dataset = CIFAR10
model = MLPModel(depth=6,width=5120,identity_val=10.0,scalar=True)
loss = radius_mix2(lam0=0.05,lam_end=0.002)
p_start = 8.0
p_end = 1000.0
eps_train = 0.03922
eps_test = 0.0078425
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 = 0
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)
  )
)
number of params:  120637451
scalar:  1.0
Epoch 0:  train loss 0.9430   train acc 0.1979   worst 0.1380   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 14.82
scalar:  0.9597
Epoch 1:  train loss 0.8163   train acc 0.3345   worst 0.2284   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 15.03
scalar:  1.2268
Epoch 2:  train loss 0.7745   train acc 0.3713   worst 0.2660   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 17.07
scalar:  1.3169
Epoch 3:  train loss 0.7503   train acc 0.3902   worst 0.2878   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 20.43
scalar:  1.353
Epoch 4:  train loss 0.7328   train acc 0.4030   worst 0.3051   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.78
Epoch 4:  test acc 0.4338   time 1.17
Calculating metrics for L_infinity dist model on training set
Epoch 4:  clean acc 0.1667   certified acc 0.1562
Calculating metrics for L_infinity dist model on test set
Epoch 4:  clean acc 0.1731   certified acc 0.1612
scalar:  1.3592
Epoch 5:  train loss 0.7172   train acc 0.4140   worst 0.3205   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.65
scalar:  1.3662
Epoch 6:  train loss 0.7046   train acc 0.4235   worst 0.3332   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.32
scalar:  1.2639
Epoch 7:  train loss 0.6922   train acc 0.4370   worst 0.3426   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 20.22
scalar:  1.2865
Epoch 8:  train loss 0.6825   train acc 0.4444   worst 0.3525   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.03
scalar:  1.2594
Epoch 9:  train loss 0.6746   train acc 0.4517   worst 0.3583   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.68
Epoch 9:  test acc 0.4812   time 1.10
Calculating metrics for L_infinity dist model on training set
Epoch 9:  clean acc 0.1731   certified acc 0.1478
Calculating metrics for L_infinity dist model on test set
Epoch 9:  clean acc 0.1798   certified acc 0.1516
scalar:  1.246
Epoch 10:  train loss 0.6662   train acc 0.4593   worst 0.3661   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.02
scalar:  1.2342
Epoch 11:  train loss 0.6591   train acc 0.4627   worst 0.3769   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.35
scalar:  1.1755
Epoch 12:  train loss 0.6532   train acc 0.4691   worst 0.3791   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.81
scalar:  1.1844
Epoch 13:  train loss 0.6459   train acc 0.4759   worst 0.3859   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 18.54
scalar:  1.1625
Epoch 14:  train loss 0.6370   train acc 0.4824   worst 0.3945   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 20.08
Epoch 14:  test acc 0.5021   time 1.12
Calculating metrics for L_infinity dist model on training set
Epoch 14:  clean acc 0.1364   certified acc 0.1216
Calculating metrics for L_infinity dist model on test set
Epoch 14:  clean acc 0.1376   certified acc 0.1227
scalar:  1.1316
Epoch 15:  train loss 0.6309   train acc 0.4889   worst 0.4013   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.38
scalar:  1.1086
Epoch 16:  train loss 0.6275   train acc 0.4894   worst 0.4046   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 18.87
scalar:  1.1195
Epoch 17:  train loss 0.6204   train acc 0.4964   worst 0.4113   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.80
scalar:  1.071
Epoch 18:  train loss 0.6180   train acc 0.4991   worst 0.4118   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.91
scalar:  1.0767
Epoch 19:  train loss 0.6116   train acc 0.5051   worst 0.4180   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 18.37
Epoch 19:  test acc 0.5244   time 1.12
Calculating metrics for L_infinity dist model on training set
Epoch 19:  clean acc 0.1369   certified acc 0.1166
Calculating metrics for L_infinity dist model on test set
Epoch 19:  clean acc 0.1423   certified acc 0.1231
scalar:  1.0692
Epoch 20:  train loss 0.6066   train acc 0.5061   worst 0.4264   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 18.98
scalar:  1.0503
Epoch 21:  train loss 0.6035   train acc 0.5100   worst 0.4290   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 20.11
scalar:  1.0653
Epoch 22:  train loss 0.6011   train acc 0.5116   worst 0.4303   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.56
scalar:  1.0164
Epoch 23:  train loss 0.5979   train acc 0.5135   worst 0.4341   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 18.70
scalar:  1.0635
Epoch 24:  train loss 0.5945   train acc 0.5165   worst 0.4374   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 18.34
Epoch 24:  test acc 0.5285   time 1.12
Calculating metrics for L_infinity dist model on training set
Epoch 24:  clean acc 0.1198   certified acc 0.1055
Calculating metrics for L_infinity dist model on test set
Epoch 24:  clean acc 0.1222   certified acc 0.1060
scalar:  1.0136
Epoch 25:  train loss 0.5927   train acc 0.5163   worst 0.4409   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.81
scalar:  0.9847
Epoch 26:  train loss 0.5873   train acc 0.5229   worst 0.4436   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.60
scalar:  0.9997
Epoch 27:  train loss 0.5840   train acc 0.5254   worst 0.4469   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.72
scalar:  0.9609
Epoch 28:  train loss 0.5836   train acc 0.5252   worst 0.4487   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.86
scalar:  0.9864
Epoch 29:  train loss 0.5801   train acc 0.5269   worst 0.4526   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.95
Epoch 29:  test acc 0.5336   time 1.10
Calculating metrics for L_infinity dist model on training set
Epoch 29:  clean acc 0.1647   certified acc 0.1406
Calculating metrics for L_infinity dist model on test set
Epoch 29:  clean acc 0.1707   certified acc 0.1454
scalar:  0.9592
Epoch 30:  train loss 0.5781   train acc 0.5286   worst 0.4557   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.38
scalar:  0.9711
Epoch 31:  train loss 0.5762   train acc 0.5298   worst 0.4565   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.10
scalar:  0.9488
Epoch 32:  train loss 0.5715   train acc 0.5343   worst 0.4608   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 18.06
scalar:  0.9499
Epoch 33:  train loss 0.5722   train acc 0.5334   worst 0.4611   lr 0.0300   p 8.00   eps 0.1952   mix 0.0500   time 19.03
scalar:  0.9559
Epoch 34:  train loss 0.5685   train acc 0.5364   worst 0.4629   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 18.81
Epoch 34:  test acc 0.5455   time 1.10
Calculating metrics for L_infinity dist model on training set
Epoch 34:  clean acc 0.1695   certified acc 0.1375
Calculating metrics for L_infinity dist model on test set
Epoch 34:  clean acc 0.1753   certified acc 0.1441
scalar:  0.9731
Epoch 35:  train loss 0.5630   train acc 0.5416   worst 0.4691   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 20.20
scalar:  0.9575
Epoch 36:  train loss 0.5632   train acc 0.5411   worst 0.4696   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.30
scalar:  0.9931
Epoch 37:  train loss 0.5615   train acc 0.5425   worst 0.4709   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 18.05
scalar:  0.9297
Epoch 38:  train loss 0.5596   train acc 0.5443   worst 0.4725   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 18.82
scalar:  0.9923
Epoch 39:  train loss 0.5606   train acc 0.5434   worst 0.4705   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 17.86
Epoch 39:  test acc 0.5532   time 1.13
Calculating metrics for L_infinity dist model on training set
Epoch 39:  clean acc 0.1451   certified acc 0.1257
Calculating metrics for L_infinity dist model on test set
Epoch 39:  clean acc 0.1486   certified acc 0.1281
scalar:  0.9401
Epoch 40:  train loss 0.5564   train acc 0.5476   worst 0.4766   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.32
scalar:  0.9717
Epoch 41:  train loss 0.5563   train acc 0.5467   worst 0.4754   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.79
scalar:  0.9557
Epoch 42:  train loss 0.5532   train acc 0.5517   worst 0.4758   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.54
scalar:  0.9528
Epoch 43:  train loss 0.5534   train acc 0.5487   worst 0.4781   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 20.27
scalar:  0.9557
Epoch 44:  train loss 0.5539   train acc 0.5487   worst 0.4791   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.41
Epoch 44:  test acc 0.5526   time 1.13
Calculating metrics for L_infinity dist model on training set
Epoch 44:  clean acc 0.1265   certified acc 0.1132
Calculating metrics for L_infinity dist model on test set
Epoch 44:  clean acc 0.1290   certified acc 0.1141
scalar:  0.9312
Epoch 45:  train loss 0.5504   train acc 0.5512   worst 0.4823   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.08
scalar:  0.917
Epoch 46:  train loss 0.5513   train acc 0.5497   worst 0.4815   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.38
scalar:  0.8909
Epoch 47:  train loss 0.5487   train acc 0.5528   worst 0.4833   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 20.32
scalar:  0.9225
Epoch 48:  train loss 0.5443   train acc 0.5555   worst 0.4885   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.14
scalar:  0.9123
Epoch 49:  train loss 0.5461   train acc 0.5543   worst 0.4870   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 18.83
Epoch 49:  test acc 0.5572   time 1.17
Calculating metrics for L_infinity dist model on training set
Epoch 49:  clean acc 0.1577   certified acc 0.1363
Calculating metrics for L_infinity dist model on test set
Epoch 49:  clean acc 0.1557   certified acc 0.1351
scalar:  0.9077
Epoch 50:  train loss 0.5501   train acc 0.5490   worst 0.4839   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.38
scalar:  0.9044
Epoch 51:  train loss 0.5442   train acc 0.5560   worst 0.4874   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.35
scalar:  0.9229
Epoch 52:  train loss 0.5472   train acc 0.5535   worst 0.4849   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.12
scalar:  0.8808
Epoch 53:  train loss 0.5401   train acc 0.5597   worst 0.4923   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.05
scalar:  0.9255
Epoch 54:  train loss 0.5387   train acc 0.5599   worst 0.4932   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.31
Epoch 54:  test acc 0.5632   time 1.13
Calculating metrics for L_infinity dist model on training set
Epoch 54:  clean acc 0.1229   certified acc 0.1079
Calculating metrics for L_infinity dist model on test set
Epoch 54:  clean acc 0.1252   certified acc 0.1095
scalar:  0.9298
Epoch 55:  train loss 0.5352   train acc 0.5641   worst 0.4970   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 18.05
scalar:  0.9185
Epoch 56:  train loss 0.5378   train acc 0.5624   worst 0.4951   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.45
scalar:  0.9196
Epoch 57:  train loss 0.5386   train acc 0.5598   worst 0.4934   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 18.99
scalar:  0.9182
Epoch 58:  train loss 0.5346   train acc 0.5652   worst 0.4970   lr 0.0299   p 8.00   eps 0.1952   mix 0.0500   time 19.57
scalar:  0.9632
Epoch 59:  train loss 0.5319   train acc 0.5680   worst 0.4988   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 18.87
Epoch 59:  test acc 0.5649   time 1.13
Calculating metrics for L_infinity dist model on training set
Epoch 59:  clean acc 0.1262   certified acc 0.0971
Calculating metrics for L_infinity dist model on test set
Epoch 59:  clean acc 0.1275   certified acc 0.0983
scalar:  0.9147
Epoch 60:  train loss 0.5333   train acc 0.5647   worst 0.4982   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 17.92
scalar:  0.9213
Epoch 61:  train loss 0.5319   train acc 0.5659   worst 0.5002   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 18.55
scalar:  0.9217
Epoch 62:  train loss 0.5270   train acc 0.5711   worst 0.5037   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 19.24
scalar:  0.9248
Epoch 63:  train loss 0.5299   train acc 0.5674   worst 0.5014   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 19.11
scalar:  0.9362
Epoch 64:  train loss 0.5286   train acc 0.5689   worst 0.5016   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 19.15
Epoch 64:  test acc 0.5698   time 1.11
Calculating metrics for L_infinity dist model on training set
Epoch 64:  clean acc 0.1324   certified acc 0.1085
Calculating metrics for L_infinity dist model on test set
Epoch 64:  clean acc 0.1374   certified acc 0.1167
scalar:  0.9173
Epoch 65:  train loss 0.5255   train acc 0.5726   worst 0.5059   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 19.67
scalar:  0.9508
Epoch 66:  train loss 0.5277   train acc 0.5716   worst 0.5013   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 19.10
scalar:  0.9311
Epoch 67:  train loss 0.5273   train acc 0.5703   worst 0.5041   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 20.59
scalar:  0.9153
Epoch 68:  train loss 0.5274   train acc 0.5716   worst 0.5022   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 19.15
scalar:  0.9254
Epoch 69:  train loss 0.5252   train acc 0.5729   worst 0.5049   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 18.53
Epoch 69:  test acc 0.5743   time 1.15
Calculating metrics for L_infinity dist model on training set
Epoch 69:  clean acc 0.1281   certified acc 0.1155
Calculating metrics for L_infinity dist model on test set
Epoch 69:  clean acc 0.1292   certified acc 0.1172
scalar:  0.9121
Epoch 70:  train loss 0.5236   train acc 0.5750   worst 0.5066   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 17.80
scalar:  0.8949
Epoch 71:  train loss 0.5221   train acc 0.5753   worst 0.5077   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 19.15
scalar:  0.9419
Epoch 72:  train loss 0.5226   train acc 0.5747   worst 0.5075   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 19.80
scalar:  0.9103
Epoch 73:  train loss 0.5211   train acc 0.5773   worst 0.5090   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 19.10
scalar:  0.9669
Epoch 74:  train loss 0.5173   train acc 0.5797   worst 0.5121   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 19.05
Epoch 74:  test acc 0.5727   time 1.09
Calculating metrics for L_infinity dist model on training set
Epoch 74:  clean acc 0.1184   certified acc 0.1031
Calculating metrics for L_infinity dist model on test set
Epoch 74:  clean acc 0.1208   certified acc 0.1036
scalar:  0.9576
Epoch 75:  train loss 0.5179   train acc 0.5789   worst 0.5116   lr 0.0298   p 8.00   eps 0.1952   mix 0.0500   time 20.04
scalar:  0.9345
Epoch 76:  train loss 0.5180   train acc 0.5799   worst 0.5107   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 19.65
scalar:  0.923
Epoch 77:  train loss 0.5165   train acc 0.5799   worst 0.5140   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 19.30
scalar:  0.9255
Epoch 78:  train loss 0.5149   train acc 0.5811   worst 0.5149   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 18.36
scalar:  0.9378
Epoch 79:  train loss 0.5161   train acc 0.5805   worst 0.5135   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 18.16
Epoch 79:  test acc 0.5765   time 1.12
Calculating metrics for L_infinity dist model on training set
Epoch 79:  clean acc 0.1255   certified acc 0.0985
Calculating metrics for L_infinity dist model on test set
Epoch 79:  clean acc 0.1280   certified acc 0.1016
scalar:  0.9284
Epoch 80:  train loss 0.5159   train acc 0.5799   worst 0.5136   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 19.05
scalar:  0.9281
Epoch 81:  train loss 0.5145   train acc 0.5814   worst 0.5155   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 19.58
scalar:  0.931
Epoch 82:  train loss 0.5132   train acc 0.5833   worst 0.5163   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 19.26
scalar:  0.9197
Epoch 83:  train loss 0.5116   train acc 0.5840   worst 0.5176   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 18.17
scalar:  0.9249
Epoch 84:  train loss 0.5149   train acc 0.5824   worst 0.5141   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 19.17
Epoch 84:  test acc 0.5783   time 1.13
Calculating metrics for L_infinity dist model on training set
Epoch 84:  clean acc 0.1258   certified acc 0.0960
Calculating metrics for L_infinity dist model on test set
Epoch 84:  clean acc 0.1300   certified acc 0.1010
scalar:  0.9543
Epoch 85:  train loss 0.5145   train acc 0.5822   worst 0.5160   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 18.96
scalar:  0.9095
Epoch 86:  train loss 0.5100   train acc 0.5854   worst 0.5190   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 18.94
scalar:  0.9278
Epoch 87:  train loss 0.5124   train acc 0.5843   worst 0.5164   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 19.58
scalar:  0.9221
Epoch 88:  train loss 0.5136   train acc 0.5824   worst 0.5168   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 19.77
scalar:  0.9429
Epoch 89:  train loss 0.5104   train acc 0.5866   worst 0.5178   lr 0.0297   p 8.00   eps 0.1952   mix 0.0500   time 19.57
Epoch 89:  test acc 0.5823   time 1.15
Calculating metrics for L_infinity dist model on training set
Epoch 89:  clean acc 0.1209   certified acc 0.1073
Calculating metrics for L_infinity dist model on test set
Epoch 89:  clean acc 0.1251   certified acc 0.1114
scalar:  0.9493
Epoch 90:  train loss 0.5057   train acc 0.5895   worst 0.5239   lr 0.0296   p 8.00   eps 0.1952   mix 0.0500   time 18.27
scalar:  0.9432
Epoch 91:  train loss 0.5060   train acc 0.5898   worst 0.5232   lr 0.0296   p 8.00   eps 0.1952   mix 0.0500   time 18.74
scalar:  0.9598
Epoch 92:  train loss 0.5074   train acc 0.5883   worst 0.5209   lr 0.0296   p 8.00   eps 0.1952   mix 0.0500   time 19.48
scalar:  0.9194
Epoch 93:  train loss 0.5053   train acc 0.5912   worst 0.5229   lr 0.0296   p 8.00   eps 0.1952   mix 0.0500   time 19.12
scalar:  0.9424
Epoch 94:  train loss 0.5058   train acc 0.5903   worst 0.5224   lr 0.0296   p 8.00   eps 0.1952   mix 0.0500   time 20.38
Epoch 94:  test acc 0.5813   time 1.15
Calculating metrics for L_infinity dist model on training set
Epoch 94:  clean acc 0.1355   certified acc 0.1177
Calculating metrics for L_infinity dist model on test set
Epoch 94:  clean acc 0.1352   certified acc 0.1193
scalar:  0.9452
Epoch 95:  train loss 0.5064   train acc 0.5892   worst 0.5208   lr 0.0296   p 8.00   eps 0.1952   mix 0.0500   time 18.57
scalar:  0.9325
Epoch 96:  train loss 0.5048   train acc 0.5918   worst 0.5227   lr 0.0296   p 8.00   eps 0.1952   mix 0.0500   time 18.72
scalar:  0.9612
Epoch 97:  train loss 0.5026   train acc 0.5937   worst 0.5243   lr 0.0296   p 8.00   eps 0.1952   mix 0.0500   time 19.28
scalar:  0.9562
Epoch 98:  train loss 0.5007   train acc 0.5956   worst 0.5268   lr 0.0296   p 8.00   eps 0.1952   mix 0.0500   time 19.20
scalar:  0.9602
Epoch 99:  train loss 0.5025   train acc 0.5927   worst 0.5235   lr 0.0296   p 8.00   eps 0.1952   mix 0.0500   time 20.20
Epoch 99:  test acc 0.5814   time 1.12
Calculating metrics for L_infinity dist model on training set
Epoch 99:  clean acc 0.1411   certified acc 0.1211
Calculating metrics for L_infinity dist model on test set
Epoch 99:  clean acc 0.1465   certified acc 0.1265
scalar:  0.9449
Epoch 100:  train loss 0.5027   train acc 0.5925   worst 0.5244   lr 0.0296   p 8.00   eps 0.1952   mix 0.0500   time 25.31
scalar:  0.9726
Epoch 101:  train loss 0.4985   train acc 0.5981   worst 0.5268   lr 0.0296   p 8.03   eps 0.1952   mix 0.0499   time 26.35
scalar:  1.0054
Epoch 102:  train loss 0.5021   train acc 0.5938   worst 0.5238   lr 0.0295   p 8.07   eps 0.1952   mix 0.0497   time 24.81
scalar:  0.9562
Epoch 103:  train loss 0.4981   train acc 0.5990   worst 0.5261   lr 0.0295   p 8.10   eps 0.1952   mix 0.0496   time 24.87
scalar:  1.0094
Epoch 104:  train loss 0.4968   train acc 0.6002   worst 0.5275   lr 0.0295   p 8.14   eps 0.1952   mix 0.0494   time 24.99
Epoch 104:  test acc 0.5873   time 2.05
Calculating metrics for L_infinity dist model on training set
Epoch 104:  clean acc 0.1148   certified acc 0.0970
Calculating metrics for L_infinity dist model on test set
Epoch 104:  clean acc 0.1126   certified acc 0.0971
scalar:  0.9994
Epoch 105:  train loss 0.4991   train acc 0.5981   worst 0.5228   lr 0.0295   p 8.17   eps 0.1952   mix 0.0493   time 24.84
scalar:  1.054
Epoch 106:  train loss 0.5007   train acc 0.5970   worst 0.5227   lr 0.0295   p 8.20   eps 0.1952   mix 0.0492   time 25.47
scalar:  1.0209
Epoch 107:  train loss 0.4940   train acc 0.6019   worst 0.5289   lr 0.0295   p 8.24   eps 0.1952   mix 0.0490   time 24.90
scalar:  1.0677
Epoch 108:  train loss 0.4990   train acc 0.5973   worst 0.5227   lr 0.0295   p 8.27   eps 0.1952   mix 0.0489   time 24.69
scalar:  1.0762
Epoch 109:  train loss 0.4988   train acc 0.5982   worst 0.5216   lr 0.0295   p 8.31   eps 0.1952   mix 0.0488   time 24.60
Epoch 109:  test acc 0.5927   time 2.03
Calculating metrics for L_infinity dist model on training set
Epoch 109:  clean acc 0.1129   certified acc 0.0902
Calculating metrics for L_infinity dist model on test set
Epoch 109:  clean acc 0.1144   certified acc 0.0930
scalar:  1.0651
Epoch 110:  train loss 0.4938   train acc 0.6026   worst 0.5263   lr 0.0295   p 8.34   eps 0.1952   mix 0.0486   time 24.75
scalar:  1.0652
Epoch 111:  train loss 0.4979   train acc 0.6004   worst 0.5215   lr 0.0295   p 8.38   eps 0.1952   mix 0.0485   time 25.48
scalar:  1.0874
Epoch 112:  train loss 0.4933   train acc 0.6054   worst 0.5250   lr 0.0295   p 8.41   eps 0.1952   mix 0.0483   time 24.94
scalar:  1.1095
Epoch 113:  train loss 0.4948   train acc 0.6037   worst 0.5213   lr 0.0294   p 8.45   eps 0.1952   mix 0.0482   time 24.99
scalar:  1.1138
Epoch 114:  train loss 0.4919   train acc 0.6052   worst 0.5272   lr 0.0294   p 8.48   eps 0.1952   mix 0.0481   time 24.81
Epoch 114:  test acc 0.5842   time 2.03
Calculating metrics for L_infinity dist model on training set
Epoch 114:  clean acc 0.1277   certified acc 0.0997
Calculating metrics for L_infinity dist model on test set
Epoch 114:  clean acc 0.1316   certified acc 0.1030
scalar:  1.1198
Epoch 115:  train loss 0.4935   train acc 0.6035   worst 0.5238   lr 0.0294   p 8.52   eps 0.1952   mix 0.0479   time 24.60
scalar:  1.0869
Epoch 116:  train loss 0.4938   train acc 0.6041   worst 0.5220   lr 0.0294   p 8.56   eps 0.1952   mix 0.0478   time 25.78
scalar:  1.1124
Epoch 117:  train loss 0.4924   train acc 0.6064   worst 0.5231   lr 0.0294   p 8.59   eps 0.1952   mix 0.0477   time 24.93
scalar:  1.1739
Epoch 118:  train loss 0.4911   train acc 0.6077   worst 0.5244   lr 0.0294   p 8.63   eps 0.1952   mix 0.0475   time 24.88
scalar:  1.1566
Epoch 119:  train loss 0.4906   train acc 0.6091   worst 0.5239   lr 0.0294   p 8.66   eps 0.1952   mix 0.0474   time 24.84
Epoch 119:  test acc 0.5931   time 1.99
Calculating metrics for L_infinity dist model on training set
Epoch 119:  clean acc 0.1424   certified acc 0.1023
Calculating metrics for L_infinity dist model on test set
Epoch 119:  clean acc 0.1478   certified acc 0.1053
scalar:  1.2024
Epoch 120:  train loss 0.4936   train acc 0.6062   worst 0.5208   lr 0.0294   p 8.70   eps 0.1952   mix 0.0473   time 24.53
scalar:  1.2157
Epoch 121:  train loss 0.4924   train acc 0.6074   worst 0.5228   lr 0.0294   p 8.74   eps 0.1952   mix 0.0471   time 25.84
scalar:  1.2198
Epoch 122:  train loss 0.4907   train acc 0.6082   worst 0.5232   lr 0.0294   p 8.77   eps 0.1952   mix 0.0470   time 25.82
scalar:  1.2337
Epoch 123:  train loss 0.4925   train acc 0.6071   worst 0.5204   lr 0.0293   p 8.81   eps 0.1952   mix 0.0469   time 24.84
scalar:  1.2675
Epoch 124:  train loss 0.4926   train acc 0.6078   worst 0.5194   lr 0.0293   p 8.85   eps 0.1952   mix 0.0468   time 24.61
Epoch 124:  test acc 0.5974   time 2.06
Calculating metrics for L_infinity dist model on training set
Epoch 124:  clean acc 0.1363   certified acc 0.1076
Calculating metrics for L_infinity dist model on test set
Epoch 124:  clean acc 0.1406   certified acc 0.1100
scalar:  1.2673
Epoch 125:  train loss 0.4914   train acc 0.6103   worst 0.5199   lr 0.0293   p 8.89   eps 0.1952   mix 0.0466   time 24.71
scalar:  1.254
Epoch 126:  train loss 0.4900   train acc 0.6096   worst 0.5214   lr 0.0293   p 8.92   eps 0.1952   mix 0.0465   time 25.01
scalar:  1.251
Epoch 127:  train loss 0.4924   train acc 0.6072   worst 0.5200   lr 0.0293   p 8.96   eps 0.1952   mix 0.0464   time 25.51
scalar:  1.2728
Epoch 128:  train loss 0.4897   train acc 0.6107   worst 0.5192   lr 0.0293   p 9.00   eps 0.1952   mix 0.0462   time 25.24
scalar:  1.2798
Epoch 129:  train loss 0.4896   train acc 0.6114   worst 0.5194   lr 0.0293   p 9.04   eps 0.1952   mix 0.0461   time 24.47
Epoch 129:  test acc 0.5951   time 2.00
Calculating metrics for L_infinity dist model on training set
Epoch 129:  clean acc 0.1352   certified acc 0.1155
Calculating metrics for L_infinity dist model on test set
Epoch 129:  clean acc 0.1390   certified acc 0.1203
scalar:  1.3062
Epoch 130:  train loss 0.4880   train acc 0.6136   worst 0.5201   lr 0.0293   p 9.07   eps 0.1952   mix 0.0460   time 24.57
scalar:  1.3011
Epoch 131:  train loss 0.4845   train acc 0.6153   worst 0.5232   lr 0.0293   p 9.11   eps 0.1952   mix 0.0458   time 25.40
scalar:  1.3374
Epoch 132:  train loss 0.4862   train acc 0.6147   worst 0.5208   lr 0.0292   p 9.15   eps 0.1952   mix 0.0457   time 25.50
scalar:  1.3567
Epoch 133:  train loss 0.4873   train acc 0.6135   worst 0.5183   lr 0.0292   p 9.19   eps 0.1952   mix 0.0456   time 24.66
scalar:  1.3627
Epoch 134:  train loss 0.4848   train acc 0.6171   worst 0.5200   lr 0.0292   p 9.23   eps 0.1952   mix 0.0455   time 24.61
Epoch 134:  test acc 0.5962   time 2.05
Calculating metrics for L_infinity dist model on training set
Epoch 134:  clean acc 0.1315   certified acc 0.1065
Calculating metrics for L_infinity dist model on test set
Epoch 134:  clean acc 0.1323   certified acc 0.1068
scalar:  1.3781
Epoch 135:  train loss 0.4869   train acc 0.6151   worst 0.5170   lr 0.0292   p 9.27   eps 0.1952   mix 0.0453   time 24.73
scalar:  1.3774
Epoch 136:  train loss 0.4896   train acc 0.6126   worst 0.5150   lr 0.0292   p 9.31   eps 0.1952   mix 0.0452   time 25.17
scalar:  1.3932
Epoch 137:  train loss 0.4847   train acc 0.6169   worst 0.5195   lr 0.0292   p 9.34   eps 0.1952   mix 0.0451   time 25.50
scalar:  1.4396
Epoch 138:  train loss 0.4858   train acc 0.6156   worst 0.5178   lr 0.0292   p 9.38   eps 0.1952   mix 0.0450   time 25.30
scalar:  1.4032
Epoch 139:  train loss 0.4872   train acc 0.6160   worst 0.5146   lr 0.0292   p 9.42   eps 0.1952   mix 0.0448   time 24.62
Epoch 139:  test acc 0.6003   time 2.00
Calculating metrics for L_infinity dist model on training set
Epoch 139:  clean acc 0.1234   certified acc 0.1024
Calculating metrics for L_infinity dist model on test set
Epoch 139:  clean acc 0.1267   certified acc 0.1059
scalar:  1.4094
Epoch 140:  train loss 0.4878   train acc 0.6149   worst 0.5146   lr 0.0291   p 9.46   eps 0.1952   mix 0.0447   time 24.68
scalar:  1.442
Epoch 141:  train loss 0.4872   train acc 0.6159   worst 0.5150   lr 0.0291   p 9.50   eps 0.1952   mix 0.0446   time 25.25
scalar:  1.4799
Epoch 142:  train loss 0.4842   train acc 0.6185   worst 0.5166   lr 0.0291   p 9.54   eps 0.1952   mix 0.0445   time 25.65
scalar:  1.506
Epoch 143:  train loss 0.4853   train acc 0.6177   worst 0.5154   lr 0.0291   p 9.58   eps 0.1952   mix 0.0443   time 24.81
scalar:  1.5028
Epoch 144:  train loss 0.4836   train acc 0.6202   worst 0.5150   lr 0.0291   p 9.62   eps 0.1952   mix 0.0442   time 25.09
Epoch 144:  test acc 0.6022   time 2.05
Calculating metrics for L_infinity dist model on training set
Epoch 144:  clean acc 0.1147   certified acc 0.1021
Calculating metrics for L_infinity dist model on test set
Epoch 144:  clean acc 0.1168   certified acc 0.1022
scalar:  1.5059
Epoch 145:  train loss 0.4862   train acc 0.6179   worst 0.5121   lr 0.0291   p 9.66   eps 0.1952   mix 0.0441   time 24.68
scalar:  1.5423
Epoch 146:  train loss 0.4863   train acc 0.6165   worst 0.5125   lr 0.0291   p 9.70   eps 0.1952   mix 0.0440   time 25.51
scalar:  1.4847
Epoch 147:  train loss 0.4833   train acc 0.6214   worst 0.5133   lr 0.0291   p 9.75   eps 0.1952   mix 0.0438   time 25.32
scalar:  1.5538
Epoch 148:  train loss 0.4860   train acc 0.6171   worst 0.5115   lr 0.0291   p 9.79   eps 0.1952   mix 0.0437   time 25.14
scalar:  1.5606
Epoch 149:  train loss 0.4823   train acc 0.6219   worst 0.5126   lr 0.0290   p 9.83   eps 0.1952   mix 0.0436   time 25.13
Epoch 149:  test acc 0.6055   time 2.04
Calculating metrics for L_infinity dist model on training set
Epoch 149:  clean acc 0.1428   certified acc 0.1108
Calculating metrics for L_infinity dist model on test set
Epoch 149:  clean acc 0.1447   certified acc 0.1119
scalar:  1.5644
Epoch 150:  train loss 0.4813   train acc 0.6254   worst 0.5116   lr 0.0290   p 9.87   eps 0.1952   mix 0.0435   time 24.63
scalar:  1.569
Epoch 151:  train loss 0.4852   train acc 0.6211   worst 0.5092   lr 0.0290   p 9.91   eps 0.1952   mix 0.0433   time 25.37
scalar:  1.5668
Epoch 152:  train loss 0.4816   train acc 0.6226   worst 0.5128   lr 0.0290   p 9.95   eps 0.1952   mix 0.0432   time 24.73
scalar:  1.591
Epoch 153:  train loss 0.4798   train acc 0.6259   worst 0.5128   lr 0.0290   p 9.99   eps 0.1952   mix 0.0431   time 26.83
scalar:  1.6211
Epoch 154:  train loss 0.4850   train acc 0.6221   worst 0.5062   lr 0.0290   p 10.04   eps 0.1952   mix 0.0430   time 27.07
Epoch 154:  test acc 0.5980   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 154:  clean acc 0.1228   certified acc 0.0965
Calculating metrics for L_infinity dist model on test set
Epoch 154:  clean acc 0.1263   certified acc 0.1007
scalar:  1.6342
Epoch 155:  train loss 0.4847   train acc 0.6234   worst 0.5078   lr 0.0290   p 10.08   eps 0.1952   mix 0.0429   time 26.91
scalar:  1.6578
Epoch 156:  train loss 0.4801   train acc 0.6274   worst 0.5097   lr 0.0289   p 10.12   eps 0.1952   mix 0.0427   time 28.11
scalar:  1.6765
Epoch 157:  train loss 0.4823   train acc 0.6256   worst 0.5075   lr 0.0289   p 10.16   eps 0.1952   mix 0.0426   time 27.61
scalar:  1.6731
Epoch 158:  train loss 0.4832   train acc 0.6262   worst 0.5057   lr 0.0289   p 10.21   eps 0.1952   mix 0.0425   time 27.12
scalar:  1.7577
Epoch 159:  train loss 0.4820   train acc 0.6255   worst 0.5068   lr 0.0289   p 10.25   eps 0.1952   mix 0.0424   time 27.17
Epoch 159:  test acc 0.6003   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 159:  clean acc 0.1314   certified acc 0.1077
Calculating metrics for L_infinity dist model on test set
Epoch 159:  clean acc 0.1310   certified acc 0.1091
scalar:  1.7166
Epoch 160:  train loss 0.4819   train acc 0.6282   worst 0.5049   lr 0.0289   p 10.29   eps 0.1952   mix 0.0423   time 27.15
scalar:  1.7238
Epoch 161:  train loss 0.4826   train acc 0.6257   worst 0.5051   lr 0.0289   p 10.34   eps 0.1952   mix 0.0422   time 27.68
scalar:  1.7738
Epoch 162:  train loss 0.4813   train acc 0.6262   worst 0.5064   lr 0.0289   p 10.38   eps 0.1952   mix 0.0420   time 27.49
scalar:  1.7229
Epoch 163:  train loss 0.4836   train acc 0.6236   worst 0.5051   lr 0.0289   p 10.42   eps 0.1952   mix 0.0419   time 27.16
scalar:  1.7506
Epoch 164:  train loss 0.4830   train acc 0.6250   worst 0.5045   lr 0.0288   p 10.47   eps 0.1952   mix 0.0418   time 26.97
Epoch 164:  test acc 0.6046   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 164:  clean acc 0.1509   certified acc 0.1237
Calculating metrics for L_infinity dist model on test set
Epoch 164:  clean acc 0.1501   certified acc 0.1261
scalar:  1.7818
Epoch 165:  train loss 0.4834   train acc 0.6276   worst 0.5012   lr 0.0288   p 10.51   eps 0.1952   mix 0.0417   time 27.07
scalar:  1.7986
Epoch 166:  train loss 0.4837   train acc 0.6257   worst 0.5022   lr 0.0288   p 10.55   eps 0.1952   mix 0.0416   time 27.78
scalar:  1.7994
Epoch 167:  train loss 0.4796   train acc 0.6283   worst 0.5061   lr 0.0288   p 10.60   eps 0.1952   mix 0.0415   time 27.87
scalar:  1.8113
Epoch 168:  train loss 0.4798   train acc 0.6283   worst 0.5040   lr 0.0288   p 10.64   eps 0.1952   mix 0.0413   time 27.56
scalar:  1.8009
Epoch 169:  train loss 0.4834   train acc 0.6292   worst 0.4969   lr 0.0288   p 10.69   eps 0.1952   mix 0.0412   time 27.08
Epoch 169:  test acc 0.6047   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 169:  clean acc 0.1574   certified acc 0.1326
Calculating metrics for L_infinity dist model on test set
Epoch 169:  clean acc 0.1613   certified acc 0.1348
scalar:  1.8469
Epoch 170:  train loss 0.4801   train acc 0.6290   worst 0.5012   lr 0.0288   p 10.73   eps 0.1952   mix 0.0411   time 26.95
scalar:  1.8548
Epoch 171:  train loss 0.4807   train acc 0.6300   worst 0.4996   lr 0.0287   p 10.78   eps 0.1952   mix 0.0410   time 27.69
scalar:  1.9013
Epoch 172:  train loss 0.4792   train acc 0.6322   worst 0.4999   lr 0.0287   p 10.82   eps 0.1952   mix 0.0409   time 27.50
scalar:  1.9071
Epoch 173:  train loss 0.4796   train acc 0.6319   worst 0.5002   lr 0.0287   p 10.87   eps 0.1952   mix 0.0408   time 27.09
scalar:  1.9326
Epoch 174:  train loss 0.4809   train acc 0.6309   worst 0.4979   lr 0.0287   p 10.91   eps 0.1952   mix 0.0406   time 27.12
Epoch 174:  test acc 0.6001   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 174:  clean acc 0.1367   certified acc 0.1135
Calculating metrics for L_infinity dist model on test set
Epoch 174:  clean acc 0.1414   certified acc 0.1168
scalar:  1.9822
Epoch 175:  train loss 0.4783   train acc 0.6349   worst 0.4992   lr 0.0287   p 10.96   eps 0.1952   mix 0.0405   time 27.03
scalar:  1.9603
Epoch 176:  train loss 0.4812   train acc 0.6316   worst 0.4965   lr 0.0287   p 11.01   eps 0.1952   mix 0.0404   time 27.98
scalar:  1.9981
Epoch 177:  train loss 0.4807   train acc 0.6318   worst 0.4960   lr 0.0286   p 11.05   eps 0.1952   mix 0.0403   time 27.70
scalar:  1.9746
Epoch 178:  train loss 0.4809   train acc 0.6315   worst 0.4955   lr 0.0286   p 11.10   eps 0.1952   mix 0.0402   time 27.53
scalar:  1.9608
Epoch 179:  train loss 0.4781   train acc 0.6347   worst 0.4982   lr 0.0286   p 11.15   eps 0.1952   mix 0.0401   time 27.31
Epoch 179:  test acc 0.6043   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 179:  clean acc 0.1431   certified acc 0.1190
Calculating metrics for L_infinity dist model on test set
Epoch 179:  clean acc 0.1488   certified acc 0.1242
scalar:  1.9774
Epoch 180:  train loss 0.4803   train acc 0.6335   worst 0.4943   lr 0.0286   p 11.19   eps 0.1952   mix 0.0400   time 26.97
scalar:  1.995
Epoch 181:  train loss 0.4780   train acc 0.6356   worst 0.4964   lr 0.0286   p 11.24   eps 0.1952   mix 0.0399   time 28.03
scalar:  2.031
Epoch 182:  train loss 0.4831   train acc 0.6323   worst 0.4907   lr 0.0286   p 11.29   eps 0.1952   mix 0.0397   time 27.16
scalar:  2.0407
Epoch 183:  train loss 0.4795   train acc 0.6355   worst 0.4916   lr 0.0286   p 11.34   eps 0.1952   mix 0.0396   time 27.22
scalar:  2.0876
Epoch 184:  train loss 0.4784   train acc 0.6356   worst 0.4952   lr 0.0285   p 11.38   eps 0.1952   mix 0.0395   time 27.26
Epoch 184:  test acc 0.6021   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 184:  clean acc 0.1589   certified acc 0.1290
Calculating metrics for L_infinity dist model on test set
Epoch 184:  clean acc 0.1600   certified acc 0.1310
scalar:  2.1172
Epoch 185:  train loss 0.4833   train acc 0.6323   worst 0.4886   lr 0.0285   p 11.43   eps 0.1952   mix 0.0394   time 26.89
scalar:  2.0814
Epoch 186:  train loss 0.4815   train acc 0.6339   worst 0.4902   lr 0.0285   p 11.48   eps 0.1952   mix 0.0393   time 27.35
scalar:  2.0771
Epoch 187:  train loss 0.4804   train acc 0.6368   worst 0.4891   lr 0.0285   p 11.53   eps 0.1952   mix 0.0392   time 27.41
scalar:  2.12
Epoch 188:  train loss 0.4819   train acc 0.6343   worst 0.4881   lr 0.0285   p 11.58   eps 0.1952   mix 0.0391   time 27.17
scalar:  2.1037
Epoch 189:  train loss 0.4832   train acc 0.6317   worst 0.4875   lr 0.0285   p 11.62   eps 0.1952   mix 0.0390   time 27.20
Epoch 189:  test acc 0.6105   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 189:  clean acc 0.1227   certified acc 0.1031
Calculating metrics for L_infinity dist model on test set
Epoch 189:  clean acc 0.1249   certified acc 0.1036
scalar:  2.1159
Epoch 190:  train loss 0.4811   train acc 0.6345   worst 0.4896   lr 0.0284   p 11.67   eps 0.1952   mix 0.0389   time 27.01
scalar:  2.1394
Epoch 191:  train loss 0.4812   train acc 0.6361   worst 0.4863   lr 0.0284   p 11.72   eps 0.1952   mix 0.0388   time 27.93
scalar:  2.17
Epoch 192:  train loss 0.4825   train acc 0.6359   worst 0.4860   lr 0.0284   p 11.77   eps 0.1952   mix 0.0386   time 27.78
scalar:  2.1621
Epoch 193:  train loss 0.4815   train acc 0.6362   worst 0.4857   lr 0.0284   p 11.82   eps 0.1952   mix 0.0385   time 27.27
scalar:  2.1946
Epoch 194:  train loss 0.4799   train acc 0.6371   worst 0.4865   lr 0.0284   p 11.87   eps 0.1952   mix 0.0384   time 27.44
Epoch 194:  test acc 0.6113   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 194:  clean acc 0.1358   certified acc 0.1075
Calculating metrics for L_infinity dist model on test set
Epoch 194:  clean acc 0.1327   certified acc 0.1034
scalar:  2.1915
Epoch 195:  train loss 0.4813   train acc 0.6356   worst 0.4848   lr 0.0284   p 11.92   eps 0.1952   mix 0.0383   time 26.92
scalar:  2.2066
Epoch 196:  train loss 0.4820   train acc 0.6356   worst 0.4837   lr 0.0283   p 11.97   eps 0.1952   mix 0.0382   time 27.60
scalar:  2.2387
Epoch 197:  train loss 0.4849   train acc 0.6332   worst 0.4817   lr 0.0283   p 12.02   eps 0.1952   mix 0.0381   time 27.22
scalar:  2.2213
Epoch 198:  train loss 0.4797   train acc 0.6388   worst 0.4833   lr 0.0283   p 12.07   eps 0.1952   mix 0.0380   time 27.13
scalar:  2.3053
Epoch 199:  train loss 0.4827   train acc 0.6340   worst 0.4822   lr 0.0283   p 12.12   eps 0.1952   mix 0.0379   time 27.11
Epoch 199:  test acc 0.6083   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 199:  clean acc 0.1648   certified acc 0.1375
Calculating metrics for L_infinity dist model on test set
Epoch 199:  clean acc 0.1654   certified acc 0.1370
scalar:  2.2689
Epoch 200:  train loss 0.4826   train acc 0.6371   worst 0.4808   lr 0.0283   p 12.17   eps 0.1952   mix 0.0378   time 26.92
scalar:  2.2814
Epoch 201:  train loss 0.4824   train acc 0.6376   worst 0.4798   lr 0.0283   p 12.23   eps 0.1952   mix 0.0377   time 27.48
scalar:  2.3129
Epoch 202:  train loss 0.4828   train acc 0.6369   worst 0.4796   lr 0.0282   p 12.28   eps 0.1952   mix 0.0376   time 27.55
scalar:  2.3165
Epoch 203:  train loss 0.4814   train acc 0.6408   worst 0.4778   lr 0.0282   p 12.33   eps 0.1952   mix 0.0375   time 27.68
scalar:  2.3429
Epoch 204:  train loss 0.4843   train acc 0.6383   worst 0.4754   lr 0.0282   p 12.38   eps 0.1952   mix 0.0374   time 27.21
Epoch 204:  test acc 0.6030   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 204:  clean acc 0.1506   certified acc 0.1212
Calculating metrics for L_infinity dist model on test set
Epoch 204:  clean acc 0.1511   certified acc 0.1196
scalar:  2.3725
Epoch 205:  train loss 0.4835   train acc 0.6373   worst 0.4751   lr 0.0282   p 12.43   eps 0.1952   mix 0.0373   time 27.07
scalar:  2.3889
Epoch 206:  train loss 0.4839   train acc 0.6363   worst 0.4767   lr 0.0282   p 12.48   eps 0.1952   mix 0.0372   time 28.04
scalar:  2.3821
Epoch 207:  train loss 0.4804   train acc 0.6407   worst 0.4774   lr 0.0282   p 12.54   eps 0.1952   mix 0.0371   time 27.45
scalar:  2.4123
Epoch 208:  train loss 0.4818   train acc 0.6394   worst 0.4767   lr 0.0281   p 12.59   eps 0.1952   mix 0.0370   time 27.34
scalar:  2.427
Epoch 209:  train loss 0.4835   train acc 0.6401   worst 0.4719   lr 0.0281   p 12.64   eps 0.1952   mix 0.0369   time 27.44
Epoch 209:  test acc 0.6087   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 209:  clean acc 0.1537   certified acc 0.1250
Calculating metrics for L_infinity dist model on test set
Epoch 209:  clean acc 0.1557   certified acc 0.1268
scalar:  2.4147
Epoch 210:  train loss 0.4824   train acc 0.6408   worst 0.4718   lr 0.0281   p 12.70   eps 0.1952   mix 0.0367   time 27.36
scalar:  2.5068
Epoch 211:  train loss 0.4806   train acc 0.6434   worst 0.4753   lr 0.0281   p 12.75   eps 0.1952   mix 0.0366   time 27.85
scalar:  2.435
Epoch 212:  train loss 0.4831   train acc 0.6406   worst 0.4710   lr 0.0281   p 12.80   eps 0.1952   mix 0.0365   time 27.18
scalar:  2.501
Epoch 213:  train loss 0.4843   train acc 0.6394   worst 0.4693   lr 0.0281   p 12.86   eps 0.1952   mix 0.0364   time 27.08
scalar:  2.4777
Epoch 214:  train loss 0.4845   train acc 0.6398   worst 0.4685   lr 0.0280   p 12.91   eps 0.1952   mix 0.0363   time 26.95
Epoch 214:  test acc 0.6096   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 214:  clean acc 0.1454   certified acc 0.1164
Calculating metrics for L_infinity dist model on test set
Epoch 214:  clean acc 0.1448   certified acc 0.1156
scalar:  2.4966
Epoch 215:  train loss 0.4831   train acc 0.6405   worst 0.4706   lr 0.0280   p 12.97   eps 0.1952   mix 0.0362   time 27.03
scalar:  2.5188
Epoch 216:  train loss 0.4848   train acc 0.6378   worst 0.4683   lr 0.0280   p 13.02   eps 0.1952   mix 0.0361   time 27.85
scalar:  2.5673
Epoch 217:  train loss 0.4860   train acc 0.6378   worst 0.4667   lr 0.0280   p 13.07   eps 0.1952   mix 0.0360   time 27.17
scalar:  2.5354
Epoch 218:  train loss 0.4842   train acc 0.6406   worst 0.4677   lr 0.0280   p 13.13   eps 0.1952   mix 0.0359   time 27.27
scalar:  2.5216
Epoch 219:  train loss 0.4874   train acc 0.6387   worst 0.4660   lr 0.0279   p 13.18   eps 0.1952   mix 0.0358   time 27.40
Epoch 219:  test acc 0.6026   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 219:  clean acc 0.1516   certified acc 0.1203
Calculating metrics for L_infinity dist model on test set
Epoch 219:  clean acc 0.1586   certified acc 0.1276
scalar:  2.5384
Epoch 220:  train loss 0.4864   train acc 0.6399   worst 0.4632   lr 0.0279   p 13.24   eps 0.1952   mix 0.0357   time 27.03
scalar:  2.5682
Epoch 221:  train loss 0.4839   train acc 0.6419   worst 0.4653   lr 0.0279   p 13.30   eps 0.1952   mix 0.0356   time 27.94
scalar:  2.6254
Epoch 222:  train loss 0.4838   train acc 0.6424   worst 0.4654   lr 0.0279   p 13.35   eps 0.1952   mix 0.0355   time 27.49
scalar:  2.6221
Epoch 223:  train loss 0.4863   train acc 0.6401   worst 0.4614   lr 0.0279   p 13.41   eps 0.1952   mix 0.0354   time 27.27
scalar:  2.5943
Epoch 224:  train loss 0.4861   train acc 0.6412   worst 0.4613   lr 0.0279   p 13.46   eps 0.1952   mix 0.0353   time 27.10
Epoch 224:  test acc 0.6091   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 224:  clean acc 0.1619   certified acc 0.1220
Calculating metrics for L_infinity dist model on test set
Epoch 224:  clean acc 0.1643   certified acc 0.1211
scalar:  2.6307
Epoch 225:  train loss 0.4865   train acc 0.6386   worst 0.4623   lr 0.0278   p 13.52   eps 0.1952   mix 0.0352   time 26.92
scalar:  2.6261
Epoch 226:  train loss 0.4842   train acc 0.6403   worst 0.4645   lr 0.0278   p 13.58   eps 0.1952   mix 0.0351   time 27.97
scalar:  2.6434
Epoch 227:  train loss 0.4903   train acc 0.6388   worst 0.4558   lr 0.0278   p 13.64   eps 0.1952   mix 0.0350   time 27.12
scalar:  2.667
Epoch 228:  train loss 0.4893   train acc 0.6380   worst 0.4576   lr 0.0278   p 13.69   eps 0.1952   mix 0.0349   time 27.35
scalar:  2.6899
Epoch 229:  train loss 0.4878   train acc 0.6407   worst 0.4555   lr 0.0278   p 13.75   eps 0.1952   mix 0.0348   time 26.86
Epoch 229:  test acc 0.6112   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 229:  clean acc 0.1500   certified acc 0.1149
Calculating metrics for L_infinity dist model on test set
Epoch 229:  clean acc 0.1535   certified acc 0.1189
scalar:  2.7107
Epoch 230:  train loss 0.4873   train acc 0.6419   worst 0.4565   lr 0.0277   p 13.81   eps 0.1952   mix 0.0347   time 26.95
scalar:  2.6918
Epoch 231:  train loss 0.4858   train acc 0.6409   worst 0.4600   lr 0.0277   p 13.87   eps 0.1952   mix 0.0347   time 27.63
scalar:  2.7135
Epoch 232:  train loss 0.4882   train acc 0.6395   worst 0.4562   lr 0.0277   p 13.92   eps 0.1952   mix 0.0346   time 27.31
scalar:  2.7391
Epoch 233:  train loss 0.4872   train acc 0.6422   worst 0.4550   lr 0.0277   p 13.98   eps 0.1952   mix 0.0345   time 27.61
scalar:  2.7741
Epoch 234:  train loss 0.4872   train acc 0.6407   worst 0.4558   lr 0.0277   p 14.04   eps 0.1952   mix 0.0344   time 26.95
Epoch 234:  test acc 0.6083   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 234:  clean acc 0.1308   certified acc 0.1066
Calculating metrics for L_infinity dist model on test set
Epoch 234:  clean acc 0.1312   certified acc 0.1085
scalar:  2.7579
Epoch 235:  train loss 0.4866   train acc 0.6429   worst 0.4540   lr 0.0276   p 14.10   eps 0.1952   mix 0.0343   time 27.32
scalar:  2.8216
Epoch 236:  train loss 0.4887   train acc 0.6408   worst 0.4537   lr 0.0276   p 14.16   eps 0.1952   mix 0.0342   time 27.92
scalar:  2.8154
Epoch 237:  train loss 0.4882   train acc 0.6396   worst 0.4524   lr 0.0276   p 14.22   eps 0.1952   mix 0.0341   time 27.14
scalar:  2.7655
Epoch 238:  train loss 0.4892   train acc 0.6404   worst 0.4503   lr 0.0276   p 14.28   eps 0.1952   mix 0.0340   time 27.38
scalar:  2.84
Epoch 239:  train loss 0.4889   train acc 0.6410   worst 0.4509   lr 0.0276   p 14.34   eps 0.1952   mix 0.0339   time 27.11
Epoch 239:  test acc 0.6014   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 239:  clean acc 0.1448   certified acc 0.1139
Calculating metrics for L_infinity dist model on test set
Epoch 239:  clean acc 0.1489   certified acc 0.1151
scalar:  2.796
Epoch 240:  train loss 0.4855   train acc 0.6428   worst 0.4544   lr 0.0275   p 14.40   eps 0.1952   mix 0.0338   time 27.38
scalar:  2.8271
Epoch 241:  train loss 0.4887   train acc 0.6422   worst 0.4509   lr 0.0275   p 14.46   eps 0.1952   mix 0.0337   time 27.81
scalar:  2.8413
Epoch 242:  train loss 0.4879   train acc 0.6433   worst 0.4504   lr 0.0275   p 14.52   eps 0.1952   mix 0.0336   time 27.22
scalar:  2.8691
Epoch 243:  train loss 0.4895   train acc 0.6411   worst 0.4480   lr 0.0275   p 14.58   eps 0.1952   mix 0.0335   time 27.37
scalar:  2.8859
Epoch 244:  train loss 0.4887   train acc 0.6412   worst 0.4501   lr 0.0275   p 14.64   eps 0.1952   mix 0.0334   time 27.14
Epoch 244:  test acc 0.6080   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 244:  clean acc 0.1504   certified acc 0.1192
Calculating metrics for L_infinity dist model on test set
Epoch 244:  clean acc 0.1515   certified acc 0.1184
scalar:  2.8667
Epoch 245:  train loss 0.4876   train acc 0.6436   worst 0.4490   lr 0.0274   p 14.71   eps 0.1952   mix 0.0333   time 27.21
scalar:  2.9587
Epoch 246:  train loss 0.4896   train acc 0.6421   worst 0.4456   lr 0.0274   p 14.77   eps 0.1952   mix 0.0332   time 27.73
scalar:  2.9265
Epoch 247:  train loss 0.4892   train acc 0.6420   worst 0.4460   lr 0.0274   p 14.83   eps 0.1952   mix 0.0331   time 27.17
scalar:  2.9581
Epoch 248:  train loss 0.4899   train acc 0.6431   worst 0.4448   lr 0.0274   p 14.89   eps 0.1952   mix 0.0330   time 27.74
scalar:  2.9677
Epoch 249:  train loss 0.4911   train acc 0.6410   worst 0.4429   lr 0.0274   p 14.95   eps 0.1952   mix 0.0329   time 26.97
Epoch 249:  test acc 0.6099   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 249:  clean acc 0.1627   certified acc 0.1293
Calculating metrics for L_infinity dist model on test set
Epoch 249:  clean acc 0.1650   certified acc 0.1314
scalar:  2.9509
Epoch 250:  train loss 0.4890   train acc 0.6451   worst 0.4451   lr 0.0273   p 15.02   eps 0.1952   mix 0.0329   time 27.31
scalar:  2.951
Epoch 251:  train loss 0.4903   train acc 0.6439   worst 0.4422   lr 0.0273   p 15.08   eps 0.1952   mix 0.0328   time 27.88
scalar:  2.9767
Epoch 252:  train loss 0.4895   train acc 0.6448   worst 0.4418   lr 0.0273   p 15.14   eps 0.1952   mix 0.0327   time 27.32
scalar:  3.0146
Epoch 253:  train loss 0.4891   train acc 0.6443   worst 0.4427   lr 0.0273   p 15.21   eps 0.1952   mix 0.0326   time 27.41
scalar:  3.0
Epoch 254:  train loss 0.4889   train acc 0.6445   worst 0.4413   lr 0.0273   p 15.27   eps 0.1952   mix 0.0325   time 27.23
Epoch 254:  test acc 0.6019   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 254:  clean acc 0.1658   certified acc 0.1399
Calculating metrics for L_infinity dist model on test set
Epoch 254:  clean acc 0.1668   certified acc 0.1424
scalar:  3.0811
Epoch 255:  train loss 0.4924   train acc 0.6420   worst 0.4393   lr 0.0272   p 15.34   eps 0.1952   mix 0.0324   time 27.45
scalar:  3.0426
Epoch 256:  train loss 0.4907   train acc 0.6426   worst 0.4405   lr 0.0272   p 15.40   eps 0.1952   mix 0.0323   time 27.69
scalar:  3.085
Epoch 257:  train loss 0.4922   train acc 0.6430   worst 0.4368   lr 0.0272   p 15.47   eps 0.1952   mix 0.0322   time 27.11
scalar:  3.1035
Epoch 258:  train loss 0.4955   train acc 0.6404   worst 0.4346   lr 0.0272   p 15.53   eps 0.1952   mix 0.0321   time 27.79
scalar:  3.0677
Epoch 259:  train loss 0.4924   train acc 0.6450   worst 0.4337   lr 0.0272   p 15.60   eps 0.1952   mix 0.0320   time 27.17
Epoch 259:  test acc 0.6106   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 259:  clean acc 0.1533   certified acc 0.1296
Calculating metrics for L_infinity dist model on test set
Epoch 259:  clean acc 0.1564   certified acc 0.1315
scalar:  3.1325
Epoch 260:  train loss 0.4915   train acc 0.6438   worst 0.4388   lr 0.0271   p 15.66   eps 0.1952   mix 0.0320   time 27.20
scalar:  3.0961
Epoch 261:  train loss 0.4943   train acc 0.6435   worst 0.4326   lr 0.0271   p 15.73   eps 0.1952   mix 0.0319   time 27.69
scalar:  3.1161
Epoch 262:  train loss 0.4927   train acc 0.6446   worst 0.4335   lr 0.0271   p 15.79   eps 0.1952   mix 0.0318   time 27.38
scalar:  3.1196
Epoch 263:  train loss 0.4923   train acc 0.6441   worst 0.4349   lr 0.0271   p 15.86   eps 0.1952   mix 0.0317   time 27.51
scalar:  3.1312
Epoch 264:  train loss 0.4916   train acc 0.6466   worst 0.4315   lr 0.0270   p 15.93   eps 0.1952   mix 0.0316   time 27.11
Epoch 264:  test acc 0.6085   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 264:  clean acc 0.1702   certified acc 0.1357
Calculating metrics for L_infinity dist model on test set
Epoch 264:  clean acc 0.1678   certified acc 0.1339
scalar:  3.2062
Epoch 265:  train loss 0.4941   train acc 0.6425   worst 0.4324   lr 0.0270   p 15.99   eps 0.1952   mix 0.0315   time 27.42
scalar:  3.149
Epoch 266:  train loss 0.4911   train acc 0.6448   worst 0.4337   lr 0.0270   p 16.06   eps 0.1952   mix 0.0314   time 27.56
scalar:  3.224
Epoch 267:  train loss 0.4951   train acc 0.6430   worst 0.4275   lr 0.0270   p 16.13   eps 0.1952   mix 0.0313   time 27.20
scalar:  3.1991
Epoch 268:  train loss 0.4936   train acc 0.6449   worst 0.4318   lr 0.0270   p 16.20   eps 0.1952   mix 0.0312   time 27.28
scalar:  3.2289
Epoch 269:  train loss 0.4954   train acc 0.6415   worst 0.4275   lr 0.0269   p 16.26   eps 0.1952   mix 0.0312   time 27.41
Epoch 269:  test acc 0.6089   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 269:  clean acc 0.1731   certified acc 0.1421
Calculating metrics for L_infinity dist model on test set
Epoch 269:  clean acc 0.1680   certified acc 0.1421
scalar:  3.2146
Epoch 270:  train loss 0.4968   train acc 0.6405   worst 0.4273   lr 0.0269   p 16.33   eps 0.1952   mix 0.0311   time 27.31
scalar:  3.1941
Epoch 271:  train loss 0.4924   train acc 0.6441   worst 0.4296   lr 0.0269   p 16.40   eps 0.1952   mix 0.0310   time 27.61
scalar:  3.2735
Epoch 272:  train loss 0.4959   train acc 0.6413   worst 0.4266   lr 0.0269   p 16.47   eps 0.1952   mix 0.0309   time 27.76
scalar:  3.2601
Epoch 273:  train loss 0.4930   train acc 0.6464   worst 0.4278   lr 0.0269   p 16.54   eps 0.1952   mix 0.0308   time 27.31
scalar:  3.2909
Epoch 274:  train loss 0.4967   train acc 0.6424   worst 0.4236   lr 0.0268   p 16.61   eps 0.1952   mix 0.0307   time 27.36
Epoch 274:  test acc 0.5982   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 274:  clean acc 0.1750   certified acc 0.1373
Calculating metrics for L_infinity dist model on test set
Epoch 274:  clean acc 0.1743   certified acc 0.1358
scalar:  3.2737
Epoch 275:  train loss 0.4951   train acc 0.6454   worst 0.4238   lr 0.0268   p 16.68   eps 0.1952   mix 0.0306   time 27.47
scalar:  3.335
Epoch 276:  train loss 0.4976   train acc 0.6428   worst 0.4214   lr 0.0268   p 16.75   eps 0.1952   mix 0.0306   time 28.12
scalar:  3.2782
Epoch 277:  train loss 0.4961   train acc 0.6427   worst 0.4245   lr 0.0268   p 16.82   eps 0.1952   mix 0.0305   time 27.25
scalar:  3.257
Epoch 278:  train loss 0.4957   train acc 0.6437   worst 0.4242   lr 0.0267   p 16.89   eps 0.1952   mix 0.0304   time 27.26
scalar:  3.2533
Epoch 279:  train loss 0.4971   train acc 0.6443   worst 0.4222   lr 0.0267   p 16.96   eps 0.1952   mix 0.0303   time 27.35
Epoch 279:  test acc 0.6103   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 279:  clean acc 0.1733   certified acc 0.1388
Calculating metrics for L_infinity dist model on test set
Epoch 279:  clean acc 0.1741   certified acc 0.1360
scalar:  3.3303
Epoch 280:  train loss 0.4937   train acc 0.6454   worst 0.4245   lr 0.0267   p 17.03   eps 0.1952   mix 0.0302   time 27.26
scalar:  3.361
Epoch 281:  train loss 0.4954   train acc 0.6448   worst 0.4233   lr 0.0267   p 17.11   eps 0.1952   mix 0.0301   time 27.87
scalar:  3.3556
Epoch 282:  train loss 0.4973   train acc 0.6435   worst 0.4184   lr 0.0266   p 17.18   eps 0.1952   mix 0.0300   time 27.30
scalar:  3.3618
Epoch 283:  train loss 0.4965   train acc 0.6448   worst 0.4211   lr 0.0266   p 17.25   eps 0.1952   mix 0.0300   time 27.27
scalar:  3.3803
Epoch 284:  train loss 0.5001   train acc 0.6415   worst 0.4171   lr 0.0266   p 17.32   eps 0.1952   mix 0.0299   time 27.42
Epoch 284:  test acc 0.6008   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 284:  clean acc 0.1672   certified acc 0.1390
Calculating metrics for L_infinity dist model on test set
Epoch 284:  clean acc 0.1620   certified acc 0.1306
scalar:  3.3609
Epoch 285:  train loss 0.4979   train acc 0.6427   worst 0.4186   lr 0.0266   p 17.39   eps 0.1952   mix 0.0298   time 27.52
scalar:  3.3957
Epoch 286:  train loss 0.4984   train acc 0.6437   worst 0.4181   lr 0.0266   p 17.47   eps 0.1952   mix 0.0297   time 27.81
scalar:  3.4029
Epoch 287:  train loss 0.4974   train acc 0.6416   worst 0.4196   lr 0.0265   p 17.54   eps 0.1952   mix 0.0296   time 27.68
scalar:  3.4187
Epoch 288:  train loss 0.4976   train acc 0.6452   worst 0.4171   lr 0.0265   p 17.62   eps 0.1952   mix 0.0295   time 27.30
scalar:  3.4477
Epoch 289:  train loss 0.4982   train acc 0.6437   worst 0.4159   lr 0.0265   p 17.69   eps 0.1952   mix 0.0295   time 27.82
Epoch 289:  test acc 0.6042   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 289:  clean acc 0.1601   certified acc 0.1251
Calculating metrics for L_infinity dist model on test set
Epoch 289:  clean acc 0.1522   certified acc 0.1200
scalar:  3.4206
Epoch 290:  train loss 0.4990   train acc 0.6433   worst 0.4174   lr 0.0265   p 17.76   eps 0.1952   mix 0.0294   time 27.18
scalar:  3.4436
Epoch 291:  train loss 0.4984   train acc 0.6448   worst 0.4159   lr 0.0264   p 17.84   eps 0.1952   mix 0.0293   time 27.85
scalar:  3.461
Epoch 292:  train loss 0.4995   train acc 0.6438   worst 0.4124   lr 0.0264   p 17.91   eps 0.1952   mix 0.0292   time 27.41
scalar:  3.493
Epoch 293:  train loss 0.4967   train acc 0.6454   worst 0.4161   lr 0.0264   p 17.99   eps 0.1952   mix 0.0291   time 27.37
scalar:  3.5287
Epoch 294:  train loss 0.5006   train acc 0.6429   worst 0.4111   lr 0.0264   p 18.06   eps 0.1952   mix 0.0290   time 27.77
Epoch 294:  test acc 0.6046   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 294:  clean acc 0.1664   certified acc 0.1329
Calculating metrics for L_infinity dist model on test set
Epoch 294:  clean acc 0.1687   certified acc 0.1333
scalar:  3.4952
Epoch 295:  train loss 0.5020   train acc 0.6424   worst 0.4103   lr 0.0263   p 18.14   eps 0.1952   mix 0.0290   time 27.20
scalar:  3.5079
Epoch 296:  train loss 0.4980   train acc 0.6458   worst 0.4118   lr 0.0263   p 18.22   eps 0.1952   mix 0.0289   time 28.11
scalar:  3.5517
Epoch 297:  train loss 0.5008   train acc 0.6437   worst 0.4106   lr 0.0263   p 18.29   eps 0.1952   mix 0.0288   time 27.16
scalar:  3.5348
Epoch 298:  train loss 0.5028   train acc 0.6409   worst 0.4092   lr 0.0263   p 18.37   eps 0.1952   mix 0.0287   time 27.19
scalar:  3.5682
Epoch 299:  train loss 0.5027   train acc 0.6425   worst 0.4068   lr 0.0263   p 18.45   eps 0.1952   mix 0.0286   time 27.45
Epoch 299:  test acc 0.6052   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 299:  clean acc 0.1810   certified acc 0.1434
Calculating metrics for L_infinity dist model on test set
Epoch 299:  clean acc 0.1765   certified acc 0.1422
scalar:  3.5541
Epoch 300:  train loss 0.5012   train acc 0.6439   worst 0.4075   lr 0.0262   p 18.53   eps 0.1952   mix 0.0286   time 27.14
scalar:  3.5758
Epoch 301:  train loss 0.5006   train acc 0.6425   worst 0.4103   lr 0.0262   p 18.60   eps 0.1952   mix 0.0285   time 28.30
scalar:  3.5722
Epoch 302:  train loss 0.4991   train acc 0.6473   worst 0.4063   lr 0.0262   p 18.68   eps 0.1952   mix 0.0284   time 27.23
scalar:  3.6158
Epoch 303:  train loss 0.5027   train acc 0.6423   worst 0.4049   lr 0.0262   p 18.76   eps 0.1952   mix 0.0283   time 27.30
scalar:  3.6351
Epoch 304:  train loss 0.5016   train acc 0.6437   worst 0.4054   lr 0.0261   p 18.84   eps 0.1952   mix 0.0282   time 27.88
Epoch 304:  test acc 0.6147   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 304:  clean acc 0.1755   certified acc 0.1381
Calculating metrics for L_infinity dist model on test set
Epoch 304:  clean acc 0.1778   certified acc 0.1382
scalar:  3.6365
Epoch 305:  train loss 0.4997   train acc 0.6461   worst 0.4063   lr 0.0261   p 18.92   eps 0.1952   mix 0.0282   time 27.12
scalar:  3.6354
Epoch 306:  train loss 0.5026   train acc 0.6431   worst 0.4053   lr 0.0261   p 19.00   eps 0.1952   mix 0.0281   time 28.19
scalar:  3.6261
Epoch 307:  train loss 0.5009   train acc 0.6450   worst 0.4057   lr 0.0261   p 19.08   eps 0.1952   mix 0.0280   time 27.22
scalar:  3.6357
Epoch 308:  train loss 0.5037   train acc 0.6417   worst 0.4041   lr 0.0260   p 19.16   eps 0.1952   mix 0.0279   time 27.24
scalar:  3.6939
Epoch 309:  train loss 0.5042   train acc 0.6442   worst 0.4015   lr 0.0260   p 19.24   eps 0.1952   mix 0.0279   time 27.59
Epoch 309:  test acc 0.6059   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 309:  clean acc 0.1880   certified acc 0.1471
Calculating metrics for L_infinity dist model on test set
Epoch 309:  clean acc 0.1899   certified acc 0.1478
scalar:  3.6542
Epoch 310:  train loss 0.5025   train acc 0.6437   worst 0.4041   lr 0.0260   p 19.32   eps 0.1952   mix 0.0278   time 27.16
scalar:  3.7181
Epoch 311:  train loss 0.5034   train acc 0.6435   worst 0.4017   lr 0.0260   p 19.40   eps 0.1952   mix 0.0277   time 27.88
scalar:  3.7196
Epoch 312:  train loss 0.5035   train acc 0.6418   worst 0.4007   lr 0.0259   p 19.48   eps 0.1952   mix 0.0276   time 27.16
scalar:  3.7065
Epoch 313:  train loss 0.5034   train acc 0.6439   worst 0.3996   lr 0.0259   p 19.56   eps 0.1952   mix 0.0275   time 27.29
scalar:  3.7132
Epoch 314:  train loss 0.5037   train acc 0.6444   worst 0.4007   lr 0.0259   p 19.65   eps 0.1952   mix 0.0275   time 27.90
Epoch 314:  test acc 0.6031   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 314:  clean acc 0.1691   certified acc 0.1294
Calculating metrics for L_infinity dist model on test set
Epoch 314:  clean acc 0.1715   certified acc 0.1272
scalar:  3.737
Epoch 315:  train loss 0.5049   train acc 0.6419   worst 0.4006   lr 0.0259   p 19.73   eps 0.1952   mix 0.0274   time 27.15
scalar:  3.6631
Epoch 316:  train loss 0.5052   train acc 0.6434   worst 0.3965   lr 0.0258   p 19.81   eps 0.1952   mix 0.0273   time 28.15
scalar:  3.7591
Epoch 317:  train loss 0.5055   train acc 0.6437   worst 0.3959   lr 0.0258   p 19.90   eps 0.1952   mix 0.0272   time 27.26
scalar:  3.7802
Epoch 318:  train loss 0.5062   train acc 0.6415   worst 0.3969   lr 0.0258   p 19.98   eps 0.1952   mix 0.0272   time 27.18
scalar:  3.765
Epoch 319:  train loss 0.5053   train acc 0.6430   worst 0.3973   lr 0.0258   p 20.06   eps 0.1952   mix 0.0271   time 27.72
Epoch 319:  test acc 0.6047   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 319:  clean acc 0.1943   certified acc 0.1513
Calculating metrics for L_infinity dist model on test set
Epoch 319:  clean acc 0.1944   certified acc 0.1509
scalar:  3.7675
Epoch 320:  train loss 0.5059   train acc 0.6445   worst 0.3947   lr 0.0257   p 20.15   eps 0.1952   mix 0.0270   time 27.39
scalar:  3.8208
Epoch 321:  train loss 0.5064   train acc 0.6414   worst 0.3957   lr 0.0257   p 20.23   eps 0.1952   mix 0.0269   time 27.94
scalar:  3.783
Epoch 322:  train loss 0.5080   train acc 0.6380   worst 0.3937   lr 0.0257   p 20.32   eps 0.1952   mix 0.0269   time 27.82
scalar:  3.7328
Epoch 323:  train loss 0.5046   train acc 0.6419   worst 0.3958   lr 0.0257   p 20.40   eps 0.1952   mix 0.0268   time 27.39
scalar:  3.8094
Epoch 324:  train loss 0.5062   train acc 0.6426   worst 0.3944   lr 0.0256   p 20.49   eps 0.1952   mix 0.0267   time 27.39
Epoch 324:  test acc 0.6055   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 324:  clean acc 0.1870   certified acc 0.1476
Calculating metrics for L_infinity dist model on test set
Epoch 324:  clean acc 0.1856   certified acc 0.1424
scalar:  3.7972
Epoch 325:  train loss 0.5037   train acc 0.6445   worst 0.3939   lr 0.0256   p 20.58   eps 0.1952   mix 0.0266   time 27.52
scalar:  3.8551
Epoch 326:  train loss 0.5085   train acc 0.6410   worst 0.3909   lr 0.0256   p 20.66   eps 0.1952   mix 0.0266   time 27.77
scalar:  3.7934
Epoch 327:  train loss 0.5050   train acc 0.6424   worst 0.3936   lr 0.0256   p 20.75   eps 0.1952   mix 0.0265   time 27.90
scalar:  3.837
Epoch 328:  train loss 0.5062   train acc 0.6435   worst 0.3916   lr 0.0255   p 20.84   eps 0.1952   mix 0.0264   time 27.13
scalar:  3.847
Epoch 329:  train loss 0.5063   train acc 0.6425   worst 0.3930   lr 0.0255   p 20.92   eps 0.1952   mix 0.0263   time 27.54
Epoch 329:  test acc 0.6041   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 329:  clean acc 0.1887   certified acc 0.1432
Calculating metrics for L_infinity dist model on test set
Epoch 329:  clean acc 0.1923   certified acc 0.1460
scalar:  3.864
Epoch 330:  train loss 0.5065   train acc 0.6434   worst 0.3911   lr 0.0255   p 21.01   eps 0.1952   mix 0.0263   time 27.53
scalar:  3.8503
Epoch 331:  train loss 0.5083   train acc 0.6424   worst 0.3881   lr 0.0255   p 21.10   eps 0.1952   mix 0.0262   time 27.57
scalar:  3.8669
Epoch 332:  train loss 0.5081   train acc 0.6431   worst 0.3888   lr 0.0254   p 21.19   eps 0.1952   mix 0.0261   time 27.98
scalar:  3.9126
Epoch 333:  train loss 0.5079   train acc 0.6423   worst 0.3882   lr 0.0254   p 21.28   eps 0.1952   mix 0.0260   time 27.06
scalar:  3.8711
Epoch 334:  train loss 0.5092   train acc 0.6398   worst 0.3881   lr 0.0254   p 21.37   eps 0.1952   mix 0.0260   time 27.26
Epoch 334:  test acc 0.6035   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 334:  clean acc 0.2035   certified acc 0.1543
Calculating metrics for L_infinity dist model on test set
Epoch 334:  clean acc 0.2067   certified acc 0.1554
scalar:  3.8632
Epoch 335:  train loss 0.5068   train acc 0.6431   worst 0.3878   lr 0.0253   p 21.46   eps 0.1952   mix 0.0259   time 27.40
scalar:  3.9355
Epoch 336:  train loss 0.5066   train acc 0.6428   worst 0.3896   lr 0.0253   p 21.55   eps 0.1952   mix 0.0258   time 27.74
scalar:  3.8998
Epoch 337:  train loss 0.5082   train acc 0.6429   worst 0.3866   lr 0.0253   p 21.64   eps 0.1952   mix 0.0258   time 27.96
scalar:  3.8987
Epoch 338:  train loss 0.5102   train acc 0.6409   worst 0.3871   lr 0.0253   p 21.73   eps 0.1952   mix 0.0257   time 27.34
scalar:  3.9319
Epoch 339:  train loss 0.5106   train acc 0.6415   worst 0.3845   lr 0.0252   p 21.82   eps 0.1952   mix 0.0256   time 27.28
Epoch 339:  test acc 0.6052   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 339:  clean acc 0.1897   certified acc 0.1408
Calculating metrics for L_infinity dist model on test set
Epoch 339:  clean acc 0.1877   certified acc 0.1393
scalar:  3.9547
Epoch 340:  train loss 0.5108   train acc 0.6421   worst 0.3837   lr 0.0252   p 21.91   eps 0.1952   mix 0.0255   time 27.17
scalar:  3.9157
Epoch 341:  train loss 0.5122   train acc 0.6418   worst 0.3812   lr 0.0252   p 22.01   eps 0.1952   mix 0.0255   time 27.53
scalar:  3.9745
Epoch 342:  train loss 0.5088   train acc 0.6429   worst 0.3850   lr 0.0252   p 22.10   eps 0.1952   mix 0.0254   time 27.95
scalar:  3.983
Epoch 343:  train loss 0.5087   train acc 0.6434   worst 0.3840   lr 0.0251   p 22.19   eps 0.1952   mix 0.0253   time 27.07
scalar:  3.9785
Epoch 344:  train loss 0.5098   train acc 0.6424   worst 0.3857   lr 0.0251   p 22.28   eps 0.1952   mix 0.0253   time 27.37
Epoch 344:  test acc 0.6008   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 344:  clean acc 0.1924   certified acc 0.1445
Calculating metrics for L_infinity dist model on test set
Epoch 344:  clean acc 0.1870   certified acc 0.1388
scalar:  4.0254
Epoch 345:  train loss 0.5112   train acc 0.6409   worst 0.3830   lr 0.0251   p 22.38   eps 0.1952   mix 0.0252   time 27.24
scalar:  4.003
Epoch 346:  train loss 0.5109   train acc 0.6412   worst 0.3810   lr 0.0251   p 22.47   eps 0.1952   mix 0.0251   time 27.91
scalar:  3.9543
Epoch 347:  train loss 0.5081   train acc 0.6433   worst 0.3841   lr 0.0250   p 22.57   eps 0.1952   mix 0.0250   time 27.75
scalar:  4.0399
Epoch 348:  train loss 0.5088   train acc 0.6431   worst 0.3827   lr 0.0250   p 22.66   eps 0.1952   mix 0.0250   time 27.34
scalar:  4.0685
Epoch 349:  train loss 0.5095   train acc 0.6409   worst 0.3822   lr 0.0250   p 22.76   eps 0.1952   mix 0.0249   time 27.21
Epoch 349:  test acc 0.5987   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 349:  clean acc 0.2133   certified acc 0.1639
Calculating metrics for L_infinity dist model on test set
Epoch 349:  clean acc 0.2161   certified acc 0.1628
scalar:  4.0386
Epoch 350:  train loss 0.5113   train acc 0.6402   worst 0.3809   lr 0.0249   p 22.85   eps 0.1952   mix 0.0248   time 27.06
scalar:  4.0668
Epoch 351:  train loss 0.5085   train acc 0.6447   worst 0.3800   lr 0.0249   p 22.95   eps 0.1952   mix 0.0248   time 27.54
scalar:  4.0504
Epoch 352:  train loss 0.5123   train acc 0.6403   worst 0.3778   lr 0.0249   p 23.05   eps 0.1952   mix 0.0247   time 28.36
scalar:  4.0931
Epoch 353:  train loss 0.5122   train acc 0.6405   worst 0.3791   lr 0.0249   p 23.14   eps 0.1952   mix 0.0246   time 27.32
scalar:  4.0781
Epoch 354:  train loss 0.5096   train acc 0.6435   worst 0.3777   lr 0.0248   p 23.24   eps 0.1952   mix 0.0246   time 27.23
Epoch 354:  test acc 0.6002   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 354:  clean acc 0.2248   certified acc 0.1736
Calculating metrics for L_infinity dist model on test set
Epoch 354:  clean acc 0.2287   certified acc 0.1750
scalar:  4.13
Epoch 355:  train loss 0.5121   train acc 0.6411   worst 0.3786   lr 0.0248   p 23.34   eps 0.1952   mix 0.0245   time 27.51
scalar:  4.0955
Epoch 356:  train loss 0.5113   train acc 0.6426   worst 0.3774   lr 0.0248   p 23.44   eps 0.1952   mix 0.0244   time 27.85
scalar:  4.1157
Epoch 357:  train loss 0.5093   train acc 0.6451   worst 0.3787   lr 0.0248   p 23.53   eps 0.1952   mix 0.0244   time 27.83
scalar:  4.1654
Epoch 358:  train loss 0.5100   train acc 0.6437   worst 0.3760   lr 0.0247   p 23.63   eps 0.1952   mix 0.0243   time 27.07
scalar:  4.1719
Epoch 359:  train loss 0.5122   train acc 0.6424   worst 0.3757   lr 0.0247   p 23.73   eps 0.1952   mix 0.0242   time 27.16
Epoch 359:  test acc 0.6053   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 359:  clean acc 0.2089   certified acc 0.1618
Calculating metrics for L_infinity dist model on test set
Epoch 359:  clean acc 0.2156   certified acc 0.1645
scalar:  4.144
Epoch 360:  train loss 0.5108   train acc 0.6429   worst 0.3773   lr 0.0247   p 23.83   eps 0.1952   mix 0.0241   time 27.35
scalar:  4.1594
Epoch 361:  train loss 0.5118   train acc 0.6448   worst 0.3751   lr 0.0246   p 23.93   eps 0.1952   mix 0.0241   time 27.78
scalar:  4.1885
Epoch 362:  train loss 0.5120   train acc 0.6423   worst 0.3735   lr 0.0246   p 24.03   eps 0.1952   mix 0.0240   time 28.10
scalar:  4.179
Epoch 363:  train loss 0.5153   train acc 0.6404   worst 0.3704   lr 0.0246   p 24.13   eps 0.1952   mix 0.0239   time 27.19
scalar:  4.1604
Epoch 364:  train loss 0.5130   train acc 0.6432   worst 0.3713   lr 0.0246   p 24.24   eps 0.1952   mix 0.0239   time 27.34
Epoch 364:  test acc 0.6028   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 364:  clean acc 0.2204   certified acc 0.1698
Calculating metrics for L_infinity dist model on test set
Epoch 364:  clean acc 0.2227   certified acc 0.1733
scalar:  4.2419
Epoch 365:  train loss 0.5129   train acc 0.6407   worst 0.3732   lr 0.0245   p 24.34   eps 0.1952   mix 0.0238   time 27.25
scalar:  4.2017
Epoch 366:  train loss 0.5127   train acc 0.6409   worst 0.3724   lr 0.0245   p 24.44   eps 0.1952   mix 0.0237   time 27.43
scalar:  4.1745
Epoch 367:  train loss 0.5138   train acc 0.6431   worst 0.3710   lr 0.0245   p 24.54   eps 0.1952   mix 0.0237   time 28.08
scalar:  4.2078
Epoch 368:  train loss 0.5129   train acc 0.6431   worst 0.3704   lr 0.0244   p 24.65   eps 0.1952   mix 0.0236   time 27.51
scalar:  4.2577
Epoch 369:  train loss 0.5121   train acc 0.6433   worst 0.3733   lr 0.0244   p 24.75   eps 0.1952   mix 0.0235   time 27.22
Epoch 369:  test acc 0.5980   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 369:  clean acc 0.2303   certified acc 0.1682
Calculating metrics for L_infinity dist model on test set
Epoch 369:  clean acc 0.2319   certified acc 0.1693
scalar:  4.2515
Epoch 370:  train loss 0.5138   train acc 0.6418   worst 0.3700   lr 0.0244   p 24.85   eps 0.1952   mix 0.0235   time 28.38
scalar:  4.2415
Epoch 371:  train loss 0.5142   train acc 0.6423   worst 0.3708   lr 0.0244   p 24.96   eps 0.1952   mix 0.0234   time 27.66
scalar:  4.2569
Epoch 372:  train loss 0.5141   train acc 0.6409   worst 0.3709   lr 0.0243   p 25.06   eps 0.1952   mix 0.0234   time 27.92
scalar:  4.2444
Epoch 373:  train loss 0.5137   train acc 0.6428   worst 0.3706   lr 0.0243   p 25.17   eps 0.1952   mix 0.0233   time 27.55
scalar:  4.2487
Epoch 374:  train loss 0.5134   train acc 0.6430   worst 0.3692   lr 0.0243   p 25.28   eps 0.1952   mix 0.0232   time 27.29
Epoch 374:  test acc 0.5982   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 374:  clean acc 0.2301   certified acc 0.1766
Calculating metrics for L_infinity dist model on test set
Epoch 374:  clean acc 0.2359   certified acc 0.1825
scalar:  4.2472
Epoch 375:  train loss 0.5158   train acc 0.6413   worst 0.3669   lr 0.0243   p 25.38   eps 0.1952   mix 0.0232   time 27.86
scalar:  4.2652
Epoch 376:  train loss 0.5135   train acc 0.6414   worst 0.3701   lr 0.0242   p 25.49   eps 0.1952   mix 0.0231   time 28.01
scalar:  4.2845
Epoch 377:  train loss 0.5139   train acc 0.6421   worst 0.3698   lr 0.0242   p 25.60   eps 0.1952   mix 0.0230   time 27.56
scalar:  4.2741
Epoch 378:  train loss 0.5134   train acc 0.6439   worst 0.3678   lr 0.0242   p 25.70   eps 0.1952   mix 0.0230   time 27.47
scalar:  4.3507
Epoch 379:  train loss 0.5167   train acc 0.6409   worst 0.3653   lr 0.0241   p 25.81   eps 0.1952   mix 0.0229   time 27.20
Epoch 379:  test acc 0.6009   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 379:  clean acc 0.2214   certified acc 0.1716
Calculating metrics for L_infinity dist model on test set
Epoch 379:  clean acc 0.2236   certified acc 0.1710
scalar:  4.2925
Epoch 380:  train loss 0.5136   train acc 0.6404   worst 0.3686   lr 0.0241   p 25.92   eps 0.1952   mix 0.0228   time 28.20
scalar:  4.2781
Epoch 381:  train loss 0.5126   train acc 0.6427   worst 0.3688   lr 0.0241   p 26.03   eps 0.1952   mix 0.0228   time 27.70
scalar:  4.2772
Epoch 382:  train loss 0.5165   train acc 0.6408   worst 0.3656   lr 0.0240   p 26.14   eps 0.1952   mix 0.0227   time 27.53
scalar:  4.3529
Epoch 383:  train loss 0.5159   train acc 0.6406   worst 0.3634   lr 0.0240   p 26.25   eps 0.1952   mix 0.0226   time 27.24
scalar:  4.3846
Epoch 384:  train loss 0.5137   train acc 0.6434   worst 0.3660   lr 0.0240   p 26.36   eps 0.1952   mix 0.0226   time 27.08
Epoch 384:  test acc 0.5983   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 384:  clean acc 0.2368   certified acc 0.1787
Calculating metrics for L_infinity dist model on test set
Epoch 384:  clean acc 0.2392   certified acc 0.1794
scalar:  4.3838
Epoch 385:  train loss 0.5146   train acc 0.6438   worst 0.3645   lr 0.0240   p 26.47   eps 0.1952   mix 0.0225   time 27.54
scalar:  4.3929
Epoch 386:  train loss 0.5153   train acc 0.6417   worst 0.3640   lr 0.0239   p 26.58   eps 0.1952   mix 0.0225   time 27.47
scalar:  4.3804
Epoch 387:  train loss 0.5150   train acc 0.6442   worst 0.3623   lr 0.0239   p 26.69   eps 0.1952   mix 0.0224   time 27.89
scalar:  4.4056
Epoch 388:  train loss 0.5158   train acc 0.6426   worst 0.3631   lr 0.0239   p 26.81   eps 0.1952   mix 0.0223   time 27.47
scalar:  4.3986
Epoch 389:  train loss 0.5159   train acc 0.6406   worst 0.3636   lr 0.0238   p 26.92   eps 0.1952   mix 0.0223   time 27.37
Epoch 389:  test acc 0.6005   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 389:  clean acc 0.2314   certified acc 0.1808
Calculating metrics for L_infinity dist model on test set
Epoch 389:  clean acc 0.2317   certified acc 0.1801
scalar:  4.4116
Epoch 390:  train loss 0.5146   train acc 0.6418   worst 0.3619   lr 0.0238   p 27.03   eps 0.1952   mix 0.0222   time 27.74
scalar:  4.4237
Epoch 391:  train loss 0.5133   train acc 0.6432   worst 0.3636   lr 0.0238   p 27.15   eps 0.1952   mix 0.0221   time 27.74
scalar:  4.4063
Epoch 392:  train loss 0.5171   train acc 0.6411   worst 0.3610   lr 0.0238   p 27.26   eps 0.1952   mix 0.0221   time 27.57
scalar:  4.3957
Epoch 393:  train loss 0.5158   train acc 0.6402   worst 0.3627   lr 0.0237   p 27.37   eps 0.1952   mix 0.0220   time 27.47
scalar:  4.4232
Epoch 394:  train loss 0.5161   train acc 0.6415   worst 0.3605   lr 0.0237   p 27.49   eps 0.1952   mix 0.0220   time 27.23
Epoch 394:  test acc 0.6007   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 394:  clean acc 0.2528   certified acc 0.1943
Calculating metrics for L_infinity dist model on test set
Epoch 394:  clean acc 0.2517   certified acc 0.1903
scalar:  4.4408
Epoch 395:  train loss 0.5156   train acc 0.6421   worst 0.3635   lr 0.0237   p 27.61   eps 0.1952   mix 0.0219   time 27.74
scalar:  4.4161
Epoch 396:  train loss 0.5184   train acc 0.6404   worst 0.3585   lr 0.0236   p 27.72   eps 0.1952   mix 0.0218   time 27.51
scalar:  4.4552
Epoch 397:  train loss 0.5166   train acc 0.6401   worst 0.3607   lr 0.0236   p 27.84   eps 0.1952   mix 0.0218   time 27.64
scalar:  4.451
Epoch 398:  train loss 0.5168   train acc 0.6424   worst 0.3605   lr 0.0236   p 27.96   eps 0.1952   mix 0.0217   time 27.55
scalar:  4.4515
Epoch 399:  train loss 0.5167   train acc 0.6425   worst 0.3609   lr 0.0236   p 28.07   eps 0.1952   mix 0.0217   time 27.10
Epoch 399:  test acc 0.6058   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 399:  clean acc 0.2324   certified acc 0.1809
Calculating metrics for L_infinity dist model on test set
Epoch 399:  clean acc 0.2325   certified acc 0.1830
scalar:  4.4779
Epoch 400:  train loss 0.5164   train acc 0.6426   worst 0.3599   lr 0.0235   p 28.19   eps 0.1952   mix 0.0216   time 27.38
scalar:  4.4889
Epoch 401:  train loss 0.5191   train acc 0.6409   worst 0.3560   lr 0.0235   p 28.31   eps 0.1952   mix 0.0215   time 27.97
scalar:  4.4793
Epoch 402:  train loss 0.5201   train acc 0.6420   worst 0.3555   lr 0.0235   p 28.43   eps 0.1952   mix 0.0215   time 27.68
scalar:  4.4994
Epoch 403:  train loss 0.5161   train acc 0.6448   worst 0.3575   lr 0.0234   p 28.55   eps 0.1952   mix 0.0214   time 27.52
scalar:  4.5142
Epoch 404:  train loss 0.5180   train acc 0.6408   worst 0.3562   lr 0.0234   p 28.67   eps 0.1952   mix 0.0214   time 27.40
Epoch 404:  test acc 0.5993   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 404:  clean acc 0.2481   certified acc 0.1892
Calculating metrics for L_infinity dist model on test set
Epoch 404:  clean acc 0.2564   certified acc 0.1932
scalar:  4.4982
Epoch 405:  train loss 0.5186   train acc 0.6396   worst 0.3561   lr 0.0234   p 28.79   eps 0.1952   mix 0.0213   time 27.27
scalar:  4.4869
Epoch 406:  train loss 0.5175   train acc 0.6410   worst 0.3584   lr 0.0233   p 28.91   eps 0.1952   mix 0.0212   time 28.09
scalar:  4.5212
Epoch 407:  train loss 0.5175   train acc 0.6429   worst 0.3552   lr 0.0233   p 29.03   eps 0.1952   mix 0.0212   time 27.59
scalar:  4.5368
Epoch 408:  train loss 0.5156   train acc 0.6436   worst 0.3570   lr 0.0233   p 29.15   eps 0.1952   mix 0.0211   time 27.60
scalar:  4.531
Epoch 409:  train loss 0.5158   train acc 0.6449   worst 0.3575   lr 0.0233   p 29.28   eps 0.1952   mix 0.0211   time 27.17
Epoch 409:  test acc 0.6038   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 409:  clean acc 0.2485   certified acc 0.1913
Calculating metrics for L_infinity dist model on test set
Epoch 409:  clean acc 0.2472   certified acc 0.1929
scalar:  4.605
Epoch 410:  train loss 0.5177   train acc 0.6406   worst 0.3548   lr 0.0232   p 29.40   eps 0.1952   mix 0.0210   time 27.38
scalar:  4.5656
Epoch 411:  train loss 0.5163   train acc 0.6436   worst 0.3557   lr 0.0232   p 29.52   eps 0.1952   mix 0.0209   time 28.27
scalar:  4.5734
Epoch 412:  train loss 0.5173   train acc 0.6429   worst 0.3571   lr 0.0232   p 29.65   eps 0.1952   mix 0.0209   time 27.62
scalar:  4.6055
Epoch 413:  train loss 0.5186   train acc 0.6420   worst 0.3522   lr 0.0231   p 29.77   eps 0.1952   mix 0.0208   time 27.71
scalar:  4.585
Epoch 414:  train loss 0.5182   train acc 0.6418   worst 0.3537   lr 0.0231   p 29.90   eps 0.1952   mix 0.0208   time 27.28
Epoch 414:  test acc 0.5924   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 414:  clean acc 0.2512   certified acc 0.1996
Calculating metrics for L_infinity dist model on test set
Epoch 414:  clean acc 0.2539   certified acc 0.2007
scalar:  4.5311
Epoch 415:  train loss 0.5195   train acc 0.6384   worst 0.3531   lr 0.0231   p 30.02   eps 0.1952   mix 0.0207   time 27.52
scalar:  4.5115
Epoch 416:  train loss 0.5171   train acc 0.6422   worst 0.3556   lr 0.0230   p 30.15   eps 0.1952   mix 0.0206   time 28.24
scalar:  4.5358
Epoch 417:  train loss 0.5177   train acc 0.6417   worst 0.3544   lr 0.0230   p 30.28   eps 0.1952   mix 0.0206   time 27.78
scalar:  4.574
Epoch 418:  train loss 0.5181   train acc 0.6433   worst 0.3520   lr 0.0230   p 30.40   eps 0.1952   mix 0.0205   time 27.38
scalar:  4.5921
Epoch 419:  train loss 0.5205   train acc 0.6406   worst 0.3506   lr 0.0229   p 30.53   eps 0.1952   mix 0.0205   time 27.30
Epoch 419:  test acc 0.6001   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 419:  clean acc 0.2873   certified acc 0.2264
Calculating metrics for L_infinity dist model on test set
Epoch 419:  clean acc 0.2970   certified acc 0.2340
scalar:  4.5754
Epoch 420:  train loss 0.5165   train acc 0.6419   worst 0.3560   lr 0.0229   p 30.66   eps 0.1952   mix 0.0204   time 27.19
scalar:  4.5861
Epoch 421:  train loss 0.5191   train acc 0.6409   worst 0.3521   lr 0.0229   p 30.79   eps 0.1952   mix 0.0204   time 28.13
scalar:  4.617
Epoch 422:  train loss 0.5169   train acc 0.6443   worst 0.3525   lr 0.0229   p 30.92   eps 0.1952   mix 0.0203   time 27.53
scalar:  4.6619
Epoch 423:  train loss 0.5176   train acc 0.6429   worst 0.3515   lr 0.0228   p 31.05   eps 0.1952   mix 0.0202   time 27.52
scalar:  4.652
Epoch 424:  train loss 0.5176   train acc 0.6434   worst 0.3518   lr 0.0228   p 31.18   eps 0.1952   mix 0.0202   time 27.34
Epoch 424:  test acc 0.5975   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 424:  clean acc 0.2755   certified acc 0.2117
Calculating metrics for L_infinity dist model on test set
Epoch 424:  clean acc 0.2734   certified acc 0.2111
scalar:  4.6613
Epoch 425:  train loss 0.5192   train acc 0.6417   worst 0.3492   lr 0.0228   p 31.31   eps 0.1952   mix 0.0201   time 27.28
scalar:  4.6512
Epoch 426:  train loss 0.5171   train acc 0.6423   worst 0.3528   lr 0.0227   p 31.44   eps 0.1952   mix 0.0201   time 28.45
scalar:  4.6684
Epoch 427:  train loss 0.5182   train acc 0.6447   worst 0.3495   lr 0.0227   p 31.57   eps 0.1952   mix 0.0200   time 27.80
scalar:  4.7286
Epoch 428:  train loss 0.5168   train acc 0.6438   worst 0.3535   lr 0.0227   p 31.71   eps 0.1952   mix 0.0200   time 27.42
scalar:  4.6833
Epoch 429:  train loss 0.5169   train acc 0.6434   worst 0.3515   lr 0.0226   p 31.84   eps 0.1952   mix 0.0199   time 27.46
Epoch 429:  test acc 0.6083   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 429:  clean acc 0.2367   certified acc 0.1870
Calculating metrics for L_infinity dist model on test set
Epoch 429:  clean acc 0.2359   certified acc 0.1821
scalar:  4.6837
Epoch 430:  train loss 0.5191   train acc 0.6427   worst 0.3486   lr 0.0226   p 31.98   eps 0.1952   mix 0.0199   time 27.08
scalar:  4.724
Epoch 431:  train loss 0.5196   train acc 0.6421   worst 0.3469   lr 0.0226   p 32.11   eps 0.1952   mix 0.0198   time 27.88
scalar:  4.7194
Epoch 432:  train loss 0.5175   train acc 0.6433   worst 0.3514   lr 0.0225   p 32.24   eps 0.1952   mix 0.0197   time 28.19
scalar:  4.6973
Epoch 433:  train loss 0.5208   train acc 0.6408   worst 0.3474   lr 0.0225   p 32.38   eps 0.1952   mix 0.0197   time 27.56
scalar:  4.7014
Epoch 434:  train loss 0.5196   train acc 0.6415   worst 0.3466   lr 0.0225   p 32.52   eps 0.1952   mix 0.0196   time 27.51
Epoch 434:  test acc 0.6036   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 434:  clean acc 0.2785   certified acc 0.2119
Calculating metrics for L_infinity dist model on test set
Epoch 434:  clean acc 0.2796   certified acc 0.2082
scalar:  4.7005
Epoch 435:  train loss 0.5194   train acc 0.6419   worst 0.3470   lr 0.0224   p 32.65   eps 0.1952   mix 0.0196   time 27.22
scalar:  4.7266
Epoch 436:  train loss 0.5171   train acc 0.6432   worst 0.3485   lr 0.0224   p 32.79   eps 0.1952   mix 0.0195   time 27.28
scalar:  4.7347
Epoch 437:  train loss 0.5193   train acc 0.6434   worst 0.3473   lr 0.0224   p 32.93   eps 0.1952   mix 0.0195   time 28.37
scalar:  4.7668
Epoch 438:  train loss 0.5210   train acc 0.6408   worst 0.3452   lr 0.0224   p 33.07   eps 0.1952   mix 0.0194   time 27.11
scalar:  4.7497
Epoch 439:  train loss 0.5215   train acc 0.6408   worst 0.3465   lr 0.0223   p 33.21   eps 0.1952   mix 0.0194   time 27.36
Epoch 439:  test acc 0.5999   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 439:  clean acc 0.2765   certified acc 0.2145
Calculating metrics for L_infinity dist model on test set
Epoch 439:  clean acc 0.2819   certified acc 0.2163
scalar:  4.7403
Epoch 440:  train loss 0.5203   train acc 0.6424   worst 0.3458   lr 0.0223   p 33.35   eps 0.1952   mix 0.0193   time 27.16
scalar:  4.7752
Epoch 441:  train loss 0.5187   train acc 0.6428   worst 0.3476   lr 0.0223   p 33.49   eps 0.1952   mix 0.0193   time 27.38
scalar:  4.7502
Epoch 442:  train loss 0.5203   train acc 0.6425   worst 0.3440   lr 0.0222   p 33.63   eps 0.1952   mix 0.0192   time 28.39
scalar:  4.7739
Epoch 443:  train loss 0.5209   train acc 0.6390   worst 0.3464   lr 0.0222   p 33.77   eps 0.1952   mix 0.0191   time 27.39
scalar:  4.7626
Epoch 444:  train loss 0.5181   train acc 0.6434   worst 0.3457   lr 0.0222   p 33.91   eps 0.1952   mix 0.0191   time 27.28
Epoch 444:  test acc 0.5951   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 444:  clean acc 0.2629   certified acc 0.2010
Calculating metrics for L_infinity dist model on test set
Epoch 444:  clean acc 0.2655   certified acc 0.2057
scalar:  4.8072
Epoch 445:  train loss 0.5196   train acc 0.6429   worst 0.3468   lr 0.0221   p 34.05   eps 0.1952   mix 0.0190   time 27.32
scalar:  4.8045
Epoch 446:  train loss 0.5173   train acc 0.6424   worst 0.3496   lr 0.0221   p 34.20   eps 0.1952   mix 0.0190   time 27.60
scalar:  4.7867
Epoch 447:  train loss 0.5200   train acc 0.6416   worst 0.3440   lr 0.0221   p 34.34   eps 0.1952   mix 0.0189   time 28.27
scalar:  4.7721
Epoch 448:  train loss 0.5208   train acc 0.6404   worst 0.3421   lr 0.0220   p 34.49   eps 0.1952   mix 0.0189   time 27.55
scalar:  4.787
Epoch 449:  train loss 0.5197   train acc 0.6418   worst 0.3447   lr 0.0220   p 34.63   eps 0.1952   mix 0.0188   time 27.49
Epoch 449:  test acc 0.6010   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 449:  clean acc 0.2644   certified acc 0.2067
Calculating metrics for L_infinity dist model on test set
Epoch 449:  clean acc 0.2671   certified acc 0.2087
scalar:  4.7501
Epoch 450:  train loss 0.5180   train acc 0.6437   worst 0.3463   lr 0.0220   p 34.78   eps 0.1952   mix 0.0188   time 27.10
scalar:  4.8133
Epoch 451:  train loss 0.5206   train acc 0.6431   worst 0.3416   lr 0.0219   p 34.92   eps 0.1952   mix 0.0187   time 27.82
scalar:  4.8463
Epoch 452:  train loss 0.5216   train acc 0.6412   worst 0.3421   lr 0.0219   p 35.07   eps 0.1952   mix 0.0187   time 28.09
scalar:  4.8159
Epoch 453:  train loss 0.5200   train acc 0.6420   worst 0.3437   lr 0.0219   p 35.22   eps 0.1952   mix 0.0186   time 27.22
scalar:  4.808
Epoch 454:  train loss 0.5174   train acc 0.6455   worst 0.3442   lr 0.0218   p 35.36   eps 0.1952   mix 0.0186   time 27.58
Epoch 454:  test acc 0.5972   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 454:  clean acc 0.2699   certified acc 0.2147
Calculating metrics for L_infinity dist model on test set
Epoch 454:  clean acc 0.2695   certified acc 0.2165
scalar:  4.8795
Epoch 455:  train loss 0.5188   train acc 0.6442   worst 0.3419   lr 0.0218   p 35.51   eps 0.1952   mix 0.0185   time 27.45
scalar:  4.8541
Epoch 456:  train loss 0.5206   train acc 0.6410   worst 0.3411   lr 0.0218   p 35.66   eps 0.1952   mix 0.0185   time 27.37
scalar:  4.8182
Epoch 457:  train loss 0.5209   train acc 0.6436   worst 0.3401   lr 0.0217   p 35.81   eps 0.1952   mix 0.0184   time 28.26
scalar:  4.8351
Epoch 458:  train loss 0.5198   train acc 0.6443   worst 0.3424   lr 0.0217   p 35.96   eps 0.1952   mix 0.0184   time 27.74
scalar:  4.8445
Epoch 459:  train loss 0.5196   train acc 0.6430   worst 0.3428   lr 0.0217   p 36.12   eps 0.1952   mix 0.0183   time 27.36
Epoch 459:  test acc 0.5980   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 459:  clean acc 0.2751   certified acc 0.2163
Calculating metrics for L_infinity dist model on test set
Epoch 459:  clean acc 0.2756   certified acc 0.2136
scalar:  4.8917
Epoch 460:  train loss 0.5171   train acc 0.6469   worst 0.3433   lr 0.0216   p 36.27   eps 0.1952   mix 0.0183   time 27.21
scalar:  4.9176
Epoch 461:  train loss 0.5195   train acc 0.6441   worst 0.3417   lr 0.0216   p 36.42   eps 0.1952   mix 0.0182   time 27.53
scalar:  4.8966
Epoch 462:  train loss 0.5184   train acc 0.6442   worst 0.3428   lr 0.0216   p 36.57   eps 0.1952   mix 0.0182   time 28.45
scalar:  4.9047
Epoch 463:  train loss 0.5221   train acc 0.6418   worst 0.3369   lr 0.0216   p 36.73   eps 0.1952   mix 0.0181   time 27.52
scalar:  4.9199
Epoch 464:  train loss 0.5216   train acc 0.6426   worst 0.3389   lr 0.0215   p 36.88   eps 0.1952   mix 0.0181   time 27.49
Epoch 464:  test acc 0.6037   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 464:  clean acc 0.2928   certified acc 0.2313
Calculating metrics for L_infinity dist model on test set
Epoch 464:  clean acc 0.3015   certified acc 0.2363
scalar:  4.9007
Epoch 465:  train loss 0.5199   train acc 0.6441   worst 0.3380   lr 0.0215   p 37.04   eps 0.1952   mix 0.0180   time 27.22
scalar:  4.92
Epoch 466:  train loss 0.5219   train acc 0.6431   worst 0.3395   lr 0.0215   p 37.19   eps 0.1952   mix 0.0179   time 27.53
scalar:  4.9184
Epoch 467:  train loss 0.5215   train acc 0.6423   worst 0.3380   lr 0.0214   p 37.35   eps 0.1952   mix 0.0179   time 27.72
scalar:  4.9254
Epoch 468:  train loss 0.5197   train acc 0.6425   worst 0.3401   lr 0.0214   p 37.51   eps 0.1952   mix 0.0178   time 27.51
scalar:  4.9387
Epoch 469:  train loss 0.5198   train acc 0.6447   worst 0.3384   lr 0.0214   p 37.66   eps 0.1952   mix 0.0178   time 27.55
Epoch 469:  test acc 0.5967   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 469:  clean acc 0.3253   certified acc 0.2516
Calculating metrics for L_infinity dist model on test set
Epoch 469:  clean acc 0.3248   certified acc 0.2502
scalar:  4.9481
Epoch 470:  train loss 0.5203   train acc 0.6413   worst 0.3409   lr 0.0213   p 37.82   eps 0.1952   mix 0.0177   time 27.36
scalar:  4.9135
Epoch 471:  train loss 0.5192   train acc 0.6439   worst 0.3408   lr 0.0213   p 37.98   eps 0.1952   mix 0.0177   time 27.82
scalar:  4.9729
Epoch 472:  train loss 0.5208   train acc 0.6412   worst 0.3389   lr 0.0213   p 38.14   eps 0.1952   mix 0.0177   time 27.66
scalar:  4.944
Epoch 473:  train loss 0.5188   train acc 0.6464   worst 0.3378   lr 0.0212   p 38.30   eps 0.1952   mix 0.0176   time 27.69
scalar:  5.032
Epoch 474:  train loss 0.5200   train acc 0.6443   worst 0.3388   lr 0.0212   p 38.46   eps 0.1952   mix 0.0176   time 27.37
Epoch 474:  test acc 0.6028   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 474:  clean acc 0.2893   certified acc 0.2287
Calculating metrics for L_infinity dist model on test set
Epoch 474:  clean acc 0.2828   certified acc 0.2235
scalar:  4.9859
Epoch 475:  train loss 0.5205   train acc 0.6441   worst 0.3362   lr 0.0212   p 38.62   eps 0.1952   mix 0.0175   time 27.33
scalar:  4.9835
Epoch 476:  train loss 0.5207   train acc 0.6443   worst 0.3372   lr 0.0211   p 38.79   eps 0.1952   mix 0.0175   time 27.80
scalar:  5.0037
Epoch 477:  train loss 0.5200   train acc 0.6455   worst 0.3366   lr 0.0211   p 38.95   eps 0.1952   mix 0.0174   time 27.36
scalar:  5.009
Epoch 478:  train loss 0.5199   train acc 0.6435   worst 0.3376   lr 0.0211   p 39.11   eps 0.1952   mix 0.0174   time 27.83
scalar:  5.0194
Epoch 479:  train loss 0.5210   train acc 0.6435   worst 0.3358   lr 0.0210   p 39.28   eps 0.1952   mix 0.0173   time 27.08
Epoch 479:  test acc 0.6019   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 479:  clean acc 0.3307   certified acc 0.2599
Calculating metrics for L_infinity dist model on test set
Epoch 479:  clean acc 0.3410   certified acc 0.2652
scalar:  5.0337
Epoch 480:  train loss 0.5236   train acc 0.6404   worst 0.3347   lr 0.0210   p 39.44   eps 0.1952   mix 0.0173   time 26.99
scalar:  5.0366
Epoch 481:  train loss 0.5211   train acc 0.6419   worst 0.3360   lr 0.0210   p 39.61   eps 0.1952   mix 0.0172   time 27.67
scalar:  5.0049
Epoch 482:  train loss 0.5190   train acc 0.6436   worst 0.3389   lr 0.0209   p 39.78   eps 0.1952   mix 0.0172   time 27.40
scalar:  5.0092
Epoch 483:  train loss 0.5193   train acc 0.6444   worst 0.3352   lr 0.0209   p 39.94   eps 0.1952   mix 0.0171   time 27.59
scalar:  5.0253
Epoch 484:  train loss 0.5207   train acc 0.6446   worst 0.3335   lr 0.0209   p 40.11   eps 0.1952   mix 0.0171   time 26.99
Epoch 484:  test acc 0.5954   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 484:  clean acc 0.3093   certified acc 0.2424
Calculating metrics for L_infinity dist model on test set
Epoch 484:  clean acc 0.3052   certified acc 0.2379
scalar:  5.0196
Epoch 485:  train loss 0.5197   train acc 0.6452   worst 0.3346   lr 0.0208   p 40.28   eps 0.1952   mix 0.0170   time 27.36
scalar:  5.026
Epoch 486:  train loss 0.5212   train acc 0.6418   worst 0.3374   lr 0.0208   p 40.45   eps 0.1952   mix 0.0170   time 27.73
scalar:  5.0009
Epoch 487:  train loss 0.5202   train acc 0.6430   worst 0.3368   lr 0.0208   p 40.62   eps 0.1952   mix 0.0169   time 27.59
scalar:  5.0367
Epoch 488:  train loss 0.5202   train acc 0.6446   worst 0.3365   lr 0.0207   p 40.79   eps 0.1952   mix 0.0169   time 27.97
scalar:  5.0639
Epoch 489:  train loss 0.5222   train acc 0.6423   worst 0.3344   lr 0.0207   p 40.96   eps 0.1952   mix 0.0168   time 27.33
Epoch 489:  test acc 0.5941   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 489:  clean acc 0.3243   certified acc 0.2527
Calculating metrics for L_infinity dist model on test set
Epoch 489:  clean acc 0.3294   certified acc 0.2571
scalar:  5.0624
Epoch 490:  train loss 0.5191   train acc 0.6442   worst 0.3357   lr 0.0207   p 41.14   eps 0.1952   mix 0.0168   time 26.99
scalar:  5.0535
Epoch 491:  train loss 0.5190   train acc 0.6442   worst 0.3369   lr 0.0206   p 41.31   eps 0.1952   mix 0.0167   time 27.84
scalar:  5.0544
Epoch 492:  train loss 0.5198   train acc 0.6446   worst 0.3349   lr 0.0206   p 41.48   eps 0.1952   mix 0.0167   time 27.58
scalar:  5.0605
Epoch 493:  train loss 0.5206   train acc 0.6429   worst 0.3347   lr 0.0206   p 41.66   eps 0.1952   mix 0.0166   time 27.33
scalar:  5.0555
Epoch 494:  train loss 0.5205   train acc 0.6428   worst 0.3345   lr 0.0205   p 41.83   eps 0.1952   mix 0.0166   time 27.13
Epoch 494:  test acc 0.5975   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 494:  clean acc 0.3616   certified acc 0.2864
Calculating metrics for L_infinity dist model on test set
Epoch 494:  clean acc 0.3655   certified acc 0.2832
scalar:  5.0881
Epoch 495:  train loss 0.5199   train acc 0.6456   worst 0.3342   lr 0.0205   p 42.01   eps 0.1952   mix 0.0166   time 27.08
scalar:  5.093
Epoch 496:  train loss 0.5197   train acc 0.6437   worst 0.3329   lr 0.0205   p 42.18   eps 0.1952   mix 0.0165   time 27.64
scalar:  5.0814
Epoch 497:  train loss 0.5219   train acc 0.6439   worst 0.3317   lr 0.0204   p 42.36   eps 0.1952   mix 0.0165   time 27.39
scalar:  5.0923
Epoch 498:  train loss 0.5202   train acc 0.6439   worst 0.3357   lr 0.0204   p 42.54   eps 0.1952   mix 0.0164   time 27.41
scalar:  5.077
Epoch 499:  train loss 0.5162   train acc 0.6471   worst 0.3377   lr 0.0204   p 42.72   eps 0.1952   mix 0.0164   time 27.00
Epoch 499:  test acc 0.5932   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 499:  clean acc 0.3665   certified acc 0.2914
Calculating metrics for L_infinity dist model on test set
Epoch 499:  clean acc 0.3674   certified acc 0.2879
scalar:  5.1156
Epoch 500:  train loss 0.5183   train acc 0.6461   worst 0.3328   lr 0.0203   p 42.90   eps 0.1952   mix 0.0163   time 27.16
scalar:  5.102
Epoch 501:  train loss 0.5183   train acc 0.6454   worst 0.3352   lr 0.0203   p 43.08   eps 0.1952   mix 0.0163   time 27.79
scalar:  5.1172
Epoch 502:  train loss 0.5222   train acc 0.6416   worst 0.3327   lr 0.0203   p 43.26   eps 0.1952   mix 0.0162   time 27.48
scalar:  5.1149
Epoch 503:  train loss 0.5208   train acc 0.6433   worst 0.3339   lr 0.0202   p 43.44   eps 0.1952   mix 0.0162   time 27.44
scalar:  5.1083
Epoch 504:  train loss 0.5188   train acc 0.6453   worst 0.3338   lr 0.0202   p 43.63   eps 0.1952   mix 0.0161   time 26.98
Epoch 504:  test acc 0.5968   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 504:  clean acc 0.3294   certified acc 0.2666
Calculating metrics for L_infinity dist model on test set
Epoch 504:  clean acc 0.3323   certified acc 0.2638
scalar:  5.1256
Epoch 505:  train loss 0.5180   train acc 0.6465   worst 0.3320   lr 0.0201   p 43.81   eps 0.1952   mix 0.0161   time 27.43
scalar:  5.1424
Epoch 506:  train loss 0.5194   train acc 0.6435   worst 0.3330   lr 0.0201   p 43.99   eps 0.1952   mix 0.0160   time 27.72
scalar:  5.1443
Epoch 507:  train loss 0.5195   train acc 0.6444   worst 0.3338   lr 0.0201   p 44.18   eps 0.1952   mix 0.0160   time 27.06
scalar:  5.1368
Epoch 508:  train loss 0.5195   train acc 0.6428   worst 0.3337   lr 0.0200   p 44.36   eps 0.1952   mix 0.0160   time 27.37
scalar:  5.1366
Epoch 509:  train loss 0.5208   train acc 0.6443   worst 0.3319   lr 0.0200   p 44.55   eps 0.1952   mix 0.0159   time 27.14
Epoch 509:  test acc 0.5957   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 509:  clean acc 0.3371   certified acc 0.2706
Calculating metrics for L_infinity dist model on test set
Epoch 509:  clean acc 0.3391   certified acc 0.2682
scalar:  5.1221
Epoch 510:  train loss 0.5205   train acc 0.6440   worst 0.3305   lr 0.0200   p 44.74   eps 0.1952   mix 0.0159   time 27.29
scalar:  5.1291
Epoch 511:  train loss 0.5199   train acc 0.6452   worst 0.3307   lr 0.0199   p 44.93   eps 0.1952   mix 0.0158   time 27.96
scalar:  5.1367
Epoch 512:  train loss 0.5187   train acc 0.6451   worst 0.3328   lr 0.0199   p 45.12   eps 0.1952   mix 0.0158   time 27.53
scalar:  5.1628
Epoch 513:  train loss 0.5196   train acc 0.6452   worst 0.3293   lr 0.0199   p 45.31   eps 0.1952   mix 0.0157   time 27.59
scalar:  5.179
Epoch 514:  train loss 0.5172   train acc 0.6474   worst 0.3328   lr 0.0198   p 45.50   eps 0.1952   mix 0.0157   time 27.49
Epoch 514:  test acc 0.6019   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 514:  clean acc 0.3689   certified acc 0.2924
Calculating metrics for L_infinity dist model on test set
Epoch 514:  clean acc 0.3667   certified acc 0.2899
scalar:  5.2056
Epoch 515:  train loss 0.5191   train acc 0.6443   worst 0.3324   lr 0.0198   p 45.69   eps 0.1952   mix 0.0156   time 27.34
scalar:  5.1736
Epoch 516:  train loss 0.5204   train acc 0.6442   worst 0.3313   lr 0.0198   p 45.88   eps 0.1952   mix 0.0156   time 27.84
scalar:  5.1678
Epoch 517:  train loss 0.5195   train acc 0.6458   worst 0.3301   lr 0.0197   p 46.07   eps 0.1952   mix 0.0156   time 27.29
scalar:  5.1946
Epoch 518:  train loss 0.5213   train acc 0.6442   worst 0.3279   lr 0.0197   p 46.27   eps 0.1952   mix 0.0155   time 27.33
scalar:  5.2051
Epoch 519:  train loss 0.5186   train acc 0.6448   worst 0.3298   lr 0.0197   p 46.46   eps 0.1952   mix 0.0155   time 27.07
Epoch 519:  test acc 0.5964   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 519:  clean acc 0.3791   certified acc 0.2991
Calculating metrics for L_infinity dist model on test set
Epoch 519:  clean acc 0.3785   certified acc 0.3016
scalar:  5.1794
Epoch 520:  train loss 0.5196   train acc 0.6458   worst 0.3283   lr 0.0196   p 46.66   eps 0.1952   mix 0.0154   time 27.33
scalar:  5.1842
Epoch 521:  train loss 0.5191   train acc 0.6459   worst 0.3309   lr 0.0196   p 46.85   eps 0.1952   mix 0.0154   time 27.67
scalar:  5.2124
Epoch 522:  train loss 0.5174   train acc 0.6478   worst 0.3317   lr 0.0196   p 47.05   eps 0.1952   mix 0.0153   time 27.35
scalar:  5.2218
Epoch 523:  train loss 0.5196   train acc 0.6446   worst 0.3305   lr 0.0195   p 47.25   eps 0.1952   mix 0.0153   time 27.44
scalar:  5.1998
Epoch 524:  train loss 0.5190   train acc 0.6464   worst 0.3300   lr 0.0195   p 47.45   eps 0.1952   mix 0.0153   time 27.15
Epoch 524:  test acc 0.5941   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 524:  clean acc 0.3498   certified acc 0.2817
Calculating metrics for L_infinity dist model on test set
Epoch 524:  clean acc 0.3539   certified acc 0.2832
scalar:  5.2143
Epoch 525:  train loss 0.5175   train acc 0.6471   worst 0.3312   lr 0.0195   p 47.65   eps 0.1952   mix 0.0152   time 27.04
scalar:  5.2362
Epoch 526:  train loss 0.5179   train acc 0.6446   worst 0.3307   lr 0.0194   p 47.85   eps 0.1952   mix 0.0152   time 27.74
scalar:  5.2509
Epoch 527:  train loss 0.5192   train acc 0.6465   worst 0.3306   lr 0.0194   p 48.05   eps 0.1952   mix 0.0151   time 27.58
scalar:  5.2646
Epoch 528:  train loss 0.5218   train acc 0.6453   worst 0.3256   lr 0.0194   p 48.25   eps 0.1952   mix 0.0151   time 27.40
scalar:  5.2422
Epoch 529:  train loss 0.5182   train acc 0.6472   worst 0.3314   lr 0.0193   p 48.45   eps 0.1952   mix 0.0150   time 27.48
Epoch 529:  test acc 0.5966   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 529:  clean acc 0.3645   certified acc 0.2885
Calculating metrics for L_infinity dist model on test set
Epoch 529:  clean acc 0.3621   certified acc 0.2827
scalar:  5.2476
Epoch 530:  train loss 0.5198   train acc 0.6464   worst 0.3284   lr 0.0193   p 48.66   eps 0.1952   mix 0.0150   time 27.25
scalar:  5.2792
Epoch 531:  train loss 0.5193   train acc 0.6466   worst 0.3275   lr 0.0193   p 48.86   eps 0.1952   mix 0.0150   time 27.79
scalar:  5.2785
Epoch 532:  train loss 0.5195   train acc 0.6466   worst 0.3260   lr 0.0192   p 49.07   eps 0.1952   mix 0.0149   time 27.23
scalar:  5.3033
Epoch 533:  train loss 0.5195   train acc 0.6452   worst 0.3277   lr 0.0192   p 49.27   eps 0.1952   mix 0.0149   time 27.35
scalar:  5.2959
Epoch 534:  train loss 0.5189   train acc 0.6456   worst 0.3273   lr 0.0192   p 49.48   eps 0.1952   mix 0.0148   time 27.51
Epoch 534:  test acc 0.6023   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 534:  clean acc 0.3559   certified acc 0.2632
Calculating metrics for L_infinity dist model on test set
Epoch 534:  clean acc 0.3523   certified acc 0.2608
scalar:  5.2901
Epoch 535:  train loss 0.5194   train acc 0.6466   worst 0.3276   lr 0.0191   p 49.69   eps 0.1952   mix 0.0148   time 27.28
scalar:  5.2811
Epoch 536:  train loss 0.5184   train acc 0.6470   worst 0.3269   lr 0.0191   p 49.90   eps 0.1952   mix 0.0148   time 27.76
scalar:  5.3027
Epoch 537:  train loss 0.5186   train acc 0.6479   worst 0.3275   lr 0.0190   p 50.11   eps 0.1952   mix 0.0147   time 27.24
scalar:  5.3189
Epoch 538:  train loss 0.5196   train acc 0.6433   worst 0.3292   lr 0.0190   p 50.32   eps 0.1952   mix 0.0147   time 27.39
scalar:  5.2578
Epoch 539:  train loss 0.5185   train acc 0.6472   worst 0.3273   lr 0.0190   p 50.53   eps 0.1952   mix 0.0146   time 27.54
Epoch 539:  test acc 0.5932   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 539:  clean acc 0.4081   certified acc 0.3259
Calculating metrics for L_infinity dist model on test set
Epoch 539:  clean acc 0.4014   certified acc 0.3231
scalar:  5.2998
Epoch 540:  train loss 0.5169   train acc 0.6493   worst 0.3274   lr 0.0189   p 50.74   eps 0.1952   mix 0.0146   time 27.12
scalar:  5.3349
Epoch 541:  train loss 0.5197   train acc 0.6465   worst 0.3262   lr 0.0189   p 50.96   eps 0.1952   mix 0.0146   time 27.81
scalar:  5.3208
Epoch 542:  train loss 0.5185   train acc 0.6471   worst 0.3269   lr 0.0189   p 51.17   eps 0.1952   mix 0.0145   time 27.09
scalar:  5.3221
Epoch 543:  train loss 0.5190   train acc 0.6474   worst 0.3247   lr 0.0188   p 51.39   eps 0.1952   mix 0.0145   time 27.06
scalar:  5.3687
Epoch 544:  train loss 0.5186   train acc 0.6475   worst 0.3269   lr 0.0188   p 51.60   eps 0.1952   mix 0.0144   time 27.59
Epoch 544:  test acc 0.5996   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 544:  clean acc 0.4091   certified acc 0.3272
Calculating metrics for L_infinity dist model on test set
Epoch 544:  clean acc 0.4072   certified acc 0.3237
scalar:  5.3367
Epoch 545:  train loss 0.5174   train acc 0.6470   worst 0.3259   lr 0.0188   p 51.82   eps 0.1952   mix 0.0144   time 27.23
scalar:  5.3345
Epoch 546:  train loss 0.5176   train acc 0.6493   worst 0.3257   lr 0.0187   p 52.04   eps 0.1952   mix 0.0143   time 27.87
scalar:  5.3804
Epoch 547:  train loss 0.5201   train acc 0.6438   worst 0.3248   lr 0.0187   p 52.26   eps 0.1952   mix 0.0143   time 27.57
scalar:  5.3418
Epoch 548:  train loss 0.5184   train acc 0.6472   worst 0.3247   lr 0.0187   p 52.48   eps 0.1952   mix 0.0143   time 27.15
scalar:  5.3433
Epoch 549:  train loss 0.5194   train acc 0.6466   worst 0.3265   lr 0.0186   p 52.70   eps 0.1952   mix 0.0142   time 27.11
Epoch 549:  test acc 0.6002   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 549:  clean acc 0.3931   certified acc 0.3119
Calculating metrics for L_infinity dist model on test set
Epoch 549:  clean acc 0.3898   certified acc 0.3083
scalar:  5.3621
Epoch 550:  train loss 0.5187   train acc 0.6474   worst 0.3251   lr 0.0186   p 52.92   eps 0.1952   mix 0.0142   time 27.30
scalar:  5.3464
Epoch 551:  train loss 0.5180   train acc 0.6489   worst 0.3261   lr 0.0186   p 53.14   eps 0.1952   mix 0.0141   time 28.09
scalar:  5.3531
Epoch 552:  train loss 0.5188   train acc 0.6466   worst 0.3250   lr 0.0185   p 53.37   eps 0.1952   mix 0.0141   time 27.45
scalar:  5.3615
Epoch 553:  train loss 0.5183   train acc 0.6485   worst 0.3246   lr 0.0185   p 53.59   eps 0.1952   mix 0.0141   time 27.05
scalar:  5.3541
Epoch 554:  train loss 0.5173   train acc 0.6487   worst 0.3263   lr 0.0184   p 53.82   eps 0.1952   mix 0.0140   time 27.82
Epoch 554:  test acc 0.5950   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 554:  clean acc 0.4050   certified acc 0.3297
Calculating metrics for L_infinity dist model on test set
Epoch 554:  clean acc 0.4051   certified acc 0.3273
scalar:  5.3783
Epoch 555:  train loss 0.5175   train acc 0.6498   worst 0.3264   lr 0.0184   p 54.04   eps 0.1952   mix 0.0140   time 26.88
scalar:  5.3871
Epoch 556:  train loss 0.5196   train acc 0.6460   worst 0.3247   lr 0.0184   p 54.27   eps 0.1952   mix 0.0140   time 27.72
scalar:  5.3961
Epoch 557:  train loss 0.5170   train acc 0.6460   worst 0.3274   lr 0.0183   p 54.50   eps 0.1952   mix 0.0139   time 27.37
scalar:  5.3578
Epoch 558:  train loss 0.5167   train acc 0.6478   worst 0.3273   lr 0.0183   p 54.73   eps 0.1952   mix 0.0139   time 27.01
scalar:  5.3875
Epoch 559:  train loss 0.5166   train acc 0.6498   worst 0.3271   lr 0.0183   p 54.96   eps 0.1952   mix 0.0138   time 27.28
Epoch 559:  test acc 0.5995   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 559:  clean acc 0.3839   certified acc 0.3091
Calculating metrics for L_infinity dist model on test set
Epoch 559:  clean acc 0.3872   certified acc 0.3135
scalar:  5.3969
Epoch 560:  train loss 0.5171   train acc 0.6470   worst 0.3279   lr 0.0182   p 55.19   eps 0.1952   mix 0.0138   time 27.24
scalar:  5.3557
Epoch 561:  train loss 0.5162   train acc 0.6479   worst 0.3280   lr 0.0182   p 55.42   eps 0.1952   mix 0.0138   time 27.79
scalar:  5.3693
Epoch 562:  train loss 0.5182   train acc 0.6477   worst 0.3258   lr 0.0182   p 55.65   eps 0.1952   mix 0.0137   time 27.38
scalar:  5.4163
Epoch 563:  train loss 0.5157   train acc 0.6498   worst 0.3265   lr 0.0181   p 55.89   eps 0.1952   mix 0.0137   time 27.07
scalar:  5.4282
Epoch 564:  train loss 0.5177   train acc 0.6476   worst 0.3257   lr 0.0181   p 56.12   eps 0.1952   mix 0.0136   time 27.51
Epoch 564:  test acc 0.5979   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 564:  clean acc 0.4002   certified acc 0.3149
Calculating metrics for L_infinity dist model on test set
Epoch 564:  clean acc 0.3959   certified acc 0.3101
scalar:  5.3936
Epoch 565:  train loss 0.5181   train acc 0.6465   worst 0.3272   lr 0.0181   p 56.36   eps 0.1952   mix 0.0136   time 27.17
scalar:  5.3493
Epoch 566:  train loss 0.5184   train acc 0.6475   worst 0.3265   lr 0.0180   p 56.60   eps 0.1952   mix 0.0136   time 27.98
scalar:  5.3745
Epoch 567:  train loss 0.5162   train acc 0.6480   worst 0.3265   lr 0.0180   p 56.84   eps 0.1952   mix 0.0135   time 27.20
scalar:  5.384
Epoch 568:  train loss 0.5174   train acc 0.6475   worst 0.3248   lr 0.0180   p 57.07   eps 0.1952   mix 0.0135   time 27.27
scalar:  5.4127
Epoch 569:  train loss 0.5166   train acc 0.6477   worst 0.3241   lr 0.0179   p 57.31   eps 0.1952   mix 0.0135   time 27.72
Epoch 569:  test acc 0.5952   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 569:  clean acc 0.4215   certified acc 0.3344
Calculating metrics for L_infinity dist model on test set
Epoch 569:  clean acc 0.4214   certified acc 0.3281
scalar:  5.4304
Epoch 570:  train loss 0.5164   train acc 0.6494   worst 0.3250   lr 0.0179   p 57.56   eps 0.1952   mix 0.0134   time 26.74
scalar:  5.4522
Epoch 571:  train loss 0.5156   train acc 0.6500   worst 0.3252   lr 0.0178   p 57.80   eps 0.1952   mix 0.0134   time 27.72
scalar:  5.4681
Epoch 572:  train loss 0.5155   train acc 0.6503   worst 0.3244   lr 0.0178   p 58.04   eps 0.1952   mix 0.0133   time 27.21
scalar:  5.4789
Epoch 573:  train loss 0.5164   train acc 0.6500   worst 0.3244   lr 0.0178   p 58.29   eps 0.1952   mix 0.0133   time 26.95
scalar:  5.4836
Epoch 574:  train loss 0.5151   train acc 0.6503   worst 0.3234   lr 0.0177   p 58.53   eps 0.1952   mix 0.0133   time 27.03
Epoch 574:  test acc 0.6054   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 574:  clean acc 0.4122   certified acc 0.3204
Calculating metrics for L_infinity dist model on test set
Epoch 574:  clean acc 0.3994   certified acc 0.3088
scalar:  5.4994
Epoch 575:  train loss 0.5144   train acc 0.6495   worst 0.3255   lr 0.0177   p 58.78   eps 0.1952   mix 0.0132   time 27.01
scalar:  5.4716
Epoch 576:  train loss 0.5149   train acc 0.6503   worst 0.3249   lr 0.0177   p 59.02   eps 0.1952   mix 0.0132   time 27.79
scalar:  5.497
Epoch 577:  train loss 0.5154   train acc 0.6480   worst 0.3257   lr 0.0176   p 59.27   eps 0.1952   mix 0.0132   time 27.29
scalar:  5.4595
Epoch 578:  train loss 0.5162   train acc 0.6495   worst 0.3240   lr 0.0176   p 59.52   eps 0.1952   mix 0.0131   time 27.19
scalar:  5.4956
Epoch 579:  train loss 0.5149   train acc 0.6510   worst 0.3261   lr 0.0176   p 59.77   eps 0.1952   mix 0.0131   time 27.35
Epoch 579:  test acc 0.6006   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 579:  clean acc 0.4135   certified acc 0.3350
Calculating metrics for L_infinity dist model on test set
Epoch 579:  clean acc 0.4029   certified acc 0.3246
scalar:  5.5109
Epoch 580:  train loss 0.5164   train acc 0.6494   worst 0.3226   lr 0.0175   p 60.02   eps 0.1952   mix 0.0130   time 27.17
scalar:  5.4941
Epoch 581:  train loss 0.5145   train acc 0.6528   worst 0.3256   lr 0.0175   p 60.28   eps 0.1952   mix 0.0130   time 27.79
scalar:  5.5227
Epoch 582:  train loss 0.5155   train acc 0.6509   worst 0.3232   lr 0.0175   p 60.53   eps 0.1952   mix 0.0130   time 27.77
scalar:  5.4856
Epoch 583:  train loss 0.5152   train acc 0.6510   worst 0.3234   lr 0.0174   p 60.78   eps 0.1952   mix 0.0129   time 27.13
scalar:  5.5207
Epoch 584:  train loss 0.5145   train acc 0.6528   worst 0.3224   lr 0.0174   p 61.04   eps 0.1952   mix 0.0129   time 27.15
Epoch 584:  test acc 0.5964   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 584:  clean acc 0.4565   certified acc 0.3589
Calculating metrics for L_infinity dist model on test set
Epoch 584:  clean acc 0.4400   certified acc 0.3444
scalar:  5.5352
Epoch 585:  train loss 0.5150   train acc 0.6519   worst 0.3211   lr 0.0173   p 61.30   eps 0.1952   mix 0.0129   time 26.73
scalar:  5.5093
Epoch 586:  train loss 0.5150   train acc 0.6512   worst 0.3236   lr 0.0173   p 61.55   eps 0.1952   mix 0.0128   time 27.50
scalar:  5.5156
Epoch 587:  train loss 0.5137   train acc 0.6529   worst 0.3227   lr 0.0173   p 61.81   eps 0.1952   mix 0.0128   time 27.36
scalar:  5.5298
Epoch 588:  train loss 0.5169   train acc 0.6483   worst 0.3236   lr 0.0172   p 62.07   eps 0.1952   mix 0.0128   time 26.84
scalar:  5.5227
Epoch 589:  train loss 0.5145   train acc 0.6495   worst 0.3247   lr 0.0172   p 62.34   eps 0.1952   mix 0.0127   time 27.10
Epoch 589:  test acc 0.6024   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 589:  clean acc 0.4387   certified acc 0.3485
Calculating metrics for L_infinity dist model on test set
Epoch 589:  clean acc 0.4319   certified acc 0.3363
scalar:  5.5104
Epoch 590:  train loss 0.5159   train acc 0.6485   worst 0.3231   lr 0.0172   p 62.60   eps 0.1952   mix 0.0127   time 26.86
scalar:  5.4866
Epoch 591:  train loss 0.5151   train acc 0.6519   worst 0.3208   lr 0.0171   p 62.86   eps 0.1952   mix 0.0127   time 27.43
scalar:  5.5248
Epoch 592:  train loss 0.5151   train acc 0.6489   worst 0.3238   lr 0.0171   p 63.13   eps 0.1952   mix 0.0126   time 28.02
scalar:  5.5606
Epoch 593:  train loss 0.5147   train acc 0.6517   worst 0.3235   lr 0.0171   p 63.39   eps 0.1952   mix 0.0126   time 26.94
scalar:  5.5422
Epoch 594:  train loss 0.5154   train acc 0.6494   worst 0.3243   lr 0.0170   p 63.66   eps 0.1952   mix 0.0125   time 27.34
Epoch 594:  test acc 0.6000   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 594:  clean acc 0.4524   certified acc 0.3634
Calculating metrics for L_infinity dist model on test set
Epoch 594:  clean acc 0.4442   certified acc 0.3562
scalar:  5.5179
Epoch 595:  train loss 0.5114   train acc 0.6544   worst 0.3261   lr 0.0170   p 63.93   eps 0.1952   mix 0.0125   time 27.17
scalar:  5.5219
Epoch 596:  train loss 0.5133   train acc 0.6502   worst 0.3240   lr 0.0170   p 64.19   eps 0.1952   mix 0.0125   time 27.24
scalar:  5.5217
Epoch 597:  train loss 0.5132   train acc 0.6508   worst 0.3253   lr 0.0169   p 64.46   eps 0.1952   mix 0.0124   time 27.72
scalar:  5.5667
Epoch 598:  train loss 0.5128   train acc 0.6510   worst 0.3237   lr 0.0169   p 64.74   eps 0.1952   mix 0.0124   time 27.31
scalar:  5.5643
Epoch 599:  train loss 0.5149   train acc 0.6498   worst 0.3222   lr 0.0168   p 65.01   eps 0.1952   mix 0.0124   time 27.05
Epoch 599:  test acc 0.6008   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 599:  clean acc 0.4946   certified acc 0.4004
Calculating metrics for L_infinity dist model on test set
Epoch 599:  clean acc 0.4826   certified acc 0.3831
scalar:  5.5387
Epoch 600:  train loss 0.5137   train acc 0.6521   worst 0.3233   lr 0.0168   p 65.28   eps 0.1952   mix 0.0123   time 26.83
scalar:  5.5552
Epoch 601:  train loss 0.5139   train acc 0.6516   worst 0.3228   lr 0.0168   p 65.56   eps 0.1952   mix 0.0123   time 27.31
scalar:  5.5383
Epoch 602:  train loss 0.5130   train acc 0.6532   worst 0.3234   lr 0.0167   p 65.83   eps 0.1952   mix 0.0123   time 27.67
scalar:  5.5858
Epoch 603:  train loss 0.5135   train acc 0.6502   worst 0.3229   lr 0.0167   p 66.11   eps 0.1952   mix 0.0122   time 27.00
scalar:  5.5528
Epoch 604:  train loss 0.5124   train acc 0.6540   worst 0.3243   lr 0.0167   p 66.39   eps 0.1952   mix 0.0122   time 26.96
Epoch 604:  test acc 0.6016   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 604:  clean acc 0.5048   certified acc 0.4102
Calculating metrics for L_infinity dist model on test set
Epoch 604:  clean acc 0.4844   certified acc 0.3957
scalar:  5.5893
Epoch 605:  train loss 0.5151   train acc 0.6522   worst 0.3204   lr 0.0166   p 66.67   eps 0.1952   mix 0.0122   time 27.00
scalar:  5.5948
Epoch 606:  train loss 0.5144   train acc 0.6531   worst 0.3221   lr 0.0166   p 66.95   eps 0.1952   mix 0.0121   time 27.43
scalar:  5.6208
Epoch 607:  train loss 0.5142   train acc 0.6512   worst 0.3208   lr 0.0166   p 67.23   eps 0.1952   mix 0.0121   time 28.10
scalar:  5.5865
Epoch 608:  train loss 0.5131   train acc 0.6531   worst 0.3231   lr 0.0165   p 67.51   eps 0.1952   mix 0.0121   time 26.91
scalar:  5.5507
Epoch 609:  train loss 0.5145   train acc 0.6505   worst 0.3225   lr 0.0165   p 67.80   eps 0.1952   mix 0.0120   time 26.92
Epoch 609:  test acc 0.6031   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 609:  clean acc 0.4802   certified acc 0.3837
Calculating metrics for L_infinity dist model on test set
Epoch 609:  clean acc 0.4674   certified acc 0.3677
scalar:  5.5681
Epoch 610:  train loss 0.5134   train acc 0.6536   worst 0.3212   lr 0.0164   p 68.08   eps 0.1952   mix 0.0120   time 27.24
scalar:  5.622
Epoch 611:  train loss 0.5122   train acc 0.6546   worst 0.3209   lr 0.0164   p 68.37   eps 0.1952   mix 0.0120   time 27.42
scalar:  5.6538
Epoch 612:  train loss 0.5121   train acc 0.6535   worst 0.3211   lr 0.0164   p 68.65   eps 0.1952   mix 0.0119   time 27.99
scalar:  5.6319
Epoch 613:  train loss 0.5123   train acc 0.6523   worst 0.3232   lr 0.0163   p 68.94   eps 0.1952   mix 0.0119   time 27.14
scalar:  5.6164
Epoch 614:  train loss 0.5132   train acc 0.6523   worst 0.3230   lr 0.0163   p 69.23   eps 0.1952   mix 0.0119   time 26.88
Epoch 614:  test acc 0.6021   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 614:  clean acc 0.4925   certified acc 0.4054
Calculating metrics for L_infinity dist model on test set
Epoch 614:  clean acc 0.4741   certified acc 0.3924
scalar:  5.6287
Epoch 615:  train loss 0.5125   train acc 0.6529   worst 0.3224   lr 0.0163   p 69.52   eps 0.1952   mix 0.0118   time 26.88
scalar:  5.6285
Epoch 616:  train loss 0.5120   train acc 0.6565   worst 0.3196   lr 0.0162   p 69.82   eps 0.1952   mix 0.0118   time 27.40
scalar:  5.6732
Epoch 617:  train loss 0.5124   train acc 0.6510   worst 0.3234   lr 0.0162   p 70.11   eps 0.1952   mix 0.0118   time 27.97
scalar:  5.6343
Epoch 618:  train loss 0.5144   train acc 0.6517   worst 0.3197   lr 0.0162   p 70.41   eps 0.1952   mix 0.0117   time 26.87
scalar:  5.6445
Epoch 619:  train loss 0.5115   train acc 0.6540   worst 0.3214   lr 0.0161   p 70.70   eps 0.1952   mix 0.0117   time 27.06
Epoch 619:  test acc 0.5965   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 619:  clean acc 0.4874   certified acc 0.4042
Calculating metrics for L_infinity dist model on test set
Epoch 619:  clean acc 0.4757   certified acc 0.3941
scalar:  5.668
Epoch 620:  train loss 0.5126   train acc 0.6520   worst 0.3226   lr 0.0161   p 71.00   eps 0.1952   mix 0.0117   time 27.16
scalar:  5.6091
Epoch 621:  train loss 0.5110   train acc 0.6534   worst 0.3228   lr 0.0161   p 71.30   eps 0.1952   mix 0.0116   time 27.17
scalar:  5.6333
Epoch 622:  train loss 0.5109   train acc 0.6543   worst 0.3228   lr 0.0160   p 71.60   eps 0.1952   mix 0.0116   time 27.40
scalar:  5.6309
Epoch 623:  train loss 0.5128   train acc 0.6525   worst 0.3210   lr 0.0160   p 71.90   eps 0.1952   mix 0.0116   time 26.88
scalar:  5.606
Epoch 624:  train loss 0.5099   train acc 0.6541   worst 0.3230   lr 0.0159   p 72.20   eps 0.1952   mix 0.0115   time 27.02
Epoch 624:  test acc 0.5910   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 624:  clean acc 0.5053   certified acc 0.4196
Calculating metrics for L_infinity dist model on test set
Epoch 624:  clean acc 0.4875   certified acc 0.4003
scalar:  5.6447
Epoch 625:  train loss 0.5122   train acc 0.6504   worst 0.3221   lr 0.0159   p 72.51   eps 0.1952   mix 0.0115   time 27.24
scalar:  5.6279
Epoch 626:  train loss 0.5133   train acc 0.6528   worst 0.3214   lr 0.0159   p 72.81   eps 0.1952   mix 0.0115   time 27.39
scalar:  5.6416
Epoch 627:  train loss 0.5106   train acc 0.6548   worst 0.3217   lr 0.0158   p 73.12   eps 0.1952   mix 0.0114   time 27.75
scalar:  5.6702
Epoch 628:  train loss 0.5120   train acc 0.6537   worst 0.3211   lr 0.0158   p 73.43   eps 0.1952   mix 0.0114   time 26.94
scalar:  5.6689
Epoch 629:  train loss 0.5110   train acc 0.6540   worst 0.3235   lr 0.0158   p 73.73   eps 0.1952   mix 0.0114   time 26.77
Epoch 629:  test acc 0.5985   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 629:  clean acc 0.5095   certified acc 0.4159
Calculating metrics for L_infinity dist model on test set
Epoch 629:  clean acc 0.4890   certified acc 0.3950
scalar:  5.6573
Epoch 630:  train loss 0.5116   train acc 0.6521   worst 0.3226   lr 0.0157   p 74.04   eps 0.1952   mix 0.0113   time 26.92
scalar:  5.6651
Epoch 631:  train loss 0.5104   train acc 0.6539   worst 0.3243   lr 0.0157   p 74.36   eps 0.1952   mix 0.0113   time 27.35
scalar:  5.6745
Epoch 632:  train loss 0.5131   train acc 0.6520   worst 0.3194   lr 0.0157   p 74.67   eps 0.1952   mix 0.0113   time 27.38
scalar:  5.6744
Epoch 633:  train loss 0.5108   train acc 0.6547   worst 0.3193   lr 0.0156   p 74.98   eps 0.1952   mix 0.0112   time 26.78
scalar:  5.6714
Epoch 634:  train loss 0.5117   train acc 0.6527   worst 0.3220   lr 0.0156   p 75.30   eps 0.1952   mix 0.0112   time 26.98
Epoch 634:  test acc 0.5964   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 634:  clean acc 0.5111   certified acc 0.4219
Calculating metrics for L_infinity dist model on test set
Epoch 634:  clean acc 0.4879   certified acc 0.3991
scalar:  5.6579
Epoch 635:  train loss 0.5103   train acc 0.6547   worst 0.3203   lr 0.0155   p 75.62   eps 0.1952   mix 0.0112   time 27.15
scalar:  5.6731
Epoch 636:  train loss 0.5098   train acc 0.6541   worst 0.3232   lr 0.0155   p 75.93   eps 0.1952   mix 0.0112   time 27.38
scalar:  5.662
Epoch 637:  train loss 0.5112   train acc 0.6561   worst 0.3207   lr 0.0155   p 76.25   eps 0.1952   mix 0.0111   time 27.25
scalar:  5.7027
Epoch 638:  train loss 0.5112   train acc 0.6551   worst 0.3204   lr 0.0154   p 76.57   eps 0.1952   mix 0.0111   time 26.91
scalar:  5.6955
Epoch 639:  train loss 0.5083   train acc 0.6556   worst 0.3213   lr 0.0154   p 76.90   eps 0.1952   mix 0.0111   time 26.84
Epoch 639:  test acc 0.6023   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 639:  clean acc 0.5115   certified acc 0.4248
Calculating metrics for L_infinity dist model on test set
Epoch 639:  clean acc 0.4886   certified acc 0.4004
scalar:  5.7076
Epoch 640:  train loss 0.5125   train acc 0.6552   worst 0.3183   lr 0.0154   p 77.22   eps 0.1952   mix 0.0110   time 27.55
scalar:  5.6808
Epoch 641:  train loss 0.5107   train acc 0.6534   worst 0.3200   lr 0.0153   p 77.54   eps 0.1952   mix 0.0110   time 27.68
scalar:  5.6945
Epoch 642:  train loss 0.5081   train acc 0.6556   worst 0.3220   lr 0.0153   p 77.87   eps 0.1952   mix 0.0110   time 27.06
scalar:  5.7003
Epoch 643:  train loss 0.5113   train acc 0.6539   worst 0.3198   lr 0.0153   p 78.20   eps 0.1952   mix 0.0109   time 27.27
scalar:  5.6952
Epoch 644:  train loss 0.5088   train acc 0.6571   worst 0.3202   lr 0.0152   p 78.53   eps 0.1952   mix 0.0109   time 26.91
Epoch 644:  test acc 0.6024   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 644:  clean acc 0.5121   certified acc 0.4247
Calculating metrics for L_infinity dist model on test set
Epoch 644:  clean acc 0.4911   certified acc 0.4013
scalar:  5.715
Epoch 645:  train loss 0.5103   train acc 0.6538   worst 0.3228   lr 0.0152   p 78.86   eps 0.1952   mix 0.0109   time 27.00
scalar:  5.6864
Epoch 646:  train loss 0.5095   train acc 0.6559   worst 0.3209   lr 0.0151   p 79.19   eps 0.1952   mix 0.0108   time 27.60
scalar:  5.7096
Epoch 647:  train loss 0.5124   train acc 0.6538   worst 0.3202   lr 0.0151   p 79.52   eps 0.1952   mix 0.0108   time 27.28
scalar:  5.7183
Epoch 648:  train loss 0.5092   train acc 0.6563   worst 0.3199   lr 0.0151   p 79.86   eps 0.1952   mix 0.0108   time 27.01
scalar:  5.7531
Epoch 649:  train loss 0.5101   train acc 0.6538   worst 0.3199   lr 0.0150   p 80.19   eps 0.1952   mix 0.0108   time 26.83
Epoch 649:  test acc 0.5996   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 649:  clean acc 0.5320   certified acc 0.4427
Calculating metrics for L_infinity dist model on test set
Epoch 649:  clean acc 0.5103   certified acc 0.4258
scalar:  5.7386
Epoch 650:  train loss 0.5077   train acc 0.6558   worst 0.3227   lr 0.0150   p 80.53   eps 0.1952   mix 0.0107   time 26.93
scalar:  5.7371
Epoch 651:  train loss 0.5117   train acc 0.6547   worst 0.3187   lr 0.0150   p 80.87   eps 0.1952   mix 0.0107   time 27.20
scalar:  5.7261
Epoch 652:  train loss 0.5087   train acc 0.6560   worst 0.3209   lr 0.0149   p 81.21   eps 0.1952   mix 0.0107   time 26.95
scalar:  5.7129
Epoch 653:  train loss 0.5115   train acc 0.6523   worst 0.3174   lr 0.0149   p 81.55   eps 0.1952   mix 0.0106   time 27.14
scalar:  5.7177
Epoch 654:  train loss 0.5103   train acc 0.6555   worst 0.3194   lr 0.0149   p 81.89   eps 0.1952   mix 0.0106   time 26.87
Epoch 654:  test acc 0.5984   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 654:  clean acc 0.5392   certified acc 0.4513
Calculating metrics for L_infinity dist model on test set
Epoch 654:  clean acc 0.5115   certified acc 0.4230
scalar:  5.7396
Epoch 655:  train loss 0.5078   train acc 0.6562   worst 0.3209   lr 0.0148   p 82.24   eps 0.1952   mix 0.0106   time 27.37
scalar:  5.7415
Epoch 656:  train loss 0.5096   train acc 0.6556   worst 0.3203   lr 0.0148   p 82.58   eps 0.1952   mix 0.0105   time 27.23
scalar:  5.7409
Epoch 657:  train loss 0.5096   train acc 0.6567   worst 0.3189   lr 0.0147   p 82.93   eps 0.1952   mix 0.0105   time 27.25
scalar:  5.7343
Epoch 658:  train loss 0.5088   train acc 0.6574   worst 0.3206   lr 0.0147   p 83.28   eps 0.1952   mix 0.0105   time 27.11
scalar:  5.7722
Epoch 659:  train loss 0.5085   train acc 0.6572   worst 0.3201   lr 0.0147   p 83.63   eps 0.1952   mix 0.0105   time 26.79
Epoch 659:  test acc 0.5990   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 659:  clean acc 0.5551   certified acc 0.4588
Calculating metrics for L_infinity dist model on test set
Epoch 659:  clean acc 0.5243   certified acc 0.4346
scalar:  5.7559
Epoch 660:  train loss 0.5080   train acc 0.6550   worst 0.3218   lr 0.0146   p 83.98   eps 0.1952   mix 0.0104   time 27.56
scalar:  5.7682
Epoch 661:  train loss 0.5062   train acc 0.6584   worst 0.3235   lr 0.0146   p 84.34   eps 0.1952   mix 0.0104   time 27.58
scalar:  5.7649
Epoch 662:  train loss 0.5087   train acc 0.6564   worst 0.3185   lr 0.0146   p 84.69   eps 0.1952   mix 0.0104   time 26.86
scalar:  5.738
Epoch 663:  train loss 0.5096   train acc 0.6548   worst 0.3203   lr 0.0145   p 85.05   eps 0.1952   mix 0.0103   time 26.94
scalar:  5.7375
Epoch 664:  train loss 0.5065   train acc 0.6562   worst 0.3219   lr 0.0145   p 85.41   eps 0.1952   mix 0.0103   time 26.81
Epoch 664:  test acc 0.6019   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 664:  clean acc 0.5615   certified acc 0.4740
Calculating metrics for L_infinity dist model on test set
Epoch 664:  clean acc 0.5338   certified acc 0.4475
scalar:  5.7572
Epoch 665:  train loss 0.5065   train acc 0.6590   worst 0.3230   lr 0.0145   p 85.77   eps 0.1952   mix 0.0103   time 27.48
scalar:  5.7805
Epoch 666:  train loss 0.5082   train acc 0.6581   worst 0.3188   lr 0.0144   p 86.13   eps 0.1952   mix 0.0103   time 27.46
scalar:  5.8184
Epoch 667:  train loss 0.5083   train acc 0.6565   worst 0.3192   lr 0.0144   p 86.49   eps 0.1952   mix 0.0102   time 27.23
scalar:  5.7945
Epoch 668:  train loss 0.5096   train acc 0.6543   worst 0.3210   lr 0.0143   p 86.85   eps 0.1952   mix 0.0102   time 26.90
scalar:  5.7561
Epoch 669:  train loss 0.5066   train acc 0.6586   worst 0.3196   lr 0.0143   p 87.22   eps 0.1952   mix 0.0102   time 26.73
Epoch 669:  test acc 0.6035   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 669:  clean acc 0.5713   certified acc 0.4839
Calculating metrics for L_infinity dist model on test set
Epoch 669:  clean acc 0.5428   certified acc 0.4488
scalar:  5.8131
Epoch 670:  train loss 0.5081   train acc 0.6564   worst 0.3202   lr 0.0143   p 87.58   eps 0.1952   mix 0.0101   time 27.37
scalar:  5.8086
Epoch 671:  train loss 0.5077   train acc 0.6566   worst 0.3193   lr 0.0142   p 87.95   eps 0.1952   mix 0.0101   time 27.63
scalar:  5.8201
Epoch 672:  train loss 0.5083   train acc 0.6569   worst 0.3184   lr 0.0142   p 88.32   eps 0.1952   mix 0.0101   time 27.12
scalar:  5.8055
Epoch 673:  train loss 0.5066   train acc 0.6557   worst 0.3222   lr 0.0142   p 88.69   eps 0.1952   mix 0.0101   time 27.08
scalar:  5.788
Epoch 674:  train loss 0.5089   train acc 0.6546   worst 0.3181   lr 0.0141   p 89.07   eps 0.1952   mix 0.0100   time 26.89
Epoch 674:  test acc 0.5994   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 674:  clean acc 0.5772   certified acc 0.4945
Calculating metrics for L_infinity dist model on test set
Epoch 674:  clean acc 0.5420   certified acc 0.4585
scalar:  5.758
Epoch 675:  train loss 0.5088   train acc 0.6565   worst 0.3184   lr 0.0141   p 89.44   eps 0.1952   mix 0.0100   time 27.00
scalar:  5.7885
Epoch 676:  train loss 0.5071   train acc 0.6579   worst 0.3198   lr 0.0141   p 89.82   eps 0.1952   mix 0.0100   time 27.55
scalar:  5.7739
Epoch 677:  train loss 0.5098   train acc 0.6539   worst 0.3183   lr 0.0140   p 90.20   eps 0.1952   mix 0.0099   time 27.24
scalar:  5.7321
Epoch 678:  train loss 0.5063   train acc 0.6601   worst 0.3206   lr 0.0140   p 90.58   eps 0.1952   mix 0.0099   time 27.14
scalar:  5.7923
Epoch 679:  train loss 0.5064   train acc 0.6587   worst 0.3190   lr 0.0139   p 90.96   eps 0.1952   mix 0.0099   time 26.94
Epoch 679:  test acc 0.6005   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 679:  clean acc 0.5811   certified acc 0.5011
Calculating metrics for L_infinity dist model on test set
Epoch 679:  clean acc 0.5501   certified acc 0.4653
scalar:  5.8011
Epoch 680:  train loss 0.5072   train acc 0.6560   worst 0.3192   lr 0.0139   p 91.34   eps 0.1952   mix 0.0099   time 27.23
scalar:  5.8024
Epoch 681:  train loss 0.5046   train acc 0.6605   worst 0.3217   lr 0.0139   p 91.72   eps 0.1952   mix 0.0098   time 28.02
scalar:  5.806
Epoch 682:  train loss 0.5063   train acc 0.6587   worst 0.3203   lr 0.0138   p 92.11   eps 0.1952   mix 0.0098   time 27.23
scalar:  5.8236
Epoch 683:  train loss 0.5051   train acc 0.6595   worst 0.3208   lr 0.0138   p 92.50   eps 0.1952   mix 0.0098   time 27.01
scalar:  5.8499
Epoch 684:  train loss 0.5067   train acc 0.6564   worst 0.3196   lr 0.0138   p 92.89   eps 0.1952   mix 0.0098   time 27.13
Epoch 684:  test acc 0.5982   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 684:  clean acc 0.5854   certified acc 0.5043
Calculating metrics for L_infinity dist model on test set
Epoch 684:  clean acc 0.5463   certified acc 0.4646
scalar:  5.8279
Epoch 685:  train loss 0.5041   train acc 0.6590   worst 0.3232   lr 0.0137   p 93.28   eps 0.1952   mix 0.0097   time 26.83
scalar:  5.8122
Epoch 686:  train loss 0.5076   train acc 0.6589   worst 0.3170   lr 0.0137   p 93.67   eps 0.1952   mix 0.0097   time 27.73
scalar:  5.8382
Epoch 687:  train loss 0.5054   train acc 0.6587   worst 0.3211   lr 0.0137   p 94.06   eps 0.1952   mix 0.0097   time 27.08
scalar:  5.8055
Epoch 688:  train loss 0.5055   train acc 0.6576   worst 0.3213   lr 0.0136   p 94.46   eps 0.1952   mix 0.0096   time 26.93
scalar:  5.8175
Epoch 689:  train loss 0.5056   train acc 0.6591   worst 0.3198   lr 0.0136   p 94.86   eps 0.1952   mix 0.0096   time 26.81
Epoch 689:  test acc 0.5995   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 689:  clean acc 0.5864   certified acc 0.5082
Calculating metrics for L_infinity dist model on test set
Epoch 689:  clean acc 0.5445   certified acc 0.4650
scalar:  5.8579
Epoch 690:  train loss 0.5073   train acc 0.6589   worst 0.3197   lr 0.0136   p 95.26   eps 0.1952   mix 0.0096   time 26.91
scalar:  5.8515
Epoch 691:  train loss 0.5050   train acc 0.6594   worst 0.3214   lr 0.0135   p 95.66   eps 0.1952   mix 0.0096   time 27.77
scalar:  5.8398
Epoch 692:  train loss 0.5054   train acc 0.6606   worst 0.3185   lr 0.0135   p 96.06   eps 0.1952   mix 0.0095   time 26.71
scalar:  5.8587
Epoch 693:  train loss 0.5042   train acc 0.6609   worst 0.3208   lr 0.0134   p 96.46   eps 0.1952   mix 0.0095   time 26.97
scalar:  5.8696
Epoch 694:  train loss 0.5055   train acc 0.6608   worst 0.3186   lr 0.0134   p 96.87   eps 0.1952   mix 0.0095   time 26.99
Epoch 694:  test acc 0.6024   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 694:  clean acc 0.6002   certified acc 0.5173
Calculating metrics for L_infinity dist model on test set
Epoch 694:  clean acc 0.5566   certified acc 0.4718
scalar:  5.8674
Epoch 695:  train loss 0.5050   train acc 0.6596   worst 0.3215   lr 0.0134   p 97.28   eps 0.1952   mix 0.0095   time 26.79
scalar:  5.8563
Epoch 696:  train loss 0.5062   train acc 0.6578   worst 0.3188   lr 0.0133   p 97.69   eps 0.1952   mix 0.0094   time 27.79
scalar:  5.8687
Epoch 697:  train loss 0.5048   train acc 0.6593   worst 0.3221   lr 0.0133   p 98.10   eps 0.1952   mix 0.0094   time 27.39
scalar:  5.8654
Epoch 698:  train loss 0.5050   train acc 0.6597   worst 0.3211   lr 0.0133   p 98.51   eps 0.1952   mix 0.0094   time 26.78
scalar:  5.8613
Epoch 699:  train loss 0.5051   train acc 0.6605   worst 0.3208   lr 0.0132   p 98.93   eps 0.1952   mix 0.0094   time 27.13
Epoch 699:  test acc 0.6029   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 699:  clean acc 0.5937   certified acc 0.5135
Calculating metrics for L_infinity dist model on test set
Epoch 699:  clean acc 0.5590   certified acc 0.4780
scalar:  5.8585
Epoch 700:  train loss 0.5054   train acc 0.6608   worst 0.3183   lr 0.0132   p 99.34   eps 0.1952   mix 0.0093   time 26.79
scalar:  5.8675
Epoch 701:  train loss 0.5041   train acc 0.6606   worst 0.3206   lr 0.0132   p 99.76   eps 0.1952   mix 0.0093   time 28.28
scalar:  5.8823
Epoch 702:  train loss 0.5063   train acc 0.6578   worst 0.3188   lr 0.0131   p 100.18   eps 0.1952   mix 0.0093   time 27.17
scalar:  5.8723
Epoch 703:  train loss 0.5039   train acc 0.6610   worst 0.3203   lr 0.0131   p 100.60   eps 0.1952   mix 0.0092   time 27.05
scalar:  5.8838
Epoch 704:  train loss 0.5019   train acc 0.6616   worst 0.3215   lr 0.0130   p 101.02   eps 0.1952   mix 0.0092   time 27.32
Epoch 704:  test acc 0.6044   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 704:  clean acc 0.5968   certified acc 0.5146
Calculating metrics for L_infinity dist model on test set
Epoch 704:  clean acc 0.5573   certified acc 0.4748
scalar:  5.8918
Epoch 705:  train loss 0.5042   train acc 0.6614   worst 0.3190   lr 0.0130   p 101.45   eps 0.1952   mix 0.0092   time 26.66
scalar:  5.8893
Epoch 706:  train loss 0.5036   train acc 0.6616   worst 0.3194   lr 0.0130   p 101.88   eps 0.1952   mix 0.0092   time 27.84
scalar:  5.9116
Epoch 707:  train loss 0.5031   train acc 0.6608   worst 0.3191   lr 0.0129   p 102.30   eps 0.1952   mix 0.0091   time 27.33
scalar:  5.914
Epoch 708:  train loss 0.5048   train acc 0.6604   worst 0.3172   lr 0.0129   p 102.73   eps 0.1952   mix 0.0091   time 26.81
scalar:  5.9099
Epoch 709:  train loss 0.5032   train acc 0.6630   worst 0.3203   lr 0.0129   p 103.17   eps 0.1952   mix 0.0091   time 27.10
Epoch 709:  test acc 0.6017   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 709:  clean acc 0.6016   certified acc 0.5218
Calculating metrics for L_infinity dist model on test set
Epoch 709:  clean acc 0.5617   certified acc 0.4792
scalar:  5.9125
Epoch 710:  train loss 0.5038   train acc 0.6604   worst 0.3181   lr 0.0128   p 103.60   eps 0.1952   mix 0.0091   time 26.79
scalar:  5.9135
Epoch 711:  train loss 0.5056   train acc 0.6599   worst 0.3210   lr 0.0128   p 104.04   eps 0.1952   mix 0.0090   time 27.76
scalar:  5.9104
Epoch 712:  train loss 0.5055   train acc 0.6588   worst 0.3188   lr 0.0128   p 104.47   eps 0.1952   mix 0.0090   time 27.22
scalar:  5.8965
Epoch 713:  train loss 0.5039   train acc 0.6590   worst 0.3205   lr 0.0127   p 104.91   eps 0.1952   mix 0.0090   time 26.82
scalar:  5.8909
Epoch 714:  train loss 0.5027   train acc 0.6610   worst 0.3215   lr 0.0127   p 105.36   eps 0.1952   mix 0.0090   time 27.02
Epoch 714:  test acc 0.6054   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 714:  clean acc 0.5983   certified acc 0.5187
Calculating metrics for L_infinity dist model on test set
Epoch 714:  clean acc 0.5580   certified acc 0.4795
scalar:  5.8977
Epoch 715:  train loss 0.5043   train acc 0.6592   worst 0.3214   lr 0.0127   p 105.80   eps 0.1952   mix 0.0089   time 26.88
scalar:  5.8847
Epoch 716:  train loss 0.5038   train acc 0.6620   worst 0.3186   lr 0.0126   p 106.24   eps 0.1952   mix 0.0089   time 27.69
scalar:  5.9101
Epoch 717:  train loss 0.5027   train acc 0.6596   worst 0.3213   lr 0.0126   p 106.69   eps 0.1952   mix 0.0089   time 27.52
scalar:  5.8941
Epoch 718:  train loss 0.5027   train acc 0.6610   worst 0.3193   lr 0.0125   p 107.14   eps 0.1952   mix 0.0089   time 26.73
scalar:  5.8977
Epoch 719:  train loss 0.5037   train acc 0.6598   worst 0.3184   lr 0.0125   p 107.59   eps 0.1952   mix 0.0088   time 27.06
Epoch 719:  test acc 0.6025   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 719:  clean acc 0.6119   certified acc 0.5375
Calculating metrics for L_infinity dist model on test set
Epoch 719:  clean acc 0.5681   certified acc 0.4859
scalar:  5.8868
Epoch 720:  train loss 0.5043   train acc 0.6603   worst 0.3175   lr 0.0125   p 108.04   eps 0.1952   mix 0.0088   time 26.97
scalar:  5.8848
Epoch 721:  train loss 0.5014   train acc 0.6621   worst 0.3212   lr 0.0124   p 108.50   eps 0.1952   mix 0.0088   time 27.48
scalar:  5.8876
Epoch 722:  train loss 0.5022   train acc 0.6619   worst 0.3205   lr 0.0124   p 108.95   eps 0.1952   mix 0.0088   time 27.51
scalar:  5.9031
Epoch 723:  train loss 0.5017   train acc 0.6603   worst 0.3204   lr 0.0124   p 109.41   eps 0.1952   mix 0.0087   time 26.65
scalar:  5.8872
Epoch 724:  train loss 0.5016   train acc 0.6629   worst 0.3189   lr 0.0123   p 109.87   eps 0.1952   mix 0.0087   time 26.90
Epoch 724:  test acc 0.5969   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 724:  clean acc 0.6152   certified acc 0.5403
Calculating metrics for L_infinity dist model on test set
Epoch 724:  clean acc 0.5606   certified acc 0.4838
scalar:  5.9312
Epoch 725:  train loss 0.5014   train acc 0.6627   worst 0.3190   lr 0.0123   p 110.34   eps 0.1952   mix 0.0087   time 26.71
scalar:  5.9227
Epoch 726:  train loss 0.5024   train acc 0.6625   worst 0.3204   lr 0.0123   p 110.80   eps 0.1952   mix 0.0087   time 27.40
scalar:  5.9199
Epoch 727:  train loss 0.5014   train acc 0.6615   worst 0.3194   lr 0.0122   p 111.27   eps 0.1952   mix 0.0086   time 27.94
scalar:  5.9178
Epoch 728:  train loss 0.5008   train acc 0.6633   worst 0.3202   lr 0.0122   p 111.73   eps 0.1952   mix 0.0086   time 26.95
scalar:  5.9296
Epoch 729:  train loss 0.5025   train acc 0.6629   worst 0.3177   lr 0.0122   p 112.20   eps 0.1952   mix 0.0086   time 26.96
Epoch 729:  test acc 0.5976   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 729:  clean acc 0.6157   certified acc 0.5438
Calculating metrics for L_infinity dist model on test set
Epoch 729:  clean acc 0.5650   certified acc 0.4913
scalar:  5.932
Epoch 730:  train loss 0.5023   train acc 0.6643   worst 0.3188   lr 0.0121   p 112.68   eps 0.1952   mix 0.0086   time 26.96
scalar:  5.9515
Epoch 731:  train loss 0.5023   train acc 0.6614   worst 0.3191   lr 0.0121   p 113.15   eps 0.1952   mix 0.0085   time 27.49
scalar:  5.927
Epoch 732:  train loss 0.5012   train acc 0.6615   worst 0.3214   lr 0.0120   p 113.63   eps 0.1952   mix 0.0085   time 27.65
scalar:  5.9269
Epoch 733:  train loss 0.5024   train acc 0.6622   worst 0.3174   lr 0.0120   p 114.10   eps 0.1952   mix 0.0085   time 26.65
scalar:  5.9386
Epoch 734:  train loss 0.5013   train acc 0.6621   worst 0.3202   lr 0.0120   p 114.58   eps 0.1952   mix 0.0085   time 27.12
Epoch 734:  test acc 0.6011   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 734:  clean acc 0.6190   certified acc 0.5441
Calculating metrics for L_infinity dist model on test set
Epoch 734:  clean acc 0.5699   certified acc 0.4912
scalar:  5.9354
Epoch 735:  train loss 0.5004   train acc 0.6645   worst 0.3209   lr 0.0119   p 115.07   eps 0.1952   mix 0.0085   time 26.91
scalar:  5.9384
Epoch 736:  train loss 0.5016   train acc 0.6613   worst 0.3176   lr 0.0119   p 115.55   eps 0.1952   mix 0.0084   time 27.07
scalar:  5.9354
Epoch 737:  train loss 0.5000   train acc 0.6632   worst 0.3207   lr 0.0119   p 116.04   eps 0.1952   mix 0.0084   time 27.72
scalar:  5.9403
Epoch 738:  train loss 0.5018   train acc 0.6604   worst 0.3206   lr 0.0118   p 116.53   eps 0.1952   mix 0.0084   time 26.79
scalar:  5.9335
Epoch 739:  train loss 0.5009   train acc 0.6630   worst 0.3210   lr 0.0118   p 117.02   eps 0.1952   mix 0.0084   time 26.87
Epoch 739:  test acc 0.5989   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 739:  clean acc 0.6217   certified acc 0.5483
Calculating metrics for L_infinity dist model on test set
Epoch 739:  clean acc 0.5640   certified acc 0.4845
scalar:  5.9508
Epoch 740:  train loss 0.4989   train acc 0.6639   worst 0.3207   lr 0.0118   p 117.51   eps 0.1952   mix 0.0083   time 26.78
scalar:  5.9618
Epoch 741:  train loss 0.5005   train acc 0.6638   worst 0.3211   lr 0.0117   p 118.00   eps 0.1952   mix 0.0083   time 27.07
scalar:  5.9592
Epoch 742:  train loss 0.5010   train acc 0.6636   worst 0.3222   lr 0.0117   p 118.50   eps 0.1952   mix 0.0083   time 28.00
scalar:  5.9547
Epoch 743:  train loss 0.4997   train acc 0.6655   worst 0.3214   lr 0.0117   p 119.00   eps 0.1952   mix 0.0083   time 26.95
scalar:  5.9564
Epoch 744:  train loss 0.5014   train acc 0.6627   worst 0.3194   lr 0.0116   p 119.50   eps 0.1952   mix 0.0082   time 27.01
Epoch 744:  test acc 0.5996   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 744:  clean acc 0.6256   certified acc 0.5550
Calculating metrics for L_infinity dist model on test set
Epoch 744:  clean acc 0.5751   certified acc 0.4916
scalar:  5.9622
Epoch 745:  train loss 0.5008   train acc 0.6621   worst 0.3215   lr 0.0116   p 120.00   eps 0.1952   mix 0.0082   time 27.10
scalar:  5.9495
Epoch 746:  train loss 0.5000   train acc 0.6643   worst 0.3188   lr 0.0116   p 120.51   eps 0.1952   mix 0.0082   time 27.02
scalar:  5.9398
Epoch 747:  train loss 0.5016   train acc 0.6599   worst 0.3189   lr 0.0115   p 121.01   eps 0.1952   mix 0.0082   time 27.65
scalar:  5.925
Epoch 748:  train loss 0.5018   train acc 0.6619   worst 0.3194   lr 0.0115   p 121.52   eps 0.1952   mix 0.0082   time 26.61
scalar:  5.9331
Epoch 749:  train loss 0.5002   train acc 0.6630   worst 0.3218   lr 0.0114   p 122.03   eps 0.1952   mix 0.0081   time 26.91
Epoch 749:  test acc 0.6002   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 749:  clean acc 0.6238   certified acc 0.5532
Calculating metrics for L_infinity dist model on test set
Epoch 749:  clean acc 0.5703   certified acc 0.4930
scalar:  5.9384
Epoch 750:  train loss 0.4996   train acc 0.6636   worst 0.3209   lr 0.0114   p 122.55   eps 0.1952   mix 0.0081   time 26.58
scalar:  5.9718
Epoch 751:  train loss 0.4995   train acc 0.6636   worst 0.3203   lr 0.0114   p 123.06   eps 0.1952   mix 0.0081   time 27.24
scalar:  5.9811
Epoch 752:  train loss 0.4990   train acc 0.6637   worst 0.3195   lr 0.0113   p 123.58   eps 0.1952   mix 0.0081   time 28.11
scalar:  5.9698
Epoch 753:  train loss 0.4974   train acc 0.6652   worst 0.3197   lr 0.0113   p 124.10   eps 0.1952   mix 0.0080   time 26.95
scalar:  5.9807
Epoch 754:  train loss 0.4987   train acc 0.6639   worst 0.3206   lr 0.0113   p 124.62   eps 0.1952   mix 0.0080   time 26.93
Epoch 754:  test acc 0.6007   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 754:  clean acc 0.6307   certified acc 0.5618
Calculating metrics for L_infinity dist model on test set
Epoch 754:  clean acc 0.5715   certified acc 0.4946
scalar:  5.9921
Epoch 755:  train loss 0.5001   train acc 0.6643   worst 0.3176   lr 0.0112   p 125.15   eps 0.1952   mix 0.0080   time 26.66
scalar:  6.0014
Epoch 756:  train loss 0.4977   train acc 0.6664   worst 0.3202   lr 0.0112   p 125.67   eps 0.1952   mix 0.0080   time 27.38
scalar:  6.0132
Epoch 757:  train loss 0.5012   train acc 0.6620   worst 0.3188   lr 0.0112   p 126.20   eps 0.1952   mix 0.0079   time 28.18
scalar:  5.9876
Epoch 758:  train loss 0.4984   train acc 0.6626   worst 0.3218   lr 0.0111   p 126.73   eps 0.1952   mix 0.0079   time 26.77
scalar:  5.9794
Epoch 759:  train loss 0.4996   train acc 0.6641   worst 0.3185   lr 0.0111   p 127.27   eps 0.1952   mix 0.0079   time 26.63
Epoch 759:  test acc 0.6013   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 759:  clean acc 0.6343   certified acc 0.5648
Calculating metrics for L_infinity dist model on test set
Epoch 759:  clean acc 0.5805   certified acc 0.5022
scalar:  5.9805
Epoch 760:  train loss 0.4974   train acc 0.6648   worst 0.3215   lr 0.0111   p 127.80   eps 0.1952   mix 0.0079   time 27.04
scalar:  6.0029
Epoch 761:  train loss 0.5007   train acc 0.6632   worst 0.3178   lr 0.0110   p 128.34   eps 0.1952   mix 0.0079   time 27.10
scalar:  6.0026
Epoch 762:  train loss 0.4983   train acc 0.6656   worst 0.3206   lr 0.0110   p 128.88   eps 0.1952   mix 0.0078   time 27.76
scalar:  6.0075
Epoch 763:  train loss 0.4976   train acc 0.6640   worst 0.3207   lr 0.0110   p 129.42   eps 0.1952   mix 0.0078   time 26.92
scalar:  6.0026
Epoch 764:  train loss 0.4986   train acc 0.6639   worst 0.3206   lr 0.0109   p 129.97   eps 0.1952   mix 0.0078   time 26.54
Epoch 764:  test acc 0.6020   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 764:  clean acc 0.6335   certified acc 0.5637
Calculating metrics for L_infinity dist model on test set
Epoch 764:  clean acc 0.5767   certified acc 0.5010
scalar:  6.0121
Epoch 765:  train loss 0.5001   train acc 0.6647   worst 0.3189   lr 0.0109   p 130.51   eps 0.1952   mix 0.0078   time 26.93
scalar:  6.0111
Epoch 766:  train loss 0.4986   train acc 0.6674   worst 0.3205   lr 0.0108   p 131.06   eps 0.1952   mix 0.0078   time 26.83
scalar:  6.0234
Epoch 767:  train loss 0.4987   train acc 0.6653   worst 0.3168   lr 0.0108   p 131.61   eps 0.1952   mix 0.0077   time 27.78
scalar:  6.0399
Epoch 768:  train loss 0.4985   train acc 0.6643   worst 0.3187   lr 0.0108   p 132.17   eps 0.1952   mix 0.0077   time 26.86
scalar:  6.0443
Epoch 769:  train loss 0.4995   train acc 0.6626   worst 0.3203   lr 0.0107   p 132.72   eps 0.1952   mix 0.0077   time 26.60
Epoch 769:  test acc 0.5992   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 769:  clean acc 0.6396   certified acc 0.5717
Calculating metrics for L_infinity dist model on test set
Epoch 769:  clean acc 0.5782   certified acc 0.5038
scalar:  6.0123
Epoch 770:  train loss 0.4966   train acc 0.6663   worst 0.3195   lr 0.0107   p 133.28   eps 0.1952   mix 0.0077   time 27.11
scalar:  6.0222
Epoch 771:  train loss 0.4995   train acc 0.6637   worst 0.3186   lr 0.0107   p 133.84   eps 0.1952   mix 0.0076   time 26.88
scalar:  6.0138
Epoch 772:  train loss 0.4959   train acc 0.6657   worst 0.3216   lr 0.0106   p 134.40   eps 0.1952   mix 0.0076   time 27.39
scalar:  6.0198
Epoch 773:  train loss 0.4979   train acc 0.6654   worst 0.3198   lr 0.0106   p 134.97   eps 0.1952   mix 0.0076   time 27.18
scalar:  6.0205
Epoch 774:  train loss 0.4984   train acc 0.6656   worst 0.3184   lr 0.0106   p 135.54   eps 0.1952   mix 0.0076   time 26.83
Epoch 774:  test acc 0.5993   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 774:  clean acc 0.6406   certified acc 0.5747
Calculating metrics for L_infinity dist model on test set
Epoch 774:  clean acc 0.5803   certified acc 0.5047
scalar:  6.0195
Epoch 775:  train loss 0.4974   train acc 0.6660   worst 0.3199   lr 0.0105   p 136.11   eps 0.1952   mix 0.0076   time 26.95
scalar:  6.0357
Epoch 776:  train loss 0.4977   train acc 0.6675   worst 0.3187   lr 0.0105   p 136.68   eps 0.1952   mix 0.0075   time 27.07
scalar:  6.0508
Epoch 777:  train loss 0.4986   train acc 0.6655   worst 0.3183   lr 0.0105   p 137.26   eps 0.1952   mix 0.0075   time 27.80
scalar:  6.0411
Epoch 778:  train loss 0.4981   train acc 0.6646   worst 0.3178   lr 0.0104   p 137.83   eps 0.1952   mix 0.0075   time 26.80
scalar:  6.0355
Epoch 779:  train loss 0.4961   train acc 0.6659   worst 0.3207   lr 0.0104   p 138.41   eps 0.1952   mix 0.0075   time 26.77
Epoch 779:  test acc 0.6022   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 779:  clean acc 0.6372   certified acc 0.5709
Calculating metrics for L_infinity dist model on test set
Epoch 779:  clean acc 0.5816   certified acc 0.5062
scalar:  6.0258
Epoch 780:  train loss 0.4969   train acc 0.6670   worst 0.3187   lr 0.0104   p 139.00   eps 0.1952   mix 0.0075   time 27.16
scalar:  6.0382
Epoch 781:  train loss 0.4971   train acc 0.6658   worst 0.3199   lr 0.0103   p 139.58   eps 0.1952   mix 0.0074   time 27.33
scalar:  6.0383
Epoch 782:  train loss 0.4964   train acc 0.6669   worst 0.3202   lr 0.0103   p 140.17   eps 0.1952   mix 0.0074   time 27.35
scalar:  6.0599
Epoch 783:  train loss 0.4964   train acc 0.6668   worst 0.3204   lr 0.0103   p 140.76   eps 0.1952   mix 0.0074   time 27.47
scalar:  6.066
Epoch 784:  train loss 0.4943   train acc 0.6672   worst 0.3233   lr 0.0102   p 141.35   eps 0.1952   mix 0.0074   time 26.85
Epoch 784:  test acc 0.5999   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 784:  clean acc 0.6483   certified acc 0.5838
Calculating metrics for L_infinity dist model on test set
Epoch 784:  clean acc 0.5868   certified acc 0.5137
scalar:  6.0489
Epoch 785:  train loss 0.4964   train acc 0.6650   worst 0.3227   lr 0.0102   p 141.94   eps 0.1952   mix 0.0073   time 27.03
scalar:  6.0321
Epoch 786:  train loss 0.4959   train acc 0.6657   worst 0.3214   lr 0.0102   p 142.54   eps 0.1952   mix 0.0073   time 26.63
scalar:  6.0539
Epoch 787:  train loss 0.4960   train acc 0.6655   worst 0.3225   lr 0.0101   p 143.14   eps 0.1952   mix 0.0073   time 27.35
scalar:  6.0438
Epoch 788:  train loss 0.4956   train acc 0.6666   worst 0.3205   lr 0.0101   p 143.74   eps 0.1952   mix 0.0073   time 27.17
scalar:  6.0333
Epoch 789:  train loss 0.4964   train acc 0.6668   worst 0.3188   lr 0.0101   p 144.35   eps 0.1952   mix 0.0073   time 26.98
Epoch 789:  test acc 0.6041   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 789:  clean acc 0.6455   certified acc 0.5828
Calculating metrics for L_infinity dist model on test set
Epoch 789:  clean acc 0.5903   certified acc 0.5168
scalar:  6.0399
Epoch 790:  train loss 0.4971   train acc 0.6645   worst 0.3196   lr 0.0100   p 144.96   eps 0.1952   mix 0.0072   time 26.86
scalar:  6.0381
Epoch 791:  train loss 0.4951   train acc 0.6648   worst 0.3220   lr 0.0100   p 145.57   eps 0.1952   mix 0.0072   time 27.03
scalar:  6.0478
Epoch 792:  train loss 0.4946   train acc 0.6672   worst 0.3192   lr 0.0100   p 146.18   eps 0.1952   mix 0.0072   time 27.11
scalar:  6.0476
Epoch 793:  train loss 0.4968   train acc 0.6644   worst 0.3195   lr 0.0099   p 146.79   eps 0.1952   mix 0.0072   time 27.24
scalar:  6.0303
Epoch 794:  train loss 0.4952   train acc 0.6660   worst 0.3220   lr 0.0099   p 147.41   eps 0.1952   mix 0.0072   time 26.53
Epoch 794:  test acc 0.6042   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 794:  clean acc 0.6475   certified acc 0.5807
Calculating metrics for L_infinity dist model on test set
Epoch 794:  clean acc 0.5861   certified acc 0.5137
scalar:  6.0148
Epoch 795:  train loss 0.4957   train acc 0.6660   worst 0.3213   lr 0.0099   p 148.03   eps 0.1952   mix 0.0071   time 27.03
scalar:  6.0284
Epoch 796:  train loss 0.4945   train acc 0.6670   worst 0.3199   lr 0.0098   p 148.65   eps 0.1952   mix 0.0071   time 26.87
scalar:  6.0486
Epoch 797:  train loss 0.4950   train acc 0.6693   worst 0.3215   lr 0.0098   p 149.28   eps 0.1952   mix 0.0071   time 27.22
scalar:  6.0722
Epoch 798:  train loss 0.4947   train acc 0.6687   worst 0.3201   lr 0.0097   p 149.91   eps 0.1952   mix 0.0071   time 27.19
scalar:  6.0752
Epoch 799:  train loss 0.4957   train acc 0.6653   worst 0.3200   lr 0.0097   p 150.54   eps 0.1952   mix 0.0071   time 26.62
Epoch 799:  test acc 0.5977   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 799:  clean acc 0.6470   certified acc 0.5833
Calculating metrics for L_infinity dist model on test set
Epoch 799:  clean acc 0.5810   certified acc 0.5073
scalar:  6.0689
Epoch 800:  train loss 0.4947   train acc 0.6675   worst 0.3206   lr 0.0097   p 151.17   eps 0.1952   mix 0.0070   time 26.64
scalar:  6.0631
Epoch 801:  train loss 0.4924   train acc 0.6688   worst 0.3223   lr 0.0096   p 151.81   eps 0.1952   mix 0.0070   time 27.01
scalar:  6.0653
Epoch 802:  train loss 0.4917   train acc 0.6701   worst 0.3245   lr 0.0096   p 152.45   eps 0.1952   mix 0.0070   time 27.30
scalar:  6.0656
Epoch 803:  train loss 0.4927   train acc 0.6691   worst 0.3211   lr 0.0096   p 153.09   eps 0.1952   mix 0.0070   time 27.34
scalar:  6.0911
Epoch 804:  train loss 0.4932   train acc 0.6679   worst 0.3226   lr 0.0095   p 153.73   eps 0.1952   mix 0.0070   time 27.00
Epoch 804:  test acc 0.5998   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 804:  clean acc 0.6510   certified acc 0.5892
Calculating metrics for L_infinity dist model on test set
Epoch 804:  clean acc 0.5882   certified acc 0.5154
scalar:  6.0927
Epoch 805:  train loss 0.4954   train acc 0.6678   worst 0.3207   lr 0.0095   p 154.38   eps 0.1952   mix 0.0069   time 26.97
scalar:  6.0859
Epoch 806:  train loss 0.4957   train acc 0.6646   worst 0.3196   lr 0.0095   p 155.03   eps 0.1952   mix 0.0069   time 27.00
scalar:  6.0608
Epoch 807:  train loss 0.4948   train acc 0.6676   worst 0.3206   lr 0.0094   p 155.68   eps 0.1952   mix 0.0069   time 27.32
scalar:  6.0668
Epoch 808:  train loss 0.4936   train acc 0.6677   worst 0.3230   lr 0.0094   p 156.34   eps 0.1952   mix 0.0069   time 27.21
scalar:  6.0888
Epoch 809:  train loss 0.4923   train acc 0.6691   worst 0.3241   lr 0.0094   p 156.99   eps 0.1952   mix 0.0069   time 27.04
Epoch 809:  test acc 0.6024   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 809:  clean acc 0.6533   certified acc 0.5906
Calculating metrics for L_infinity dist model on test set
Epoch 809:  clean acc 0.5882   certified acc 0.5190
scalar:  6.0841
Epoch 810:  train loss 0.4937   train acc 0.6666   worst 0.3219   lr 0.0093   p 157.65   eps 0.1952   mix 0.0069   time 26.47
scalar:  6.0678
Epoch 811:  train loss 0.4928   train acc 0.6682   worst 0.3220   lr 0.0093   p 158.32   eps 0.1952   mix 0.0068   time 27.27
scalar:  6.0658
Epoch 812:  train loss 0.4923   train acc 0.6683   worst 0.3213   lr 0.0093   p 158.98   eps 0.1952   mix 0.0068   time 27.52
scalar:  6.0643
Epoch 813:  train loss 0.4936   train acc 0.6666   worst 0.3206   lr 0.0092   p 159.65   eps 0.1952   mix 0.0068   time 27.16
scalar:  6.0758
Epoch 814:  train loss 0.4952   train acc 0.6671   worst 0.3238   lr 0.0092   p 160.32   eps 0.1952   mix 0.0068   time 26.66
Epoch 814:  test acc 0.6026   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 814:  clean acc 0.6552   certified acc 0.5954
Calculating metrics for L_infinity dist model on test set
Epoch 814:  clean acc 0.5904   certified acc 0.5171
scalar:  6.0837
Epoch 815:  train loss 0.4955   train acc 0.6661   worst 0.3201   lr 0.0092   p 161.00   eps 0.1952   mix 0.0068   time 26.62
scalar:  6.0804
Epoch 816:  train loss 0.4916   train acc 0.6679   worst 0.3234   lr 0.0091   p 161.68   eps 0.1952   mix 0.0067   time 27.16
scalar:  6.0884
Epoch 817:  train loss 0.4916   train acc 0.6719   worst 0.3212   lr 0.0091   p 162.36   eps 0.1952   mix 0.0067   time 27.22
scalar:  6.1164
Epoch 818:  train loss 0.4927   train acc 0.6678   worst 0.3219   lr 0.0091   p 163.04   eps 0.1952   mix 0.0067   time 26.89
scalar:  6.119
Epoch 819:  train loss 0.4922   train acc 0.6702   worst 0.3203   lr 0.0090   p 163.72   eps 0.1952   mix 0.0067   time 26.87
Epoch 819:  test acc 0.6038   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 819:  clean acc 0.6557   certified acc 0.5942
Calculating metrics for L_infinity dist model on test set
Epoch 819:  clean acc 0.5907   certified acc 0.5177
scalar:  6.125
Epoch 820:  train loss 0.4914   train acc 0.6682   worst 0.3238   lr 0.0090   p 164.41   eps 0.1952   mix 0.0067   time 26.92
scalar:  6.0985
Epoch 821:  train loss 0.4927   train acc 0.6707   worst 0.3203   lr 0.0090   p 165.11   eps 0.1952   mix 0.0066   time 27.45
scalar:  6.1005
Epoch 822:  train loss 0.4940   train acc 0.6677   worst 0.3206   lr 0.0089   p 165.80   eps 0.1952   mix 0.0066   time 27.63
scalar:  6.1245
Epoch 823:  train loss 0.4928   train acc 0.6684   worst 0.3218   lr 0.0089   p 166.50   eps 0.1952   mix 0.0066   time 26.72
scalar:  6.106
Epoch 824:  train loss 0.4945   train acc 0.6667   worst 0.3191   lr 0.0089   p 167.20   eps 0.1952   mix 0.0066   time 26.90
Epoch 824:  test acc 0.6036   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 824:  clean acc 0.6574   certified acc 0.5959
Calculating metrics for L_infinity dist model on test set
Epoch 824:  clean acc 0.5910   certified acc 0.5140
scalar:  6.1047
Epoch 825:  train loss 0.4921   train acc 0.6687   worst 0.3227   lr 0.0088   p 167.90   eps 0.1952   mix 0.0066   time 26.86
scalar:  6.1128
Epoch 826:  train loss 0.4932   train acc 0.6698   worst 0.3197   lr 0.0088   p 168.61   eps 0.1952   mix 0.0066   time 27.52
scalar:  6.1232
Epoch 827:  train loss 0.4927   train acc 0.6695   worst 0.3200   lr 0.0088   p 169.32   eps 0.1952   mix 0.0065   time 27.30
scalar:  6.1379
Epoch 828:  train loss 0.4928   train acc 0.6658   worst 0.3228   lr 0.0087   p 170.03   eps 0.1952   mix 0.0065   time 27.24
scalar:  6.13
Epoch 829:  train loss 0.4904   train acc 0.6697   worst 0.3245   lr 0.0087   p 170.75   eps 0.1952   mix 0.0065   time 26.68
Epoch 829:  test acc 0.6001   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 829:  clean acc 0.6611   certified acc 0.5991
Calculating metrics for L_infinity dist model on test set
Epoch 829:  clean acc 0.5938   certified acc 0.5220
scalar:  6.1433
Epoch 830:  train loss 0.4903   train acc 0.6711   worst 0.3228   lr 0.0087   p 171.46   eps 0.1952   mix 0.0065   time 26.56
scalar:  6.1517
Epoch 831:  train loss 0.4926   train acc 0.6672   worst 0.3201   lr 0.0086   p 172.19   eps 0.1952   mix 0.0065   time 27.24
scalar:  6.1332
Epoch 832:  train loss 0.4912   train acc 0.6691   worst 0.3227   lr 0.0086   p 172.91   eps 0.1952   mix 0.0064   time 27.46
scalar:  6.1252
Epoch 833:  train loss 0.4906   train acc 0.6687   worst 0.3219   lr 0.0086   p 173.64   eps 0.1952   mix 0.0064   time 26.91
scalar:  6.1347
Epoch 834:  train loss 0.4902   train acc 0.6695   worst 0.3240   lr 0.0085   p 174.37   eps 0.1952   mix 0.0064   time 26.82
Epoch 834:  test acc 0.6004   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 834:  clean acc 0.6598   certified acc 0.6035
Calculating metrics for L_infinity dist model on test set
Epoch 834:  clean acc 0.5882   certified acc 0.5223
scalar:  6.13
Epoch 835:  train loss 0.4906   train acc 0.6699   worst 0.3220   lr 0.0085   p 175.10   eps 0.1952   mix 0.0064   time 26.71
scalar:  6.1298
Epoch 836:  train loss 0.4886   train acc 0.6726   worst 0.3230   lr 0.0085   p 175.84   eps 0.1952   mix 0.0064   time 27.31
scalar:  6.1386
Epoch 837:  train loss 0.4933   train acc 0.6702   worst 0.3175   lr 0.0084   p 176.58   eps 0.1952   mix 0.0064   time 27.22
scalar:  6.1547
Epoch 838:  train loss 0.4905   train acc 0.6714   worst 0.3223   lr 0.0084   p 177.32   eps 0.1952   mix 0.0063   time 26.74
scalar:  6.1503
Epoch 839:  train loss 0.4922   train acc 0.6682   worst 0.3228   lr 0.0084   p 178.07   eps 0.1952   mix 0.0063   time 27.14
Epoch 839:  test acc 0.5994   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 839:  clean acc 0.6604   certified acc 0.6020
Calculating metrics for L_infinity dist model on test set
Epoch 839:  clean acc 0.5929   certified acc 0.5205
scalar:  6.1402
Epoch 840:  train loss 0.4903   train acc 0.6710   worst 0.3238   lr 0.0084   p 178.82   eps 0.1952   mix 0.0063   time 26.54
scalar:  6.1382
Epoch 841:  train loss 0.4908   train acc 0.6682   worst 0.3240   lr 0.0083   p 179.57   eps 0.1952   mix 0.0063   time 27.21
scalar:  6.1432
Epoch 842:  train loss 0.4911   train acc 0.6702   worst 0.3208   lr 0.0083   p 180.32   eps 0.1952   mix 0.0063   time 27.57
scalar:  6.1413
Epoch 843:  train loss 0.4898   train acc 0.6677   worst 0.3231   lr 0.0083   p 181.08   eps 0.1952   mix 0.0062   time 26.98
scalar:  6.1238
Epoch 844:  train loss 0.4913   train acc 0.6696   worst 0.3187   lr 0.0082   p 181.84   eps 0.1952   mix 0.0062   time 27.61
Epoch 844:  test acc 0.6040   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 844:  clean acc 0.6657   certified acc 0.6068
Calculating metrics for L_infinity dist model on test set
Epoch 844:  clean acc 0.5960   certified acc 0.5265
scalar:  6.126
Epoch 845:  train loss 0.4905   train acc 0.6703   worst 0.3224   lr 0.0082   p 182.61   eps 0.1952   mix 0.0062   time 26.74
scalar:  6.1457
Epoch 846:  train loss 0.4898   train acc 0.6721   worst 0.3214   lr 0.0082   p 183.38   eps 0.1952   mix 0.0062   time 26.95
scalar:  6.1585
Epoch 847:  train loss 0.4883   train acc 0.6704   worst 0.3252   lr 0.0081   p 184.15   eps 0.1952   mix 0.0062   time 27.46
scalar:  6.1528
Epoch 848:  train loss 0.4901   train acc 0.6708   worst 0.3224   lr 0.0081   p 184.92   eps 0.1952   mix 0.0062   time 27.00
scalar:  6.1472
Epoch 849:  train loss 0.4901   train acc 0.6681   worst 0.3240   lr 0.0081   p 185.70   eps 0.1952   mix 0.0061   time 26.91
Epoch 849:  test acc 0.6047   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 849:  clean acc 0.6646   certified acc 0.6061
Calculating metrics for L_infinity dist model on test set
Epoch 849:  clean acc 0.5940   certified acc 0.5179
scalar:  6.1279
Epoch 850:  train loss 0.4890   train acc 0.6722   worst 0.3213   lr 0.0080   p 186.48   eps 0.1952   mix 0.0061   time 26.91
scalar:  6.1388
Epoch 851:  train loss 0.4884   train acc 0.6722   worst 0.3229   lr 0.0080   p 187.27   eps 0.1952   mix 0.0061   time 26.86
scalar:  6.1506
Epoch 852:  train loss 0.4888   train acc 0.6708   worst 0.3241   lr 0.0080   p 188.06   eps 0.1952   mix 0.0061   time 27.65
scalar:  6.1482
Epoch 853:  train loss 0.4899   train acc 0.6692   worst 0.3225   lr 0.0079   p 188.85   eps 0.1952   mix 0.0061   time 27.02
scalar:  6.1432
Epoch 854:  train loss 0.4905   train acc 0.6701   worst 0.3213   lr 0.0079   p 189.64   eps 0.1952   mix 0.0061   time 26.98
Epoch 854:  test acc 0.6044   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 854:  clean acc 0.6611   certified acc 0.6043
Calculating metrics for L_infinity dist model on test set
Epoch 854:  clean acc 0.5941   certified acc 0.5236
scalar:  6.1545
Epoch 855:  train loss 0.4873   train acc 0.6704   worst 0.3232   lr 0.0079   p 190.44   eps 0.1952   mix 0.0060   time 26.59
scalar:  6.1662
Epoch 856:  train loss 0.4890   train acc 0.6703   worst 0.3222   lr 0.0078   p 191.24   eps 0.1952   mix 0.0060   time 26.89
scalar:  6.1535
Epoch 857:  train loss 0.4903   train acc 0.6709   worst 0.3213   lr 0.0078   p 192.05   eps 0.1952   mix 0.0060   time 27.50
scalar:  6.145
Epoch 858:  train loss 0.4889   train acc 0.6714   worst 0.3229   lr 0.0078   p 192.85   eps 0.1952   mix 0.0060   time 26.83
scalar:  6.1577
Epoch 859:  train loss 0.4890   train acc 0.6706   worst 0.3253   lr 0.0077   p 193.66   eps 0.1952   mix 0.0060   time 27.02
Epoch 859:  test acc 0.6018   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 859:  clean acc 0.6634   certified acc 0.6065
Calculating metrics for L_infinity dist model on test set
Epoch 859:  clean acc 0.5908   certified acc 0.5220
scalar:  6.1508
Epoch 860:  train loss 0.4869   train acc 0.6724   worst 0.3260   lr 0.0077   p 194.48   eps 0.1952   mix 0.0060   time 26.86
scalar:  6.1449
Epoch 861:  train loss 0.4897   train acc 0.6698   worst 0.3226   lr 0.0077   p 195.30   eps 0.1952   mix 0.0059   time 27.18
scalar:  6.1503
Epoch 862:  train loss 0.4879   train acc 0.6725   worst 0.3243   lr 0.0076   p 196.12   eps 0.1952   mix 0.0059   time 27.61
scalar:  6.1603
Epoch 863:  train loss 0.4893   train acc 0.6718   worst 0.3234   lr 0.0076   p 196.94   eps 0.1952   mix 0.0059   time 26.95
scalar:  6.1862
Epoch 864:  train loss 0.4873   train acc 0.6714   worst 0.3251   lr 0.0076   p 197.77   eps 0.1952   mix 0.0059   time 27.10
Epoch 864:  test acc 0.6031   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 864:  clean acc 0.6656   certified acc 0.6079
Calculating metrics for L_infinity dist model on test set
Epoch 864:  clean acc 0.5941   certified acc 0.5225
scalar:  6.1729
Epoch 865:  train loss 0.4908   train acc 0.6696   worst 0.3204   lr 0.0076   p 198.61   eps 0.1952   mix 0.0059   time 26.67
scalar:  6.1815
Epoch 866:  train loss 0.4874   train acc 0.6733   worst 0.3236   lr 0.0075   p 199.44   eps 0.1952   mix 0.0059   time 27.19
scalar:  6.1777
Epoch 867:  train loss 0.4890   train acc 0.6718   worst 0.3224   lr 0.0075   p 200.28   eps 0.1952   mix 0.0058   time 27.60
scalar:  6.1799
Epoch 868:  train loss 0.4877   train acc 0.6716   worst 0.3254   lr 0.0075   p 201.12   eps 0.1952   mix 0.0058   time 26.66
scalar:  6.16
Epoch 869:  train loss 0.4870   train acc 0.6722   worst 0.3239   lr 0.0074   p 201.97   eps 0.1952   mix 0.0058   time 27.19
Epoch 869:  test acc 0.6033   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 869:  clean acc 0.6699   certified acc 0.6144
Calculating metrics for L_infinity dist model on test set
Epoch 869:  clean acc 0.5955   certified acc 0.5253
scalar:  6.1667
Epoch 870:  train loss 0.4885   train acc 0.6710   worst 0.3221   lr 0.0074   p 202.82   eps 0.1952   mix 0.0058   time 26.83
scalar:  6.165
Epoch 871:  train loss 0.4869   train acc 0.6709   worst 0.3241   lr 0.0074   p 203.67   eps 0.1952   mix 0.0058   time 26.92
scalar:  6.1673
Epoch 872:  train loss 0.4868   train acc 0.6721   worst 0.3274   lr 0.0073   p 204.53   eps 0.1952   mix 0.0058   time 27.53
scalar:  6.1641
Epoch 873:  train loss 0.4877   train acc 0.6690   worst 0.3249   lr 0.0073   p 205.39   eps 0.1952   mix 0.0057   time 26.86
scalar:  6.1502
Epoch 874:  train loss 0.4870   train acc 0.6733   worst 0.3253   lr 0.0073   p 206.25   eps 0.1952   mix 0.0057   time 26.99
Epoch 874:  test acc 0.6016   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 874:  clean acc 0.6678   certified acc 0.6114
Calculating metrics for L_infinity dist model on test set
Epoch 874:  clean acc 0.5969   certified acc 0.5294
scalar:  6.1662
Epoch 875:  train loss 0.4884   train acc 0.6715   worst 0.3234   lr 0.0072   p 207.12   eps 0.1952   mix 0.0057   time 26.92
scalar:  6.1768
Epoch 876:  train loss 0.4894   train acc 0.6705   worst 0.3205   lr 0.0072   p 207.99   eps 0.1952   mix 0.0057   time 27.01
scalar:  6.1853
Epoch 877:  train loss 0.4850   train acc 0.6735   worst 0.3266   lr 0.0072   p 208.87   eps 0.1952   mix 0.0057   time 27.76
scalar:  6.1733
Epoch 878:  train loss 0.4863   train acc 0.6725   worst 0.3265   lr 0.0071   p 209.75   eps 0.1952   mix 0.0057   time 26.73
scalar:  6.1785
Epoch 879:  train loss 0.4886   train acc 0.6720   worst 0.3245   lr 0.0071   p 210.63   eps 0.1952   mix 0.0056   time 26.99
Epoch 879:  test acc 0.6057   time 2.69
Calculating metrics for L_infinity dist model on training set
Epoch 879:  clean acc 0.6677   certified acc 0.6136
Calculating metrics for L_infinity dist model on test set
Epoch 879:  clean acc 0.5947   certified acc 0.5247
scalar:  6.1807
Epoch 880:  train loss 0.4864   train acc 0.6739   worst 0.3234   lr 0.0071   p 211.52   eps 0.1952   mix 0.0056   time 26.76
scalar:  6.1865
Epoch 881:  train loss 0.4876   train acc 0.6731   worst 0.3202   lr 0.0071   p 212.41   eps 0.1952   mix 0.0056   time 27.12
scalar:  6.1921
Epoch 882:  train loss 0.4884   train acc 0.6712   worst 0.3227   lr 0.0070   p 213.30   eps 0.1952   mix 0.0056   time 27.46
scalar:  6.1796
Epoch 883:  train loss 0.4854   train acc 0.6740   worst 0.3262   lr 0.0070   p 214.20   eps 0.1952   mix 0.0056   time 27.01
scalar:  6.1842
Epoch 884:  train loss 0.4863   train acc 0.6732   worst 0.3239   lr 0.0070   p 215.10   eps 0.1952   mix 0.0056   time 27.02
Epoch 884:  test acc 0.6035   time 2.66
Calculating metrics for L_infinity dist model on training set
Epoch 884:  clean acc 0.6679   certified acc 0.6137
Calculating metrics for L_infinity dist model on test set
Epoch 884:  clean acc 0.5935   certified acc 0.5236
scalar:  6.1809
Epoch 885:  train loss 0.4851   train acc 0.6738   worst 0.3249   lr 0.0069   p 216.00   eps 0.1952   mix 0.0056   time 26.57
scalar:  6.1949
Epoch 886:  train loss 0.4876   train acc 0.6716   worst 0.3238   lr 0.0069   p 216.91   eps 0.1952   mix 0.0055   time 26.81
scalar:  6.1884
Epoch 887:  train loss 0.4866   train acc 0.6729   worst 0.3230   lr 0.0069   p 217.82   eps 0.1952   mix 0.0055   time 27.50
scalar:  6.1818
Epoch 888:  train loss 0.4866   train acc 0.6716   worst 0.3262   lr 0.0068   p 218.74   eps 0.1952   mix 0.0055   time 27.18
scalar:  6.1781
Epoch 889:  train loss 0.4873   train acc 0.6717   worst 0.3216   lr 0.0068   p 219.66   eps 0.1952   mix 0.0055   time 27.44
Epoch 889:  test acc 0.6032   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 889:  clean acc 0.6682   certified acc 0.6159
Calculating metrics for L_infinity dist model on test set
Epoch 889:  clean acc 0.6006   certified acc 0.5318
scalar:  6.1665
Epoch 890:  train loss 0.4853   train acc 0.6721   worst 0.3252   lr 0.0068   p 220.59   eps 0.1952   mix 0.0055   time 26.78
scalar:  6.1653
Epoch 891:  train loss 0.4853   train acc 0.6737   worst 0.3268   lr 0.0067   p 221.51   eps 0.1952   mix 0.0055   time 26.97
scalar:  6.188
Epoch 892:  train loss 0.4840   train acc 0.6741   worst 0.3270   lr 0.0067   p 222.45   eps 0.1952   mix 0.0054   time 27.50
scalar:  6.1986
Epoch 893:  train loss 0.4861   train acc 0.6724   worst 0.3248   lr 0.0067   p 223.38   eps 0.1952   mix 0.0054   time 27.05
scalar:  6.1973
Epoch 894:  train loss 0.4838   train acc 0.6735   worst 0.3254   lr 0.0067   p 224.32   eps 0.1952   mix 0.0054   time 27.24
Epoch 894:  test acc 0.6047   time 2.68
Calculating metrics for L_infinity dist model on training set
Epoch 894:  clean acc 0.6704   certified acc 0.6199
Calculating metrics for L_infinity dist model on test set
Epoch 894:  clean acc 0.5968   certified acc 0.5263
scalar:  6.1996
Epoch 895:  train loss 0.4842   train acc 0.6723   worst 0.3264   lr 0.0066   p 225.26   eps 0.1952   mix 0.0054   time 26.90
scalar:  6.2
Epoch 896:  train loss 0.4849   train acc 0.6745   worst 0.3249   lr 0.0066   p 226.21   eps 0.1952   mix 0.0054   time 26.92
scalar:  6.2049
Epoch 897:  train loss 0.4844   train acc 0.6749   worst 0.3233   lr 0.0066   p 227.16   eps 0.1952   mix 0.0054   time 27.28
scalar:  6.207
Epoch 898:  train loss 0.4851   train acc 0.6721   worst 0.3276   lr 0.0065   p 228.12   eps 0.1952   mix 0.0054   time 27.28
scalar:  6.204
Epoch 899:  train loss 0.4830   train acc 0.6765   worst 0.3264   lr 0.0065   p 229.08   eps 0.1952   mix 0.0053   time 27.40
Epoch 899:  test acc 0.6027   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 899:  clean acc 0.6705   certified acc 0.6183
Calculating metrics for L_infinity dist model on test set
Epoch 899:  clean acc 0.5978   certified acc 0.5255
scalar:  6.2156
Epoch 900:  train loss 0.4844   train acc 0.6753   worst 0.3280   lr 0.0065   p 230.04   eps 0.1952   mix 0.0053   time 26.73
scalar:  6.2142
Epoch 901:  train loss 0.4850   train acc 0.6744   worst 0.3251   lr 0.0064   p 231.01   eps 0.1952   mix 0.0053   time 26.67
scalar:  6.2074
Epoch 902:  train loss 0.4838   train acc 0.6756   worst 0.3269   lr 0.0064   p 231.98   eps 0.1952   mix 0.0053   time 26.93
scalar:  6.1978
Epoch 903:  train loss 0.4837   train acc 0.6735   worst 0.3255   lr 0.0064   p 232.96   eps 0.1952   mix 0.0053   time 26.99
scalar:  6.1939
Epoch 904:  train loss 0.4826   train acc 0.6749   worst 0.3289   lr 0.0064   p 233.94   eps 0.1952   mix 0.0053   time 26.85
Epoch 904:  test acc 0.6040   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 904:  clean acc 0.6721   certified acc 0.6193
Calculating metrics for L_infinity dist model on test set
Epoch 904:  clean acc 0.5983   certified acc 0.5281
scalar:  6.1925
Epoch 905:  train loss 0.4840   train acc 0.6753   worst 0.3258   lr 0.0063   p 234.92   eps 0.1952   mix 0.0053   time 26.52
scalar:  6.2086
Epoch 906:  train loss 0.4834   train acc 0.6765   worst 0.3255   lr 0.0063   p 235.91   eps 0.1952   mix 0.0052   time 26.93
scalar:  6.2124
Epoch 907:  train loss 0.4838   train acc 0.6742   worst 0.3269   lr 0.0063   p 236.90   eps 0.1952   mix 0.0052   time 27.48
scalar:  6.212
Epoch 908:  train loss 0.4843   train acc 0.6743   worst 0.3258   lr 0.0062   p 237.90   eps 0.1952   mix 0.0052   time 27.03
scalar:  6.2071
Epoch 909:  train loss 0.4838   train acc 0.6746   worst 0.3258   lr 0.0062   p 238.90   eps 0.1952   mix 0.0052   time 27.03
Epoch 909:  test acc 0.6058   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 909:  clean acc 0.6724   certified acc 0.6198
Calculating metrics for L_infinity dist model on test set
Epoch 909:  clean acc 0.5941   certified acc 0.5272
scalar:  6.2069
Epoch 910:  train loss 0.4821   train acc 0.6756   worst 0.3264   lr 0.0062   p 239.91   eps 0.1952   mix 0.0052   time 26.72
scalar:  6.2099
Epoch 911:  train loss 0.4833   train acc 0.6752   worst 0.3277   lr 0.0062   p 240.92   eps 0.1952   mix 0.0052   time 26.89
scalar:  6.1994
Epoch 912:  train loss 0.4829   train acc 0.6765   worst 0.3242   lr 0.0061   p 241.93   eps 0.1952   mix 0.0052   time 27.07
scalar:  6.2083
Epoch 913:  train loss 0.4831   train acc 0.6748   worst 0.3260   lr 0.0061   p 242.95   eps 0.1952   mix 0.0051   time 26.91
scalar:  6.2093
Epoch 914:  train loss 0.4820   train acc 0.6742   worst 0.3266   lr 0.0061   p 243.97   eps 0.1952   mix 0.0051   time 26.81
Epoch 914:  test acc 0.6034   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 914:  clean acc 0.6749   certified acc 0.6226
Calculating metrics for L_infinity dist model on test set
Epoch 914:  clean acc 0.5999   certified acc 0.5260
scalar:  6.2147
Epoch 915:  train loss 0.4826   train acc 0.6751   worst 0.3277   lr 0.0060   p 245.00   eps 0.1952   mix 0.0051   time 26.83
scalar:  6.2155
Epoch 916:  train loss 0.4828   train acc 0.6755   worst 0.3267   lr 0.0060   p 246.03   eps 0.1952   mix 0.0051   time 26.93
scalar:  6.1974
Epoch 917:  train loss 0.4822   train acc 0.6765   worst 0.3283   lr 0.0060   p 247.06   eps 0.1952   mix 0.0051   time 27.06
scalar:  6.2007
Epoch 918:  train loss 0.4826   train acc 0.6753   worst 0.3251   lr 0.0060   p 248.10   eps 0.1952   mix 0.0051   time 26.78
scalar:  6.2153
Epoch 919:  train loss 0.4834   train acc 0.6760   worst 0.3257   lr 0.0059   p 249.15   eps 0.1952   mix 0.0051   time 27.07
Epoch 919:  test acc 0.5996   time 2.65
Calculating metrics for L_infinity dist model on training set
Epoch 919:  clean acc 0.6722   certified acc 0.6219
Calculating metrics for L_infinity dist model on test set
Epoch 919:  clean acc 0.5976   certified acc 0.5303
scalar:  6.2182
Epoch 920:  train loss 0.4800   train acc 0.6759   worst 0.3290   lr 0.0059   p 250.19   eps 0.1952   mix 0.0050   time 26.59
scalar:  6.2226
Epoch 921:  train loss 0.4808   train acc 0.6769   worst 0.3269   lr 0.0059   p 251.25   eps 0.1952   mix 0.0050   time 27.07
scalar:  6.2234
Epoch 922:  train loss 0.4826   train acc 0.6758   worst 0.3267   lr 0.0058   p 252.30   eps 0.1952   mix 0.0050   time 27.27
scalar:  6.2163
Epoch 923:  train loss 0.4814   train acc 0.6739   worst 0.3289   lr 0.0058   p 253.37   eps 0.1952   mix 0.0050   time 27.13
scalar:  6.2033
Epoch 924:  train loss 0.4823   train acc 0.6759   worst 0.3281   lr 0.0058   p 254.43   eps 0.1952   mix 0.0050   time 26.71
Epoch 924:  test acc 0.6021   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 924:  clean acc 0.6755   certified acc 0.6231
Calculating metrics for L_infinity dist model on test set
Epoch 924:  clean acc 0.5960   certified acc 0.5288
scalar:  6.2012
Epoch 925:  train loss 0.4791   train acc 0.6779   worst 0.3303   lr 0.0057   p 255.50   eps 0.1952   mix 0.0050   time 26.53
scalar:  6.2138
Epoch 926:  train loss 0.4795   train acc 0.6760   worst 0.3287   lr 0.0057   p 256.58   eps 0.1952   mix 0.0050   time 26.83
scalar:  6.2141
Epoch 927:  train loss 0.4823   train acc 0.6742   worst 0.3263   lr 0.0057   p 257.66   eps 0.1952   mix 0.0049   time 26.89
scalar:  6.2139
Epoch 928:  train loss 0.4825   train acc 0.6763   worst 0.3275   lr 0.0057   p 258.74   eps 0.1952   mix 0.0049   time 26.97
scalar:  6.2195
Epoch 929:  train loss 0.4804   train acc 0.6749   worst 0.3303   lr 0.0056   p 259.83   eps 0.1952   mix 0.0049   time 26.75
Epoch 929:  test acc 0.6028   time 2.51
Calculating metrics for L_infinity dist model on training set
Epoch 929:  clean acc 0.6742   certified acc 0.6241
Calculating metrics for L_infinity dist model on test set
Epoch 929:  clean acc 0.5997   certified acc 0.5307
scalar:  6.2087
Epoch 930:  train loss 0.4807   train acc 0.6774   worst 0.3275   lr 0.0056   p 260.92   eps 0.1952   mix 0.0049   time 26.58
scalar:  6.2108
Epoch 931:  train loss 0.4805   train acc 0.6778   worst 0.3271   lr 0.0056   p 262.02   eps 0.1952   mix 0.0049   time 26.91
scalar:  6.2143
Epoch 932:  train loss 0.4813   train acc 0.6779   worst 0.3273   lr 0.0056   p 263.12   eps 0.1952   mix 0.0049   time 27.83
scalar:  6.2092
Epoch 933:  train loss 0.4806   train acc 0.6784   worst 0.3278   lr 0.0055   p 264.23   eps 0.1952   mix 0.0049   time 26.81
scalar:  6.216
Epoch 934:  train loss 0.4814   train acc 0.6756   worst 0.3271   lr 0.0055   p 265.34   eps 0.1952   mix 0.0048   time 26.77
Epoch 934:  test acc 0.6030   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 934:  clean acc 0.6750   certified acc 0.6246
Calculating metrics for L_infinity dist model on test set
Epoch 934:  clean acc 0.6014   certified acc 0.5332
scalar:  6.2057
Epoch 935:  train loss 0.4779   train acc 0.6767   worst 0.3328   lr 0.0055   p 266.46   eps 0.1952   mix 0.0048   time 26.98
scalar:  6.217
Epoch 936:  train loss 0.4793   train acc 0.6782   worst 0.3282   lr 0.0054   p 267.58   eps 0.1952   mix 0.0048   time 26.68
scalar:  6.2131
Epoch 937:  train loss 0.4791   train acc 0.6762   worst 0.3297   lr 0.0054   p 268.71   eps 0.1952   mix 0.0048   time 27.60
scalar:  6.2153
Epoch 938:  train loss 0.4782   train acc 0.6782   worst 0.3315   lr 0.0054   p 269.84   eps 0.1952   mix 0.0048   time 26.83
scalar:  6.2085
Epoch 939:  train loss 0.4804   train acc 0.6757   worst 0.3277   lr 0.0054   p 270.97   eps 0.1952   mix 0.0048   time 27.01
Epoch 939:  test acc 0.6057   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 939:  clean acc 0.6757   certified acc 0.6231
Calculating metrics for L_infinity dist model on test set
Epoch 939:  clean acc 0.6011   certified acc 0.5323
scalar:  6.2187
Epoch 940:  train loss 0.4788   train acc 0.6771   worst 0.3274   lr 0.0053   p 272.11   eps 0.1952   mix 0.0048   time 27.11
scalar:  6.2081
Epoch 941:  train loss 0.4799   train acc 0.6762   worst 0.3295   lr 0.0053   p 273.26   eps 0.1952   mix 0.0047   time 26.55
scalar:  6.2182
Epoch 942:  train loss 0.4794   train acc 0.6773   worst 0.3287   lr 0.0053   p 274.41   eps 0.1952   mix 0.0047   time 27.55
scalar:  6.2113
Epoch 943:  train loss 0.4798   train acc 0.6761   worst 0.3314   lr 0.0052   p 275.56   eps 0.1952   mix 0.0047   time 26.79
scalar:  6.216
Epoch 944:  train loss 0.4795   train acc 0.6779   worst 0.3291   lr 0.0052   p 276.72   eps 0.1952   mix 0.0047   time 26.85
Epoch 944:  test acc 0.6070   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 944:  clean acc 0.6778   certified acc 0.6264
Calculating metrics for L_infinity dist model on test set
Epoch 944:  clean acc 0.6020   certified acc 0.5299
scalar:  6.2194
Epoch 945:  train loss 0.4777   train acc 0.6789   worst 0.3300   lr 0.0052   p 277.88   eps 0.1952   mix 0.0047   time 27.30
scalar:  6.2259
Epoch 946:  train loss 0.4788   train acc 0.6785   worst 0.3301   lr 0.0052   p 279.05   eps 0.1952   mix 0.0047   time 26.50
scalar:  6.2269
Epoch 947:  train loss 0.4794   train acc 0.6759   worst 0.3302   lr 0.0051   p 280.23   eps 0.1952   mix 0.0047   time 27.35
scalar:  6.2324
Epoch 948:  train loss 0.4814   train acc 0.6765   worst 0.3263   lr 0.0051   p 281.41   eps 0.1952   mix 0.0047   time 26.75
scalar:  6.2201
Epoch 949:  train loss 0.4782   train acc 0.6779   worst 0.3304   lr 0.0051   p 282.59   eps 0.1952   mix 0.0046   time 26.74
Epoch 949:  test acc 0.6063   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 949:  clean acc 0.6766   certified acc 0.6246
Calculating metrics for L_infinity dist model on test set
Epoch 949:  clean acc 0.6007   certified acc 0.5306
scalar:  6.2144
Epoch 950:  train loss 0.4796   train acc 0.6775   worst 0.3291   lr 0.0051   p 283.78   eps 0.1952   mix 0.0046   time 27.12
scalar:  6.2124
Epoch 951:  train loss 0.4787   train acc 0.6761   worst 0.3300   lr 0.0050   p 284.97   eps 0.1952   mix 0.0046   time 26.77
scalar:  6.2193
Epoch 952:  train loss 0.4785   train acc 0.6763   worst 0.3301   lr 0.0050   p 286.17   eps 0.1952   mix 0.0046   time 27.48
scalar:  6.2126
Epoch 953:  train loss 0.4793   train acc 0.6785   worst 0.3288   lr 0.0050   p 287.38   eps 0.1952   mix 0.0046   time 26.55
scalar:  6.2227
Epoch 954:  train loss 0.4787   train acc 0.6776   worst 0.3291   lr 0.0049   p 288.58   eps 0.1952   mix 0.0046   time 26.86
Epoch 954:  test acc 0.6031   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 954:  clean acc 0.6756   certified acc 0.6251
Calculating metrics for L_infinity dist model on test set
Epoch 954:  clean acc 0.6003   certified acc 0.5335
scalar:  6.2218
Epoch 955:  train loss 0.4772   train acc 0.6771   worst 0.3332   lr 0.0049   p 289.80   eps 0.1952   mix 0.0046   time 27.47
scalar:  6.2213
Epoch 956:  train loss 0.4765   train acc 0.6796   worst 0.3322   lr 0.0049   p 291.02   eps 0.1952   mix 0.0046   time 26.57
scalar:  6.2311
Epoch 957:  train loss 0.4782   train acc 0.6772   worst 0.3296   lr 0.0049   p 292.24   eps 0.1952   mix 0.0045   time 27.69
scalar:  6.2264
Epoch 958:  train loss 0.4776   train acc 0.6776   worst 0.3317   lr 0.0048   p 293.47   eps 0.1952   mix 0.0045   time 26.62
scalar:  6.2264
Epoch 959:  train loss 0.4769   train acc 0.6779   worst 0.3314   lr 0.0048   p 294.71   eps 0.1952   mix 0.0045   time 27.21
Epoch 959:  test acc 0.6034   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 959:  clean acc 0.6776   certified acc 0.6276
Calculating metrics for L_infinity dist model on test set
Epoch 959:  clean acc 0.5966   certified acc 0.5291
scalar:  6.2226
Epoch 960:  train loss 0.4766   train acc 0.6785   worst 0.3324   lr 0.0048   p 295.95   eps 0.1952   mix 0.0045   time 27.31
scalar:  6.2243
Epoch 961:  train loss 0.4762   train acc 0.6797   worst 0.3332   lr 0.0048   p 297.19   eps 0.1952   mix 0.0045   time 26.97
scalar:  6.2275
Epoch 962:  train loss 0.4761   train acc 0.6805   worst 0.3301   lr 0.0047   p 298.44   eps 0.1952   mix 0.0045   time 27.29
scalar:  6.2259
Epoch 963:  train loss 0.4779   train acc 0.6769   worst 0.3308   lr 0.0047   p 299.70   eps 0.1952   mix 0.0045   time 26.83
scalar:  6.2272
Epoch 964:  train loss 0.4769   train acc 0.6795   worst 0.3293   lr 0.0047   p 300.96   eps 0.1952   mix 0.0045   time 26.95
Epoch 964:  test acc 0.6049   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 964:  clean acc 0.6779   certified acc 0.6276
Calculating metrics for L_infinity dist model on test set
Epoch 964:  clean acc 0.6043   certified acc 0.5349
scalar:  6.2264
Epoch 965:  train loss 0.4762   train acc 0.6772   worst 0.3321   lr 0.0047   p 302.23   eps 0.1952   mix 0.0044   time 26.67
scalar:  6.2202
Epoch 966:  train loss 0.4767   train acc 0.6784   worst 0.3301   lr 0.0046   p 303.50   eps 0.1952   mix 0.0044   time 26.80
scalar:  6.2297
Epoch 967:  train loss 0.4766   train acc 0.6777   worst 0.3331   lr 0.0046   p 304.77   eps 0.1952   mix 0.0044   time 27.42
scalar:  6.2319
Epoch 968:  train loss 0.4770   train acc 0.6787   worst 0.3340   lr 0.0046   p 306.06   eps 0.1952   mix 0.0044   time 26.67
scalar:  6.2328
Epoch 969:  train loss 0.4765   train acc 0.6779   worst 0.3314   lr 0.0045   p 307.34   eps 0.1952   mix 0.0044   time 26.63
Epoch 969:  test acc 0.6038   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 969:  clean acc 0.6792   certified acc 0.6284
Calculating metrics for L_infinity dist model on test set
Epoch 969:  clean acc 0.6008   certified acc 0.5327
scalar:  6.2297
Epoch 970:  train loss 0.4757   train acc 0.6799   worst 0.3314   lr 0.0045   p 308.64   eps 0.1952   mix 0.0044   time 27.08
scalar:  6.2326
Epoch 971:  train loss 0.4764   train acc 0.6804   worst 0.3313   lr 0.0045   p 309.94   eps 0.1952   mix 0.0044   time 26.56
scalar:  6.2334
Epoch 972:  train loss 0.4766   train acc 0.6782   worst 0.3332   lr 0.0045   p 311.24   eps 0.1952   mix 0.0044   time 27.05
scalar:  6.2283
Epoch 973:  train loss 0.4772   train acc 0.6810   worst 0.3305   lr 0.0044   p 312.55   eps 0.1952   mix 0.0043   time 26.63
scalar:  6.2392
Epoch 974:  train loss 0.4742   train acc 0.6805   worst 0.3317   lr 0.0044   p 313.86   eps 0.1952   mix 0.0043   time 26.65
Epoch 974:  test acc 0.6020   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 974:  clean acc 0.6802   certified acc 0.6314
Calculating metrics for L_infinity dist model on test set
Epoch 974:  clean acc 0.6014   certified acc 0.5345
scalar:  6.2395
Epoch 975:  train loss 0.4753   train acc 0.6802   worst 0.3314   lr 0.0044   p 315.18   eps 0.1952   mix 0.0043   time 27.29
scalar:  6.2438
Epoch 976:  train loss 0.4734   train acc 0.6804   worst 0.3330   lr 0.0044   p 316.51   eps 0.1952   mix 0.0043   time 26.95
scalar:  6.2419
Epoch 977:  train loss 0.4754   train acc 0.6785   worst 0.3326   lr 0.0043   p 317.84   eps 0.1952   mix 0.0043   time 27.26
scalar:  6.2418
Epoch 978:  train loss 0.4759   train acc 0.6775   worst 0.3293   lr 0.0043   p 319.18   eps 0.1952   mix 0.0043   time 26.60
scalar:  6.2366
Epoch 979:  train loss 0.4761   train acc 0.6793   worst 0.3312   lr 0.0043   p 320.52   eps 0.1952   mix 0.0043   time 26.90
Epoch 979:  test acc 0.6047   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 979:  clean acc 0.6802   certified acc 0.6305
Calculating metrics for L_infinity dist model on test set
Epoch 979:  clean acc 0.6030   certified acc 0.5338
scalar:  6.2357
Epoch 980:  train loss 0.4749   train acc 0.6794   worst 0.3325   lr 0.0043   p 321.87   eps 0.1952   mix 0.0043   time 27.44
scalar:  6.2338
Epoch 981:  train loss 0.4746   train acc 0.6820   worst 0.3345   lr 0.0042   p 323.23   eps 0.1952   mix 0.0042   time 26.76
scalar:  6.238
Epoch 982:  train loss 0.4754   train acc 0.6782   worst 0.3332   lr 0.0042   p 324.59   eps 0.1952   mix 0.0042   time 27.22
scalar:  6.232
Epoch 983:  train loss 0.4733   train acc 0.6813   worst 0.3334   lr 0.0042   p 325.95   eps 0.1952   mix 0.0042   time 26.62
scalar:  6.2362
Epoch 984:  train loss 0.4733   train acc 0.6827   worst 0.3332   lr 0.0042   p 327.32   eps 0.1952   mix 0.0042   time 27.00
Epoch 984:  test acc 0.6018   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 984:  clean acc 0.6801   certified acc 0.6322
Calculating metrics for L_infinity dist model on test set
Epoch 984:  clean acc 0.6007   certified acc 0.5304
scalar:  6.2399
Epoch 985:  train loss 0.4732   train acc 0.6790   worst 0.3347   lr 0.0041   p 328.70   eps 0.1952   mix 0.0042   time 27.26
scalar:  6.2317
Epoch 986:  train loss 0.4727   train acc 0.6823   worst 0.3337   lr 0.0041   p 330.08   eps 0.1952   mix 0.0042   time 26.76
scalar:  6.2374
Epoch 987:  train loss 0.4716   train acc 0.6819   worst 0.3348   lr 0.0041   p 331.47   eps 0.1952   mix 0.0042   time 26.84
scalar:  6.2389
Epoch 988:  train loss 0.4750   train acc 0.6793   worst 0.3328   lr 0.0041   p 332.87   eps 0.1952   mix 0.0042   time 26.80
scalar:  6.2415
Epoch 989:  train loss 0.4741   train acc 0.6800   worst 0.3332   lr 0.0040   p 334.27   eps 0.1952   mix 0.0042   time 26.86
Epoch 989:  test acc 0.6015   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 989:  clean acc 0.6809   certified acc 0.6323
Calculating metrics for L_infinity dist model on test set
Epoch 989:  clean acc 0.6023   certified acc 0.5324
scalar:  6.2388
Epoch 990:  train loss 0.4713   train acc 0.6815   worst 0.3355   lr 0.0040   p 335.67   eps 0.1952   mix 0.0041   time 27.20
scalar:  6.2385
Epoch 991:  train loss 0.4734   train acc 0.6799   worst 0.3363   lr 0.0040   p 337.08   eps 0.1952   mix 0.0041   time 27.11
scalar:  6.2402
Epoch 992:  train loss 0.4712   train acc 0.6821   worst 0.3364   lr 0.0040   p 338.50   eps 0.1952   mix 0.0041   time 27.34
scalar:  6.2316
Epoch 993:  train loss 0.4725   train acc 0.6825   worst 0.3357   lr 0.0039   p 339.93   eps 0.1952   mix 0.0041   time 26.85
scalar:  6.2329
Epoch 994:  train loss 0.4731   train acc 0.6794   worst 0.3344   lr 0.0039   p 341.36   eps 0.1952   mix 0.0041   time 26.89
Epoch 994:  test acc 0.6051   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 994:  clean acc 0.6805   certified acc 0.6336
Calculating metrics for L_infinity dist model on test set
Epoch 994:  clean acc 0.6037   certified acc 0.5377
scalar:  6.2254
Epoch 995:  train loss 0.4710   train acc 0.6815   worst 0.3374   lr 0.0039   p 342.79   eps 0.1952   mix 0.0041   time 27.13
scalar:  6.22
Epoch 996:  train loss 0.4707   train acc 0.6829   worst 0.3367   lr 0.0039   p 344.24   eps 0.1952   mix 0.0041   time 26.96
scalar:  6.2275
Epoch 997:  train loss 0.4727   train acc 0.6798   worst 0.3352   lr 0.0038   p 345.68   eps 0.1952   mix 0.0041   time 26.81
scalar:  6.2223
Epoch 998:  train loss 0.4725   train acc 0.6810   worst 0.3341   lr 0.0038   p 347.14   eps 0.1952   mix 0.0040   time 26.69
scalar:  6.2248
Epoch 999:  train loss 0.4731   train acc 0.6816   worst 0.3338   lr 0.0038   p 348.60   eps 0.1952   mix 0.0040   time 26.69
Epoch 999:  test acc 0.6053   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 999:  clean acc 0.6801   certified acc 0.6338
Calculating metrics for L_infinity dist model on test set
Epoch 999:  clean acc 0.6016   certified acc 0.5345
scalar:  6.2253
Epoch 1000:  train loss 0.4711   train acc 0.6832   worst 0.3355   lr 0.0038   p 350.07   eps 0.1952   mix 0.0040   time 26.82
scalar:  6.2315
Epoch 1001:  train loss 0.4711   train acc 0.6827   worst 0.3348   lr 0.0037   p 351.54   eps 0.1952   mix 0.0040   time 27.33
scalar:  6.2288
Epoch 1002:  train loss 0.4712   train acc 0.6808   worst 0.3381   lr 0.0037   p 353.02   eps 0.1952   mix 0.0040   time 27.34
scalar:  6.2246
Epoch 1003:  train loss 0.4724   train acc 0.6803   worst 0.3352   lr 0.0037   p 354.50   eps 0.1952   mix 0.0040   time 26.67
scalar:  6.2233
Epoch 1004:  train loss 0.4721   train acc 0.6809   worst 0.3361   lr 0.0037   p 355.99   eps 0.1952   mix 0.0040   time 26.67
Epoch 1004:  test acc 0.6059   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1004:  clean acc 0.6809   certified acc 0.6323
Calculating metrics for L_infinity dist model on test set
Epoch 1004:  clean acc 0.6015   certified acc 0.5380
scalar:  6.2274
Epoch 1005:  train loss 0.4715   train acc 0.6814   worst 0.3356   lr 0.0037   p 357.49   eps 0.1952   mix 0.0040   time 27.22
scalar:  6.2261
Epoch 1006:  train loss 0.4714   train acc 0.6808   worst 0.3383   lr 0.0036   p 359.00   eps 0.1952   mix 0.0040   time 27.07
scalar:  6.2273
Epoch 1007:  train loss 0.4706   train acc 0.6817   worst 0.3350   lr 0.0036   p 360.51   eps 0.1952   mix 0.0039   time 27.21
scalar:  6.2238
Epoch 1008:  train loss 0.4706   train acc 0.6833   worst 0.3361   lr 0.0036   p 362.02   eps 0.1952   mix 0.0039   time 26.56
scalar:  6.2258
Epoch 1009:  train loss 0.4708   train acc 0.6814   worst 0.3360   lr 0.0036   p 363.55   eps 0.1952   mix 0.0039   time 26.91
Epoch 1009:  test acc 0.6035   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 1009:  clean acc 0.6819   certified acc 0.6344
Calculating metrics for L_infinity dist model on test set
Epoch 1009:  clean acc 0.6023   certified acc 0.5351
scalar:  6.2278
Epoch 1010:  train loss 0.4712   train acc 0.6812   worst 0.3366   lr 0.0035   p 365.08   eps 0.1952   mix 0.0039   time 26.94
scalar:  6.2264
Epoch 1011:  train loss 0.4712   train acc 0.6822   worst 0.3368   lr 0.0035   p 366.61   eps 0.1952   mix 0.0039   time 27.17
scalar:  6.2282
Epoch 1012:  train loss 0.4697   train acc 0.6818   worst 0.3370   lr 0.0035   p 368.16   eps 0.1952   mix 0.0039   time 27.17
scalar:  6.2263
Epoch 1013:  train loss 0.4697   train acc 0.6824   worst 0.3369   lr 0.0035   p 369.70   eps 0.1952   mix 0.0039   time 26.76
scalar:  6.2345
Epoch 1014:  train loss 0.4685   train acc 0.6822   worst 0.3393   lr 0.0034   p 371.26   eps 0.1952   mix 0.0039   time 26.63
Epoch 1014:  test acc 0.6072   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 1014:  clean acc 0.6836   certified acc 0.6351
Calculating metrics for L_infinity dist model on test set
Epoch 1014:  clean acc 0.6032   certified acc 0.5323
scalar:  6.2288
Epoch 1015:  train loss 0.4695   train acc 0.6808   worst 0.3413   lr 0.0034   p 372.82   eps 0.1952   mix 0.0039   time 26.89
scalar:  6.2273
Epoch 1016:  train loss 0.4698   train acc 0.6822   worst 0.3391   lr 0.0034   p 374.39   eps 0.1952   mix 0.0039   time 27.49
scalar:  6.2236
Epoch 1017:  train loss 0.4683   train acc 0.6835   worst 0.3387   lr 0.0034   p 375.97   eps 0.1952   mix 0.0038   time 26.97
scalar:  6.2232
Epoch 1018:  train loss 0.4692   train acc 0.6836   worst 0.3384   lr 0.0034   p 377.55   eps 0.1952   mix 0.0038   time 26.30
scalar:  6.2259
Epoch 1019:  train loss 0.4692   train acc 0.6826   worst 0.3380   lr 0.0033   p 379.14   eps 0.1952   mix 0.0038   time 26.65
Epoch 1019:  test acc 0.6052   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1019:  clean acc 0.6824   certified acc 0.6353
Calculating metrics for L_infinity dist model on test set
Epoch 1019:  clean acc 0.6056   certified acc 0.5384
scalar:  6.2261
Epoch 1020:  train loss 0.4701   train acc 0.6817   worst 0.3385   lr 0.0033   p 380.73   eps 0.1952   mix 0.0038   time 27.32
scalar:  6.2229
Epoch 1021:  train loss 0.4715   train acc 0.6793   worst 0.3383   lr 0.0033   p 382.33   eps 0.1952   mix 0.0038   time 26.87
scalar:  6.2177
Epoch 1022:  train loss 0.4686   train acc 0.6820   worst 0.3385   lr 0.0033   p 383.94   eps 0.1952   mix 0.0038   time 26.99
scalar:  6.2212
Epoch 1023:  train loss 0.4678   train acc 0.6837   worst 0.3383   lr 0.0032   p 385.56   eps 0.1952   mix 0.0038   time 26.60
scalar:  6.2253
Epoch 1024:  train loss 0.4682   train acc 0.6854   worst 0.3388   lr 0.0032   p 387.18   eps 0.1952   mix 0.0038   time 26.86
Epoch 1024:  test acc 0.6067   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1024:  clean acc 0.6826   certified acc 0.6359
Calculating metrics for L_infinity dist model on test set
Epoch 1024:  clean acc 0.6051   certified acc 0.5364
scalar:  6.2299
Epoch 1025:  train loss 0.4679   train acc 0.6835   worst 0.3399   lr 0.0032   p 388.81   eps 0.1952   mix 0.0038   time 27.23
scalar:  6.2308
Epoch 1026:  train loss 0.4673   train acc 0.6837   worst 0.3406   lr 0.0032   p 390.44   eps 0.1952   mix 0.0037   time 27.24
scalar:  6.2331
Epoch 1027:  train loss 0.4688   train acc 0.6833   worst 0.3371   lr 0.0031   p 392.09   eps 0.1952   mix 0.0037   time 27.04
scalar:  6.2385
Epoch 1028:  train loss 0.4666   train acc 0.6853   worst 0.3412   lr 0.0031   p 393.74   eps 0.1952   mix 0.0037   time 26.57
scalar:  6.2447
Epoch 1029:  train loss 0.4679   train acc 0.6834   worst 0.3407   lr 0.0031   p 395.39   eps 0.1952   mix 0.0037   time 26.73
Epoch 1029:  test acc 0.6049   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1029:  clean acc 0.6848   certified acc 0.6381
Calculating metrics for L_infinity dist model on test set
Epoch 1029:  clean acc 0.6066   certified acc 0.5371
scalar:  6.2331
Epoch 1030:  train loss 0.4690   train acc 0.6833   worst 0.3392   lr 0.0031   p 397.06   eps 0.1952   mix 0.0037   time 27.10
scalar:  6.2398
Epoch 1031:  train loss 0.4708   train acc 0.6823   worst 0.3353   lr 0.0031   p 398.73   eps 0.1952   mix 0.0037   time 27.76
scalar:  6.2325
Epoch 1032:  train loss 0.4666   train acc 0.6862   worst 0.3397   lr 0.0030   p 400.40   eps 0.1952   mix 0.0037   time 27.06
scalar:  6.2355
Epoch 1033:  train loss 0.4679   train acc 0.6832   worst 0.3398   lr 0.0030   p 402.09   eps 0.1952   mix 0.0037   time 27.01
scalar:  6.2316
Epoch 1034:  train loss 0.4695   train acc 0.6810   worst 0.3366   lr 0.0030   p 403.78   eps 0.1952   mix 0.0037   time 26.69
Epoch 1034:  test acc 0.6020   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1034:  clean acc 0.6846   certified acc 0.6351
Calculating metrics for L_infinity dist model on test set
Epoch 1034:  clean acc 0.6020   certified acc 0.5329
scalar:  6.2272
Epoch 1035:  train loss 0.4680   train acc 0.6828   worst 0.3375   lr 0.0030   p 405.48   eps 0.1952   mix 0.0037   time 26.90
scalar:  6.2234
Epoch 1036:  train loss 0.4684   train acc 0.6823   worst 0.3397   lr 0.0030   p 407.19   eps 0.1952   mix 0.0036   time 27.82
scalar:  6.2213
Epoch 1037:  train loss 0.4679   train acc 0.6812   worst 0.3408   lr 0.0029   p 408.90   eps 0.1952   mix 0.0036   time 27.16
scalar:  6.2215
Epoch 1038:  train loss 0.4641   train acc 0.6850   worst 0.3441   lr 0.0029   p 410.62   eps 0.1952   mix 0.0036   time 26.58
scalar:  6.2257
Epoch 1039:  train loss 0.4658   train acc 0.6840   worst 0.3407   lr 0.0029   p 412.35   eps 0.1952   mix 0.0036   time 26.66
Epoch 1039:  test acc 0.6013   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 1039:  clean acc 0.6839   certified acc 0.6366
Calculating metrics for L_infinity dist model on test set
Epoch 1039:  clean acc 0.6011   certified acc 0.5342
scalar:  6.2277
Epoch 1040:  train loss 0.4655   train acc 0.6844   worst 0.3443   lr 0.0029   p 414.08   eps 0.1952   mix 0.0036   time 26.84
scalar:  6.2265
Epoch 1041:  train loss 0.4667   train acc 0.6828   worst 0.3400   lr 0.0028   p 415.82   eps 0.1952   mix 0.0036   time 27.56
scalar:  6.2267
Epoch 1042:  train loss 0.4654   train acc 0.6860   worst 0.3407   lr 0.0028   p 417.57   eps 0.1952   mix 0.0036   time 27.02
scalar:  6.2275
Epoch 1043:  train loss 0.4663   train acc 0.6822   worst 0.3411   lr 0.0028   p 419.33   eps 0.1952   mix 0.0036   time 26.74
scalar:  6.2278
Epoch 1044:  train loss 0.4644   train acc 0.6849   worst 0.3421   lr 0.0028   p 421.09   eps 0.1952   mix 0.0036   time 26.58
Epoch 1044:  test acc 0.6040   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1044:  clean acc 0.6830   certified acc 0.6348
Calculating metrics for L_infinity dist model on test set
Epoch 1044:  clean acc 0.6030   certified acc 0.5336
scalar:  6.2231
Epoch 1045:  train loss 0.4670   train acc 0.6830   worst 0.3391   lr 0.0028   p 422.87   eps 0.1952   mix 0.0035   time 26.83
scalar:  6.2208
Epoch 1046:  train loss 0.4654   train acc 0.6853   worst 0.3418   lr 0.0027   p 424.65   eps 0.1952   mix 0.0035   time 27.73
scalar:  6.2182
Epoch 1047:  train loss 0.4663   train acc 0.6835   worst 0.3410   lr 0.0027   p 426.43   eps 0.1952   mix 0.0035   time 27.17
scalar:  6.2207
Epoch 1048:  train loss 0.4654   train acc 0.6824   worst 0.3430   lr 0.0027   p 428.23   eps 0.1952   mix 0.0035   time 26.71
scalar:  6.2205
Epoch 1049:  train loss 0.4655   train acc 0.6835   worst 0.3418   lr 0.0027   p 430.03   eps 0.1952   mix 0.0035   time 26.67
Epoch 1049:  test acc 0.6048   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 1049:  clean acc 0.6840   certified acc 0.6375
Calculating metrics for L_infinity dist model on test set
Epoch 1049:  clean acc 0.6043   certified acc 0.5355
scalar:  6.2225
Epoch 1050:  train loss 0.4656   train acc 0.6860   worst 0.3425   lr 0.0027   p 431.84   eps 0.1952   mix 0.0035   time 26.73
scalar:  6.2251
Epoch 1051:  train loss 0.4641   train acc 0.6864   worst 0.3414   lr 0.0026   p 433.65   eps 0.1952   mix 0.0035   time 27.22
scalar:  6.2272
Epoch 1052:  train loss 0.4658   train acc 0.6843   worst 0.3403   lr 0.0026   p 435.48   eps 0.1952   mix 0.0035   time 27.62
scalar:  6.2292
Epoch 1053:  train loss 0.4641   train acc 0.6852   worst 0.3427   lr 0.0026   p 437.31   eps 0.1952   mix 0.0035   time 26.60
scalar:  6.2296
Epoch 1054:  train loss 0.4655   train acc 0.6835   worst 0.3418   lr 0.0026   p 439.15   eps 0.1952   mix 0.0035   time 26.58
Epoch 1054:  test acc 0.6064   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1054:  clean acc 0.6852   certified acc 0.6380
Calculating metrics for L_infinity dist model on test set
Epoch 1054:  clean acc 0.6045   certified acc 0.5332
scalar:  6.2329
Epoch 1055:  train loss 0.4653   train acc 0.6851   worst 0.3425   lr 0.0026   p 441.00   eps 0.1952   mix 0.0035   time 26.99
scalar:  6.232
Epoch 1056:  train loss 0.4635   train acc 0.6861   worst 0.3429   lr 0.0025   p 442.85   eps 0.1952   mix 0.0034   time 27.32
scalar:  6.2349
Epoch 1057:  train loss 0.4652   train acc 0.6838   worst 0.3426   lr 0.0025   p 444.72   eps 0.1952   mix 0.0034   time 27.46
scalar:  6.2333
Epoch 1058:  train loss 0.4622   train acc 0.6854   worst 0.3443   lr 0.0025   p 446.59   eps 0.1952   mix 0.0034   time 26.76
scalar:  6.2387
Epoch 1059:  train loss 0.4650   train acc 0.6836   worst 0.3425   lr 0.0025   p 448.47   eps 0.1952   mix 0.0034   time 26.66
Epoch 1059:  test acc 0.6045   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1059:  clean acc 0.6873   certified acc 0.6398
Calculating metrics for L_infinity dist model on test set
Epoch 1059:  clean acc 0.6034   certified acc 0.5348
scalar:  6.2372
Epoch 1060:  train loss 0.4634   train acc 0.6887   worst 0.3411   lr 0.0025   p 450.35   eps 0.1952   mix 0.0034   time 26.91
scalar:  6.2411
Epoch 1061:  train loss 0.4641   train acc 0.6846   worst 0.3427   lr 0.0024   p 452.25   eps 0.1952   mix 0.0034   time 27.63
scalar:  6.2392
Epoch 1062:  train loss 0.4625   train acc 0.6870   worst 0.3436   lr 0.0024   p 454.15   eps 0.1952   mix 0.0034   time 27.41
scalar:  6.2468
Epoch 1063:  train loss 0.4633   train acc 0.6859   worst 0.3416   lr 0.0024   p 456.06   eps 0.1952   mix 0.0034   time 26.40
scalar:  6.2427
Epoch 1064:  train loss 0.4622   train acc 0.6857   worst 0.3448   lr 0.0024   p 457.98   eps 0.1952   mix 0.0034   time 26.69
Epoch 1064:  test acc 0.6033   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1064:  clean acc 0.6848   certified acc 0.6389
Calculating metrics for L_infinity dist model on test set
Epoch 1064:  clean acc 0.6011   certified acc 0.5323
scalar:  6.2405
Epoch 1065:  train loss 0.4638   train acc 0.6838   worst 0.3432   lr 0.0024   p 459.91   eps 0.1952   mix 0.0034   time 26.68
scalar:  6.2405
Epoch 1066:  train loss 0.4631   train acc 0.6868   worst 0.3444   lr 0.0023   p 461.84   eps 0.1952   mix 0.0033   time 27.42
scalar:  6.2383
Epoch 1067:  train loss 0.4633   train acc 0.6839   worst 0.3456   lr 0.0023   p 463.79   eps 0.1952   mix 0.0033   time 27.25
scalar:  6.2341
Epoch 1068:  train loss 0.4625   train acc 0.6856   worst 0.3444   lr 0.0023   p 465.74   eps 0.1952   mix 0.0033   time 26.52
scalar:  6.2325
Epoch 1069:  train loss 0.4616   train acc 0.6883   worst 0.3440   lr 0.0023   p 467.70   eps 0.1952   mix 0.0033   time 26.65
Epoch 1069:  test acc 0.6063   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1069:  clean acc 0.6902   certified acc 0.6427
Calculating metrics for L_infinity dist model on test set
Epoch 1069:  clean acc 0.6033   certified acc 0.5347
scalar:  6.2368
Epoch 1070:  train loss 0.4614   train acc 0.6875   worst 0.3458   lr 0.0023   p 469.66   eps 0.1952   mix 0.0033   time 26.44
scalar:  6.2359
Epoch 1071:  train loss 0.4645   train acc 0.6854   worst 0.3423   lr 0.0022   p 471.64   eps 0.1952   mix 0.0033   time 27.59
scalar:  6.2372
Epoch 1072:  train loss 0.4615   train acc 0.6861   worst 0.3438   lr 0.0022   p 473.63   eps 0.1952   mix 0.0033   time 27.27
scalar:  6.2356
Epoch 1073:  train loss 0.4615   train acc 0.6855   worst 0.3455   lr 0.0022   p 475.62   eps 0.1952   mix 0.0033   time 26.63
scalar:  6.2349
Epoch 1074:  train loss 0.4615   train acc 0.6858   worst 0.3471   lr 0.0022   p 477.62   eps 0.1952   mix 0.0033   time 26.59
Epoch 1074:  test acc 0.6072   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1074:  clean acc 0.6884   certified acc 0.6433
Calculating metrics for L_infinity dist model on test set
Epoch 1074:  clean acc 0.6051   certified acc 0.5348
scalar:  6.2339
Epoch 1075:  train loss 0.4613   train acc 0.6877   worst 0.3437   lr 0.0022   p 479.63   eps 0.1952   mix 0.0033   time 26.68
scalar:  6.2353
Epoch 1076:  train loss 0.4621   train acc 0.6855   worst 0.3454   lr 0.0021   p 481.65   eps 0.1952   mix 0.0033   time 26.91
scalar:  6.2341
Epoch 1077:  train loss 0.4618   train acc 0.6863   worst 0.3453   lr 0.0021   p 483.67   eps 0.1952   mix 0.0032   time 27.79
scalar:  6.2365
Epoch 1078:  train loss 0.4592   train acc 0.6871   worst 0.3465   lr 0.0021   p 485.71   eps 0.1952   mix 0.0032   time 26.44
scalar:  6.2368
Epoch 1079:  train loss 0.4620   train acc 0.6874   worst 0.3452   lr 0.0021   p 487.75   eps 0.1952   mix 0.0032   time 26.70
Epoch 1079:  test acc 0.6080   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1079:  clean acc 0.6878   certified acc 0.6419
Calculating metrics for L_infinity dist model on test set
Epoch 1079:  clean acc 0.6074   certified acc 0.5370
scalar:  6.2357
Epoch 1080:  train loss 0.4616   train acc 0.6872   worst 0.3443   lr 0.0021   p 489.80   eps 0.1952   mix 0.0032   time 26.45
scalar:  6.2404
Epoch 1081:  train loss 0.4598   train acc 0.6872   worst 0.3470   lr 0.0021   p 491.86   eps 0.1952   mix 0.0032   time 27.46
scalar:  6.2432
Epoch 1082:  train loss 0.4608   train acc 0.6872   worst 0.3451   lr 0.0020   p 493.93   eps 0.1952   mix 0.0032   time 27.43
scalar:  6.2429
Epoch 1083:  train loss 0.4606   train acc 0.6865   worst 0.3475   lr 0.0020   p 496.01   eps 0.1952   mix 0.0032   time 26.79
scalar:  6.2421
Epoch 1084:  train loss 0.4594   train acc 0.6892   worst 0.3462   lr 0.0020   p 498.10   eps 0.1952   mix 0.0032   time 26.66
Epoch 1084:  test acc 0.6078   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1084:  clean acc 0.6890   certified acc 0.6446
Calculating metrics for L_infinity dist model on test set
Epoch 1084:  clean acc 0.6086   certified acc 0.5379
scalar:  6.2461
Epoch 1085:  train loss 0.4591   train acc 0.6882   worst 0.3475   lr 0.0020   p 500.19   eps 0.1952   mix 0.0032   time 26.74
scalar:  6.2501
Epoch 1086:  train loss 0.4612   train acc 0.6859   worst 0.3440   lr 0.0020   p 502.30   eps 0.1952   mix 0.0032   time 27.18
scalar:  6.2507
Epoch 1087:  train loss 0.4605   train acc 0.6873   worst 0.3446   lr 0.0019   p 504.41   eps 0.1952   mix 0.0032   time 27.74
scalar:  6.2499
Epoch 1088:  train loss 0.4604   train acc 0.6867   worst 0.3446   lr 0.0019   p 506.53   eps 0.1952   mix 0.0031   time 26.58
scalar:  6.2497
Epoch 1089:  train loss 0.4594   train acc 0.6875   worst 0.3471   lr 0.0019   p 508.67   eps 0.1952   mix 0.0031   time 26.53
Epoch 1089:  test acc 0.6055   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 1089:  clean acc 0.6878   certified acc 0.6435
Calculating metrics for L_infinity dist model on test set
Epoch 1089:  clean acc 0.6049   certified acc 0.5369
scalar:  6.2493
Epoch 1090:  train loss 0.4618   train acc 0.6852   worst 0.3434   lr 0.0019   p 510.81   eps 0.1952   mix 0.0031   time 26.57
scalar:  6.2484
Epoch 1091:  train loss 0.4603   train acc 0.6857   worst 0.3489   lr 0.0019   p 512.96   eps 0.1952   mix 0.0031   time 27.13
scalar:  6.2457
Epoch 1092:  train loss 0.4578   train acc 0.6874   worst 0.3493   lr 0.0019   p 515.11   eps 0.1952   mix 0.0031   time 27.46
scalar:  6.2434
Epoch 1093:  train loss 0.4581   train acc 0.6869   worst 0.3495   lr 0.0018   p 517.28   eps 0.1952   mix 0.0031   time 26.77
scalar:  6.242
Epoch 1094:  train loss 0.4581   train acc 0.6889   worst 0.3472   lr 0.0018   p 519.46   eps 0.1952   mix 0.0031   time 26.34
Epoch 1094:  test acc 0.6036   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 1094:  clean acc 0.6876   certified acc 0.6439
Calculating metrics for L_infinity dist model on test set
Epoch 1094:  clean acc 0.6053   certified acc 0.5368
scalar:  6.2404
Epoch 1095:  train loss 0.4601   train acc 0.6870   worst 0.3477   lr 0.0018   p 521.64   eps 0.1952   mix 0.0031   time 26.38
scalar:  6.2387
Epoch 1096:  train loss 0.4588   train acc 0.6898   worst 0.3444   lr 0.0018   p 523.84   eps 0.1952   mix 0.0031   time 27.22
scalar:  6.2393
Epoch 1097:  train loss 0.4586   train acc 0.6894   worst 0.3486   lr 0.0018   p 526.04   eps 0.1952   mix 0.0031   time 27.37
scalar:  6.2402
Epoch 1098:  train loss 0.4581   train acc 0.6880   worst 0.3496   lr 0.0018   p 528.25   eps 0.1952   mix 0.0031   time 26.77
scalar:  6.2413
Epoch 1099:  train loss 0.4582   train acc 0.6890   worst 0.3479   lr 0.0017   p 530.48   eps 0.1952   mix 0.0031   time 26.70
Epoch 1099:  test acc 0.6075   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1099:  clean acc 0.6869   certified acc 0.6429
Calculating metrics for L_infinity dist model on test set
Epoch 1099:  clean acc 0.6060   certified acc 0.5357
scalar:  6.243
Epoch 1100:  train loss 0.4576   train acc 0.6881   worst 0.3508   lr 0.0017   p 532.71   eps 0.1952   mix 0.0030   time 26.81
scalar:  6.2442
Epoch 1101:  train loss 0.4574   train acc 0.6889   worst 0.3509   lr 0.0017   p 534.95   eps 0.1952   mix 0.0030   time 27.07
scalar:  6.2438
Epoch 1102:  train loss 0.4598   train acc 0.6876   worst 0.3476   lr 0.0017   p 537.20   eps 0.1952   mix 0.0030   time 27.45
scalar:  6.2455
Epoch 1103:  train loss 0.4593   train acc 0.6869   worst 0.3497   lr 0.0017   p 539.46   eps 0.1952   mix 0.0030   time 26.84
scalar:  6.2436
Epoch 1104:  train loss 0.4578   train acc 0.6880   worst 0.3508   lr 0.0017   p 541.73   eps 0.1952   mix 0.0030   time 26.77
Epoch 1104:  test acc 0.6087   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1104:  clean acc 0.6901   certified acc 0.6465
Calculating metrics for L_infinity dist model on test set
Epoch 1104:  clean acc 0.6084   certified acc 0.5385
scalar:  6.2445
Epoch 1105:  train loss 0.4590   train acc 0.6879   worst 0.3493   lr 0.0016   p 544.01   eps 0.1952   mix 0.0030   time 26.69
scalar:  6.2413
Epoch 1106:  train loss 0.4577   train acc 0.6874   worst 0.3504   lr 0.0016   p 546.30   eps 0.1952   mix 0.0030   time 26.78
scalar:  6.2409
Epoch 1107:  train loss 0.4589   train acc 0.6872   worst 0.3484   lr 0.0016   p 548.60   eps 0.1952   mix 0.0030   time 27.32
scalar:  6.2402
Epoch 1108:  train loss 0.4570   train acc 0.6881   worst 0.3513   lr 0.0016   p 550.91   eps 0.1952   mix 0.0030   time 27.32
scalar:  6.2422
Epoch 1109:  train loss 0.4562   train acc 0.6885   worst 0.3527   lr 0.0016   p 553.22   eps 0.1952   mix 0.0030   time 26.74
Epoch 1109:  test acc 0.6060   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1109:  clean acc 0.6893   certified acc 0.6453
Calculating metrics for L_infinity dist model on test set
Epoch 1109:  clean acc 0.6048   certified acc 0.5360
scalar:  6.2413
Epoch 1110:  train loss 0.4570   train acc 0.6889   worst 0.3538   lr 0.0016   p 555.55   eps 0.1952   mix 0.0030   time 26.56
scalar:  6.2391
Epoch 1111:  train loss 0.4588   train acc 0.6874   worst 0.3499   lr 0.0015   p 557.89   eps 0.1952   mix 0.0030   time 26.97
scalar:  6.2391
Epoch 1112:  train loss 0.4572   train acc 0.6894   worst 0.3491   lr 0.0015   p 560.24   eps 0.1952   mix 0.0029   time 27.21
scalar:  6.2406
Epoch 1113:  train loss 0.4584   train acc 0.6880   worst 0.3497   lr 0.0015   p 562.59   eps 0.1952   mix 0.0029   time 27.22
scalar:  6.2378
Epoch 1114:  train loss 0.4574   train acc 0.6878   worst 0.3513   lr 0.0015   p 564.96   eps 0.1952   mix 0.0029   time 26.74
Epoch 1114:  test acc 0.6057   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1114:  clean acc 0.6879   certified acc 0.6430
Calculating metrics for L_infinity dist model on test set
Epoch 1114:  clean acc 0.6036   certified acc 0.5391
scalar:  6.2372
Epoch 1115:  train loss 0.4588   train acc 0.6878   worst 0.3494   lr 0.0015   p 567.34   eps 0.1952   mix 0.0029   time 26.63
scalar:  6.2342
Epoch 1116:  train loss 0.4552   train acc 0.6912   worst 0.3504   lr 0.0015   p 569.72   eps 0.1952   mix 0.0029   time 26.96
scalar:  6.235
Epoch 1117:  train loss 0.4553   train acc 0.6885   worst 0.3529   lr 0.0014   p 572.12   eps 0.1952   mix 0.0029   time 27.66
scalar:  6.2351
Epoch 1118:  train loss 0.4564   train acc 0.6894   worst 0.3523   lr 0.0014   p 574.53   eps 0.1952   mix 0.0029   time 27.02
scalar:  6.2375
Epoch 1119:  train loss 0.4562   train acc 0.6885   worst 0.3510   lr 0.0014   p 576.95   eps 0.1952   mix 0.0029   time 26.69
Epoch 1119:  test acc 0.6063   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1119:  clean acc 0.6914   certified acc 0.6471
Calculating metrics for L_infinity dist model on test set
Epoch 1119:  clean acc 0.6044   certified acc 0.5394
scalar:  6.2362
Epoch 1120:  train loss 0.4565   train acc 0.6905   worst 0.3495   lr 0.0014   p 579.37   eps 0.1952   mix 0.0029   time 26.75
scalar:  6.2394
Epoch 1121:  train loss 0.4564   train acc 0.6893   worst 0.3498   lr 0.0014   p 581.81   eps 0.1952   mix 0.0029   time 27.21
scalar:  6.2387
Epoch 1122:  train loss 0.4563   train acc 0.6890   worst 0.3510   lr 0.0014   p 584.26   eps 0.1952   mix 0.0029   time 27.40
scalar:  6.2388
Epoch 1123:  train loss 0.4552   train acc 0.6904   worst 0.3514   lr 0.0014   p 586.72   eps 0.1952   mix 0.0029   time 27.13
scalar:  6.2391
Epoch 1124:  train loss 0.4548   train acc 0.6896   worst 0.3514   lr 0.0013   p 589.18   eps 0.1952   mix 0.0028   time 26.65
Epoch 1124:  test acc 0.6083   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 1124:  clean acc 0.6894   certified acc 0.6443
Calculating metrics for L_infinity dist model on test set
Epoch 1124:  clean acc 0.6061   certified acc 0.5383
scalar:  6.2372
Epoch 1125:  train loss 0.4554   train acc 0.6900   worst 0.3520   lr 0.0013   p 591.66   eps 0.1952   mix 0.0028   time 26.44
scalar:  6.2393
Epoch 1126:  train loss 0.4545   train acc 0.6900   worst 0.3545   lr 0.0013   p 594.15   eps 0.1952   mix 0.0028   time 27.16
scalar:  6.2385
Epoch 1127:  train loss 0.4549   train acc 0.6908   worst 0.3512   lr 0.0013   p 596.65   eps 0.1952   mix 0.0028   time 27.25
scalar:  6.2388
Epoch 1128:  train loss 0.4550   train acc 0.6895   worst 0.3508   lr 0.0013   p 599.16   eps 0.1952   mix 0.0028   time 27.54
scalar:  6.2402
Epoch 1129:  train loss 0.4557   train acc 0.6896   worst 0.3534   lr 0.0013   p 601.68   eps 0.1952   mix 0.0028   time 26.73
Epoch 1129:  test acc 0.6092   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1129:  clean acc 0.6909   certified acc 0.6459
Calculating metrics for L_infinity dist model on test set
Epoch 1129:  clean acc 0.6103   certified acc 0.5413
scalar:  6.2386
Epoch 1130:  train loss 0.4556   train acc 0.6899   worst 0.3522   lr 0.0012   p 604.22   eps 0.1952   mix 0.0028   time 26.77
scalar:  6.2383
Epoch 1131:  train loss 0.4545   train acc 0.6901   worst 0.3532   lr 0.0012   p 606.76   eps 0.1952   mix 0.0028   time 26.96
scalar:  6.2396
Epoch 1132:  train loss 0.4553   train acc 0.6885   worst 0.3519   lr 0.0012   p 609.31   eps 0.1952   mix 0.0028   time 27.45
scalar:  6.2409
Epoch 1133:  train loss 0.4550   train acc 0.6899   worst 0.3514   lr 0.0012   p 611.87   eps 0.1952   mix 0.0028   time 27.08
scalar:  6.2406
Epoch 1134:  train loss 0.4553   train acc 0.6886   worst 0.3526   lr 0.0012   p 614.45   eps 0.1952   mix 0.0028   time 26.63
Epoch 1134:  test acc 0.6087   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 1134:  clean acc 0.6913   certified acc 0.6474
Calculating metrics for L_infinity dist model on test set
Epoch 1134:  clean acc 0.6073   certified acc 0.5391
scalar:  6.2412
Epoch 1135:  train loss 0.4547   train acc 0.6893   worst 0.3508   lr 0.0012   p 617.03   eps 0.1952   mix 0.0028   time 26.53
scalar:  6.2414
Epoch 1136:  train loss 0.4539   train acc 0.6916   worst 0.3510   lr 0.0012   p 619.63   eps 0.1952   mix 0.0028   time 26.98
scalar:  6.2432
Epoch 1137:  train loss 0.4548   train acc 0.6899   worst 0.3531   lr 0.0011   p 622.24   eps 0.1952   mix 0.0027   time 27.23
scalar:  6.2432
Epoch 1138:  train loss 0.4528   train acc 0.6902   worst 0.3544   lr 0.0011   p 624.85   eps 0.1952   mix 0.0027   time 27.22
scalar:  6.2439
Epoch 1139:  train loss 0.4540   train acc 0.6894   worst 0.3544   lr 0.0011   p 627.48   eps 0.1952   mix 0.0027   time 26.67
Epoch 1139:  test acc 0.6098   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1139:  clean acc 0.6921   certified acc 0.6498
Calculating metrics for L_infinity dist model on test set
Epoch 1139:  clean acc 0.6091   certified acc 0.5366
scalar:  6.243
Epoch 1140:  train loss 0.4554   train acc 0.6893   worst 0.3505   lr 0.0011   p 630.12   eps 0.1952   mix 0.0027   time 26.71
scalar:  6.2416
Epoch 1141:  train loss 0.4538   train acc 0.6911   worst 0.3524   lr 0.0011   p 632.78   eps 0.1952   mix 0.0027   time 27.32
scalar:  6.2406
Epoch 1142:  train loss 0.4541   train acc 0.6898   worst 0.3530   lr 0.0011   p 635.44   eps 0.1952   mix 0.0027   time 27.22
scalar:  6.2411
Epoch 1143:  train loss 0.4534   train acc 0.6882   worst 0.3530   lr 0.0011   p 638.11   eps 0.1952   mix 0.0027   time 27.51
scalar:  6.2395
Epoch 1144:  train loss 0.4541   train acc 0.6910   worst 0.3524   lr 0.0011   p 640.80   eps 0.1952   mix 0.0027   time 26.59
Epoch 1144:  test acc 0.6086   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 1144:  clean acc 0.6927   certified acc 0.6488
Calculating metrics for L_infinity dist model on test set
Epoch 1144:  clean acc 0.6088   certified acc 0.5396
scalar:  6.2413
Epoch 1145:  train loss 0.4543   train acc 0.6906   worst 0.3516   lr 0.0010   p 643.49   eps 0.1952   mix 0.0027   time 26.43
scalar:  6.242
Epoch 1146:  train loss 0.4527   train acc 0.6910   worst 0.3540   lr 0.0010   p 646.20   eps 0.1952   mix 0.0027   time 27.00
scalar:  6.2429
Epoch 1147:  train loss 0.4533   train acc 0.6907   worst 0.3524   lr 0.0010   p 648.92   eps 0.1952   mix 0.0027   time 26.80
scalar:  6.2435
Epoch 1148:  train loss 0.4540   train acc 0.6899   worst 0.3534   lr 0.0010   p 651.65   eps 0.1952   mix 0.0027   time 27.44
scalar:  6.2438
Epoch 1149:  train loss 0.4528   train acc 0.6915   worst 0.3531   lr 0.0010   p 654.39   eps 0.1952   mix 0.0027   time 26.59
Epoch 1149:  test acc 0.6076   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1149:  clean acc 0.6913   certified acc 0.6487
Calculating metrics for L_infinity dist model on test set
Epoch 1149:  clean acc 0.6083   certified acc 0.5405
scalar:  6.2419
Epoch 1150:  train loss 0.4528   train acc 0.6912   worst 0.3549   lr 0.0010   p 657.14   eps 0.1952   mix 0.0026   time 26.52
scalar:  6.2427
Epoch 1151:  train loss 0.4514   train acc 0.6928   worst 0.3555   lr 0.0010   p 659.91   eps 0.1952   mix 0.0026   time 27.06
scalar:  6.2435
Epoch 1152:  train loss 0.4525   train acc 0.6911   worst 0.3538   lr 0.0009   p 662.68   eps 0.1952   mix 0.0026   time 26.72
scalar:  6.2429
Epoch 1153:  train loss 0.4551   train acc 0.6879   worst 0.3513   lr 0.0009   p 665.47   eps 0.1952   mix 0.0026   time 27.52
scalar:  6.2424
Epoch 1154:  train loss 0.4537   train acc 0.6905   worst 0.3534   lr 0.0009   p 668.27   eps 0.1952   mix 0.0026   time 26.40
Epoch 1154:  test acc 0.6070   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1154:  clean acc 0.6915   certified acc 0.6475
Calculating metrics for L_infinity dist model on test set
Epoch 1154:  clean acc 0.6072   certified acc 0.5393
scalar:  6.2421
Epoch 1155:  train loss 0.4528   train acc 0.6886   worst 0.3551   lr 0.0009   p 671.08   eps 0.1952   mix 0.0026   time 26.46
scalar:  6.2409
Epoch 1156:  train loss 0.4514   train acc 0.6904   worst 0.3553   lr 0.0009   p 673.91   eps 0.1952   mix 0.0026   time 26.89
scalar:  6.24
Epoch 1157:  train loss 0.4533   train acc 0.6892   worst 0.3535   lr 0.0009   p 676.74   eps 0.1952   mix 0.0026   time 26.70
scalar:  6.2375
Epoch 1158:  train loss 0.4520   train acc 0.6905   worst 0.3558   lr 0.0009   p 679.59   eps 0.1952   mix 0.0026   time 27.37
scalar:  6.2372
Epoch 1159:  train loss 0.4524   train acc 0.6896   worst 0.3575   lr 0.0009   p 682.45   eps 0.1952   mix 0.0026   time 26.45
Epoch 1159:  test acc 0.6074   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1159:  clean acc 0.6934   certified acc 0.6497
Calculating metrics for L_infinity dist model on test set
Epoch 1159:  clean acc 0.6056   certified acc 0.5408
scalar:  6.236
Epoch 1160:  train loss 0.4525   train acc 0.6907   worst 0.3575   lr 0.0009   p 685.32   eps 0.1952   mix 0.0026   time 26.78
scalar:  6.2354
Epoch 1161:  train loss 0.4520   train acc 0.6904   worst 0.3566   lr 0.0008   p 688.20   eps 0.1952   mix 0.0026   time 27.05
scalar:  6.2361
Epoch 1162:  train loss 0.4511   train acc 0.6917   worst 0.3537   lr 0.0008   p 691.10   eps 0.1952   mix 0.0026   time 26.72
scalar:  6.2371
Epoch 1163:  train loss 0.4522   train acc 0.6916   worst 0.3549   lr 0.0008   p 694.01   eps 0.1952   mix 0.0026   time 27.31
scalar:  6.2377
Epoch 1164:  train loss 0.4520   train acc 0.6920   worst 0.3559   lr 0.0008   p 696.93   eps 0.1952   mix 0.0025   time 26.63
Epoch 1164:  test acc 0.6071   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1164:  clean acc 0.6948   certified acc 0.6525
Calculating metrics for L_infinity dist model on test set
Epoch 1164:  clean acc 0.6062   certified acc 0.5408
scalar:  6.2383
Epoch 1165:  train loss 0.4509   train acc 0.6923   worst 0.3554   lr 0.0008   p 699.86   eps 0.1952   mix 0.0025   time 26.65
scalar:  6.2385
Epoch 1166:  train loss 0.4507   train acc 0.6925   worst 0.3569   lr 0.0008   p 702.80   eps 0.1952   mix 0.0025   time 27.14
scalar:  6.2391
Epoch 1167:  train loss 0.4508   train acc 0.6908   worst 0.3563   lr 0.0008   p 705.76   eps 0.1952   mix 0.0025   time 26.88
scalar:  6.2389
Epoch 1168:  train loss 0.4505   train acc 0.6922   worst 0.3569   lr 0.0008   p 708.73   eps 0.1952   mix 0.0025   time 27.72
scalar:  6.2385
Epoch 1169:  train loss 0.4508   train acc 0.6937   worst 0.3541   lr 0.0007   p 711.71   eps 0.1952   mix 0.0025   time 26.67
Epoch 1169:  test acc 0.6082   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 1169:  clean acc 0.6933   certified acc 0.6520
Calculating metrics for L_infinity dist model on test set
Epoch 1169:  clean acc 0.6075   certified acc 0.5410
scalar:  6.2386
Epoch 1170:  train loss 0.4508   train acc 0.6921   worst 0.3571   lr 0.0007   p 714.71   eps 0.1952   mix 0.0025   time 26.68
scalar:  6.2398
Epoch 1171:  train loss 0.4533   train acc 0.6879   worst 0.3541   lr 0.0007   p 717.71   eps 0.1952   mix 0.0025   time 27.14
scalar:  6.239
Epoch 1172:  train loss 0.4515   train acc 0.6910   worst 0.3558   lr 0.0007   p 720.73   eps 0.1952   mix 0.0025   time 26.88
scalar:  6.2384
Epoch 1173:  train loss 0.4492   train acc 0.6916   worst 0.3575   lr 0.0007   p 723.77   eps 0.1952   mix 0.0025   time 27.24
scalar:  6.2391
Epoch 1174:  train loss 0.4510   train acc 0.6917   worst 0.3550   lr 0.0007   p 726.81   eps 0.1952   mix 0.0025   time 26.96
Epoch 1174:  test acc 0.6089   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1174:  clean acc 0.6929   certified acc 0.6513
Calculating metrics for L_infinity dist model on test set
Epoch 1174:  clean acc 0.6075   certified acc 0.5414
scalar:  6.2399
Epoch 1175:  train loss 0.4494   train acc 0.6932   worst 0.3578   lr 0.0007   p 729.87   eps 0.1952   mix 0.0025   time 27.02
scalar:  6.2405
Epoch 1176:  train loss 0.4489   train acc 0.6932   worst 0.3581   lr 0.0007   p 732.94   eps 0.1952   mix 0.0025   time 27.20
scalar:  6.2401
Epoch 1177:  train loss 0.4498   train acc 0.6909   worst 0.3562   lr 0.0007   p 736.02   eps 0.1952   mix 0.0025   time 27.01
scalar:  6.2405
Epoch 1178:  train loss 0.4509   train acc 0.6912   worst 0.3554   lr 0.0006   p 739.12   eps 0.1952   mix 0.0024   time 27.35
scalar:  6.2397
Epoch 1179:  train loss 0.4503   train acc 0.6933   worst 0.3564   lr 0.0006   p 742.23   eps 0.1952   mix 0.0024   time 27.06
Epoch 1179:  test acc 0.6081   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1179:  clean acc 0.6928   certified acc 0.6510
Calculating metrics for L_infinity dist model on test set
Epoch 1179:  clean acc 0.6077   certified acc 0.5432
scalar:  6.2399
Epoch 1180:  train loss 0.4505   train acc 0.6918   worst 0.3550   lr 0.0006   p 745.35   eps 0.1952   mix 0.0024   time 26.77
scalar:  6.2405
Epoch 1181:  train loss 0.4484   train acc 0.6919   worst 0.3611   lr 0.0006   p 748.49   eps 0.1952   mix 0.0024   time 27.16
scalar:  6.2408
Epoch 1182:  train loss 0.4527   train acc 0.6913   worst 0.3545   lr 0.0006   p 751.64   eps 0.1952   mix 0.0024   time 26.65
scalar:  6.2393
Epoch 1183:  train loss 0.4500   train acc 0.6906   worst 0.3590   lr 0.0006   p 754.80   eps 0.1952   mix 0.0024   time 26.79
scalar:  6.2383
Epoch 1184:  train loss 0.4500   train acc 0.6920   worst 0.3560   lr 0.0006   p 757.98   eps 0.1952   mix 0.0024   time 27.33
Epoch 1184:  test acc 0.6089   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1184:  clean acc 0.6937   certified acc 0.6527
Calculating metrics for L_infinity dist model on test set
Epoch 1184:  clean acc 0.6054   certified acc 0.5400
scalar:  6.2378
Epoch 1185:  train loss 0.4511   train acc 0.6900   worst 0.3575   lr 0.0006   p 761.17   eps 0.1952   mix 0.0024   time 26.73
scalar:  6.2378
Epoch 1186:  train loss 0.4501   train acc 0.6917   worst 0.3597   lr 0.0006   p 764.37   eps 0.1952   mix 0.0024   time 27.41
scalar:  6.237
Epoch 1187:  train loss 0.4484   train acc 0.6922   worst 0.3598   lr 0.0006   p 767.58   eps 0.1952   mix 0.0024   time 26.70
scalar:  6.2372
Epoch 1188:  train loss 0.4500   train acc 0.6908   worst 0.3593   lr 0.0005   p 770.81   eps 0.1952   mix 0.0024   time 26.82
scalar:  6.2373
Epoch 1189:  train loss 0.4495   train acc 0.6910   worst 0.3593   lr 0.0005   p 774.06   eps 0.1952   mix 0.0024   time 27.54
Epoch 1189:  test acc 0.6057   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1189:  clean acc 0.6956   certified acc 0.6544
Calculating metrics for L_infinity dist model on test set
Epoch 1189:  clean acc 0.6049   certified acc 0.5396
scalar:  6.2361
Epoch 1190:  train loss 0.4492   train acc 0.6923   worst 0.3556   lr 0.0005   p 777.31   eps 0.1952   mix 0.0024   time 26.78
scalar:  6.2366
Epoch 1191:  train loss 0.4482   train acc 0.6918   worst 0.3578   lr 0.0005   p 780.58   eps 0.1952   mix 0.0024   time 27.09
scalar:  6.2368
Epoch 1192:  train loss 0.4485   train acc 0.6919   worst 0.3565   lr 0.0005   p 783.87   eps 0.1952   mix 0.0024   time 26.54
scalar:  6.2366
Epoch 1193:  train loss 0.4491   train acc 0.6931   worst 0.3562   lr 0.0005   p 787.17   eps 0.1952   mix 0.0023   time 26.76
scalar:  6.2364
Epoch 1194:  train loss 0.4489   train acc 0.6951   worst 0.3568   lr 0.0005   p 790.48   eps 0.1952   mix 0.0023   time 27.46
Epoch 1194:  test acc 0.6081   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1194:  clean acc 0.6965   certified acc 0.6539
Calculating metrics for L_infinity dist model on test set
Epoch 1194:  clean acc 0.6090   certified acc 0.5408
scalar:  6.2362
Epoch 1195:  train loss 0.4484   train acc 0.6911   worst 0.3584   lr 0.0005   p 793.80   eps 0.1952   mix 0.0023   time 26.47
scalar:  6.2364
Epoch 1196:  train loss 0.4483   train acc 0.6935   worst 0.3594   lr 0.0005   p 797.14   eps 0.1952   mix 0.0023   time 27.03
scalar:  6.2377
Epoch 1197:  train loss 0.4495   train acc 0.6912   worst 0.3565   lr 0.0005   p 800.50   eps 0.1952   mix 0.0023   time 26.72
scalar:  6.238
Epoch 1198:  train loss 0.4498   train acc 0.6903   worst 0.3576   lr 0.0005   p 803.87   eps 0.1952   mix 0.0023   time 26.82
scalar:  6.2377
Epoch 1199:  train loss 0.4462   train acc 0.6945   worst 0.3601   lr 0.0004   p 807.25   eps 0.1952   mix 0.0023   time 27.55
Epoch 1199:  test acc 0.6086   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1199:  clean acc 0.6948   certified acc 0.6526
Calculating metrics for L_infinity dist model on test set
Epoch 1199:  clean acc 0.6101   certified acc 0.5390
Generate adversarial examples on test dataset
adversarial attack acc 54.0200
scalar:  6.2375
Epoch 1200:  train loss 0.4484   train acc 0.6926   worst 0.3606   lr 0.0004   p 810.64   eps 0.1952   mix 0.0023   time 25.67
scalar:  6.2379
Epoch 1201:  train loss 0.4487   train acc 0.6917   worst 0.3602   lr 0.0004   p 814.05   eps 0.1952   mix 0.0023   time 25.81
scalar:  6.2377
Epoch 1202:  train loss 0.4488   train acc 0.6926   worst 0.3570   lr 0.0004   p 817.48   eps 0.1952   mix 0.0023   time 26.28
scalar:  6.2373
Epoch 1203:  train loss 0.4468   train acc 0.6942   worst 0.3603   lr 0.0004   p 820.92   eps 0.1952   mix 0.0023   time 26.51
scalar:  6.2375
Epoch 1204:  train loss 0.4496   train acc 0.6934   worst 0.3571   lr 0.0004   p 824.37   eps 0.1952   mix 0.0023   time 27.32
Epoch 1204:  test acc 0.6098   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1204:  clean acc 0.6941   certified acc 0.6520
Calculating metrics for L_infinity dist model on test set
Epoch 1204:  clean acc 0.6103   certified acc 0.5410
scalar:  6.2374
Epoch 1205:  train loss 0.4462   train acc 0.6933   worst 0.3617   lr 0.0004   p 827.84   eps 0.1952   mix 0.0023   time 26.13
scalar:  6.2377
Epoch 1206:  train loss 0.4479   train acc 0.6925   worst 0.3589   lr 0.0004   p 831.32   eps 0.1952   mix 0.0023   time 26.46
scalar:  6.2375
Epoch 1207:  train loss 0.4489   train acc 0.6922   worst 0.3591   lr 0.0004   p 834.82   eps 0.1952   mix 0.0023   time 26.23
scalar:  6.2379
Epoch 1208:  train loss 0.4487   train acc 0.6931   worst 0.3596   lr 0.0004   p 838.33   eps 0.1952   mix 0.0022   time 26.13
scalar:  6.2379
Epoch 1209:  train loss 0.4493   train acc 0.6922   worst 0.3565   lr 0.0004   p 841.86   eps 0.1952   mix 0.0022   time 25.69
Epoch 1209:  test acc 0.6076   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 1209:  clean acc 0.6959   certified acc 0.6537
Calculating metrics for L_infinity dist model on test set
Epoch 1209:  clean acc 0.6080   certified acc 0.5421
scalar:  6.2375
Epoch 1210:  train loss 0.4467   train acc 0.6928   worst 0.3600   lr 0.0004   p 845.40   eps 0.1952   mix 0.0022   time 25.79
scalar:  6.2378
Epoch 1211:  train loss 0.4470   train acc 0.6925   worst 0.3598   lr 0.0003   p 848.96   eps 0.1952   mix 0.0022   time 25.89
scalar:  6.2378
Epoch 1212:  train loss 0.4480   train acc 0.6903   worst 0.3610   lr 0.0003   p 852.53   eps 0.1952   mix 0.0022   time 25.64
scalar:  6.2373
Epoch 1213:  train loss 0.4467   train acc 0.6940   worst 0.3608   lr 0.0003   p 856.12   eps 0.1952   mix 0.0022   time 25.85
scalar:  6.238
Epoch 1214:  train loss 0.4463   train acc 0.6931   worst 0.3607   lr 0.0003   p 859.72   eps 0.1952   mix 0.0022   time 25.66
Epoch 1214:  test acc 0.6062   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1214:  clean acc 0.6924   certified acc 0.6512
Calculating metrics for L_infinity dist model on test set
Epoch 1214:  clean acc 0.6059   certified acc 0.5406
scalar:  6.238
Epoch 1215:  train loss 0.4462   train acc 0.6949   worst 0.3616   lr 0.0003   p 863.34   eps 0.1952   mix 0.0022   time 25.70
scalar:  6.2385
Epoch 1216:  train loss 0.4455   train acc 0.6948   worst 0.3621   lr 0.0003   p 866.97   eps 0.1952   mix 0.0022   time 26.17
scalar:  6.2388
Epoch 1217:  train loss 0.4482   train acc 0.6929   worst 0.3606   lr 0.0003   p 870.62   eps 0.1952   mix 0.0022   time 25.68
scalar:  6.239
Epoch 1218:  train loss 0.4474   train acc 0.6919   worst 0.3597   lr 0.0003   p 874.28   eps 0.1952   mix 0.0022   time 26.00
scalar:  6.2387
Epoch 1219:  train loss 0.4493   train acc 0.6919   worst 0.3591   lr 0.0003   p 877.96   eps 0.1952   mix 0.0022   time 25.80
Epoch 1219:  test acc 0.6065   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1219:  clean acc 0.6943   certified acc 0.6522
Calculating metrics for L_infinity dist model on test set
Epoch 1219:  clean acc 0.6067   certified acc 0.5410
scalar:  6.2391
Epoch 1220:  train loss 0.4477   train acc 0.6933   worst 0.3579   lr 0.0003   p 881.65   eps 0.1952   mix 0.0022   time 26.04
scalar:  6.2397
Epoch 1221:  train loss 0.4466   train acc 0.6942   worst 0.3608   lr 0.0003   p 885.36   eps 0.1952   mix 0.0022   time 26.92
scalar:  6.2398
Epoch 1222:  train loss 0.4480   train acc 0.6930   worst 0.3587   lr 0.0003   p 889.09   eps 0.1952   mix 0.0022   time 26.20
scalar:  6.2399
Epoch 1223:  train loss 0.4473   train acc 0.6926   worst 0.3612   lr 0.0003   p 892.83   eps 0.1952   mix 0.0022   time 26.35
scalar:  6.2399
Epoch 1224:  train loss 0.4456   train acc 0.6940   worst 0.3625   lr 0.0003   p 896.59   eps 0.1952   mix 0.0022   time 25.94
Epoch 1224:  test acc 0.6073   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1224:  clean acc 0.6965   certified acc 0.6552
Calculating metrics for L_infinity dist model on test set
Epoch 1224:  clean acc 0.6067   certified acc 0.5410
scalar:  6.2405
Epoch 1225:  train loss 0.4464   train acc 0.6951   worst 0.3602   lr 0.0002   p 900.36   eps 0.1952   mix 0.0021   time 27.05
scalar:  6.2405
Epoch 1226:  train loss 0.4467   train acc 0.6931   worst 0.3600   lr 0.0002   p 904.15   eps 0.1952   mix 0.0021   time 27.20
scalar:  6.2406
Epoch 1227:  train loss 0.4480   train acc 0.6912   worst 0.3580   lr 0.0002   p 907.95   eps 0.1952   mix 0.0021   time 26.75
scalar:  6.2405
Epoch 1228:  train loss 0.4471   train acc 0.6926   worst 0.3587   lr 0.0002   p 911.77   eps 0.1952   mix 0.0021   time 27.05
scalar:  6.2401
Epoch 1229:  train loss 0.4462   train acc 0.6944   worst 0.3596   lr 0.0002   p 915.61   eps 0.1952   mix 0.0021   time 26.55
Epoch 1229:  test acc 0.6072   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1229:  clean acc 0.6937   certified acc 0.6544
Calculating metrics for L_infinity dist model on test set
Epoch 1229:  clean acc 0.6071   certified acc 0.5402
scalar:  6.2402
Epoch 1230:  train loss 0.4474   train acc 0.6935   worst 0.3606   lr 0.0002   p 919.46   eps 0.1952   mix 0.0021   time 26.98
scalar:  6.2403
Epoch 1231:  train loss 0.4466   train acc 0.6922   worst 0.3614   lr 0.0002   p 923.33   eps 0.1952   mix 0.0021   time 27.31
scalar:  6.2403
Epoch 1232:  train loss 0.4463   train acc 0.6929   worst 0.3607   lr 0.0002   p 927.21   eps 0.1952   mix 0.0021   time 27.04
scalar:  6.2406
Epoch 1233:  train loss 0.4467   train acc 0.6922   worst 0.3595   lr 0.0002   p 931.11   eps 0.1952   mix 0.0021   time 26.69
scalar:  6.2407
Epoch 1234:  train loss 0.4455   train acc 0.6923   worst 0.3609   lr 0.0002   p 935.03   eps 0.1952   mix 0.0021   time 26.41
Epoch 1234:  test acc 0.6072   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 1234:  clean acc 0.6950   certified acc 0.6546
Calculating metrics for L_infinity dist model on test set
Epoch 1234:  clean acc 0.6061   certified acc 0.5425
scalar:  6.2407
Epoch 1235:  train loss 0.4460   train acc 0.6934   worst 0.3613   lr 0.0002   p 938.96   eps 0.1952   mix 0.0021   time 27.37
scalar:  6.2407
Epoch 1236:  train loss 0.4468   train acc 0.6946   worst 0.3609   lr 0.0002   p 942.91   eps 0.1952   mix 0.0021   time 26.92
scalar:  6.2408
Epoch 1237:  train loss 0.4461   train acc 0.6944   worst 0.3623   lr 0.0002   p 946.88   eps 0.1952   mix 0.0021   time 26.98
scalar:  6.2408
Epoch 1238:  train loss 0.4464   train acc 0.6938   worst 0.3576   lr 0.0002   p 950.87   eps 0.1952   mix 0.0021   time 27.19
scalar:  6.2408
Epoch 1239:  train loss 0.4479   train acc 0.6936   worst 0.3591   lr 0.0002   p 954.87   eps 0.1952   mix 0.0021   time 26.44
Epoch 1239:  test acc 0.6072   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1239:  clean acc 0.6938   certified acc 0.6540
Calculating metrics for L_infinity dist model on test set
Epoch 1239:  clean acc 0.6062   certified acc 0.5403
scalar:  6.241
Epoch 1240:  train loss 0.4462   train acc 0.6944   worst 0.3602   lr 0.0002   p 958.88   eps 0.1952   mix 0.0021   time 27.34
scalar:  6.2409
Epoch 1241:  train loss 0.4443   train acc 0.6956   worst 0.3635   lr 0.0002   p 962.92   eps 0.1952   mix 0.0021   time 27.09
scalar:  6.241
Epoch 1242:  train loss 0.4469   train acc 0.6942   worst 0.3603   lr 0.0001   p 966.97   eps 0.1952   mix 0.0020   time 26.93
scalar:  6.2414
Epoch 1243:  train loss 0.4463   train acc 0.6946   worst 0.3608   lr 0.0001   p 971.04   eps 0.1952   mix 0.0020   time 26.99
scalar:  6.2416
Epoch 1244:  train loss 0.4483   train acc 0.6918   worst 0.3587   lr 0.0001   p 975.12   eps 0.1952   mix 0.0020   time 26.74
Epoch 1244:  test acc 0.6080   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1244:  clean acc 0.6964   certified acc 0.6553
Calculating metrics for L_infinity dist model on test set
Epoch 1244:  clean acc 0.6072   certified acc 0.5425
scalar:  6.2415
Epoch 1245:  train loss 0.4477   train acc 0.6939   worst 0.3601   lr 0.0001   p 979.23   eps 0.1952   mix 0.0020   time 27.29
scalar:  6.2416
Epoch 1246:  train loss 0.4457   train acc 0.6925   worst 0.3605   lr 0.0001   p 983.35   eps 0.1952   mix 0.0020   time 27.23
scalar:  6.2416
Epoch 1247:  train loss 0.4461   train acc 0.6933   worst 0.3619   lr 0.0001   p 987.48   eps 0.1952   mix 0.0020   time 26.88
scalar:  6.2418
Epoch 1248:  train loss 0.4475   train acc 0.6948   worst 0.3566   lr 0.0001   p 991.64   eps 0.1952   mix 0.0020   time 27.23
scalar:  6.2419
Epoch 1249:  train loss 0.4481   train acc 0.6931   worst 0.3598   lr 0.0001   p 995.81   eps 0.1952   mix 0.0020   time 26.68
Epoch 1249:  test acc 0.6086   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 1249:  clean acc 0.6942   certified acc 0.6524
Calculating metrics for L_infinity dist model on test set
Epoch 1249:  clean acc 0.6075   certified acc 0.5440
Generate adversarial examples on test dataset
adversarial attack acc 54.3500
scalar:  6.2419
Epoch 1250:  train loss 0.4472   train acc 0.6930   worst 0.3603   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.07
scalar:  6.2419
Epoch 1251:  train loss 0.4480   train acc 0.6928   worst 0.3571   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.15
scalar:  6.2418
Epoch 1252:  train loss 0.4472   train acc 0.6931   worst 0.3584   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.12
scalar:  6.242
Epoch 1253:  train loss 0.4467   train acc 0.6942   worst 0.3577   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.08
scalar:  6.2422
Epoch 1254:  train loss 0.4481   train acc 0.6924   worst 0.3579   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.10
Epoch 1254:  test acc 0.6071   time 1.10
Calculating metrics for L_infinity dist model on training set
Epoch 1254:  clean acc 0.6948   certified acc 0.6538
Calculating metrics for L_infinity dist model on test set
Epoch 1254:  clean acc 0.6071   certified acc 0.5420
scalar:  6.2421
Epoch 1255:  train loss 0.4483   train acc 0.6937   worst 0.3569   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.07
scalar:  6.2422
Epoch 1256:  train loss 0.4484   train acc 0.6918   worst 0.3569   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.12
scalar:  6.2422
Epoch 1257:  train loss 0.4463   train acc 0.6949   worst 0.3584   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.17
scalar:  6.2423
Epoch 1258:  train loss 0.4478   train acc 0.6936   worst 0.3587   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.14
scalar:  6.2422
Epoch 1259:  train loss 0.4469   train acc 0.6957   worst 0.3573   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.18
Epoch 1259:  test acc 0.6073   time 0.86
Calculating metrics for L_infinity dist model on training set
Epoch 1259:  clean acc 0.6957   certified acc 0.6554
Calculating metrics for L_infinity dist model on test set
Epoch 1259:  clean acc 0.6073   certified acc 0.5427
scalar:  6.2423
Epoch 1260:  train loss 0.4483   train acc 0.6934   worst 0.3578   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.13
scalar:  6.2423
Epoch 1261:  train loss 0.4488   train acc 0.6919   worst 0.3583   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.16
scalar:  6.2422
Epoch 1262:  train loss 0.4466   train acc 0.6929   worst 0.3597   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.10
scalar:  6.2421
Epoch 1263:  train loss 0.4482   train acc 0.6937   worst 0.3576   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.08
scalar:  6.2422
Epoch 1264:  train loss 0.4486   train acc 0.6923   worst 0.3606   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.10
Epoch 1264:  test acc 0.6066   time 0.82
Calculating metrics for L_infinity dist model on training set
Epoch 1264:  clean acc 0.6942   certified acc 0.6538
Calculating metrics for L_infinity dist model on test set
Epoch 1264:  clean acc 0.6066   certified acc 0.5421
scalar:  6.2422
Epoch 1265:  train loss 0.4481   train acc 0.6928   worst 0.3582   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.12
scalar:  6.2421
Epoch 1266:  train loss 0.4461   train acc 0.6931   worst 0.3610   lr 0.0001   p inf   eps 0.1952   mix 0.0020   time 6.08
scalar:  6.2421
Epoch 1267:  train loss 0.4471   train acc 0.6928   worst 0.3587   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.08
scalar:  6.2422
Epoch 1268:  train loss 0.4478   train acc 0.6926   worst 0.3578   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.12
scalar:  6.2422
Epoch 1269:  train loss 0.4469   train acc 0.6928   worst 0.3598   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.15
Epoch 1269:  test acc 0.6071   time 0.84
Calculating metrics for L_infinity dist model on training set
Epoch 1269:  clean acc 0.6955   certified acc 0.6539
Calculating metrics for L_infinity dist model on test set
Epoch 1269:  clean acc 0.6071   certified acc 0.5415
scalar:  6.2422
Epoch 1270:  train loss 0.4465   train acc 0.6921   worst 0.3597   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.16
scalar:  6.2422
Epoch 1271:  train loss 0.4466   train acc 0.6931   worst 0.3611   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.26
scalar:  6.2422
Epoch 1272:  train loss 0.4473   train acc 0.6938   worst 0.3585   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.15
scalar:  6.2422
Epoch 1273:  train loss 0.4464   train acc 0.6943   worst 0.3596   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.07
scalar:  6.2421
Epoch 1274:  train loss 0.4468   train acc 0.6934   worst 0.3585   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.10
Epoch 1274:  test acc 0.6061   time 0.82
Calculating metrics for L_infinity dist model on training set
Epoch 1274:  clean acc 0.6955   certified acc 0.6520
Calculating metrics for L_infinity dist model on test set
Epoch 1274:  clean acc 0.6061   certified acc 0.5426
scalar:  6.2421
Epoch 1275:  train loss 0.4464   train acc 0.6947   worst 0.3612   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.13
scalar:  6.2422
Epoch 1276:  train loss 0.4486   train acc 0.6914   worst 0.3588   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.13
scalar:  6.2421
Epoch 1277:  train loss 0.4478   train acc 0.6906   worst 0.3598   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.25
scalar:  6.2421
Epoch 1278:  train loss 0.4478   train acc 0.6951   worst 0.3566   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.21
scalar:  6.2421
Epoch 1279:  train loss 0.4465   train acc 0.6935   worst 0.3600   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.11
Epoch 1279:  test acc 0.6050   time 0.80
Calculating metrics for L_infinity dist model on training set
Epoch 1279:  clean acc 0.6952   certified acc 0.6548
Calculating metrics for L_infinity dist model on test set
Epoch 1279:  clean acc 0.6050   certified acc 0.5411
scalar:  6.2421
Epoch 1280:  train loss 0.4461   train acc 0.6931   worst 0.3595   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.16
scalar:  6.2421
Epoch 1281:  train loss 0.4471   train acc 0.6946   worst 0.3589   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.18
scalar:  6.2421
Epoch 1282:  train loss 0.4479   train acc 0.6912   worst 0.3610   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.14
scalar:  6.2421
Epoch 1283:  train loss 0.4451   train acc 0.6946   worst 0.3630   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.14
scalar:  6.242
Epoch 1284:  train loss 0.4468   train acc 0.6918   worst 0.3620   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.11
Epoch 1284:  test acc 0.6081   time 0.85
Calculating metrics for L_infinity dist model on training set
Epoch 1284:  clean acc 0.6939   certified acc 0.6535
Calculating metrics for L_infinity dist model on test set
Epoch 1284:  clean acc 0.6081   certified acc 0.5429
scalar:  6.242
Epoch 1285:  train loss 0.4474   train acc 0.6926   worst 0.3587   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.09
scalar:  6.242
Epoch 1286:  train loss 0.4460   train acc 0.6940   worst 0.3615   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.08
scalar:  6.242
Epoch 1287:  train loss 0.4480   train acc 0.6917   worst 0.3590   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.17
scalar:  6.242
Epoch 1288:  train loss 0.4461   train acc 0.6955   worst 0.3569   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.12
scalar:  6.242
Epoch 1289:  train loss 0.4477   train acc 0.6931   worst 0.3599   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.14
Epoch 1289:  test acc 0.6077   time 0.80
Calculating metrics for L_infinity dist model on training set
Epoch 1289:  clean acc 0.6956   certified acc 0.6529
Calculating metrics for L_infinity dist model on test set
Epoch 1289:  clean acc 0.6077   certified acc 0.5418
scalar:  6.242
Epoch 1290:  train loss 0.4472   train acc 0.6919   worst 0.3613   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.08
scalar:  6.242
Epoch 1291:  train loss 0.4457   train acc 0.6950   worst 0.3590   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.08
scalar:  6.242
Epoch 1292:  train loss 0.4472   train acc 0.6934   worst 0.3567   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.17
scalar:  6.2421
Epoch 1293:  train loss 0.4481   train acc 0.6925   worst 0.3579   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.24
scalar:  6.2421
Epoch 1294:  train loss 0.4452   train acc 0.6949   worst 0.3609   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.11
Epoch 1294:  test acc 0.6066   time 0.78
Calculating metrics for L_infinity dist model on training set
Epoch 1294:  clean acc 0.6966   certified acc 0.6561
Calculating metrics for L_infinity dist model on test set
Epoch 1294:  clean acc 0.6066   certified acc 0.5416
scalar:  6.2421
Epoch 1295:  train loss 0.4462   train acc 0.6940   worst 0.3596   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.12
Epoch 1295:  test acc 0.6087   time 0.76
Calculating metrics for L_infinity dist model on training set
Epoch 1295:  clean acc 0.6972   certified acc 0.6565
Calculating metrics for L_infinity dist model on test set
Epoch 1295:  clean acc 0.6087   certified acc 0.5421
Generate adversarial examples on test dataset
adversarial attack acc 54.4200
scalar:  6.2421
Epoch 1296:  train loss 0.4465   train acc 0.6945   worst 0.3588   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.03
Epoch 1296:  test acc 0.6072   time 0.73
Calculating metrics for L_infinity dist model on training set
Epoch 1296:  clean acc 0.6951   certified acc 0.6539
Calculating metrics for L_infinity dist model on test set
Epoch 1296:  clean acc 0.6072   certified acc 0.5437
Generate adversarial examples on test dataset
adversarial attack acc 54.6200
scalar:  6.2421
Epoch 1297:  train loss 0.4455   train acc 0.6934   worst 0.3596   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.08
Epoch 1297:  test acc 0.6070   time 0.73
Calculating metrics for L_infinity dist model on training set
Epoch 1297:  clean acc 0.6964   certified acc 0.6544
Calculating metrics for L_infinity dist model on test set
Epoch 1297:  clean acc 0.6070   certified acc 0.5423
Generate adversarial examples on test dataset
adversarial attack acc 54.5300
scalar:  6.2421
Epoch 1298:  train loss 0.4478   train acc 0.6932   worst 0.3577   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.09
Epoch 1298:  test acc 0.6077   time 0.76
Calculating metrics for L_infinity dist model on training set
Epoch 1298:  clean acc 0.6967   certified acc 0.6561
Calculating metrics for L_infinity dist model on test set
Epoch 1298:  clean acc 0.6077   certified acc 0.5426
Generate adversarial examples on test dataset
adversarial attack acc 54.5800
scalar:  6.2421
Epoch 1299:  train loss 0.4463   train acc 0.6944   worst 0.3598   lr 0.0000   p inf   eps 0.1952   mix 0.0020   time 6.12
Epoch 1299:  test acc 0.6081   time 0.73
Calculating metrics for L_infinity dist model on training set
Epoch 1299:  clean acc 0.6954   certified acc 0.6530
Calculating metrics for L_infinity dist model on test set
Epoch 1299:  clean acc 0.6081   certified acc 0.5431
Generate adversarial examples on test dataset
adversarial attack acc 54.5400
============Training completes===========
Generate adversarial examples on test dataset
adversarial attack acc 54.6700
Calculating test acc and certified test acc
Epoch 1300:  clean acc 0.6081   certified acc 0.5431
