Epoch: 0001 train_loss= 2.08635 train_acc= 0.15472 val_loss= 2.08381 val_acc= 0.17241 time= 0.23872
Epoch: 0002 train_loss= 2.08258 train_acc= 0.15094 val_loss= 2.08105 val_acc= 0.17241 time= 0.01567
Epoch: 0003 train_loss= 2.07794 train_acc= 0.15094 val_loss= 2.07878 val_acc= 0.17241 time= 0.00000
Epoch: 0004 train_loss= 2.07336 train_acc= 0.15094 val_loss= 2.07706 val_acc= 0.17241 time= 0.01563
Epoch: 0005 train_loss= 2.06922 train_acc= 0.15094 val_loss= 2.07596 val_acc= 0.17241 time= 0.00000
Epoch: 0006 train_loss= 2.06609 train_acc= 0.15094 val_loss= 2.07545 val_acc= 0.17241 time= 0.01563
Epoch: 0007 train_loss= 2.06216 train_acc= 0.15094 val_loss= 2.07548 val_acc= 0.17241 time= 0.01563
Epoch: 0008 train_loss= 2.05873 train_acc= 0.14717 val_loss= 2.07602 val_acc= 0.17241 time= 0.00000
Epoch: 0009 train_loss= 2.05465 train_acc= 0.15094 val_loss= 2.07703 val_acc= 0.17241 time= 0.01563
Epoch: 0010 train_loss= 2.05335 train_acc= 0.15094 val_loss= 2.07851 val_acc= 0.17241 time= 0.01563
Epoch: 0011 train_loss= 2.05092 train_acc= 0.15472 val_loss= 2.08023 val_acc= 0.17241 time= 0.00000
Epoch: 0012 train_loss= 2.04872 train_acc= 0.16604 val_loss= 2.08205 val_acc= 0.17241 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.12097 accuracy= 0.08475 time= 0.00000 
