Epoch: 0001 train_loss= 0.70102 train_acc= 0.46970 val_loss= 0.69799 val_acc= 0.50820 time= 0.14020
Epoch: 0002 train_loss= 0.69808 train_acc= 0.52727 val_loss= 0.69586 val_acc= 0.50820 time= 0.01563
Epoch: 0003 train_loss= 0.69594 train_acc= 0.51818 val_loss= 0.69439 val_acc= 0.50820 time= 0.01562
Epoch: 0004 train_loss= 0.69464 train_acc= 0.52424 val_loss= 0.69347 val_acc= 0.50820 time= 0.01563
Epoch: 0005 train_loss= 0.69360 train_acc= 0.54242 val_loss= 0.69294 val_acc= 0.50820 time= 0.00000
Epoch: 0006 train_loss= 0.69321 train_acc= 0.53333 val_loss= 0.69271 val_acc= 0.50820 time= 0.01563
Epoch: 0007 train_loss= 0.69299 train_acc= 0.53636 val_loss= 0.69268 val_acc= 0.50820 time= 0.01563
Epoch: 0008 train_loss= 0.69298 train_acc= 0.53939 val_loss= 0.69273 val_acc= 0.50820 time= 0.00000
Epoch: 0009 train_loss= 0.69275 train_acc= 0.54242 val_loss= 0.69281 val_acc= 0.50820 time= 0.01563
Epoch: 0010 train_loss= 0.69304 train_acc= 0.53030 val_loss= 0.69289 val_acc= 0.50820 time= 0.01563
Epoch: 0011 train_loss= 0.69310 train_acc= 0.53333 val_loss= 0.69290 val_acc= 0.50820 time= 0.00000
Epoch: 0012 train_loss= 0.69279 train_acc= 0.53939 val_loss= 0.69281 val_acc= 0.50820 time= 0.01563
Epoch: 0013 train_loss= 0.69266 train_acc= 0.53939 val_loss= 0.69273 val_acc= 0.50820 time= 0.01563
Epoch: 0014 train_loss= 0.69240 train_acc= 0.53636 val_loss= 0.69261 val_acc= 0.50820 time= 0.01563
Epoch: 0015 train_loss= 0.69251 train_acc= 0.53636 val_loss= 0.69254 val_acc= 0.50820 time= 0.00000
Epoch: 0016 train_loss= 0.69214 train_acc= 0.54545 val_loss= 0.69255 val_acc= 0.50820 time= 0.01563
Epoch: 0017 train_loss= 0.69187 train_acc= 0.55455 val_loss= 0.69252 val_acc= 0.52459 time= 0.01563
Epoch: 0018 train_loss= 0.69124 train_acc= 0.54848 val_loss= 0.69250 val_acc= 0.52459 time= 0.00000
Epoch: 0019 train_loss= 0.69161 train_acc= 0.56061 val_loss= 0.69239 val_acc= 0.52459 time= 0.01563
Epoch: 0020 train_loss= 0.69084 train_acc= 0.57273 val_loss= 0.69217 val_acc= 0.52459 time= 0.01563
Epoch: 0021 train_loss= 0.69144 train_acc= 0.54545 val_loss= 0.69202 val_acc= 0.52459 time= 0.01563
Epoch: 0022 train_loss= 0.69047 train_acc= 0.55455 val_loss= 0.69192 val_acc= 0.52459 time= 0.00000
Epoch: 0023 train_loss= 0.69060 train_acc= 0.55152 val_loss= 0.69194 val_acc= 0.52459 time= 0.01563
Epoch: 0024 train_loss= 0.69020 train_acc= 0.54848 val_loss= 0.69202 val_acc= 0.52459 time= 0.01563
Epoch: 0025 train_loss= 0.69073 train_acc= 0.56061 val_loss= 0.69223 val_acc= 0.52459 time= 0.00000
Epoch: 0026 train_loss= 0.68964 train_acc= 0.55758 val_loss= 0.69252 val_acc= 0.55738 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 0.69190 accuracy= 0.55738 time= 0.00000 
