8.395833 %
validation error 8.197917 %
validation error 8.031250 %
validation error 8.031250 %
validation error 7.968750 %
Optimization complete with best validation score of 7.968750 %, with test performance 7.968750 %
mlp
... loading data
... building the model
... training
epoch 1, minibatch 2500/2500, validation error 9.620000 %
epoch 1, minibatch 2500/2500, test error of best model 10.090000 %
epoch 2, minibatch 2500/2500, validation error 8.610000 %
epoch 2, minibatch 2500/2500, test error of best model 8.740000 %
epoch 3, minibatch 2500/2500, validation error 8.000000 %
epoch 3, minibatch 2500/2500, test error of best model 8.160000 %
epoch 4, minibatch 2500/2500, validation error 7.600000 %
epoch 4, minibatch 2500/2500, test error of best model 7.790000 %
epoch 5, minibatch 2500/2500, validation error 7.300000 %
epoch 5, minibatch 2500/2500, test error of best model 7.580000 %
Optimization complete. Best validation score of 7.300000 % obtained at iteration 12500, with test performance 7.580000 %
convolutional_mlp
... loading data
... building the model
... training
training @ iter = 0
epoch 1, minibatch 100/100, validation error 13.290000 %
epoch 1, minibatch 100/100, test error of best model 13.670000 %
training @ iter = 100
epoch 2, minibatch 100/100, validation error 9.830000 %
epoch 2, minibatch 100/100, test error of best model 10.200000 %
training @ iter = 200
epoch 3, minibatch 100/100, validation error 8.410000 %
epoch 3, minibatch 100/100, test error of best model 8.770000 %
training @ iter = 300
epoch 4, minibatch 100/100, validation error 7.330000 %
epoch 4, minibatch 100/100, test error of best model 7.620000 %
training @ iter = 400
epoch 5, minibatch 100/100, validation error 6.500000 %
epoch 5, minibatch 100/100, test error of best model 6.890000 %
Optimization complete.
Best validation score of 6.500000 % obtained at iteration 500, with test performance 6.890000 %
dA
... loading data
Training epoch 0, cost 63.2891690659
Training epoch 1, cost 55.7866565292
Training epoch 0, cost 81.7649854492
Training epoch 1, cost 73.4434465851
SdA
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost 194.516141
Pre-training layer 1, epoch 0, cost 695.487604
Pre-training layer 2, epoch 0, cost 529.058429
... getting the finetuning functions
... finetunning the model
epoch 1, minibatch 166/166, validation error 14.848485 %
epoch 1, minibatch 166/166, test error of best model 15.727273 %
epoch 2, minibatch 166/166, validation error 11.606061 %
epoch 2, minibatch 166/166, test error of best model 11.717172 %
Optimization complete with best validation score of 11.606061 %, on iteration 332, with test performance 11.717172 %
DBN
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost -165.613303219
Pre-training layer 1, epoch 0, cost -620.403747559
Pre-training layer 2, epoch 0, cost -134.217854373
... getting the finetuning functions
... finetuning the model
epoch 1, minibatch 166/166, validation error 28.222222 %
epoch 1, minibatch 166/166, test error of best model 30.191919 %
epoch 2, minibatch 166/166, validation error 20.292929 %
epoch 2, minibatch 166/166, test error of best model 21.434343 %
Optimization complete with best validation score of 20.292929 %, obtained at iteration 332, with test performance 21.434343 %
rbm
... loading data
Training epoch 0, cost is -51.6296
Training took 0.055968 minutes
... plotting sample 0
rnnrbm
Epoch 1/1
-15.0075673556
rnnslu
{'verbose': 1, 'win': 7, 'savemodel': False, 'fold': 3, 'seed': 345, 'emb_dimension': 50, 'nepochs': 1, 'data': 'atis', 'nhidden': 200, 'decay': True, 'lr': 0.0970806646812754}
lstm
model options {'encoder': 'lstm', 'optimizer': <function adadelta at 0x7f8514f589b0>, 'validFreq': 370, 'lrate': 0.0001, 'batch_size': 16, 'decay_c': 0.0, 'patience': 10, 'reload_model': None, 'n_words': 10000, 'max_epochs': 1, 'dispFreq': 10, 'dataset': 'imdb', 'valid_batch_size': 64, 'use_dropout': True, 'dim_proj': 128, 'maxlen': 100, 'saveto': '', 'noise_std': 0.0, 'test_size': 1000, 'saveFreq': 1110}
Loading data
Building model
Optimization
1998 train examples
105 valid examples
1000 test examples
Epoch 0 Update 10 Cost 0.689358711243
Epoch 0 Update 20 Cost 0.710573196411
Epoch 0 Update 30 Cost 0.680263280869
Epoch 0 Update 40 Cost 0.667281806469
Epoch 0 Update 50 Cost 0.637676477432
Epoch 0 Update 60 Cost 0.685400247574
Epoch 0 Update 70 Cost 0.736423969269
Epoch 0 Update 80 Cost 0.641846060753
Epoch 0 Update 90 Cost 0.709488868713
Epoch 0 Update 100 Cost 0.627746403217
Epoch 0 Update 110 Cost 0.669853687286
Epoch 0 Update 120 Cost 0.734875559807
Seen 1998 samples
Train 0.423923923924 Valid 0.4 Test 0.524
The code run for 1 epochs, with 3.901582 sec/epochs
--------------------- >> end captured stdout << ----------------------
jenkins...@mila.quebec
Oct 26, 2018, 9:17:09 AM10/26/18
to theano-...@googlegroups.com
Theano_buildbot_dlt_speed - Build # 333 - Unstable -
Tot=24 Skip=0 Fail=0
See https://jenkins.mila.quebec/job/Theano_buildbot_dlt_speed/333/ to view the results.
Failed tests:
All tests passed
Tot=24 Skip=0 Fail=0
See https://jenkins.mila.quebec/job/Theano_buildbot_dlt_speed/333/ to view the results.
Failed tests:
All tests passed
jenkins...@mila.quebec
Oct 26, 2018, 8:55:38 PM10/26/18
to theano-...@googlegroups.com
Theano_buildbot_dlt_speed - Build # 334 - Fixed -
Tot=24 Skip=0 Fail=0
See https://jenkins.mila.quebec/job/Theano_buildbot_dlt_speed/334/ to view the results.
Tot=24 Skip=0 Fail=0
See https://jenkins.mila.quebec/job/Theano_buildbot_dlt_speed/334/ to view the results.
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