Epoch: 0001 train_loss= 2.08714 train_acc= 0.17610 val_loss= 2.08545 val_acc= 0.06897 time= 0.21876
Epoch: 0002 train_loss= 2.08445 train_acc= 0.15094 val_loss= 2.08368 val_acc= 0.06897 time= 0.00000
Epoch: 0003 train_loss= 2.08229 train_acc= 0.14465 val_loss= 2.08209 val_acc= 0.06897 time= 0.01563
Epoch: 0004 train_loss= 2.07961 train_acc= 0.14465 val_loss= 2.08063 val_acc= 0.06897 time= 0.00000
Epoch: 0005 train_loss= 2.07804 train_acc= 0.15094 val_loss= 2.07921 val_acc= 0.06897 time= 0.01563
Epoch: 0006 train_loss= 2.07670 train_acc= 0.14465 val_loss= 2.07775 val_acc= 0.06897 time= 0.00000
Epoch: 0007 train_loss= 2.07581 train_acc= 0.15094 val_loss= 2.07642 val_acc= 0.06897 time= 0.01563
Epoch: 0008 train_loss= 2.07421 train_acc= 0.14465 val_loss= 2.07504 val_acc= 0.06897 time= 0.00000
Epoch: 0009 train_loss= 2.07359 train_acc= 0.15094 val_loss= 2.07370 val_acc= 0.06897 time= 0.01563
Epoch: 0010 train_loss= 2.07234 train_acc= 0.15094 val_loss= 2.07243 val_acc= 0.06897 time= 0.00000
Epoch: 0011 train_loss= 2.07321 train_acc= 0.15094 val_loss= 2.07130 val_acc= 0.06897 time= 0.01563
Epoch: 0012 train_loss= 2.07068 train_acc= 0.15094 val_loss= 2.07007 val_acc= 0.06897 time= 0.00000
Epoch: 0013 train_loss= 2.06959 train_acc= 0.15094 val_loss= 2.06897 val_acc= 0.06897 time= 0.01563
Epoch: 0014 train_loss= 2.07016 train_acc= 0.15094 val_loss= 2.06800 val_acc= 0.06897 time= 0.00000
Epoch: 0015 train_loss= 2.06900 train_acc= 0.15094 val_loss= 2.06700 val_acc= 0.06897 time= 0.01563
Epoch: 0016 train_loss= 2.06829 train_acc= 0.15094 val_loss= 2.06608 val_acc= 0.06897 time= 0.01563
Epoch: 0017 train_loss= 2.06633 train_acc= 0.15094 val_loss= 2.06484 val_acc= 0.06897 time= 0.00000
Epoch: 0018 train_loss= 2.06488 train_acc= 0.15094 val_loss= 2.06346 val_acc= 0.06897 time= 0.01563
Epoch: 0019 train_loss= 2.06641 train_acc= 0.15094 val_loss= 2.06194 val_acc= 0.06897 time= 0.00000
Epoch: 0020 train_loss= 2.06357 train_acc= 0.15094 val_loss= 2.06023 val_acc= 0.06897 time= 0.01563
Epoch: 0021 train_loss= 2.06151 train_acc= 0.15094 val_loss= 2.05854 val_acc= 0.06897 time= 0.00000
Epoch: 0022 train_loss= 2.06048 train_acc= 0.15094 val_loss= 2.05704 val_acc= 0.06897 time= 0.01563
Epoch: 0023 train_loss= 2.05961 train_acc= 0.15094 val_loss= 2.05534 val_acc= 0.06897 time= 0.00000
Epoch: 0024 train_loss= 2.05588 train_acc= 0.15094 val_loss= 2.05355 val_acc= 0.06897 time= 0.01563
Epoch: 0025 train_loss= 2.05713 train_acc= 0.15094 val_loss= 2.05158 val_acc= 0.06897 time= 0.00000
Epoch: 0026 train_loss= 2.05654 train_acc= 0.15094 val_loss= 2.04927 val_acc= 0.06897 time= 0.01563
Epoch: 0027 train_loss= 2.05636 train_acc= 0.15094 val_loss= 2.04715 val_acc= 0.06897 time= 0.00000
Epoch: 0028 train_loss= 2.05448 train_acc= 0.15094 val_loss= 2.04501 val_acc= 0.06897 time= 0.01563
Epoch: 0029 train_loss= 2.05375 train_acc= 0.15723 val_loss= 2.04310 val_acc= 0.06897 time= 0.00000
Epoch: 0030 train_loss= 2.05147 train_acc= 0.17610 val_loss= 2.04145 val_acc= 0.06897 time= 0.01562
Epoch: 0031 train_loss= 2.04851 train_acc= 0.17610 val_loss= 2.03985 val_acc= 0.06897 time= 0.01563
Epoch: 0032 train_loss= 2.05168 train_acc= 0.16352 val_loss= 2.03844 val_acc= 0.06897 time= 0.00000
Epoch: 0033 train_loss= 2.05055 train_acc= 0.16981 val_loss= 2.03712 val_acc= 0.31034 time= 0.01563
Epoch: 0034 train_loss= 2.04831 train_acc= 0.22013 val_loss= 2.03583 val_acc= 0.37931 time= 0.01563
Epoch: 0035 train_loss= 2.04825 train_acc= 0.23270 val_loss= 2.03480 val_acc= 0.34483 time= 0.00000
Epoch: 0036 train_loss= 2.04804 train_acc= 0.16981 val_loss= 2.03415 val_acc= 0.31034 time= 0.01563
Epoch: 0037 train_loss= 2.05102 train_acc= 0.20126 val_loss= 2.03351 val_acc= 0.31034 time= 0.00000
Epoch: 0038 train_loss= 2.04627 train_acc= 0.19497 val_loss= 2.03307 val_acc= 0.31034 time= 0.01563
Epoch: 0039 train_loss= 2.04824 train_acc= 0.20126 val_loss= 2.03264 val_acc= 0.31034 time= 0.00000
Epoch: 0040 train_loss= 2.04345 train_acc= 0.22013 val_loss= 2.03242 val_acc= 0.31034 time= 0.01563
Epoch: 0041 train_loss= 2.04415 train_acc= 0.20126 val_loss= 2.03239 val_acc= 0.34483 time= 0.00000
Epoch: 0042 train_loss= 2.04661 train_acc= 0.17610 val_loss= 2.03230 val_acc= 0.37931 time= 0.01563
Epoch: 0043 train_loss= 2.04506 train_acc= 0.16352 val_loss= 2.03240 val_acc= 0.37931 time= 0.00000
Epoch: 0044 train_loss= 2.04226 train_acc= 0.20755 val_loss= 2.03244 val_acc= 0.41379 time= 0.01563
Epoch: 0045 train_loss= 2.04595 train_acc= 0.16352 val_loss= 2.03242 val_acc= 0.37931 time= 0.00000
Epoch: 0046 train_loss= 2.04052 train_acc= 0.17610 val_loss= 2.03246 val_acc= 0.41379 time= 0.01563
Epoch: 0047 train_loss= 2.04156 train_acc= 0.17610 val_loss= 2.03265 val_acc= 0.41379 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.11477 accuracy= 0.18644 time= 0.00000 
