Epoch: 0001 train_loss= 2.18984 train_acc= 0.12938 val_loss= 2.07714 val_acc= 0.13793 time= 0.71961
Epoch: 0002 train_loss= 2.17453 train_acc= 0.11321 val_loss= 2.08111 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.19316 train_acc= 0.12938 val_loss= 2.08590 val_acc= 0.17241 time= 0.01562
Epoch: 0004 train_loss= 2.18401 train_acc= 0.12938 val_loss= 2.08920 val_acc= 0.17241 time= 0.01563
Epoch: 0005 train_loss= 2.18817 train_acc= 0.15633 val_loss= 2.09240 val_acc= 0.17241 time= 0.00000
Epoch: 0006 train_loss= 2.11217 train_acc= 0.15094 val_loss= 2.09541 val_acc= 0.17241 time= 0.01563
Epoch: 0007 train_loss= 2.09334 train_acc= 0.15094 val_loss= 2.09763 val_acc= 0.17241 time= 0.01563
Epoch: 0008 train_loss= 2.16957 train_acc= 0.15364 val_loss= 2.09886 val_acc= 0.17241 time= 0.01563
Epoch: 0009 train_loss= 2.13517 train_acc= 0.14286 val_loss= 2.10017 val_acc= 0.10345 time= 0.00000
Epoch: 0010 train_loss= 2.11417 train_acc= 0.15364 val_loss= 2.10153 val_acc= 0.10345 time= 0.01563
Epoch: 0011 train_loss= 2.09450 train_acc= 0.15364 val_loss= 2.10175 val_acc= 0.03448 time= 0.01563
Epoch: 0012 train_loss= 2.06899 train_acc= 0.18868 val_loss= 2.10197 val_acc= 0.10345 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.08796 accuracy= 0.15254 time= 0.01563 
