Commit 2881e892 authored by schubert.draco's avatar schubert.draco
Browse files

added more out files

parent 09e9920e
/mpcdf/soft/SLE_12_SP3/packages/x86_64/scikit-learn/anaconda_3_5_1/0.19.1/lib/python3.6/site-packages/sklearn/model_selection/_split.py:605: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.
% (min_groups, self.n_splits)), Warning)
srun: Job step aborted: Waiting up to 302 seconds for job step to finish.
slurmstepd: error: *** JOB 8888619 ON dra0702 CANCELLED AT 2019-07-07T14:15:25 ***
slurmstepd: error: *** STEP 8888619.0 ON dra0702 CANCELLED AT 2019-07-07T14:15:25 ***
load data
...for category author
start grid search for classifier LOG and author
=================================
Global information about the job:
=================================
Job owner: mschuber(62502)
Job name: grid_log_500
Node list: dra0702
Job start: Sat Jul 6 19:51:48 CEST 2019
Job end: Sun Jul 7 14:15:25 CEST 2019
Work dir: /u/mschuber/PAN/attributionfeatures/Scripts/./
Command: /draco/u/mschuber/PAN/attributionfeatures/Scripts/logistic/grid_log_500.cmd
================================================================================
Information on jobsteps (Note: MaxRSS is per task, cf. "man sacct"):
================================================================================
JobID JobName NNodes NTasks NCPUS MaxRSS Elapsed ExitCode
------------ ---------- ------ ------ ------ ---------- ---------- --------
8888619.0 python 1 1 64 82027629K 18:23:38 0:15
Maximum memory per node: 82.0276 GB
CPU utilization: 23.4 %
This diff is collapsed.
read data
size is nan...look at single lines only
make train-test split
subset the data accordingly
make vocab
save the data
...for category author
start grid search for classifier SVM and author
finished GridSearch...continue with predictions
finished grid search for classifier SVM and author after 16.918499475253952 hours
...for category gender
start grid search for classifier SVM and gender
finished GridSearch...continue with predictions
finished grid search for classifier SVM and gender after 0.04477418820063273 hours
...for category age
start grid search for classifier SVM and age
finished GridSearch...continue with predictions
finished grid search for classifier SVM and age after 0.2710791090461943 hours
done
job finished
=================================
Global information about the job:
=================================
Job owner: mschuber(62502)
Job name: grid_svm_500
Node list: dra0506
Job start: Sat Jul 6 17:21:00 CEST 2019
Job end: Sun Jul 7 10:39:10 CEST 2019
Work dir: /u/mschuber/PAN/attributionfeatures/Scripts/./
Command: /draco/u/mschuber/PAN/attributionfeatures/Scripts/svm/grid_strat_svm_500.cmd
================================================================================
Information on jobsteps (Note: MaxRSS is per task, cf. "man sacct"):
================================================================================
JobID JobName NNodes NTasks NCPUS MaxRSS Elapsed ExitCode
------------ ---------- ------ ------ ------ ---------- ---------- --------
8887652.0 python 1 1 64 62334314K 17:18:06 0:0
Maximum memory per node: 62.3343 GB
CPU utilization: 0.1 %
/mpcdf/soft/SLE_12_SP3/packages/x86_64/scikit-learn/anaconda_3_5_1/0.19.1/lib/python3.6/site-packages/sklearn/model_selection/_split.py:605: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.
% (min_groups, self.n_splits)), Warning)
srun: Job step aborted: Waiting up to 302 seconds for job step to finish.
slurmstepd: error: *** JOB 8900050 ON dra0543 CANCELLED AT 2019-07-07T20:18:21 ***
slurmstepd: error: *** STEP 8900050.0 ON dra0543 CANCELLED AT 2019-07-07T20:18:21 ***
/mpcdf/soft/SLE_12_SP3/packages/x86_64/scikit-learn/anaconda_3_5_1/0.19.1/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/mpcdf/soft/SLE_12_SP3/packages/x86_64/scikit-learn/anaconda_3_5_1/0.19.1/lib/python3.6/site-packages/sklearn/metrics/classification.py:1137: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
'recall', 'true', average, warn_for)
/mpcdf/soft/SLE_12_SP3/packages/x86_64/scikit-learn/anaconda_3_5_1/0.19.1/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/mpcdf/soft/SLE_12_SP3/packages/x86_64/scikit-learn/anaconda_3_5_1/0.19.1/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/mpcdf/soft/SLE_12_SP3/packages/x86_64/scikit-learn/anaconda_3_5_1/0.19.1/lib/python3.6/site-packages/sklearn/metrics/classification.py:1137: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
'recall', 'true', average, warn_for)
/mpcdf/soft/SLE_12_SP3/packages/x86_64/scikit-learn/anaconda_3_5_1/0.19.1/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/mpcdf/soft/SLE_12_SP3/packages/x86_64/scikit-learn/anaconda_3_5_1/0.19.1/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/mpcdf/soft/SLE_12_SP3/packages/x86_64/scikit-learn/anaconda_3_5_1/0.19.1/lib/python3.6/site-packages/sklearn/metrics/classification.py:1137: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
'recall', 'true', average, warn_for)
srun: error: dra0543: task 0: Terminated
srun: Terminating job step 8900050.0
load data
...for category author
start grid search for classifier SVM and author
=================================
Global information about the job:
=================================
Job owner: mschuber(62502)
Job name: grid_svmC_500
Node list: dra0543
Job start: Sun Jul 7 15:16:39 CEST 2019
Job end: Sun Jul 7 20:18:21 CEST 2019
Work dir: /u/mschuber/PAN/attributionfeatures/Scripts/./
Command: /draco/u/mschuber/PAN/attributionfeatures/Scripts/svm/grid_strat_svm_count_500.cmd
================================================================================
Information on jobsteps (Note: MaxRSS is per task, cf. "man sacct"):
================================================================================
JobID JobName NNodes NTasks NCPUS MaxRSS Elapsed ExitCode
------------ ---------- ------ ------ ------ ---------- ---------- --------
8900050.0 python 1 1 64 34235926K 05:01:42 0:15
Maximum memory per node: 34.2359 GB
CPU utilization: 6.9 %
This diff is collapsed.
read data
size is nan...look at single lines only
make train-test split
subset the data accordingly
make vocab
save the data
...for category author
start grid search for classifier SVM and author
finished GridSearch...continue with predictions
finished grid search for classifier SVM and author after 7.428743487265375 hours
...for category gender
start grid search for classifier SVM and gender
finished GridSearch...continue with predictions
finished grid search for classifier SVM and gender after 0.021005861692958407 hours
...for category age
start grid search for classifier SVM and age
finished GridSearch...continue with predictions
finished grid search for classifier SVM and age after 0.1522822411192788 hours
done
job finished
=================================
Global information about the job:
=================================
Job owner: mschuber(62502)
Job name: grid_svm_mT_500
Node list: dra0758
Job start: Sat Jul 6 17:23:24 CEST 2019
Job end: Sun Jul 7 01:01:18 CEST 2019
Work dir: /u/mschuber/PAN/attributionfeatures/Scripts/./
Command: /draco/u/mschuber/PAN/attributionfeatures/Scripts/svm/grid_strat_svm_mT_500.cmd
================================================================================
Information on jobsteps (Note: MaxRSS is per task, cf. "man sacct"):
================================================================================
JobID JobName NNodes NTasks NCPUS MaxRSS Elapsed ExitCode
------------ ---------- ------ ------ ------ ---------- ---------- --------
8887685.0 python 1 1 64 34144054K 07:37:50 0:0
Maximum memory per node: 34.1441 GB
CPU utilization: 0.2 %
srun: Job step aborted: Waiting up to 302 seconds for job step to finish.
slurmstepd: error: *** JOB 8888513 ON dra0895 CANCELLED AT 2019-07-07T14:15:19 ***
slurmstepd: error: *** STEP 8888513.0 ON dra0895 CANCELLED AT 2019-07-07T14:15:19 ***
load data
...for category author
fit classifier LOG and author
=================================
Global information about the job:
=================================
Job owner: mschuber(62502)
Job name: log_1000
Node list: dra0895
Job start: Sat Jul 6 18:50:24 CEST 2019
Job end: Sun Jul 7 14:15:19 CEST 2019
Work dir: /u/mschuber/PAN/attributionfeatures/Scripts/./
Command: /draco/u/mschuber/PAN/attributionfeatures/Scripts/logistic/log_1000.cmd
================================================================================
Information on jobsteps (Note: MaxRSS is per task, cf. "man sacct"):
================================================================================
JobID JobName NNodes NTasks NCPUS MaxRSS Elapsed ExitCode
------------ ---------- ------ ------ ------ ---------- ---------- --------
8888513.0 python 1 1 64 24166366K 19:24:54 0:15
Maximum memory per node: 24.1664 GB
CPU utilization: 1.6 %
Traceback (most recent call last):
File "/draco/u/mschuber/PAN/attributionfeatures/Scripts/logistic.py", line 149, in <module>
train, test = train_test_split(indices, random_state = random_state, stratify= author_id, test_size=test_size)
File "/mpcdf/soft/SLE_12_SP3/packages/x86_64/scikit-learn/anaconda_3_5_1/0.19.1/lib/python3.6/site-packages/sklearn/model_selection/_split.py", line 2056, in train_test_split
train, test = next(cv.split(X=arrays[0], y=stratify))
File "/mpcdf/soft/SLE_12_SP3/packages/x86_64/scikit-learn/anaconda_3_5_1/0.19.1/lib/python3.6/site-packages/sklearn/model_selection/_split.py", line 1204, in split
for train, test in self._iter_indices(X, y, groups):
File "/mpcdf/soft/SLE_12_SP3/packages/x86_64/scikit-learn/anaconda_3_5_1/0.19.1/lib/python3.6/site-packages/sklearn/model_selection/_split.py", line 1546, in _iter_indices
raise ValueError("The least populated class in y has only 1"
ValueError: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2.
srun: error: dra0606: task 0: Exited with exit code 1
srun: Terminating job step 8879864.0
read data
size is nan...look at single lines only
make train-test split
job finished
=================================
Global information about the job:
=================================
Job owner: mschuber(62502)
Job name: log_1880
Node list: dra0606
Job start: Fri Jul 5 15:38:46 CEST 2019
Job end: Fri Jul 5 15:45:14 CEST 2019
Work dir: /u/mschuber/PAN/attributionfeatures/Scripts/./
Command: /draco/u/mschuber/PAN/attributionfeatures/Scripts/logistic/log_2000.cmd
================================================================================
Information on jobsteps (Note: MaxRSS is per task, cf. "man sacct"):
================================================================================
JobID JobName NNodes NTasks NCPUS MaxRSS Elapsed ExitCode
------------ ---------- ------ ------ ------ ---------- ---------- --------
8879864.0 python 1 1 64 23980752K 00:06:24 1:0
Maximum memory per node: 23.9808 GB
CPU utilization: 1.4 %
srun: Job step aborted: Waiting up to 302 seconds for job step to finish.
slurmstepd: error: *** JOB 8888505 ON dra0893 CANCELLED AT 2019-07-07T18:50:25 DUE TO TIME LIMIT ***
slurmstepd: error: *** STEP 8888505.0 ON dra0893 CANCELLED AT 2019-07-07T18:50:25 DUE TO TIME LIMIT ***
load data
...for category author
fit classifier LOG and author
Epoch 1, change: 1.00000000
Epoch 2, change: 0.24362509
Epoch 3, change: 0.14624081
Epoch 4, change: 0.10321990
Epoch 5, change: 0.07675505
Epoch 6, change: 0.05599297
Epoch 7, change: 0.04945009
Epoch 8, change: 0.04425908
Epoch 9, change: 0.03755645
Epoch 10, change: 0.03396808
Epoch 11, change: 0.03171070
Epoch 12, change: 0.02836757
Epoch 13, change: 0.02813148
Epoch 14, change: 0.02494015
Epoch 15, change: 0.02334223
Epoch 16, change: 0.02274432
Epoch 17, change: 0.02158292
Epoch 18, change: 0.02043164
Epoch 19, change: 0.01911447
Epoch 20, change: 0.01813703
Epoch 21, change: 0.01729056
Epoch 22, change: 0.01661869
Epoch 23, change: 0.01600272
Epoch 24, change: 0.01526733
Epoch 25, change: 0.01483753
Epoch 26, change: 0.01428053
Epoch 27, change: 0.01374196
Epoch 28, change: 0.01317656
Epoch 29, change: 0.01237733
Epoch 30, change: 0.01234432
Epoch 31, change: 0.01204986
Epoch 32, change: 0.01152445
Epoch 33, change: 0.01132346
Epoch 34, change: 0.01080497
Epoch 35, change: 0.01032414
Epoch 36, change: 0.01007014
Epoch 37, change: 0.00993623
Epoch 38, change: 0.00937219
Epoch 39, change: 0.00915080
Epoch 40, change: 0.00917056
Epoch 41, change: 0.00883428
Epoch 42, change: 0.00873779
Epoch 43, change: 0.00839343
Epoch 44, change: 0.00807678
Epoch 45, change: 0.00804156
Epoch 46, change: 0.00776412
Epoch 47, change: 0.00752721
Epoch 48, change: 0.00742031
Epoch 49, change: 0.00716359
Epoch 50, change: 0.00695583
Epoch 51, change: 0.00703399
Epoch 52, change: 0.00673934
Epoch 53, change: 0.00662625
Epoch 54, change: 0.00648619
Epoch 55, change: 0.00638634
Epoch 56, change: 0.00630977
Epoch 57, change: 0.00620819
Epoch 58, change: 0.00610767
Epoch 59, change: 0.00601952
Epoch 60, change: 0.00590557
Epoch 61, change: 0.00586246
Epoch 62, change: 0.00571393
Epoch 63, change: 0.00559073
Epoch 64, change: 0.00552641
Epoch 65, change: 0.00544090
Epoch 66, change: 0.00532791
Epoch 67, change: 0.00513737
Epoch 68, change: 0.00501175
Epoch 69, change: 0.00492007
Epoch 70, change: 0.00479364
Epoch 71, change: 0.00466900
Epoch 72, change: 0.00457318
Epoch 73, change: 0.00445488
Epoch 74, change: 0.00434979
Epoch 75, change: 0.00426212
Epoch 76, change: 0.00422673
Epoch 77, change: 0.00408987
Epoch 78, change: 0.00400365
Epoch 79, change: 0.00389209
Epoch 80, change: 0.00385503
Epoch 81, change: 0.00377934
Epoch 82, change: 0.00371553
Epoch 83, change: 0.00365821
Epoch 84, change: 0.00357894
Epoch 85, change: 0.00358318
Epoch 86, change: 0.00346400
Epoch 87, change: 0.00337148
Epoch 88, change: 0.00331845
Epoch 89, change: 0.00325342
Epoch 90, change: 0.00319959
Epoch 91, change: 0.00313833
Epoch 92, change: 0.00308232
Epoch 93, change: 0.00301886
Epoch 94, change: 0.00298086
Epoch 95, change: 0.00294208
Epoch 96, change: 0.00289193
Epoch 97, change: 0.00286136
Epoch 98, change: 0.00279557
Epoch 99, change: 0.00275929
Epoch 100, change: 0.00270524
Epoch 101, change: 0.00267581
Epoch 102, change: 0.00263806
Epoch 103, change: 0.00259414
Epoch 104, change: 0.00256919
Epoch 105, change: 0.00253805
Epoch 106, change: 0.00248224
Epoch 107, change: 0.00245671
Epoch 108, change: 0.00241302
Epoch 109, change: 0.00238782
Epoch 110, change: 0.00234716
Epoch 111, change: 0.00231082
Epoch 112, change: 0.00228622
Epoch 113, change: 0.00225889
Epoch 114, change: 0.00223234
Epoch 115, change: 0.00220153
Epoch 116, change: 0.00216595
Epoch 117, change: 0.00214198
Epoch 118, change: 0.00211249
Epoch 119, change: 0.00210162
Epoch 120, change: 0.00208313
Epoch 121, change: 0.00207273
Epoch 122, change: 0.00205596
Epoch 123, change: 0.00205850
Epoch 124, change: 0.00203953
Epoch 125, change: 0.00202740
Epoch 126, change: 0.00200768
Epoch 127, change: 0.00199734
Epoch 128, change: 0.00198453
Epoch 129, change: 0.00197000
Epoch 130, change: 0.00195810
Epoch 131, change: 0.00194413
Epoch 132, change: 0.00194472
Epoch 133, change: 0.00192686
Epoch 134, change: 0.00191570
Epoch 135, change: 0.00189224
Epoch 136, change: 0.00189931
Epoch 137, change: 0.00187080
Epoch 138, change: 0.00187028
Epoch 139, change: 0.00185575
Epoch 140, change: 0.00185181
Epoch 141, change: 0.00183687
Epoch 142, change: 0.00182810
Epoch 143, change: 0.00182834
Epoch 144, change: 0.00180574
Epoch 145, change: 0.00179240
Epoch 146, change: 0.00178894
Epoch 147, change: 0.00176975
Epoch 148, change: 0.00177017
Epoch 149, change: 0.00175021
Epoch 150, change: 0.00174031
Epoch 151, change: 0.00173768
Epoch 152, change: 0.00171671
Epoch 153, change: 0.00171009
Epoch 154, change: 0.00170386
Epoch 155, change: 0.00169175
Epoch 156, change: 0.00167938
Epoch 157, change: 0.00166606
Epoch 158, change: 0.00165990
Epoch 159, change: 0.00165539
Epoch 160, change: 0.00164735
Epoch 161, change: 0.00163503
Epoch 162, change: 0.00162959
Epoch 163, change: 0.00161980
Epoch 164, change: 0.00161242
Epoch 165, change: 0.00160114
Epoch 166, change: 0.00159028
Epoch 167, change: 0.00158182
Epoch 168, change: 0.00157856
Epoch 169, change: 0.00156321
Epoch 170, change: 0.00155477
Epoch 171, change: 0.00154510
Epoch 172, change: 0.00154219
Epoch 173, change: 0.00153058
Epoch 174, change: 0.00152201
Epoch 175, change: 0.00151001
Epoch 176, change: 0.00150538
Epoch 177, change: 0.00149560
Epoch 178, change: 0.00148807
Epoch 179, change: 0.00148639
Epoch 180, change: 0.00147394
Epoch 181, change: 0.00145429
Epoch 182, change: 0.00145416
Epoch 183, change: 0.00144330
Epoch 184, change: 0.00144592
Epoch 185, change: 0.00143408
Epoch 186, change: 0.00142552
Epoch 187, change: 0.00142039
Epoch 188, change: 0.00141354
Epoch 189, change: 0.00140022
Epoch 190, change: 0.00139979
Epoch 191, change: 0.00139121
Epoch 192, change: 0.00138854
Epoch 193, change: 0.00137564
Epoch 194, change: 0.00137071
Epoch 195, change: 0.00136240
Epoch 196, change: 0.00135808
Epoch 197, change: 0.00134936
Epoch 198, change: 0.00134638
Epoch 199, change: 0.00133882
Epoch 200, change: 0.00133247
Epoch 201, change: 0.00132146
Epoch 202, change: 0.00131904
Epoch 203, change: 0.00130918
Epoch 204, change: 0.00130400
Epoch 205, change: 0.00130158
Epoch 206, change: 0.00129737
Epoch 207, change: 0.00128531
Epoch 208, change: 0.00128078
Epoch 209, change: 0.00127470
Epoch 210, change: 0.00126574
Epoch 211, change: 0.00126206
Epoch 212, change: 0.00125681
Epoch 213, change: 0.00125062
Epoch 214, change: 0.00124445
Epoch 215, change: 0.00123901
Epoch 216, change: 0.00123507
Epoch 217, change: 0.00122513
Epoch 218, change: 0.00121913
Epoch 219, change: 0.00121413
Epoch 220, change: 0.00120956
Epoch 221, change: 0.00120479
Epoch 222, change: 0.00119717
Epoch 223, change: 0.00119385
Epoch 224, change: 0.00118944
Epoch 225, change: 0.00118521
Epoch 226, change: 0.00117113
Epoch 227, change: 0.00116463
Epoch 228, change: 0.00116905
Epoch 229, change: 0.00116522
Epoch 230, change: 0.00115721
Epoch 231, change: 0.00115222
Epoch 232, change: 0.00114518
Epoch 233, change: 0.00114248
Epoch 234, change: 0.00113320
Epoch 235, change: 0.00112574
Epoch 236, change: 0.00111981
Epoch 237, change: 0.00112089
Epoch 238, change: 0.00111775
Epoch 239, change: 0.00111491
Epoch 240, change: 0.00110548
Epoch 241, change: 0.00110068
Epoch 242, change: 0.00109694
Epoch 243, change: 0.00109460
Epoch 244, change: 0.00108392
Epoch 245, change: 0.00107992
Epoch 246, change: 0.00108032
Epoch 247, change: 0.00107211
Epoch 248, change: 0.00106883
Epoch 249, change: 0.00106524
Epoch 250, change: 0.00106208
Epoch 251, change: 0.00105745
Epoch 252, change: 0.00105329
Epoch 253, change: 0.00104777
Epoch 254, change: 0.00104131
Epoch 255, change: 0.00104086
Epoch 256, change: 0.00103790
Epoch 257, change: 0.00102960
Epoch 258, change: 0.00102650
Epoch 259, change: 0.00102206
Epoch 260, change: 0.00101572
Epoch 261, change: 0.00101221
Epoch 262, change: 0.00100895
Epoch 263, change: 0.00100567
Epoch 264, change: 0.00100373
Epoch 265, change: 0.00100130
Epoch 266, change: 0.00099387
Epoch 267, change: 0.00099393
Epoch 268, change: 0.00098182
Epoch 269, change: 0.00098194
Epoch 270, change: 0.00097628
Epoch 271, change: 0.00097399
Epoch 272, change: 0.00097202
Epoch 273, change: 0.00096553
Epoch 274, change: 0.00096044
Epoch 275, change: 0.00096049
Epoch 276, change: 0.00095320
Epoch 277, change: 0.00095038
Epoch 278, change: 0.00094570
Epoch 279, change: 0.00094552
Epoch 280, change: 0.00094118
Epoch 281, change: 0.00093596
Epoch 282, change: 0.00093304
Epoch 283, change: 0.00093109
Epoch 284, change: 0.00092706
Epoch 285, change: 0.00092197
Epoch 286, change: 0.00091747
Epoch 287, change: 0.00091349
Epoch 288, change: 0.00091017
Epoch 289, change: 0.00090856
Epoch 290, change: 0.00090019
Epoch 291, change: 0.00090166
Epoch 292, change: 0.00089639
Epoch 293, change: 0.00089401
Epoch 294, change: 0.00089313
Epoch 295, change: 0.00088879
Epoch 296, change: 0.00088655
Epoch 297, change: 0.00087441
Epoch 298, change: 0.00085882
Epoch 299, change: 0.00084247
Epoch 300, change: 0.00082200
Epoch 301, change: 0.00080862
Epoch 302, change: 0.00079804
Epoch 303, change: 0.00077773
Epoch 304, change: 0.00077006
Epoch 305, change: 0.00075935
Epoch 306, change: 0.00074278
Epoch 307, change: 0.00073168
Epoch 308, change: 0.00072070
Epoch 309, change: 0.00071227
Epoch 310, change: 0.00070087
Epoch 311, change: 0.00069426
Epoch 312, change: 0.00068061
Epoch 313, change: 0.00067593
Epoch 314, change: 0.00066283
Epoch 315, change: 0.00065534
Epoch 316, change: 0.00064996
Epoch 317, change: 0.00064874
Epoch 318, change: 0.00063555
Epoch 319, change: 0.00062796
Epoch 320, change: 0.00062382
Epoch 321, change: 0.00061820
Epoch 322, change: 0.00060861
Epoch 323, change: 0.00060691
Epoch 324, change: 0.00059762
Epoch 325, change: 0.00059463
Epoch 326, change: 0.00058699
Epoch 327, change: 0.00058615
Epoch 328, change: 0.00057588
Epoch 329, change: 0.00057333
Epoch 330, change: 0.00057058
Epoch 331, change: 0.00056333
Epoch 332, change: 0.00056103
Epoch 333, change: 0.00055248
Epoch 334, change: 0.00054844
Epoch 335, change: 0.00054648
Epoch 336, change: 0.00053939
Epoch 337, change: 0.00053848
Epoch 338, change: 0.00053004
Epoch 339, change: 0.00053155
Epoch 340, change: 0.00052451
Epoch 341, change: 0.00052157
Epoch 342, change: 0.00051624
Epoch 343, change: 0.00051602
Epoch 344, change: 0.00051125
Epoch 345, change: 0.00050791
Epoch 346, change: 0.00050213
Epoch 347, change: 0.00049991
Epoch 348, change: 0.00049808
Epoch 349, change: 0.00049350
Epoch 350, change: 0.00049323
Epoch 351, change: 0.00048959
Epoch 352, change: 0.00048516
Epoch 353, change: 0.00048068
Epoch 354, change: 0.00047891
Epoch 355, change: 0.00047799
Epoch 356, change: 0.00047412
Epoch 357, change: 0.00047464
Epoch 358, change: 0.00047014
Epoch 359, change: 0.00046369
Epoch 360, change: 0.00046437
Epoch 361, change: 0.00046193
Epoch 362, change: 0.00046221
Epoch 363, change: 0.00045739
Epoch 364, change: 0.00045696
Epoch 365, change: 0.00045428
Epoch 366, change: 0.00045141
Epoch 367, change: 0.00045126
Epoch 368, change: 0.00044953
Epoch 369, change: 0.00043191
Epoch 370, change: 0.00044008
Epoch 371, change: 0.00043540