Epoch: 0001 train_loss= 2.08496 train_acc= 0.10943 val_loss= 2.08458 val_acc= 0.10345 time= 0.36425
Epoch: 0002 train_loss= 2.08374 train_acc= 0.16226 val_loss= 2.08396 val_acc= 0.10345 time= 0.01100
Epoch: 0003 train_loss= 2.08250 train_acc= 0.16604 val_loss= 2.08311 val_acc= 0.10345 time= 0.00000
Epoch: 0004 train_loss= 2.08188 train_acc= 0.16226 val_loss= 2.08238 val_acc= 0.10345 time= 0.01563
Epoch: 0005 train_loss= 2.08003 train_acc= 0.16981 val_loss= 2.08149 val_acc= 0.10345 time= 0.00000
Epoch: 0006 train_loss= 2.07925 train_acc= 0.16604 val_loss= 2.08044 val_acc= 0.10345 time= 0.01563
Epoch: 0007 train_loss= 2.07744 train_acc= 0.16604 val_loss= 2.07941 val_acc= 0.10345 time= 0.01563
Epoch: 0008 train_loss= 2.07585 train_acc= 0.16604 val_loss= 2.07839 val_acc= 0.10345 time= 0.00000
Epoch: 0009 train_loss= 2.07466 train_acc= 0.16604 val_loss= 2.07740 val_acc= 0.10345 time= 0.01562
Epoch: 0010 train_loss= 2.07364 train_acc= 0.16981 val_loss= 2.07650 val_acc= 0.10345 time= 0.00000
Epoch: 0011 train_loss= 2.07108 train_acc= 0.16604 val_loss= 2.07569 val_acc= 0.10345 time= 0.01563
Epoch: 0012 train_loss= 2.06979 train_acc= 0.16981 val_loss= 2.07503 val_acc= 0.10345 time= 0.01563
Epoch: 0013 train_loss= 2.06861 train_acc= 0.16604 val_loss= 2.07457 val_acc= 0.10345 time= 0.00000
Epoch: 0014 train_loss= 2.06580 train_acc= 0.16604 val_loss= 2.07428 val_acc= 0.10345 time= 0.01563
Epoch: 0015 train_loss= 2.06387 train_acc= 0.16604 val_loss= 2.07416 val_acc= 0.10345 time= 0.01563
Epoch: 0016 train_loss= 2.06301 train_acc= 0.16981 val_loss= 2.07409 val_acc= 0.10345 time= 0.00000
Epoch: 0017 train_loss= 2.06333 train_acc= 0.16981 val_loss= 2.07411 val_acc= 0.10345 time= 0.01562
Epoch: 0018 train_loss= 2.06366 train_acc= 0.16604 val_loss= 2.07411 val_acc= 0.10345 time= 0.00000
Epoch: 0019 train_loss= 2.06411 train_acc= 0.16604 val_loss= 2.07399 val_acc= 0.10345 time= 0.01563
Epoch: 0020 train_loss= 2.06210 train_acc= 0.16981 val_loss= 2.07380 val_acc= 0.10345 time= 0.00000
Epoch: 0021 train_loss= 2.06308 train_acc= 0.16981 val_loss= 2.07351 val_acc= 0.10345 time= 0.01563
Epoch: 0022 train_loss= 2.06339 train_acc= 0.16981 val_loss= 2.07280 val_acc= 0.10345 time= 0.01563
Epoch: 0023 train_loss= 2.06168 train_acc= 0.15849 val_loss= 2.07191 val_acc= 0.10345 time= 0.00000
Epoch: 0024 train_loss= 2.06193 train_acc= 0.16981 val_loss= 2.07098 val_acc= 0.10345 time= 0.01563
Epoch: 0025 train_loss= 2.06282 train_acc= 0.16981 val_loss= 2.07029 val_acc= 0.10345 time= 0.00000
Epoch: 0026 train_loss= 2.06327 train_acc= 0.17358 val_loss= 2.06972 val_acc= 0.10345 time= 0.01563
Epoch: 0027 train_loss= 2.05923 train_acc= 0.19245 val_loss= 2.06933 val_acc= 0.10345 time= 0.01563
Epoch: 0028 train_loss= 2.06172 train_acc= 0.17358 val_loss= 2.06906 val_acc= 0.10345 time= 0.00000
Epoch: 0029 train_loss= 2.05955 train_acc= 0.16226 val_loss= 2.06887 val_acc= 0.10345 time= 0.01563
Epoch: 0030 train_loss= 2.06079 train_acc= 0.18491 val_loss= 2.06840 val_acc= 0.10345 time= 0.00000
Epoch: 0031 train_loss= 2.06021 train_acc= 0.15849 val_loss= 2.06787 val_acc= 0.10345 time= 0.01563
Epoch: 0032 train_loss= 2.06089 train_acc= 0.15472 val_loss= 2.06742 val_acc= 0.10345 time= 0.00000
Epoch: 0033 train_loss= 2.06013 train_acc= 0.16226 val_loss= 2.06679 val_acc= 0.10345 time= 0.01563
Epoch: 0034 train_loss= 2.05982 train_acc= 0.16226 val_loss= 2.06616 val_acc= 0.10345 time= 0.00000
Epoch: 0035 train_loss= 2.05982 train_acc= 0.17358 val_loss= 2.06551 val_acc= 0.10345 time= 0.01563
Epoch: 0036 train_loss= 2.05765 train_acc= 0.16226 val_loss= 2.06481 val_acc= 0.10345 time= 0.00000
Epoch: 0037 train_loss= 2.05989 train_acc= 0.17358 val_loss= 2.06416 val_acc= 0.10345 time= 0.00000
Epoch: 0038 train_loss= 2.05932 train_acc= 0.16981 val_loss= 2.06367 val_acc= 0.10345 time= 0.01563
Epoch: 0039 train_loss= 2.05989 train_acc= 0.16981 val_loss= 2.06322 val_acc= 0.10345 time= 0.01563
Epoch: 0040 train_loss= 2.05841 train_acc= 0.16981 val_loss= 2.06302 val_acc= 0.10345 time= 0.00000
Epoch: 0041 train_loss= 2.06005 train_acc= 0.16604 val_loss= 2.06279 val_acc= 0.10345 time= 0.01563
Epoch: 0042 train_loss= 2.05772 train_acc= 0.16981 val_loss= 2.06243 val_acc= 0.10345 time= 0.00000
Epoch: 0043 train_loss= 2.05651 train_acc= 0.16981 val_loss= 2.06241 val_acc= 0.10345 time= 0.01563
Epoch: 0044 train_loss= 2.05682 train_acc= 0.16604 val_loss= 2.06248 val_acc= 0.10345 time= 0.00000
Epoch: 0045 train_loss= 2.05944 train_acc= 0.16226 val_loss= 2.06260 val_acc= 0.10345 time= 0.01563
Epoch: 0046 train_loss= 2.05726 train_acc= 0.17358 val_loss= 2.06283 val_acc= 0.10345 time= 0.00000
Epoch: 0047 train_loss= 2.05715 train_acc= 0.16604 val_loss= 2.06307 val_acc= 0.10345 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07703 accuracy= 0.18644 time= 0.00000 
