Epoch: 0001 train_loss= 0.70135 train_acc= 0.45584 val_loss= 0.69783 val_acc= 0.60656 time= 0.87008
Epoch: 0002 train_loss= 0.69810 train_acc= 0.53896 val_loss= 0.69517 val_acc= 0.65574 time= 0.01300
Epoch: 0003 train_loss= 0.69574 train_acc= 0.54935 val_loss= 0.69311 val_acc= 0.63934 time= 0.01200
Epoch: 0004 train_loss= 0.69374 train_acc= 0.55325 val_loss= 0.69156 val_acc= 0.63934 time= 0.01100
Epoch: 0005 train_loss= 0.69262 train_acc= 0.55325 val_loss= 0.69055 val_acc= 0.63934 time= 0.01200
Epoch: 0006 train_loss= 0.69161 train_acc= 0.56234 val_loss= 0.68995 val_acc= 0.63934 time= 0.01200
Epoch: 0007 train_loss= 0.69086 train_acc= 0.55325 val_loss= 0.68951 val_acc= 0.63934 time= 0.01343
Epoch: 0008 train_loss= 0.69021 train_acc= 0.57143 val_loss= 0.68919 val_acc= 0.63934 time= 0.01200
Epoch: 0009 train_loss= 0.69038 train_acc= 0.57013 val_loss= 0.68898 val_acc= 0.63934 time= 0.00700
Epoch: 0010 train_loss= 0.69016 train_acc= 0.58182 val_loss= 0.68865 val_acc= 0.63934 time= 0.01566
Epoch: 0011 train_loss= 0.68930 train_acc= 0.58961 val_loss= 0.68819 val_acc= 0.63934 time= 0.00000
Epoch: 0012 train_loss= 0.68825 train_acc= 0.59740 val_loss= 0.68750 val_acc= 0.63934 time= 0.01563
Epoch: 0013 train_loss= 0.68755 train_acc= 0.60390 val_loss= 0.68685 val_acc= 0.63934 time= 0.01563
Epoch: 0014 train_loss= 0.68795 train_acc= 0.58182 val_loss= 0.68630 val_acc= 0.63934 time= 0.01463
Epoch: 0015 train_loss= 0.68636 train_acc= 0.60909 val_loss= 0.68585 val_acc= 0.65574 time= 0.01300
Epoch: 0016 train_loss= 0.68666 train_acc= 0.61948 val_loss= 0.68542 val_acc= 0.67213 time= 0.01300
Epoch: 0017 train_loss= 0.68485 train_acc= 0.62078 val_loss= 0.68505 val_acc= 0.68852 time= 0.01100
Epoch: 0018 train_loss= 0.68403 train_acc= 0.60390 val_loss= 0.68488 val_acc= 0.68852 time= 0.01400
Epoch: 0019 train_loss= 0.68433 train_acc= 0.63247 val_loss= 0.68456 val_acc= 0.70492 time= 0.01100
Epoch: 0020 train_loss= 0.68295 train_acc= 0.62468 val_loss= 0.68454 val_acc= 0.73770 time= 0.01200
Epoch: 0021 train_loss= 0.68240 train_acc= 0.62857 val_loss= 0.68450 val_acc= 0.72131 time= 0.01300
Epoch: 0022 train_loss= 0.68057 train_acc= 0.68182 val_loss= 0.68402 val_acc= 0.72131 time= 0.00800
Epoch: 0023 train_loss= 0.68033 train_acc= 0.65455 val_loss= 0.68346 val_acc= 0.72131 time= 0.00000
Epoch: 0024 train_loss= 0.68116 train_acc= 0.63247 val_loss= 0.68296 val_acc= 0.72131 time= 0.01566
Epoch: 0025 train_loss= 0.68111 train_acc= 0.64026 val_loss= 0.68255 val_acc= 0.72131 time= 0.02039
Epoch: 0026 train_loss= 0.67749 train_acc= 0.65455 val_loss= 0.68227 val_acc= 0.72131 time= 0.01000
Epoch: 0027 train_loss= 0.67836 train_acc= 0.64675 val_loss= 0.68207 val_acc= 0.73770 time= 0.01279
Epoch: 0028 train_loss= 0.67684 train_acc= 0.66753 val_loss= 0.68183 val_acc= 0.73770 time= 0.01200
Epoch: 0029 train_loss= 0.67794 train_acc= 0.66753 val_loss= 0.68160 val_acc= 0.75410 time= 0.01214
Epoch: 0030 train_loss= 0.67201 train_acc= 0.68442 val_loss= 0.68149 val_acc= 0.77049 time= 0.01220
Epoch: 0031 train_loss= 0.67590 train_acc= 0.66104 val_loss= 0.68103 val_acc= 0.73770 time= 0.01422
Epoch: 0032 train_loss= 0.67317 train_acc= 0.70130 val_loss= 0.67954 val_acc= 0.73770 time= 0.01200
Epoch: 0033 train_loss= 0.67177 train_acc= 0.66753 val_loss= 0.67842 val_acc= 0.72131 time= 0.01109
Epoch: 0034 train_loss= 0.67261 train_acc= 0.67532 val_loss= 0.67727 val_acc= 0.72131 time= 0.01100
Epoch: 0035 train_loss= 0.66898 train_acc= 0.66364 val_loss= 0.67692 val_acc= 0.72131 time= 0.01100
Epoch: 0036 train_loss= 0.66715 train_acc= 0.67792 val_loss= 0.67722 val_acc= 0.72131 time= 0.01100
Epoch: 0037 train_loss= 0.66985 train_acc= 0.66104 val_loss= 0.67789 val_acc= 0.77049 time= 0.01100
Epoch: 0038 train_loss= 0.66453 train_acc= 0.70390 val_loss= 0.67804 val_acc= 0.70492 time= 0.01100
Epoch: 0039 train_loss= 0.66760 train_acc= 0.67273 val_loss= 0.67861 val_acc= 0.72131 time= 0.01200
Epoch: 0040 train_loss= 0.66710 train_acc= 0.68312 val_loss= 0.67802 val_acc= 0.72131 time= 0.01100
Epoch: 0041 train_loss= 0.66676 train_acc= 0.68182 val_loss= 0.67540 val_acc= 0.77049 time= 0.01207
Epoch: 0042 train_loss= 0.66329 train_acc= 0.68182 val_loss= 0.67394 val_acc= 0.72131 time= 0.00530
Epoch: 0043 train_loss= 0.66248 train_acc= 0.67403 val_loss= 0.67351 val_acc= 0.75410 time= 0.01501
Epoch: 0044 train_loss= 0.66060 train_acc= 0.71299 val_loss= 0.67316 val_acc= 0.75410 time= 0.00287
Epoch: 0045 train_loss= 0.66158 train_acc= 0.66494 val_loss= 0.67391 val_acc= 0.75410 time= 0.01562
Epoch: 0046 train_loss= 0.66097 train_acc= 0.71039 val_loss= 0.67256 val_acc= 0.75410 time= 0.00000
Epoch: 0047 train_loss= 0.65731 train_acc= 0.69351 val_loss= 0.67066 val_acc= 0.73770 time= 0.01563
Epoch: 0048 train_loss= 0.66249 train_acc= 0.68052 val_loss= 0.67025 val_acc= 0.73770 time= 0.01563
Epoch: 0049 train_loss= 0.66113 train_acc= 0.66234 val_loss= 0.67100 val_acc= 0.75410 time= 0.00000
Epoch: 0050 train_loss= 0.65677 train_acc= 0.67662 val_loss= 0.67220 val_acc= 0.72131 time= 0.02476
Epoch: 0051 train_loss= 0.65806 train_acc= 0.70649 val_loss= 0.67262 val_acc= 0.73770 time= 0.01300
Epoch: 0052 train_loss= 0.66022 train_acc= 0.66234 val_loss= 0.67331 val_acc= 0.73770 time= 0.01000
Early stopping...
Optimization Finished!
Test set results: cost= 0.66571 accuracy= 0.74590 time= 0.00600 
