Epoch: 0001 train_loss= 1.39396 train_acc= 0.27734 val_loss= 1.39150 val_acc= 0.19643 time= 0.50017
Epoch: 0002 train_loss= 1.39069 train_acc= 0.27539 val_loss= 1.38953 val_acc= 0.19643 time= 0.01563
Epoch: 0003 train_loss= 1.38782 train_acc= 0.27930 val_loss= 1.38834 val_acc= 0.19643 time= 0.01563
Epoch: 0004 train_loss= 1.38588 train_acc= 0.27539 val_loss= 1.38782 val_acc= 0.19643 time= 0.01563
Epoch: 0005 train_loss= 1.38413 train_acc= 0.27734 val_loss= 1.38769 val_acc= 0.19643 time= 0.01563
Epoch: 0006 train_loss= 1.38297 train_acc= 0.27539 val_loss= 1.38778 val_acc= 0.19643 time= 0.01562
Epoch: 0007 train_loss= 1.38229 train_acc= 0.27734 val_loss= 1.38802 val_acc= 0.19643 time= 0.01563
Epoch: 0008 train_loss= 1.38183 train_acc= 0.27734 val_loss= 1.38830 val_acc= 0.19643 time= 0.01563
Epoch: 0009 train_loss= 1.38170 train_acc= 0.27734 val_loss= 1.38841 val_acc= 0.19643 time= 0.01563
Epoch: 0010 train_loss= 1.38173 train_acc= 0.27734 val_loss= 1.38821 val_acc= 0.19643 time= 0.01563
Epoch: 0011 train_loss= 1.38106 train_acc= 0.27930 val_loss= 1.38771 val_acc= 0.19643 time= 0.00000
Epoch: 0012 train_loss= 1.38093 train_acc= 0.29102 val_loss= 1.38689 val_acc= 0.19643 time= 0.01563
Epoch: 0013 train_loss= 1.38156 train_acc= 0.27148 val_loss= 1.38592 val_acc= 0.19643 time= 0.01563
Epoch: 0014 train_loss= 1.38042 train_acc= 0.25195 val_loss= 1.38514 val_acc= 0.35714 time= 0.03125
Epoch: 0015 train_loss= 1.38096 train_acc= 0.29688 val_loss= 1.38445 val_acc= 0.39286 time= 0.01563
Epoch: 0016 train_loss= 1.38019 train_acc= 0.29492 val_loss= 1.38391 val_acc= 0.37500 time= 0.01563
Epoch: 0017 train_loss= 1.38017 train_acc= 0.33008 val_loss= 1.38343 val_acc= 0.37500 time= 0.01563
Epoch: 0018 train_loss= 1.37952 train_acc= 0.29883 val_loss= 1.38306 val_acc= 0.37500 time= 0.01563
Epoch: 0019 train_loss= 1.37902 train_acc= 0.31250 val_loss= 1.38280 val_acc= 0.37500 time= 0.01563
Epoch: 0020 train_loss= 1.37826 train_acc= 0.32031 val_loss= 1.38268 val_acc= 0.37500 time= 0.01563
Epoch: 0021 train_loss= 1.37914 train_acc= 0.30859 val_loss= 1.38259 val_acc= 0.37500 time= 0.01563
Epoch: 0022 train_loss= 1.37841 train_acc= 0.31250 val_loss= 1.38251 val_acc= 0.37500 time= 0.01562
Epoch: 0023 train_loss= 1.37825 train_acc= 0.30859 val_loss= 1.38240 val_acc= 0.37500 time= 0.01563
Epoch: 0024 train_loss= 1.37837 train_acc= 0.30469 val_loss= 1.38215 val_acc= 0.37500 time= 0.01563
Epoch: 0025 train_loss= 1.37756 train_acc= 0.32031 val_loss= 1.38194 val_acc= 0.37500 time= 0.00000
Epoch: 0026 train_loss= 1.37779 train_acc= 0.30469 val_loss= 1.38173 val_acc= 0.37500 time= 0.03146
Epoch: 0027 train_loss= 1.37812 train_acc= 0.29883 val_loss= 1.38140 val_acc= 0.37500 time= 0.01563
Epoch: 0028 train_loss= 1.37788 train_acc= 0.30078 val_loss= 1.38111 val_acc= 0.37500 time= 0.01563
Epoch: 0029 train_loss= 1.37761 train_acc= 0.30859 val_loss= 1.38078 val_acc= 0.37500 time= 0.01563
Epoch: 0030 train_loss= 1.37690 train_acc= 0.30078 val_loss= 1.38059 val_acc= 0.37500 time= 0.00000
Epoch: 0031 train_loss= 1.37636 train_acc= 0.30859 val_loss= 1.38052 val_acc= 0.37500 time= 0.01563
Epoch: 0032 train_loss= 1.37622 train_acc= 0.30664 val_loss= 1.38045 val_acc= 0.37500 time= 0.01563
Epoch: 0033 train_loss= 1.37652 train_acc= 0.30469 val_loss= 1.38036 val_acc= 0.37500 time= 0.01563
Epoch: 0034 train_loss= 1.37606 train_acc= 0.29492 val_loss= 1.38022 val_acc= 0.37500 time= 0.01563
Epoch: 0035 train_loss= 1.37630 train_acc= 0.29492 val_loss= 1.37980 val_acc= 0.37500 time= 0.01563
Epoch: 0036 train_loss= 1.37681 train_acc= 0.30273 val_loss= 1.37947 val_acc= 0.37500 time= 0.01563
Epoch: 0037 train_loss= 1.37654 train_acc= 0.30469 val_loss= 1.37930 val_acc= 0.37500 time= 0.01563
Epoch: 0038 train_loss= 1.37661 train_acc= 0.30273 val_loss= 1.37919 val_acc= 0.37500 time= 0.01563
Epoch: 0039 train_loss= 1.37562 train_acc= 0.30469 val_loss= 1.37907 val_acc= 0.37500 time= 0.01563
Epoch: 0040 train_loss= 1.37650 train_acc= 0.30664 val_loss= 1.37886 val_acc= 0.37500 time= 0.01563
Epoch: 0041 train_loss= 1.37560 train_acc= 0.30078 val_loss= 1.37855 val_acc= 0.37500 time= 0.01562
Epoch: 0042 train_loss= 1.37554 train_acc= 0.30469 val_loss= 1.37849 val_acc= 0.37500 time= 0.01563
Epoch: 0043 train_loss= 1.37497 train_acc= 0.31055 val_loss= 1.37832 val_acc= 0.37500 time= 0.01563
Epoch: 0044 train_loss= 1.37444 train_acc= 0.30664 val_loss= 1.37817 val_acc= 0.37500 time= 0.02092
Epoch: 0045 train_loss= 1.37447 train_acc= 0.30664 val_loss= 1.37811 val_acc= 0.37500 time= 0.01050
Epoch: 0046 train_loss= 1.37421 train_acc= 0.30664 val_loss= 1.37782 val_acc= 0.37500 time= 0.01563
Epoch: 0047 train_loss= 1.37463 train_acc= 0.30469 val_loss= 1.37740 val_acc= 0.37500 time= 0.01563
Epoch: 0048 train_loss= 1.37499 train_acc= 0.31055 val_loss= 1.37730 val_acc= 0.37500 time= 0.01563
Epoch: 0049 train_loss= 1.37406 train_acc= 0.30859 val_loss= 1.37712 val_acc= 0.37500 time= 0.01563
Epoch: 0050 train_loss= 1.37353 train_acc= 0.30859 val_loss= 1.37699 val_acc= 0.37500 time= 0.01562
Epoch: 0051 train_loss= 1.37328 train_acc= 0.31641 val_loss= 1.37669 val_acc= 0.37500 time= 0.00000
Epoch: 0052 train_loss= 1.37403 train_acc= 0.30664 val_loss= 1.37665 val_acc= 0.37500 time= 0.03125
Epoch: 0053 train_loss= 1.37403 train_acc= 0.30664 val_loss= 1.37605 val_acc= 0.37500 time= 0.00000
Epoch: 0054 train_loss= 1.37334 train_acc= 0.30664 val_loss= 1.37583 val_acc= 0.37500 time= 0.01563
Epoch: 0055 train_loss= 1.37338 train_acc= 0.30664 val_loss= 1.37565 val_acc= 0.37500 time= 0.01563
Epoch: 0056 train_loss= 1.37341 train_acc= 0.30859 val_loss= 1.37563 val_acc= 0.37500 time= 0.01563
Epoch: 0057 train_loss= 1.37212 train_acc= 0.30273 val_loss= 1.37606 val_acc= 0.37500 time= 0.01562
Epoch: 0058 train_loss= 1.37293 train_acc= 0.30469 val_loss= 1.37657 val_acc= 0.37500 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37991 accuracy= 0.31858 time= 0.01563 
