dataset = CIFAR10
model = MLPModel(depth=6,width=5120,identity_val=10.0,scalar=True)
loss = radius_mix2(lam0=0.1,lam_end=0.0005)
p_start = 8.0
p_end = 1000.0
eps_train = 0.09411
eps_test = 0.03137
eps_smooth = 0
epochs = 0,0,100,1250,1300
decays = None
batch_size = 512
lr = 0.03
scalar_lr = 0.006
beta1 = 0.9
beta2 = 0.99
epsilon = 1e-10
start_epoch = 0
checkpoint = None
gpu = 5
dist_url = tcp://localhost:23456
world_size = 1
rank = 0
print_freq = 200
result_dir = result
filter_name = 
seed = 2021
visualize = True
Compose(
    RandomCrop(size=(32, 32), padding=3)
    RandomHorizontalFlip(p=0.5)
    ToTensor()
    Normalize(mean=[0.4914 0.4822 0.4465], std=[0.2009, 0.2009, 0.2009])
)
MLPModel(
  (fc_dist): BoundSequential(
    (0): NormDist(
      in_features=3072, out_features=5120, bias=False
      (mean_shift): MeanShift(5120, affine=False)
    )
    (1): NormDist(
      in_features=5120, out_features=5120, bias=False
      (mean_shift): MeanShift(5120, affine=False)
    )
    (2): NormDist(
      in_features=5120, out_features=5120, bias=False
      (mean_shift): MeanShift(5120, affine=False)
    )
    (3): NormDist(
      in_features=5120, out_features=5120, bias=False
      (mean_shift): MeanShift(5120, affine=False)
    )
    (4): NormDist(
      in_features=5120, out_features=5120, bias=False
      (mean_shift): MeanShift(5120, affine=False)
    )
    (5): NormDist(in_features=5120, out_features=10, bias=True)
  )
  (fc_linear): BoundSequential()
)
number of params:  120637451
scalar:  1.0
Epoch 0:  train loss 1.0759   train acc 0.1837   worst 0.0987   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 17.61
scalar:  0.8808
Epoch 1:  train loss 0.9446   train acc 0.3078   worst 0.1864   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.93
scalar:  0.8878
Epoch 2:  train loss 0.8929   train acc 0.3416   worst 0.2365   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.47
scalar:  0.8054
Epoch 3:  train loss 0.8619   train acc 0.3610   worst 0.2669   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.27
scalar:  0.7645
Epoch 4:  train loss 0.8272   train acc 0.3969   worst 0.2881   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.46
Epoch 4:  test acc 0.4286   time 1.07
Calculating metrics for L_infinity dist model on training set
Epoch 4:  clean acc 0.1409   certified acc 0.1057
Calculating metrics for L_infinity dist model on test set
Epoch 4:  clean acc 0.1436   certified acc 0.1104
scalar:  0.7155
Epoch 5:  train loss 0.8023   train acc 0.4118   worst 0.3126   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.00
scalar:  0.6939
Epoch 6:  train loss 0.7837   train acc 0.4265   worst 0.3279   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.70
scalar:  0.6892
Epoch 7:  train loss 0.7684   train acc 0.4367   worst 0.3424   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.72
scalar:  0.6974
Epoch 8:  train loss 0.7569   train acc 0.4442   worst 0.3534   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.74
scalar:  0.6611
Epoch 9:  train loss 0.7503   train acc 0.4502   worst 0.3588   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.56
Epoch 9:  test acc 0.4738   time 1.10
Calculating metrics for L_infinity dist model on training set
Epoch 9:  clean acc 0.1560   certified acc 0.1261
Calculating metrics for L_infinity dist model on test set
Epoch 9:  clean acc 0.1591   certified acc 0.1294
scalar:  0.629
Epoch 10:  train loss 0.7404   train acc 0.4562   worst 0.3682   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.71
scalar:  0.6372
Epoch 11:  train loss 0.7334   train acc 0.4621   worst 0.3730   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.62
scalar:  0.6441
Epoch 12:  train loss 0.7232   train acc 0.4717   worst 0.3797   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.82
scalar:  0.6491
Epoch 13:  train loss 0.7169   train acc 0.4736   worst 0.3882   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.71
scalar:  0.6545
Epoch 14:  train loss 0.7098   train acc 0.4845   worst 0.3896   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.60
Epoch 14:  test acc 0.5056   time 1.12
Calculating metrics for L_infinity dist model on training set
Epoch 14:  clean acc 0.1353   certified acc 0.0973
Calculating metrics for L_infinity dist model on test set
Epoch 14:  clean acc 0.1391   certified acc 0.0980
scalar:  0.6659
Epoch 15:  train loss 0.7072   train acc 0.4863   worst 0.3924   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.31
scalar:  0.6296
Epoch 16:  train loss 0.6963   train acc 0.4972   worst 0.3977   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.70
scalar:  0.6485
Epoch 17:  train loss 0.6891   train acc 0.5035   worst 0.4036   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.89
scalar:  0.6482
Epoch 18:  train loss 0.6861   train acc 0.5062   worst 0.4058   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.12
scalar:  0.6565
Epoch 19:  train loss 0.6774   train acc 0.5137   worst 0.4118   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.51
Epoch 19:  test acc 0.5312   time 1.09
Calculating metrics for L_infinity dist model on training set
Epoch 19:  clean acc 0.1698   certified acc 0.0844
Calculating metrics for L_infinity dist model on test set
Epoch 19:  clean acc 0.1705   certified acc 0.0892
scalar:  0.6561
Epoch 20:  train loss 0.6763   train acc 0.5157   worst 0.4126   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.68
scalar:  0.6334
Epoch 21:  train loss 0.6683   train acc 0.5217   worst 0.4193   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.38
scalar:  0.6406
Epoch 22:  train loss 0.6631   train acc 0.5273   worst 0.4245   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.62
scalar:  0.6586
Epoch 23:  train loss 0.6592   train acc 0.5290   worst 0.4267   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.58
scalar:  0.6629
Epoch 24:  train loss 0.6563   train acc 0.5337   worst 0.4276   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.44
Epoch 24:  test acc 0.5430   time 1.13
Calculating metrics for L_infinity dist model on training set
Epoch 24:  clean acc 0.1809   certified acc 0.0794
Calculating metrics for L_infinity dist model on test set
Epoch 24:  clean acc 0.1840   certified acc 0.0828
scalar:  0.6358
Epoch 25:  train loss 0.6527   train acc 0.5347   worst 0.4313   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.24
scalar:  0.6498
Epoch 26:  train loss 0.6485   train acc 0.5391   worst 0.4344   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.49
scalar:  0.6392
Epoch 27:  train loss 0.6428   train acc 0.5446   worst 0.4377   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.40
scalar:  0.6205
Epoch 28:  train loss 0.6408   train acc 0.5444   worst 0.4408   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.62
scalar:  0.6335
Epoch 29:  train loss 0.6356   train acc 0.5498   worst 0.4457   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.86
Epoch 29:  test acc 0.5496   time 1.15
Calculating metrics for L_infinity dist model on training set
Epoch 29:  clean acc 0.1849   certified acc 0.0744
Calculating metrics for L_infinity dist model on test set
Epoch 29:  clean acc 0.1908   certified acc 0.0765
scalar:  0.6356
Epoch 30:  train loss 0.6354   train acc 0.5495   worst 0.4465   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.20
scalar:  0.6625
Epoch 31:  train loss 0.6310   train acc 0.5528   worst 0.4499   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.21
scalar:  0.6293
Epoch 32:  train loss 0.6268   train acc 0.5566   worst 0.4516   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 20.26
scalar:  0.6432
Epoch 33:  train loss 0.6236   train acc 0.5603   worst 0.4544   lr 0.0300   p 8.00   eps 0.4684   mix 0.1000   time 19.82
scalar:  0.6548
Epoch 34:  train loss 0.6228   train acc 0.5590   worst 0.4561   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 20.28
Epoch 34:  test acc 0.5635   time 1.12
Calculating metrics for L_infinity dist model on training set
Epoch 34:  clean acc 0.1615   certified acc 0.0717
Calculating metrics for L_infinity dist model on test set
Epoch 34:  clean acc 0.1632   certified acc 0.0749
scalar:  0.6357
Epoch 35:  train loss 0.6182   train acc 0.5645   worst 0.4590   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 19.43
scalar:  0.6557
Epoch 36:  train loss 0.6165   train acc 0.5661   worst 0.4594   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 19.15
scalar:  0.674
Epoch 37:  train loss 0.6153   train acc 0.5661   worst 0.4609   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 19.69
scalar:  0.6552
Epoch 38:  train loss 0.6097   train acc 0.5701   worst 0.4648   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 20.10
scalar:  0.6699
Epoch 39:  train loss 0.6107   train acc 0.5713   worst 0.4643   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 19.58
Epoch 39:  test acc 0.5677   time 1.15
Calculating metrics for L_infinity dist model on training set
Epoch 39:  clean acc 0.1718   certified acc 0.0745
Calculating metrics for L_infinity dist model on test set
Epoch 39:  clean acc 0.1755   certified acc 0.0745
scalar:  0.6438
Epoch 40:  train loss 0.6069   train acc 0.5727   worst 0.4677   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 19.88
scalar:  0.6718
Epoch 41:  train loss 0.6037   train acc 0.5758   worst 0.4696   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 19.55
scalar:  0.6582
Epoch 42:  train loss 0.6065   train acc 0.5730   worst 0.4676   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 19.89
scalar:  0.6546
Epoch 43:  train loss 0.5991   train acc 0.5788   worst 0.4741   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 21.09
scalar:  0.6429
Epoch 44:  train loss 0.6002   train acc 0.5771   worst 0.4732   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 20.12
Epoch 44:  test acc 0.5743   time 1.10
Calculating metrics for L_infinity dist model on training set
Epoch 44:  clean acc 0.1564   certified acc 0.0462
Calculating metrics for L_infinity dist model on test set
Epoch 44:  clean acc 0.1662   certified acc 0.0501
scalar:  0.6608
Epoch 45:  train loss 0.5958   train acc 0.5813   worst 0.4769   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 19.07
scalar:  0.6706
Epoch 46:  train loss 0.5997   train acc 0.5777   worst 0.4734   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 20.07
scalar:  0.6545
Epoch 47:  train loss 0.5929   train acc 0.5826   worst 0.4799   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 20.57
scalar:  0.6469
Epoch 48:  train loss 0.5921   train acc 0.5837   worst 0.4796   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 20.09
scalar:  0.6606
Epoch 49:  train loss 0.5943   train acc 0.5837   worst 0.4772   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 19.77
Epoch 49:  test acc 0.5841   time 1.11
Calculating metrics for L_infinity dist model on training set
Epoch 49:  clean acc 0.1666   certified acc 0.0476
Calculating metrics for L_infinity dist model on test set
Epoch 49:  clean acc 0.1705   certified acc 0.0524
scalar:  0.6602
Epoch 50:  train loss 0.5918   train acc 0.5849   worst 0.4795   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 19.40
scalar:  0.669
Epoch 51:  train loss 0.5895   train acc 0.5857   worst 0.4822   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 19.50
scalar:  0.6538
Epoch 52:  train loss 0.5884   train acc 0.5886   worst 0.4797   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 20.01
scalar:  0.6702
Epoch 53:  train loss 0.5823   train acc 0.5920   worst 0.4878   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 20.24
scalar:  0.6841
Epoch 54:  train loss 0.5813   train acc 0.5926   worst 0.4883   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 21.17
Epoch 54:  test acc 0.5823   time 1.08
Calculating metrics for L_infinity dist model on training set
Epoch 54:  clean acc 0.1582   certified acc 0.0581
Calculating metrics for L_infinity dist model on test set
Epoch 54:  clean acc 0.1597   certified acc 0.0600
scalar:  0.6885
Epoch 55:  train loss 0.5804   train acc 0.5946   worst 0.4866   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 20.20
scalar:  0.6914
Epoch 56:  train loss 0.5799   train acc 0.5942   worst 0.4896   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 20.30
scalar:  0.6885
Epoch 57:  train loss 0.5809   train acc 0.5927   worst 0.4880   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 20.21
scalar:  0.6795
Epoch 58:  train loss 0.5758   train acc 0.5986   worst 0.4921   lr 0.0299   p 8.00   eps 0.4684   mix 0.1000   time 20.70
scalar:  0.679
Epoch 59:  train loss 0.5748   train acc 0.5990   worst 0.4920   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 19.50
Epoch 59:  test acc 0.5928   time 1.11
Calculating metrics for L_infinity dist model on training set
Epoch 59:  clean acc 0.1414   certified acc 0.0396
Calculating metrics for L_infinity dist model on test set
Epoch 59:  clean acc 0.1451   certified acc 0.0432
scalar:  0.6688
Epoch 60:  train loss 0.5751   train acc 0.5991   worst 0.4903   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 20.36
scalar:  0.6926
Epoch 61:  train loss 0.5743   train acc 0.5983   worst 0.4927   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 20.23
scalar:  0.7056
Epoch 62:  train loss 0.5703   train acc 0.6032   worst 0.4956   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 20.19
scalar:  0.674
Epoch 63:  train loss 0.5697   train acc 0.6040   worst 0.4939   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 19.02
scalar:  0.7261
Epoch 64:  train loss 0.5665   train acc 0.6070   worst 0.4966   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 18.90
Epoch 64:  test acc 0.5957   time 1.11
Calculating metrics for L_infinity dist model on training set
Epoch 64:  clean acc 0.1371   certified acc 0.0421
Calculating metrics for L_infinity dist model on test set
Epoch 64:  clean acc 0.1382   certified acc 0.0447
scalar:  0.698
Epoch 65:  train loss 0.5668   train acc 0.6069   worst 0.4983   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 18.01
scalar:  0.7162
Epoch 66:  train loss 0.5663   train acc 0.6071   worst 0.4969   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 18.31
scalar:  0.7131
Epoch 67:  train loss 0.5642   train acc 0.6090   worst 0.4985   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 19.99
scalar:  0.7239
Epoch 68:  train loss 0.5637   train acc 0.6099   worst 0.4989   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 20.15
scalar:  0.6988
Epoch 69:  train loss 0.5626   train acc 0.6101   worst 0.4996   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 20.14
Epoch 69:  test acc 0.5989   time 1.09
Calculating metrics for L_infinity dist model on training set
Epoch 69:  clean acc 0.1049   certified acc 0.0629
Calculating metrics for L_infinity dist model on test set
Epoch 69:  clean acc 0.1063   certified acc 0.0646
scalar:  0.7195
Epoch 70:  train loss 0.5618   train acc 0.6099   worst 0.5006   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 20.61
scalar:  0.7076
Epoch 71:  train loss 0.5613   train acc 0.6118   worst 0.5005   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 19.84
scalar:  0.723
Epoch 72:  train loss 0.5616   train acc 0.6104   worst 0.5004   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 19.77
scalar:  0.6874
Epoch 73:  train loss 0.5580   train acc 0.6133   worst 0.5040   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 18.92
scalar:  0.7412
Epoch 74:  train loss 0.5569   train acc 0.6157   worst 0.5023   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 19.79
Epoch 74:  test acc 0.5954   time 1.14
Calculating metrics for L_infinity dist model on training set
Epoch 74:  clean acc 0.1181   certified acc 0.0581
Calculating metrics for L_infinity dist model on test set
Epoch 74:  clean acc 0.1179   certified acc 0.0581
scalar:  0.71
Epoch 75:  train loss 0.5571   train acc 0.6141   worst 0.5035   lr 0.0298   p 8.00   eps 0.4684   mix 0.1000   time 20.11
scalar:  0.6985
Epoch 76:  train loss 0.5598   train acc 0.6126   worst 0.5012   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 19.93
scalar:  0.7197
Epoch 77:  train loss 0.5550   train acc 0.6165   worst 0.5061   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 20.68
scalar:  0.7078
Epoch 78:  train loss 0.5554   train acc 0.6161   worst 0.5052   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 19.89
scalar:  0.7092
Epoch 79:  train loss 0.5531   train acc 0.6190   worst 0.5075   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 20.05
Epoch 79:  test acc 0.6030   time 1.14
Calculating metrics for L_infinity dist model on training set
Epoch 79:  clean acc 0.1218   certified acc 0.0574
Calculating metrics for L_infinity dist model on test set
Epoch 79:  clean acc 0.1211   certified acc 0.0566
scalar:  0.7278
Epoch 80:  train loss 0.5520   train acc 0.6190   worst 0.5069   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 20.16
scalar:  0.7292
Epoch 81:  train loss 0.5513   train acc 0.6199   worst 0.5087   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 19.92
scalar:  0.7388
Epoch 82:  train loss 0.5485   train acc 0.6222   worst 0.5094   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 20.16
scalar:  0.7329
Epoch 83:  train loss 0.5486   train acc 0.6202   worst 0.5107   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 20.83
scalar:  0.7302
Epoch 84:  train loss 0.5500   train acc 0.6202   worst 0.5094   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 20.70
Epoch 84:  test acc 0.5997   time 1.09
Calculating metrics for L_infinity dist model on training set
Epoch 84:  clean acc 0.1064   certified acc 0.0485
Calculating metrics for L_infinity dist model on test set
Epoch 84:  clean acc 0.1046   certified acc 0.0485
scalar:  0.7298
Epoch 85:  train loss 0.5489   train acc 0.6219   worst 0.5085   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 20.36
scalar:  0.74
Epoch 86:  train loss 0.5497   train acc 0.6214   worst 0.5077   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 20.23
scalar:  0.7267
Epoch 87:  train loss 0.5474   train acc 0.6227   worst 0.5103   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 21.13
scalar:  0.729
Epoch 88:  train loss 0.5439   train acc 0.6264   worst 0.5128   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 20.07
scalar:  0.7717
Epoch 89:  train loss 0.5436   train acc 0.6261   worst 0.5131   lr 0.0297   p 8.00   eps 0.4684   mix 0.1000   time 20.36
Epoch 89:  test acc 0.6051   time 1.08
Calculating metrics for L_infinity dist model on training set
Epoch 89:  clean acc 0.1423   certified acc 0.0543
Calculating metrics for L_infinity dist model on test set
Epoch 89:  clean acc 0.1401   certified acc 0.0507
scalar:  0.754
Epoch 90:  train loss 0.5442   train acc 0.6265   worst 0.5106   lr 0.0296   p 8.00   eps 0.4684   mix 0.1000   time 20.45
scalar:  0.7827
Epoch 91:  train loss 0.5404   train acc 0.6290   worst 0.5150   lr 0.0296   p 8.00   eps 0.4684   mix 0.1000   time 20.25
scalar:  0.7564
Epoch 92:  train loss 0.5438   train acc 0.6256   worst 0.5121   lr 0.0296   p 8.00   eps 0.4684   mix 0.1000   time 20.31
scalar:  0.7368
Epoch 93:  train loss 0.5415   train acc 0.6278   worst 0.5144   lr 0.0296   p 8.00   eps 0.4684   mix 0.1000   time 20.14
scalar:  0.752
Epoch 94:  train loss 0.5408   train acc 0.6289   worst 0.5135   lr 0.0296   p 8.00   eps 0.4684   mix 0.1000   time 19.99
Epoch 94:  test acc 0.6101   time 1.11
Calculating metrics for L_infinity dist model on training set
Epoch 94:  clean acc 0.1403   certified acc 0.0570
Calculating metrics for L_infinity dist model on test set
Epoch 94:  clean acc 0.1390   certified acc 0.0571
scalar:  0.7889
Epoch 95:  train loss 0.5391   train acc 0.6293   worst 0.5174   lr 0.0296   p 8.00   eps 0.4684   mix 0.1000   time 19.80
scalar:  0.7228
Epoch 96:  train loss 0.5401   train acc 0.6307   worst 0.5159   lr 0.0296   p 8.00   eps 0.4684   mix 0.1000   time 20.14
scalar:  0.7524
Epoch 97:  train loss 0.5402   train acc 0.6295   worst 0.5153   lr 0.0296   p 8.00   eps 0.4684   mix 0.1000   time 18.84
scalar:  0.7499
Epoch 98:  train loss 0.5360   train acc 0.6343   worst 0.5164   lr 0.0296   p 8.00   eps 0.4684   mix 0.1000   time 18.68
scalar:  0.7724
Epoch 99:  train loss 0.5358   train acc 0.6335   worst 0.5180   lr 0.0296   p 8.00   eps 0.4684   mix 0.1000   time 18.64
Epoch 99:  test acc 0.6116   time 1.12
Calculating metrics for L_infinity dist model on training set
Epoch 99:  clean acc 0.1540   certified acc 0.0370
Calculating metrics for L_infinity dist model on test set
Epoch 99:  clean acc 0.1571   certified acc 0.0358
scalar:  0.7738
Epoch 100:  train loss 0.5370   train acc 0.6324   worst 0.5159   lr 0.0296   p 8.00   eps 0.4684   mix 0.1000   time 25.37
scalar:  0.8002
Epoch 101:  train loss 0.5335   train acc 0.6362   worst 0.5179   lr 0.0296   p 8.03   eps 0.4684   mix 0.0995   time 25.42
scalar:  0.7882
Epoch 102:  train loss 0.5371   train acc 0.6328   worst 0.5138   lr 0.0295   p 8.07   eps 0.4684   mix 0.0991   time 26.05
scalar:  0.7852
Epoch 103:  train loss 0.5340   train acc 0.6360   worst 0.5142   lr 0.0295   p 8.10   eps 0.4684   mix 0.0986   time 26.05
scalar:  0.8085
Epoch 104:  train loss 0.5353   train acc 0.6367   worst 0.5115   lr 0.0295   p 8.14   eps 0.4684   mix 0.0982   time 25.93
Epoch 104:  test acc 0.6105   time 2.06
Calculating metrics for L_infinity dist model on training set
Epoch 104:  clean acc 0.1387   certified acc 0.0321
Calculating metrics for L_infinity dist model on test set
Epoch 104:  clean acc 0.1452   certified acc 0.0308
scalar:  0.8035
Epoch 105:  train loss 0.5331   train acc 0.6366   worst 0.5122   lr 0.0295   p 8.17   eps 0.4684   mix 0.0977   time 25.37
scalar:  0.8251
Epoch 106:  train loss 0.5328   train acc 0.6387   worst 0.5100   lr 0.0295   p 8.20   eps 0.4684   mix 0.0973   time 26.10
scalar:  0.837
Epoch 107:  train loss 0.5333   train acc 0.6373   worst 0.5094   lr 0.0295   p 8.24   eps 0.4684   mix 0.0968   time 25.93
scalar:  0.8346
Epoch 108:  train loss 0.5329   train acc 0.6381   worst 0.5079   lr 0.0295   p 8.27   eps 0.4684   mix 0.0964   time 25.75
scalar:  0.8651
Epoch 109:  train loss 0.5310   train acc 0.6405   worst 0.5079   lr 0.0295   p 8.31   eps 0.4684   mix 0.0959   time 26.18
Epoch 109:  test acc 0.6101   time 2.05
Calculating metrics for L_infinity dist model on training set
Epoch 109:  clean acc 0.1254   certified acc 0.0448
Calculating metrics for L_infinity dist model on test set
Epoch 109:  clean acc 0.1245   certified acc 0.0467
scalar:  0.8772
Epoch 110:  train loss 0.5276   train acc 0.6427   worst 0.5099   lr 0.0295   p 8.34   eps 0.4684   mix 0.0955   time 25.58
scalar:  0.8762
Epoch 111:  train loss 0.5313   train acc 0.6388   worst 0.5060   lr 0.0295   p 8.38   eps 0.4684   mix 0.0951   time 25.87
scalar:  0.8684
Epoch 112:  train loss 0.5319   train acc 0.6388   worst 0.5044   lr 0.0295   p 8.41   eps 0.4684   mix 0.0946   time 26.03
scalar:  0.8672
Epoch 113:  train loss 0.5318   train acc 0.6397   worst 0.5035   lr 0.0294   p 8.45   eps 0.4684   mix 0.0942   time 25.92
scalar:  0.8854
Epoch 114:  train loss 0.5293   train acc 0.6433   worst 0.5034   lr 0.0294   p 8.48   eps 0.4684   mix 0.0938   time 25.68
Epoch 114:  test acc 0.6124   time 2.07
Calculating metrics for L_infinity dist model on training set
Epoch 114:  clean acc 0.1045   certified acc 0.0924
Calculating metrics for L_infinity dist model on test set
Epoch 114:  clean acc 0.1042   certified acc 0.0931
scalar:  0.9093
Epoch 115:  train loss 0.5290   train acc 0.6423   worst 0.5029   lr 0.0294   p 8.52   eps 0.4684   mix 0.0933   time 25.40
scalar:  0.9316
Epoch 116:  train loss 0.5299   train acc 0.6414   worst 0.5002   lr 0.0294   p 8.56   eps 0.4684   mix 0.0929   time 26.31
scalar:  0.9343
Epoch 117:  train loss 0.5270   train acc 0.6438   worst 0.5023   lr 0.0294   p 8.59   eps 0.4684   mix 0.0925   time 26.04
scalar:  0.9419
Epoch 118:  train loss 0.5276   train acc 0.6439   worst 0.4990   lr 0.0294   p 8.63   eps 0.4684   mix 0.0920   time 25.87
scalar:  0.9535
Epoch 119:  train loss 0.5268   train acc 0.6462   worst 0.4975   lr 0.0294   p 8.66   eps 0.4684   mix 0.0916   time 25.99
Epoch 119:  test acc 0.6141   time 2.06
Calculating metrics for L_infinity dist model on training set
Epoch 119:  clean acc 0.1003   certified acc 0.0710
Calculating metrics for L_infinity dist model on test set
Epoch 119:  clean acc 0.1004   certified acc 0.0696
scalar:  0.9734
Epoch 120:  train loss 0.5265   train acc 0.6445   worst 0.4985   lr 0.0294   p 8.70   eps 0.4684   mix 0.0912   time 25.74
scalar:  0.9784
Epoch 121:  train loss 0.5265   train acc 0.6447   worst 0.4977   lr 0.0294   p 8.74   eps 0.4684   mix 0.0908   time 25.55
scalar:  1.0001
Epoch 122:  train loss 0.5287   train acc 0.6439   worst 0.4940   lr 0.0294   p 8.77   eps 0.4684   mix 0.0904   time 26.31
scalar:  0.986
Epoch 123:  train loss 0.5294   train acc 0.6427   worst 0.4928   lr 0.0293   p 8.81   eps 0.4684   mix 0.0899   time 25.82
scalar:  0.9933
Epoch 124:  train loss 0.5276   train acc 0.6469   worst 0.4908   lr 0.0293   p 8.85   eps 0.4684   mix 0.0895   time 25.89
Epoch 124:  test acc 0.6185   time 2.02
Calculating metrics for L_infinity dist model on training set
Epoch 124:  clean acc 0.1026   certified acc 0.0893
Calculating metrics for L_infinity dist model on test set
Epoch 124:  clean acc 0.1027   certified acc 0.0878
scalar:  1.0033
Epoch 125:  train loss 0.5267   train acc 0.6466   worst 0.4908   lr 0.0293   p 8.89   eps 0.4684   mix 0.0891   time 25.66
scalar:  1.0145
Epoch 126:  train loss 0.5268   train acc 0.6451   worst 0.4917   lr 0.0293   p 8.92   eps 0.4684   mix 0.0887   time 26.45
scalar:  1.0291
Epoch 127:  train loss 0.5273   train acc 0.6462   worst 0.4890   lr 0.0293   p 8.96   eps 0.4684   mix 0.0883   time 25.91
scalar:  1.0589
Epoch 128:  train loss 0.5253   train acc 0.6479   worst 0.4902   lr 0.0293   p 9.00   eps 0.4684   mix 0.0879   time 26.03
scalar:  1.0275
Epoch 129:  train loss 0.5273   train acc 0.6480   worst 0.4869   lr 0.0293   p 9.04   eps 0.4684   mix 0.0875   time 25.95
Epoch 129:  test acc 0.6144   time 2.08
Calculating metrics for L_infinity dist model on training set
Epoch 129:  clean acc 0.1007   certified acc 0.0900
Calculating metrics for L_infinity dist model on test set
Epoch 129:  clean acc 0.0986   certified acc 0.0886
scalar:  1.0795
Epoch 130:  train loss 0.5248   train acc 0.6477   worst 0.4879   lr 0.0293   p 9.07   eps 0.4684   mix 0.0871   time 25.54
scalar:  1.0693
Epoch 131:  train loss 0.5264   train acc 0.6467   worst 0.4857   lr 0.0293   p 9.11   eps 0.4684   mix 0.0867   time 26.23
scalar:  1.0717
Epoch 132:  train loss 0.5258   train acc 0.6488   worst 0.4834   lr 0.0292   p 9.15   eps 0.4684   mix 0.0863   time 25.80
scalar:  1.1014
Epoch 133:  train loss 0.5262   train acc 0.6482   worst 0.4820   lr 0.0292   p 9.19   eps 0.4684   mix 0.0859   time 25.78
scalar:  1.1245
Epoch 134:  train loss 0.5250   train acc 0.6490   worst 0.4834   lr 0.0292   p 9.23   eps 0.4684   mix 0.0855   time 25.74
Epoch 134:  test acc 0.6111   time 2.07
Calculating metrics for L_infinity dist model on training set
Epoch 134:  clean acc 0.1003   certified acc 0.0959
Calculating metrics for L_infinity dist model on test set
Epoch 134:  clean acc 0.0995   certified acc 0.0944
scalar:  1.1018
Epoch 135:  train loss 0.5260   train acc 0.6482   worst 0.4805   lr 0.0292   p 9.27   eps 0.4684   mix 0.0851   time 25.80
scalar:  1.1356
Epoch 136:  train loss 0.5257   train acc 0.6479   worst 0.4797   lr 0.0292   p 9.31   eps 0.4684   mix 0.0847   time 25.96
scalar:  1.1428
Epoch 137:  train loss 0.5273   train acc 0.6476   worst 0.4787   lr 0.0292   p 9.34   eps 0.4684   mix 0.0843   time 26.24
scalar:  1.1631
Epoch 138:  train loss 0.5267   train acc 0.6485   worst 0.4770   lr 0.0292   p 9.38   eps 0.4684   mix 0.0839   time 25.71
scalar:  1.1394
Epoch 139:  train loss 0.5253   train acc 0.6488   worst 0.4765   lr 0.0292   p 9.42   eps 0.4684   mix 0.0836   time 26.32
Epoch 139:  test acc 0.6175   time 2.09
Calculating metrics for L_infinity dist model on training set
Epoch 139:  clean acc 0.1000   certified acc 0.0999
Calculating metrics for L_infinity dist model on test set
Epoch 139:  clean acc 0.1000   certified acc 0.1000
scalar:  1.148
Epoch 140:  train loss 0.5273   train acc 0.6483   worst 0.4725   lr 0.0291   p 9.46   eps 0.4684   mix 0.0832   time 25.55
scalar:  1.1342
Epoch 141:  train loss 0.5270   train acc 0.6494   worst 0.4728   lr 0.0291   p 9.50   eps 0.4684   mix 0.0828   time 26.10
scalar:  1.2
Epoch 142:  train loss 0.5260   train acc 0.6497   worst 0.4726   lr 0.0291   p 9.54   eps 0.4684   mix 0.0824   time 25.82
scalar:  1.2016
Epoch 143:  train loss 0.5247   train acc 0.6507   worst 0.4723   lr 0.0291   p 9.58   eps 0.4684   mix 0.0820   time 25.14
scalar:  1.2093
Epoch 144:  train loss 0.5258   train acc 0.6504   worst 0.4700   lr 0.0291   p 9.62   eps 0.4684   mix 0.0817   time 25.99
Epoch 144:  test acc 0.6171   time 2.05
Calculating metrics for L_infinity dist model on training set
Epoch 144:  clean acc 0.1031   certified acc 0.0965
Calculating metrics for L_infinity dist model on test set
Epoch 144:  clean acc 0.1029   certified acc 0.0962
scalar:  1.206
Epoch 145:  train loss 0.5281   train acc 0.6477   worst 0.4670   lr 0.0291   p 9.66   eps 0.4684   mix 0.0813   time 26.01
scalar:  1.2102
Epoch 146:  train loss 0.5291   train acc 0.6498   worst 0.4652   lr 0.0291   p 9.70   eps 0.4684   mix 0.0809   time 26.03
scalar:  1.1987
Epoch 147:  train loss 0.5254   train acc 0.6532   worst 0.4661   lr 0.0291   p 9.75   eps 0.4684   mix 0.0805   time 25.93
scalar:  1.2379
Epoch 148:  train loss 0.5278   train acc 0.6513   worst 0.4633   lr 0.0291   p 9.79   eps 0.4684   mix 0.0802   time 25.39
scalar:  1.2616
Epoch 149:  train loss 0.5289   train acc 0.6466   worst 0.4646   lr 0.0290   p 9.83   eps 0.4684   mix 0.0798   time 25.73
Epoch 149:  test acc 0.6157   time 2.06
Calculating metrics for L_infinity dist model on training set
Epoch 149:  clean acc 0.1090   certified acc 0.0616
Calculating metrics for L_infinity dist model on test set
Epoch 149:  clean acc 0.1113   certified acc 0.0636
scalar:  1.2384
Epoch 150:  train loss 0.5266   train acc 0.6520   worst 0.4590   lr 0.0290   p 9.87   eps 0.4684   mix 0.0794   time 25.68
scalar:  1.2711
Epoch 151:  train loss 0.5292   train acc 0.6491   worst 0.4601   lr 0.0290   p 9.91   eps 0.4684   mix 0.0791   time 25.88
scalar:  1.2517
Epoch 152:  train loss 0.5281   train acc 0.6494   worst 0.4588   lr 0.0290   p 9.95   eps 0.4684   mix 0.0787   time 26.14
scalar:  1.2665
Epoch 153:  train loss 0.5261   train acc 0.6540   worst 0.4577   lr 0.0290   p 9.99   eps 0.4684   mix 0.0783   time 27.43
scalar:  1.2893
Epoch 154:  train loss 0.5274   train acc 0.6512   worst 0.4581   lr 0.0290   p 10.04   eps 0.4684   mix 0.0780   time 28.45
Epoch 154:  test acc 0.6082   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 154:  clean acc 0.1105   certified acc 0.0593
Calculating metrics for L_infinity dist model on test set
Epoch 154:  clean acc 0.1144   certified acc 0.0605
scalar:  1.2808
Epoch 155:  train loss 0.5275   train acc 0.6511   worst 0.4549   lr 0.0290   p 10.08   eps 0.4684   mix 0.0776   time 28.22
scalar:  1.2999
Epoch 156:  train loss 0.5279   train acc 0.6511   worst 0.4549   lr 0.0289   p 10.12   eps 0.4684   mix 0.0773   time 27.87
scalar:  1.294
Epoch 157:  train loss 0.5291   train acc 0.6500   worst 0.4529   lr 0.0289   p 10.16   eps 0.4684   mix 0.0769   time 28.66
scalar:  1.3429
Epoch 158:  train loss 0.5285   train acc 0.6497   worst 0.4521   lr 0.0289   p 10.21   eps 0.4684   mix 0.0766   time 28.22
scalar:  1.3594
Epoch 159:  train loss 0.5290   train acc 0.6502   worst 0.4516   lr 0.0289   p 10.25   eps 0.4684   mix 0.0762   time 28.35
Epoch 159:  test acc 0.6187   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 159:  clean acc 0.1053   certified acc 0.0798
Calculating metrics for L_infinity dist model on test set
Epoch 159:  clean acc 0.1057   certified acc 0.0783
scalar:  1.3567
Epoch 160:  train loss 0.5305   train acc 0.6479   worst 0.4501   lr 0.0289   p 10.29   eps 0.4684   mix 0.0758   time 28.53
scalar:  1.3482
Epoch 161:  train loss 0.5314   train acc 0.6469   worst 0.4480   lr 0.0289   p 10.34   eps 0.4684   mix 0.0755   time 28.33
scalar:  1.3649
Epoch 162:  train loss 0.5297   train acc 0.6492   worst 0.4474   lr 0.0289   p 10.38   eps 0.4684   mix 0.0752   time 28.39
scalar:  1.3406
Epoch 163:  train loss 0.5293   train acc 0.6519   worst 0.4457   lr 0.0289   p 10.42   eps 0.4684   mix 0.0748   time 28.24
scalar:  1.3998
Epoch 164:  train loss 0.5278   train acc 0.6513   worst 0.4462   lr 0.0288   p 10.47   eps 0.4684   mix 0.0745   time 28.20
Epoch 164:  test acc 0.6182   time 2.68
Calculating metrics for L_infinity dist model on training set
Epoch 164:  clean acc 0.1453   certified acc 0.0217
Calculating metrics for L_infinity dist model on test set
Epoch 164:  clean acc 0.1478   certified acc 0.0237
scalar:  1.401
Epoch 165:  train loss 0.5313   train acc 0.6479   worst 0.4442   lr 0.0288   p 10.51   eps 0.4684   mix 0.0741   time 28.33
scalar:  1.3692
Epoch 166:  train loss 0.5302   train acc 0.6523   worst 0.4400   lr 0.0288   p 10.55   eps 0.4684   mix 0.0738   time 28.18
scalar:  1.4117
Epoch 167:  train loss 0.5296   train acc 0.6489   worst 0.4437   lr 0.0288   p 10.60   eps 0.4684   mix 0.0734   time 27.97
scalar:  1.4125
Epoch 168:  train loss 0.5287   train acc 0.6524   worst 0.4397   lr 0.0288   p 10.64   eps 0.4684   mix 0.0731   time 28.73
scalar:  1.4248
Epoch 169:  train loss 0.5317   train acc 0.6494   worst 0.4390   lr 0.0288   p 10.69   eps 0.4684   mix 0.0728   time 28.23
Epoch 169:  test acc 0.6191   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 169:  clean acc 0.1366   certified acc 0.0164
Calculating metrics for L_infinity dist model on test set
Epoch 169:  clean acc 0.1389   certified acc 0.0168
scalar:  1.4113
Epoch 170:  train loss 0.5340   train acc 0.6478   worst 0.4366   lr 0.0288   p 10.73   eps 0.4684   mix 0.0724   time 28.32
scalar:  1.4288
Epoch 171:  train loss 0.5325   train acc 0.6514   worst 0.4339   lr 0.0287   p 10.78   eps 0.4684   mix 0.0721   time 28.00
scalar:  1.4611
Epoch 172:  train loss 0.5321   train acc 0.6497   worst 0.4364   lr 0.0287   p 10.82   eps 0.4684   mix 0.0718   time 28.61
scalar:  1.4822
Epoch 173:  train loss 0.5299   train acc 0.6516   worst 0.4357   lr 0.0287   p 10.87   eps 0.4684   mix 0.0714   time 28.13
scalar:  1.4869
Epoch 174:  train loss 0.5342   train acc 0.6470   worst 0.4319   lr 0.0287   p 10.91   eps 0.4684   mix 0.0711   time 28.02
Epoch 174:  test acc 0.6132   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 174:  clean acc 0.1455   certified acc 0.0844
Calculating metrics for L_infinity dist model on test set
Epoch 174:  clean acc 0.1508   certified acc 0.0866
scalar:  1.5184
Epoch 175:  train loss 0.5332   train acc 0.6496   worst 0.4313   lr 0.0287   p 10.96   eps 0.4684   mix 0.0708   time 28.59
scalar:  1.4983
Epoch 176:  train loss 0.5342   train acc 0.6472   worst 0.4304   lr 0.0287   p 11.01   eps 0.4684   mix 0.0705   time 27.98
scalar:  1.4862
Epoch 177:  train loss 0.5352   train acc 0.6491   worst 0.4268   lr 0.0286   p 11.05   eps 0.4684   mix 0.0701   time 28.46
scalar:  1.4848
Epoch 178:  train loss 0.5353   train acc 0.6465   worst 0.4267   lr 0.0286   p 11.10   eps 0.4684   mix 0.0698   time 27.79
scalar:  1.4936
Epoch 179:  train loss 0.5326   train acc 0.6508   worst 0.4259   lr 0.0286   p 11.15   eps 0.4684   mix 0.0695   time 27.82
Epoch 179:  test acc 0.6146   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 179:  clean acc 0.1253   certified acc 0.0872
Calculating metrics for L_infinity dist model on test set
Epoch 179:  clean acc 0.1247   certified acc 0.0864
scalar:  1.5
Epoch 180:  train loss 0.5352   train acc 0.6488   worst 0.4212   lr 0.0286   p 11.19   eps 0.4684   mix 0.0692   time 28.17
scalar:  1.515
Epoch 181:  train loss 0.5332   train acc 0.6509   worst 0.4242   lr 0.0286   p 11.24   eps 0.4684   mix 0.0689   time 28.38
scalar:  1.5453
Epoch 182:  train loss 0.5362   train acc 0.6476   worst 0.4205   lr 0.0286   p 11.29   eps 0.4684   mix 0.0685   time 27.96
scalar:  1.5649
Epoch 183:  train loss 0.5354   train acc 0.6485   worst 0.4220   lr 0.0286   p 11.34   eps 0.4684   mix 0.0682   time 27.96
scalar:  1.5536
Epoch 184:  train loss 0.5366   train acc 0.6494   worst 0.4194   lr 0.0285   p 11.38   eps 0.4684   mix 0.0679   time 27.94
Epoch 184:  test acc 0.6122   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 184:  clean acc 0.1366   certified acc 0.0816
Calculating metrics for L_infinity dist model on test set
Epoch 184:  clean acc 0.1388   certified acc 0.0818
scalar:  1.5751
Epoch 185:  train loss 0.5394   train acc 0.6473   worst 0.4162   lr 0.0285   p 11.43   eps 0.4684   mix 0.0676   time 28.44
scalar:  1.5689
Epoch 186:  train loss 0.5393   train acc 0.6471   worst 0.4145   lr 0.0285   p 11.48   eps 0.4684   mix 0.0673   time 28.49
scalar:  1.5683
Epoch 187:  train loss 0.5382   train acc 0.6494   worst 0.4133   lr 0.0285   p 11.53   eps 0.4684   mix 0.0670   time 28.12
scalar:  1.6171
Epoch 188:  train loss 0.5386   train acc 0.6475   worst 0.4125   lr 0.0285   p 11.58   eps 0.4684   mix 0.0667   time 27.95
scalar:  1.5771
Epoch 189:  train loss 0.5387   train acc 0.6455   worst 0.4141   lr 0.0285   p 11.62   eps 0.4684   mix 0.0664   time 28.39
Epoch 189:  test acc 0.6120   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 189:  clean acc 0.1650   certified acc 0.0330
Calculating metrics for L_infinity dist model on test set
Epoch 189:  clean acc 0.1658   certified acc 0.0347
scalar:  1.6165
Epoch 190:  train loss 0.5373   train acc 0.6478   worst 0.4128   lr 0.0284   p 11.67   eps 0.4684   mix 0.0661   time 28.43
scalar:  1.6273
Epoch 191:  train loss 0.5401   train acc 0.6452   worst 0.4111   lr 0.0284   p 11.72   eps 0.4684   mix 0.0658   time 28.58
scalar:  1.6189
Epoch 192:  train loss 0.5380   train acc 0.6491   worst 0.4084   lr 0.0284   p 11.77   eps 0.4684   mix 0.0655   time 28.01
scalar:  1.6214
Epoch 193:  train loss 0.5408   train acc 0.6480   worst 0.4059   lr 0.0284   p 11.82   eps 0.4684   mix 0.0652   time 28.25
scalar:  1.6429
Epoch 194:  train loss 0.5395   train acc 0.6481   worst 0.4052   lr 0.0284   p 11.87   eps 0.4684   mix 0.0649   time 28.01
Epoch 194:  test acc 0.6118   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 194:  clean acc 0.1320   certified acc 0.0412
Calculating metrics for L_infinity dist model on test set
Epoch 194:  clean acc 0.1329   certified acc 0.0446
scalar:  1.6505
Epoch 195:  train loss 0.5408   train acc 0.6450   worst 0.4065   lr 0.0284   p 11.92   eps 0.4684   mix 0.0646   time 28.16
scalar:  1.6201
Epoch 196:  train loss 0.5426   train acc 0.6472   worst 0.4021   lr 0.0283   p 11.97   eps 0.4684   mix 0.0643   time 28.40
scalar:  1.6859
Epoch 197:  train loss 0.5425   train acc 0.6448   worst 0.4044   lr 0.0283   p 12.02   eps 0.4684   mix 0.0640   time 28.25
scalar:  1.6652
Epoch 198:  train loss 0.5399   train acc 0.6475   worst 0.4031   lr 0.0283   p 12.07   eps 0.4684   mix 0.0637   time 28.07
scalar:  1.7026
Epoch 199:  train loss 0.5430   train acc 0.6457   worst 0.3993   lr 0.0283   p 12.12   eps 0.4684   mix 0.0634   time 28.08
Epoch 199:  test acc 0.6070   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 199:  clean acc 0.1226   certified acc 0.0777
Calculating metrics for L_infinity dist model on test set
Epoch 199:  clean acc 0.1205   certified acc 0.0776
scalar:  1.7199
Epoch 200:  train loss 0.5415   train acc 0.6475   worst 0.3995   lr 0.0283   p 12.17   eps 0.4684   mix 0.0631   time 28.03
scalar:  1.6622
Epoch 201:  train loss 0.5426   train acc 0.6428   worst 0.4004   lr 0.0283   p 12.23   eps 0.4684   mix 0.0628   time 28.77
scalar:  1.6838
Epoch 202:  train loss 0.5434   train acc 0.6439   worst 0.3971   lr 0.0282   p 12.28   eps 0.4684   mix 0.0625   time 28.27
scalar:  1.6844
Epoch 203:  train loss 0.5440   train acc 0.6448   worst 0.3959   lr 0.0282   p 12.33   eps 0.4684   mix 0.0622   time 28.23
scalar:  1.7207
Epoch 204:  train loss 0.5445   train acc 0.6446   worst 0.3946   lr 0.0282   p 12.38   eps 0.4684   mix 0.0619   time 28.17
Epoch 204:  test acc 0.6144   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 204:  clean acc 0.1521   certified acc 0.0168
Calculating metrics for L_infinity dist model on test set
Epoch 204:  clean acc 0.1586   certified acc 0.0166
scalar:  1.7276
Epoch 205:  train loss 0.5464   train acc 0.6426   worst 0.3942   lr 0.0282   p 12.43   eps 0.4684   mix 0.0616   time 28.20
scalar:  1.7191
Epoch 206:  train loss 0.5456   train acc 0.6427   worst 0.3921   lr 0.0282   p 12.48   eps 0.4684   mix 0.0614   time 28.39
scalar:  1.7876
Epoch 207:  train loss 0.5441   train acc 0.6454   worst 0.3926   lr 0.0282   p 12.54   eps 0.4684   mix 0.0611   time 28.06
scalar:  1.7603
Epoch 208:  train loss 0.5448   train acc 0.6459   worst 0.3879   lr 0.0281   p 12.59   eps 0.4684   mix 0.0608   time 27.90
scalar:  1.7668
Epoch 209:  train loss 0.5456   train acc 0.6473   worst 0.3879   lr 0.0281   p 12.64   eps 0.4684   mix 0.0605   time 28.47
Epoch 209:  test acc 0.6110   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 209:  clean acc 0.1607   certified acc 0.0474
Calculating metrics for L_infinity dist model on test set
Epoch 209:  clean acc 0.1638   certified acc 0.0499
scalar:  1.8031
Epoch 210:  train loss 0.5466   train acc 0.6439   worst 0.3853   lr 0.0281   p 12.70   eps 0.4684   mix 0.0602   time 28.62
scalar:  1.8014
Epoch 211:  train loss 0.5448   train acc 0.6445   worst 0.3875   lr 0.0281   p 12.75   eps 0.4684   mix 0.0600   time 28.28
scalar:  1.77
Epoch 212:  train loss 0.5488   train acc 0.6441   worst 0.3839   lr 0.0281   p 12.80   eps 0.4684   mix 0.0597   time 28.51
scalar:  1.79
Epoch 213:  train loss 0.5495   train acc 0.6409   worst 0.3822   lr 0.0281   p 12.86   eps 0.4684   mix 0.0594   time 27.61
scalar:  1.7803
Epoch 214:  train loss 0.5468   train acc 0.6433   worst 0.3840   lr 0.0280   p 12.91   eps 0.4684   mix 0.0591   time 28.25
Epoch 214:  test acc 0.6069   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 214:  clean acc 0.1569   certified acc 0.0498
Calculating metrics for L_infinity dist model on test set
Epoch 214:  clean acc 0.1571   certified acc 0.0513
scalar:  1.8233
Epoch 215:  train loss 0.5478   train acc 0.6434   worst 0.3828   lr 0.0280   p 12.97   eps 0.4684   mix 0.0589   time 28.44
scalar:  1.8469
Epoch 216:  train loss 0.5492   train acc 0.6411   worst 0.3813   lr 0.0280   p 13.02   eps 0.4684   mix 0.0586   time 28.17
scalar:  1.8465
Epoch 217:  train loss 0.5506   train acc 0.6427   worst 0.3785   lr 0.0280   p 13.07   eps 0.4684   mix 0.0583   time 28.29
scalar:  1.8483
Epoch 218:  train loss 0.5495   train acc 0.6432   worst 0.3776   lr 0.0280   p 13.13   eps 0.4684   mix 0.0581   time 28.03
scalar:  1.8343
Epoch 219:  train loss 0.5514   train acc 0.6398   worst 0.3761   lr 0.0279   p 13.18   eps 0.4684   mix 0.0578   time 28.77
Epoch 219:  test acc 0.6020   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 219:  clean acc 0.1593   certified acc 0.0558
Calculating metrics for L_infinity dist model on test set
Epoch 219:  clean acc 0.1638   certified acc 0.0560
scalar:  1.8614
Epoch 220:  train loss 0.5519   train acc 0.6394   worst 0.3743   lr 0.0279   p 13.24   eps 0.4684   mix 0.0575   time 28.34
scalar:  1.8564
Epoch 221:  train loss 0.5505   train acc 0.6416   worst 0.3757   lr 0.0279   p 13.30   eps 0.4684   mix 0.0573   time 28.07
scalar:  1.8846
Epoch 222:  train loss 0.5498   train acc 0.6409   worst 0.3760   lr 0.0279   p 13.35   eps 0.4684   mix 0.0570   time 27.66
scalar:  1.8609
Epoch 223:  train loss 0.5548   train acc 0.6385   worst 0.3697   lr 0.0279   p 13.41   eps 0.4684   mix 0.0567   time 28.05
scalar:  1.8464
Epoch 224:  train loss 0.5505   train acc 0.6389   worst 0.3759   lr 0.0279   p 13.46   eps 0.4684   mix 0.0565   time 28.27
Epoch 224:  test acc 0.6010   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 224:  clean acc 0.1596   certified acc 0.0583
Calculating metrics for L_infinity dist model on test set
Epoch 224:  clean acc 0.1617   certified acc 0.0596
scalar:  1.8557
Epoch 225:  train loss 0.5528   train acc 0.6386   worst 0.3728   lr 0.0278   p 13.52   eps 0.4684   mix 0.0562   time 28.78
scalar:  1.8829
Epoch 226:  train loss 0.5515   train acc 0.6387   worst 0.3726   lr 0.0278   p 13.58   eps 0.4684   mix 0.0560   time 28.16
scalar:  1.8807
Epoch 227:  train loss 0.5563   train acc 0.6365   worst 0.3673   lr 0.0278   p 13.64   eps 0.4684   mix 0.0557   time 28.32
scalar:  1.8844
Epoch 228:  train loss 0.5544   train acc 0.6403   worst 0.3651   lr 0.0278   p 13.69   eps 0.4684   mix 0.0554   time 28.01
scalar:  1.9123
Epoch 229:  train loss 0.5558   train acc 0.6362   worst 0.3679   lr 0.0278   p 13.75   eps 0.4684   mix 0.0552   time 28.52
Epoch 229:  test acc 0.6095   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 229:  clean acc 0.1536   certified acc 0.0660
Calculating metrics for L_infinity dist model on test set
Epoch 229:  clean acc 0.1593   certified acc 0.0668
scalar:  1.8641
Epoch 230:  train loss 0.5561   train acc 0.6363   worst 0.3646   lr 0.0277   p 13.81   eps 0.4684   mix 0.0549   time 28.34
scalar:  1.8968
Epoch 231:  train loss 0.5552   train acc 0.6394   worst 0.3636   lr 0.0277   p 13.87   eps 0.4684   mix 0.0547   time 28.09
scalar:  1.9604
Epoch 232:  train loss 0.5574   train acc 0.6380   worst 0.3626   lr 0.0277   p 13.92   eps 0.4684   mix 0.0544   time 28.10
scalar:  1.9434
Epoch 233:  train loss 0.5580   train acc 0.6361   worst 0.3633   lr 0.0277   p 13.98   eps 0.4684   mix 0.0542   time 28.15
scalar:  1.9299
Epoch 234:  train loss 0.5558   train acc 0.6378   worst 0.3618   lr 0.0277   p 14.04   eps 0.4684   mix 0.0539   time 28.39
Epoch 234:  test acc 0.6028   time 2.67
Calculating metrics for L_infinity dist model on training set
Epoch 234:  clean acc 0.1438   certified acc 0.0190
Calculating metrics for L_infinity dist model on test set
Epoch 234:  clean acc 0.1373   certified acc 0.0193
scalar:  1.9479
Epoch 235:  train loss 0.5565   train acc 0.6394   worst 0.3574   lr 0.0276   p 14.10   eps 0.4684   mix 0.0537   time 28.57
scalar:  1.9427
Epoch 236:  train loss 0.5578   train acc 0.6358   worst 0.3603   lr 0.0276   p 14.16   eps 0.4684   mix 0.0534   time 28.27
scalar:  1.9544
Epoch 237:  train loss 0.5566   train acc 0.6376   worst 0.3587   lr 0.0276   p 14.22   eps 0.4684   mix 0.0532   time 28.28
scalar:  1.9585
Epoch 238:  train loss 0.5596   train acc 0.6363   worst 0.3549   lr 0.0276   p 14.28   eps 0.4684   mix 0.0530   time 28.50
scalar:  2.0009
Epoch 239:  train loss 0.5592   train acc 0.6373   worst 0.3533   lr 0.0276   p 14.34   eps 0.4684   mix 0.0527   time 28.35
Epoch 239:  test acc 0.6003   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 239:  clean acc 0.1530   certified acc 0.0371
Calculating metrics for L_infinity dist model on test set
Epoch 239:  clean acc 0.1485   certified acc 0.0364
scalar:  1.9846
Epoch 240:  train loss 0.5579   train acc 0.6364   worst 0.3549   lr 0.0275   p 14.40   eps 0.4684   mix 0.0525   time 28.26
scalar:  2.0158
Epoch 241:  train loss 0.5595   train acc 0.6340   worst 0.3551   lr 0.0275   p 14.46   eps 0.4684   mix 0.0522   time 28.34
scalar:  2.0138
Epoch 242:  train loss 0.5593   train acc 0.6373   worst 0.3516   lr 0.0275   p 14.52   eps 0.4684   mix 0.0520   time 28.15
scalar:  2.0578
Epoch 243:  train loss 0.5604   train acc 0.6351   worst 0.3529   lr 0.0275   p 14.58   eps 0.4684   mix 0.0517   time 28.28
scalar:  2.0513
Epoch 244:  train loss 0.5599   train acc 0.6347   worst 0.3513   lr 0.0275   p 14.64   eps 0.4684   mix 0.0515   time 28.04
Epoch 244:  test acc 0.5992   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 244:  clean acc 0.1489   certified acc 0.0710
Calculating metrics for L_infinity dist model on test set
Epoch 244:  clean acc 0.1441   certified acc 0.0674
scalar:  2.0067
Epoch 245:  train loss 0.5598   train acc 0.6360   worst 0.3499   lr 0.0274   p 14.71   eps 0.4684   mix 0.0513   time 28.45
scalar:  2.0671
Epoch 246:  train loss 0.5621   train acc 0.6352   worst 0.3474   lr 0.0274   p 14.77   eps 0.4684   mix 0.0510   time 28.19
scalar:  2.0568
Epoch 247:  train loss 0.5631   train acc 0.6323   worst 0.3472   lr 0.0274   p 14.83   eps 0.4684   mix 0.0508   time 28.30
scalar:  2.0572
Epoch 248:  train loss 0.5646   train acc 0.6336   worst 0.3444   lr 0.0274   p 14.89   eps 0.4684   mix 0.0506   time 28.55
scalar:  2.0408
Epoch 249:  train loss 0.5645   train acc 0.6325   worst 0.3441   lr 0.0274   p 14.95   eps 0.4684   mix 0.0503   time 28.53
Epoch 249:  test acc 0.6007   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 249:  clean acc 0.1572   certified acc 0.0468
Calculating metrics for L_infinity dist model on test set
Epoch 249:  clean acc 0.1519   certified acc 0.0420
scalar:  2.0251
Epoch 250:  train loss 0.5628   train acc 0.6317   worst 0.3454   lr 0.0273   p 15.02   eps 0.4684   mix 0.0501   time 28.10
scalar:  2.035
Epoch 251:  train loss 0.5640   train acc 0.6300   worst 0.3442   lr 0.0273   p 15.08   eps 0.4684   mix 0.0499   time 28.25
scalar:  2.0586
Epoch 252:  train loss 0.5650   train acc 0.6321   worst 0.3432   lr 0.0273   p 15.14   eps 0.4684   mix 0.0496   time 28.19
scalar:  2.0686
Epoch 253:  train loss 0.5642   train acc 0.6323   worst 0.3400   lr 0.0273   p 15.21   eps 0.4684   mix 0.0494   time 28.71
scalar:  2.0868
Epoch 254:  train loss 0.5650   train acc 0.6311   worst 0.3414   lr 0.0273   p 15.27   eps 0.4684   mix 0.0492   time 28.49
Epoch 254:  test acc 0.6003   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 254:  clean acc 0.1522   certified acc 0.0628
Calculating metrics for L_infinity dist model on test set
Epoch 254:  clean acc 0.1498   certified acc 0.0575
scalar:  2.1033
Epoch 255:  train loss 0.5659   train acc 0.6292   worst 0.3396   lr 0.0272   p 15.34   eps 0.4684   mix 0.0490   time 28.27
scalar:  2.1036
Epoch 256:  train loss 0.5652   train acc 0.6311   worst 0.3366   lr 0.0272   p 15.40   eps 0.4684   mix 0.0487   time 28.51
scalar:  2.0717
Epoch 257:  train loss 0.5660   train acc 0.6314   worst 0.3389   lr 0.0272   p 15.47   eps 0.4684   mix 0.0485   time 28.20
scalar:  2.1033
Epoch 258:  train loss 0.5667   train acc 0.6295   worst 0.3376   lr 0.0272   p 15.53   eps 0.4684   mix 0.0483   time 28.38
scalar:  2.1369
Epoch 259:  train loss 0.5674   train acc 0.6286   worst 0.3359   lr 0.0272   p 15.60   eps 0.4684   mix 0.0481   time 28.63
Epoch 259:  test acc 0.5945   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 259:  clean acc 0.1593   certified acc 0.0796
Calculating metrics for L_infinity dist model on test set
Epoch 259:  clean acc 0.1609   certified acc 0.0822
scalar:  2.1302
Epoch 260:  train loss 0.5655   train acc 0.6309   worst 0.3351   lr 0.0271   p 15.66   eps 0.4684   mix 0.0478   time 28.18
scalar:  2.1258
Epoch 261:  train loss 0.5681   train acc 0.6287   worst 0.3345   lr 0.0271   p 15.73   eps 0.4684   mix 0.0476   time 28.03
scalar:  2.1401
Epoch 262:  train loss 0.5700   train acc 0.6275   worst 0.3328   lr 0.0271   p 15.79   eps 0.4684   mix 0.0474   time 28.62
scalar:  2.1012
Epoch 263:  train loss 0.5673   train acc 0.6283   worst 0.3318   lr 0.0271   p 15.86   eps 0.4684   mix 0.0472   time 28.23
scalar:  2.1192
Epoch 264:  train loss 0.5691   train acc 0.6294   worst 0.3310   lr 0.0270   p 15.93   eps 0.4684   mix 0.0470   time 28.84
Epoch 264:  test acc 0.5930   time 2.65
Calculating metrics for L_infinity dist model on training set
Epoch 264:  clean acc 0.1626   certified acc 0.0381
Calculating metrics for L_infinity dist model on test set
Epoch 264:  clean acc 0.1641   certified acc 0.0385
scalar:  2.165
Epoch 265:  train loss 0.5702   train acc 0.6260   worst 0.3297   lr 0.0270   p 15.99   eps 0.4684   mix 0.0468   time 28.57
scalar:  2.1556
Epoch 266:  train loss 0.5692   train acc 0.6289   worst 0.3283   lr 0.0270   p 16.06   eps 0.4684   mix 0.0465   time 28.29
scalar:  2.173
Epoch 267:  train loss 0.5721   train acc 0.6249   worst 0.3281   lr 0.0270   p 16.13   eps 0.4684   mix 0.0463   time 28.44
scalar:  2.1753
Epoch 268:  train loss 0.5691   train acc 0.6263   worst 0.3289   lr 0.0270   p 16.20   eps 0.4684   mix 0.0461   time 28.19
scalar:  2.1724
Epoch 269:  train loss 0.5729   train acc 0.6253   worst 0.3263   lr 0.0269   p 16.26   eps 0.4684   mix 0.0459   time 29.11
Epoch 269:  test acc 0.5940   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 269:  clean acc 0.1539   certified acc 0.0592
Calculating metrics for L_infinity dist model on test set
Epoch 269:  clean acc 0.1486   certified acc 0.0586
scalar:  2.1664
Epoch 270:  train loss 0.5739   train acc 0.6234   worst 0.3260   lr 0.0269   p 16.33   eps 0.4684   mix 0.0457   time 28.49
scalar:  2.1598
Epoch 271:  train loss 0.5703   train acc 0.6281   worst 0.3267   lr 0.0269   p 16.40   eps 0.4684   mix 0.0455   time 28.02
scalar:  2.1859
Epoch 272:  train loss 0.5728   train acc 0.6266   worst 0.3232   lr 0.0269   p 16.47   eps 0.4684   mix 0.0453   time 28.43
scalar:  2.2162
Epoch 273:  train loss 0.5717   train acc 0.6293   worst 0.3226   lr 0.0269   p 16.54   eps 0.4684   mix 0.0451   time 27.99
scalar:  2.2279
Epoch 274:  train loss 0.5728   train acc 0.6259   worst 0.3213   lr 0.0268   p 16.61   eps 0.4684   mix 0.0449   time 28.82
Epoch 274:  test acc 0.5936   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 274:  clean acc 0.1578   certified acc 0.0603
Calculating metrics for L_infinity dist model on test set
Epoch 274:  clean acc 0.1578   certified acc 0.0578
scalar:  2.2024
Epoch 275:  train loss 0.5751   train acc 0.6222   worst 0.3206   lr 0.0268   p 16.68   eps 0.4684   mix 0.0447   time 28.22
scalar:  2.213
Epoch 276:  train loss 0.5747   train acc 0.6243   worst 0.3214   lr 0.0268   p 16.75   eps 0.4684   mix 0.0444   time 28.11
scalar:  2.2102
Epoch 277:  train loss 0.5726   train acc 0.6250   worst 0.3215   lr 0.0268   p 16.82   eps 0.4684   mix 0.0442   time 28.30
scalar:  2.2175
Epoch 278:  train loss 0.5730   train acc 0.6258   worst 0.3192   lr 0.0267   p 16.89   eps 0.4684   mix 0.0440   time 28.45
scalar:  2.2163
Epoch 279:  train loss 0.5759   train acc 0.6229   worst 0.3173   lr 0.0267   p 16.96   eps 0.4684   mix 0.0438   time 28.72
Epoch 279:  test acc 0.5913   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 279:  clean acc 0.1493   certified acc 0.0674
Calculating metrics for L_infinity dist model on test set
Epoch 279:  clean acc 0.1465   certified acc 0.0643
scalar:  2.2146
Epoch 280:  train loss 0.5734   train acc 0.6266   worst 0.3194   lr 0.0267   p 17.03   eps 0.4684   mix 0.0436   time 28.02
scalar:  2.2687
Epoch 281:  train loss 0.5751   train acc 0.6241   worst 0.3165   lr 0.0267   p 17.11   eps 0.4684   mix 0.0434   time 28.29
scalar:  2.2522
Epoch 282:  train loss 0.5761   train acc 0.6231   worst 0.3161   lr 0.0266   p 17.18   eps 0.4684   mix 0.0432   time 28.24
scalar:  2.2402
Epoch 283:  train loss 0.5764   train acc 0.6227   worst 0.3164   lr 0.0266   p 17.25   eps 0.4684   mix 0.0430   time 28.40
scalar:  2.2542
Epoch 284:  train loss 0.5767   train acc 0.6240   worst 0.3134   lr 0.0266   p 17.32   eps 0.4684   mix 0.0428   time 28.93
Epoch 284:  test acc 0.5855   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 284:  clean acc 0.1405   certified acc 0.0665
Calculating metrics for L_infinity dist model on test set
Epoch 284:  clean acc 0.1390   certified acc 0.0653
scalar:  2.2393
Epoch 285:  train loss 0.5776   train acc 0.6213   worst 0.3133   lr 0.0266   p 17.39   eps 0.4684   mix 0.0426   time 28.64
scalar:  2.2793
Epoch 286:  train loss 0.5788   train acc 0.6230   worst 0.3113   lr 0.0266   p 17.47   eps 0.4684   mix 0.0424   time 28.13
scalar:  2.2718
Epoch 287:  train loss 0.5776   train acc 0.6215   worst 0.3111   lr 0.0265   p 17.54   eps 0.4684   mix 0.0423   time 28.60
scalar:  2.3063
Epoch 288:  train loss 0.5797   train acc 0.6206   worst 0.3108   lr 0.0265   p 17.62   eps 0.4684   mix 0.0421   time 28.36
scalar:  2.3124
Epoch 289:  train loss 0.5785   train acc 0.6226   worst 0.3110   lr 0.0265   p 17.69   eps 0.4684   mix 0.0419   time 28.43
Epoch 289:  test acc 0.5916   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 289:  clean acc 0.1235   certified acc 0.0419
Calculating metrics for L_infinity dist model on test set
Epoch 289:  clean acc 0.1195   certified acc 0.0418
scalar:  2.2774
Epoch 290:  train loss 0.5794   train acc 0.6180   worst 0.3098   lr 0.0265   p 17.76   eps 0.4684   mix 0.0417   time 28.24
scalar:  2.2962
Epoch 291:  train loss 0.5805   train acc 0.6183   worst 0.3069   lr 0.0264   p 17.84   eps 0.4684   mix 0.0415   time 28.50
scalar:  2.3017
Epoch 292:  train loss 0.5814   train acc 0.6188   worst 0.3063   lr 0.0264   p 17.91   eps 0.4684   mix 0.0413   time 28.60
scalar:  2.2675
Epoch 293:  train loss 0.5804   train acc 0.6181   worst 0.3089   lr 0.0264   p 17.99   eps 0.4684   mix 0.0411   time 28.45
scalar:  2.3244
Epoch 294:  train loss 0.5831   train acc 0.6184   worst 0.3029   lr 0.0264   p 18.06   eps 0.4684   mix 0.0409   time 28.88
Epoch 294:  test acc 0.5909   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 294:  clean acc 0.1457   certified acc 0.0690
Calculating metrics for L_infinity dist model on test set
Epoch 294:  clean acc 0.1441   certified acc 0.0688
scalar:  2.329
Epoch 295:  train loss 0.5827   train acc 0.6176   worst 0.3025   lr 0.0263   p 18.14   eps 0.4684   mix 0.0407   time 28.14
scalar:  2.2934
Epoch 296:  train loss 0.5827   train acc 0.6182   worst 0.3042   lr 0.0263   p 18.22   eps 0.4684   mix 0.0405   time 28.32
scalar:  2.3259
Epoch 297:  train loss 0.5818   train acc 0.6160   worst 0.3048   lr 0.0263   p 18.29   eps 0.4684   mix 0.0403   time 28.55
scalar:  2.2896
Epoch 298:  train loss 0.5847   train acc 0.6162   worst 0.2989   lr 0.0263   p 18.37   eps 0.4684   mix 0.0402   time 28.47
scalar:  2.3679
Epoch 299:  train loss 0.5836   train acc 0.6167   worst 0.3024   lr 0.0263   p 18.45   eps 0.4684   mix 0.0400   time 29.02
Epoch 299:  test acc 0.5845   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 299:  clean acc 0.1355   certified acc 0.0575
Calculating metrics for L_infinity dist model on test set
Epoch 299:  clean acc 0.1378   certified acc 0.0574
scalar:  2.3301
Epoch 300:  train loss 0.5847   train acc 0.6167   worst 0.2999   lr 0.0262   p 18.53   eps 0.4684   mix 0.0398   time 28.33
scalar:  2.3473
Epoch 301:  train loss 0.5829   train acc 0.6161   worst 0.3028   lr 0.0262   p 18.60   eps 0.4684   mix 0.0396   time 28.80
scalar:  2.3761
Epoch 302:  train loss 0.5842   train acc 0.6180   worst 0.2971   lr 0.0262   p 18.68   eps 0.4684   mix 0.0394   time 28.44
scalar:  2.3319
Epoch 303:  train loss 0.5851   train acc 0.6177   worst 0.2944   lr 0.0262   p 18.76   eps 0.4684   mix 0.0392   time 28.50
scalar:  2.387
Epoch 304:  train loss 0.5869   train acc 0.6134   worst 0.2955   lr 0.0261   p 18.84   eps 0.4684   mix 0.0391   time 28.41
Epoch 304:  test acc 0.5862   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 304:  clean acc 0.1367   certified acc 0.0601
Calculating metrics for L_infinity dist model on test set
Epoch 304:  clean acc 0.1331   certified acc 0.0555
scalar:  2.4009
Epoch 305:  train loss 0.5830   train acc 0.6188   worst 0.2972   lr 0.0261   p 18.92   eps 0.4684   mix 0.0389   time 28.39
scalar:  2.3892
Epoch 306:  train loss 0.5840   train acc 0.6170   worst 0.2947   lr 0.0261   p 19.00   eps 0.4684   mix 0.0387   time 29.22
scalar:  2.3848
Epoch 307:  train loss 0.5841   train acc 0.6189   worst 0.2945   lr 0.0261   p 19.08   eps 0.4684   mix 0.0385   time 28.54
scalar:  2.3866
Epoch 308:  train loss 0.5879   train acc 0.6134   worst 0.2928   lr 0.0260   p 19.16   eps 0.4684   mix 0.0384   time 28.66
scalar:  2.3843
Epoch 309:  train loss 0.5874   train acc 0.6158   worst 0.2913   lr 0.0260   p 19.24   eps 0.4684   mix 0.0382   time 28.64
Epoch 309:  test acc 0.5843   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 309:  clean acc 0.1255   certified acc 0.0687
Calculating metrics for L_infinity dist model on test set
Epoch 309:  clean acc 0.1208   certified acc 0.0667
scalar:  2.419
Epoch 310:  train loss 0.5872   train acc 0.6152   worst 0.2935   lr 0.0260   p 19.32   eps 0.4684   mix 0.0380   time 28.42
scalar:  2.4006
Epoch 311:  train loss 0.5884   train acc 0.6138   worst 0.2909   lr 0.0260   p 19.40   eps 0.4684   mix 0.0378   time 28.52
scalar:  2.4377
Epoch 312:  train loss 0.5885   train acc 0.6142   worst 0.2886   lr 0.0259   p 19.48   eps 0.4684   mix 0.0377   time 28.21
scalar:  2.4303
Epoch 313:  train loss 0.5878   train acc 0.6131   worst 0.2911   lr 0.0259   p 19.56   eps 0.4684   mix 0.0375   time 28.71
scalar:  2.3923
Epoch 314:  train loss 0.5877   train acc 0.6130   worst 0.2906   lr 0.0259   p 19.65   eps 0.4684   mix 0.0373   time 28.77
Epoch 314:  test acc 0.5835   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 314:  clean acc 0.1549   certified acc 0.0673
Calculating metrics for L_infinity dist model on test set
Epoch 314:  clean acc 0.1506   certified acc 0.0617
scalar:  2.4152
Epoch 315:  train loss 0.5899   train acc 0.6125   worst 0.2881   lr 0.0259   p 19.73   eps 0.4684   mix 0.0371   time 28.17
scalar:  2.4197
Epoch 316:  train loss 0.5904   train acc 0.6127   worst 0.2870   lr 0.0258   p 19.81   eps 0.4684   mix 0.0370   time 28.69
scalar:  2.4808
Epoch 317:  train loss 0.5888   train acc 0.6130   worst 0.2894   lr 0.0258   p 19.90   eps 0.4684   mix 0.0368   time 28.34
scalar:  2.3959
Epoch 318:  train loss 0.5893   train acc 0.6120   worst 0.2863   lr 0.0258   p 19.98   eps 0.4684   mix 0.0366   time 28.90
scalar:  2.4541
Epoch 319:  train loss 0.5900   train acc 0.6115   worst 0.2887   lr 0.0258   p 20.06   eps 0.4684   mix 0.0365   time 28.39
Epoch 319:  test acc 0.5787   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 319:  clean acc 0.1316   certified acc 0.0684
Calculating metrics for L_infinity dist model on test set
Epoch 319:  clean acc 0.1272   certified acc 0.0649
scalar:  2.4485
Epoch 320:  train loss 0.5925   train acc 0.6102   worst 0.2836   lr 0.0257   p 20.15   eps 0.4684   mix 0.0363   time 28.20
scalar:  2.4544
Epoch 321:  train loss 0.5906   train acc 0.6118   worst 0.2833   lr 0.0257   p 20.23   eps 0.4684   mix 0.0361   time 28.70
scalar:  2.4529
Epoch 322:  train loss 0.5931   train acc 0.6088   worst 0.2832   lr 0.0257   p 20.32   eps 0.4684   mix 0.0360   time 28.51
scalar:  2.4193
Epoch 323:  train loss 0.5938   train acc 0.6099   worst 0.2801   lr 0.0257   p 20.40   eps 0.4684   mix 0.0358   time 28.88
scalar:  2.4879
Epoch 324:  train loss 0.5925   train acc 0.6107   worst 0.2820   lr 0.0256   p 20.49   eps 0.4684   mix 0.0356   time 28.44
Epoch 324:  test acc 0.5763   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 324:  clean acc 0.1289   certified acc 0.0673
Calculating metrics for L_infinity dist model on test set
Epoch 324:  clean acc 0.1273   certified acc 0.0636
scalar:  2.4631
Epoch 325:  train loss 0.5919   train acc 0.6111   worst 0.2826   lr 0.0256   p 20.58   eps 0.4684   mix 0.0355   time 28.50
scalar:  2.5038
Epoch 326:  train loss 0.5939   train acc 0.6069   worst 0.2827   lr 0.0256   p 20.66   eps 0.4684   mix 0.0353   time 28.69
scalar:  2.4123
Epoch 327:  train loss 0.5933   train acc 0.6110   worst 0.2800   lr 0.0256   p 20.75   eps 0.4684   mix 0.0351   time 28.37
scalar:  2.513
Epoch 328:  train loss 0.5930   train acc 0.6094   worst 0.2779   lr 0.0255   p 20.84   eps 0.4684   mix 0.0350   time 28.46
scalar:  2.4818
Epoch 329:  train loss 0.5929   train acc 0.6086   worst 0.2800   lr 0.0255   p 20.92   eps 0.4684   mix 0.0348   time 28.94
Epoch 329:  test acc 0.5805   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 329:  clean acc 0.1494   certified acc 0.0672
Calculating metrics for L_infinity dist model on test set
Epoch 329:  clean acc 0.1466   certified acc 0.0642
scalar:  2.5001
Epoch 330:  train loss 0.5928   train acc 0.6095   worst 0.2788   lr 0.0255   p 21.01   eps 0.4684   mix 0.0347   time 28.48
scalar:  2.4876
Epoch 331:  train loss 0.5942   train acc 0.6105   worst 0.2772   lr 0.0255   p 21.10   eps 0.4684   mix 0.0345   time 28.17
scalar:  2.5355
Epoch 332:  train loss 0.5946   train acc 0.6103   worst 0.2752   lr 0.0254   p 21.19   eps 0.4684   mix 0.0343   time 28.31
scalar:  2.5114
Epoch 333:  train loss 0.5939   train acc 0.6098   worst 0.2766   lr 0.0254   p 21.28   eps 0.4684   mix 0.0342   time 28.44
scalar:  2.5165
Epoch 334:  train loss 0.5982   train acc 0.6070   worst 0.2751   lr 0.0254   p 21.37   eps 0.4684   mix 0.0340   time 28.41
Epoch 334:  test acc 0.5770   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 334:  clean acc 0.1445   certified acc 0.0735
Calculating metrics for L_infinity dist model on test set
Epoch 334:  clean acc 0.1452   certified acc 0.0742
scalar:  2.5247
Epoch 335:  train loss 0.5946   train acc 0.6074   worst 0.2765   lr 0.0253   p 21.46   eps 0.4684   mix 0.0339   time 28.51
scalar:  2.5319
Epoch 336:  train loss 0.5953   train acc 0.6097   worst 0.2740   lr 0.0253   p 21.55   eps 0.4684   mix 0.0337   time 28.75
scalar:  2.5285
Epoch 337:  train loss 0.5967   train acc 0.6051   worst 0.2726   lr 0.0253   p 21.64   eps 0.4684   mix 0.0336   time 28.48
scalar:  2.5205
Epoch 338:  train loss 0.5977   train acc 0.6061   worst 0.2707   lr 0.0253   p 21.73   eps 0.4684   mix 0.0334   time 28.68
scalar:  2.5313
Epoch 339:  train loss 0.5972   train acc 0.6059   worst 0.2715   lr 0.0252   p 21.82   eps 0.4684   mix 0.0332   time 28.61
Epoch 339:  test acc 0.5775   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 339:  clean acc 0.1324   certified acc 0.0595
Calculating metrics for L_infinity dist model on test set
Epoch 339:  clean acc 0.1301   certified acc 0.0570
scalar:  2.5337
Epoch 340:  train loss 0.5970   train acc 0.6080   worst 0.2720   lr 0.0252   p 21.91   eps 0.4684   mix 0.0331   time 28.62
scalar:  2.5371
Epoch 341:  train loss 0.5986   train acc 0.6047   worst 0.2708   lr 0.0252   p 22.01   eps 0.4684   mix 0.0329   time 28.60
scalar:  2.5589
Epoch 342:  train loss 0.5993   train acc 0.6056   worst 0.2689   lr 0.0252   p 22.10   eps 0.4684   mix 0.0328   time 28.34
scalar:  2.5308
Epoch 343:  train loss 0.5984   train acc 0.6052   worst 0.2691   lr 0.0251   p 22.19   eps 0.4684   mix 0.0326   time 28.36
scalar:  2.5304
Epoch 344:  train loss 0.5993   train acc 0.6060   worst 0.2684   lr 0.0251   p 22.28   eps 0.4684   mix 0.0325   time 28.79
Epoch 344:  test acc 0.5754   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 344:  clean acc 0.1598   certified acc 0.0595
Calculating metrics for L_infinity dist model on test set
Epoch 344:  clean acc 0.1577   certified acc 0.0554
scalar:  2.563
Epoch 345:  train loss 0.5997   train acc 0.6013   worst 0.2688   lr 0.0251   p 22.38   eps 0.4684   mix 0.0323   time 28.82
scalar:  2.5373
Epoch 346:  train loss 0.5983   train acc 0.6057   worst 0.2696   lr 0.0251   p 22.47   eps 0.4684   mix 0.0322   time 28.60
scalar:  2.5778
Epoch 347:  train loss 0.5984   train acc 0.6041   worst 0.2685   lr 0.0250   p 22.57   eps 0.4684   mix 0.0320   time 28.35
scalar:  2.581
Epoch 348:  train loss 0.6000   train acc 0.6024   worst 0.2656   lr 0.0250   p 22.66   eps 0.4684   mix 0.0319   time 28.51
scalar:  2.553
Epoch 349:  train loss 0.6003   train acc 0.6017   worst 0.2667   lr 0.0250   p 22.76   eps 0.4684   mix 0.0318   time 28.78
Epoch 349:  test acc 0.5705   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 349:  clean acc 0.1842   certified acc 0.0772
Calculating metrics for L_infinity dist model on test set
Epoch 349:  clean acc 0.1793   certified acc 0.0735
scalar:  2.5838
Epoch 350:  train loss 0.6001   train acc 0.6033   worst 0.2664   lr 0.0249   p 22.85   eps 0.4684   mix 0.0316   time 29.09
scalar:  2.5603
Epoch 351:  train loss 0.6006   train acc 0.6049   worst 0.2639   lr 0.0249   p 22.95   eps 0.4684   mix 0.0315   time 28.30
scalar:  2.5901
Epoch 352:  train loss 0.6007   train acc 0.6034   worst 0.2622   lr 0.0249   p 23.05   eps 0.4684   mix 0.0313   time 28.50
scalar:  2.6038
Epoch 353:  train loss 0.6028   train acc 0.6006   worst 0.2638   lr 0.0249   p 23.14   eps 0.4684   mix 0.0312   time 28.30
scalar:  2.5956
Epoch 354:  train loss 0.6009   train acc 0.6032   worst 0.2624   lr 0.0248   p 23.24   eps 0.4684   mix 0.0310   time 28.50
Epoch 354:  test acc 0.5762   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 354:  clean acc 0.1560   certified acc 0.0640
Calculating metrics for L_infinity dist model on test set
Epoch 354:  clean acc 0.1508   certified acc 0.0594
scalar:  2.6193
Epoch 355:  train loss 0.6026   train acc 0.6036   worst 0.2608   lr 0.0248   p 23.34   eps 0.4684   mix 0.0309   time 28.88
scalar:  2.6285
Epoch 356:  train loss 0.6025   train acc 0.6037   worst 0.2597   lr 0.0248   p 23.44   eps 0.4684   mix 0.0307   time 28.13
scalar:  2.6352
Epoch 357:  train loss 0.6041   train acc 0.6018   worst 0.2610   lr 0.0248   p 23.53   eps 0.4684   mix 0.0306   time 28.93
scalar:  2.5935
Epoch 358:  train loss 0.6032   train acc 0.6027   worst 0.2595   lr 0.0247   p 23.63   eps 0.4684   mix 0.0305   time 28.91
scalar:  2.6448
Epoch 359:  train loss 0.6036   train acc 0.6001   worst 0.2602   lr 0.0247   p 23.73   eps 0.4684   mix 0.0303   time 28.15
Epoch 359:  test acc 0.5731   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 359:  clean acc 0.1629   certified acc 0.0602
Calculating metrics for L_infinity dist model on test set
Epoch 359:  clean acc 0.1601   certified acc 0.0537
scalar:  2.6203
Epoch 360:  train loss 0.6026   train acc 0.6002   worst 0.2609   lr 0.0247   p 23.83   eps 0.4684   mix 0.0302   time 28.51
scalar:  2.5967
Epoch 361:  train loss 0.6032   train acc 0.6024   worst 0.2568   lr 0.0246   p 23.93   eps 0.4684   mix 0.0300   time 27.98
scalar:  2.6477
Epoch 362:  train loss 0.6041   train acc 0.6001   worst 0.2569   lr 0.0246   p 24.03   eps 0.4684   mix 0.0299   time 28.91
scalar:  2.6374
Epoch 363:  train loss 0.6056   train acc 0.6007   worst 0.2541   lr 0.0246   p 24.13   eps 0.4684   mix 0.0298   time 28.77
scalar:  2.6177
Epoch 364:  train loss 0.6046   train acc 0.6009   worst 0.2556   lr 0.0246   p 24.24   eps 0.4684   mix 0.0296   time 28.71
Epoch 364:  test acc 0.5738   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 364:  clean acc 0.1646   certified acc 0.0524
Calculating metrics for L_infinity dist model on test set
Epoch 364:  clean acc 0.1663   certified acc 0.0511
scalar:  2.6691
Epoch 365:  train loss 0.6056   train acc 0.5989   worst 0.2562   lr 0.0245   p 24.34   eps 0.4684   mix 0.0295   time 28.96
scalar:  2.6669
Epoch 366:  train loss 0.6054   train acc 0.5988   worst 0.2561   lr 0.0245   p 24.44   eps 0.4684   mix 0.0294   time 28.72
scalar:  2.6798
Epoch 367:  train loss 0.6070   train acc 0.5983   worst 0.2524   lr 0.0245   p 24.54   eps 0.4684   mix 0.0292   time 28.82
scalar:  2.667
Epoch 368:  train loss 0.6072   train acc 0.5998   worst 0.2515   lr 0.0244   p 24.65   eps 0.4684   mix 0.0291   time 28.65
scalar:  2.6887
Epoch 369:  train loss 0.6046   train acc 0.5998   worst 0.2541   lr 0.0244   p 24.75   eps 0.4684   mix 0.0290   time 28.88
Epoch 369:  test acc 0.5729   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 369:  clean acc 0.1622   certified acc 0.0767
Calculating metrics for L_infinity dist model on test set
Epoch 369:  clean acc 0.1605   certified acc 0.0729
scalar:  2.6649
Epoch 370:  train loss 0.6074   train acc 0.5971   worst 0.2526   lr 0.0244   p 24.85   eps 0.4684   mix 0.0288   time 28.50
scalar:  2.6499
Epoch 371:  train loss 0.6063   train acc 0.5990   worst 0.2514   lr 0.0244   p 24.96   eps 0.4684   mix 0.0287   time 28.79
scalar:  2.6707
Epoch 372:  train loss 0.6070   train acc 0.5967   worst 0.2531   lr 0.0243   p 25.06   eps 0.4684   mix 0.0286   time 28.39
scalar:  2.668
Epoch 373:  train loss 0.6057   train acc 0.5993   worst 0.2526   lr 0.0243   p 25.17   eps 0.4684   mix 0.0284   time 28.47
scalar:  2.6691
Epoch 374:  train loss 0.6082   train acc 0.5974   worst 0.2504   lr 0.0243   p 25.28   eps 0.4684   mix 0.0283   time 29.07
Epoch 374:  test acc 0.5745   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 374:  clean acc 0.1592   certified acc 0.0598
Calculating metrics for L_infinity dist model on test set
Epoch 374:  clean acc 0.1529   certified acc 0.0544
scalar:  2.6855
Epoch 375:  train loss 0.6092   train acc 0.5956   worst 0.2490   lr 0.0243   p 25.38   eps 0.4684   mix 0.0282   time 28.49
scalar:  2.6595
Epoch 376:  train loss 0.6083   train acc 0.5986   worst 0.2480   lr 0.0242   p 25.49   eps 0.4684   mix 0.0280   time 28.77
scalar:  2.6642
Epoch 377:  train loss 0.6075   train acc 0.5978   worst 0.2491   lr 0.0242   p 25.60   eps 0.4684   mix 0.0279   time 28.91
scalar:  2.7006
Epoch 378:  train loss 0.6082   train acc 0.5989   worst 0.2476   lr 0.0242   p 25.70   eps 0.4684   mix 0.0278   time 28.46
scalar:  2.7201
Epoch 379:  train loss 0.6091   train acc 0.5985   worst 0.2461   lr 0.0241   p 25.81   eps 0.4684   mix 0.0277   time 29.42
Epoch 379:  test acc 0.5741   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 379:  clean acc 0.1608   certified acc 0.0577
Calculating metrics for L_infinity dist model on test set
Epoch 379:  clean acc 0.1544   certified acc 0.0504
scalar:  2.689
Epoch 380:  train loss 0.6087   train acc 0.5962   worst 0.2488   lr 0.0241   p 25.92   eps 0.4684   mix 0.0275   time 28.41
scalar:  2.6948
Epoch 381:  train loss 0.6091   train acc 0.5953   worst 0.2473   lr 0.0241   p 26.03   eps 0.4684   mix 0.0274   time 28.52
scalar:  2.67
Epoch 382:  train loss 0.6093   train acc 0.5944   worst 0.2479   lr 0.0240   p 26.14   eps 0.4684   mix 0.0273   time 28.57
scalar:  2.7002
Epoch 383:  train loss 0.6119   train acc 0.5931   worst 0.2453   lr 0.0240   p 26.25   eps 0.4684   mix 0.0271   time 28.51
scalar:  2.7249
Epoch 384:  train loss 0.6097   train acc 0.5972   worst 0.2441   lr 0.0240   p 26.36   eps 0.4684   mix 0.0270   time 28.97
Epoch 384:  test acc 0.5709   time 2.69
Calculating metrics for L_infinity dist model on training set
Epoch 384:  clean acc 0.1776   certified acc 0.0515
Calculating metrics for L_infinity dist model on test set
Epoch 384:  clean acc 0.1727   certified acc 0.0477
scalar:  2.7284
Epoch 385:  train loss 0.6089   train acc 0.5976   worst 0.2430   lr 0.0240   p 26.47   eps 0.4684   mix 0.0269   time 28.28
scalar:  2.7543
Epoch 386:  train loss 0.6095   train acc 0.5952   worst 0.2428   lr 0.0239   p 26.58   eps 0.4684   mix 0.0268   time 28.84
scalar:  2.7406
Epoch 387:  train loss 0.6105   train acc 0.5965   worst 0.2432   lr 0.0239   p 26.69   eps 0.4684   mix 0.0267   time 28.40
scalar:  2.7391
Epoch 388:  train loss 0.6096   train acc 0.5942   worst 0.2447   lr 0.0239   p 26.81   eps 0.4684   mix 0.0265   time 28.69
scalar:  2.7201
Epoch 389:  train loss 0.6105   train acc 0.5937   worst 0.2415   lr 0.0238   p 26.92   eps 0.4684   mix 0.0264   time 28.97
Epoch 389:  test acc 0.5690   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 389:  clean acc 0.1919   certified acc 0.0569
Calculating metrics for L_infinity dist model on test set
Epoch 389:  clean acc 0.1944   certified acc 0.0563
scalar:  2.7742
Epoch 390:  train loss 0.6123   train acc 0.5931   worst 0.2412   lr 0.0238   p 27.03   eps 0.4684   mix 0.0263   time 28.47
scalar:  2.7395
Epoch 391:  train loss 0.6113   train acc 0.5940   worst 0.2427   lr 0.0238   p 27.15   eps 0.4684   mix 0.0262   time 28.56
scalar:  2.7195
Epoch 392:  train loss 0.6116   train acc 0.5940   worst 0.2401   lr 0.0238   p 27.26   eps 0.4684   mix 0.0260   time 28.49
scalar:  2.7174
Epoch 393:  train loss 0.6128   train acc 0.5907   worst 0.2407   lr 0.0237   p 27.37   eps 0.4684   mix 0.0259   time 28.33
scalar:  2.727
Epoch 394:  train loss 0.6120   train acc 0.5932   worst 0.2400   lr 0.0237   p 27.49   eps 0.4684   mix 0.0258   time 28.72
Epoch 394:  test acc 0.5685   time 2.65
Calculating metrics for L_infinity dist model on training set
Epoch 394:  clean acc 0.1752   certified acc 0.0587
Calculating metrics for L_infinity dist model on test set
Epoch 394:  clean acc 0.1732   certified acc 0.0576
scalar:  2.7405
Epoch 395:  train loss 0.6125   train acc 0.5927   worst 0.2384   lr 0.0237   p 27.61   eps 0.4684   mix 0.0257   time 28.37
scalar:  2.7323
Epoch 396:  train loss 0.6165   train acc 0.5907   worst 0.2380   lr 0.0236   p 27.72   eps 0.4684   mix 0.0256   time 28.71
scalar:  2.7629
Epoch 397:  train loss 0.6141   train acc 0.5918   worst 0.2396   lr 0.0236   p 27.84   eps 0.4684   mix 0.0255   time 28.46
scalar:  2.7635
Epoch 398:  train loss 0.6136   train acc 0.5916   worst 0.2390   lr 0.0236   p 27.96   eps 0.4684   mix 0.0253   time 28.43
scalar:  2.7211
Epoch 399:  train loss 0.6118   train acc 0.5920   worst 0.2397   lr 0.0236   p 28.07   eps 0.4684   mix 0.0252   time 29.35
Epoch 399:  test acc 0.5707   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 399:  clean acc 0.1237   certified acc 0.0646
Calculating metrics for L_infinity dist model on test set
Epoch 399:  clean acc 0.1201   certified acc 0.0586
scalar:  2.7534
Epoch 400:  train loss 0.6139   train acc 0.5938   worst 0.2359   lr 0.0235   p 28.19   eps 0.4684   mix 0.0251   time 28.57
scalar:  2.7571
Epoch 401:  train loss 0.6148   train acc 0.5914   worst 0.2357   lr 0.0235   p 28.31   eps 0.4684   mix 0.0250   time 28.66
scalar:  2.784
Epoch 402:  train loss 0.6157   train acc 0.5899   worst 0.2346   lr 0.0235   p 28.43   eps 0.4684   mix 0.0249   time 28.47
scalar:  2.7501
Epoch 403:  train loss 0.6156   train acc 0.5890   worst 0.2355   lr 0.0234   p 28.55   eps 0.4684   mix 0.0248   time 28.64
scalar:  2.76
Epoch 404:  train loss 0.6154   train acc 0.5893   worst 0.2350   lr 0.0234   p 28.67   eps 0.4684   mix 0.0246   time 28.89
Epoch 404:  test acc 0.5638   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 404:  clean acc 0.1719   certified acc 0.0581
Calculating metrics for L_infinity dist model on test set
Epoch 404:  clean acc 0.1720   certified acc 0.0519
scalar:  2.7755
Epoch 405:  train loss 0.6169   train acc 0.5884   worst 0.2344   lr 0.0234   p 28.79   eps 0.4684   mix 0.0245   time 28.79
scalar:  2.7783
Epoch 406:  train loss 0.6154   train acc 0.5905   worst 0.2348   lr 0.0233   p 28.91   eps 0.4684   mix 0.0244   time 28.33
scalar:  2.7883
Epoch 407:  train loss 0.6170   train acc 0.5894   worst 0.2327   lr 0.0233   p 29.03   eps 0.4684   mix 0.0243   time 28.71
scalar:  2.7852
Epoch 408:  train loss 0.6159   train acc 0.5901   worst 0.2345   lr 0.0233   p 29.15   eps 0.4684   mix 0.0242   time 29.05
scalar:  2.7685
Epoch 409:  train loss 0.6147   train acc 0.5919   worst 0.2346   lr 0.0233   p 29.28   eps 0.4684   mix 0.0241   time 28.82
Epoch 409:  test acc 0.5643   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 409:  clean acc 0.1621   certified acc 0.0635
Calculating metrics for L_infinity dist model on test set
Epoch 409:  clean acc 0.1584   certified acc 0.0618
scalar:  2.7971
Epoch 410:  train loss 0.6156   train acc 0.5919   worst 0.2326   lr 0.0232   p 29.40   eps 0.4684   mix 0.0240   time 28.66
scalar:  2.8077
Epoch 411:  train loss 0.6162   train acc 0.5892   worst 0.2333   lr 0.0232   p 29.52   eps 0.4684   mix 0.0239   time 28.24
scalar:  2.7967
Epoch 412:  train loss 0.6152   train acc 0.5896   worst 0.2322   lr 0.0232   p 29.65   eps 0.4684   mix 0.0238   time 28.66
scalar:  2.8064
Epoch 413:  train loss 0.6179   train acc 0.5886   worst 0.2309   lr 0.0231   p 29.77   eps 0.4684   mix 0.0236   time 29.40
scalar:  2.8142
Epoch 414:  train loss 0.6184   train acc 0.5889   worst 0.2282   lr 0.0231   p 29.90   eps 0.4684   mix 0.0235   time 28.67
Epoch 414:  test acc 0.5622   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 414:  clean acc 0.1667   certified acc 0.0583
Calculating metrics for L_infinity dist model on test set
Epoch 414:  clean acc 0.1631   certified acc 0.0571
scalar:  2.7609
Epoch 415:  train loss 0.6187   train acc 0.5880   worst 0.2285   lr 0.0231   p 30.02   eps 0.4684   mix 0.0234   time 28.59
scalar:  2.7597
Epoch 416:  train loss 0.6173   train acc 0.5871   worst 0.2316   lr 0.0230   p 30.15   eps 0.4684   mix 0.0233   time 28.84
scalar:  2.7584
Epoch 417:  train loss 0.6186   train acc 0.5868   worst 0.2292   lr 0.0230   p 30.28   eps 0.4684   mix 0.0232   time 28.23
scalar:  2.8195
Epoch 418:  train loss 0.6177   train acc 0.5901   worst 0.2281   lr 0.0230   p 30.40   eps 0.4684   mix 0.0231   time 28.64
scalar:  2.7833
Epoch 419:  train loss 0.6186   train acc 0.5868   worst 0.2274   lr 0.0229   p 30.53   eps 0.4684   mix 0.0230   time 28.73
Epoch 419:  test acc 0.5611   time 2.65
Calculating metrics for L_infinity dist model on training set
Epoch 419:  clean acc 0.1258   certified acc 0.0796
Calculating metrics for L_infinity dist model on test set
Epoch 419:  clean acc 0.1225   certified acc 0.0783
scalar:  2.7776
Epoch 420:  train loss 0.6166   train acc 0.5870   worst 0.2294   lr 0.0229   p 30.66   eps 0.4684   mix 0.0229   time 28.91
scalar:  2.7864
Epoch 421:  train loss 0.6188   train acc 0.5865   worst 0.2288   lr 0.0229   p 30.79   eps 0.4684   mix 0.0228   time 29.02
scalar:  2.796
Epoch 422:  train loss 0.6187   train acc 0.5871   worst 0.2282   lr 0.0229   p 30.92   eps 0.4684   mix 0.0227   time 28.44
scalar:  2.8077
Epoch 423:  train loss 0.6186   train acc 0.5895   worst 0.2264   lr 0.0228   p 31.05   eps 0.4684   mix 0.0226   time 28.46
scalar:  2.8486
Epoch 424:  train loss 0.6201   train acc 0.5877   worst 0.2251   lr 0.0228   p 31.18   eps 0.4684   mix 0.0225   time 28.33
Epoch 424:  test acc 0.5624   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 424:  clean acc 0.1779   certified acc 0.0627
Calculating metrics for L_infinity dist model on test set
Epoch 424:  clean acc 0.1716   certified acc 0.0590
scalar:  2.8334
Epoch 425:  train loss 0.6202   train acc 0.5874   worst 0.2245   lr 0.0228   p 31.31   eps 0.4684   mix 0.0224   time 28.91
scalar:  2.8497
Epoch 426:  train loss 0.6172   train acc 0.5891   worst 0.2255   lr 0.0227   p 31.44   eps 0.4684   mix 0.0223   time 27.96
scalar:  2.8297
Epoch 427:  train loss 0.6194   train acc 0.5866   worst 0.2273   lr 0.0227   p 31.57   eps 0.4684   mix 0.0222   time 28.62
scalar:  2.8437
Epoch 428:  train loss 0.6209   train acc 0.5842   worst 0.2249   lr 0.0227   p 31.71   eps 0.4684   mix 0.0221   time 28.31
scalar:  2.8167
Epoch 429:  train loss 0.6203   train acc 0.5857   worst 0.2252   lr 0.0226   p 31.84   eps 0.4684   mix 0.0220   time 28.64
Epoch 429:  test acc 0.5633   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 429:  clean acc 0.1955   certified acc 0.0786
Calculating metrics for L_infinity dist model on test set
Epoch 429:  clean acc 0.1940   certified acc 0.0758
scalar:  2.8058
Epoch 430:  train loss 0.6210   train acc 0.5858   worst 0.2248   lr 0.0226   p 31.98   eps 0.4684   mix 0.0219   time 28.81
scalar:  2.839
Epoch 431:  train loss 0.6211   train acc 0.5830   worst 0.2242   lr 0.0226   p 32.11   eps 0.4684   mix 0.0218   time 28.50
scalar:  2.8253
Epoch 432:  train loss 0.6210   train acc 0.5845   worst 0.2225   lr 0.0225   p 32.24   eps 0.4684   mix 0.0217   time 28.47
scalar:  2.846
Epoch 433:  train loss 0.6225   train acc 0.5834   worst 0.2227   lr 0.0225   p 32.38   eps 0.4684   mix 0.0216   time 28.97
scalar:  2.8599
Epoch 434:  train loss 0.6229   train acc 0.5836   worst 0.2214   lr 0.0225   p 32.52   eps 0.4684   mix 0.0215   time 28.46
Epoch 434:  test acc 0.5600   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 434:  clean acc 0.1834   certified acc 0.0649
Calculating metrics for L_infinity dist model on test set
Epoch 434:  clean acc 0.1810   certified acc 0.0606
scalar:  2.8472
Epoch 435:  train loss 0.6223   train acc 0.5836   worst 0.2213   lr 0.0224   p 32.65   eps 0.4684   mix 0.0214   time 28.76
scalar:  2.8474
Epoch 436:  train loss 0.6198   train acc 0.5851   worst 0.2231   lr 0.0224   p 32.79   eps 0.4684   mix 0.0213   time 28.51
scalar:  2.8376
Epoch 437:  train loss 0.6234   train acc 0.5820   worst 0.2200   lr 0.0224   p 32.93   eps 0.4684   mix 0.0212   time 28.84
scalar:  2.8356
Epoch 438:  train loss 0.6224   train acc 0.5844   worst 0.2199   lr 0.0224   p 33.07   eps 0.4684   mix 0.0211   time 28.63
scalar:  2.8587
Epoch 439:  train loss 0.6240   train acc 0.5814   worst 0.2205   lr 0.0223   p 33.21   eps 0.4684   mix 0.0210   time 28.59
Epoch 439:  test acc 0.5575   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 439:  clean acc 0.1649   certified acc 0.0808
Calculating metrics for L_infinity dist model on test set
Epoch 439:  clean acc 0.1567   certified acc 0.0745
scalar:  2.8275
Epoch 440:  train loss 0.6238   train acc 0.5832   worst 0.2179   lr 0.0223   p 33.35   eps 0.4684   mix 0.0209   time 28.79
scalar:  2.8453
Epoch 441:  train loss 0.6233   train acc 0.5797   worst 0.2206   lr 0.0223   p 33.49   eps 0.4684   mix 0.0208   time 28.70
scalar:  2.8059
Epoch 442:  train loss 0.6234   train acc 0.5817   worst 0.2186   lr 0.0222   p 33.63   eps 0.4684   mix 0.0207   time 28.46
scalar:  2.8453
Epoch 443:  train loss 0.6236   train acc 0.5833   worst 0.2180   lr 0.0222   p 33.77   eps 0.4684   mix 0.0206   time 28.79
scalar:  2.8808
Epoch 444:  train loss 0.6224   train acc 0.5836   worst 0.2191   lr 0.0222   p 33.91   eps 0.4684   mix 0.0205   time 28.78
Epoch 444:  test acc 0.5557   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 444:  clean acc 0.1848   certified acc 0.0732
Calculating metrics for L_infinity dist model on test set
Epoch 444:  clean acc 0.1814   certified acc 0.0685
scalar:  2.8843
Epoch 445:  train loss 0.6229   train acc 0.5820   worst 0.2183   lr 0.0221   p 34.05   eps 0.4684   mix 0.0204   time 28.20
scalar:  2.887
Epoch 446:  train loss 0.6226   train acc 0.5812   worst 0.2207   lr 0.0221   p 34.20   eps 0.4684   mix 0.0203   time 28.65
scalar:  2.8617
Epoch 447:  train loss 0.6256   train acc 0.5817   worst 0.2161   lr 0.0221   p 34.34   eps 0.4684   mix 0.0202   time 29.01
scalar:  2.8686
Epoch 448:  train loss 0.6245   train acc 0.5811   worst 0.2164   lr 0.0220   p 34.49   eps 0.4684   mix 0.0201   time 28.37
scalar:  2.8919
Epoch 449:  train loss 0.6238   train acc 0.5821   worst 0.2155   lr 0.0220   p 34.63   eps 0.4684   mix 0.0200   time 28.47
Epoch 449:  test acc 0.5611   time 2.67
Calculating metrics for L_infinity dist model on training set
Epoch 449:  clean acc 0.1798   certified acc 0.0642
Calculating metrics for L_infinity dist model on test set
Epoch 449:  clean acc 0.1737   certified acc 0.0604
scalar:  2.8494
Epoch 450:  train loss 0.6229   train acc 0.5821   worst 0.2133   lr 0.0220   p 34.78   eps 0.4684   mix 0.0199   time 28.49
scalar:  2.8961
Epoch 451:  train loss 0.6266   train acc 0.5790   worst 0.2147   lr 0.0219   p 34.92   eps 0.4684   mix 0.0198   time 28.72
scalar:  2.9002
Epoch 452:  train loss 0.6254   train acc 0.5803   worst 0.2145   lr 0.0219   p 35.07   eps 0.4684   mix 0.0198   time 28.95
scalar:  2.8755
Epoch 453:  train loss 0.6252   train acc 0.5835   worst 0.2139   lr 0.0219   p 35.22   eps 0.4684   mix 0.0197   time 28.28
scalar:  2.9018
Epoch 454:  train loss 0.6233   train acc 0.5825   worst 0.2167   lr 0.0218   p 35.36   eps 0.4684   mix 0.0196   time 28.48
Epoch 454:  test acc 0.5550   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 454:  clean acc 0.2079   certified acc 0.0743
Calculating metrics for L_infinity dist model on test set
Epoch 454:  clean acc 0.2083   certified acc 0.0746
scalar:  2.9098
Epoch 455:  train loss 0.6240   train acc 0.5838   worst 0.2144   lr 0.0218   p 35.51   eps 0.4684   mix 0.0195   time 28.63
scalar:  2.9309
Epoch 456:  train loss 0.6250   train acc 0.5828   worst 0.2161   lr 0.0218   p 35.66   eps 0.4684   mix 0.0194   time 28.29
scalar:  2.8981
Epoch 457:  train loss 0.6268   train acc 0.5798   worst 0.2123   lr 0.0217   p 35.81   eps 0.4684   mix 0.0193   time 28.32
scalar:  2.8825
Epoch 458:  train loss 0.6255   train acc 0.5806   worst 0.2133   lr 0.0217   p 35.96   eps 0.4684   mix 0.0192   time 28.49
scalar:  2.8928
Epoch 459:  train loss 0.6263   train acc 0.5787   worst 0.2140   lr 0.0217   p 36.12   eps 0.4684   mix 0.0191   time 28.65
Epoch 459:  test acc 0.5564   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 459:  clean acc 0.2184   certified acc 0.0619
Calculating metrics for L_infinity dist model on test set
Epoch 459:  clean acc 0.2203   certified acc 0.0606
scalar:  2.8751
Epoch 460:  train loss 0.6246   train acc 0.5815   worst 0.2134   lr 0.0216   p 36.27   eps 0.4684   mix 0.0190   time 28.44
scalar:  2.9069
Epoch 461:  train loss 0.6274   train acc 0.5776   worst 0.2112   lr 0.0216   p 36.42   eps 0.4684   mix 0.0190   time 28.12
scalar:  2.9177
Epoch 462:  train loss 0.6258   train acc 0.5810   worst 0.2121   lr 0.0216   p 36.57   eps 0.4684   mix 0.0189   time 28.35
scalar:  2.9347
Epoch 463:  train loss 0.6273   train acc 0.5801   worst 0.2088   lr 0.0216   p 36.73   eps 0.4684   mix 0.0188   time 28.26
scalar:  2.8981
Epoch 464:  train loss 0.6274   train acc 0.5806   worst 0.2099   lr 0.0215   p 36.88   eps 0.4684   mix 0.0187   time 28.84
Epoch 464:  test acc 0.5567   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 464:  clean acc 0.2157   certified acc 0.0852
Calculating metrics for L_infinity dist model on test set
Epoch 464:  clean acc 0.2127   certified acc 0.0820
scalar:  2.9353
Epoch 465:  train loss 0.6269   train acc 0.5813   worst 0.2088   lr 0.0215   p 37.04   eps 0.4684   mix 0.0186   time 28.96
scalar:  2.9332
Epoch 466:  train loss 0.6270   train acc 0.5818   worst 0.2095   lr 0.0215   p 37.19   eps 0.4684   mix 0.0185   time 28.18
scalar:  2.9345
Epoch 467:  train loss 0.6272   train acc 0.5802   worst 0.2096   lr 0.0214   p 37.35   eps 0.4684   mix 0.0184   time 28.38
scalar:  2.9085
Epoch 468:  train loss 0.6266   train acc 0.5797   worst 0.2108   lr 0.0214   p 37.51   eps 0.4684   mix 0.0184   time 28.11
scalar:  2.9296
Epoch 469:  train loss 0.6270   train acc 0.5798   worst 0.2070   lr 0.0214   p 37.66   eps 0.4684   mix 0.0183   time 28.98
Epoch 469:  test acc 0.5559   time 2.70
Calculating metrics for L_infinity dist model on training set
Epoch 469:  clean acc 0.2228   certified acc 0.0782
Calculating metrics for L_infinity dist model on test set
Epoch 469:  clean acc 0.2204   certified acc 0.0752
scalar:  2.9465
Epoch 470:  train loss 0.6276   train acc 0.5788   worst 0.2096   lr 0.0213   p 37.82   eps 0.4684   mix 0.0182   time 28.47
scalar:  2.9352
Epoch 471:  train loss 0.6273   train acc 0.5794   worst 0.2078   lr 0.0213   p 37.98   eps 0.4684   mix 0.0181   time 28.62
scalar:  2.905
Epoch 472:  train loss 0.6271   train acc 0.5793   worst 0.2084   lr 0.0213   p 38.14   eps 0.4684   mix 0.0180   time 28.07
scalar:  2.9158
Epoch 473:  train loss 0.6279   train acc 0.5796   worst 0.2068   lr 0.0212   p 38.30   eps 0.4684   mix 0.0179   time 28.25
scalar:  2.9514
Epoch 474:  train loss 0.6290   train acc 0.5762   worst 0.2080   lr 0.0212   p 38.46   eps 0.4684   mix 0.0179   time 28.84
Epoch 474:  test acc 0.5651   time 2.67
Calculating metrics for L_infinity dist model on training set
Epoch 474:  clean acc 0.2229   certified acc 0.0818
Calculating metrics for L_infinity dist model on test set
Epoch 474:  clean acc 0.2190   certified acc 0.0778
scalar:  2.9122
Epoch 475:  train loss 0.6272   train acc 0.5792   worst 0.2085   lr 0.0212   p 38.62   eps 0.4684   mix 0.0178   time 28.33
scalar:  2.9239
Epoch 476:  train loss 0.6290   train acc 0.5781   worst 0.2056   lr 0.0211   p 38.79   eps 0.4684   mix 0.0177   time 28.49
scalar:  2.9866
Epoch 477:  train loss 0.6268   train acc 0.5796   worst 0.2093   lr 0.0211   p 38.95   eps 0.4684   mix 0.0176   time 28.37
scalar:  2.9524
Epoch 478:  train loss 0.6286   train acc 0.5802   worst 0.2051   lr 0.0211   p 39.11   eps 0.4684   mix 0.0175   time 28.16
scalar:  2.9453
Epoch 479:  train loss 0.6300   train acc 0.5790   worst 0.2050   lr 0.0210   p 39.28   eps 0.4684   mix 0.0174   time 28.65
Epoch 479:  test acc 0.5567   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 479:  clean acc 0.2529   certified acc 0.0850
Calculating metrics for L_infinity dist model on test set
Epoch 479:  clean acc 0.2514   certified acc 0.0804
scalar:  2.9376
Epoch 480:  train loss 0.6300   train acc 0.5780   worst 0.2053   lr 0.0210   p 39.44   eps 0.4684   mix 0.0174   time 28.63
scalar:  2.9725
Epoch 481:  train loss 0.6286   train acc 0.5777   worst 0.2070   lr 0.0210   p 39.61   eps 0.4684   mix 0.0173   time 28.42
scalar:  2.938
Epoch 482:  train loss 0.6274   train acc 0.5789   worst 0.2070   lr 0.0209   p 39.78   eps 0.4684   mix 0.0172   time 28.14
scalar:  2.9683
Epoch 483:  train loss 0.6275   train acc 0.5795   worst 0.2053   lr 0.0209   p 39.94   eps 0.4684   mix 0.0171   time 28.10
scalar:  2.9833
Epoch 484:  train loss 0.6297   train acc 0.5784   worst 0.2051   lr 0.0209   p 40.11   eps 0.4684   mix 0.0170   time 28.51
Epoch 484:  test acc 0.5546   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 484:  clean acc 0.2642   certified acc 0.0935
Calculating metrics for L_infinity dist model on test set
Epoch 484:  clean acc 0.2601   certified acc 0.0921
scalar:  2.9673
Epoch 485:  train loss 0.6284   train acc 0.5772   worst 0.2017   lr 0.0208   p 40.28   eps 0.4684   mix 0.0170   time 28.55
scalar:  2.9725
Epoch 486:  train loss 0.6298   train acc 0.5763   worst 0.2035   lr 0.0208   p 40.45   eps 0.4684   mix 0.0169   time 28.71
scalar:  2.9716
Epoch 487:  train loss 0.6305   train acc 0.5756   worst 0.2024   lr 0.0208   p 40.62   eps 0.4684   mix 0.0168   time 28.37
scalar:  2.9816
Epoch 488:  train loss 0.6301   train acc 0.5764   worst 0.2034   lr 0.0207   p 40.79   eps 0.4684   mix 0.0167   time 28.57
scalar:  2.9746
Epoch 489:  train loss 0.6304   train acc 0.5767   worst 0.2033   lr 0.0207   p 40.96   eps 0.4684   mix 0.0167   time 28.52
Epoch 489:  test acc 0.5526   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 489:  clean acc 0.2515   certified acc 0.0973
Calculating metrics for L_infinity dist model on test set
Epoch 489:  clean acc 0.2509   certified acc 0.0936
scalar:  2.9452
Epoch 490:  train loss 0.6284   train acc 0.5758   worst 0.2038   lr 0.0207   p 41.14   eps 0.4684   mix 0.0166   time 28.68
scalar:  2.9438
Epoch 491:  train loss 0.6292   train acc 0.5759   worst 0.2023   lr 0.0206   p 41.31   eps 0.4684   mix 0.0165   time 28.35
scalar:  2.9533
Epoch 492:  train loss 0.6304   train acc 0.5769   worst 0.2033   lr 0.0206   p 41.48   eps 0.4684   mix 0.0164   time 28.22
scalar:  2.9727
Epoch 493:  train loss 0.6315   train acc 0.5756   worst 0.2011   lr 0.0206   p 41.66   eps 0.4684   mix 0.0164   time 28.58
scalar:  2.9807
Epoch 494:  train loss 0.6302   train acc 0.5777   worst 0.2012   lr 0.0205   p 41.83   eps 0.4684   mix 0.0163   time 28.45
Epoch 494:  test acc 0.5502   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 494:  clean acc 0.2768   certified acc 0.1043
Calculating metrics for L_infinity dist model on test set
Epoch 494:  clean acc 0.2731   certified acc 0.1014
scalar:  2.9882
Epoch 495:  train loss 0.6315   train acc 0.5770   worst 0.1988   lr 0.0205   p 42.01   eps 0.4684   mix 0.0162   time 28.43
scalar:  2.9937
Epoch 496:  train loss 0.6300   train acc 0.5771   worst 0.2000   lr 0.0205   p 42.18   eps 0.4684   mix 0.0161   time 27.92
scalar:  2.9883
Epoch 497:  train loss 0.6305   train acc 0.5762   worst 0.2005   lr 0.0204   p 42.36   eps 0.4684   mix 0.0161   time 28.34
scalar:  2.9886
Epoch 498:  train loss 0.6318   train acc 0.5748   worst 0.2011   lr 0.0204   p 42.54   eps 0.4684   mix 0.0160   time 28.58
scalar:  2.9436
Epoch 499:  train loss 0.6285   train acc 0.5791   worst 0.1998   lr 0.0204   p 42.72   eps 0.4684   mix 0.0159   time 28.67
Epoch 499:  test acc 0.5523   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 499:  clean acc 0.2946   certified acc 0.1056
Calculating metrics for L_infinity dist model on test set
Epoch 499:  clean acc 0.2951   certified acc 0.1005
scalar:  3.0148
Epoch 500:  train loss 0.6322   train acc 0.5751   worst 0.1966   lr 0.0203   p 42.90   eps 0.4684   mix 0.0158   time 28.93
scalar:  3.0134
Epoch 501:  train loss 0.6307   train acc 0.5742   worst 0.1998   lr 0.0203   p 43.08   eps 0.4684   mix 0.0158   time 28.27
scalar:  2.9955
Epoch 502:  train loss 0.6339   train acc 0.5735   worst 0.1971   lr 0.0203   p 43.26   eps 0.4684   mix 0.0157   time 28.01
scalar:  2.9845
Epoch 503:  train loss 0.6311   train acc 0.5763   worst 0.1994   lr 0.0202   p 43.44   eps 0.4684   mix 0.0156   time 28.53
scalar:  3.0044
Epoch 504:  train loss 0.6326   train acc 0.5761   worst 0.1965   lr 0.0202   p 43.63   eps 0.4684   mix 0.0155   time 28.76
Epoch 504:  test acc 0.5540   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 504:  clean acc 0.2711   certified acc 0.1067
Calculating metrics for L_infinity dist model on test set
Epoch 504:  clean acc 0.2720   certified acc 0.1041
scalar:  3.0433
Epoch 505:  train loss 0.6309   train acc 0.5777   worst 0.1982   lr 0.0201   p 43.81   eps 0.4684   mix 0.0155   time 28.34
scalar:  3.0163
Epoch 506:  train loss 0.6316   train acc 0.5768   worst 0.1966   lr 0.0201   p 43.99   eps 0.4684   mix 0.0154   time 28.05
scalar:  3.0359
Epoch 507:  train loss 0.6306   train acc 0.5759   worst 0.1985   lr 0.0201   p 44.18   eps 0.4684   mix 0.0153   time 28.11
scalar:  3.0009
Epoch 508:  train loss 0.6322   train acc 0.5743   worst 0.1964   lr 0.0200   p 44.36   eps 0.4684   mix 0.0153   time 28.55
scalar:  3.0223
Epoch 509:  train loss 0.6338   train acc 0.5750   worst 0.1969   lr 0.0200   p 44.55   eps 0.4684   mix 0.0152   time 28.86
Epoch 509:  test acc 0.5516   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 509:  clean acc 0.2865   certified acc 0.1053
Calculating metrics for L_infinity dist model on test set
Epoch 509:  clean acc 0.2854   certified acc 0.1062
scalar:  3.0153
Epoch 510:  train loss 0.6331   train acc 0.5737   worst 0.1937   lr 0.0200   p 44.74   eps 0.4684   mix 0.0151   time 28.58
scalar:  2.9964
Epoch 511:  train loss 0.6321   train acc 0.5764   worst 0.1950   lr 0.0199   p 44.93   eps 0.4684   mix 0.0151   time 28.34
scalar:  3.0377
Epoch 512:  train loss 0.6322   train acc 0.5753   worst 0.1950   lr 0.0199   p 45.12   eps 0.4684   mix 0.0150   time 28.54
scalar:  3.0454
Epoch 513:  train loss 0.6325   train acc 0.5778   worst 0.1948   lr 0.0199   p 45.31   eps 0.4684   mix 0.0149   time 28.73
scalar:  3.0485
Epoch 514:  train loss 0.6324   train acc 0.5759   worst 0.1946   lr 0.0198   p 45.50   eps 0.4684   mix 0.0148   time 29.01
Epoch 514:  test acc 0.5586   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 514:  clean acc 0.2949   certified acc 0.1041
Calculating metrics for L_infinity dist model on test set
Epoch 514:  clean acc 0.2975   certified acc 0.1027
scalar:  3.0378
Epoch 515:  train loss 0.6336   train acc 0.5741   worst 0.1923   lr 0.0198   p 45.69   eps 0.4684   mix 0.0148   time 28.26
scalar:  3.0509
Epoch 516:  train loss 0.6338   train acc 0.5747   worst 0.1951   lr 0.0198   p 45.88   eps 0.4684   mix 0.0147   time 28.04
scalar:  3.0556
Epoch 517:  train loss 0.6328   train acc 0.5767   worst 0.1922   lr 0.0197   p 46.07   eps 0.4684   mix 0.0146   time 28.36
scalar:  3.0377
Epoch 518:  train loss 0.6338   train acc 0.5756   worst 0.1930   lr 0.0197   p 46.27   eps 0.4684   mix 0.0146   time 28.35
scalar:  3.0257
Epoch 519:  train loss 0.6320   train acc 0.5749   worst 0.1936   lr 0.0197   p 46.46   eps 0.4684   mix 0.0145   time 28.70
Epoch 519:  test acc 0.5527   time 2.65
Calculating metrics for L_infinity dist model on training set
Epoch 519:  clean acc 0.3354   certified acc 0.1286
Calculating metrics for L_infinity dist model on test set
Epoch 519:  clean acc 0.3369   certified acc 0.1267
scalar:  3.0436
Epoch 520:  train loss 0.6326   train acc 0.5755   worst 0.1906   lr 0.0196   p 46.66   eps 0.4684   mix 0.0144   time 28.34
scalar:  3.0545
Epoch 521:  train loss 0.6327   train acc 0.5763   worst 0.1937   lr 0.0196   p 46.85   eps 0.4684   mix 0.0144   time 28.23
scalar:  3.0562
Epoch 522:  train loss 0.6331   train acc 0.5758   worst 0.1914   lr 0.0196   p 47.05   eps 0.4684   mix 0.0143   time 28.25
scalar:  3.0461
Epoch 523:  train loss 0.6343   train acc 0.5729   worst 0.1914   lr 0.0195   p 47.25   eps 0.4684   mix 0.0142   time 28.47
scalar:  3.0525
Epoch 524:  train loss 0.6323   train acc 0.5745   worst 0.1944   lr 0.0195   p 47.45   eps 0.4684   mix 0.0142   time 29.18
Epoch 524:  test acc 0.5452   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 524:  clean acc 0.3629   certified acc 0.1060
Calculating metrics for L_infinity dist model on test set
Epoch 524:  clean acc 0.3653   certified acc 0.1072
scalar:  3.0488
Epoch 525:  train loss 0.6328   train acc 0.5733   worst 0.1948   lr 0.0195   p 47.65   eps 0.4684   mix 0.0141   time 28.00
scalar:  3.0335
Epoch 526:  train loss 0.6334   train acc 0.5725   worst 0.1922   lr 0.0194   p 47.85   eps 0.4684   mix 0.0140   time 28.10
scalar:  2.9979
Epoch 527:  train loss 0.6344   train acc 0.5746   worst 0.1898   lr 0.0194   p 48.05   eps 0.4684   mix 0.0140   time 28.77
scalar:  3.0607
Epoch 528:  train loss 0.6335   train acc 0.5753   worst 0.1912   lr 0.0194   p 48.25   eps 0.4684   mix 0.0139   time 28.29
scalar:  3.0793
Epoch 529:  train loss 0.6333   train acc 0.5777   worst 0.1904   lr 0.0193   p 48.45   eps 0.4684   mix 0.0139   time 28.19
Epoch 529:  test acc 0.5533   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 529:  clean acc 0.3236   certified acc 0.1123
Calculating metrics for L_infinity dist model on test set
Epoch 529:  clean acc 0.3253   certified acc 0.1117
scalar:  3.0381
Epoch 530:  train loss 0.6339   train acc 0.5729   worst 0.1924   lr 0.0193   p 48.66   eps 0.4684   mix 0.0138   time 28.07
scalar:  3.0665
Epoch 531:  train loss 0.6355   train acc 0.5736   worst 0.1897   lr 0.0193   p 48.86   eps 0.4684   mix 0.0137   time 27.85
scalar:  3.0789
Epoch 532:  train loss 0.6340   train acc 0.5717   worst 0.1904   lr 0.0192   p 49.07   eps 0.4684   mix 0.0137   time 28.40
scalar:  3.0709
Epoch 533:  train loss 0.6349   train acc 0.5744   worst 0.1883   lr 0.0192   p 49.27   eps 0.4684   mix 0.0136   time 28.19
scalar:  3.0906
Epoch 534:  train loss 0.6350   train acc 0.5746   worst 0.1887   lr 0.0192   p 49.48   eps 0.4684   mix 0.0135   time 28.84
Epoch 534:  test acc 0.5513   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 534:  clean acc 0.3419   certified acc 0.1277
Calculating metrics for L_infinity dist model on test set
Epoch 534:  clean acc 0.3473   certified acc 0.1277
scalar:  3.104
Epoch 535:  train loss 0.6335   train acc 0.5758   worst 0.1902   lr 0.0191   p 49.69   eps 0.4684   mix 0.0135   time 27.99
scalar:  3.0865
Epoch 536:  train loss 0.6342   train acc 0.5750   worst 0.1879   lr 0.0191   p 49.90   eps 0.4684   mix 0.0134   time 28.27
scalar:  3.1017
Epoch 537:  train loss 0.6348   train acc 0.5742   worst 0.1885   lr 0.0190   p 50.11   eps 0.4684   mix 0.0134   time 28.43
scalar:  3.0613
Epoch 538:  train loss 0.6338   train acc 0.5733   worst 0.1885   lr 0.0190   p 50.32   eps 0.4684   mix 0.0133   time 28.35
scalar:  3.0725
Epoch 539:  train loss 0.6331   train acc 0.5758   worst 0.1895   lr 0.0190   p 50.53   eps 0.4684   mix 0.0132   time 29.33
Epoch 539:  test acc 0.5470   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 539:  clean acc 0.3726   certified acc 0.1528
Calculating metrics for L_infinity dist model on test set
Epoch 539:  clean acc 0.3749   certified acc 0.1503
scalar:  3.0857
Epoch 540:  train loss 0.6348   train acc 0.5727   worst 0.1878   lr 0.0189   p 50.74   eps 0.4684   mix 0.0132   time 27.93
scalar:  3.0846
Epoch 541:  train loss 0.6361   train acc 0.5697   worst 0.1890   lr 0.0189   p 50.96   eps 0.4684   mix 0.0131   time 28.37
scalar:  3.0635
Epoch 542:  train loss 0.6352   train acc 0.5724   worst 0.1870   lr 0.0189   p 51.17   eps 0.4684   mix 0.0130   time 28.21
scalar:  3.0993
Epoch 543:  train loss 0.6358   train acc 0.5751   worst 0.1855   lr 0.0188   p 51.39   eps 0.4684   mix 0.0130   time 28.32
scalar:  3.1278
Epoch 544:  train loss 0.6354   train acc 0.5707   worst 0.1880   lr 0.0188   p 51.60   eps 0.4684   mix 0.0129   time 28.57
Epoch 544:  test acc 0.5455   time 2.70
Calculating metrics for L_infinity dist model on training set
Epoch 544:  clean acc 0.3729   certified acc 0.1470
Calculating metrics for L_infinity dist model on test set
Epoch 544:  clean acc 0.3672   certified acc 0.1448
scalar:  3.0878
Epoch 545:  train loss 0.6332   train acc 0.5765   worst 0.1871   lr 0.0188   p 51.82   eps 0.4684   mix 0.0129   time 28.25
scalar:  3.1018
Epoch 546:  train loss 0.6344   train acc 0.5729   worst 0.1868   lr 0.0187   p 52.04   eps 0.4684   mix 0.0128   time 27.98
scalar:  3.0872
Epoch 547:  train loss 0.6359   train acc 0.5736   worst 0.1881   lr 0.0187   p 52.26   eps 0.4684   mix 0.0128   time 28.24
scalar:  3.1037
Epoch 548:  train loss 0.6362   train acc 0.5737   worst 0.1864   lr 0.0187   p 52.48   eps 0.4684   mix 0.0127   time 28.44
scalar:  3.1359
Epoch 549:  train loss 0.6369   train acc 0.5723   worst 0.1853   lr 0.0186   p 52.70   eps 0.4684   mix 0.0126   time 28.58
Epoch 549:  test acc 0.5530   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 549:  clean acc 0.3699   certified acc 0.1520
Calculating metrics for L_infinity dist model on test set
Epoch 549:  clean acc 0.3651   certified acc 0.1523
scalar:  3.1108
Epoch 550:  train loss 0.6350   train acc 0.5731   worst 0.1848   lr 0.0186   p 52.92   eps 0.4684   mix 0.0126   time 27.90
scalar:  3.098
Epoch 551:  train loss 0.6356   train acc 0.5738   worst 0.1844   lr 0.0186   p 53.14   eps 0.4684   mix 0.0125   time 28.07
scalar:  3.0805
Epoch 552:  train loss 0.6354   train acc 0.5735   worst 0.1859   lr 0.0185   p 53.37   eps 0.4684   mix 0.0125   time 28.11
scalar:  3.1226
Epoch 553:  train loss 0.6369   train acc 0.5723   worst 0.1845   lr 0.0185   p 53.59   eps 0.4684   mix 0.0124   time 28.60
scalar:  3.1186
Epoch 554:  train loss 0.6366   train acc 0.5722   worst 0.1834   lr 0.0184   p 53.82   eps 0.4684   mix 0.0123   time 28.16
Epoch 554:  test acc 0.5502   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 554:  clean acc 0.3979   certified acc 0.1712
Calculating metrics for L_infinity dist model on test set
Epoch 554:  clean acc 0.4024   certified acc 0.1750
scalar:  3.0956
Epoch 555:  train loss 0.6358   train acc 0.5734   worst 0.1849   lr 0.0184   p 54.04   eps 0.4684   mix 0.0123   time 27.94
scalar:  3.1281
Epoch 556:  train loss 0.6360   train acc 0.5735   worst 0.1822   lr 0.0184   p 54.27   eps 0.4684   mix 0.0122   time 28.44
scalar:  3.1199
Epoch 557:  train loss 0.6370   train acc 0.5727   worst 0.1827   lr 0.0183   p 54.50   eps 0.4684   mix 0.0122   time 28.31
scalar:  3.111
Epoch 558:  train loss 0.6357   train acc 0.5710   worst 0.1842   lr 0.0183   p 54.73   eps 0.4684   mix 0.0121   time 28.38
scalar:  3.1106
Epoch 559:  train loss 0.6359   train acc 0.5714   worst 0.1847   lr 0.0183   p 54.96   eps 0.4684   mix 0.0121   time 28.48
Epoch 559:  test acc 0.5479   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 559:  clean acc 0.3693   certified acc 0.1591
Calculating metrics for L_infinity dist model on test set
Epoch 559:  clean acc 0.3662   certified acc 0.1543
scalar:  3.1191
Epoch 560:  train loss 0.6357   train acc 0.5735   worst 0.1840   lr 0.0182   p 55.19   eps 0.4684   mix 0.0120   time 27.80
scalar:  3.0944
Epoch 561:  train loss 0.6366   train acc 0.5692   worst 0.1821   lr 0.0182   p 55.42   eps 0.4684   mix 0.0120   time 28.48
scalar:  3.0872
Epoch 562:  train loss 0.6346   train acc 0.5732   worst 0.1841   lr 0.0182   p 55.65   eps 0.4684   mix 0.0119   time 28.64
scalar:  3.1108
Epoch 563:  train loss 0.6365   train acc 0.5702   worst 0.1820   lr 0.0181   p 55.89   eps 0.4684   mix 0.0118   time 28.25
scalar:  3.1192
Epoch 564:  train loss 0.6386   train acc 0.5703   worst 0.1815   lr 0.0181   p 56.12   eps 0.4684   mix 0.0118   time 28.22
Epoch 564:  test acc 0.5477   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 564:  clean acc 0.3937   certified acc 0.1646
Calculating metrics for L_infinity dist model on test set
Epoch 564:  clean acc 0.3956   certified acc 0.1684
scalar:  3.09
Epoch 565:  train loss 0.6391   train acc 0.5676   worst 0.1812   lr 0.0181   p 56.36   eps 0.4684   mix 0.0117   time 28.08
scalar:  3.113
Epoch 566:  train loss 0.6364   train acc 0.5709   worst 0.1838   lr 0.0180   p 56.60   eps 0.4684   mix 0.0117   time 28.08
scalar:  3.1218
Epoch 567:  train loss 0.6359   train acc 0.5735   worst 0.1816   lr 0.0180   p 56.84   eps 0.4684   mix 0.0116   time 28.20
scalar:  3.1517
Epoch 568:  train loss 0.6361   train acc 0.5722   worst 0.1825   lr 0.0180   p 57.07   eps 0.4684   mix 0.0116   time 28.75
scalar:  3.1295
Epoch 569:  train loss 0.6367   train acc 0.5703   worst 0.1808   lr 0.0179   p 57.31   eps 0.4684   mix 0.0115   time 28.36
Epoch 569:  test acc 0.5432   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 569:  clean acc 0.4063   certified acc 0.1747
Calculating metrics for L_infinity dist model on test set
Epoch 569:  clean acc 0.4008   certified acc 0.1773
scalar:  3.1472
Epoch 570:  train loss 0.6366   train acc 0.5701   worst 0.1823   lr 0.0179   p 57.56   eps 0.4684   mix 0.0115   time 27.96
scalar:  3.1545
Epoch 571:  train loss 0.6371   train acc 0.5712   worst 0.1819   lr 0.0178   p 57.80   eps 0.4684   mix 0.0114   time 28.31
scalar:  3.1576
Epoch 572:  train loss 0.6370   train acc 0.5716   worst 0.1789   lr 0.0178   p 58.04   eps 0.4684   mix 0.0114   time 28.11
scalar:  3.1451
Epoch 573:  train loss 0.6368   train acc 0.5703   worst 0.1803   lr 0.0178   p 58.29   eps 0.4684   mix 0.0113   time 28.48
scalar:  3.1302
Epoch 574:  train loss 0.6378   train acc 0.5711   worst 0.1795   lr 0.0177   p 58.53   eps 0.4684   mix 0.0113   time 28.14
Epoch 574:  test acc 0.5430   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 574:  clean acc 0.4374   certified acc 0.1964
Calculating metrics for L_infinity dist model on test set
Epoch 574:  clean acc 0.4305   certified acc 0.1940
scalar:  3.1515
Epoch 575:  train loss 0.6369   train acc 0.5708   worst 0.1800   lr 0.0177   p 58.78   eps 0.4684   mix 0.0112   time 27.97
scalar:  3.1423
Epoch 576:  train loss 0.6372   train acc 0.5722   worst 0.1791   lr 0.0177   p 59.02   eps 0.4684   mix 0.0112   time 28.11
scalar:  3.1555
Epoch 577:  train loss 0.6359   train acc 0.5718   worst 0.1815   lr 0.0176   p 59.27   eps 0.4684   mix 0.0111   time 28.06
scalar:  3.1312
Epoch 578:  train loss 0.6371   train acc 0.5724   worst 0.1786   lr 0.0176   p 59.52   eps 0.4684   mix 0.0111   time 28.41
scalar:  3.1756
Epoch 579:  train loss 0.6360   train acc 0.5743   worst 0.1822   lr 0.0176   p 59.77   eps 0.4684   mix 0.0110   time 28.27
Epoch 579:  test acc 0.5503   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 579:  clean acc 0.4366   certified acc 0.2049
Calculating metrics for L_infinity dist model on test set
Epoch 579:  clean acc 0.4307   certified acc 0.1996
scalar:  3.168
Epoch 580:  train loss 0.6361   train acc 0.5715   worst 0.1803   lr 0.0175   p 60.02   eps 0.4684   mix 0.0110   time 27.88
scalar:  3.1714
Epoch 581:  train loss 0.6368   train acc 0.5688   worst 0.1819   lr 0.0175   p 60.28   eps 0.4684   mix 0.0109   time 28.47
scalar:  3.1525
Epoch 582:  train loss 0.6379   train acc 0.5700   worst 0.1794   lr 0.0175   p 60.53   eps 0.4684   mix 0.0109   time 28.08
scalar:  3.1252
Epoch 583:  train loss 0.6376   train acc 0.5706   worst 0.1794   lr 0.0174   p 60.78   eps 0.4684   mix 0.0108   time 28.74
scalar:  3.1647
Epoch 584:  train loss 0.6374   train acc 0.5724   worst 0.1778   lr 0.0174   p 61.04   eps 0.4684   mix 0.0108   time 27.99
Epoch 584:  test acc 0.5483   time 2.69
Calculating metrics for L_infinity dist model on training set
Epoch 584:  clean acc 0.4248   certified acc 0.1935
Calculating metrics for L_infinity dist model on test set
Epoch 584:  clean acc 0.4215   certified acc 0.1955
scalar:  3.1553
Epoch 585:  train loss 0.6389   train acc 0.5706   worst 0.1773   lr 0.0173   p 61.30   eps 0.4684   mix 0.0107   time 28.17
scalar:  3.1309
Epoch 586:  train loss 0.6367   train acc 0.5708   worst 0.1771   lr 0.0173   p 61.55   eps 0.4684   mix 0.0107   time 28.37
scalar:  3.1512
Epoch 587:  train loss 0.6366   train acc 0.5723   worst 0.1792   lr 0.0173   p 61.81   eps 0.4684   mix 0.0106   time 28.19
scalar:  3.1651
Epoch 588:  train loss 0.6377   train acc 0.5711   worst 0.1779   lr 0.0172   p 62.07   eps 0.4684   mix 0.0106   time 28.13
scalar:  3.1508
Epoch 589:  train loss 0.6381   train acc 0.5700   worst 0.1782   lr 0.0172   p 62.34   eps 0.4684   mix 0.0105   time 27.98
Epoch 589:  test acc 0.5469   time 2.65
Calculating metrics for L_infinity dist model on training set
Epoch 589:  clean acc 0.4427   certified acc 0.2059
Calculating metrics for L_infinity dist model on test set
Epoch 589:  clean acc 0.4367   certified acc 0.2046
scalar:  3.1718
Epoch 590:  train loss 0.6376   train acc 0.5680   worst 0.1800   lr 0.0172   p 62.60   eps 0.4684   mix 0.0105   time 28.05
scalar:  3.1312
Epoch 591:  train loss 0.6385   train acc 0.5685   worst 0.1781   lr 0.0171   p 62.86   eps 0.4684   mix 0.0104   time 27.96
scalar:  3.1338
Epoch 592:  train loss 0.6392   train acc 0.5710   worst 0.1771   lr 0.0171   p 63.13   eps 0.4684   mix 0.0104   time 28.06
scalar:  3.1848
Epoch 593:  train loss 0.6378   train acc 0.5710   worst 0.1760   lr 0.0171   p 63.39   eps 0.4684   mix 0.0103   time 28.41
scalar:  3.1791
Epoch 594:  train loss 0.6387   train acc 0.5722   worst 0.1743   lr 0.0170   p 63.66   eps 0.4684   mix 0.0103   time 27.90
Epoch 594:  test acc 0.5434   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 594:  clean acc 0.4528   certified acc 0.2243
Calculating metrics for L_infinity dist model on test set
Epoch 594:  clean acc 0.4493   certified acc 0.2193
scalar:  3.2035
Epoch 595:  train loss 0.6357   train acc 0.5722   worst 0.1778   lr 0.0170   p 63.93   eps 0.4684   mix 0.0102   time 28.48
scalar:  3.1685
Epoch 596:  train loss 0.6379   train acc 0.5713   worst 0.1766   lr 0.0170   p 64.19   eps 0.4684   mix 0.0102   time 28.30
scalar:  3.1793
Epoch 597:  train loss 0.6375   train acc 0.5715   worst 0.1778   lr 0.0169   p 64.46   eps 0.4684   mix 0.0101   time 28.49
scalar:  3.18
Epoch 598:  train loss 0.6367   train acc 0.5707   worst 0.1779   lr 0.0169   p 64.74   eps 0.4684   mix 0.0101   time 27.90
scalar:  3.1597
Epoch 599:  train loss 0.6379   train acc 0.5703   worst 0.1753   lr 0.0168   p 65.01   eps 0.4684   mix 0.0100   time 28.20
Epoch 599:  test acc 0.5420   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 599:  clean acc 0.4733   certified acc 0.2367
Calculating metrics for L_infinity dist model on test set
Epoch 599:  clean acc 0.4620   certified acc 0.2260
scalar:  3.1512
Epoch 600:  train loss 0.6391   train acc 0.5687   worst 0.1747   lr 0.0168   p 65.28   eps 0.4684   mix 0.0100   time 28.21
scalar:  3.1634
Epoch 601:  train loss 0.6384   train acc 0.5718   worst 0.1744   lr 0.0168   p 65.56   eps 0.4684   mix 0.0099   time 28.63
scalar:  3.177
Epoch 602:  train loss 0.6377   train acc 0.5709   worst 0.1747   lr 0.0167   p 65.83   eps 0.4684   mix 0.0099   time 28.80
scalar:  3.1868
Epoch 603:  train loss 0.6382   train acc 0.5715   worst 0.1744   lr 0.0167   p 66.11   eps 0.4684   mix 0.0099   time 27.79
scalar:  3.1922
Epoch 604:  train loss 0.6370   train acc 0.5725   worst 0.1748   lr 0.0167   p 66.39   eps 0.4684   mix 0.0098   time 28.32
Epoch 604:  test acc 0.5510   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 604:  clean acc 0.4926   certified acc 0.2457
Calculating metrics for L_infinity dist model on test set
Epoch 604:  clean acc 0.4829   certified acc 0.2452
scalar:  3.1867
Epoch 605:  train loss 0.6362   train acc 0.5716   worst 0.1769   lr 0.0166   p 66.67   eps 0.4684   mix 0.0098   time 28.15
scalar:  3.19
Epoch 606:  train loss 0.6381   train acc 0.5717   worst 0.1737   lr 0.0166   p 66.95   eps 0.4684   mix 0.0097   time 28.35
scalar:  3.2221
Epoch 607:  train loss 0.6406   train acc 0.5685   worst 0.1736   lr 0.0166   p 67.23   eps 0.4684   mix 0.0097   time 28.50
scalar:  3.1863
Epoch 608:  train loss 0.6381   train acc 0.5691   worst 0.1750   lr 0.0165   p 67.51   eps 0.4684   mix 0.0096   time 27.85
scalar:  3.1848
Epoch 609:  train loss 0.6390   train acc 0.5685   worst 0.1731   lr 0.0165   p 67.80   eps 0.4684   mix 0.0096   time 27.97
Epoch 609:  test acc 0.5444   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 609:  clean acc 0.4631   certified acc 0.2281
Calculating metrics for L_infinity dist model on test set
Epoch 609:  clean acc 0.4530   certified acc 0.2253
scalar:  3.1989
Epoch 610:  train loss 0.6395   train acc 0.5705   worst 0.1726   lr 0.0164   p 68.08   eps 0.4684   mix 0.0095   time 28.31
scalar:  3.2169
Epoch 611:  train loss 0.6377   train acc 0.5720   worst 0.1728   lr 0.0164   p 68.37   eps 0.4684   mix 0.0095   time 28.29
scalar:  3.2225
Epoch 612:  train loss 0.6373   train acc 0.5689   worst 0.1753   lr 0.0164   p 68.65   eps 0.4684   mix 0.0095   time 28.23
scalar:  3.1824
Epoch 613:  train loss 0.6390   train acc 0.5710   worst 0.1734   lr 0.0163   p 68.94   eps 0.4684   mix 0.0094   time 28.08
scalar:  3.2114
Epoch 614:  train loss 0.6402   train acc 0.5697   worst 0.1724   lr 0.0163   p 69.23   eps 0.4684   mix 0.0094   time 27.96
Epoch 614:  test acc 0.5458   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 614:  clean acc 0.4673   certified acc 0.2236
Calculating metrics for L_infinity dist model on test set
Epoch 614:  clean acc 0.4513   certified acc 0.2215
scalar:  3.1888
Epoch 615:  train loss 0.6393   train acc 0.5709   worst 0.1729   lr 0.0163   p 69.52   eps 0.4684   mix 0.0093   time 28.38
scalar:  3.202
Epoch 616:  train loss 0.6377   train acc 0.5736   worst 0.1713   lr 0.0162   p 69.82   eps 0.4684   mix 0.0093   time 28.17
scalar:  3.2084
Epoch 617:  train loss 0.6392   train acc 0.5689   worst 0.1713   lr 0.0162   p 70.11   eps 0.4684   mix 0.0092   time 28.98
scalar:  3.215
Epoch 618:  train loss 0.6391   train acc 0.5691   worst 0.1738   lr 0.0162   p 70.41   eps 0.4684   mix 0.0092   time 28.08
scalar:  3.1955
Epoch 619:  train loss 0.6390   train acc 0.5711   worst 0.1711   lr 0.0161   p 70.70   eps 0.4684   mix 0.0092   time 27.94
Epoch 619:  test acc 0.5435   time 2.65
Calculating metrics for L_infinity dist model on training set
Epoch 619:  clean acc 0.4762   certified acc 0.2366
Calculating metrics for L_infinity dist model on test set
Epoch 619:  clean acc 0.4686   certified acc 0.2346
scalar:  3.2086
Epoch 620:  train loss 0.6397   train acc 0.5699   worst 0.1745   lr 0.0161   p 71.00   eps 0.4684   mix 0.0091   time 28.17
scalar:  3.2009
Epoch 621:  train loss 0.6382   train acc 0.5730   worst 0.1730   lr 0.0161   p 71.30   eps 0.4684   mix 0.0091   time 27.81
scalar:  3.2316
Epoch 622:  train loss 0.6394   train acc 0.5688   worst 0.1723   lr 0.0160   p 71.60   eps 0.4684   mix 0.0090   time 28.33
scalar:  3.2232
Epoch 623:  train loss 0.6390   train acc 0.5690   worst 0.1710   lr 0.0160   p 71.90   eps 0.4684   mix 0.0090   time 27.72
scalar:  3.2083
Epoch 624:  train loss 0.6386   train acc 0.5715   worst 0.1714   lr 0.0159   p 72.20   eps 0.4684   mix 0.0089   time 28.13
Epoch 624:  test acc 0.5431   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 624:  clean acc 0.4891   certified acc 0.2388
Calculating metrics for L_infinity dist model on test set
Epoch 624:  clean acc 0.4766   certified acc 0.2357
scalar:  3.2308
Epoch 625:  train loss 0.6389   train acc 0.5685   worst 0.1702   lr 0.0159   p 72.51   eps 0.4684   mix 0.0089   time 28.67
scalar:  3.23
Epoch 626:  train loss 0.6387   train acc 0.5700   worst 0.1697   lr 0.0159   p 72.81   eps 0.4684   mix 0.0089   time 27.95
scalar:  3.2426
Epoch 627:  train loss 0.6395   train acc 0.5691   worst 0.1709   lr 0.0158   p 73.12   eps 0.4684   mix 0.0088   time 28.21
scalar:  3.2272
Epoch 628:  train loss 0.6379   train acc 0.5688   worst 0.1718   lr 0.0158   p 73.43   eps 0.4684   mix 0.0088   time 27.83
scalar:  3.2208
Epoch 629:  train loss 0.6391   train acc 0.5692   worst 0.1709   lr 0.0158   p 73.73   eps 0.4684   mix 0.0087   time 28.03
Epoch 629:  test acc 0.5443   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 629:  clean acc 0.4830   certified acc 0.2442
Calculating metrics for L_infinity dist model on test set
Epoch 629:  clean acc 0.4758   certified acc 0.2398
scalar:  3.2361
Epoch 630:  train loss 0.6389   train acc 0.5688   worst 0.1714   lr 0.0157   p 74.04   eps 0.4684   mix 0.0087   time 28.22
scalar:  3.2323
Epoch 631:  train loss 0.6384   train acc 0.5711   worst 0.1700   lr 0.0157   p 74.36   eps 0.4684   mix 0.0087   time 28.02
scalar:  3.2276
Epoch 632:  train loss 0.6407   train acc 0.5672   worst 0.1704   lr 0.0157   p 74.67   eps 0.4684   mix 0.0086   time 28.31
scalar:  3.1905
Epoch 633:  train loss 0.6398   train acc 0.5693   worst 0.1709   lr 0.0156   p 74.98   eps 0.4684   mix 0.0086   time 27.83
scalar:  3.2467
Epoch 634:  train loss 0.6400   train acc 0.5691   worst 0.1693   lr 0.0156   p 75.30   eps 0.4684   mix 0.0085   time 28.22
Epoch 634:  test acc 0.5394   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 634:  clean acc 0.4894   certified acc 0.2539
Calculating metrics for L_infinity dist model on test set
Epoch 634:  clean acc 0.4790   certified acc 0.2501
scalar:  3.2406
Epoch 635:  train loss 0.6389   train acc 0.5700   worst 0.1715   lr 0.0155   p 75.62   eps 0.4684   mix 0.0085   time 28.08
scalar:  3.2446
Epoch 636:  train loss 0.6380   train acc 0.5707   worst 0.1723   lr 0.0155   p 75.93   eps 0.4684   mix 0.0085   time 27.87
scalar:  3.2378
Epoch 637:  train loss 0.6381   train acc 0.5707   worst 0.1711   lr 0.0155   p 76.25   eps 0.4684   mix 0.0084   time 28.10
scalar:  3.2142
Epoch 638:  train loss 0.6402   train acc 0.5712   worst 0.1677   lr 0.0154   p 76.57   eps 0.4684   mix 0.0084   time 27.81
scalar:  3.2327
Epoch 639:  train loss 0.6374   train acc 0.5715   worst 0.1691   lr 0.0154   p 76.90   eps 0.4684   mix 0.0083   time 28.36
Epoch 639:  test acc 0.5427   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 639:  clean acc 0.5013   certified acc 0.2565
Calculating metrics for L_infinity dist model on test set
Epoch 639:  clean acc 0.4847   certified acc 0.2466
scalar:  3.2461
Epoch 640:  train loss 0.6398   train acc 0.5688   worst 0.1693   lr 0.0154   p 77.22   eps 0.4684   mix 0.0083   time 28.34
scalar:  3.2346
Epoch 641:  train loss 0.6391   train acc 0.5701   worst 0.1703   lr 0.0153   p 77.54   eps 0.4684   mix 0.0083   time 28.12
scalar:  3.263
Epoch 642:  train loss 0.6387   train acc 0.5693   worst 0.1691   lr 0.0153   p 77.87   eps 0.4684   mix 0.0082   time 27.57
scalar:  3.2398
Epoch 643:  train loss 0.6405   train acc 0.5673   worst 0.1688   lr 0.0153   p 78.20   eps 0.4684   mix 0.0082   time 27.92
scalar:  3.2291
Epoch 644:  train loss 0.6402   train acc 0.5693   worst 0.1685   lr 0.0152   p 78.53   eps 0.4684   mix 0.0082   time 28.01
Epoch 644:  test acc 0.5389   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 644:  clean acc 0.4965   certified acc 0.2546
Calculating metrics for L_infinity dist model on test set
Epoch 644:  clean acc 0.4765   certified acc 0.2463
scalar:  3.251
Epoch 645:  train loss 0.6400   train acc 0.5691   worst 0.1686   lr 0.0152   p 78.86   eps 0.4684   mix 0.0081   time 28.09
scalar:  3.2547
Epoch 646:  train loss 0.6389   train acc 0.5703   worst 0.1676   lr 0.0151   p 79.19   eps 0.4684   mix 0.0081   time 28.12
scalar:  3.2505
Epoch 647:  train loss 0.6395   train acc 0.5684   worst 0.1675   lr 0.0151   p 79.52   eps 0.4684   mix 0.0080   time 27.59
scalar:  3.2566
Epoch 648:  train loss 0.6405   train acc 0.5693   worst 0.1668   lr 0.0151   p 79.86   eps 0.4684   mix 0.0080   time 27.83
scalar:  3.2376
Epoch 649:  train loss 0.6404   train acc 0.5711   worst 0.1666   lr 0.0150   p 80.19   eps 0.4684   mix 0.0080   time 28.52
Epoch 649:  test acc 0.5409   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 649:  clean acc 0.5048   certified acc 0.2613
Calculating metrics for L_infinity dist model on test set
Epoch 649:  clean acc 0.4940   certified acc 0.2554
scalar:  3.238
Epoch 650:  train loss 0.6378   train acc 0.5703   worst 0.1687   lr 0.0150   p 80.53   eps 0.4684   mix 0.0079   time 28.22
scalar:  3.2695
Epoch 651:  train loss 0.6389   train acc 0.5686   worst 0.1689   lr 0.0150   p 80.87   eps 0.4684   mix 0.0079   time 28.22
scalar:  3.2327
Epoch 652:  train loss 0.6385   train acc 0.5691   worst 0.1690   lr 0.0149   p 81.21   eps 0.4684   mix 0.0079   time 28.01
scalar:  3.2487
Epoch 653:  train loss 0.6400   train acc 0.5663   worst 0.1686   lr 0.0149   p 81.55   eps 0.4684   mix 0.0078   time 27.94
scalar:  3.2296
Epoch 654:  train loss 0.6405   train acc 0.5677   worst 0.1676   lr 0.0149   p 81.89   eps 0.4684   mix 0.0078   time 28.63
Epoch 654:  test acc 0.5388   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 654:  clean acc 0.5062   certified acc 0.2760
Calculating metrics for L_infinity dist model on test set
Epoch 654:  clean acc 0.4914   certified acc 0.2619
scalar:  3.2186
Epoch 655:  train loss 0.6401   train acc 0.5682   worst 0.1672   lr 0.0148   p 82.24   eps 0.4684   mix 0.0078   time 27.86
scalar:  3.2233
Epoch 656:  train loss 0.6412   train acc 0.5675   worst 0.1659   lr 0.0148   p 82.58   eps 0.4684   mix 0.0077   time 28.07
scalar:  3.2402
Epoch 657:  train loss 0.6389   train acc 0.5699   worst 0.1682   lr 0.0147   p 82.93   eps 0.4684   mix 0.0077   time 27.78
scalar:  3.2576
Epoch 658:  train loss 0.6388   train acc 0.5707   worst 0.1678   lr 0.0147   p 83.28   eps 0.4684   mix 0.0076   time 28.00
scalar:  3.2664
Epoch 659:  train loss 0.6404   train acc 0.5713   worst 0.1646   lr 0.0147   p 83.63   eps 0.4684   mix 0.0076   time 28.24
Epoch 659:  test acc 0.5428   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 659:  clean acc 0.5166   certified acc 0.2786
Calculating metrics for L_infinity dist model on test set
Epoch 659:  clean acc 0.4977   certified acc 0.2659
scalar:  3.2579
Epoch 660:  train loss 0.6393   train acc 0.5683   worst 0.1684   lr 0.0146   p 83.98   eps 0.4684   mix 0.0076   time 27.96
scalar:  3.2699
Epoch 661:  train loss 0.6387   train acc 0.5731   worst 0.1675   lr 0.0146   p 84.34   eps 0.4684   mix 0.0075   time 28.40
scalar:  3.2788
Epoch 662:  train loss 0.6399   train acc 0.5699   worst 0.1671   lr 0.0146   p 84.69   eps 0.4684   mix 0.0075   time 28.04
scalar:  3.2659
Epoch 663:  train loss 0.6402   train acc 0.5679   worst 0.1674   lr 0.0145   p 85.05   eps 0.4684   mix 0.0075   time 27.76
scalar:  3.2477
Epoch 664:  train loss 0.6388   train acc 0.5695   worst 0.1667   lr 0.0145   p 85.41   eps 0.4684   mix 0.0074   time 28.55
Epoch 664:  test acc 0.5390   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 664:  clean acc 0.5223   certified acc 0.2775
Calculating metrics for L_infinity dist model on test set
Epoch 664:  clean acc 0.5010   certified acc 0.2603
scalar:  3.2525
Epoch 665:  train loss 0.6392   train acc 0.5699   worst 0.1675   lr 0.0145   p 85.77   eps 0.4684   mix 0.0074   time 28.24
scalar:  3.2717
Epoch 666:  train loss 0.6395   train acc 0.5703   worst 0.1667   lr 0.0144   p 86.13   eps 0.4684   mix 0.0074   time 27.97
scalar:  3.2831
Epoch 667:  train loss 0.6401   train acc 0.5672   worst 0.1649   lr 0.0144   p 86.49   eps 0.4684   mix 0.0073   time 27.79
scalar:  3.2532
Epoch 668:  train loss 0.6411   train acc 0.5697   worst 0.1643   lr 0.0143   p 86.85   eps 0.4684   mix 0.0073   time 27.99
scalar:  3.2699
Epoch 669:  train loss 0.6387   train acc 0.5710   worst 0.1648   lr 0.0143   p 87.22   eps 0.4684   mix 0.0073   time 28.39
Epoch 669:  test acc 0.5377   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 669:  clean acc 0.5238   certified acc 0.2858
Calculating metrics for L_infinity dist model on test set
Epoch 669:  clean acc 0.5017   certified acc 0.2723
scalar:  3.2889
Epoch 670:  train loss 0.6393   train acc 0.5702   worst 0.1644   lr 0.0143   p 87.58   eps 0.4684   mix 0.0072   time 28.06
scalar:  3.2623
Epoch 671:  train loss 0.6391   train acc 0.5702   worst 0.1653   lr 0.0142   p 87.95   eps 0.4684   mix 0.0072   time 28.43
scalar:  3.3004
Epoch 672:  train loss 0.6398   train acc 0.5696   worst 0.1653   lr 0.0142   p 88.32   eps 0.4684   mix 0.0072   time 27.99
scalar:  3.2787
Epoch 673:  train loss 0.6381   train acc 0.5701   worst 0.1672   lr 0.0142   p 88.69   eps 0.4684   mix 0.0071   time 28.10
scalar:  3.2681
Epoch 674:  train loss 0.6396   train acc 0.5686   worst 0.1654   lr 0.0141   p 89.07   eps 0.4684   mix 0.0071   time 28.34
Epoch 674:  test acc 0.5429   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 674:  clean acc 0.5180   certified acc 0.2852
Calculating metrics for L_infinity dist model on test set
Epoch 674:  clean acc 0.5024   certified acc 0.2691
scalar:  3.2672
Epoch 675:  train loss 0.6401   train acc 0.5686   worst 0.1644   lr 0.0141   p 89.44   eps 0.4684   mix 0.0071   time 28.13
scalar:  3.2806
Epoch 676:  train loss 0.6399   train acc 0.5695   worst 0.1655   lr 0.0141   p 89.82   eps 0.4684   mix 0.0070   time 27.84
scalar:  3.267
Epoch 677:  train loss 0.6396   train acc 0.5698   worst 0.1648   lr 0.0140   p 90.20   eps 0.4684   mix 0.0070   time 27.91
scalar:  3.2791
Epoch 678:  train loss 0.6390   train acc 0.5681   worst 0.1650   lr 0.0140   p 90.58   eps 0.4684   mix 0.0070   time 28.33
scalar:  3.2776
Epoch 679:  train loss 0.6402   train acc 0.5700   worst 0.1641   lr 0.0139   p 90.96   eps 0.4684   mix 0.0069   time 28.32
Epoch 679:  test acc 0.5415   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 679:  clean acc 0.5282   certified acc 0.2970
Calculating metrics for L_infinity dist model on test set
Epoch 679:  clean acc 0.5099   certified acc 0.2817
scalar:  3.2799
Epoch 680:  train loss 0.6399   train acc 0.5679   worst 0.1634   lr 0.0139   p 91.34   eps 0.4684   mix 0.0069   time 28.17
scalar:  3.2691
Epoch 681:  train loss 0.6390   train acc 0.5705   worst 0.1647   lr 0.0139   p 91.72   eps 0.4684   mix 0.0069   time 27.89
scalar:  3.2877
Epoch 682:  train loss 0.6394   train acc 0.5715   worst 0.1623   lr 0.0138   p 92.11   eps 0.4684   mix 0.0068   time 27.99
scalar:  3.2897
Epoch 683:  train loss 0.6396   train acc 0.5694   worst 0.1639   lr 0.0138   p 92.50   eps 0.4684   mix 0.0068   time 27.76
scalar:  3.2824
Epoch 684:  train loss 0.6386   train acc 0.5705   worst 0.1645   lr 0.0138   p 92.89   eps 0.4684   mix 0.0068   time 28.25
Epoch 684:  test acc 0.5378   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 684:  clean acc 0.5283   certified acc 0.3017
Calculating metrics for L_infinity dist model on test set
Epoch 684:  clean acc 0.5079   certified acc 0.2860
scalar:  3.3033
Epoch 685:  train loss 0.6387   train acc 0.5703   worst 0.1634   lr 0.0137   p 93.28   eps 0.4684   mix 0.0068   time 28.31
scalar:  3.2879
Epoch 686:  train loss 0.6423   train acc 0.5688   worst 0.1636   lr 0.0137   p 93.67   eps 0.4684   mix 0.0067   time 27.60
scalar:  3.2814
Epoch 687:  train loss 0.6401   train acc 0.5699   worst 0.1620   lr 0.0137   p 94.06   eps 0.4684   mix 0.0067   time 27.76
scalar:  3.2993
Epoch 688:  train loss 0.6393   train acc 0.5720   worst 0.1620   lr 0.0136   p 94.46   eps 0.4684   mix 0.0067   time 28.04
scalar:  3.3113
Epoch 689:  train loss 0.6401   train acc 0.5699   worst 0.1628   lr 0.0136   p 94.86   eps 0.4684   mix 0.0066   time 28.35
Epoch 689:  test acc 0.5408   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 689:  clean acc 0.5365   certified acc 0.3084
Calculating metrics for L_infinity dist model on test set
Epoch 689:  clean acc 0.5182   certified acc 0.2892
scalar:  3.2932
Epoch 690:  train loss 0.6394   train acc 0.5701   worst 0.1623   lr 0.0136   p 95.26   eps 0.4684   mix 0.0066   time 27.82
scalar:  3.2933
Epoch 691:  train loss 0.6389   train acc 0.5690   worst 0.1641   lr 0.0135   p 95.66   eps 0.4684   mix 0.0066   time 27.40
scalar:  3.3107
Epoch 692:  train loss 0.6397   train acc 0.5695   worst 0.1633   lr 0.0135   p 96.06   eps 0.4684   mix 0.0065   time 27.82
scalar:  3.2944
Epoch 693:  train loss 0.6380   train acc 0.5705   worst 0.1640   lr 0.0134   p 96.46   eps 0.4684   mix 0.0065   time 28.17
scalar:  3.2771
Epoch 694:  train loss 0.6402   train acc 0.5697   worst 0.1646   lr 0.0134   p 96.87   eps 0.4684   mix 0.0065   time 28.22
Epoch 694:  test acc 0.5377   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 694:  clean acc 0.5455   certified acc 0.3176
Calculating metrics for L_infinity dist model on test set
Epoch 694:  clean acc 0.5237   certified acc 0.2993
scalar:  3.2871
Epoch 695:  train loss 0.6396   train acc 0.5678   worst 0.1639   lr 0.0134   p 97.28   eps 0.4684   mix 0.0064   time 27.88
scalar:  3.2995
Epoch 696:  train loss 0.6410   train acc 0.5688   worst 0.1617   lr 0.0133   p 97.69   eps 0.4684   mix 0.0064   time 27.85
scalar:  3.3058
Epoch 697:  train loss 0.6395   train acc 0.5679   worst 0.1654   lr 0.0133   p 98.10   eps 0.4684   mix 0.0064   time 28.20
scalar:  3.2816
Epoch 698:  train loss 0.6387   train acc 0.5702   worst 0.1633   lr 0.0133   p 98.51   eps 0.4684   mix 0.0064   time 28.10
scalar:  3.2961
Epoch 699:  train loss 0.6403   train acc 0.5687   worst 0.1618   lr 0.0132   p 98.93   eps 0.4684   mix 0.0063   time 28.41
Epoch 699:  test acc 0.5335   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 699:  clean acc 0.5337   certified acc 0.3113
Calculating metrics for L_infinity dist model on test set
Epoch 699:  clean acc 0.5157   certified acc 0.2979
scalar:  3.2863
Epoch 700:  train loss 0.6398   train acc 0.5689   worst 0.1628   lr 0.0132   p 99.34   eps 0.4684   mix 0.0063   time 27.78
scalar:  3.2929
Epoch 701:  train loss 0.6387   train acc 0.5686   worst 0.1628   lr 0.0132   p 99.76   eps 0.4684   mix 0.0063   time 27.85
scalar:  3.3083
Epoch 702:  train loss 0.6412   train acc 0.5688   worst 0.1627   lr 0.0131   p 100.18   eps 0.4684   mix 0.0062   time 27.89
scalar:  3.3005
Epoch 703:  train loss 0.6398   train acc 0.5710   worst 0.1625   lr 0.0131   p 100.60   eps 0.4684   mix 0.0062   time 28.10
scalar:  3.3211
Epoch 704:  train loss 0.6404   train acc 0.5709   worst 0.1615   lr 0.0130   p 101.02   eps 0.4684   mix 0.0062   time 28.40
Epoch 704:  test acc 0.5376   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 704:  clean acc 0.5378   certified acc 0.3063
Calculating metrics for L_infinity dist model on test set
Epoch 704:  clean acc 0.5136   certified acc 0.2850
scalar:  3.3113
Epoch 705:  train loss 0.6400   train acc 0.5698   worst 0.1629   lr 0.0130   p 101.45   eps 0.4684   mix 0.0062   time 28.14
scalar:  3.3057
Epoch 706:  train loss 0.6397   train acc 0.5691   worst 0.1623   lr 0.0130   p 101.88   eps 0.4684   mix 0.0061   time 27.51
scalar:  3.3132
Epoch 707:  train loss 0.6395   train acc 0.5679   worst 0.1619   lr 0.0129   p 102.30   eps 0.4684   mix 0.0061   time 28.13
scalar:  3.3265
Epoch 708:  train loss 0.6404   train acc 0.5710   worst 0.1604   lr 0.0129   p 102.73   eps 0.4684   mix 0.0061   time 28.01
scalar:  3.3357
Epoch 709:  train loss 0.6408   train acc 0.5706   worst 0.1617   lr 0.0129   p 103.17   eps 0.4684   mix 0.0060   time 27.88
Epoch 709:  test acc 0.5370   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 709:  clean acc 0.5372   certified acc 0.3179
Calculating metrics for L_infinity dist model on test set
Epoch 709:  clean acc 0.5155   certified acc 0.2951
scalar:  3.3299
Epoch 710:  train loss 0.6391   train acc 0.5709   worst 0.1605   lr 0.0128   p 103.60   eps 0.4684   mix 0.0060   time 27.92
scalar:  3.3319
Epoch 711:  train loss 0.6391   train acc 0.5684   worst 0.1611   lr 0.0128   p 104.04   eps 0.4684   mix 0.0060   time 27.82
scalar:  3.319
Epoch 712:  train loss 0.6399   train acc 0.5691   worst 0.1623   lr 0.0128   p 104.47   eps 0.4684   mix 0.0060   time 27.93
scalar:  3.3064
Epoch 713:  train loss 0.6400   train acc 0.5699   worst 0.1612   lr 0.0127   p 104.91   eps 0.4684   mix 0.0059   time 28.00
scalar:  3.3302
Epoch 714:  train loss 0.6390   train acc 0.5705   worst 0.1616   lr 0.0127   p 105.36   eps 0.4684   mix 0.0059   time 28.25
Epoch 714:  test acc 0.5395   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 714:  clean acc 0.5513   certified acc 0.3327
Calculating metrics for L_infinity dist model on test set
Epoch 714:  clean acc 0.5271   certified acc 0.3096
scalar:  3.3231
Epoch 715:  train loss 0.6385   train acc 0.5711   worst 0.1617   lr 0.0127   p 105.80   eps 0.4684   mix 0.0059   time 28.10
scalar:  3.3245
Epoch 716:  train loss 0.6405   train acc 0.5713   worst 0.1599   lr 0.0126   p 106.24   eps 0.4684   mix 0.0059   time 27.86
scalar:  3.3347
Epoch 717:  train loss 0.6386   train acc 0.5715   worst 0.1608   lr 0.0126   p 106.69   eps 0.4684   mix 0.0058   time 28.19
scalar:  3.3404
Epoch 718:  train loss 0.6409   train acc 0.5710   worst 0.1585   lr 0.0125   p 107.14   eps 0.4684   mix 0.0058   time 27.71
scalar:  3.3318
Epoch 719:  train loss 0.6395   train acc 0.5710   worst 0.1586   lr 0.0125   p 107.59   eps 0.4684   mix 0.0058   time 27.85
Epoch 719:  test acc 0.5350   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 719:  clean acc 0.5483   certified acc 0.3207
Calculating metrics for L_infinity dist model on test set
Epoch 719:  clean acc 0.5225   certified acc 0.2983
scalar:  3.3213
Epoch 720:  train loss 0.6400   train acc 0.5713   worst 0.1617   lr 0.0125   p 108.04   eps 0.4684   mix 0.0057   time 27.59
scalar:  3.3304
Epoch 721:  train loss 0.6405   train acc 0.5694   worst 0.1606   lr 0.0124   p 108.50   eps 0.4684   mix 0.0057   time 27.99
scalar:  3.3228
Epoch 722:  train loss 0.6382   train acc 0.5720   worst 0.1619   lr 0.0124   p 108.95   eps 0.4684   mix 0.0057   time 28.61
scalar:  3.3408
Epoch 723:  train loss 0.6394   train acc 0.5709   worst 0.1612   lr 0.0124   p 109.41   eps 0.4684   mix 0.0057   time 27.87
scalar:  3.3395
Epoch 724:  train loss 0.6390   train acc 0.5695   worst 0.1607   lr 0.0123   p 109.87   eps 0.4684   mix 0.0056   time 27.71
Epoch 724:  test acc 0.5362   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 724:  clean acc 0.5410   certified acc 0.3287
Calculating metrics for L_infinity dist model on test set
Epoch 724:  clean acc 0.5136   certified acc 0.3082
scalar:  3.332
Epoch 725:  train loss 0.6402   train acc 0.5690   worst 0.1607   lr 0.0123   p 110.34   eps 0.4684   mix 0.0056   time 27.55
scalar:  3.3275
Epoch 726:  train loss 0.6395   train acc 0.5717   worst 0.1595   lr 0.0123   p 110.80   eps 0.4684   mix 0.0056   time 27.64
scalar:  3.3172
Epoch 727:  train loss 0.6388   train acc 0.5697   worst 0.1606   lr 0.0122   p 111.27   eps 0.4684   mix 0.0056   time 28.25
scalar:  3.3311
Epoch 728:  train loss 0.6378   train acc 0.5707   worst 0.1618   lr 0.0122   p 111.73   eps 0.4684   mix 0.0055   time 27.66
scalar:  3.3357
Epoch 729:  train loss 0.6395   train acc 0.5690   worst 0.1610   lr 0.0122   p 112.20   eps 0.4684   mix 0.0055   time 28.33
Epoch 729:  test acc 0.5351   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 729:  clean acc 0.5487   certified acc 0.3386
Calculating metrics for L_infinity dist model on test set
Epoch 729:  clean acc 0.5237   certified acc 0.3160
scalar:  3.3335
Epoch 730:  train loss 0.6403   train acc 0.5670   worst 0.1607   lr 0.0121   p 112.68   eps 0.4684   mix 0.0055   time 27.68
scalar:  3.3378
Epoch 731:  train loss 0.6387   train acc 0.5714   worst 0.1585   lr 0.0121   p 113.15   eps 0.4684   mix 0.0055   time 28.09
scalar:  3.3424
Epoch 732:  train loss 0.6385   train acc 0.5706   worst 0.1604   lr 0.0120   p 113.63   eps 0.4684   mix 0.0054   time 28.09
scalar:  3.3587
Epoch 733:  train loss 0.6401   train acc 0.5690   worst 0.1585   lr 0.0120   p 114.10   eps 0.4684   mix 0.0054   time 27.71
scalar:  3.346
Epoch 734:  train loss 0.6390   train acc 0.5683   worst 0.1610   lr 0.0120   p 114.58   eps 0.4684   mix 0.0054   time 28.20
Epoch 734:  test acc 0.5386   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 734:  clean acc 0.5533   certified acc 0.3446
Calculating metrics for L_infinity dist model on test set
Epoch 734:  clean acc 0.5261   certified acc 0.3218
scalar:  3.3313
Epoch 735:  train loss 0.6394   train acc 0.5681   worst 0.1619   lr 0.0119   p 115.07   eps 0.4684   mix 0.0054   time 27.76
scalar:  3.3327
Epoch 736:  train loss 0.6398   train acc 0.5680   worst 0.1585   lr 0.0119   p 115.55   eps 0.4684   mix 0.0053   time 27.98
scalar:  3.3195
Epoch 737:  train loss 0.6376   train acc 0.5725   worst 0.1605   lr 0.0119   p 116.04   eps 0.4684   mix 0.0053   time 28.00
scalar:  3.352
Epoch 738:  train loss 0.6409   train acc 0.5679   worst 0.1590   lr 0.0118   p 116.53   eps 0.4684   mix 0.0053   time 28.38
scalar:  3.3357
Epoch 739:  train loss 0.6393   train acc 0.5684   worst 0.1603   lr 0.0118   p 117.02   eps 0.4684   mix 0.0053   time 28.06
Epoch 739:  test acc 0.5351   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 739:  clean acc 0.5573   certified acc 0.3517
Calculating metrics for L_infinity dist model on test set
Epoch 739:  clean acc 0.5318   certified acc 0.3237
scalar:  3.3324
Epoch 740:  train loss 0.6394   train acc 0.5696   worst 0.1603   lr 0.0118   p 117.51   eps 0.4684   mix 0.0052   time 27.62
scalar:  3.3446
Epoch 741:  train loss 0.6389   train acc 0.5729   worst 0.1584   lr 0.0117   p 118.00   eps 0.4684   mix 0.0052   time 28.34
scalar:  3.369
Epoch 742:  train loss 0.6400   train acc 0.5679   worst 0.1585   lr 0.0117   p 118.50   eps 0.4684   mix 0.0052   time 28.21
scalar:  3.3419
Epoch 743:  train loss 0.6393   train acc 0.5706   worst 0.1594   lr 0.0117   p 119.00   eps 0.4684   mix 0.0052   time 27.99
scalar:  3.3519
Epoch 744:  train loss 0.6398   train acc 0.5697   worst 0.1585   lr 0.0116   p 119.50   eps 0.4684   mix 0.0051   time 27.94
Epoch 744:  test acc 0.5367   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 744:  clean acc 0.5614   certified acc 0.3582
Calculating metrics for L_infinity dist model on test set
Epoch 744:  clean acc 0.5334   certified acc 0.3284
scalar:  3.3538
Epoch 745:  train loss 0.6396   train acc 0.5693   worst 0.1592   lr 0.0116   p 120.00   eps 0.4684   mix 0.0051   time 27.68
scalar:  3.3318
Epoch 746:  train loss 0.6391   train acc 0.5698   worst 0.1577   lr 0.0116   p 120.51   eps 0.4684   mix 0.0051   time 28.42
scalar:  3.3337
Epoch 747:  train loss 0.6418   train acc 0.5659   worst 0.1554   lr 0.0115   p 121.01   eps 0.4684   mix 0.0051   time 28.06
scalar:  3.3456
Epoch 748:  train loss 0.6393   train acc 0.5714   worst 0.1592   lr 0.0115   p 121.52   eps 0.4684   mix 0.0051   time 27.95
scalar:  3.3529
Epoch 749:  train loss 0.6410   train acc 0.5669   worst 0.1589   lr 0.0114   p 122.03   eps 0.4684   mix 0.0050   time 27.88
Epoch 749:  test acc 0.5338   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 749:  clean acc 0.5599   certified acc 0.3610
Calculating metrics for L_infinity dist model on test set
Epoch 749:  clean acc 0.5320   certified acc 0.3337
scalar:  3.3271
Epoch 750:  train loss 0.6392   train acc 0.5682   worst 0.1595   lr 0.0114   p 122.55   eps 0.4684   mix 0.0050   time 27.58
scalar:  3.354
Epoch 751:  train loss 0.6380   train acc 0.5711   worst 0.1607   lr 0.0114   p 123.06   eps 0.4684   mix 0.0050   time 28.38
scalar:  3.3551
Epoch 752:  train loss 0.6403   train acc 0.5686   worst 0.1579   lr 0.0113   p 123.58   eps 0.4684   mix 0.0050   time 28.16
scalar:  3.3367
Epoch 753:  train loss 0.6383   train acc 0.5707   worst 0.1586   lr 0.0113   p 124.10   eps 0.4684   mix 0.0049   time 28.36
scalar:  3.3635
Epoch 754:  train loss 0.6392   train acc 0.5699   worst 0.1594   lr 0.0113   p 124.62   eps 0.4684   mix 0.0049   time 27.94
Epoch 754:  test acc 0.5348   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 754:  clean acc 0.5592   certified acc 0.3644
Calculating metrics for L_infinity dist model on test set
Epoch 754:  clean acc 0.5308   certified acc 0.3329
scalar:  3.3494
Epoch 755:  train loss 0.6395   train acc 0.5717   worst 0.1569   lr 0.0112   p 125.15   eps 0.4684   mix 0.0049   time 27.84
scalar:  3.372
Epoch 756:  train loss 0.6372   train acc 0.5740   worst 0.1591   lr 0.0112   p 125.67   eps 0.4684   mix 0.0049   time 28.18
scalar:  3.4007
Epoch 757:  train loss 0.6406   train acc 0.5691   worst 0.1568   lr 0.0112   p 126.20   eps 0.4684   mix 0.0048   time 27.86
scalar:  3.3548
Epoch 758:  train loss 0.6399   train acc 0.5690   worst 0.1575   lr 0.0111   p 126.73   eps 0.4684   mix 0.0048   time 27.81
scalar:  3.3539
Epoch 759:  train loss 0.6385   train acc 0.5696   worst 0.1576   lr 0.0111   p 127.27   eps 0.4684   mix 0.0048   time 27.91
Epoch 759:  test acc 0.5356   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 759:  clean acc 0.5623   certified acc 0.3627
Calculating metrics for L_infinity dist model on test set
Epoch 759:  clean acc 0.5319   certified acc 0.3308
scalar:  3.3411
Epoch 760:  train loss 0.6391   train acc 0.5701   worst 0.1588   lr 0.0111   p 127.80   eps 0.4684   mix 0.0048   time 27.74
scalar:  3.3475
Epoch 761:  train loss 0.6401   train acc 0.5713   worst 0.1564   lr 0.0110   p 128.34   eps 0.4684   mix 0.0048   time 28.30
scalar:  3.3962
Epoch 762:  train loss 0.6389   train acc 0.5704   worst 0.1565   lr 0.0110   p 128.88   eps 0.4684   mix 0.0047   time 27.89
scalar:  3.3925
Epoch 763:  train loss 0.6402   train acc 0.5719   worst 0.1565   lr 0.0110   p 129.42   eps 0.4684   mix 0.0047   time 28.09
scalar:  3.3754
Epoch 764:  train loss 0.6386   train acc 0.5700   worst 0.1581   lr 0.0109   p 129.97   eps 0.4684   mix 0.0047   time 28.01
Epoch 764:  test acc 0.5382   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 764:  clean acc 0.5656   certified acc 0.3679
Calculating metrics for L_infinity dist model on test set
Epoch 764:  clean acc 0.5355   certified acc 0.3372
scalar:  3.3706
Epoch 765:  train loss 0.6381   train acc 0.5716   worst 0.1583   lr 0.0109   p 130.51   eps 0.4684   mix 0.0047   time 27.92
scalar:  3.3631
Epoch 766:  train loss 0.6392   train acc 0.5702   worst 0.1586   lr 0.0108   p 131.06   eps 0.4684   mix 0.0046   time 28.04
scalar:  3.3702
Epoch 767:  train loss 0.6385   train acc 0.5690   worst 0.1580   lr 0.0108   p 131.61   eps 0.4684   mix 0.0046   time 28.11
scalar:  3.3743
Epoch 768:  train loss 0.6393   train acc 0.5709   worst 0.1564   lr 0.0108   p 132.17   eps 0.4684   mix 0.0046   time 27.84
scalar:  3.387
Epoch 769:  train loss 0.6388   train acc 0.5695   worst 0.1572   lr 0.0107   p 132.72   eps 0.4684   mix 0.0046   time 28.42
Epoch 769:  test acc 0.5358   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 769:  clean acc 0.5636   certified acc 0.3707
Calculating metrics for L_infinity dist model on test set
Epoch 769:  clean acc 0.5290   certified acc 0.3404
scalar:  3.3741
Epoch 770:  train loss 0.6396   train acc 0.5702   worst 0.1578   lr 0.0107   p 133.28   eps 0.4684   mix 0.0046   time 27.83
scalar:  3.3836
Epoch 771:  train loss 0.6388   train acc 0.5699   worst 0.1581   lr 0.0107   p 133.84   eps 0.4684   mix 0.0045   time 28.42
scalar:  3.3713
Epoch 772:  train loss 0.6383   train acc 0.5719   worst 0.1569   lr 0.0106   p 134.40   eps 0.4684   mix 0.0045   time 28.02
scalar:  3.3739
Epoch 773:  train loss 0.6386   train acc 0.5705   worst 0.1574   lr 0.0106   p 134.97   eps 0.4684   mix 0.0045   time 28.15
scalar:  3.363
Epoch 774:  train loss 0.6388   train acc 0.5701   worst 0.1575   lr 0.0106   p 135.54   eps 0.4684   mix 0.0045   time 28.00
Epoch 774:  test acc 0.5368   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 774:  clean acc 0.5656   certified acc 0.3766
Calculating metrics for L_infinity dist model on test set
Epoch 774:  clean acc 0.5337   certified acc 0.3457
scalar:  3.3611
Epoch 775:  train loss 0.6393   train acc 0.5709   worst 0.1550   lr 0.0105   p 136.11   eps 0.4684   mix 0.0045   time 27.94
scalar:  3.3833
Epoch 776:  train loss 0.6397   train acc 0.5702   worst 0.1567   lr 0.0105   p 136.68   eps 0.4684   mix 0.0044   time 28.31
scalar:  3.3734
Epoch 777:  train loss 0.6394   train acc 0.5697   worst 0.1578   lr 0.0105   p 137.26   eps 0.4684   mix 0.0044   time 28.49
scalar:  3.3614
Epoch 778:  train loss 0.6397   train acc 0.5696   worst 0.1553   lr 0.0104   p 137.83   eps 0.4684   mix 0.0044   time 27.54
scalar:  3.37
Epoch 779:  train loss 0.6389   train acc 0.5717   worst 0.1560   lr 0.0104   p 138.41   eps 0.4684   mix 0.0044   time 27.90
Epoch 779:  test acc 0.5373   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 779:  clean acc 0.5686   certified acc 0.3844
Calculating metrics for L_infinity dist model on test set
Epoch 779:  clean acc 0.5362   certified acc 0.3466
scalar:  3.3753
Epoch 780:  train loss 0.6382   train acc 0.5707   worst 0.1561   lr 0.0104   p 139.00   eps 0.4684   mix 0.0044   time 28.20
scalar:  3.3703
Epoch 781:  train loss 0.6391   train acc 0.5703   worst 0.1570   lr 0.0103   p 139.58   eps 0.4684   mix 0.0043   time 27.89
scalar:  3.369
Epoch 782:  train loss 0.6393   train acc 0.5716   worst 0.1564   lr 0.0103   p 140.17   eps 0.4684   mix 0.0043   time 28.14
scalar:  3.3944
Epoch 783:  train loss 0.6390   train acc 0.5694   worst 0.1568   lr 0.0103   p 140.76   eps 0.4684   mix 0.0043   time 27.77
scalar:  3.3744
Epoch 784:  train loss 0.6367   train acc 0.5731   worst 0.1579   lr 0.0102   p 141.35   eps 0.4684   mix 0.0043   time 27.85
Epoch 784:  test acc 0.5353   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 784:  clean acc 0.5710   certified acc 0.3842
Calculating metrics for L_infinity dist model on test set
Epoch 784:  clean acc 0.5351   certified acc 0.3444
scalar:  3.3894
Epoch 785:  train loss 0.6380   train acc 0.5706   worst 0.1576   lr 0.0102   p 141.94   eps 0.4684   mix 0.0043   time 27.86
scalar:  3.4005
Epoch 786:  train loss 0.6392   train acc 0.5717   worst 0.1556   lr 0.0102   p 142.54   eps 0.4684   mix 0.0042   time 28.28
scalar:  3.4052
Epoch 787:  train loss 0.6381   train acc 0.5716   worst 0.1562   lr 0.0101   p 143.14   eps 0.4684   mix 0.0042   time 27.72
scalar:  3.3971
Epoch 788:  train loss 0.6400   train acc 0.5714   worst 0.1548   lr 0.0101   p 143.74   eps 0.4684   mix 0.0042   time 27.68
scalar:  3.3842
Epoch 789:  train loss 0.6397   train acc 0.5694   worst 0.1561   lr 0.0101   p 144.35   eps 0.4684   mix 0.0042   time 28.27
Epoch 789:  test acc 0.5401   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 789:  clean acc 0.5691   certified acc 0.3891
Calculating metrics for L_infinity dist model on test set
Epoch 789:  clean acc 0.5381   certified acc 0.3533
scalar:  3.399
Epoch 790:  train loss 0.6383   train acc 0.5715   worst 0.1564   lr 0.0100   p 144.96   eps 0.4684   mix 0.0042   time 28.09
scalar:  3.3952
Epoch 791:  train loss 0.6377   train acc 0.5696   worst 0.1562   lr 0.0100   p 145.57   eps 0.4684   mix 0.0041   time 28.02
scalar:  3.3907
Epoch 792:  train loss 0.6382   train acc 0.5736   worst 0.1556   lr 0.0100   p 146.18   eps 0.4684   mix 0.0041   time 28.23
scalar:  3.4075
Epoch 793:  train loss 0.6400   train acc 0.5733   worst 0.1551   lr 0.0099   p 146.79   eps 0.4684   mix 0.0041   time 27.74
scalar:  3.4214
Epoch 794:  train loss 0.6384   train acc 0.5716   worst 0.1554   lr 0.0099   p 147.41   eps 0.4684   mix 0.0041   time 27.97
Epoch 794:  test acc 0.5361   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 794:  clean acc 0.5709   certified acc 0.3895
Calculating metrics for L_infinity dist model on test set
Epoch 794:  clean acc 0.5397   certified acc 0.3479
scalar:  3.4011
Epoch 795:  train loss 0.6390   train acc 0.5718   worst 0.1537   lr 0.0099   p 148.03   eps 0.4684   mix 0.0041   time 28.04
scalar:  3.3976
Epoch 796:  train loss 0.6373   train acc 0.5705   worst 0.1565   lr 0.0098   p 148.65   eps 0.4684   mix 0.0040   time 27.88
scalar:  3.3799
Epoch 797:  train loss 0.6377   train acc 0.5724   worst 0.1565   lr 0.0098   p 149.28   eps 0.4684   mix 0.0040   time 27.60
scalar:  3.3832
Epoch 798:  train loss 0.6388   train acc 0.5713   worst 0.1540   lr 0.0097   p 149.91   eps 0.4684   mix 0.0040   time 28.12
scalar:  3.3927
Epoch 799:  train loss 0.6384   train acc 0.5712   worst 0.1554   lr 0.0097   p 150.54   eps 0.4684   mix 0.0040   time 27.95
Epoch 799:  test acc 0.5381   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 799:  clean acc 0.5706   certified acc 0.3927
Calculating metrics for L_infinity dist model on test set
Epoch 799:  clean acc 0.5416   certified acc 0.3541
scalar:  3.3953
Epoch 800:  train loss 0.6375   train acc 0.5720   worst 0.1560   lr 0.0097   p 151.17   eps 0.4684   mix 0.0040   time 28.13
scalar:  3.3983
Epoch 801:  train loss 0.6385   train acc 0.5722   worst 0.1546   lr 0.0096   p 151.81   eps 0.4684   mix 0.0040   time 27.83
scalar:  3.4151
Epoch 802:  train loss 0.6369   train acc 0.5732   worst 0.1547   lr 0.0096   p 152.45   eps 0.4684   mix 0.0039   time 27.90
scalar:  3.4189
Epoch 803:  train loss 0.6379   train acc 0.5705   worst 0.1542   lr 0.0096   p 153.09   eps 0.4684   mix 0.0039   time 28.22
scalar:  3.4075
Epoch 804:  train loss 0.6380   train acc 0.5691   worst 0.1558   lr 0.0095   p 153.73   eps 0.4684   mix 0.0039   time 27.80
Epoch 804:  test acc 0.5416   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 804:  clean acc 0.5726   certified acc 0.3933
Calculating metrics for L_infinity dist model on test set
Epoch 804:  clean acc 0.5436   certified acc 0.3553
scalar:  3.4052
Epoch 805:  train loss 0.6379   train acc 0.5722   worst 0.1541   lr 0.0095   p 154.38   eps 0.4684   mix 0.0039   time 28.03
scalar:  3.4034
Epoch 806:  train loss 0.6384   train acc 0.5729   worst 0.1552   lr 0.0095   p 155.03   eps 0.4684   mix 0.0039   time 27.88
scalar:  3.4083
Epoch 807:  train loss 0.6372   train acc 0.5727   worst 0.1548   lr 0.0094   p 155.68   eps 0.4684   mix 0.0038   time 27.43
scalar:  3.4124
Epoch 808:  train loss 0.6384   train acc 0.5693   worst 0.1549   lr 0.0094   p 156.34   eps 0.4684   mix 0.0038   time 28.01
scalar:  3.4039
Epoch 809:  train loss 0.6384   train acc 0.5680   worst 0.1563   lr 0.0094   p 156.99   eps 0.4684   mix 0.0038   time 27.97
Epoch 809:  test acc 0.5350   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 809:  clean acc 0.5744   certified acc 0.3962
Calculating metrics for L_infinity dist model on test set
Epoch 809:  clean acc 0.5361   certified acc 0.3561
scalar:  3.3922
Epoch 810:  train loss 0.6382   train acc 0.5719   worst 0.1523   lr 0.0093   p 157.65   eps 0.4684   mix 0.0038   time 27.60
scalar:  3.4162
Epoch 811:  train loss 0.6373   train acc 0.5701   worst 0.1562   lr 0.0093   p 158.32   eps 0.4684   mix 0.0038   time 28.00
scalar:  3.4132
Epoch 812:  train loss 0.6372   train acc 0.5730   worst 0.1550   lr 0.0093   p 158.98   eps 0.4684   mix 0.0038   time 27.67
scalar:  3.435
Epoch 813:  train loss 0.6377   train acc 0.5709   worst 0.1536   lr 0.0092   p 159.65   eps 0.4684   mix 0.0037   time 27.50
scalar:  3.4095
Epoch 814:  train loss 0.6386   train acc 0.5690   worst 0.1561   lr 0.0092   p 160.32   eps 0.4684   mix 0.0037   time 27.90
Epoch 814:  test acc 0.5378   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 814:  clean acc 0.5708   certified acc 0.3965
Calculating metrics for L_infinity dist model on test set
Epoch 814:  clean acc 0.5409   certified acc 0.3567
scalar:  3.3995
Epoch 815:  train loss 0.6387   train acc 0.5697   worst 0.1552   lr 0.0092   p 161.00   eps 0.4684   mix 0.0037   time 28.09
scalar:  3.3932
Epoch 816:  train loss 0.6368   train acc 0.5710   worst 0.1565   lr 0.0091   p 161.68   eps 0.4684   mix 0.0037   time 28.04
scalar:  3.3957
Epoch 817:  train loss 0.6374   train acc 0.5717   worst 0.1556   lr 0.0091   p 162.36   eps 0.4684   mix 0.0037   time 27.71
scalar:  3.4083
Epoch 818:  train loss 0.6369   train acc 0.5726   worst 0.1540   lr 0.0091   p 163.04   eps 0.4684   mix 0.0037   time 27.77
scalar:  3.4211
Epoch 819:  train loss 0.6379   train acc 0.5712   worst 0.1543   lr 0.0090   p 163.72   eps 0.4684   mix 0.0036   time 27.91
Epoch 819:  test acc 0.5389   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 819:  clean acc 0.5755   certified acc 0.3994
Calculating metrics for L_infinity dist model on test set
Epoch 819:  clean acc 0.5440   certified acc 0.3581
scalar:  3.4209
Epoch 820:  train loss 0.6366   train acc 0.5741   worst 0.1552   lr 0.0090   p 164.41   eps 0.4684   mix 0.0036   time 27.90
scalar:  3.428
Epoch 821:  train loss 0.6364   train acc 0.5743   worst 0.1558   lr 0.0090   p 165.11   eps 0.4684   mix 0.0036   time 28.14
scalar:  3.4273
Epoch 822:  train loss 0.6385   train acc 0.5696   worst 0.1536   lr 0.0089   p 165.80   eps 0.4684   mix 0.0036   time 27.93
scalar:  3.4094
Epoch 823:  train loss 0.6367   train acc 0.5715   worst 0.1558   lr 0.0089   p 166.50   eps 0.4684   mix 0.0036   time 27.86
scalar:  3.399
Epoch 824:  train loss 0.6378   train acc 0.5721   worst 0.1538   lr 0.0089   p 167.20   eps 0.4684   mix 0.0036   time 28.20
Epoch 824:  test acc 0.5355   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 824:  clean acc 0.5756   certified acc 0.4082
Calculating metrics for L_infinity dist model on test set
Epoch 824:  clean acc 0.5415   certified acc 0.3653
scalar:  3.4141
Epoch 825:  train loss 0.6364   train acc 0.5726   worst 0.1532   lr 0.0088   p 167.90   eps 0.4684   mix 0.0035   time 28.51
scalar:  3.4223
Epoch 826:  train loss 0.6370   train acc 0.5711   worst 0.1562   lr 0.0088   p 168.61   eps 0.4684   mix 0.0035   time 27.67
scalar:  3.4292
Epoch 827:  train loss 0.6378   train acc 0.5716   worst 0.1550   lr 0.0088   p 169.32   eps 0.4684   mix 0.0035   time 27.99
scalar:  3.426
Epoch 828:  train loss 0.6371   train acc 0.5718   worst 0.1565   lr 0.0087   p 170.03   eps 0.4684   mix 0.0035   time 27.56
scalar:  3.4229
Epoch 829:  train loss 0.6370   train acc 0.5736   worst 0.1538   lr 0.0087   p 170.75   eps 0.4684   mix 0.0035   time 28.06
Epoch 829:  test acc 0.5371   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 829:  clean acc 0.5771   certified acc 0.4088
Calculating metrics for L_infinity dist model on test set
Epoch 829:  clean acc 0.5444   certified acc 0.3597
scalar:  3.4233
Epoch 830:  train loss 0.6371   train acc 0.5714   worst 0.1553   lr 0.0087   p 171.46   eps 0.4684   mix 0.0035   time 28.06
scalar:  3.4205
Epoch 831:  train loss 0.6378   train acc 0.5710   worst 0.1538   lr 0.0086   p 172.19   eps 0.4684   mix 0.0034   time 27.80
scalar:  3.4163
Epoch 832:  train loss 0.6380   train acc 0.5694   worst 0.1559   lr 0.0086   p 172.91   eps 0.4684   mix 0.0034   time 27.79
scalar:  3.4009
Epoch 833:  train loss 0.6366   train acc 0.5703   worst 0.1564   lr 0.0086   p 173.64   eps 0.4684   mix 0.0034   time 27.98
scalar:  3.4098
Epoch 834:  train loss 0.6368   train acc 0.5716   worst 0.1547   lr 0.0085   p 174.37   eps 0.4684   mix 0.0034   time 28.58
Epoch 834:  test acc 0.5345   time 2.68
Calculating metrics for L_infinity dist model on training set
Epoch 834:  clean acc 0.5717   certified acc 0.4096
Calculating metrics for L_infinity dist model on test set
Epoch 834:  clean acc 0.5418   certified acc 0.3643
scalar:  3.4105
Epoch 835:  train loss 0.6364   train acc 0.5727   worst 0.1550   lr 0.0085   p 175.10   eps 0.4684   mix 0.0034   time 28.38
scalar:  3.4028
Epoch 836:  train loss 0.6349   train acc 0.5741   worst 0.1558   lr 0.0085   p 175.84   eps 0.4684   mix 0.0034   time 27.44
scalar:  3.4211
Epoch 837:  train loss 0.6378   train acc 0.5717   worst 0.1542   lr 0.0084   p 176.58   eps 0.4684   mix 0.0034   time 28.35
scalar:  3.4208
Epoch 838:  train loss 0.6375   train acc 0.5727   worst 0.1514   lr 0.0084   p 177.32   eps 0.4684   mix 0.0033   time 27.97
scalar:  3.4358
Epoch 839:  train loss 0.6363   train acc 0.5725   worst 0.1551   lr 0.0084   p 178.07   eps 0.4684   mix 0.0033   time 28.16
Epoch 839:  test acc 0.5391   time 2.65
Calculating metrics for L_infinity dist model on training set
Epoch 839:  clean acc 0.5724   certified acc 0.4062
Calculating metrics for L_infinity dist model on test set
Epoch 839:  clean acc 0.5440   certified acc 0.3657
scalar:  3.4213
Epoch 840:  train loss 0.6362   train acc 0.5723   worst 0.1553   lr 0.0084   p 178.82   eps 0.4684   mix 0.0033   time 27.96
scalar:  3.4145
Epoch 841:  train loss 0.6370   train acc 0.5714   worst 0.1548   lr 0.0083   p 179.57   eps 0.4684   mix 0.0033   time 27.59
scalar:  3.4206
Epoch 842:  train loss 0.6375   train acc 0.5695   worst 0.1552   lr 0.0083   p 180.32   eps 0.4684   mix 0.0033   time 27.82
scalar:  3.4184
Epoch 843:  train loss 0.6384   train acc 0.5716   worst 0.1530   lr 0.0083   p 181.08   eps 0.4684   mix 0.0033   time 28.00
scalar:  3.4179
Epoch 844:  train loss 0.6375   train acc 0.5725   worst 0.1526   lr 0.0082   p 181.84   eps 0.4684   mix 0.0032   time 27.90
Epoch 844:  test acc 0.5372   time 2.65
Calculating metrics for L_infinity dist model on training set
Epoch 844:  clean acc 0.5809   certified acc 0.4130
Calculating metrics for L_infinity dist model on test set
Epoch 844:  clean acc 0.5442   certified acc 0.3659
scalar:  3.4292
Epoch 845:  train loss 0.6366   train acc 0.5747   worst 0.1538   lr 0.0082   p 182.61   eps 0.4684   mix 0.0032   time 28.26
scalar:  3.4451
Epoch 846:  train loss 0.6372   train acc 0.5747   worst 0.1539   lr 0.0082   p 183.38   eps 0.4684   mix 0.0032   time 27.98
scalar:  3.4474
Epoch 847:  train loss 0.6360   train acc 0.5753   worst 0.1543   lr 0.0081   p 184.15   eps 0.4684   mix 0.0032   time 27.69
scalar:  3.4383
Epoch 848:  train loss 0.6382   train acc 0.5728   worst 0.1515   lr 0.0081   p 184.92   eps 0.4684   mix 0.0032   time 27.89
scalar:  3.4325
Epoch 849:  train loss 0.6371   train acc 0.5729   worst 0.1539   lr 0.0081   p 185.70   eps 0.4684   mix 0.0032   time 28.04
Epoch 849:  test acc 0.5358   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 849:  clean acc 0.5793   certified acc 0.4124
Calculating metrics for L_infinity dist model on test set
Epoch 849:  clean acc 0.5420   certified acc 0.3652
scalar:  3.4356
Epoch 850:  train loss 0.6378   train acc 0.5696   worst 0.1522   lr 0.0080   p 186.48   eps 0.4684   mix 0.0032   time 27.99
scalar:  3.4229
Epoch 851:  train loss 0.6363   train acc 0.5723   worst 0.1543   lr 0.0080   p 187.27   eps 0.4684   mix 0.0031   time 27.88
scalar:  3.4391
Epoch 852:  train loss 0.6355   train acc 0.5737   worst 0.1533   lr 0.0080   p 188.06   eps 0.4684   mix 0.0031   time 27.98
scalar:  3.4437
Epoch 853:  train loss 0.6372   train acc 0.5727   worst 0.1522   lr 0.0079   p 188.85   eps 0.4684   mix 0.0031   time 27.89
scalar:  3.4384
Epoch 854:  train loss 0.6373   train acc 0.5726   worst 0.1523   lr 0.0079   p 189.64   eps 0.4684   mix 0.0031   time 28.36
Epoch 854:  test acc 0.5345   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 854:  clean acc 0.5751   certified acc 0.4178
Calculating metrics for L_infinity dist model on test set
Epoch 854:  clean acc 0.5371   certified acc 0.3690
scalar:  3.4431
Epoch 855:  train loss 0.6361   train acc 0.5726   worst 0.1530   lr 0.0079   p 190.44   eps 0.4684   mix 0.0031   time 27.91
scalar:  3.4527
Epoch 856:  train loss 0.6353   train acc 0.5753   worst 0.1515   lr 0.0078   p 191.24   eps 0.4684   mix 0.0031   time 28.32
scalar:  3.4536
Epoch 857:  train loss 0.6356   train acc 0.5726   worst 0.1525   lr 0.0078   p 192.05   eps 0.4684   mix 0.0031   time 28.01
scalar:  3.4461
Epoch 858:  train loss 0.6359   train acc 0.5741   worst 0.1527   lr 0.0078   p 192.85   eps 0.4684   mix 0.0030   time 27.91
scalar:  3.4571
Epoch 859:  train loss 0.6357   train acc 0.5730   worst 0.1533   lr 0.0077   p 193.66   eps 0.4684   mix 0.0030   time 28.35
Epoch 859:  test acc 0.5346   time 2.70
Calculating metrics for L_infinity dist model on training set
Epoch 859:  clean acc 0.5773   certified acc 0.4184
Calculating metrics for L_infinity dist model on test set
Epoch 859:  clean acc 0.5389   certified acc 0.3711
scalar:  3.4556
Epoch 860:  train loss 0.6357   train acc 0.5733   worst 0.1521   lr 0.0077   p 194.48   eps 0.4684   mix 0.0030   time 27.84
scalar:  3.4482
Epoch 861:  train loss 0.6349   train acc 0.5732   worst 0.1549   lr 0.0077   p 195.30   eps 0.4684   mix 0.0030   time 27.74
scalar:  3.4422
Epoch 862:  train loss 0.6356   train acc 0.5710   worst 0.1544   lr 0.0076   p 196.12   eps 0.4684   mix 0.0030   time 27.71
scalar:  3.4366
Epoch 863:  train loss 0.6370   train acc 0.5735   worst 0.1522   lr 0.0076   p 196.94   eps 0.4684   mix 0.0030   time 27.84
scalar:  3.4604
Epoch 864:  train loss 0.6353   train acc 0.5743   worst 0.1534   lr 0.0076   p 197.77   eps 0.4684   mix 0.0030   time 28.26
Epoch 864:  test acc 0.5372   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 864:  clean acc 0.5767   certified acc 0.4197
Calculating metrics for L_infinity dist model on test set
Epoch 864:  clean acc 0.5378   certified acc 0.3724
scalar:  3.4607
Epoch 865:  train loss 0.6373   train acc 0.5713   worst 0.1531   lr 0.0076   p 198.61   eps 0.4684   mix 0.0029   time 27.72
scalar:  3.4463
Epoch 866:  train loss 0.6361   train acc 0.5732   worst 0.1541   lr 0.0075   p 199.44   eps 0.4684   mix 0.0029   time 28.50
scalar:  3.4445
Epoch 867:  train loss 0.6353   train acc 0.5717   worst 0.1553   lr 0.0075   p 200.28   eps 0.4684   mix 0.0029   time 28.52
scalar:  3.4375
Epoch 868:  train loss 0.6355   train acc 0.5752   worst 0.1532   lr 0.0075   p 201.12   eps 0.4684   mix 0.0029   time 27.53
scalar:  3.4448
Epoch 869:  train loss 0.6365   train acc 0.5742   worst 0.1518   lr 0.0074   p 201.97   eps 0.4684   mix 0.0029   time 27.79
Epoch 869:  test acc 0.5367   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 869:  clean acc 0.5806   certified acc 0.4236
Calculating metrics for L_infinity dist model on test set
Epoch 869:  clean acc 0.5402   certified acc 0.3722
scalar:  3.4611
Epoch 870:  train loss 0.6362   train acc 0.5732   worst 0.1513   lr 0.0074   p 202.82   eps 0.4684   mix 0.0029   time 27.62
scalar:  3.4605
Epoch 871:  train loss 0.6347   train acc 0.5741   worst 0.1532   lr 0.0074   p 203.67   eps 0.4684   mix 0.0029   time 28.07
scalar:  3.454
Epoch 872:  train loss 0.6355   train acc 0.5739   worst 0.1516   lr 0.0073   p 204.53   eps 0.4684   mix 0.0029   time 27.91
scalar:  3.4558
Epoch 873:  train loss 0.6349   train acc 0.5730   worst 0.1533   lr 0.0073   p 205.39   eps 0.4684   mix 0.0028   time 27.54
scalar:  3.4562
Epoch 874:  train loss 0.6361   train acc 0.5743   worst 0.1539   lr 0.0073   p 206.25   eps 0.4684   mix 0.0028   time 28.13
Epoch 874:  test acc 0.5375   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 874:  clean acc 0.5773   certified acc 0.4208
Calculating metrics for L_infinity dist model on test set
Epoch 874:  clean acc 0.5455   certified acc 0.3762
scalar:  3.4579
Epoch 875:  train loss 0.6353   train acc 0.5723   worst 0.1532   lr 0.0072   p 207.12   eps 0.4684   mix 0.0028   time 27.66
scalar:  3.4558
Epoch 876:  train loss 0.6371   train acc 0.5720   worst 0.1531   lr 0.0072   p 207.99   eps 0.4684   mix 0.0028   time 27.98
scalar:  3.4526
Epoch 877:  train loss 0.6358   train acc 0.5734   worst 0.1537   lr 0.0072   p 208.87   eps 0.4684   mix 0.0028   time 27.74
scalar:  3.4458
Epoch 878:  train loss 0.6350   train acc 0.5729   worst 0.1530   lr 0.0071   p 209.75   eps 0.4684   mix 0.0028   time 27.92
scalar:  3.4623
Epoch 879:  train loss 0.6357   train acc 0.5744   worst 0.1526   lr 0.0071   p 210.63   eps 0.4684   mix 0.0028   time 28.42
Epoch 879:  test acc 0.5412   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 879:  clean acc 0.5782   certified acc 0.4210
Calculating metrics for L_infinity dist model on test set
Epoch 879:  clean acc 0.5459   certified acc 0.3749
scalar:  3.4693
Epoch 880:  train loss 0.6354   train acc 0.5746   worst 0.1516   lr 0.0071   p 211.52   eps 0.4684   mix 0.0027   time 27.79
scalar:  3.4629
Epoch 881:  train loss 0.6343   train acc 0.5742   worst 0.1527   lr 0.0071   p 212.41   eps 0.4684   mix 0.0027   time 28.07
scalar:  3.4662
Epoch 882:  train loss 0.6357   train acc 0.5731   worst 0.1529   lr 0.0070   p 213.30   eps 0.4684   mix 0.0027   time 27.55
scalar:  3.4464
Epoch 883:  train loss 0.6348   train acc 0.5726   worst 0.1534   lr 0.0070   p 214.20   eps 0.4684   mix 0.0027   time 27.79
scalar:  3.4484
Epoch 884:  train loss 0.6347   train acc 0.5753   worst 0.1528   lr 0.0070   p 215.10   eps 0.4684   mix 0.0027   time 28.57
Epoch 884:  test acc 0.5349   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 884:  clean acc 0.5785   certified acc 0.4228
Calculating metrics for L_infinity dist model on test set
Epoch 884:  clean acc 0.5457   certified acc 0.3751
scalar:  3.4463
Epoch 885:  train loss 0.6342   train acc 0.5764   worst 0.1532   lr 0.0069   p 216.00   eps 0.4684   mix 0.0027   time 27.67
scalar:  3.4652
Epoch 886:  train loss 0.6374   train acc 0.5731   worst 0.1527   lr 0.0069   p 216.91   eps 0.4684   mix 0.0027   time 27.60
scalar:  3.4674
Epoch 887:  train loss 0.6368   train acc 0.5739   worst 0.1528   lr 0.0069   p 217.82   eps 0.4684   mix 0.0027   time 27.65
scalar:  3.4572
Epoch 888:  train loss 0.6343   train acc 0.5746   worst 0.1534   lr 0.0068   p 218.74   eps 0.4684   mix 0.0027   time 27.83
scalar:  3.4531
Epoch 889:  train loss 0.6365   train acc 0.5724   worst 0.1526   lr 0.0068   p 219.66   eps 0.4684   mix 0.0026   time 28.38
Epoch 889:  test acc 0.5349   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 889:  clean acc 0.5786   certified acc 0.4283
Calculating metrics for L_infinity dist model on test set
Epoch 889:  clean acc 0.5414   certified acc 0.3762
scalar:  3.4548
Epoch 890:  train loss 0.6358   train acc 0.5752   worst 0.1500   lr 0.0068   p 220.59   eps 0.4684   mix 0.0026   time 28.07
scalar:  3.4681
Epoch 891:  train loss 0.6340   train acc 0.5730   worst 0.1531   lr 0.0067   p 221.51   eps 0.4684   mix 0.0026   time 27.97
scalar:  3.4637
Epoch 892:  train loss 0.6352   train acc 0.5737   worst 0.1508   lr 0.0067   p 222.45   eps 0.4684   mix 0.0026   time 27.53
scalar:  3.4624
Epoch 893:  train loss 0.6348   train acc 0.5754   worst 0.1518   lr 0.0067   p 223.38   eps 0.4684   mix 0.0026   time 28.06
scalar:  3.4725
Epoch 894:  train loss 0.6350   train acc 0.5756   worst 0.1532   lr 0.0067   p 224.32   eps 0.4684   mix 0.0026   time 28.29
Epoch 894:  test acc 0.5348   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 894:  clean acc 0.5792   certified acc 0.4287
Calculating metrics for L_infinity dist model on test set
Epoch 894:  clean acc 0.5399   certified acc 0.3792
scalar:  3.4716
Epoch 895:  train loss 0.6363   train acc 0.5729   worst 0.1508   lr 0.0066   p 225.26   eps 0.4684   mix 0.0026   time 27.90
scalar:  3.4656
Epoch 896:  train loss 0.6352   train acc 0.5737   worst 0.1523   lr 0.0066   p 226.21   eps 0.4684   mix 0.0026   time 27.89
scalar:  3.4615
Epoch 897:  train loss 0.6351   train acc 0.5739   worst 0.1539   lr 0.0066   p 227.16   eps 0.4684   mix 0.0025   time 27.66
scalar:  3.4671
Epoch 898:  train loss 0.6347   train acc 0.5733   worst 0.1529   lr 0.0065   p 228.12   eps 0.4684   mix 0.0025   time 27.66
scalar:  3.4679
Epoch 899:  train loss 0.6340   train acc 0.5764   worst 0.1523   lr 0.0065   p 229.08   eps 0.4684   mix 0.0025   time 28.26
Epoch 899:  test acc 0.5352   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 899:  clean acc 0.5783   certified acc 0.4269
Calculating metrics for L_infinity dist model on test set
Epoch 899:  clean acc 0.5414   certified acc 0.3783
scalar:  3.4788
Epoch 900:  train loss 0.6336   train acc 0.5754   worst 0.1509   lr 0.0065   p 230.04   eps 0.4684   mix 0.0025   time 27.82
scalar:  3.4733
Epoch 901:  train loss 0.6346   train acc 0.5743   worst 0.1527   lr 0.0064   p 231.01   eps 0.4684   mix 0.0025   time 28.01
scalar:  3.4757
Epoch 902:  train loss 0.6337   train acc 0.5752   worst 0.1516   lr 0.0064   p 231.98   eps 0.4684   mix 0.0025   time 27.58
scalar:  3.4774
Epoch 903:  train loss 0.6328   train acc 0.5763   worst 0.1519   lr 0.0064   p 232.96   eps 0.4684   mix 0.0025   time 27.77
scalar:  3.4723
Epoch 904:  train loss 0.6338   train acc 0.5753   worst 0.1524   lr 0.0064   p 233.94   eps 0.4684   mix 0.0025   time 28.11
Epoch 904:  test acc 0.5360   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 904:  clean acc 0.5801   certified acc 0.4297
Calculating metrics for L_infinity dist model on test set
Epoch 904:  clean acc 0.5404   certified acc 0.3813
scalar:  3.4775
Epoch 905:  train loss 0.6352   train acc 0.5749   worst 0.1505   lr 0.0063   p 234.92   eps 0.4684   mix 0.0025   time 28.03
scalar:  3.4765
Epoch 906:  train loss 0.6342   train acc 0.5721   worst 0.1522   lr 0.0063   p 235.91   eps 0.4684   mix 0.0024   time 27.90
scalar:  3.472
Epoch 907:  train loss 0.6343   train acc 0.5753   worst 0.1514   lr 0.0063   p 236.90   eps 0.4684   mix 0.0024   time 27.66
scalar:  3.4741
Epoch 908:  train loss 0.6359   train acc 0.5744   worst 0.1514   lr 0.0062   p 237.90   eps 0.4684   mix 0.0024   time 27.99
scalar:  3.4758
Epoch 909:  train loss 0.6346   train acc 0.5763   worst 0.1504   lr 0.0062   p 238.90   eps 0.4684   mix 0.0024   time 28.33
Epoch 909:  test acc 0.5389   time 2.65
Calculating metrics for L_infinity dist model on training set
Epoch 909:  clean acc 0.5790   certified acc 0.4304
Calculating metrics for L_infinity dist model on test set
Epoch 909:  clean acc 0.5440   certified acc 0.3786
scalar:  3.4793
Epoch 910:  train loss 0.6345   train acc 0.5756   worst 0.1501   lr 0.0062   p 239.91   eps 0.4684   mix 0.0024   time 27.80
scalar:  3.4851
Epoch 911:  train loss 0.6328   train acc 0.5767   worst 0.1531   lr 0.0062   p 240.92   eps 0.4684   mix 0.0024   time 27.86
scalar:  3.482
Epoch 912:  train loss 0.6332   train acc 0.5773   worst 0.1518   lr 0.0061   p 241.93   eps 0.4684   mix 0.0024   time 27.40
scalar:  3.4841
Epoch 913:  train loss 0.6340   train acc 0.5753   worst 0.1517   lr 0.0061   p 242.95   eps 0.4684   mix 0.0024   time 28.38
scalar:  3.4839
Epoch 914:  train loss 0.6343   train acc 0.5735   worst 0.1537   lr 0.0061   p 243.97   eps 0.4684   mix 0.0024   time 28.05
Epoch 914:  test acc 0.5365   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 914:  clean acc 0.5810   certified acc 0.4335
Calculating metrics for L_infinity dist model on test set
Epoch 914:  clean acc 0.5378   certified acc 0.3793
scalar:  3.486
Epoch 915:  train loss 0.6328   train acc 0.5779   worst 0.1515   lr 0.0060   p 245.00   eps 0.4684   mix 0.0023   time 28.47
scalar:  3.4835
Epoch 916:  train loss 0.6355   train acc 0.5752   worst 0.1495   lr 0.0060   p 246.03   eps 0.4684   mix 0.0023   time 27.59
scalar:  3.4808
Epoch 917:  train loss 0.6329   train acc 0.5757   worst 0.1537   lr 0.0060   p 247.06   eps 0.4684   mix 0.0023   time 27.84
scalar:  3.5011
Epoch 918:  train loss 0.6342   train acc 0.5746   worst 0.1529   lr 0.0060   p 248.10   eps 0.4684   mix 0.0023   time 27.99
scalar:  3.4935
Epoch 919:  train loss 0.6335   train acc 0.5756   worst 0.1494   lr 0.0059   p 249.15   eps 0.4684   mix 0.0023   time 28.01
Epoch 919:  test acc 0.5379   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 919:  clean acc 0.5790   certified acc 0.4323
Calculating metrics for L_infinity dist model on test set
Epoch 919:  clean acc 0.5374   certified acc 0.3803
scalar:  3.4951
Epoch 920:  train loss 0.6338   train acc 0.5768   worst 0.1502   lr 0.0059   p 250.19   eps 0.4684   mix 0.0023   time 28.12
scalar:  3.504
Epoch 921:  train loss 0.6314   train acc 0.5785   worst 0.1537   lr 0.0059   p 251.25   eps 0.4684   mix 0.0023   time 27.60
scalar:  3.5022
Epoch 922:  train loss 0.6338   train acc 0.5766   worst 0.1515   lr 0.0058   p 252.30   eps 0.4684   mix 0.0023   time 27.84
scalar:  3.5053
Epoch 923:  train loss 0.6335   train acc 0.5742   worst 0.1510   lr 0.0058   p 253.37   eps 0.4684   mix 0.0023   time 27.75
scalar:  3.4973
Epoch 924:  train loss 0.6348   train acc 0.5755   worst 0.1516   lr 0.0058   p 254.43   eps 0.4684   mix 0.0022   time 27.87
Epoch 924:  test acc 0.5366   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 924:  clean acc 0.5792   certified acc 0.4356
Calculating metrics for L_infinity dist model on test set
Epoch 924:  clean acc 0.5399   certified acc 0.3862
scalar:  3.4923
Epoch 925:  train loss 0.6325   train acc 0.5782   worst 0.1530   lr 0.0057   p 255.50   eps 0.4684   mix 0.0022   time 28.03
scalar:  3.4938
Epoch 926:  train loss 0.6338   train acc 0.5767   worst 0.1515   lr 0.0057   p 256.58   eps 0.4684   mix 0.0022   time 27.61
scalar:  3.5002
Epoch 927:  train loss 0.6332   train acc 0.5747   worst 0.1519   lr 0.0057   p 257.66   eps 0.4684   mix 0.0022   time 27.85
scalar:  3.4868
Epoch 928:  train loss 0.6341   train acc 0.5734   worst 0.1525   lr 0.0057   p 258.74   eps 0.4684   mix 0.0022   time 28.02
scalar:  3.4855
Epoch 929:  train loss 0.6319   train acc 0.5770   worst 0.1506   lr 0.0056   p 259.83   eps 0.4684   mix 0.0022   time 28.45
Epoch 929:  test acc 0.5328   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 929:  clean acc 0.5796   certified acc 0.4344
Calculating metrics for L_infinity dist model on test set
Epoch 929:  clean acc 0.5383   certified acc 0.3817
scalar:  3.486
Epoch 930:  train loss 0.6319   train acc 0.5773   worst 0.1516   lr 0.0056   p 260.92   eps 0.4684   mix 0.0022   time 27.73
scalar:  3.4921
Epoch 931:  train loss 0.6331   train acc 0.5757   worst 0.1499   lr 0.0056   p 262.02   eps 0.4684   mix 0.0022   time 27.49
scalar:  3.4953
Epoch 932:  train loss 0.6320   train acc 0.5789   worst 0.1510   lr 0.0056   p 263.12   eps 0.4684   mix 0.0022   time 27.92
scalar:  3.5082
Epoch 933:  train loss 0.6320   train acc 0.5763   worst 0.1515   lr 0.0055   p 264.23   eps 0.4684   mix 0.0022   time 27.97
scalar:  3.508
Epoch 934:  train loss 0.6335   train acc 0.5756   worst 0.1524   lr 0.0055   p 265.34   eps 0.4684   mix 0.0021   time 27.99
Epoch 934:  test acc 0.5366   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 934:  clean acc 0.5811   certified acc 0.4363
Calculating metrics for L_infinity dist model on test set
Epoch 934:  clean acc 0.5413   certified acc 0.3886
scalar:  3.4926
Epoch 935:  train loss 0.6303   train acc 0.5787   worst 0.1521   lr 0.0055   p 266.46   eps 0.4684   mix 0.0021   time 28.34
scalar:  3.5014
Epoch 936:  train loss 0.6325   train acc 0.5763   worst 0.1514   lr 0.0054   p 267.58   eps 0.4684   mix 0.0021   time 27.70
scalar:  3.502
Epoch 937:  train loss 0.6315   train acc 0.5759   worst 0.1519   lr 0.0054   p 268.71   eps 0.4684   mix 0.0021   time 28.17
scalar:  3.5024
Epoch 938:  train loss 0.6319   train acc 0.5768   worst 0.1523   lr 0.0054   p 269.84   eps 0.4684   mix 0.0021   time 28.09
scalar:  3.4955
Epoch 939:  train loss 0.6324   train acc 0.5748   worst 0.1513   lr 0.0054   p 270.97   eps 0.4684   mix 0.0021   time 27.64
Epoch 939:  test acc 0.5362   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 939:  clean acc 0.5787   certified acc 0.4361
Calculating metrics for L_infinity dist model on test set
Epoch 939:  clean acc 0.5422   certified acc 0.3858
scalar:  3.5051
Epoch 940:  train loss 0.6327   train acc 0.5768   worst 0.1504   lr 0.0053   p 272.11   eps 0.4684   mix 0.0021   time 27.76
scalar:  3.5022
Epoch 941:  train loss 0.6319   train acc 0.5773   worst 0.1498   lr 0.0053   p 273.26   eps 0.4684   mix 0.0021   time 27.31
scalar:  3.5226
Epoch 942:  train loss 0.6323   train acc 0.5753   worst 0.1533   lr 0.0053   p 274.41   eps 0.4684   mix 0.0021   time 27.95
scalar:  3.5108
Epoch 943:  train loss 0.6318   train acc 0.5771   worst 0.1530   lr 0.0052   p 275.56   eps 0.4684   mix 0.0021   time 27.43
scalar:  3.5062
Epoch 944:  train loss 0.6332   train acc 0.5739   worst 0.1531   lr 0.0052   p 276.72   eps 0.4684   mix 0.0020   time 27.98
Epoch 944:  test acc 0.5339   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 944:  clean acc 0.5809   certified acc 0.4396
Calculating metrics for L_infinity dist model on test set
Epoch 944:  clean acc 0.5390   certified acc 0.3883
scalar:  3.5069
Epoch 945:  train loss 0.6321   train acc 0.5759   worst 0.1519   lr 0.0052   p 277.88   eps 0.4684   mix 0.0020   time 27.47
scalar:  3.5041
Epoch 946:  train loss 0.6315   train acc 0.5763   worst 0.1526   lr 0.0052   p 279.05   eps 0.4684   mix 0.0020   time 27.54
scalar:  3.5017
Epoch 947:  train loss 0.6318   train acc 0.5778   worst 0.1506   lr 0.0051   p 280.23   eps 0.4684   mix 0.0020   time 27.97
scalar:  3.516
Epoch 948:  train loss 0.6333   train acc 0.5752   worst 0.1507   lr 0.0051   p 281.41   eps 0.4684   mix 0.0020   time 27.83
scalar:  3.514
Epoch 949:  train loss 0.6324   train acc 0.5774   worst 0.1524   lr 0.0051   p 282.59   eps 0.4684   mix 0.0020   time 28.02
Epoch 949:  test acc 0.5376   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 949:  clean acc 0.5817   certified acc 0.4384
Calculating metrics for L_infinity dist model on test set
Epoch 949:  clean acc 0.5425   certified acc 0.3860
scalar:  3.5154
Epoch 950:  train loss 0.6314   train acc 0.5781   worst 0.1507   lr 0.0051   p 283.78   eps 0.4684   mix 0.0020   time 27.65
scalar:  3.5179
Epoch 951:  train loss 0.6315   train acc 0.5791   worst 0.1501   lr 0.0050   p 284.97   eps 0.4684   mix 0.0020   time 27.70
scalar:  3.5274
Epoch 952:  train loss 0.6302   train acc 0.5790   worst 0.1536   lr 0.0050   p 286.17   eps 0.4684   mix 0.0020   time 27.72
scalar:  3.5228
Epoch 953:  train loss 0.6309   train acc 0.5775   worst 0.1522   lr 0.0050   p 287.38   eps 0.4684   mix 0.0020   time 27.94
scalar:  3.5193
Epoch 954:  train loss 0.6331   train acc 0.5773   worst 0.1509   lr 0.0049   p 288.58   eps 0.4684   mix 0.0020   time 28.75
Epoch 954:  test acc 0.5345   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 954:  clean acc 0.5819   certified acc 0.4389
Calculating metrics for L_infinity dist model on test set
Epoch 954:  clean acc 0.5407   certified acc 0.3865
scalar:  3.5225
Epoch 955:  train loss 0.6306   train acc 0.5794   worst 0.1527   lr 0.0049   p 289.80   eps 0.4684   mix 0.0019   time 27.47
scalar:  3.5292
Epoch 956:  train loss 0.6317   train acc 0.5769   worst 0.1529   lr 0.0049   p 291.02   eps 0.4684   mix 0.0019   time 27.62
scalar:  3.5345
Epoch 957:  train loss 0.6303   train acc 0.5795   worst 0.1506   lr 0.0049   p 292.24   eps 0.4684   mix 0.0019   time 27.91
scalar:  3.5435
Epoch 958:  train loss 0.6298   train acc 0.5804   worst 0.1507   lr 0.0048   p 293.47   eps 0.4684   mix 0.0019   time 27.68
scalar:  3.5351
Epoch 959:  train loss 0.6307   train acc 0.5783   worst 0.1521   lr 0.0048   p 294.71   eps 0.4684   mix 0.0019   time 28.22
Epoch 959:  test acc 0.5389   time 2.67
Calculating metrics for L_infinity dist model on training set
Epoch 959:  clean acc 0.5821   certified acc 0.4409
Calculating metrics for L_infinity dist model on test set
Epoch 959:  clean acc 0.5431   certified acc 0.3913
scalar:  3.5324
Epoch 960:  train loss 0.6317   train acc 0.5773   worst 0.1507   lr 0.0048   p 295.95   eps 0.4684   mix 0.0019   time 27.61
scalar:  3.5313
Epoch 961:  train loss 0.6297   train acc 0.5811   worst 0.1533   lr 0.0048   p 297.19   eps 0.4684   mix 0.0019   time 28.18
scalar:  3.5393
Epoch 962:  train loss 0.6305   train acc 0.5798   worst 0.1505   lr 0.0047   p 298.44   eps 0.4684   mix 0.0019   time 27.85
scalar:  3.5273
Epoch 963:  train loss 0.6306   train acc 0.5779   worst 0.1523   lr 0.0047   p 299.70   eps 0.4684   mix 0.0019   time 28.03
scalar:  3.5274
Epoch 964:  train loss 0.6322   train acc 0.5758   worst 0.1522   lr 0.0047   p 300.96   eps 0.4684   mix 0.0019   time 28.07
Epoch 964:  test acc 0.5344   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 964:  clean acc 0.5804   certified acc 0.4411
Calculating metrics for L_infinity dist model on test set
Epoch 964:  clean acc 0.5388   certified acc 0.3892
scalar:  3.5217
Epoch 965:  train loss 0.6305   train acc 0.5771   worst 0.1538   lr 0.0047   p 302.23   eps 0.4684   mix 0.0019   time 27.76
scalar:  3.5124
Epoch 966:  train loss 0.6305   train acc 0.5781   worst 0.1529   lr 0.0046   p 303.50   eps 0.4684   mix 0.0019   time 27.98
scalar:  3.5143
Epoch 967:  train loss 0.6303   train acc 0.5788   worst 0.1520   lr 0.0046   p 304.77   eps 0.4684   mix 0.0018   time 27.65
scalar:  3.5251
Epoch 968:  train loss 0.6311   train acc 0.5781   worst 0.1527   lr 0.0046   p 306.06   eps 0.4684   mix 0.0018   time 27.81
scalar:  3.5181
Epoch 969:  train loss 0.6315   train acc 0.5787   worst 0.1504   lr 0.0045   p 307.34   eps 0.4684   mix 0.0018   time 28.27
Epoch 969:  test acc 0.5352   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 969:  clean acc 0.5806   certified acc 0.4437
Calculating metrics for L_infinity dist model on test set
Epoch 969:  clean acc 0.5400   certified acc 0.3910
scalar:  3.5263
Epoch 970:  train loss 0.6319   train acc 0.5760   worst 0.1485   lr 0.0045   p 308.64   eps 0.4684   mix 0.0018   time 27.88
scalar:  3.5148
Epoch 971:  train loss 0.6304   train acc 0.5779   worst 0.1516   lr 0.0045   p 309.94   eps 0.4684   mix 0.0018   time 27.95
scalar:  3.5177
Epoch 972:  train loss 0.6292   train acc 0.5809   worst 0.1518   lr 0.0045   p 311.24   eps 0.4684   mix 0.0018   time 28.15
scalar:  3.5192
Epoch 973:  train loss 0.6295   train acc 0.5787   worst 0.1509   lr 0.0044   p 312.55   eps 0.4684   mix 0.0018   time 27.59
scalar:  3.5177
Epoch 974:  train loss 0.6303   train acc 0.5792   worst 0.1527   lr 0.0044   p 313.86   eps 0.4684   mix 0.0018   time 28.09
Epoch 974:  test acc 0.5362   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 974:  clean acc 0.5806   certified acc 0.4457
Calculating metrics for L_infinity dist model on test set
Epoch 974:  clean acc 0.5374   certified acc 0.3925
scalar:  3.5203
Epoch 975:  train loss 0.6304   train acc 0.5776   worst 0.1533   lr 0.0044   p 315.18   eps 0.4684   mix 0.0018   time 27.64
scalar:  3.5147
Epoch 976:  train loss 0.6295   train acc 0.5792   worst 0.1521   lr 0.0044   p 316.51   eps 0.4684   mix 0.0018   time 28.05
scalar:  3.5209
Epoch 977:  train loss 0.6310   train acc 0.5767   worst 0.1511   lr 0.0043   p 317.84   eps 0.4684   mix 0.0018   time 27.77
scalar:  3.5206
Epoch 978:  train loss 0.6313   train acc 0.5767   worst 0.1507   lr 0.0043   p 319.18   eps 0.4684   mix 0.0018   time 27.73
scalar:  3.5242
Epoch 979:  train loss 0.6308   train acc 0.5778   worst 0.1510   lr 0.0043   p 320.52   eps 0.4684   mix 0.0017   time 27.98
Epoch 979:  test acc 0.5372   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 979:  clean acc 0.5827   certified acc 0.4444
Calculating metrics for L_infinity dist model on test set
Epoch 979:  clean acc 0.5417   certified acc 0.3898
scalar:  3.5211
Epoch 980:  train loss 0.6297   train acc 0.5790   worst 0.1528   lr 0.0043   p 321.87   eps 0.4684   mix 0.0017   time 27.91
scalar:  3.526
Epoch 981:  train loss 0.6286   train acc 0.5806   worst 0.1520   lr 0.0042   p 323.23   eps 0.4684   mix 0.0017   time 28.14
scalar:  3.5303
Epoch 982:  train loss 0.6308   train acc 0.5777   worst 0.1519   lr 0.0042   p 324.59   eps 0.4684   mix 0.0017   time 28.06
scalar:  3.5286
Epoch 983:  train loss 0.6302   train acc 0.5777   worst 0.1532   lr 0.0042   p 325.95   eps 0.4684   mix 0.0017   time 28.12
scalar:  3.5314
Epoch 984:  train loss 0.6281   train acc 0.5811   worst 0.1524   lr 0.0042   p 327.32   eps 0.4684   mix 0.0017   time 28.54
Epoch 984:  test acc 0.5397   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 984:  clean acc 0.5837   certified acc 0.4445
Calculating metrics for L_infinity dist model on test set
Epoch 984:  clean acc 0.5452   certified acc 0.3896
scalar:  3.5408
Epoch 985:  train loss 0.6282   train acc 0.5784   worst 0.1545   lr 0.0041   p 328.70   eps 0.4684   mix 0.0017   time 27.44
scalar:  3.5325
Epoch 986:  train loss 0.6290   train acc 0.5782   worst 0.1517   lr 0.0041   p 330.08   eps 0.4684   mix 0.0017   time 27.45
scalar:  3.5303
Epoch 987:  train loss 0.6289   train acc 0.5774   worst 0.1531   lr 0.0041   p 331.47   eps 0.4684   mix 0.0017   time 28.38
scalar:  3.5317
Epoch 988:  train loss 0.6311   train acc 0.5758   worst 0.1526   lr 0.0041   p 332.87   eps 0.4684   mix 0.0017   time 27.73
scalar:  3.5305
Epoch 989:  train loss 0.6300   train acc 0.5790   worst 0.1504   lr 0.0040   p 334.27   eps 0.4684   mix 0.0017   time 27.98
Epoch 989:  test acc 0.5384   time 2.65
Calculating metrics for L_infinity dist model on training set
Epoch 989:  clean acc 0.5822   certified acc 0.4438
Calculating metrics for L_infinity dist model on test set
Epoch 989:  clean acc 0.5426   certified acc 0.3920
scalar:  3.5324
Epoch 990:  train loss 0.6289   train acc 0.5779   worst 0.1528   lr 0.0040   p 335.67   eps 0.4684   mix 0.0017   time 27.87
scalar:  3.5256
Epoch 991:  train loss 0.6315   train acc 0.5776   worst 0.1493   lr 0.0040   p 337.08   eps 0.4684   mix 0.0016   time 27.50
scalar:  3.5312
Epoch 992:  train loss 0.6293   train acc 0.5800   worst 0.1508   lr 0.0040   p 338.50   eps 0.4684   mix 0.0016   time 28.10
scalar:  3.5318
Epoch 993:  train loss 0.6285   train acc 0.5796   worst 0.1520   lr 0.0039   p 339.93   eps 0.4684   mix 0.0016   time 27.72
scalar:  3.5318
Epoch 994:  train loss 0.6307   train acc 0.5772   worst 0.1517   lr 0.0039   p 341.36   eps 0.4684   mix 0.0016   time 27.96
Epoch 994:  test acc 0.5388   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 994:  clean acc 0.5830   certified acc 0.4481
Calculating metrics for L_infinity dist model on test set
Epoch 994:  clean acc 0.5409   certified acc 0.3904
scalar:  3.525
Epoch 995:  train loss 0.6287   train acc 0.5798   worst 0.1524   lr 0.0039   p 342.79   eps 0.4684   mix 0.0016   time 28.06
scalar:  3.5291
Epoch 996:  train loss 0.6281   train acc 0.5811   worst 0.1512   lr 0.0039   p 344.24   eps 0.4684   mix 0.0016   time 27.67
scalar:  3.5368
Epoch 997:  train loss 0.6304   train acc 0.5786   worst 0.1499   lr 0.0038   p 345.68   eps 0.4684   mix 0.0016   time 28.14
scalar:  3.5351
Epoch 998:  train loss 0.6280   train acc 0.5792   worst 0.1539   lr 0.0038   p 347.14   eps 0.4684   mix 0.0016   time 27.76
scalar:  3.5326
Epoch 999:  train loss 0.6292   train acc 0.5786   worst 0.1531   lr 0.0038   p 348.60   eps 0.4684   mix 0.0016   time 28.12
Epoch 999:  test acc 0.5380   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 999:  clean acc 0.5828   certified acc 0.4471
Calculating metrics for L_infinity dist model on test set
Epoch 999:  clean acc 0.5405   certified acc 0.3905
scalar:  3.5343
Epoch 1000:  train loss 0.6288   train acc 0.5799   worst 0.1529   lr 0.0038   p 350.07   eps 0.4684   mix 0.0016   time 28.04
scalar:  3.5383
Epoch 1001:  train loss 0.6282   train acc 0.5811   worst 0.1513   lr 0.0037   p 351.54   eps 0.4684   mix 0.0016   time 27.58
scalar:  3.537
Epoch 1002:  train loss 0.6282   train acc 0.5790   worst 0.1520   lr 0.0037   p 353.02   eps 0.4684   mix 0.0016   time 28.28
scalar:  3.5401
Epoch 1003:  train loss 0.6291   train acc 0.5806   worst 0.1505   lr 0.0037   p 354.50   eps 0.4684   mix 0.0016   time 28.08
scalar:  3.5456
Epoch 1004:  train loss 0.6284   train acc 0.5786   worst 0.1519   lr 0.0037   p 355.99   eps 0.4684   mix 0.0016   time 27.96
Epoch 1004:  test acc 0.5375   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 1004:  clean acc 0.5826   certified acc 0.4476
Calculating metrics for L_infinity dist model on test set
Epoch 1004:  clean acc 0.5414   certified acc 0.3894
scalar:  3.5427
Epoch 1005:  train loss 0.6279   train acc 0.5806   worst 0.1521   lr 0.0037   p 357.49   eps 0.4684   mix 0.0015   time 28.05
scalar:  3.5428
Epoch 1006:  train loss 0.6286   train acc 0.5808   worst 0.1510   lr 0.0036   p 359.00   eps 0.4684   mix 0.0015   time 27.74
scalar:  3.5438
Epoch 1007:  train loss 0.6282   train acc 0.5804   worst 0.1528   lr 0.0036   p 360.51   eps 0.4684   mix 0.0015   time 28.06
scalar:  3.5425
Epoch 1008:  train loss 0.6278   train acc 0.5803   worst 0.1540   lr 0.0036   p 362.02   eps 0.4684   mix 0.0015   time 28.02
scalar:  3.5405
Epoch 1009:  train loss 0.6275   train acc 0.5811   worst 0.1530   lr 0.0036   p 363.55   eps 0.4684   mix 0.0015   time 27.74
Epoch 1009:  test acc 0.5408   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 1009:  clean acc 0.5851   certified acc 0.4505
Calculating metrics for L_infinity dist model on test set
Epoch 1009:  clean acc 0.5429   certified acc 0.3901
scalar:  3.5441
Epoch 1010:  train loss 0.6291   train acc 0.5789   worst 0.1508   lr 0.0035   p 365.08   eps 0.4684   mix 0.0015   time 28.28
scalar:  3.5418
Epoch 1011:  train loss 0.6282   train acc 0.5806   worst 0.1531   lr 0.0035   p 366.61   eps 0.4684   mix 0.0015   time 27.74
scalar:  3.544
Epoch 1012:  train loss 0.6283   train acc 0.5793   worst 0.1513   lr 0.0035   p 368.16   eps 0.4684   mix 0.0015   time 28.60
scalar:  3.54
Epoch 1013:  train loss 0.6276   train acc 0.5803   worst 0.1540   lr 0.0035   p 369.70   eps 0.4684   mix 0.0015   time 27.76
scalar:  3.5449
Epoch 1014:  train loss 0.6272   train acc 0.5797   worst 0.1527   lr 0.0034   p 371.26   eps 0.4684   mix 0.0015   time 27.75
Epoch 1014:  test acc 0.5395   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 1014:  clean acc 0.5852   certified acc 0.4497
Calculating metrics for L_infinity dist model on test set
Epoch 1014:  clean acc 0.5409   certified acc 0.3928
scalar:  3.5467
Epoch 1015:  train loss 0.6275   train acc 0.5816   worst 0.1521   lr 0.0034   p 372.82   eps 0.4684   mix 0.0015   time 28.20
scalar:  3.5467
Epoch 1016:  train loss 0.6286   train acc 0.5806   worst 0.1504   lr 0.0034   p 374.39   eps 0.4684   mix 0.0015   time 27.77
scalar:  3.5484
Epoch 1017:  train loss 0.6273   train acc 0.5820   worst 0.1515   lr 0.0034   p 375.97   eps 0.4684   mix 0.0015   time 27.81
scalar:  3.5443
Epoch 1018:  train loss 0.6278   train acc 0.5788   worst 0.1528   lr 0.0034   p 377.55   eps 0.4684   mix 0.0015   time 27.65
scalar:  3.5448
Epoch 1019:  train loss 0.6270   train acc 0.5831   worst 0.1520   lr 0.0033   p 379.14   eps 0.4684   mix 0.0014   time 27.56
Epoch 1019:  test acc 0.5400   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 1019:  clean acc 0.5818   certified acc 0.4507
Calculating metrics for L_infinity dist model on test set
Epoch 1019:  clean acc 0.5421   certified acc 0.3923
scalar:  3.5509
Epoch 1020:  train loss 0.6251   train acc 0.5815   worst 0.1539   lr 0.0033   p 380.73   eps 0.4684   mix 0.0014   time 28.66
scalar:  3.555
Epoch 1021:  train loss 0.6277   train acc 0.5784   worst 0.1536   lr 0.0033   p 382.33   eps 0.4684   mix 0.0014   time 28.15
scalar:  3.5462
Epoch 1022:  train loss 0.6270   train acc 0.5798   worst 0.1527   lr 0.0033   p 383.94   eps 0.4684   mix 0.0014   time 27.81
scalar:  3.5431
Epoch 1023:  train loss 0.6268   train acc 0.5821   worst 0.1525   lr 0.0032   p 385.56   eps 0.4684   mix 0.0014   time 27.76
scalar:  3.5497
Epoch 1024:  train loss 0.6280   train acc 0.5808   worst 0.1536   lr 0.0032   p 387.18   eps 0.4684   mix 0.0014   time 27.90
Epoch 1024:  test acc 0.5399   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 1024:  clean acc 0.5835   certified acc 0.4510
Calculating metrics for L_infinity dist model on test set
Epoch 1024:  clean acc 0.5411   certified acc 0.3910
scalar:  3.5478
Epoch 1025:  train loss 0.6271   train acc 0.5821   worst 0.1535   lr 0.0032   p 388.81   eps 0.4684   mix 0.0014   time 27.74
scalar:  3.553
Epoch 1026:  train loss 0.6278   train acc 0.5797   worst 0.1532   lr 0.0032   p 390.44   eps 0.4684   mix 0.0014   time 27.94
scalar:  3.5518
Epoch 1027:  train loss 0.6275   train acc 0.5810   worst 0.1511   lr 0.0031   p 392.09   eps 0.4684   mix 0.0014   time 27.85
scalar:  3.5506
Epoch 1028:  train loss 0.6264   train acc 0.5808   worst 0.1553   lr 0.0031   p 393.74   eps 0.4684   mix 0.0014   time 27.57
scalar:  3.5506
Epoch 1029:  train loss 0.6265   train acc 0.5807   worst 0.1534   lr 0.0031   p 395.39   eps 0.4684   mix 0.0014   time 27.80
Epoch 1029:  test acc 0.5403   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 1029:  clean acc 0.5829   certified acc 0.4509
Calculating metrics for L_infinity dist model on test set
Epoch 1029:  clean acc 0.5432   certified acc 0.3953
scalar:  3.5501
Epoch 1030:  train loss 0.6268   train acc 0.5828   worst 0.1542   lr 0.0031   p 397.06   eps 0.4684   mix 0.0014   time 27.97
scalar:  3.557
Epoch 1031:  train loss 0.6271   train acc 0.5816   worst 0.1514   lr 0.0031   p 398.73   eps 0.4684   mix 0.0014   time 28.02
scalar:  3.5511
Epoch 1032:  train loss 0.6250   train acc 0.5823   worst 0.1523   lr 0.0030   p 400.40   eps 0.4684   mix 0.0014   time 27.90
scalar:  3.553
Epoch 1033:  train loss 0.6247   train acc 0.5813   worst 0.1542   lr 0.0030   p 402.09   eps 0.4684   mix 0.0014   time 27.80
scalar:  3.5522
Epoch 1034:  train loss 0.6265   train acc 0.5799   worst 0.1535   lr 0.0030   p 403.78   eps 0.4684   mix 0.0014   time 27.82
Epoch 1034:  test acc 0.5402   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 1034:  clean acc 0.5838   certified acc 0.4515
Calculating metrics for L_infinity dist model on test set
Epoch 1034:  clean acc 0.5402   certified acc 0.3926
scalar:  3.5484
Epoch 1035:  train loss 0.6273   train acc 0.5809   worst 0.1505   lr 0.0030   p 405.48   eps 0.4684   mix 0.0013   time 27.53
scalar:  3.543
Epoch 1036:  train loss 0.6254   train acc 0.5824   worst 0.1527   lr 0.0030   p 407.19   eps 0.4684   mix 0.0013   time 28.54
scalar:  3.5468
Epoch 1037:  train loss 0.6263   train acc 0.5807   worst 0.1527   lr 0.0029   p 408.90   eps 0.4684   mix 0.0013   time 27.87
scalar:  3.5506
Epoch 1038:  train loss 0.6236   train acc 0.5826   worst 0.1545   lr 0.0029   p 410.62   eps 0.4684   mix 0.0013   time 27.86
scalar:  3.5478
Epoch 1039:  train loss 0.6255   train acc 0.5815   worst 0.1533   lr 0.0029   p 412.35   eps 0.4684   mix 0.0013   time 27.91
Epoch 1039:  test acc 0.5372   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1039:  clean acc 0.5851   certified acc 0.4515
Calculating metrics for L_infinity dist model on test set
Epoch 1039:  clean acc 0.5372   certified acc 0.3922
scalar:  3.5526
Epoch 1040:  train loss 0.6253   train acc 0.5801   worst 0.1533   lr 0.0029   p 414.08   eps 0.4684   mix 0.0013   time 27.36
scalar:  3.5512
Epoch 1041:  train loss 0.6271   train acc 0.5799   worst 0.1536   lr 0.0028   p 415.82   eps 0.4684   mix 0.0013   time 28.40
scalar:  3.5485
Epoch 1042:  train loss 0.6254   train acc 0.5822   worst 0.1533   lr 0.0028   p 417.57   eps 0.4684   mix 0.0013   time 27.74
scalar:  3.5484
Epoch 1043:  train loss 0.6269   train acc 0.5821   worst 0.1530   lr 0.0028   p 419.33   eps 0.4684   mix 0.0013   time 27.81
scalar:  3.554
Epoch 1044:  train loss 0.6251   train acc 0.5815   worst 0.1520   lr 0.0028   p 421.09   eps 0.4684   mix 0.0013   time 28.32
Epoch 1044:  test acc 0.5378   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 1044:  clean acc 0.5854   certified acc 0.4514
Calculating metrics for L_infinity dist model on test set
Epoch 1044:  clean acc 0.5394   certified acc 0.3915
scalar:  3.5544
Epoch 1045:  train loss 0.6254   train acc 0.5813   worst 0.1513   lr 0.0028   p 422.87   eps 0.4684   mix 0.0013   time 27.82
scalar:  3.5522
Epoch 1046:  train loss 0.6263   train acc 0.5810   worst 0.1525   lr 0.0027   p 424.65   eps 0.4684   mix 0.0013   time 28.04
scalar:  3.5538
Epoch 1047:  train loss 0.6254   train acc 0.5801   worst 0.1527   lr 0.0027   p 426.43   eps 0.4684   mix 0.0013   time 27.79
scalar:  3.5537
Epoch 1048:  train loss 0.6250   train acc 0.5805   worst 0.1534   lr 0.0027   p 428.23   eps 0.4684   mix 0.0013   time 27.69
scalar:  3.5504
Epoch 1049:  train loss 0.6258   train acc 0.5828   worst 0.1533   lr 0.0027   p 430.03   eps 0.4684   mix 0.0013   time 28.05
Epoch 1049:  test acc 0.5400   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1049:  clean acc 0.5848   certified acc 0.4528
Calculating metrics for L_infinity dist model on test set
Epoch 1049:  clean acc 0.5418   certified acc 0.3926
scalar:  3.5532
Epoch 1050:  train loss 0.6263   train acc 0.5824   worst 0.1541   lr 0.0027   p 431.84   eps 0.4684   mix 0.0013   time 27.87
scalar:  3.557
Epoch 1051:  train loss 0.6257   train acc 0.5832   worst 0.1514   lr 0.0026   p 433.65   eps 0.4684   mix 0.0013   time 27.75
scalar:  3.5602
Epoch 1052:  train loss 0.6254   train acc 0.5827   worst 0.1525   lr 0.0026   p 435.48   eps 0.4684   mix 0.0012   time 27.87
scalar:  3.5607
Epoch 1053:  train loss 0.6255   train acc 0.5824   worst 0.1548   lr 0.0026   p 437.31   eps 0.4684   mix 0.0012   time 27.70
scalar:  3.564
Epoch 1054:  train loss 0.6246   train acc 0.5816   worst 0.1539   lr 0.0026   p 439.15   eps 0.4684   mix 0.0012   time 28.10
Epoch 1054:  test acc 0.5394   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1054:  clean acc 0.5861   certified acc 0.4525
Calculating metrics for L_infinity dist model on test set
Epoch 1054:  clean acc 0.5398   certified acc 0.3959
scalar:  3.5639
Epoch 1055:  train loss 0.6251   train acc 0.5829   worst 0.1532   lr 0.0026   p 441.00   eps 0.4684   mix 0.0012   time 27.64
scalar:  3.5634
Epoch 1056:  train loss 0.6246   train acc 0.5804   worst 0.1532   lr 0.0025   p 442.85   eps 0.4684   mix 0.0012   time 27.39
scalar:  3.559
Epoch 1057:  train loss 0.6245   train acc 0.5827   worst 0.1529   lr 0.0025   p 444.72   eps 0.4684   mix 0.0012   time 27.79
scalar:  3.5599
Epoch 1058:  train loss 0.6235   train acc 0.5817   worst 0.1548   lr 0.0025   p 446.59   eps 0.4684   mix 0.0012   time 27.78
scalar:  3.5602
Epoch 1059:  train loss 0.6258   train acc 0.5825   worst 0.1526   lr 0.0025   p 448.47   eps 0.4684   mix 0.0012   time 28.30
Epoch 1059:  test acc 0.5370   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 1059:  clean acc 0.5858   certified acc 0.4526
Calculating metrics for L_infinity dist model on test set
Epoch 1059:  clean acc 0.5396   certified acc 0.3955
scalar:  3.5611
Epoch 1060:  train loss 0.6247   train acc 0.5820   worst 0.1523   lr 0.0025   p 450.35   eps 0.4684   mix 0.0012   time 27.94
scalar:  3.562
Epoch 1061:  train loss 0.6240   train acc 0.5834   worst 0.1533   lr 0.0024   p 452.25   eps 0.4684   mix 0.0012   time 27.75
scalar:  3.5591
Epoch 1062:  train loss 0.6233   train acc 0.5812   worst 0.1555   lr 0.0024   p 454.15   eps 0.4684   mix 0.0012   time 27.87
scalar:  3.5565
Epoch 1063:  train loss 0.6243   train acc 0.5822   worst 0.1530   lr 0.0024   p 456.06   eps 0.4684   mix 0.0012   time 27.64
scalar:  3.5572
Epoch 1064:  train loss 0.6232   train acc 0.5819   worst 0.1538   lr 0.0024   p 457.98   eps 0.4684   mix 0.0012   time 28.51
Epoch 1064:  test acc 0.5395   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 1064:  clean acc 0.5881   certified acc 0.4554
Calculating metrics for L_infinity dist model on test set
Epoch 1064:  clean acc 0.5424   certified acc 0.3972
scalar:  3.5594
Epoch 1065:  train loss 0.6232   train acc 0.5838   worst 0.1519   lr 0.0024   p 459.91   eps 0.4684   mix 0.0012   time 28.43
scalar:  3.5653
Epoch 1066:  train loss 0.6255   train acc 0.5809   worst 0.1541   lr 0.0023   p 461.84   eps 0.4684   mix 0.0012   time 27.71
scalar:  3.5639
Epoch 1067:  train loss 0.6252   train acc 0.5806   worst 0.1525   lr 0.0023   p 463.79   eps 0.4684   mix 0.0012   time 28.02
scalar:  3.5608
Epoch 1068:  train loss 0.6228   train acc 0.5825   worst 0.1531   lr 0.0023   p 465.74   eps 0.4684   mix 0.0012   time 27.83
scalar:  3.5617
Epoch 1069:  train loss 0.6224   train acc 0.5829   worst 0.1537   lr 0.0023   p 467.70   eps 0.4684   mix 0.0012   time 28.46
Epoch 1069:  test acc 0.5386   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 1069:  clean acc 0.5875   certified acc 0.4576
Calculating metrics for L_infinity dist model on test set
Epoch 1069:  clean acc 0.5433   certified acc 0.3948
scalar:  3.5642
Epoch 1070:  train loss 0.6228   train acc 0.5841   worst 0.1548   lr 0.0023   p 469.66   eps 0.4684   mix 0.0011   time 28.26
scalar:  3.5641
Epoch 1071:  train loss 0.6246   train acc 0.5826   worst 0.1539   lr 0.0022   p 471.64   eps 0.4684   mix 0.0011   time 27.52
scalar:  3.5625
Epoch 1072:  train loss 0.6239   train acc 0.5820   worst 0.1539   lr 0.0022   p 473.63   eps 0.4684   mix 0.0011   time 27.77
scalar:  3.5598
Epoch 1073:  train loss 0.6217   train acc 0.5836   worst 0.1549   lr 0.0022   p 475.62   eps 0.4684   mix 0.0011   time 27.98
scalar:  3.5619
Epoch 1074:  train loss 0.6239   train acc 0.5827   worst 0.1536   lr 0.0022   p 477.62   eps 0.4684   mix 0.0011   time 27.95
Epoch 1074:  test acc 0.5389   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1074:  clean acc 0.5870   certified acc 0.4575
Calculating metrics for L_infinity dist model on test set
Epoch 1074:  clean acc 0.5415   certified acc 0.3968
scalar:  3.5607
Epoch 1075:  train loss 0.6249   train acc 0.5831   worst 0.1529   lr 0.0022   p 479.63   eps 0.4684   mix 0.0011   time 28.11
scalar:  3.5634
Epoch 1076:  train loss 0.6244   train acc 0.5824   worst 0.1529   lr 0.0021   p 481.65   eps 0.4684   mix 0.0011   time 27.49
scalar:  3.5648
Epoch 1077:  train loss 0.6230   train acc 0.5841   worst 0.1540   lr 0.0021   p 483.67   eps 0.4684   mix 0.0011   time 27.88
scalar:  3.5654
Epoch 1078:  train loss 0.6224   train acc 0.5826   worst 0.1555   lr 0.0021   p 485.71   eps 0.4684   mix 0.0011   time 27.93
scalar:  3.5648
Epoch 1079:  train loss 0.6235   train acc 0.5828   worst 0.1562   lr 0.0021   p 487.75   eps 0.4684   mix 0.0011   time 27.89
Epoch 1079:  test acc 0.5386   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 1079:  clean acc 0.5832   certified acc 0.4551
Calculating metrics for L_infinity dist model on test set
Epoch 1079:  clean acc 0.5363   certified acc 0.3950
scalar:  3.5677
Epoch 1080:  train loss 0.6236   train acc 0.5837   worst 0.1549   lr 0.0021   p 489.80   eps 0.4684   mix 0.0011   time 27.97
scalar:  3.5683
Epoch 1081:  train loss 0.6224   train acc 0.5832   worst 0.1553   lr 0.0021   p 491.86   eps 0.4684   mix 0.0011   time 27.77
scalar:  3.5672
Epoch 1082:  train loss 0.6226   train acc 0.5842   worst 0.1541   lr 0.0020   p 493.93   eps 0.4684   mix 0.0011   time 27.38
scalar:  3.5714
Epoch 1083:  train loss 0.6225   train acc 0.5839   worst 0.1541   lr 0.0020   p 496.01   eps 0.4684   mix 0.0011   time 27.77
scalar:  3.5686
Epoch 1084:  train loss 0.6215   train acc 0.5857   worst 0.1542   lr 0.0020   p 498.10   eps 0.4684   mix 0.0011   time 28.21
Epoch 1084:  test acc 0.5377   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 1084:  clean acc 0.5882   certified acc 0.4566
Calculating metrics for L_infinity dist model on test set
Epoch 1084:  clean acc 0.5390   certified acc 0.3949
scalar:  3.5728
Epoch 1085:  train loss 0.6230   train acc 0.5827   worst 0.1543   lr 0.0020   p 500.19   eps 0.4684   mix 0.0011   time 27.82
scalar:  3.5723
Epoch 1086:  train loss 0.6243   train acc 0.5824   worst 0.1531   lr 0.0020   p 502.30   eps 0.4684   mix 0.0011   time 27.62
scalar:  3.5669
Epoch 1087:  train loss 0.6225   train acc 0.5859   worst 0.1528   lr 0.0019   p 504.41   eps 0.4684   mix 0.0011   time 27.81
scalar:  3.5711
Epoch 1088:  train loss 0.6228   train acc 0.5845   worst 0.1538   lr 0.0019   p 506.53   eps 0.4684   mix 0.0011   time 27.42
scalar:  3.571
Epoch 1089:  train loss 0.6211   train acc 0.5851   worst 0.1549   lr 0.0019   p 508.67   eps 0.4684   mix 0.0010   time 28.31
Epoch 1089:  test acc 0.5395   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 1089:  clean acc 0.5860   certified acc 0.4583
Calculating metrics for L_infinity dist model on test set
Epoch 1089:  clean acc 0.5403   certified acc 0.3959
scalar:  3.5723
Epoch 1090:  train loss 0.6215   train acc 0.5858   worst 0.1553   lr 0.0019   p 510.81   eps 0.4684   mix 0.0010   time 27.87
scalar:  3.5747
Epoch 1091:  train loss 0.6226   train acc 0.5836   worst 0.1537   lr 0.0019   p 512.96   eps 0.4684   mix 0.0010   time 27.78
scalar:  3.5752
Epoch 1092:  train loss 0.6213   train acc 0.5853   worst 0.1545   lr 0.0019   p 515.11   eps 0.4684   mix 0.0010   time 27.46
scalar:  3.5782
Epoch 1093:  train loss 0.6216   train acc 0.5842   worst 0.1540   lr 0.0018   p 517.28   eps 0.4684   mix 0.0010   time 27.58
scalar:  3.5798
Epoch 1094:  train loss 0.6213   train acc 0.5868   worst 0.1539   lr 0.0018   p 519.46   eps 0.4684   mix 0.0010   time 28.46
Epoch 1094:  test acc 0.5395   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 1094:  clean acc 0.5865   certified acc 0.4585
Calculating metrics for L_infinity dist model on test set
Epoch 1094:  clean acc 0.5394   certified acc 0.3954
scalar:  3.5788
Epoch 1095:  train loss 0.6233   train acc 0.5818   worst 0.1543   lr 0.0018   p 521.64   eps 0.4684   mix 0.0010   time 27.35
scalar:  3.5768
Epoch 1096:  train loss 0.6226   train acc 0.5832   worst 0.1538   lr 0.0018   p 523.84   eps 0.4684   mix 0.0010   time 27.74
scalar:  3.5744
Epoch 1097:  train loss 0.6222   train acc 0.5824   worst 0.1540   lr 0.0018   p 526.04   eps 0.4684   mix 0.0010   time 27.65
scalar:  3.574
Epoch 1098:  train loss 0.6226   train acc 0.5819   worst 0.1550   lr 0.0018   p 528.25   eps 0.4684   mix 0.0010   time 28.17
scalar:  3.5719
Epoch 1099:  train loss 0.6213   train acc 0.5853   worst 0.1547   lr 0.0017   p 530.48   eps 0.4684   mix 0.0010   time 28.30
Epoch 1099:  test acc 0.5406   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1099:  clean acc 0.5865   certified acc 0.4584
Calculating metrics for L_infinity dist model on test set
Epoch 1099:  clean acc 0.5414   certified acc 0.3955
scalar:  3.5742
Epoch 1100:  train loss 0.6205   train acc 0.5847   worst 0.1567   lr 0.0017   p 532.71   eps 0.4684   mix 0.0010   time 27.41
scalar:  3.5768
Epoch 1101:  train loss 0.6215   train acc 0.5868   worst 0.1533   lr 0.0017   p 534.95   eps 0.4684   mix 0.0010   time 27.53
scalar:  3.5811
Epoch 1102:  train loss 0.6216   train acc 0.5851   worst 0.1544   lr 0.0017   p 537.20   eps 0.4684   mix 0.0010   time 27.83
scalar:  3.5811
Epoch 1103:  train loss 0.6221   train acc 0.5841   worst 0.1552   lr 0.0017   p 539.46   eps 0.4684   mix 0.0010   time 27.72
scalar:  3.5797
Epoch 1104:  train loss 0.6216   train acc 0.5854   worst 0.1537   lr 0.0017   p 541.73   eps 0.4684   mix 0.0010   time 28.15
Epoch 1104:  test acc 0.5392   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 1104:  clean acc 0.5881   certified acc 0.4586
Calculating metrics for L_infinity dist model on test set
Epoch 1104:  clean acc 0.5404   certified acc 0.3978
scalar:  3.5804
Epoch 1105:  train loss 0.6232   train acc 0.5828   worst 0.1543   lr 0.0016   p 544.01   eps 0.4684   mix 0.0010   time 27.74
scalar:  3.5801
Epoch 1106:  train loss 0.6211   train acc 0.5844   worst 0.1526   lr 0.0016   p 546.30   eps 0.4684   mix 0.0010   time 27.96
scalar:  3.5787
Epoch 1107:  train loss 0.6216   train acc 0.5837   worst 0.1561   lr 0.0016   p 548.60   eps 0.4684   mix 0.0010   time 27.58
scalar:  3.5778
Epoch 1108:  train loss 0.6215   train acc 0.5838   worst 0.1551   lr 0.0016   p 550.91   eps 0.4684   mix 0.0010   time 27.92
scalar:  3.5777
Epoch 1109:  train loss 0.6192   train acc 0.5837   worst 0.1561   lr 0.0016   p 553.22   eps 0.4684   mix 0.0010   time 28.35
Epoch 1109:  test acc 0.5394   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 1109:  clean acc 0.5880   certified acc 0.4596
Calculating metrics for L_infinity dist model on test set
Epoch 1109:  clean acc 0.5422   certified acc 0.3975
scalar:  3.5738
Epoch 1110:  train loss 0.6203   train acc 0.5836   worst 0.1562   lr 0.0016   p 555.55   eps 0.4684   mix 0.0010   time 27.37
scalar:  3.5704
Epoch 1111:  train loss 0.6216   train acc 0.5848   worst 0.1551   lr 0.0015   p 557.89   eps 0.4684   mix 0.0009   time 27.82
scalar:  3.5698
Epoch 1112:  train loss 0.6209   train acc 0.5842   worst 0.1559   lr 0.0015   p 560.24   eps 0.4684   mix 0.0009   time 27.63
scalar:  3.5701
Epoch 1113:  train loss 0.6209   train acc 0.5849   worst 0.1532   lr 0.0015   p 562.59   eps 0.4684   mix 0.0009   time 28.01
scalar:  3.5717
Epoch 1114:  train loss 0.6207   train acc 0.5840   worst 0.1558   lr 0.0015   p 564.96   eps 0.4684   mix 0.0009   time 28.19
Epoch 1114:  test acc 0.5419   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 1114:  clean acc 0.5864   certified acc 0.4586
Calculating metrics for L_infinity dist model on test set
Epoch 1114:  clean acc 0.5413   certified acc 0.3970
scalar:  3.5724
Epoch 1115:  train loss 0.6218   train acc 0.5838   worst 0.1562   lr 0.0015   p 567.34   eps 0.4684   mix 0.0009   time 27.52
scalar:  3.5729
Epoch 1116:  train loss 0.6207   train acc 0.5876   worst 0.1527   lr 0.0015   p 569.72   eps 0.4684   mix 0.0009   time 27.80
scalar:  3.5757
Epoch 1117:  train loss 0.6204   train acc 0.5860   worst 0.1554   lr 0.0014   p 572.12   eps 0.4684   mix 0.0009   time 27.78
scalar:  3.577
Epoch 1118:  train loss 0.6211   train acc 0.5849   worst 0.1555   lr 0.0014   p 574.53   eps 0.4684   mix 0.0009   time 28.04
scalar:  3.5762
Epoch 1119:  train loss 0.6207   train acc 0.5843   worst 0.1550   lr 0.0014   p 576.95   eps 0.4684   mix 0.0009   time 28.40
Epoch 1119:  test acc 0.5410   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 1119:  clean acc 0.5867   certified acc 0.4600
Calculating metrics for L_infinity dist model on test set
Epoch 1119:  clean acc 0.5434   certified acc 0.3972
scalar:  3.5749
Epoch 1120:  train loss 0.6216   train acc 0.5826   worst 0.1547   lr 0.0014   p 579.37   eps 0.4684   mix 0.0009   time 27.69
scalar:  3.5759
Epoch 1121:  train loss 0.6206   train acc 0.5861   worst 0.1558   lr 0.0014   p 581.81   eps 0.4684   mix 0.0009   time 27.59
scalar:  3.5764
Epoch 1122:  train loss 0.6211   train acc 0.5852   worst 0.1554   lr 0.0014   p 584.26   eps 0.4684   mix 0.0009   time 27.57
scalar:  3.5781
Epoch 1123:  train loss 0.6204   train acc 0.5822   worst 0.1561   lr 0.0014   p 586.72   eps 0.4684   mix 0.0009   time 28.02
scalar:  3.5788
Epoch 1124:  train loss 0.6209   train acc 0.5836   worst 0.1562   lr 0.0013   p 589.18   eps 0.4684   mix 0.0009   time 28.23
Epoch 1124:  test acc 0.5404   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 1124:  clean acc 0.5881   certified acc 0.4615
Calculating metrics for L_infinity dist model on test set
Epoch 1124:  clean acc 0.5416   certified acc 0.3963
scalar:  3.5773
Epoch 1125:  train loss 0.6200   train acc 0.5848   worst 0.1542   lr 0.0013   p 591.66   eps 0.4684   mix 0.0009   time 27.94
scalar:  3.5782
Epoch 1126:  train loss 0.6202   train acc 0.5849   worst 0.1569   lr 0.0013   p 594.15   eps 0.4684   mix 0.0009   time 27.90
scalar:  3.5776
Epoch 1127:  train loss 0.6208   train acc 0.5849   worst 0.1565   lr 0.0013   p 596.65   eps 0.4684   mix 0.0009   time 27.82
scalar:  3.5778
Epoch 1128:  train loss 0.6201   train acc 0.5868   worst 0.1539   lr 0.0013   p 599.16   eps 0.4684   mix 0.0009   time 28.38
scalar:  3.5792
Epoch 1129:  train loss 0.6201   train acc 0.5858   worst 0.1552   lr 0.0013   p 601.68   eps 0.4684   mix 0.0009   time 28.14
Epoch 1129:  test acc 0.5424   time 2.67
Calculating metrics for L_infinity dist model on training set
Epoch 1129:  clean acc 0.5882   certified acc 0.4613
Calculating metrics for L_infinity dist model on test set
Epoch 1129:  clean acc 0.5408   certified acc 0.3985
scalar:  3.5813
Epoch 1130:  train loss 0.6206   train acc 0.5854   worst 0.1546   lr 0.0012   p 604.22   eps 0.4684   mix 0.0009   time 27.75
scalar:  3.5804
Epoch 1131:  train loss 0.6194   train acc 0.5861   worst 0.1537   lr 0.0012   p 606.76   eps 0.4684   mix 0.0009   time 27.92
scalar:  3.5811
Epoch 1132:  train loss 0.6199   train acc 0.5863   worst 0.1553   lr 0.0012   p 609.31   eps 0.4684   mix 0.0009   time 28.15
scalar:  3.5816
Epoch 1133:  train loss 0.6198   train acc 0.5871   worst 0.1545   lr 0.0012   p 611.87   eps 0.4684   mix 0.0009   time 28.15
scalar:  3.5819
Epoch 1134:  train loss 0.6196   train acc 0.5840   worst 0.1577   lr 0.0012   p 614.45   eps 0.4684   mix 0.0009   time 28.46
Epoch 1134:  test acc 0.5399   time 2.65
Calculating metrics for L_infinity dist model on training set
Epoch 1134:  clean acc 0.5884   certified acc 0.4617
Calculating metrics for L_infinity dist model on test set
Epoch 1134:  clean acc 0.5425   certified acc 0.3953
scalar:  3.5808
Epoch 1135:  train loss 0.6195   train acc 0.5857   worst 0.1554   lr 0.0012   p 617.03   eps 0.4684   mix 0.0008   time 28.15
scalar:  3.5794
Epoch 1136:  train loss 0.6190   train acc 0.5843   worst 0.1561   lr 0.0012   p 619.63   eps 0.4684   mix 0.0008   time 28.16
scalar:  3.5769
Epoch 1137:  train loss 0.6171   train acc 0.5870   worst 0.1563   lr 0.0011   p 622.24   eps 0.4684   mix 0.0008   time 27.63
scalar:  3.5804
Epoch 1138:  train loss 0.6197   train acc 0.5847   worst 0.1538   lr 0.0011   p 624.85   eps 0.4684   mix 0.0008   time 28.36
scalar:  3.5808
Epoch 1139:  train loss 0.6196   train acc 0.5858   worst 0.1563   lr 0.0011   p 627.48   eps 0.4684   mix 0.0008   time 28.42
Epoch 1139:  test acc 0.5397   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 1139:  clean acc 0.5903   certified acc 0.4619
Calculating metrics for L_infinity dist model on test set
Epoch 1139:  clean acc 0.5416   certified acc 0.3960
scalar:  3.5803
Epoch 1140:  train loss 0.6198   train acc 0.5861   worst 0.1539   lr 0.0011   p 630.12   eps 0.4684   mix 0.0008   time 27.77
scalar:  3.5816
Epoch 1141:  train loss 0.6187   train acc 0.5857   worst 0.1568   lr 0.0011   p 632.78   eps 0.4684   mix 0.0008   time 27.74
scalar:  3.5816
Epoch 1142:  train loss 0.6206   train acc 0.5846   worst 0.1540   lr 0.0011   p 635.44   eps 0.4684   mix 0.0008   time 27.83
scalar:  3.5812
Epoch 1143:  train loss 0.6189   train acc 0.5861   worst 0.1549   lr 0.0011   p 638.11   eps 0.4684   mix 0.0008   time 28.16
scalar:  3.58
Epoch 1144:  train loss 0.6201   train acc 0.5862   worst 0.1553   lr 0.0011   p 640.80   eps 0.4684   mix 0.0008   time 27.88
Epoch 1144:  test acc 0.5403   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 1144:  clean acc 0.5871   certified acc 0.4609
Calculating metrics for L_infinity dist model on test set
Epoch 1144:  clean acc 0.5423   certified acc 0.3950
scalar:  3.5825
Epoch 1145:  train loss 0.6189   train acc 0.5859   worst 0.1559   lr 0.0010   p 643.49   eps 0.4684   mix 0.0008   time 27.90
scalar:  3.5838
Epoch 1146:  train loss 0.6195   train acc 0.5857   worst 0.1562   lr 0.0010   p 646.20   eps 0.4684   mix 0.0008   time 27.87
scalar:  3.5836
Epoch 1147:  train loss 0.6198   train acc 0.5860   worst 0.1568   lr 0.0010   p 648.92   eps 0.4684   mix 0.0008   time 28.06
scalar:  3.5843
Epoch 1148:  train loss 0.6205   train acc 0.5841   worst 0.1528   lr 0.0010   p 651.65   eps 0.4684   mix 0.0008   time 27.76
scalar:  3.5847
Epoch 1149:  train loss 0.6187   train acc 0.5867   worst 0.1572   lr 0.0010   p 654.39   eps 0.4684   mix 0.0008   time 27.81
Epoch 1149:  test acc 0.5428   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 1149:  clean acc 0.5897   certified acc 0.4595
Calculating metrics for L_infinity dist model on test set
Epoch 1149:  clean acc 0.5440   certified acc 0.3973
scalar:  3.5838
Epoch 1150:  train loss 0.6188   train acc 0.5876   worst 0.1572   lr 0.0010   p 657.14   eps 0.4684   mix 0.0008   time 27.71
scalar:  3.5845
Epoch 1151:  train loss 0.6178   train acc 0.5878   worst 0.1561   lr 0.0010   p 659.91   eps 0.4684   mix 0.0008   time 27.88
scalar:  3.5868
Epoch 1152:  train loss 0.6184   train acc 0.5842   worst 0.1574   lr 0.0009   p 662.68   eps 0.4684   mix 0.0008   time 28.07
scalar:  3.5855
Epoch 1153:  train loss 0.6193   train acc 0.5850   worst 0.1557   lr 0.0009   p 665.47   eps 0.4684   mix 0.0008   time 27.90
scalar:  3.585
Epoch 1154:  train loss 0.6173   train acc 0.5845   worst 0.1562   lr 0.0009   p 668.27   eps 0.4684   mix 0.0008   time 28.30
Epoch 1154:  test acc 0.5404   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 1154:  clean acc 0.5865   certified acc 0.4622
Calculating metrics for L_infinity dist model on test set
Epoch 1154:  clean acc 0.5412   certified acc 0.3979
scalar:  3.5842
Epoch 1155:  train loss 0.6193   train acc 0.5857   worst 0.1555   lr 0.0009   p 671.08   eps 0.4684   mix 0.0008   time 28.05
scalar:  3.5834
Epoch 1156:  train loss 0.6179   train acc 0.5871   worst 0.1570   lr 0.0009   p 673.91   eps 0.4684   mix 0.0008   time 27.55
scalar:  3.5844
Epoch 1157:  train loss 0.6188   train acc 0.5867   worst 0.1579   lr 0.0009   p 676.74   eps 0.4684   mix 0.0008   time 28.13
scalar:  3.5829
Epoch 1158:  train loss 0.6187   train acc 0.5860   worst 0.1567   lr 0.0009   p 679.59   eps 0.4684   mix 0.0008   time 27.40
scalar:  3.584
Epoch 1159:  train loss 0.6176   train acc 0.5884   worst 0.1559   lr 0.0009   p 682.45   eps 0.4684   mix 0.0008   time 28.24
Epoch 1159:  test acc 0.5409   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 1159:  clean acc 0.5880   certified acc 0.4617
Calculating metrics for L_infinity dist model on test set
Epoch 1159:  clean acc 0.5399   certified acc 0.3961
scalar:  3.585
Epoch 1160:  train loss 0.6177   train acc 0.5857   worst 0.1578   lr 0.0009   p 685.32   eps 0.4684   mix 0.0008   time 28.10
scalar:  3.5853
Epoch 1161:  train loss 0.6179   train acc 0.5868   worst 0.1576   lr 0.0008   p 688.20   eps 0.4684   mix 0.0008   time 27.59
scalar:  3.5849
Epoch 1162:  train loss 0.6178   train acc 0.5877   worst 0.1567   lr 0.0008   p 691.10   eps 0.4684   mix 0.0007   time 28.20
scalar:  3.585
Epoch 1163:  train loss 0.6188   train acc 0.5858   worst 0.1563   lr 0.0008   p 694.01   eps 0.4684   mix 0.0007   time 27.19
scalar:  3.5851
Epoch 1164:  train loss 0.6173   train acc 0.5873   worst 0.1563   lr 0.0008   p 696.93   eps 0.4684   mix 0.0007   time 27.46
Epoch 1164:  test acc 0.5411   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 1164:  clean acc 0.5901   certified acc 0.4647
Calculating metrics for L_infinity dist model on test set
Epoch 1164:  clean acc 0.5415   certified acc 0.3987
scalar:  3.5861
Epoch 1165:  train loss 0.6188   train acc 0.5861   worst 0.1552   lr 0.0008   p 699.86   eps 0.4684   mix 0.0007   time 27.30
scalar:  3.5854
Epoch 1166:  train loss 0.6176   train acc 0.5887   worst 0.1568   lr 0.0008   p 702.80   eps 0.4684   mix 0.0007   time 27.23
scalar:  3.5868
Epoch 1167:  train loss 0.6177   train acc 0.5862   worst 0.1572   lr 0.0008   p 705.76   eps 0.4684   mix 0.0007   time 27.09
scalar:  3.5868
Epoch 1168:  train loss 0.6178   train acc 0.5874   worst 0.1565   lr 0.0008   p 708.73   eps 0.4684   mix 0.0007   time 26.97
scalar:  3.5875
Epoch 1169:  train loss 0.6184   train acc 0.5863   worst 0.1549   lr 0.0007   p 711.71   eps 0.4684   mix 0.0007   time 27.27
Epoch 1169:  test acc 0.5399   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 1169:  clean acc 0.5862   certified acc 0.4623
Calculating metrics for L_infinity dist model on test set
Epoch 1169:  clean acc 0.5418   certified acc 0.3999
scalar:  3.5863
Epoch 1170:  train loss 0.6176   train acc 0.5869   worst 0.1584   lr 0.0007   p 714.71   eps 0.4684   mix 0.0007   time 26.93
scalar:  3.5861
Epoch 1171:  train loss 0.6183   train acc 0.5853   worst 0.1567   lr 0.0007   p 717.71   eps 0.4684   mix 0.0007   time 27.13
scalar:  3.5864
Epoch 1172:  train loss 0.6164   train acc 0.5867   worst 0.1573   lr 0.0007   p 720.73   eps 0.4684   mix 0.0007   time 26.58
scalar:  3.5855
Epoch 1173:  train loss 0.6165   train acc 0.5879   worst 0.1577   lr 0.0007   p 723.77   eps 0.4684   mix 0.0007   time 27.00
scalar:  3.5869
Epoch 1174:  train loss 0.6181   train acc 0.5858   worst 0.1564   lr 0.0007   p 726.81   eps 0.4684   mix 0.0007   time 26.80
Epoch 1174:  test acc 0.5426   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 1174:  clean acc 0.5878   certified acc 0.4621
Calculating metrics for L_infinity dist model on test set
Epoch 1174:  clean acc 0.5433   certified acc 0.4009
scalar:  3.5872
Epoch 1175:  train loss 0.6183   train acc 0.5873   worst 0.1551   lr 0.0007   p 729.87   eps 0.4684   mix 0.0007   time 26.67
scalar:  3.5866
Epoch 1176:  train loss 0.6167   train acc 0.5877   worst 0.1590   lr 0.0007   p 732.94   eps 0.4684   mix 0.0007   time 26.66
scalar:  3.5873
Epoch 1177:  train loss 0.6186   train acc 0.5846   worst 0.1564   lr 0.0007   p 736.02   eps 0.4684   mix 0.0007   time 26.54
scalar:  3.586
Epoch 1178:  train loss 0.6180   train acc 0.5854   worst 0.1548   lr 0.0006   p 739.12   eps 0.4684   mix 0.0007   time 26.78
scalar:  3.5857
Epoch 1179:  train loss 0.6174   train acc 0.5861   worst 0.1574   lr 0.0006   p 742.23   eps 0.4684   mix 0.0007   time 26.80
Epoch 1179:  test acc 0.5404   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1179:  clean acc 0.5902   certified acc 0.4639
Calculating metrics for L_infinity dist model on test set
Epoch 1179:  clean acc 0.5415   certified acc 0.3997
scalar:  3.5862
Epoch 1180:  train loss 0.6172   train acc 0.5879   worst 0.1571   lr 0.0006   p 745.35   eps 0.4684   mix 0.0007   time 26.74
scalar:  3.5871
Epoch 1181:  train loss 0.6164   train acc 0.5874   worst 0.1564   lr 0.0006   p 748.49   eps 0.4684   mix 0.0007   time 26.35
scalar:  3.5868
Epoch 1182:  train loss 0.6174   train acc 0.5872   worst 0.1562   lr 0.0006   p 751.64   eps 0.4684   mix 0.0007   time 26.64
scalar:  3.5861
Epoch 1183:  train loss 0.6154   train acc 0.5874   worst 0.1594   lr 0.0006   p 754.80   eps 0.4684   mix 0.0007   time 26.64
scalar:  3.5863
Epoch 1184:  train loss 0.6174   train acc 0.5874   worst 0.1570   lr 0.0006   p 757.98   eps 0.4684   mix 0.0007   time 26.50
Epoch 1184:  test acc 0.5398   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1184:  clean acc 0.5885   certified acc 0.4642
Calculating metrics for L_infinity dist model on test set
Epoch 1184:  clean acc 0.5405   certified acc 0.3991
scalar:  3.5869
Epoch 1185:  train loss 0.6182   train acc 0.5842   worst 0.1566   lr 0.0006   p 761.17   eps 0.4684   mix 0.0007   time 26.51
scalar:  3.5875
Epoch 1186:  train loss 0.6189   train acc 0.5842   worst 0.1558   lr 0.0006   p 764.37   eps 0.4684   mix 0.0007   time 26.68
scalar:  3.5873
Epoch 1187:  train loss 0.6164   train acc 0.5865   worst 0.1561   lr 0.0006   p 767.58   eps 0.4684   mix 0.0007   time 26.34
scalar:  3.5869
Epoch 1188:  train loss 0.6177   train acc 0.5848   worst 0.1572   lr 0.0005   p 770.81   eps 0.4684   mix 0.0007   time 26.54
scalar:  3.5865
Epoch 1189:  train loss 0.6178   train acc 0.5853   worst 0.1583   lr 0.0005   p 774.06   eps 0.4684   mix 0.0007   time 26.30
Epoch 1189:  test acc 0.5408   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 1189:  clean acc 0.5896   certified acc 0.4660
Calculating metrics for L_infinity dist model on test set
Epoch 1189:  clean acc 0.5404   certified acc 0.3992
scalar:  3.5862
Epoch 1190:  train loss 0.6174   train acc 0.5848   worst 0.1553   lr 0.0005   p 777.31   eps 0.4684   mix 0.0007   time 26.56
scalar:  3.5858
Epoch 1191:  train loss 0.6151   train acc 0.5873   worst 0.1583   lr 0.0005   p 780.58   eps 0.4684   mix 0.0007   time 26.48
scalar:  3.5858
Epoch 1192:  train loss 0.6171   train acc 0.5878   worst 0.1571   lr 0.0005   p 783.87   eps 0.4684   mix 0.0007   time 26.32
scalar:  3.5857
Epoch 1193:  train loss 0.6185   train acc 0.5858   worst 0.1578   lr 0.0005   p 787.17   eps 0.4684   mix 0.0007   time 26.47
scalar:  3.5859
Epoch 1194:  train loss 0.6164   train acc 0.5865   worst 0.1576   lr 0.0005   p 790.48   eps 0.4684   mix 0.0006   time 26.62
Epoch 1194:  test acc 0.5416   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 1194:  clean acc 0.5888   certified acc 0.4650
Calculating metrics for L_infinity dist model on test set
Epoch 1194:  clean acc 0.5424   certified acc 0.4000
scalar:  3.5867
Epoch 1195:  train loss 0.6161   train acc 0.5856   worst 0.1575   lr 0.0005   p 793.80   eps 0.4684   mix 0.0006   time 26.67
scalar:  3.5867
Epoch 1196:  train loss 0.6169   train acc 0.5872   worst 0.1584   lr 0.0005   p 797.14   eps 0.4684   mix 0.0006   time 26.48
scalar:  3.5878
Epoch 1197:  train loss 0.6162   train acc 0.5869   worst 0.1574   lr 0.0005   p 800.50   eps 0.4684   mix 0.0006   time 26.57
scalar:  3.5876
Epoch 1198:  train loss 0.6177   train acc 0.5865   worst 0.1549   lr 0.0005   p 803.87   eps 0.4684   mix 0.0006   time 26.51
scalar:  3.5878
Epoch 1199:  train loss 0.6156   train acc 0.5873   worst 0.1584   lr 0.0004   p 807.25   eps 0.4684   mix 0.0006   time 26.73
Epoch 1199:  test acc 0.5426   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 1199:  clean acc 0.5900   certified acc 0.4649
Calculating metrics for L_infinity dist model on test set
Epoch 1199:  clean acc 0.5419   certified acc 0.3991
Generate adversarial examples on test dataset
adversarial attack acc 40.2400
scalar:  3.5868
Epoch 1200:  train loss 0.6177   train acc 0.5878   worst 0.1549   lr 0.0004   p 810.64   eps 0.4684   mix 0.0006   time 26.89
scalar:  3.5871
Epoch 1201:  train loss 0.6166   train acc 0.5872   worst 0.1568   lr 0.0004   p 814.05   eps 0.4684   mix 0.0006   time 26.46
scalar:  3.587
Epoch 1202:  train loss 0.6161   train acc 0.5876   worst 0.1576   lr 0.0004   p 817.48   eps 0.4684   mix 0.0006   time 26.48
scalar:  3.5868
Epoch 1203:  train loss 0.6165   train acc 0.5876   worst 0.1568   lr 0.0004   p 820.92   eps 0.4684   mix 0.0006   time 26.50
scalar:  3.5877
Epoch 1204:  train loss 0.6169   train acc 0.5866   worst 0.1582   lr 0.0004   p 824.37   eps 0.4684   mix 0.0006   time 26.71
Epoch 1204:  test acc 0.5422   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 1204:  clean acc 0.5913   certified acc 0.4670
Calculating metrics for L_infinity dist model on test set
Epoch 1204:  clean acc 0.5420   certified acc 0.3993
scalar:  3.5879
Epoch 1205:  train loss 0.6172   train acc 0.5863   worst 0.1578   lr 0.0004   p 827.84   eps 0.4684   mix 0.0006   time 26.68
scalar:  3.5881
Epoch 1206:  train loss 0.6163   train acc 0.5879   worst 0.1579   lr 0.0004   p 831.32   eps 0.4684   mix 0.0006   time 26.38
scalar:  3.5885
Epoch 1207:  train loss 0.6158   train acc 0.5883   worst 0.1581   lr 0.0004   p 834.82   eps 0.4684   mix 0.0006   time 26.62
scalar:  3.5888
Epoch 1208:  train loss 0.6170   train acc 0.5872   worst 0.1562   lr 0.0004   p 838.33   eps 0.4684   mix 0.0006   time 26.50
scalar:  3.5891
Epoch 1209:  train loss 0.6160   train acc 0.5897   worst 0.1565   lr 0.0004   p 841.86   eps 0.4684   mix 0.0006   time 26.59
Epoch 1209:  test acc 0.5426   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 1209:  clean acc 0.5882   certified acc 0.4682
Calculating metrics for L_infinity dist model on test set
Epoch 1209:  clean acc 0.5426   certified acc 0.4003
scalar:  3.5891
Epoch 1210:  train loss 0.6152   train acc 0.5893   worst 0.1556   lr 0.0004   p 845.40   eps 0.4684   mix 0.0006   time 26.51
scalar:  3.5892
Epoch 1211:  train loss 0.6159   train acc 0.5882   worst 0.1571   lr 0.0003   p 848.96   eps 0.4684   mix 0.0006   time 26.42
scalar:  3.5895
Epoch 1212:  train loss 0.6177   train acc 0.5852   worst 0.1567   lr 0.0003   p 852.53   eps 0.4684   mix 0.0006   time 26.47
scalar:  3.5893
Epoch 1213:  train loss 0.6153   train acc 0.5873   worst 0.1594   lr 0.0003   p 856.12   eps 0.4684   mix 0.0006   time 26.72
scalar:  3.5897
Epoch 1214:  train loss 0.6162   train acc 0.5861   worst 0.1576   lr 0.0003   p 859.72   eps 0.4684   mix 0.0006   time 26.45
Epoch 1214:  test acc 0.5402   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 1214:  clean acc 0.5863   certified acc 0.4651
Calculating metrics for L_infinity dist model on test set
Epoch 1214:  clean acc 0.5410   certified acc 0.3992
scalar:  3.5892
Epoch 1215:  train loss 0.6154   train acc 0.5866   worst 0.1581   lr 0.0003   p 863.34   eps 0.4684   mix 0.0006   time 26.51
scalar:  3.5893
Epoch 1216:  train loss 0.6150   train acc 0.5876   worst 0.1578   lr 0.0003   p 866.97   eps 0.4684   mix 0.0006   time 26.29
scalar:  3.5889
Epoch 1217:  train loss 0.6162   train acc 0.5868   worst 0.1579   lr 0.0003   p 870.62   eps 0.4684   mix 0.0006   time 26.34
scalar:  3.589
Epoch 1218:  train loss 0.6165   train acc 0.5868   worst 0.1564   lr 0.0003   p 874.28   eps 0.4684   mix 0.0006   time 26.33
scalar:  3.589
Epoch 1219:  train loss 0.6154   train acc 0.5878   worst 0.1585   lr 0.0003   p 877.96   eps 0.4684   mix 0.0006   time 26.35
Epoch 1219:  test acc 0.5411   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 1219:  clean acc 0.5885   certified acc 0.4663
Calculating metrics for L_infinity dist model on test set
Epoch 1219:  clean acc 0.5435   certified acc 0.3998
scalar:  3.5894
Epoch 1220:  train loss 0.6166   train acc 0.5882   worst 0.1563   lr 0.0003   p 881.65   eps 0.4684   mix 0.0006   time 26.48
scalar:  3.5893
Epoch 1221:  train loss 0.6162   train acc 0.5881   worst 0.1566   lr 0.0003   p 885.36   eps 0.4684   mix 0.0006   time 26.50
scalar:  3.5901
Epoch 1222:  train loss 0.6170   train acc 0.5870   worst 0.1544   lr 0.0003   p 889.09   eps 0.4684   mix 0.0006   time 26.30
scalar:  3.5898
Epoch 1223:  train loss 0.6152   train acc 0.5891   worst 0.1563   lr 0.0003   p 892.83   eps 0.4684   mix 0.0006   time 26.32
scalar:  3.5901
Epoch 1224:  train loss 0.6164   train acc 0.5844   worst 0.1581   lr 0.0003   p 896.59   eps 0.4684   mix 0.0006   time 26.26
Epoch 1224:  test acc 0.5420   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 1224:  clean acc 0.5902   certified acc 0.4662
Calculating metrics for L_infinity dist model on test set
Epoch 1224:  clean acc 0.5430   certified acc 0.3981
scalar:  3.5904
Epoch 1225:  train loss 0.6151   train acc 0.5877   worst 0.1594   lr 0.0002   p 900.36   eps 0.4684   mix 0.0006   time 26.26
scalar:  3.5902
Epoch 1226:  train loss 0.6157   train acc 0.5869   worst 0.1587   lr 0.0002   p 904.15   eps 0.4684   mix 0.0006   time 26.17
scalar:  3.5902
Epoch 1227:  train loss 0.6173   train acc 0.5857   worst 0.1557   lr 0.0002   p 907.95   eps 0.4684   mix 0.0006   time 26.25
scalar:  3.59
Epoch 1228:  train loss 0.6164   train acc 0.5871   worst 0.1570   lr 0.0002   p 911.77   eps 0.4684   mix 0.0006   time 26.20
scalar:  3.59
Epoch 1229:  train loss 0.6158   train acc 0.5885   worst 0.1579   lr 0.0002   p 915.61   eps 0.4684   mix 0.0006   time 26.22
Epoch 1229:  test acc 0.5414   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 1229:  clean acc 0.5892   certified acc 0.4683
Calculating metrics for L_infinity dist model on test set
Epoch 1229:  clean acc 0.5420   certified acc 0.3992
scalar:  3.5901
Epoch 1230:  train loss 0.6161   train acc 0.5868   worst 0.1578   lr 0.0002   p 919.46   eps 0.4684   mix 0.0005   time 26.20
scalar:  3.5901
Epoch 1231:  train loss 0.6167   train acc 0.5876   worst 0.1561   lr 0.0002   p 923.33   eps 0.4684   mix 0.0005   time 26.18
scalar:  3.5902
Epoch 1232:  train loss 0.6162   train acc 0.5863   worst 0.1591   lr 0.0002   p 927.21   eps 0.4684   mix 0.0005   time 26.27
scalar:  3.5907
Epoch 1233:  train loss 0.6152   train acc 0.5893   worst 0.1575   lr 0.0002   p 931.11   eps 0.4684   mix 0.0005   time 26.19
scalar:  3.5909
Epoch 1234:  train loss 0.6144   train acc 0.5887   worst 0.1595   lr 0.0002   p 935.03   eps 0.4684   mix 0.0005   time 26.20
Epoch 1234:  test acc 0.5428   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 1234:  clean acc 0.5894   certified acc 0.4672
Calculating metrics for L_infinity dist model on test set
Epoch 1234:  clean acc 0.5422   certified acc 0.3990
scalar:  3.5912
Epoch 1235:  train loss 0.6147   train acc 0.5890   worst 0.1574   lr 0.0002   p 938.96   eps 0.4684   mix 0.0005   time 26.29
scalar:  3.5915
Epoch 1236:  train loss 0.6163   train acc 0.5870   worst 0.1587   lr 0.0002   p 942.91   eps 0.4684   mix 0.0005   time 26.16
scalar:  3.5917
Epoch 1237:  train loss 0.6147   train acc 0.5879   worst 0.1586   lr 0.0002   p 946.88   eps 0.4684   mix 0.0005   time 26.25
scalar:  3.5916
Epoch 1238:  train loss 0.6162   train acc 0.5881   worst 0.1577   lr 0.0002   p 950.87   eps 0.4684   mix 0.0005   time 26.27
scalar:  3.592
Epoch 1239:  train loss 0.6169   train acc 0.5866   worst 0.1579   lr 0.0002   p 954.87   eps 0.4684   mix 0.0005   time 26.12
Epoch 1239:  test acc 0.5398   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 1239:  clean acc 0.5894   certified acc 0.4676
Calculating metrics for L_infinity dist model on test set
Epoch 1239:  clean acc 0.5406   certified acc 0.3998
scalar:  3.592
Epoch 1240:  train loss 0.6147   train acc 0.5889   worst 0.1573   lr 0.0002   p 958.88   eps 0.4684   mix 0.0005   time 26.25
scalar:  3.5919
Epoch 1241:  train loss 0.6144   train acc 0.5883   worst 0.1600   lr 0.0002   p 962.92   eps 0.4684   mix 0.0005   time 26.21
scalar:  3.5921
Epoch 1242:  train loss 0.6162   train acc 0.5866   worst 0.1573   lr 0.0001   p 966.97   eps 0.4684   mix 0.0005   time 26.22
scalar:  3.5923
Epoch 1243:  train loss 0.6148   train acc 0.5871   worst 0.1581   lr 0.0001   p 971.04   eps 0.4684   mix 0.0005   time 26.20
scalar:  3.5923
Epoch 1244:  train loss 0.6170   train acc 0.5831   worst 0.1552   lr 0.0001   p 975.12   eps 0.4684   mix 0.0005   time 26.30
Epoch 1244:  test acc 0.5418   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 1244:  clean acc 0.5897   certified acc 0.4685
Calculating metrics for L_infinity dist model on test set
Epoch 1244:  clean acc 0.5425   certified acc 0.3991
scalar:  3.592
Epoch 1245:  train loss 0.6170   train acc 0.5881   worst 0.1559   lr 0.0001   p 979.23   eps 0.4684   mix 0.0005   time 26.13
scalar:  3.5921
Epoch 1246:  train loss 0.6147   train acc 0.5896   worst 0.1571   lr 0.0001   p 983.35   eps 0.4684   mix 0.0005   time 26.21
scalar:  3.5921
Epoch 1247:  train loss 0.6161   train acc 0.5861   worst 0.1567   lr 0.0001   p 987.48   eps 0.4684   mix 0.0005   time 26.16
scalar:  3.5924
Epoch 1248:  train loss 0.6153   train acc 0.5860   worst 0.1579   lr 0.0001   p 991.64   eps 0.4684   mix 0.0005   time 26.24
scalar:  3.5924
Epoch 1249:  train loss 0.6161   train acc 0.5856   worst 0.1598   lr 0.0001   p 995.81   eps 0.4684   mix 0.0005   time 26.33
Epoch 1249:  test acc 0.5424   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1249:  clean acc 0.5880   certified acc 0.4652
Calculating metrics for L_infinity dist model on test set
Epoch 1249:  clean acc 0.5439   certified acc 0.4005
Generate adversarial examples on test dataset
adversarial attack acc 40.1500
scalar:  3.5923
Epoch 1250:  train loss 0.6173   train acc 0.5888   worst 0.1532   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.11
scalar:  3.5925
Epoch 1251:  train loss 0.6178   train acc 0.5884   worst 0.1528   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.15
scalar:  3.5926
Epoch 1252:  train loss 0.6184   train acc 0.5864   worst 0.1517   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.13
scalar:  3.5928
Epoch 1253:  train loss 0.6173   train acc 0.5898   worst 0.1540   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.16
scalar:  3.5931
Epoch 1254:  train loss 0.6174   train acc 0.5881   worst 0.1542   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.12
Epoch 1254:  test acc 0.5425   time 0.76
Calculating metrics for L_infinity dist model on training set
Epoch 1254:  clean acc 0.5877   certified acc 0.4659
Calculating metrics for L_infinity dist model on test set
Epoch 1254:  clean acc 0.5425   certified acc 0.3982
scalar:  3.5931
Epoch 1255:  train loss 0.6162   train acc 0.5893   worst 0.1542   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.12
scalar:  3.5934
Epoch 1256:  train loss 0.6169   train acc 0.5874   worst 0.1533   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.12
scalar:  3.5936
Epoch 1257:  train loss 0.6179   train acc 0.5887   worst 0.1517   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.12
scalar:  3.5939
Epoch 1258:  train loss 0.6169   train acc 0.5871   worst 0.1551   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.10
scalar:  3.594
Epoch 1259:  train loss 0.6166   train acc 0.5889   worst 0.1541   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.09
Epoch 1259:  test acc 0.5423   time 0.71
Calculating metrics for L_infinity dist model on training set
Epoch 1259:  clean acc 0.5909   certified acc 0.4680
Calculating metrics for L_infinity dist model on test set
Epoch 1259:  clean acc 0.5423   certified acc 0.4008
scalar:  3.5942
Epoch 1260:  train loss 0.6181   train acc 0.5878   worst 0.1520   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.18
scalar:  3.5943
Epoch 1261:  train loss 0.6192   train acc 0.5867   worst 0.1551   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.16
scalar:  3.5943
Epoch 1262:  train loss 0.6180   train acc 0.5858   worst 0.1529   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.15
scalar:  3.5944
Epoch 1263:  train loss 0.6192   train acc 0.5866   worst 0.1527   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.10
scalar:  3.5946
Epoch 1264:  train loss 0.6175   train acc 0.5889   worst 0.1524   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.11
Epoch 1264:  test acc 0.5424   time 0.70
Calculating metrics for L_infinity dist model on training set
Epoch 1264:  clean acc 0.5876   certified acc 0.4666
Calculating metrics for L_infinity dist model on test set
Epoch 1264:  clean acc 0.5424   certified acc 0.3993
scalar:  3.5947
Epoch 1265:  train loss 0.6183   train acc 0.5877   worst 0.1522   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.10
scalar:  3.5949
Epoch 1266:  train loss 0.6180   train acc 0.5859   worst 0.1558   lr 0.0001   p inf   eps 0.4684   mix 0.0005   time 6.14
scalar:  3.595
Epoch 1267:  train loss 0.6164   train acc 0.5865   worst 0.1549   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.12
scalar:  3.5951
Epoch 1268:  train loss 0.6174   train acc 0.5868   worst 0.1527   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.11
scalar:  3.5952
Epoch 1269:  train loss 0.6177   train acc 0.5872   worst 0.1549   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.12
Epoch 1269:  test acc 0.5426   time 0.66
Calculating metrics for L_infinity dist model on training set
Epoch 1269:  clean acc 0.5903   certified acc 0.4684
Calculating metrics for L_infinity dist model on test set
Epoch 1269:  clean acc 0.5426   certified acc 0.4004
scalar:  3.5953
Epoch 1270:  train loss 0.6168   train acc 0.5879   worst 0.1545   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.13
scalar:  3.5954
Epoch 1271:  train loss 0.6171   train acc 0.5897   worst 0.1534   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.16
scalar:  3.5955
Epoch 1272:  train loss 0.6175   train acc 0.5870   worst 0.1557   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.12
scalar:  3.5955
Epoch 1273:  train loss 0.6171   train acc 0.5876   worst 0.1540   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.10
scalar:  3.5956
Epoch 1274:  train loss 0.6158   train acc 0.5863   worst 0.1567   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.13
Epoch 1274:  test acc 0.5421   time 0.75
Calculating metrics for L_infinity dist model on training set
Epoch 1274:  clean acc 0.5877   certified acc 0.4674
Calculating metrics for L_infinity dist model on test set
Epoch 1274:  clean acc 0.5421   certified acc 0.4000
scalar:  3.5957
Epoch 1275:  train loss 0.6164   train acc 0.5882   worst 0.1541   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.09
scalar:  3.5958
Epoch 1276:  train loss 0.6174   train acc 0.5866   worst 0.1539   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.11
scalar:  3.5958
Epoch 1277:  train loss 0.6173   train acc 0.5860   worst 0.1541   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.13
scalar:  3.5959
Epoch 1278:  train loss 0.6188   train acc 0.5866   worst 0.1537   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.16
scalar:  3.5959
Epoch 1279:  train loss 0.6173   train acc 0.5868   worst 0.1544   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.18
Epoch 1279:  test acc 0.5421   time 0.82
Calculating metrics for L_infinity dist model on training set
Epoch 1279:  clean acc 0.5888   certified acc 0.4672
Calculating metrics for L_infinity dist model on test set
Epoch 1279:  clean acc 0.5421   certified acc 0.3999
scalar:  3.596
Epoch 1280:  train loss 0.6173   train acc 0.5866   worst 0.1537   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.15
scalar:  3.596
Epoch 1281:  train loss 0.6183   train acc 0.5892   worst 0.1545   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.12
scalar:  3.596
Epoch 1282:  train loss 0.6167   train acc 0.5868   worst 0.1546   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.21
scalar:  3.5961
Epoch 1283:  train loss 0.6176   train acc 0.5896   worst 0.1534   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.22
scalar:  3.5961
Epoch 1284:  train loss 0.6169   train acc 0.5884   worst 0.1524   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.20
Epoch 1284:  test acc 0.5415   time 0.69
Calculating metrics for L_infinity dist model on training set
Epoch 1284:  clean acc 0.5906   certified acc 0.4662
Calculating metrics for L_infinity dist model on test set
Epoch 1284:  clean acc 0.5415   certified acc 0.3989
scalar:  3.5961
Epoch 1285:  train loss 0.6168   train acc 0.5881   worst 0.1559   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.15
scalar:  3.5962
Epoch 1286:  train loss 0.6178   train acc 0.5871   worst 0.1544   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.14
scalar:  3.5962
Epoch 1287:  train loss 0.6174   train acc 0.5876   worst 0.1552   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.11
scalar:  3.5962
Epoch 1288:  train loss 0.6164   train acc 0.5871   worst 0.1559   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.12
scalar:  3.5962
Epoch 1289:  train loss 0.6191   train acc 0.5883   worst 0.1532   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.14
Epoch 1289:  test acc 0.5400   time 0.75
Calculating metrics for L_infinity dist model on training set
Epoch 1289:  clean acc 0.5872   certified acc 0.4677
Calculating metrics for L_infinity dist model on test set
Epoch 1289:  clean acc 0.5400   certified acc 0.4003
scalar:  3.5962
Epoch 1290:  train loss 0.6167   train acc 0.5871   worst 0.1543   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.23
scalar:  3.5962
Epoch 1291:  train loss 0.6163   train acc 0.5885   worst 0.1573   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.16
scalar:  3.5962
Epoch 1292:  train loss 0.6167   train acc 0.5883   worst 0.1559   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.12
scalar:  3.5962
Epoch 1293:  train loss 0.6171   train acc 0.5874   worst 0.1542   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.17
scalar:  3.5962
Epoch 1294:  train loss 0.6183   train acc 0.5866   worst 0.1539   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.14
Epoch 1294:  test acc 0.5416   time 0.71
Calculating metrics for L_infinity dist model on training set
Epoch 1294:  clean acc 0.5889   certified acc 0.4682
Calculating metrics for L_infinity dist model on test set
Epoch 1294:  clean acc 0.5416   certified acc 0.3993
scalar:  3.5963
Epoch 1295:  train loss 0.6167   train acc 0.5872   worst 0.1556   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.14
Epoch 1295:  test acc 0.5432   time 0.71
Calculating metrics for L_infinity dist model on training set
Epoch 1295:  clean acc 0.5895   certified acc 0.4678
Calculating metrics for L_infinity dist model on test set
Epoch 1295:  clean acc 0.5432   certified acc 0.3999
Generate adversarial examples on test dataset
adversarial attack acc 42.1900
scalar:  3.5963
Epoch 1296:  train loss 0.6171   train acc 0.5890   worst 0.1554   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.15
Epoch 1296:  test acc 0.5429   time 0.71
Calculating metrics for L_infinity dist model on training set
Epoch 1296:  clean acc 0.5907   certified acc 0.4666
Calculating metrics for L_infinity dist model on test set
Epoch 1296:  clean acc 0.5429   certified acc 0.3996
Generate adversarial examples on test dataset
adversarial attack acc 42.0700
scalar:  3.5963
Epoch 1297:  train loss 0.6166   train acc 0.5890   worst 0.1558   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.12
Epoch 1297:  test acc 0.5417   time 0.68
Calculating metrics for L_infinity dist model on training set
Epoch 1297:  clean acc 0.5884   certified acc 0.4673
Calculating metrics for L_infinity dist model on test set
Epoch 1297:  clean acc 0.5417   certified acc 0.4010
Generate adversarial examples on test dataset
adversarial attack acc 42.2100
scalar:  3.5963
Epoch 1298:  train loss 0.6177   train acc 0.5880   worst 0.1552   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.12
Epoch 1298:  test acc 0.5425   time 0.72
Calculating metrics for L_infinity dist model on training set
Epoch 1298:  clean acc 0.5898   certified acc 0.4667
Calculating metrics for L_infinity dist model on test set
Epoch 1298:  clean acc 0.5425   certified acc 0.4007
Generate adversarial examples on test dataset
adversarial attack acc 42.3000
scalar:  3.5963
Epoch 1299:  train loss 0.6172   train acc 0.5885   worst 0.1559   lr 0.0000   p inf   eps 0.4684   mix 0.0005   time 6.11
Epoch 1299:  test acc 0.5421   time 0.71
Calculating metrics for L_infinity dist model on training set
Epoch 1299:  clean acc 0.5892   certified acc 0.4687
Calculating metrics for L_infinity dist model on test set
Epoch 1299:  clean acc 0.5421   certified acc 0.4013
Generate adversarial examples on test dataset
adversarial attack acc 42.2500
Generate adversarial examples on test dataset
adversarial attack acc 42.2800
Calculating test acc and certified test acc
Epoch 1300:  clean acc 0.5421   certified acc 0.4013
