epoch 0: {'accuracy': 0.8601501006772836} , current_best_acc: 0.8601501006772836 train_loss: 0.22423352301120758
epoch 1: {'accuracy': 0.8813838550247117} , current_best_acc: 0.8813838550247117 train_loss: 0.12009488791227341
epoch 2: {'accuracy': 0.8751601684056379} , current_best_acc: 0.8813838550247117 train_loss: 0.3511312007904053
epoch 3: {'accuracy': 0.8819330038440417} , current_best_acc: 0.8819330038440417 train_loss: 0.224882110953331
epoch 4: {'accuracy': 0.9004210140948197} , current_best_acc: 0.9004210140948197 train_loss: 0.37503933906555176
epoch 5: {'accuracy': 0.9040820062236866} , current_best_acc: 0.9040820062236866 train_loss: 0.354200541973114
epoch 6: {'accuracy': 0.8941973274757459} , current_best_acc: 0.9040820062236866 train_loss: 0.5873671770095825
epoch 7: {'accuracy': 0.8996888156690463} , current_best_acc: 0.9040820062236866 train_loss: 0.1264803260564804
epoch 8: {'accuracy': 0.9049972542559034} , current_best_acc: 0.9049972542559034 train_loss: 0.49953895807266235
epoch 9: {'accuracy': 0.9040820062236866} , current_best_acc: 0.9049972542559034 train_loss: 0.33977511525154114
epoch 10: {'accuracy': 0.9104887424492037} , current_best_acc: 0.9104887424492037 train_loss: 0.6609187722206116
epoch 11: {'accuracy': 0.9075599487461102} , current_best_acc: 0.9104887424492037 train_loss: 0.07604140043258667
epoch 12: {'accuracy': 0.9051803038623467} , current_best_acc: 0.9104887424492037 train_loss: 0.3824803829193115
epoch 13: {'accuracy': 0.9007871133077063} , current_best_acc: 0.9104887424492037 train_loss: 0.21191559731960297
epoch 14: {'accuracy': 0.9088412959912137} , current_best_acc: 0.9104887424492037 train_loss: 0.39995965361595154
epoch 15: {'accuracy': 0.9106717920556471} , current_best_acc: 0.9106717920556471 train_loss: 0.11825834959745407
epoch 16: {'accuracy': 0.9055464030752334} , current_best_acc: 0.9106717920556471 train_loss: 0.44584569334983826
epoch 17: {'accuracy': 0.8965769723595094} , current_best_acc: 0.9106717920556471 train_loss: 0.2419937700033188
epoch 18: {'accuracy': 0.9134175361522973} , current_best_acc: 0.9134175361522973 train_loss: 0.08389459550380707
epoch 19: {'accuracy': 0.9081090975654402} , current_best_acc: 0.9134175361522973 train_loss: 0.3655228316783905
epoch 20: {'accuracy': 0.9104887424492037} , current_best_acc: 0.9134175361522973 train_loss: 0.456352174282074
epoch 21: {'accuracy': 0.9093904448105437} , current_best_acc: 0.9134175361522973 train_loss: 0.7216575741767883
epoch 22: {'accuracy': 0.9064616511074501} , current_best_acc: 0.9134175361522973 train_loss: 0.05909716337919235
epoch 23: {'accuracy': 0.9073768991396669} , current_best_acc: 0.9134175361522973 train_loss: 0.11260940134525299
epoch 24: {'accuracy': 0.8971261211788395} , current_best_acc: 0.9134175361522973 train_loss: 0.12486638873815536
epoch 25: {'accuracy': 0.9108548416620904} , current_best_acc: 0.9134175361522973 train_loss: 0.6027513146400452
epoch 26: {'accuracy': 0.9130514369394106} , current_best_acc: 0.9134175361522973 train_loss: 0.32880517840385437
epoch 27: {'accuracy': 0.9071938495332235} , current_best_acc: 0.9134175361522973 train_loss: 0.08031763136386871
epoch 28: {'accuracy': 0.9170785282811642} , current_best_acc: 0.9170785282811642 train_loss: 0.03817947581410408
epoch 29: {'accuracy': 0.9033498077979132} , current_best_acc: 0.9170785282811642 train_loss: 1.2077770233154297
epoch 30: {'accuracy': 0.9110378912685337} , current_best_acc: 0.9170785282811642 train_loss: 0.22897018492221832
epoch 31: {'accuracy': 0.9145158337909574} , current_best_acc: 0.9170785282811642 train_loss: 0.09251221269369125
epoch 32: {'accuracy': 0.918542925132711} , current_best_acc: 0.918542925132711 train_loss: 0.8858433365821838
epoch 33: {'accuracy': 0.9132344865458539} , current_best_acc: 0.918542925132711 train_loss: 0.21418669819831848
epoch 34: {'accuracy': 0.9187259747391543} , current_best_acc: 0.9187259747391543 train_loss: 0.030841859057545662
epoch 35: {'accuracy': 0.9203734211971444} , current_best_acc: 0.9203734211971444 train_loss: 0.5795441269874573
epoch 36: {'accuracy': 0.9216547684422478} , current_best_acc: 0.9216547684422478 train_loss: 0.028284931555390358
epoch 37: {'accuracy': 0.9212886692293611} , current_best_acc: 0.9216547684422478 train_loss: 0.5624893307685852
epoch 38: {'accuracy': 0.9218378180486912} , current_best_acc: 0.9218378180486912 train_loss: 0.2805929481983185
epoch 39: {'accuracy': 0.9189090243455976} , current_best_acc: 0.9218378180486912 train_loss: 0.40551140904426575
