Epoch: 0001 train_loss= 2.06466 train_acc= 0.15094 val_loss= 2.08489 val_acc= 0.10345 time= 0.26555
Epoch: 0002 train_loss= 2.06061 train_acc= 0.16442 val_loss= 2.08249 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.06450 train_acc= 0.17520 val_loss= 2.08053 val_acc= 0.10345 time= 0.00000
Epoch: 0004 train_loss= 2.06010 train_acc= 0.17520 val_loss= 2.07716 val_acc= 0.10345 time= 0.01563
Epoch: 0005 train_loss= 2.06036 train_acc= 0.18329 val_loss= 2.07410 val_acc= 0.10345 time= 0.01563
Epoch: 0006 train_loss= 2.05498 train_acc= 0.17520 val_loss= 2.07153 val_acc= 0.10345 time= 0.00000
Epoch: 0007 train_loss= 2.05744 train_acc= 0.17790 val_loss= 2.06867 val_acc= 0.10345 time= 0.01563
Epoch: 0008 train_loss= 2.06390 train_acc= 0.17520 val_loss= 2.06571 val_acc= 0.10345 time= 0.00000
Epoch: 0009 train_loss= 2.06002 train_acc= 0.17520 val_loss= 2.06316 val_acc= 0.10345 time= 0.01563
Epoch: 0010 train_loss= 2.06053 train_acc= 0.17251 val_loss= 2.06119 val_acc= 0.10345 time= 0.00000
Epoch: 0011 train_loss= 2.05609 train_acc= 0.17520 val_loss= 2.05925 val_acc= 0.10345 time= 0.01563
Epoch: 0012 train_loss= 2.05013 train_acc= 0.18868 val_loss= 2.05806 val_acc= 0.10345 time= 0.01563
Epoch: 0013 train_loss= 2.05354 train_acc= 0.18059 val_loss= 2.05741 val_acc= 0.10345 time= 0.00000
Epoch: 0014 train_loss= 2.05165 train_acc= 0.17520 val_loss= 2.05710 val_acc= 0.10345 time= 0.01563
Epoch: 0015 train_loss= 2.05229 train_acc= 0.17251 val_loss= 2.05693 val_acc= 0.10345 time= 0.00000
Epoch: 0016 train_loss= 2.05348 train_acc= 0.18868 val_loss= 2.05681 val_acc= 0.10345 time= 0.01562
Epoch: 0017 train_loss= 2.05486 train_acc= 0.18598 val_loss= 2.05717 val_acc= 0.10345 time= 0.01563
Epoch: 0018 train_loss= 2.04795 train_acc= 0.18598 val_loss= 2.05744 val_acc= 0.10345 time= 0.00000
Epoch: 0019 train_loss= 2.04968 train_acc= 0.16981 val_loss= 2.05746 val_acc= 0.10345 time= 0.01563
Epoch: 0020 train_loss= 2.04929 train_acc= 0.18059 val_loss= 2.05743 val_acc= 0.10345 time= 0.00000
Epoch: 0021 train_loss= 2.04565 train_acc= 0.16173 val_loss= 2.05728 val_acc= 0.10345 time= 0.01563
Epoch: 0022 train_loss= 2.05130 train_acc= 0.16981 val_loss= 2.05708 val_acc= 0.10345 time= 0.01563
Epoch: 0023 train_loss= 2.04791 train_acc= 0.19137 val_loss= 2.05691 val_acc= 0.10345 time= 0.00000
Epoch: 0024 train_loss= 2.04614 train_acc= 0.19137 val_loss= 2.05683 val_acc= 0.10345 time= 0.01562
Epoch: 0025 train_loss= 2.05128 train_acc= 0.16981 val_loss= 2.05653 val_acc= 0.10345 time= 0.00000
Epoch: 0026 train_loss= 2.04632 train_acc= 0.17520 val_loss= 2.05615 val_acc= 0.10345 time= 0.01563
Epoch: 0027 train_loss= 2.04500 train_acc= 0.19407 val_loss= 2.05571 val_acc= 0.10345 time= 0.00000
Epoch: 0028 train_loss= 2.04555 train_acc= 0.16981 val_loss= 2.05506 val_acc= 0.10345 time= 0.01563
Epoch: 0029 train_loss= 2.04538 train_acc= 0.17251 val_loss= 2.05408 val_acc= 0.10345 time= 0.01563
Epoch: 0030 train_loss= 2.03991 train_acc= 0.18059 val_loss= 2.05286 val_acc= 0.10345 time= 0.00000
Epoch: 0031 train_loss= 2.04269 train_acc= 0.18868 val_loss= 2.05192 val_acc= 0.10345 time= 0.01563
Epoch: 0032 train_loss= 2.04442 train_acc= 0.20485 val_loss= 2.05098 val_acc= 0.10345 time= 0.00000
Epoch: 0033 train_loss= 2.04055 train_acc= 0.21294 val_loss= 2.05015 val_acc= 0.13793 time= 0.01563
Epoch: 0034 train_loss= 2.04095 train_acc= 0.18329 val_loss= 2.04943 val_acc= 0.13793 time= 0.01563
Epoch: 0035 train_loss= 2.04276 train_acc= 0.19407 val_loss= 2.04883 val_acc= 0.13793 time= 0.00000
Epoch: 0036 train_loss= 2.04110 train_acc= 0.18868 val_loss= 2.04796 val_acc= 0.13793 time= 0.01563
Epoch: 0037 train_loss= 2.04224 train_acc= 0.19407 val_loss= 2.04714 val_acc= 0.13793 time= 0.00000
Epoch: 0038 train_loss= 2.03711 train_acc= 0.19946 val_loss= 2.04624 val_acc= 0.13793 time= 0.01563
Epoch: 0039 train_loss= 2.03835 train_acc= 0.19677 val_loss= 2.04539 val_acc= 0.13793 time= 0.01563
Epoch: 0040 train_loss= 2.03340 train_acc= 0.21024 val_loss= 2.04445 val_acc= 0.17241 time= 0.00000
Epoch: 0041 train_loss= 2.04037 train_acc= 0.18329 val_loss= 2.04317 val_acc= 0.17241 time= 0.01563
Epoch: 0042 train_loss= 2.04108 train_acc= 0.21833 val_loss= 2.04237 val_acc= 0.17241 time= 0.00000
Epoch: 0043 train_loss= 2.03159 train_acc= 0.22911 val_loss= 2.04151 val_acc= 0.24138 time= 0.01563
Epoch: 0044 train_loss= 2.03409 train_acc= 0.19946 val_loss= 2.04083 val_acc= 0.24138 time= 0.00000
Epoch: 0045 train_loss= 2.04239 train_acc= 0.20755 val_loss= 2.04058 val_acc= 0.24138 time= 0.01563
Epoch: 0046 train_loss= 2.04166 train_acc= 0.19137 val_loss= 2.04063 val_acc= 0.24138 time= 0.01563
Epoch: 0047 train_loss= 2.03630 train_acc= 0.21024 val_loss= 2.04107 val_acc= 0.24138 time= 0.00000
Epoch: 0048 train_loss= 2.03705 train_acc= 0.19946 val_loss= 2.04208 val_acc= 0.20690 time= 0.01563
Epoch: 0049 train_loss= 2.03353 train_acc= 0.21563 val_loss= 2.04268 val_acc= 0.20690 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.07336 accuracy= 0.15254 time= 0.01563 
