Epoch: 0001 train_loss= 0.70036 train_acc= 0.48182 val_loss= 0.69991 val_acc= 0.44262 time= 0.64065
Epoch: 0002 train_loss= 0.69925 train_acc= 0.47403 val_loss= 0.69865 val_acc= 0.44262 time= 0.01200
Epoch: 0003 train_loss= 0.69873 train_acc= 0.48052 val_loss= 0.69750 val_acc= 0.55738 time= 0.01200
Epoch: 0004 train_loss= 0.69797 train_acc= 0.52078 val_loss= 0.69645 val_acc= 0.55738 time= 0.01200
Epoch: 0005 train_loss= 0.69736 train_acc= 0.52078 val_loss= 0.69549 val_acc= 0.55738 time= 0.01200
Epoch: 0006 train_loss= 0.69662 train_acc= 0.51948 val_loss= 0.69473 val_acc= 0.55738 time= 0.01500
Epoch: 0007 train_loss= 0.69636 train_acc= 0.52208 val_loss= 0.69411 val_acc= 0.55738 time= 0.01400
Epoch: 0008 train_loss= 0.69569 train_acc= 0.52338 val_loss= 0.69352 val_acc= 0.55738 time= 0.01300
Epoch: 0009 train_loss= 0.69570 train_acc= 0.52078 val_loss= 0.69298 val_acc= 0.55738 time= 0.01300
Epoch: 0010 train_loss= 0.69532 train_acc= 0.51948 val_loss= 0.69250 val_acc= 0.55738 time= 0.01300
Epoch: 0011 train_loss= 0.69491 train_acc= 0.52338 val_loss= 0.69209 val_acc= 0.55738 time= 0.01200
Epoch: 0012 train_loss= 0.69436 train_acc= 0.52597 val_loss= 0.69172 val_acc= 0.55738 time= 0.01500
Epoch: 0013 train_loss= 0.69460 train_acc= 0.52078 val_loss= 0.69138 val_acc= 0.55738 time= 0.01300
Epoch: 0014 train_loss= 0.69455 train_acc= 0.52208 val_loss= 0.69110 val_acc= 0.55738 time= 0.01300
Epoch: 0015 train_loss= 0.69437 train_acc= 0.52468 val_loss= 0.69088 val_acc= 0.55738 time= 0.01400
Epoch: 0016 train_loss= 0.69352 train_acc= 0.52727 val_loss= 0.69068 val_acc= 0.55738 time= 0.01402
Epoch: 0017 train_loss= 0.69337 train_acc= 0.52597 val_loss= 0.69054 val_acc= 0.55738 time= 0.01200
Epoch: 0018 train_loss= 0.69386 train_acc= 0.52338 val_loss= 0.69044 val_acc= 0.55738 time= 0.01200
Epoch: 0019 train_loss= 0.69378 train_acc= 0.52208 val_loss= 0.69038 val_acc= 0.55738 time= 0.01100
Epoch: 0020 train_loss= 0.69360 train_acc= 0.52078 val_loss= 0.69035 val_acc= 0.55738 time= 0.01300
Epoch: 0021 train_loss= 0.69324 train_acc= 0.52468 val_loss= 0.69035 val_acc= 0.55738 time= 0.01100
Epoch: 0022 train_loss= 0.69334 train_acc= 0.52208 val_loss= 0.69039 val_acc= 0.55738 time= 0.01200
Epoch: 0023 train_loss= 0.69241 train_acc= 0.52727 val_loss= 0.69040 val_acc= 0.55738 time= 0.01200
Epoch: 0024 train_loss= 0.69288 train_acc= 0.52727 val_loss= 0.69037 val_acc= 0.55738 time= 0.01200
Epoch: 0025 train_loss= 0.69348 train_acc= 0.52468 val_loss= 0.69039 val_acc= 0.55738 time= 0.01200
Epoch: 0026 train_loss= 0.69238 train_acc= 0.52338 val_loss= 0.69034 val_acc= 0.55738 time= 0.01200
Epoch: 0027 train_loss= 0.69256 train_acc= 0.52078 val_loss= 0.69025 val_acc= 0.55738 time= 0.01200
Epoch: 0028 train_loss= 0.69281 train_acc= 0.52468 val_loss= 0.69017 val_acc= 0.55738 time= 0.01300
Epoch: 0029 train_loss= 0.69278 train_acc= 0.52208 val_loss= 0.69010 val_acc= 0.55738 time= 0.01417
Epoch: 0030 train_loss= 0.69272 train_acc= 0.52468 val_loss= 0.69007 val_acc= 0.55738 time= 0.01200
Epoch: 0031 train_loss= 0.69245 train_acc= 0.52208 val_loss= 0.69004 val_acc= 0.55738 time= 0.01200
Epoch: 0032 train_loss= 0.69224 train_acc= 0.52468 val_loss= 0.69001 val_acc= 0.55738 time= 0.01100
Epoch: 0033 train_loss= 0.69250 train_acc= 0.52597 val_loss= 0.68997 val_acc= 0.55738 time= 0.01300
Epoch: 0034 train_loss= 0.69274 train_acc= 0.52078 val_loss= 0.68997 val_acc= 0.55738 time= 0.01500
Epoch: 0035 train_loss= 0.69281 train_acc= 0.52468 val_loss= 0.68997 val_acc= 0.55738 time= 0.01300
Epoch: 0036 train_loss= 0.69218 train_acc= 0.52468 val_loss= 0.68994 val_acc= 0.55738 time= 0.01200
Epoch: 0037 train_loss= 0.69271 train_acc= 0.52338 val_loss= 0.68992 val_acc= 0.55738 time= 0.01100
Epoch: 0038 train_loss= 0.69290 train_acc= 0.52338 val_loss= 0.68990 val_acc= 0.55738 time= 0.01200
Epoch: 0039 train_loss= 0.69271 train_acc= 0.52338 val_loss= 0.68990 val_acc= 0.55738 time= 0.01200
Epoch: 0040 train_loss= 0.69228 train_acc= 0.52338 val_loss= 0.68991 val_acc= 0.55738 time= 0.01300
Epoch: 0041 train_loss= 0.69222 train_acc= 0.52468 val_loss= 0.68990 val_acc= 0.55738 time= 0.01200
Epoch: 0042 train_loss= 0.69244 train_acc= 0.52338 val_loss= 0.68989 val_acc= 0.55738 time= 0.01200
Epoch: 0043 train_loss= 0.69217 train_acc= 0.52468 val_loss= 0.68986 val_acc= 0.55738 time= 0.01200
Epoch: 0044 train_loss= 0.69203 train_acc= 0.52338 val_loss= 0.68982 val_acc= 0.55738 time= 0.01200
Epoch: 0045 train_loss= 0.69233 train_acc= 0.52338 val_loss= 0.68982 val_acc= 0.55738 time= 0.01300
Epoch: 0046 train_loss= 0.69199 train_acc= 0.52208 val_loss= 0.68980 val_acc= 0.55738 time= 0.01200
Epoch: 0047 train_loss= 0.69235 train_acc= 0.52338 val_loss= 0.68979 val_acc= 0.55738 time= 0.01502
Epoch: 0048 train_loss= 0.69264 train_acc= 0.52208 val_loss= 0.68979 val_acc= 0.55738 time= 0.01300
Epoch: 0049 train_loss= 0.69235 train_acc= 0.52208 val_loss= 0.68972 val_acc= 0.55738 time= 0.01500
Epoch: 0050 train_loss= 0.69235 train_acc= 0.52468 val_loss= 0.68967 val_acc= 0.55738 time= 0.01200
Epoch: 0051 train_loss= 0.69251 train_acc= 0.52078 val_loss= 0.68966 val_acc= 0.55738 time= 0.01200
Epoch: 0052 train_loss= 0.69241 train_acc= 0.52208 val_loss= 0.68969 val_acc= 0.55738 time= 0.01100
Epoch: 0053 train_loss= 0.69239 train_acc= 0.52208 val_loss= 0.68978 val_acc= 0.55738 time= 0.01200
Early stopping...
Optimization Finished!
Test set results: cost= 0.68775 accuracy= 0.55738 time= 0.00600 
