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README.md

chainer-eve

An Eve optimizer implementation in Chainer. See https://arxiv.org/abs/1611.01505v3

Results

Eve (python train_mnist.py -g 0 --noplot):

GPU: 0
# unit: 1000
# Minibatch-size: 100
# epoch: 20

epoch       main/loss   validation/main/loss  main/accuracy  validation/main/accuracy  elapsed_time  lr          d_tilde     f
1           0.192833    0.0895319             0.942116       0.971                     11.7518       0.000717226  0.936704    0.0903957
2           0.0674112   0.0852023             0.978349       0.9729                    14.278        0.000680821  1.22801     0.037441
3           0.0345352   0.0543691             0.989148       0.9824                    16.794        0.000518859  1.76098     0.0136122
4           0.0162165   0.059911              0.995082       0.9823                    19.3123       0.000377031  2.52929     0.0187974
5           0.00777608  0.0536649             0.998099       0.9855                    21.8096       0.000348165  2.7999      0.00704089
6           0.00429833  0.0578413             0.998933       0.9848                    24.4582       0.000302475  3.26066     0.0426459
7           0.00338734  0.0688266             0.9991         0.9825                    27.0248       0.00029957  3.31304     0.00327397
8           0.00296734  0.0658664             0.99915        0.9827                    29.53         0.000274632  3.62626     0.00115538
9           0.00325209  0.0634611             0.9991         0.9849                    32.0536       0.000258712  3.85659     0.000464668
10          0.000825565  0.0625149             0.999933       0.9864                    34.5527       0.00025892  3.85742     7.86829e-05
11          0.000624758  0.0642727             0.9999         0.986                     37.0814       0.00027492  3.63496     0.000105081
12          0.00577406  0.0750651             0.997999       0.9837                    39.6146       0.000241942  4.13169     0.000458369
13          0.00321643  0.0686685             0.998983       0.9845                    42.1558       0.000228902  4.36779     9.47857e-05
14          0.000467157  0.0661637             0.999933       0.986                     44.6661       0.000242063  4.1307      8.46958e-05
15          0.000369986  0.0698587             0.999933       0.9854                    47.248        0.000253236  3.94864     0.000226092
16          0.000150206  0.0655219             0.999983       0.9864                    49.7427       0.000281945  3.54667     3.27778e-05
17          0.000162297  0.0734155             0.999967       0.9864                    52.2742       0.000299217  3.34199     0.0001577
18          0.0115315   0.0659616             0.9968         0.9847                    54.777        0.000240933  4.15049     0.000924358
19          0.00045691  0.0709715             0.999983       0.9852                    57.2734       0.000239325  4.17839     0.000353003
20          0.000153304  0.068501              0.999983       0.986                     59.7914       0.000263265  3.79844     7.4482e-06

Adam (python train_mnist.py -g 0 --noplot --opt Adam):

GPU: 0
# unit: 1000
# Minibatch-size: 100
# epoch: 20

epoch       main/loss   validation/main/loss  main/accuracy  validation/main/accuracy  elapsed_time  lr          d_tilde     f
1           0.194638    0.0945798             0.94185        0.9701                    10.8974       0.000671828
2           0.0723721   0.0831307             0.977699       0.9746                    13.3633       0.000836054
3           0.0480908   0.0951917             0.985065       0.9713                    15.8534       0.000913701
4           0.0362504   0.0869371             0.988315       0.974                     18.308        0.00095362
5           0.0281614   0.0787104             0.990698       0.9809                    20.7644       0.000974827
6           0.023696    0.0860714             0.992248       0.9785                    23.2302       0.000986268
7           0.0212671   0.0768599             0.993182       0.9808                    25.6866       0.00099249
8           0.0163347   0.0968411             0.994965       0.9768                    28.1884       0.000995887
9           0.0195261   0.0759767             0.993699       0.9812                    30.6438       0.000997745
10          0.0123887   0.0825914             0.996032       0.9806                    33.1139       0.000998764
11          0.0156228   0.0821682             0.995099       0.9828                    35.6785       0.000999322
12          0.0134235   0.0771391             0.996066       0.9831                    38.1389       0.000999628
13          0.0118061   0.107356              0.996182       0.978                     40.634        0.000999796
14          0.0130808   0.092472              0.995798       0.9821                    43.1157       0.000999888
15          0.00811215  0.0897111             0.997332       0.9823                    45.5749       0.000999939
16          0.00924951  0.0950187             0.997099       0.9818                    48.1076       0.000999966
17          0.0106534   0.0962093             0.996849       0.9827                    50.569        0.000999982
18          0.00851079  0.100032              0.997649       0.9826                    53.0719       0.00099999
19          0.00668295  0.121721              0.997916       0.9789                    55.5379       0.000999994
20          0.0142575   0.113598              0.996066       0.9819                    58.0259       0.000999997

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An Eve optimizer implementation in Chainer

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