Epoch: 0001 train_loss= 1.11756 train_acc= 0.48727 val_loss= 1.04489 val_acc= 0.60656 time= 0.59351
Epoch: 0002 train_loss= 1.35576 train_acc= 0.52545 val_loss= 1.20656 val_acc= 0.60656 time= 0.01563
Epoch: 0003 train_loss= 1.93244 train_acc= 0.51091 val_loss= 0.99191 val_acc= 0.57377 time= 0.01563
Epoch: 0004 train_loss= 1.14158 train_acc= 0.50364 val_loss= 0.78829 val_acc= 0.57377 time= 0.03125
Epoch: 0005 train_loss= 1.12983 train_acc= 0.50727 val_loss= 0.72327 val_acc= 0.47541 time= 0.01562
Epoch: 0006 train_loss= 0.95764 train_acc= 0.52182 val_loss= 0.73829 val_acc= 0.45902 time= 0.03125
Epoch: 0007 train_loss= 1.12559 train_acc= 0.49636 val_loss= 0.76961 val_acc= 0.44262 time= 0.01563
Epoch: 0008 train_loss= 0.84645 train_acc= 0.54364 val_loss= 0.91829 val_acc= 0.44262 time= 0.03125
Epoch: 0009 train_loss= 0.82121 train_acc= 0.48727 val_loss= 0.92535 val_acc= 0.44262 time= 0.01563
Epoch: 0010 train_loss= 0.92969 train_acc= 0.54364 val_loss= 0.97268 val_acc= 0.42623 time= 0.03125
Epoch: 0011 train_loss= 1.98282 train_acc= 0.48364 val_loss= 0.89864 val_acc= 0.42623 time= 0.01563
Epoch: 0012 train_loss= 0.75820 train_acc= 0.48727 val_loss= 0.85645 val_acc= 0.42623 time= 0.03125
Epoch: 0013 train_loss= 0.96916 train_acc= 0.46545 val_loss= 0.80037 val_acc= 0.42623 time= 0.01563
Epoch: 0014 train_loss= 1.24636 train_acc= 0.49455 val_loss= 0.75331 val_acc= 0.44262 time= 0.03125
Epoch: 0015 train_loss= 0.74018 train_acc= 0.52909 val_loss= 0.72781 val_acc= 0.44262 time= 0.01563
Epoch: 0016 train_loss= 0.85405 train_acc= 0.49091 val_loss= 0.71649 val_acc= 0.47541 time= 0.03125
Epoch: 0017 train_loss= 0.75599 train_acc= 0.48364 val_loss= 0.71181 val_acc= 0.49180 time= 0.01563
Epoch: 0018 train_loss= 1.19002 train_acc= 0.52000 val_loss= 0.70859 val_acc= 0.49180 time= 0.03125
Epoch: 0019 train_loss= 0.72239 train_acc= 0.52364 val_loss= 0.70571 val_acc= 0.45902 time= 0.03741
Epoch: 0020 train_loss= 0.69821 train_acc= 0.52000 val_loss= 0.70319 val_acc= 0.47541 time= 0.02137
Epoch: 0021 train_loss= 0.72762 train_acc= 0.50727 val_loss= 0.70093 val_acc= 0.49180 time= 0.02964
Epoch: 0022 train_loss= 0.82239 train_acc= 0.51636 val_loss= 0.69936 val_acc= 0.49180 time= 0.01563
Epoch: 0023 train_loss= 0.74280 train_acc= 0.47818 val_loss= 0.69807 val_acc= 0.49180 time= 0.03125
Epoch: 0024 train_loss= 0.71476 train_acc= 0.50727 val_loss= 0.69700 val_acc= 0.52459 time= 0.01563
Epoch: 0025 train_loss= 0.72511 train_acc= 0.50364 val_loss= 0.69596 val_acc= 0.52459 time= 0.01563
Epoch: 0026 train_loss= 0.70746 train_acc= 0.50364 val_loss= 0.69493 val_acc= 0.57377 time= 0.03125
Epoch: 0027 train_loss= 0.72996 train_acc= 0.51273 val_loss= 0.69416 val_acc= 0.57377 time= 0.01563
Epoch: 0028 train_loss= 0.71111 train_acc= 0.50909 val_loss= 0.69356 val_acc= 0.57377 time= 0.01563
Epoch: 0029 train_loss= 0.71989 train_acc= 0.52000 val_loss= 0.69312 val_acc= 0.59016 time= 0.01563
Epoch: 0030 train_loss= 0.71303 train_acc= 0.52000 val_loss= 0.69278 val_acc= 0.57377 time= 0.01562
Epoch: 0031 train_loss= 0.73146 train_acc= 0.53818 val_loss= 0.69255 val_acc= 0.57377 time= 0.03125
Epoch: 0032 train_loss= 0.69881 train_acc= 0.53273 val_loss= 0.69234 val_acc= 0.57377 time= 0.02443
Epoch: 0033 train_loss= 0.77020 train_acc= 0.49091 val_loss= 0.69225 val_acc= 0.57377 time= 0.02497
Epoch: 0034 train_loss= 0.70661 train_acc= 0.53273 val_loss= 0.69220 val_acc= 0.57377 time= 0.01670
Epoch: 0035 train_loss= 0.70009 train_acc= 0.51818 val_loss= 0.69211 val_acc= 0.57377 time= 0.01563
Epoch: 0036 train_loss= 0.70353 train_acc= 0.52545 val_loss= 0.69208 val_acc= 0.55738 time= 0.03125
Epoch: 0037 train_loss= 0.70259 train_acc= 0.53091 val_loss= 0.69202 val_acc= 0.55738 time= 0.01563
Epoch: 0038 train_loss= 0.70796 train_acc= 0.53818 val_loss= 0.69207 val_acc= 0.55738 time= 0.02884
Epoch: 0039 train_loss= 0.71943 train_acc= 0.52727 val_loss= 0.69220 val_acc= 0.54098 time= 0.02201
Epoch: 0040 train_loss= 0.70198 train_acc= 0.51273 val_loss= 0.69235 val_acc= 0.54098 time= 0.02200
Early stopping...
Optimization Finished!
Test set results: cost= 0.69726 accuracy= 0.59016 time= 0.01103 
