Epoch: 0001 train_loss= 0.70098 train_acc= 0.48909 val_loss= 0.69799 val_acc= 0.52459 time= 0.46897
Epoch: 0002 train_loss= 0.69773 train_acc= 0.51455 val_loss= 0.69585 val_acc= 0.49180 time= 0.00000
Epoch: 0003 train_loss= 0.69554 train_acc= 0.52909 val_loss= 0.69437 val_acc= 0.49180 time= 0.01563
Epoch: 0004 train_loss= 0.69330 train_acc= 0.52727 val_loss= 0.69341 val_acc= 0.49180 time= 0.01563
Epoch: 0005 train_loss= 0.69285 train_acc= 0.53636 val_loss= 0.69278 val_acc= 0.50820 time= 0.00000
Epoch: 0006 train_loss= 0.69214 train_acc= 0.55455 val_loss= 0.69242 val_acc= 0.50820 time= 0.01563
Epoch: 0007 train_loss= 0.69155 train_acc= 0.56545 val_loss= 0.69222 val_acc= 0.54098 time= 0.01563
Epoch: 0008 train_loss= 0.69090 train_acc= 0.60000 val_loss= 0.69208 val_acc= 0.55738 time= 0.00000
Epoch: 0009 train_loss= 0.68999 train_acc= 0.60364 val_loss= 0.69196 val_acc= 0.52459 time= 0.01563
Epoch: 0010 train_loss= 0.68918 train_acc= 0.63273 val_loss= 0.69181 val_acc= 0.50820 time= 0.01563
Epoch: 0011 train_loss= 0.68938 train_acc= 0.66364 val_loss= 0.69163 val_acc= 0.54098 time= 0.00000
Epoch: 0012 train_loss= 0.68817 train_acc= 0.65091 val_loss= 0.69141 val_acc= 0.54098 time= 0.01562
Epoch: 0013 train_loss= 0.68821 train_acc= 0.66000 val_loss= 0.69113 val_acc= 0.52459 time= 0.00000
Epoch: 0014 train_loss= 0.68798 train_acc= 0.68000 val_loss= 0.69083 val_acc= 0.52459 time= 0.01563
Epoch: 0015 train_loss= 0.68686 train_acc= 0.65818 val_loss= 0.69051 val_acc= 0.52459 time= 0.01563
Epoch: 0016 train_loss= 0.68495 train_acc= 0.69455 val_loss= 0.69018 val_acc= 0.52459 time= 0.00000
Epoch: 0017 train_loss= 0.68666 train_acc= 0.66364 val_loss= 0.68984 val_acc= 0.54098 time= 0.01563
Epoch: 0018 train_loss= 0.68460 train_acc= 0.62000 val_loss= 0.68948 val_acc= 0.54098 time= 0.01563
Epoch: 0019 train_loss= 0.68381 train_acc= 0.64364 val_loss= 0.68912 val_acc= 0.54098 time= 0.01563
Epoch: 0020 train_loss= 0.68205 train_acc= 0.68909 val_loss= 0.68872 val_acc= 0.59016 time= 0.00000
Epoch: 0021 train_loss= 0.68056 train_acc= 0.67636 val_loss= 0.68835 val_acc= 0.62295 time= 0.01563
Epoch: 0022 train_loss= 0.68055 train_acc= 0.70364 val_loss= 0.68798 val_acc= 0.62295 time= 0.00000
Epoch: 0023 train_loss= 0.68023 train_acc= 0.67636 val_loss= 0.68760 val_acc= 0.62295 time= 0.01563
Epoch: 0024 train_loss= 0.68055 train_acc= 0.68000 val_loss= 0.68726 val_acc= 0.63934 time= 0.01563
Epoch: 0025 train_loss= 0.68012 train_acc= 0.67818 val_loss= 0.68696 val_acc= 0.62295 time= 0.00000
Epoch: 0026 train_loss= 0.67692 train_acc= 0.69091 val_loss= 0.68675 val_acc= 0.60656 time= 0.01563
Epoch: 0027 train_loss= 0.67567 train_acc= 0.66545 val_loss= 0.68644 val_acc= 0.60656 time= 0.02005
Epoch: 0028 train_loss= 0.67568 train_acc= 0.68182 val_loss= 0.68613 val_acc= 0.62295 time= 0.01200
Epoch: 0029 train_loss= 0.67868 train_acc= 0.69455 val_loss= 0.68578 val_acc= 0.59016 time= 0.01567
Epoch: 0030 train_loss= 0.67865 train_acc= 0.64000 val_loss= 0.68531 val_acc= 0.59016 time= 0.01563
Epoch: 0031 train_loss= 0.67165 train_acc= 0.68364 val_loss= 0.68474 val_acc= 0.60656 time= 0.01563
Epoch: 0032 train_loss= 0.67454 train_acc= 0.66545 val_loss= 0.68415 val_acc= 0.63934 time= 0.01563
Epoch: 0033 train_loss= 0.67297 train_acc= 0.68364 val_loss= 0.68365 val_acc= 0.59016 time= 0.01563
Epoch: 0034 train_loss= 0.67392 train_acc= 0.67273 val_loss= 0.68326 val_acc= 0.59016 time= 0.01563
Epoch: 0035 train_loss= 0.67281 train_acc= 0.64364 val_loss= 0.68284 val_acc= 0.60656 time= 0.01563
Epoch: 0036 train_loss= 0.66668 train_acc= 0.67091 val_loss= 0.68242 val_acc= 0.60656 time= 0.01563
Epoch: 0037 train_loss= 0.66800 train_acc= 0.67455 val_loss= 0.68199 val_acc= 0.65574 time= 0.00000
Epoch: 0038 train_loss= 0.66725 train_acc= 0.68545 val_loss= 0.68162 val_acc= 0.62295 time= 0.01713
Epoch: 0039 train_loss= 0.66478 train_acc= 0.68545 val_loss= 0.68133 val_acc= 0.62295 time= 0.01651
Epoch: 0040 train_loss= 0.66618 train_acc= 0.67818 val_loss= 0.68113 val_acc= 0.62295 time= 0.01651
Epoch: 0041 train_loss= 0.66546 train_acc= 0.68000 val_loss= 0.68108 val_acc= 0.62295 time= 0.01351
Epoch: 0042 train_loss= 0.66518 train_acc= 0.70000 val_loss= 0.68095 val_acc= 0.57377 time= 0.00301
Epoch: 0043 train_loss= 0.66431 train_acc= 0.67818 val_loss= 0.68045 val_acc= 0.62295 time= 0.01301
Epoch: 0044 train_loss= 0.66494 train_acc= 0.66182 val_loss= 0.67990 val_acc= 0.62295 time= 0.01563
Epoch: 0045 train_loss= 0.66568 train_acc= 0.65091 val_loss= 0.67965 val_acc= 0.63934 time= 0.01563
Epoch: 0046 train_loss= 0.66243 train_acc= 0.69818 val_loss= 0.67945 val_acc= 0.62295 time= 0.00000
Epoch: 0047 train_loss= 0.66000 train_acc= 0.65818 val_loss= 0.67912 val_acc= 0.60656 time= 0.01563
Epoch: 0048 train_loss= 0.65731 train_acc= 0.67818 val_loss= 0.67896 val_acc= 0.60656 time= 0.01563
Epoch: 0049 train_loss= 0.66235 train_acc= 0.62545 val_loss= 0.67815 val_acc= 0.63934 time= 0.00000
Epoch: 0050 train_loss= 0.66141 train_acc= 0.67818 val_loss= 0.67754 val_acc= 0.65574 time= 0.02037
Epoch: 0051 train_loss= 0.65613 train_acc= 0.68545 val_loss= 0.67719 val_acc= 0.65574 time= 0.01101
Epoch: 0052 train_loss= 0.65646 train_acc= 0.70182 val_loss= 0.67684 val_acc= 0.60656 time= 0.00000
Epoch: 0053 train_loss= 0.65489 train_acc= 0.68182 val_loss= 0.67693 val_acc= 0.63934 time= 0.01563
Epoch: 0054 train_loss= 0.66048 train_acc= 0.67636 val_loss= 0.67738 val_acc= 0.55738 time= 0.01563
Epoch: 0055 train_loss= 0.65233 train_acc= 0.68000 val_loss= 0.67782 val_acc= 0.54098 time= 0.00000
Epoch: 0056 train_loss= 0.65971 train_acc= 0.66000 val_loss= 0.67701 val_acc= 0.55738 time= 0.01562
Epoch: 0057 train_loss= 0.65373 train_acc= 0.66545 val_loss= 0.67537 val_acc= 0.63934 time= 0.01563
Epoch: 0058 train_loss= 0.65270 train_acc= 0.67091 val_loss= 0.67472 val_acc= 0.62295 time= 0.00000
Epoch: 0059 train_loss= 0.66035 train_acc= 0.64182 val_loss= 0.67449 val_acc= 0.63934 time= 0.01563
Epoch: 0060 train_loss= 0.64899 train_acc= 0.69818 val_loss= 0.67406 val_acc= 0.63934 time= 0.01563
Epoch: 0061 train_loss= 0.65513 train_acc= 0.68182 val_loss= 0.67378 val_acc= 0.62295 time= 0.00000
Epoch: 0062 train_loss= 0.65459 train_acc= 0.67273 val_loss= 0.67374 val_acc= 0.63934 time= 0.01563
Epoch: 0063 train_loss= 0.64396 train_acc= 0.68364 val_loss= 0.67399 val_acc= 0.63934 time= 0.01563
Epoch: 0064 train_loss= 0.63969 train_acc= 0.70909 val_loss= 0.67428 val_acc= 0.59016 time= 0.00000
Epoch: 0065 train_loss= 0.65051 train_acc= 0.70364 val_loss= 0.67475 val_acc= 0.57377 time= 0.01563
Epoch: 0066 train_loss= 0.65169 train_acc= 0.65636 val_loss= 0.67375 val_acc= 0.59016 time= 0.00000
Epoch: 0067 train_loss= 0.65223 train_acc= 0.67636 val_loss= 0.67259 val_acc= 0.63934 time= 0.01563
Epoch: 0068 train_loss= 0.65276 train_acc= 0.69091 val_loss= 0.67202 val_acc= 0.62295 time= 0.01563
Epoch: 0069 train_loss= 0.64206 train_acc= 0.71455 val_loss= 0.67181 val_acc= 0.63934 time= 0.00000
Epoch: 0070 train_loss= 0.64553 train_acc= 0.68000 val_loss= 0.67161 val_acc= 0.65574 time= 0.01563
Epoch: 0071 train_loss= 0.65350 train_acc= 0.69273 val_loss= 0.67144 val_acc= 0.65574 time= 0.01563
Epoch: 0072 train_loss= 0.64569 train_acc= 0.68727 val_loss= 0.67150 val_acc= 0.65574 time= 0.00483
Epoch: 0073 train_loss= 0.64355 train_acc= 0.66909 val_loss= 0.67200 val_acc= 0.63934 time= 0.01100
Epoch: 0074 train_loss= 0.64002 train_acc= 0.68545 val_loss= 0.67459 val_acc= 0.54098 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.67008 accuracy= 0.64754 time= 0.01563 
