Namespace(batch=128, dataset='CIFAR10', epochs=10, lr=0.05, net='MyLeNetMatNormalNoceil', postfix='_noCrop_k3', res_folder='res/', resume=False, scheduler='cosine', stoch=False, warmup_ep=5, warmup_mul=0)
==> Preparing data..
==> Building model..
==> Training model..
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Epoch: 0
Acc : 35.84 / 47.42
Loss : 1.73 / 1.45
Time: 247.71314711589366
---------

Epoch: 1
Acc : 53.34 / 60.40
Loss : 1.30 / 1.14
Time: 495.9195477189496
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Epoch: 2
Acc : 60.44 / 60.80
Loss : 1.11 / 1.08
Time: 743.7779585365206
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Epoch: 3
Acc : 65.48 / 65.42
Loss : 0.98 / 0.98
Time: 991.7510572513565
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Epoch: 4
Acc : 69.41 / 69.60
Loss : 0.87 / 0.86
Time: 1239.6623603589833
---------

Epoch: 5
Acc : 72.88 / 71.52
Loss : 0.78 / 0.80
Time: 1487.751311905682
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Epoch: 6
Acc : 75.24 / 73.46
Loss : 0.71 / 0.76
Time: 1735.6824928615242
---------

Epoch: 7
Acc : 77.73 / 74.89
Loss : 0.64 / 0.72
Time: 1983.8558715265244
---------

Epoch: 8
Acc : 79.20 / 77.12
Loss : 0.60 / 0.66
Time: 2231.74981981609
---------

Epoch: 9
Acc : 80.25 / 77.54
Loss : 0.57 / 0.65
Time: 2479.908313873224
---------
Best Acc : 77.54
Training time (min): 41.33180533062356
Log :" res/log/MyLeNetMatNormalNoceil-10epochs_noCrop_k3.json " saved !
Plot :" res/MyLeNetMatNormalNoceil-10epochs_noCrop_k3 " saved !

real	41m40.414s
user	32m50.590s
sys	14m15.666s