Epoch: 0001 train_loss= 2.08743 train_acc= 0.07170 val_loss= 2.08670 val_acc= 0.13793 time= 0.21877
Epoch: 0002 train_loss= 2.08394 train_acc= 0.15472 val_loss= 2.08266 val_acc= 0.13793 time= 0.00000
Epoch: 0003 train_loss= 2.08027 train_acc= 0.18113 val_loss= 2.07850 val_acc= 0.13793 time= 0.01562
Epoch: 0004 train_loss= 2.07751 train_acc= 0.18491 val_loss= 2.07450 val_acc= 0.13793 time= 0.01562
Epoch: 0005 train_loss= 2.07457 train_acc= 0.18491 val_loss= 2.07057 val_acc= 0.13793 time= 0.00000
Epoch: 0006 train_loss= 2.07148 train_acc= 0.18491 val_loss= 2.06671 val_acc= 0.13793 time= 0.01563
Epoch: 0007 train_loss= 2.06832 train_acc= 0.18113 val_loss= 2.06338 val_acc= 0.13793 time= 0.01563
Epoch: 0008 train_loss= 2.06633 train_acc= 0.18491 val_loss= 2.05995 val_acc= 0.13793 time= 0.00000
Epoch: 0009 train_loss= 2.06471 train_acc= 0.18491 val_loss= 2.05646 val_acc= 0.13793 time= 0.01563
Epoch: 0010 train_loss= 2.06136 train_acc= 0.18491 val_loss= 2.05303 val_acc= 0.13793 time= 0.01563
Epoch: 0011 train_loss= 2.06028 train_acc= 0.18491 val_loss= 2.04972 val_acc= 0.13793 time= 0.00000
Epoch: 0012 train_loss= 2.05790 train_acc= 0.18113 val_loss= 2.04654 val_acc= 0.13793 time= 0.01563
Epoch: 0013 train_loss= 2.05568 train_acc= 0.18491 val_loss= 2.04362 val_acc= 0.13793 time= 0.01563
Epoch: 0014 train_loss= 2.05342 train_acc= 0.18491 val_loss= 2.04112 val_acc= 0.13793 time= 0.00000
Epoch: 0015 train_loss= 2.05352 train_acc= 0.18491 val_loss= 2.03896 val_acc= 0.13793 time= 0.01563
Epoch: 0016 train_loss= 2.05015 train_acc= 0.18113 val_loss= 2.03712 val_acc= 0.13793 time= 0.01563
Epoch: 0017 train_loss= 2.05155 train_acc= 0.18491 val_loss= 2.03565 val_acc= 0.13793 time= 0.00000
Epoch: 0018 train_loss= 2.04974 train_acc= 0.18491 val_loss= 2.03452 val_acc= 0.13793 time= 0.01563
Epoch: 0019 train_loss= 2.04819 train_acc= 0.18113 val_loss= 2.03358 val_acc= 0.13793 time= 0.01563
Epoch: 0020 train_loss= 2.04693 train_acc= 0.18113 val_loss= 2.03283 val_acc= 0.13793 time= 0.00000
Epoch: 0021 train_loss= 2.04876 train_acc= 0.18113 val_loss= 2.03216 val_acc= 0.13793 time= 0.01563
Epoch: 0022 train_loss= 2.04767 train_acc= 0.18113 val_loss= 2.03153 val_acc= 0.13793 time= 0.01563
Epoch: 0023 train_loss= 2.04568 train_acc= 0.18113 val_loss= 2.03097 val_acc= 0.13793 time= 0.00000
Epoch: 0024 train_loss= 2.04642 train_acc= 0.18491 val_loss= 2.03038 val_acc= 0.13793 time= 0.01563
Epoch: 0025 train_loss= 2.04312 train_acc= 0.18491 val_loss= 2.02977 val_acc= 0.13793 time= 0.01563
Epoch: 0026 train_loss= 2.04276 train_acc= 0.18491 val_loss= 2.02917 val_acc= 0.13793 time= 0.00000
Epoch: 0027 train_loss= 2.04242 train_acc= 0.18491 val_loss= 2.02847 val_acc= 0.13793 time= 0.01563
Epoch: 0028 train_loss= 2.04239 train_acc= 0.18113 val_loss= 2.02775 val_acc= 0.13793 time= 0.01563
Epoch: 0029 train_loss= 2.04025 train_acc= 0.18491 val_loss= 2.02701 val_acc= 0.13793 time= 0.00000
Epoch: 0030 train_loss= 2.04325 train_acc= 0.18491 val_loss= 2.02624 val_acc= 0.13793 time= 0.01563
Epoch: 0031 train_loss= 2.04179 train_acc= 0.18491 val_loss= 2.02548 val_acc= 0.13793 time= 0.01563
Epoch: 0032 train_loss= 2.04203 train_acc= 0.18491 val_loss= 2.02479 val_acc= 0.13793 time= 0.01563
Epoch: 0033 train_loss= 2.03951 train_acc= 0.18491 val_loss= 2.02417 val_acc= 0.13793 time= 0.00000
Epoch: 0034 train_loss= 2.04053 train_acc= 0.18491 val_loss= 2.02347 val_acc= 0.13793 time= 0.01563
Epoch: 0035 train_loss= 2.04081 train_acc= 0.18491 val_loss= 2.02276 val_acc= 0.13793 time= 0.01562
Epoch: 0036 train_loss= 2.03943 train_acc= 0.18491 val_loss= 2.02213 val_acc= 0.13793 time= 0.00000
Epoch: 0037 train_loss= 2.03811 train_acc= 0.18491 val_loss= 2.02154 val_acc= 0.13793 time= 0.01563
Epoch: 0038 train_loss= 2.04099 train_acc= 0.18113 val_loss= 2.02089 val_acc= 0.13793 time= 0.01563
Epoch: 0039 train_loss= 2.03908 train_acc= 0.18491 val_loss= 2.02031 val_acc= 0.13793 time= 0.00000
Epoch: 0040 train_loss= 2.03876 train_acc= 0.18491 val_loss= 2.01975 val_acc= 0.13793 time= 0.01563
Epoch: 0041 train_loss= 2.03821 train_acc= 0.18113 val_loss= 2.01923 val_acc= 0.13793 time= 0.01563
Epoch: 0042 train_loss= 2.04076 train_acc= 0.18113 val_loss= 2.01867 val_acc= 0.13793 time= 0.00000
Epoch: 0043 train_loss= 2.04006 train_acc= 0.18491 val_loss= 2.01811 val_acc= 0.13793 time= 0.01563
Epoch: 0044 train_loss= 2.03959 train_acc= 0.18491 val_loss= 2.01755 val_acc= 0.13793 time= 0.01563
Epoch: 0045 train_loss= 2.03875 train_acc= 0.18491 val_loss= 2.01693 val_acc= 0.13793 time= 0.01563
Epoch: 0046 train_loss= 2.03985 train_acc= 0.18491 val_loss= 2.01646 val_acc= 0.13793 time= 0.00000
Epoch: 0047 train_loss= 2.03922 train_acc= 0.18113 val_loss= 2.01619 val_acc= 0.13793 time= 0.01563
Epoch: 0048 train_loss= 2.03921 train_acc= 0.18491 val_loss= 2.01617 val_acc= 0.13793 time= 0.01563
Epoch: 0049 train_loss= 2.03822 train_acc= 0.18113 val_loss= 2.01615 val_acc= 0.13793 time= 0.00000
Epoch: 0050 train_loss= 2.03963 train_acc= 0.18491 val_loss= 2.01617 val_acc= 0.13793 time= 0.01563
Epoch: 0051 train_loss= 2.03933 train_acc= 0.18491 val_loss= 2.01611 val_acc= 0.13793 time= 0.01563
Epoch: 0052 train_loss= 2.03854 train_acc= 0.18491 val_loss= 2.01606 val_acc= 0.13793 time= 0.00000
Epoch: 0053 train_loss= 2.03834 train_acc= 0.18491 val_loss= 2.01609 val_acc= 0.13793 time= 0.01563
Epoch: 0054 train_loss= 2.03873 train_acc= 0.18491 val_loss= 2.01614 val_acc= 0.13793 time= 0.01563
Epoch: 0055 train_loss= 2.03959 train_acc= 0.18113 val_loss= 2.01630 val_acc= 0.13793 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.09127 accuracy= 0.11864 time= 0.01563 
