dataset = CIFAR10
model = MLPModel(depth=6,width=5120,identity_val=10.0,scalar=True)
loss = radius_mix2(lam0=0.1,lam_end=0.0002)
p_start = 8.0
p_end = 1000.0
eps_train = 0.15685
eps_test = 0.06274
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 1.0822   train acc 0.1855   worst 0.0878   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 14.85
scalar:  0.667
Epoch 1:  train loss 0.9711   train acc 0.2869   worst 0.1626   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 15.50
scalar:  0.5992
Epoch 2:  train loss 0.9044   train acc 0.3270   worst 0.2326   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 17.48
scalar:  0.5346
Epoch 3:  train loss 0.8671   train acc 0.3477   worst 0.2703   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 20.29
scalar:  0.4977
Epoch 4:  train loss 0.8471   train acc 0.3613   worst 0.2913   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.20
Epoch 4:  test acc 0.3860   time 1.09
Calculating metrics for L_infinity dist model on training set
Epoch 4:  clean acc 0.1470   certified acc 0.0631
Calculating metrics for L_infinity dist model on test set
Epoch 4:  clean acc 0.1541   certified acc 0.0652
scalar:  0.476
Epoch 5:  train loss 0.8339   train acc 0.3716   worst 0.2985   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.69
scalar:  0.4751
Epoch 6:  train loss 0.8189   train acc 0.3837   worst 0.3094   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.87
scalar:  0.4631
Epoch 7:  train loss 0.8079   train acc 0.3904   worst 0.3200   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.10
scalar:  0.459
Epoch 8:  train loss 0.7980   train acc 0.3995   worst 0.3270   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.78
scalar:  0.4779
Epoch 9:  train loss 0.7917   train acc 0.4047   worst 0.3312   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.35
Epoch 9:  test acc 0.4231   time 1.10
Calculating metrics for L_infinity dist model on training set
Epoch 9:  clean acc 0.1373   certified acc 0.0783
Calculating metrics for L_infinity dist model on test set
Epoch 9:  clean acc 0.1388   certified acc 0.0792
scalar:  0.4523
Epoch 10:  train loss 0.7830   train acc 0.4112   worst 0.3372   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.35
scalar:  0.4568
Epoch 11:  train loss 0.7699   train acc 0.4299   worst 0.3398   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.48
scalar:  0.459
Epoch 12:  train loss 0.7536   train acc 0.4466   worst 0.3525   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.35
scalar:  0.4643
Epoch 13:  train loss 0.7443   train acc 0.4537   worst 0.3616   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.51
scalar:  0.4611
Epoch 14:  train loss 0.7347   train acc 0.4623   worst 0.3678   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.65
Epoch 14:  test acc 0.4837   time 1.06
Calculating metrics for L_infinity dist model on training set
Epoch 14:  clean acc 0.1513   certified acc 0.0913
Calculating metrics for L_infinity dist model on test set
Epoch 14:  clean acc 0.1509   certified acc 0.0924
scalar:  0.4699
Epoch 15:  train loss 0.7285   train acc 0.4672   worst 0.3729   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.84
scalar:  0.4367
Epoch 16:  train loss 0.7239   train acc 0.4730   worst 0.3749   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.64
scalar:  0.4605
Epoch 17:  train loss 0.7161   train acc 0.4798   worst 0.3815   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 20.89
scalar:  0.4601
Epoch 18:  train loss 0.7139   train acc 0.4807   worst 0.3826   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.00
scalar:  0.4565
Epoch 19:  train loss 0.7056   train acc 0.4895   worst 0.3883   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.61
Epoch 19:  test acc 0.5046   time 1.20
Calculating metrics for L_infinity dist model on training set
Epoch 19:  clean acc 0.1467   certified acc 0.0372
Calculating metrics for L_infinity dist model on test set
Epoch 19:  clean acc 0.1452   certified acc 0.0366
scalar:  0.4694
Epoch 20:  train loss 0.7006   train acc 0.4936   worst 0.3915   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 18.84
scalar:  0.4717
Epoch 21:  train loss 0.6962   train acc 0.4960   worst 0.3947   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 18.62
scalar:  0.4907
Epoch 22:  train loss 0.6911   train acc 0.5011   worst 0.3988   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.80
scalar:  0.483
Epoch 23:  train loss 0.6870   train acc 0.5070   worst 0.4012   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.45
scalar:  0.4817
Epoch 24:  train loss 0.6817   train acc 0.5102   worst 0.4048   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.33
Epoch 24:  test acc 0.5252   time 1.12
Calculating metrics for L_infinity dist model on training set
Epoch 24:  clean acc 0.1243   certified acc 0.0155
Calculating metrics for L_infinity dist model on test set
Epoch 24:  clean acc 0.1220   certified acc 0.0150
scalar:  0.478
Epoch 25:  train loss 0.6788   train acc 0.5128   worst 0.4073   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.72
scalar:  0.4739
Epoch 26:  train loss 0.6760   train acc 0.5155   worst 0.4083   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.13
scalar:  0.478
Epoch 27:  train loss 0.6715   train acc 0.5225   worst 0.4099   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.91
scalar:  0.4816
Epoch 28:  train loss 0.6659   train acc 0.5252   worst 0.4144   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.43
scalar:  0.4962
Epoch 29:  train loss 0.6631   train acc 0.5257   worst 0.4187   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 18.96
Epoch 29:  test acc 0.5394   time 1.10
Calculating metrics for L_infinity dist model on training set
Epoch 29:  clean acc 0.1354   certified acc 0.0076
Calculating metrics for L_infinity dist model on test set
Epoch 29:  clean acc 0.1339   certified acc 0.0080
scalar:  0.4828
Epoch 30:  train loss 0.6611   train acc 0.5286   worst 0.4191   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.27
scalar:  0.487
Epoch 31:  train loss 0.6575   train acc 0.5322   worst 0.4204   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.27
scalar:  0.4876
Epoch 32:  train loss 0.6552   train acc 0.5341   worst 0.4236   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.13
scalar:  0.4872
Epoch 33:  train loss 0.6509   train acc 0.5373   worst 0.4269   lr 0.0300   p 8.00   eps 0.7807   mix 0.1000   time 19.62
scalar:  0.4965
Epoch 34:  train loss 0.6514   train acc 0.5364   worst 0.4257   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.35
Epoch 34:  test acc 0.5493   time 1.10
Calculating metrics for L_infinity dist model on training set
Epoch 34:  clean acc 0.1572   certified acc 0.0192
Calculating metrics for L_infinity dist model on test set
Epoch 34:  clean acc 0.1584   certified acc 0.0199
scalar:  0.4694
Epoch 35:  train loss 0.6445   train acc 0.5429   worst 0.4303   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 18.50
scalar:  0.4945
Epoch 36:  train loss 0.6463   train acc 0.5428   worst 0.4299   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.36
scalar:  0.5118
Epoch 37:  train loss 0.6435   train acc 0.5438   worst 0.4312   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.06
scalar:  0.4871
Epoch 38:  train loss 0.6382   train acc 0.5471   worst 0.4354   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.86
scalar:  0.5159
Epoch 39:  train loss 0.6350   train acc 0.5498   worst 0.4377   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.88
Epoch 39:  test acc 0.5548   time 1.10
Calculating metrics for L_infinity dist model on training set
Epoch 39:  clean acc 0.1565   certified acc 0.0235
Calculating metrics for L_infinity dist model on test set
Epoch 39:  clean acc 0.1571   certified acc 0.0234
scalar:  0.5009
Epoch 40:  train loss 0.6359   train acc 0.5496   worst 0.4383   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.38
scalar:  0.4998
Epoch 41:  train loss 0.6327   train acc 0.5510   worst 0.4406   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.53
scalar:  0.5066
Epoch 42:  train loss 0.6327   train acc 0.5526   worst 0.4377   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.17
scalar:  0.498
Epoch 43:  train loss 0.6289   train acc 0.5545   worst 0.4440   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.88
scalar:  0.5108
Epoch 44:  train loss 0.6257   train acc 0.5579   worst 0.4461   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.58
Epoch 44:  test acc 0.5619   time 1.07
Calculating metrics for L_infinity dist model on training set
Epoch 44:  clean acc 0.1402   certified acc 0.0064
Calculating metrics for L_infinity dist model on test set
Epoch 44:  clean acc 0.1404   certified acc 0.0070
scalar:  0.5091
Epoch 45:  train loss 0.6271   train acc 0.5567   worst 0.4450   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.17
scalar:  0.5184
Epoch 46:  train loss 0.6210   train acc 0.5617   worst 0.4471   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.77
scalar:  0.5142
Epoch 47:  train loss 0.6209   train acc 0.5634   worst 0.4462   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.28
scalar:  0.4992
Epoch 48:  train loss 0.6210   train acc 0.5633   worst 0.4462   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 18.95
scalar:  0.52
Epoch 49:  train loss 0.6192   train acc 0.5662   worst 0.4484   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.58
Epoch 49:  test acc 0.5676   time 1.15
Calculating metrics for L_infinity dist model on training set
Epoch 49:  clean acc 0.1457   certified acc 0.0332
Calculating metrics for L_infinity dist model on test set
Epoch 49:  clean acc 0.1462   certified acc 0.0323
scalar:  0.5018
Epoch 50:  train loss 0.6195   train acc 0.5650   worst 0.4470   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.22
scalar:  0.5326
Epoch 51:  train loss 0.6157   train acc 0.5665   worst 0.4507   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.77
scalar:  0.5169
Epoch 52:  train loss 0.6130   train acc 0.5697   worst 0.4518   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 20.00
scalar:  0.5415
Epoch 53:  train loss 0.6123   train acc 0.5715   worst 0.4537   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 20.31
scalar:  0.5289
Epoch 54:  train loss 0.6082   train acc 0.5732   worst 0.4577   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 20.03
Epoch 54:  test acc 0.5736   time 1.15
Calculating metrics for L_infinity dist model on training set
Epoch 54:  clean acc 0.1345   certified acc 0.0592
Calculating metrics for L_infinity dist model on test set
Epoch 54:  clean acc 0.1402   certified acc 0.0579
scalar:  0.5471
Epoch 55:  train loss 0.6071   train acc 0.5745   worst 0.4594   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.73
scalar:  0.5482
Epoch 56:  train loss 0.6066   train acc 0.5754   worst 0.4578   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.39
scalar:  0.5195
Epoch 57:  train loss 0.6080   train acc 0.5742   worst 0.4570   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.43
scalar:  0.5383
Epoch 58:  train loss 0.6051   train acc 0.5764   worst 0.4578   lr 0.0299   p 8.00   eps 0.7807   mix 0.1000   time 19.40
scalar:  0.5444
Epoch 59:  train loss 0.6022   train acc 0.5783   worst 0.4622   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.46
Epoch 59:  test acc 0.5764   time 1.14
Calculating metrics for L_infinity dist model on training set
Epoch 59:  clean acc 0.1465   certified acc 0.0254
Calculating metrics for L_infinity dist model on test set
Epoch 59:  clean acc 0.1438   certified acc 0.0242
scalar:  0.537
Epoch 60:  train loss 0.6007   train acc 0.5812   worst 0.4610   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 18.10
scalar:  0.5378
Epoch 61:  train loss 0.6016   train acc 0.5807   worst 0.4594   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.10
scalar:  0.5491
Epoch 62:  train loss 0.5989   train acc 0.5814   worst 0.4614   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.38
scalar:  0.5499
Epoch 63:  train loss 0.5961   train acc 0.5851   worst 0.4631   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.73
scalar:  0.5602
Epoch 64:  train loss 0.5970   train acc 0.5842   worst 0.4620   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.61
Epoch 64:  test acc 0.5853   time 1.14
Calculating metrics for L_infinity dist model on training set
Epoch 64:  clean acc 0.1385   certified acc 0.0298
Calculating metrics for L_infinity dist model on test set
Epoch 64:  clean acc 0.1334   certified acc 0.0305
scalar:  0.559
Epoch 65:  train loss 0.5950   train acc 0.5871   worst 0.4628   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.71
scalar:  0.5661
Epoch 66:  train loss 0.5948   train acc 0.5865   worst 0.4642   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.55
scalar:  0.5614
Epoch 67:  train loss 0.5921   train acc 0.5909   worst 0.4649   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.68
scalar:  0.5677
Epoch 68:  train loss 0.5920   train acc 0.5905   worst 0.4642   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.16
scalar:  0.5291
Epoch 69:  train loss 0.5917   train acc 0.5907   worst 0.4652   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.65
Epoch 69:  test acc 0.5818   time 1.17
Calculating metrics for L_infinity dist model on training set
Epoch 69:  clean acc 0.1290   certified acc 0.0462
Calculating metrics for L_infinity dist model on test set
Epoch 69:  clean acc 0.1273   certified acc 0.0462
scalar:  0.566
Epoch 70:  train loss 0.5902   train acc 0.5903   worst 0.4671   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.60
scalar:  0.5341
Epoch 71:  train loss 0.5905   train acc 0.5905   worst 0.4673   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.06
scalar:  0.5564
Epoch 72:  train loss 0.5899   train acc 0.5929   worst 0.4653   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.61
scalar:  0.5443
Epoch 73:  train loss 0.5876   train acc 0.5956   worst 0.4675   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 19.81
scalar:  0.5827
Epoch 74:  train loss 0.5864   train acc 0.5951   worst 0.4688   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 20.11
Epoch 74:  test acc 0.5834   time 1.12
Calculating metrics for L_infinity dist model on training set
Epoch 74:  clean acc 0.1279   certified acc 0.0366
Calculating metrics for L_infinity dist model on test set
Epoch 74:  clean acc 0.1282   certified acc 0.0366
scalar:  0.5653
Epoch 75:  train loss 0.5847   train acc 0.5984   worst 0.4682   lr 0.0298   p 8.00   eps 0.7807   mix 0.1000   time 18.75
scalar:  0.5496
Epoch 76:  train loss 0.5832   train acc 0.5990   worst 0.4686   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 19.37
scalar:  0.5535
Epoch 77:  train loss 0.5830   train acc 0.5995   worst 0.4690   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 19.90
scalar:  0.5716
Epoch 78:  train loss 0.5809   train acc 0.6013   worst 0.4724   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 20.57
scalar:  0.5656
Epoch 79:  train loss 0.5829   train acc 0.6013   worst 0.4702   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 19.57
Epoch 79:  test acc 0.5845   time 1.12
Calculating metrics for L_infinity dist model on training set
Epoch 79:  clean acc 0.1265   certified acc 0.0308
Calculating metrics for L_infinity dist model on test set
Epoch 79:  clean acc 0.1257   certified acc 0.0301
scalar:  0.5734
Epoch 80:  train loss 0.5838   train acc 0.6008   worst 0.4677   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 19.21
scalar:  0.573
Epoch 81:  train loss 0.5787   train acc 0.6041   worst 0.4727   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 19.00
scalar:  0.5563
Epoch 82:  train loss 0.5779   train acc 0.6072   worst 0.4710   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 19.34
scalar:  0.5631
Epoch 83:  train loss 0.5753   train acc 0.6087   worst 0.4744   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 19.40
scalar:  0.5672
Epoch 84:  train loss 0.5789   train acc 0.6065   worst 0.4694   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 20.12
Epoch 84:  test acc 0.5934   time 1.10
Calculating metrics for L_infinity dist model on training set
Epoch 84:  clean acc 0.1317   certified acc 0.0200
Calculating metrics for L_infinity dist model on test set
Epoch 84:  clean acc 0.1338   certified acc 0.0216
scalar:  0.5614
Epoch 85:  train loss 0.5750   train acc 0.6082   worst 0.4729   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 20.37
scalar:  0.5727
Epoch 86:  train loss 0.5747   train acc 0.6109   worst 0.4719   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 19.18
scalar:  0.5714
Epoch 87:  train loss 0.5714   train acc 0.6130   worst 0.4758   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 19.64
scalar:  0.5606
Epoch 88:  train loss 0.5718   train acc 0.6143   worst 0.4741   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 19.61
scalar:  0.5732
Epoch 89:  train loss 0.5725   train acc 0.6128   worst 0.4741   lr 0.0297   p 8.00   eps 0.7807   mix 0.1000   time 18.71
Epoch 89:  test acc 0.5946   time 1.13
Calculating metrics for L_infinity dist model on training set
Epoch 89:  clean acc 0.1161   certified acc 0.0415
Calculating metrics for L_infinity dist model on test set
Epoch 89:  clean acc 0.1174   certified acc 0.0405
scalar:  0.5672
Epoch 90:  train loss 0.5695   train acc 0.6160   worst 0.4756   lr 0.0296   p 8.00   eps 0.7807   mix 0.1000   time 19.32
scalar:  0.5796
Epoch 91:  train loss 0.5657   train acc 0.6180   worst 0.4786   lr 0.0296   p 8.00   eps 0.7807   mix 0.1000   time 19.09
scalar:  0.5922
Epoch 92:  train loss 0.5688   train acc 0.6163   worst 0.4754   lr 0.0296   p 8.00   eps 0.7807   mix 0.1000   time 19.95
scalar:  0.5811
Epoch 93:  train loss 0.5670   train acc 0.6163   worst 0.4784   lr 0.0296   p 8.00   eps 0.7807   mix 0.1000   time 19.63
scalar:  0.5838
Epoch 94:  train loss 0.5657   train acc 0.6187   worst 0.4785   lr 0.0296   p 8.00   eps 0.7807   mix 0.1000   time 19.84
Epoch 94:  test acc 0.6041   time 1.09
Calculating metrics for L_infinity dist model on training set
Epoch 94:  clean acc 0.1002   certified acc 0.0981
Calculating metrics for L_infinity dist model on test set
Epoch 94:  clean acc 0.1006   certified acc 0.0971
scalar:  0.6018
Epoch 95:  train loss 0.5654   train acc 0.6196   worst 0.4784   lr 0.0296   p 8.00   eps 0.7807   mix 0.1000   time 18.39
scalar:  0.5881
Epoch 96:  train loss 0.5674   train acc 0.6189   worst 0.4766   lr 0.0296   p 8.00   eps 0.7807   mix 0.1000   time 18.32
scalar:  0.5784
Epoch 97:  train loss 0.5665   train acc 0.6184   worst 0.4772   lr 0.0296   p 8.00   eps 0.7807   mix 0.1000   time 19.79
scalar:  0.5633
Epoch 98:  train loss 0.5645   train acc 0.6210   worst 0.4790   lr 0.0296   p 8.00   eps 0.7807   mix 0.1000   time 20.26
scalar:  0.5876
Epoch 99:  train loss 0.5641   train acc 0.6210   worst 0.4795   lr 0.0296   p 8.00   eps 0.7807   mix 0.1000   time 20.13
Epoch 99:  test acc 0.6016   time 1.09
Calculating metrics for L_infinity dist model on training set
Epoch 99:  clean acc 0.1000   certified acc 0.1000
Calculating metrics for L_infinity dist model on test set
Epoch 99:  clean acc 0.1000   certified acc 0.0998
scalar:  0.5992
Epoch 100:  train loss 0.5630   train acc 0.6211   worst 0.4785   lr 0.0296   p 8.00   eps 0.7807   mix 0.1000   time 25.96
scalar:  0.5962
Epoch 101:  train loss 0.5628   train acc 0.6236   worst 0.4769   lr 0.0296   p 8.03   eps 0.7807   mix 0.0995   time 26.65
scalar:  0.5988
Epoch 102:  train loss 0.5636   train acc 0.6229   worst 0.4764   lr 0.0295   p 8.07   eps 0.7807   mix 0.0989   time 24.90
scalar:  0.5969
Epoch 103:  train loss 0.5614   train acc 0.6256   worst 0.4732   lr 0.0295   p 8.10   eps 0.7807   mix 0.0984   time 25.33
scalar:  0.6197
Epoch 104:  train loss 0.5604   train acc 0.6265   worst 0.4740   lr 0.0295   p 8.14   eps 0.7807   mix 0.0979   time 24.66
Epoch 104:  test acc 0.6072   time 2.01
Calculating metrics for L_infinity dist model on training set
Epoch 104:  clean acc 0.1000   certified acc 0.0997
Calculating metrics for L_infinity dist model on test set
Epoch 104:  clean acc 0.1000   certified acc 0.0996
scalar:  0.6106
Epoch 105:  train loss 0.5597   train acc 0.6246   worst 0.4743   lr 0.0295   p 8.17   eps 0.7807   mix 0.0973   time 25.02
scalar:  0.6287
Epoch 106:  train loss 0.5611   train acc 0.6233   worst 0.4719   lr 0.0295   p 8.20   eps 0.7807   mix 0.0968   time 25.48
scalar:  0.6216
Epoch 107:  train loss 0.5605   train acc 0.6237   worst 0.4723   lr 0.0295   p 8.24   eps 0.7807   mix 0.0963   time 25.57
scalar:  0.638
Epoch 108:  train loss 0.5613   train acc 0.6260   worst 0.4674   lr 0.0295   p 8.27   eps 0.7807   mix 0.0958   time 25.25
scalar:  0.6729
Epoch 109:  train loss 0.5594   train acc 0.6281   worst 0.4681   lr 0.0295   p 8.31   eps 0.7807   mix 0.0953   time 24.98
Epoch 109:  test acc 0.6031   time 1.99
Calculating metrics for L_infinity dist model on training set
Epoch 109:  clean acc 0.1000   certified acc 0.0995
Calculating metrics for L_infinity dist model on test set
Epoch 109:  clean acc 0.0999   certified acc 0.0994
scalar:  0.6655
Epoch 110:  train loss 0.5560   train acc 0.6297   worst 0.4686   lr 0.0295   p 8.34   eps 0.7807   mix 0.0947   time 25.25
scalar:  0.6643
Epoch 111:  train loss 0.5597   train acc 0.6271   worst 0.4645   lr 0.0295   p 8.38   eps 0.7807   mix 0.0942   time 25.86
scalar:  0.6675
Epoch 112:  train loss 0.5576   train acc 0.6295   worst 0.4636   lr 0.0295   p 8.41   eps 0.7807   mix 0.0937   time 25.26
scalar:  0.6597
Epoch 113:  train loss 0.5611   train acc 0.6269   worst 0.4610   lr 0.0294   p 8.45   eps 0.7807   mix 0.0932   time 25.15
scalar:  0.675
Epoch 114:  train loss 0.5585   train acc 0.6278   worst 0.4617   lr 0.0294   p 8.48   eps 0.7807   mix 0.0927   time 24.78
Epoch 114:  test acc 0.6035   time 2.02
Calculating metrics for L_infinity dist model on training set
Epoch 114:  clean acc 0.1003   certified acc 0.0989
Calculating metrics for L_infinity dist model on test set
Epoch 114:  clean acc 0.1002   certified acc 0.0989
scalar:  0.7001
Epoch 115:  train loss 0.5583   train acc 0.6289   worst 0.4612   lr 0.0294   p 8.52   eps 0.7807   mix 0.0922   time 24.72
scalar:  0.6975
Epoch 116:  train loss 0.5575   train acc 0.6309   worst 0.4596   lr 0.0294   p 8.56   eps 0.7807   mix 0.0917   time 25.24
scalar:  0.7001
Epoch 117:  train loss 0.5592   train acc 0.6282   worst 0.4573   lr 0.0294   p 8.59   eps 0.7807   mix 0.0912   time 25.22
scalar:  0.7036
Epoch 118:  train loss 0.5572   train acc 0.6307   worst 0.4564   lr 0.0294   p 8.63   eps 0.7807   mix 0.0907   time 24.98
scalar:  0.7166
Epoch 119:  train loss 0.5568   train acc 0.6300   worst 0.4584   lr 0.0294   p 8.66   eps 0.7807   mix 0.0902   time 24.52
Epoch 119:  test acc 0.6041   time 1.98
Calculating metrics for L_infinity dist model on training set
Epoch 119:  clean acc 0.1001   certified acc 0.0995
Calculating metrics for L_infinity dist model on test set
Epoch 119:  clean acc 0.1000   certified acc 0.0993
scalar:  0.7292
Epoch 120:  train loss 0.5591   train acc 0.6304   worst 0.4518   lr 0.0294   p 8.70   eps 0.7807   mix 0.0898   time 24.84
scalar:  0.7359
Epoch 121:  train loss 0.5585   train acc 0.6307   worst 0.4504   lr 0.0294   p 8.74   eps 0.7807   mix 0.0893   time 25.74
scalar:  0.7289
Epoch 122:  train loss 0.5582   train acc 0.6306   worst 0.4512   lr 0.0294   p 8.77   eps 0.7807   mix 0.0888   time 25.25
scalar:  0.7606
Epoch 123:  train loss 0.5586   train acc 0.6299   worst 0.4482   lr 0.0293   p 8.81   eps 0.7807   mix 0.0883   time 24.85
scalar:  0.7415
Epoch 124:  train loss 0.5572   train acc 0.6323   worst 0.4472   lr 0.0293   p 8.85   eps 0.7807   mix 0.0878   time 24.43
Epoch 124:  test acc 0.5979   time 2.05
Calculating metrics for L_infinity dist model on training set
Epoch 124:  clean acc 0.1036   certified acc 0.0943
Calculating metrics for L_infinity dist model on test set
Epoch 124:  clean acc 0.1040   certified acc 0.0954
scalar:  0.7511
Epoch 125:  train loss 0.5619   train acc 0.6270   worst 0.4433   lr 0.0293   p 8.89   eps 0.7807   mix 0.0874   time 24.79
scalar:  0.7751
Epoch 126:  train loss 0.5585   train acc 0.6312   worst 0.4450   lr 0.0293   p 8.92   eps 0.7807   mix 0.0869   time 26.18
scalar:  0.7713
Epoch 127:  train loss 0.5592   train acc 0.6302   worst 0.4428   lr 0.0293   p 8.96   eps 0.7807   mix 0.0864   time 25.29
scalar:  0.7775
Epoch 128:  train loss 0.5574   train acc 0.6323   worst 0.4418   lr 0.0293   p 9.00   eps 0.7807   mix 0.0860   time 24.98
scalar:  0.7595
Epoch 129:  train loss 0.5580   train acc 0.6309   worst 0.4408   lr 0.0293   p 9.04   eps 0.7807   mix 0.0855   time 24.75
Epoch 129:  test acc 0.6004   time 1.98
Calculating metrics for L_infinity dist model on training set
Epoch 129:  clean acc 0.1288   certified acc 0.0314
Calculating metrics for L_infinity dist model on test set
Epoch 129:  clean acc 0.1280   certified acc 0.0317
scalar:  0.7845
Epoch 130:  train loss 0.5575   train acc 0.6327   worst 0.4412   lr 0.0293   p 9.07   eps 0.7807   mix 0.0850   time 24.44
scalar:  0.7869
Epoch 131:  train loss 0.5591   train acc 0.6306   worst 0.4390   lr 0.0293   p 9.11   eps 0.7807   mix 0.0846   time 25.46
scalar:  0.7826
Epoch 132:  train loss 0.5580   train acc 0.6329   worst 0.4356   lr 0.0292   p 9.15   eps 0.7807   mix 0.0841   time 25.22
scalar:  0.8165
Epoch 133:  train loss 0.5573   train acc 0.6329   worst 0.4344   lr 0.0292   p 9.19   eps 0.7807   mix 0.0837   time 24.78
scalar:  0.826
Epoch 134:  train loss 0.5585   train acc 0.6323   worst 0.4342   lr 0.0292   p 9.23   eps 0.7807   mix 0.0832   time 24.45
Epoch 134:  test acc 0.6050   time 1.99
Calculating metrics for L_infinity dist model on training set
Epoch 134:  clean acc 0.1146   certified acc 0.0389
Calculating metrics for L_infinity dist model on test set
Epoch 134:  clean acc 0.1154   certified acc 0.0405
scalar:  0.8071
Epoch 135:  train loss 0.5585   train acc 0.6315   worst 0.4332   lr 0.0292   p 9.27   eps 0.7807   mix 0.0828   time 24.36
scalar:  0.8547
Epoch 136:  train loss 0.5623   train acc 0.6290   worst 0.4267   lr 0.0292   p 9.31   eps 0.7807   mix 0.0823   time 26.04
scalar:  0.8312
Epoch 137:  train loss 0.5597   train acc 0.6311   worst 0.4317   lr 0.0292   p 9.34   eps 0.7807   mix 0.0819   time 24.89
scalar:  0.8475
Epoch 138:  train loss 0.5598   train acc 0.6309   worst 0.4269   lr 0.0292   p 9.38   eps 0.7807   mix 0.0814   time 24.82
scalar:  0.8391
Epoch 139:  train loss 0.5614   train acc 0.6312   worst 0.4239   lr 0.0292   p 9.42   eps 0.7807   mix 0.0810   time 24.78
Epoch 139:  test acc 0.6075   time 2.02
Calculating metrics for L_infinity dist model on training set
Epoch 139:  clean acc 0.1232   certified acc 0.0523
Calculating metrics for L_infinity dist model on test set
Epoch 139:  clean acc 0.1233   certified acc 0.0510
scalar:  0.8365
Epoch 140:  train loss 0.5618   train acc 0.6294   worst 0.4255   lr 0.0291   p 9.46   eps 0.7807   mix 0.0806   time 24.51
scalar:  0.8473
Epoch 141:  train loss 0.5620   train acc 0.6326   worst 0.4215   lr 0.0291   p 9.50   eps 0.7807   mix 0.0801   time 25.58
scalar:  0.8587
Epoch 142:  train loss 0.5599   train acc 0.6325   worst 0.4218   lr 0.0291   p 9.54   eps 0.7807   mix 0.0797   time 25.28
scalar:  0.887
Epoch 143:  train loss 0.5597   train acc 0.6314   worst 0.4202   lr 0.0291   p 9.58   eps 0.7807   mix 0.0793   time 24.79
scalar:  0.8884
Epoch 144:  train loss 0.5604   train acc 0.6318   worst 0.4191   lr 0.0291   p 9.62   eps 0.7807   mix 0.0788   time 24.74
Epoch 144:  test acc 0.6088   time 2.02
Calculating metrics for L_infinity dist model on training set
Epoch 144:  clean acc 0.1146   certified acc 0.0669
Calculating metrics for L_infinity dist model on test set
Epoch 144:  clean acc 0.1132   certified acc 0.0672
scalar:  0.8785
Epoch 145:  train loss 0.5621   train acc 0.6289   worst 0.4187   lr 0.0291   p 9.66   eps 0.7807   mix 0.0784   time 24.49
scalar:  0.8941
Epoch 146:  train loss 0.5638   train acc 0.6302   worst 0.4142   lr 0.0291   p 9.70   eps 0.7807   mix 0.0780   time 25.27
scalar:  0.8592
Epoch 147:  train loss 0.5619   train acc 0.6301   worst 0.4158   lr 0.0291   p 9.75   eps 0.7807   mix 0.0776   time 25.05
scalar:  0.9042
Epoch 148:  train loss 0.5648   train acc 0.6287   worst 0.4121   lr 0.0291   p 9.79   eps 0.7807   mix 0.0772   time 25.16
scalar:  0.8997
Epoch 149:  train loss 0.5632   train acc 0.6305   worst 0.4124   lr 0.0290   p 9.83   eps 0.7807   mix 0.0767   time 25.07
Epoch 149:  test acc 0.6036   time 1.98
Calculating metrics for L_infinity dist model on training set
Epoch 149:  clean acc 0.1056   certified acc 0.0088
Calculating metrics for L_infinity dist model on test set
Epoch 149:  clean acc 0.1059   certified acc 0.0090
scalar:  0.8748
Epoch 150:  train loss 0.5614   train acc 0.6313   worst 0.4124   lr 0.0290   p 9.87   eps 0.7807   mix 0.0763   time 24.49
scalar:  0.9196
Epoch 151:  train loss 0.5638   train acc 0.6301   worst 0.4096   lr 0.0290   p 9.91   eps 0.7807   mix 0.0759   time 25.41
scalar:  0.8987
Epoch 152:  train loss 0.5651   train acc 0.6296   worst 0.4060   lr 0.0290   p 9.95   eps 0.7807   mix 0.0755   time 24.99
scalar:  0.9236
Epoch 153:  train loss 0.5649   train acc 0.6295   worst 0.4052   lr 0.0290   p 9.99   eps 0.7807   mix 0.0751   time 27.18
scalar:  0.9358
Epoch 154:  train loss 0.5649   train acc 0.6287   worst 0.4064   lr 0.0290   p 10.04   eps 0.7807   mix 0.0747   time 27.33
Epoch 154:  test acc 0.6062   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 154:  clean acc 0.1059   certified acc 0.0013
Calculating metrics for L_infinity dist model on test set
Epoch 154:  clean acc 0.1082   certified acc 0.0013
scalar:  0.9357
Epoch 155:  train loss 0.5654   train acc 0.6279   worst 0.4043   lr 0.0290   p 10.08   eps 0.7807   mix 0.0743   time 27.22
scalar:  0.9337
Epoch 156:  train loss 0.5649   train acc 0.6285   worst 0.4025   lr 0.0289   p 10.12   eps 0.7807   mix 0.0739   time 28.11
scalar:  0.944
Epoch 157:  train loss 0.5656   train acc 0.6279   worst 0.4005   lr 0.0289   p 10.16   eps 0.7807   mix 0.0735   time 27.36
scalar:  0.9558
Epoch 158:  train loss 0.5677   train acc 0.6281   worst 0.4000   lr 0.0289   p 10.21   eps 0.7807   mix 0.0731   time 27.62
scalar:  0.9656
Epoch 159:  train loss 0.5660   train acc 0.6283   worst 0.3971   lr 0.0289   p 10.25   eps 0.7807   mix 0.0727   time 27.23
Epoch 159:  test acc 0.6073   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 159:  clean acc 0.1007   certified acc 0.0108
Calculating metrics for L_infinity dist model on test set
Epoch 159:  clean acc 0.1004   certified acc 0.0111
scalar:  0.9701
Epoch 160:  train loss 0.5696   train acc 0.6259   worst 0.3957   lr 0.0289   p 10.29   eps 0.7807   mix 0.0723   time 27.21
scalar:  0.9555
Epoch 161:  train loss 0.5683   train acc 0.6261   worst 0.3953   lr 0.0289   p 10.34   eps 0.7807   mix 0.0719   time 27.48
scalar:  0.9849
Epoch 162:  train loss 0.5682   train acc 0.6268   worst 0.3934   lr 0.0289   p 10.38   eps 0.7807   mix 0.0715   time 27.53
scalar:  0.9789
Epoch 163:  train loss 0.5660   train acc 0.6296   worst 0.3939   lr 0.0289   p 10.42   eps 0.7807   mix 0.0711   time 27.55
scalar:  0.9984
Epoch 164:  train loss 0.5701   train acc 0.6232   worst 0.3932   lr 0.0288   p 10.47   eps 0.7807   mix 0.0708   time 27.08
Epoch 164:  test acc 0.5988   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 164:  clean acc 0.1033   certified acc 0.0048
Calculating metrics for L_infinity dist model on test set
Epoch 164:  clean acc 0.1001   certified acc 0.0048
scalar:  0.9957
Epoch 165:  train loss 0.5718   train acc 0.6251   worst 0.3860   lr 0.0288   p 10.51   eps 0.7807   mix 0.0704   time 27.57
scalar:  0.9987
Epoch 166:  train loss 0.5706   train acc 0.6272   worst 0.3858   lr 0.0288   p 10.55   eps 0.7807   mix 0.0700   time 27.69
scalar:  0.9858
Epoch 167:  train loss 0.5689   train acc 0.6242   worst 0.3912   lr 0.0288   p 10.60   eps 0.7807   mix 0.0696   time 27.75
scalar:  1.0026
Epoch 168:  train loss 0.5702   train acc 0.6265   worst 0.3850   lr 0.0288   p 10.64   eps 0.7807   mix 0.0692   time 27.57
scalar:  1.0046
Epoch 169:  train loss 0.5729   train acc 0.6249   worst 0.3814   lr 0.0288   p 10.69   eps 0.7807   mix 0.0689   time 26.97
Epoch 169:  test acc 0.5984   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 169:  clean acc 0.1032   certified acc 0.0385
Calculating metrics for L_infinity dist model on test set
Epoch 169:  clean acc 0.1033   certified acc 0.0390
scalar:  1.0199
Epoch 170:  train loss 0.5725   train acc 0.6250   worst 0.3804   lr 0.0288   p 10.73   eps 0.7807   mix 0.0685   time 27.73
scalar:  1.0319
Epoch 171:  train loss 0.5721   train acc 0.6257   worst 0.3795   lr 0.0287   p 10.78   eps 0.7807   mix 0.0681   time 27.22
scalar:  1.0274
Epoch 172:  train loss 0.5721   train acc 0.6259   worst 0.3801   lr 0.0287   p 10.82   eps 0.7807   mix 0.0678   time 27.44
scalar:  1.0605
Epoch 173:  train loss 0.5694   train acc 0.6282   worst 0.3810   lr 0.0287   p 10.87   eps 0.7807   mix 0.0674   time 27.41
scalar:  1.065
Epoch 174:  train loss 0.5710   train acc 0.6277   worst 0.3753   lr 0.0287   p 10.91   eps 0.7807   mix 0.0670   time 27.07
Epoch 174:  test acc 0.5940   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 174:  clean acc 0.1029   certified acc 0.0508
Calculating metrics for L_infinity dist model on test set
Epoch 174:  clean acc 0.1029   certified acc 0.0499
scalar:  1.0978
Epoch 175:  train loss 0.5721   train acc 0.6270   worst 0.3766   lr 0.0287   p 10.96   eps 0.7807   mix 0.0667   time 27.77
scalar:  1.0801
Epoch 176:  train loss 0.5747   train acc 0.6234   worst 0.3739   lr 0.0287   p 11.01   eps 0.7807   mix 0.0663   time 27.34
scalar:  1.0756
Epoch 177:  train loss 0.5735   train acc 0.6254   worst 0.3735   lr 0.0286   p 11.05   eps 0.7807   mix 0.0660   time 27.14
scalar:  1.0817
Epoch 178:  train loss 0.5765   train acc 0.6225   worst 0.3694   lr 0.0286   p 11.10   eps 0.7807   mix 0.0656   time 27.81
scalar:  1.0475
Epoch 179:  train loss 0.5755   train acc 0.6251   worst 0.3667   lr 0.0286   p 11.15   eps 0.7807   mix 0.0653   time 26.76
Epoch 179:  test acc 0.5927   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 179:  clean acc 0.1154   certified acc 0.0327
Calculating metrics for L_infinity dist model on test set
Epoch 179:  clean acc 0.1174   certified acc 0.0342
scalar:  1.084
Epoch 180:  train loss 0.5769   train acc 0.6213   worst 0.3679   lr 0.0286   p 11.19   eps 0.7807   mix 0.0649   time 27.64
scalar:  1.0763
Epoch 181:  train loss 0.5730   train acc 0.6256   worst 0.3686   lr 0.0286   p 11.24   eps 0.7807   mix 0.0646   time 27.30
scalar:  1.0987
Epoch 182:  train loss 0.5792   train acc 0.6202   worst 0.3631   lr 0.0286   p 11.29   eps 0.7807   mix 0.0642   time 27.31
scalar:  1.1107
Epoch 183:  train loss 0.5772   train acc 0.6228   worst 0.3619   lr 0.0286   p 11.34   eps 0.7807   mix 0.0639   time 27.53
scalar:  1.1081
Epoch 184:  train loss 0.5778   train acc 0.6233   worst 0.3638   lr 0.0285   p 11.38   eps 0.7807   mix 0.0635   time 27.05
Epoch 184:  test acc 0.5946   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 184:  clean acc 0.1039   certified acc 0.0645
Calculating metrics for L_infinity dist model on test set
Epoch 184:  clean acc 0.1062   certified acc 0.0657
scalar:  1.1384
Epoch 185:  train loss 0.5806   train acc 0.6189   worst 0.3585   lr 0.0285   p 11.43   eps 0.7807   mix 0.0632   time 27.67
scalar:  1.0977
Epoch 186:  train loss 0.5806   train acc 0.6214   worst 0.3579   lr 0.0285   p 11.48   eps 0.7807   mix 0.0628   time 27.59
scalar:  1.1036
Epoch 187:  train loss 0.5805   train acc 0.6216   worst 0.3565   lr 0.0285   p 11.53   eps 0.7807   mix 0.0625   time 27.25
scalar:  1.1558
Epoch 188:  train loss 0.5802   train acc 0.6225   worst 0.3562   lr 0.0285   p 11.58   eps 0.7807   mix 0.0622   time 27.81
scalar:  1.1371
Epoch 189:  train loss 0.5811   train acc 0.6169   worst 0.3564   lr 0.0285   p 11.62   eps 0.7807   mix 0.0618   time 26.93
Epoch 189:  test acc 0.5958   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 189:  clean acc 0.1087   certified acc 0.0700
Calculating metrics for L_infinity dist model on test set
Epoch 189:  clean acc 0.1081   certified acc 0.0701
scalar:  1.1337
Epoch 190:  train loss 0.5800   train acc 0.6215   worst 0.3534   lr 0.0284   p 11.67   eps 0.7807   mix 0.0615   time 27.68
scalar:  1.1584
Epoch 191:  train loss 0.5828   train acc 0.6173   worst 0.3522   lr 0.0284   p 11.72   eps 0.7807   mix 0.0612   time 27.27
scalar:  1.1524
Epoch 192:  train loss 0.5800   train acc 0.6207   worst 0.3532   lr 0.0284   p 11.77   eps 0.7807   mix 0.0608   time 26.85
scalar:  1.1461
Epoch 193:  train loss 0.5839   train acc 0.6184   worst 0.3484   lr 0.0284   p 11.82   eps 0.7807   mix 0.0605   time 27.63
scalar:  1.1638
Epoch 194:  train loss 0.5837   train acc 0.6196   worst 0.3460   lr 0.0284   p 11.87   eps 0.7807   mix 0.0602   time 27.23
Epoch 194:  test acc 0.5954   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 194:  clean acc 0.1135   certified acc 0.0653
Calculating metrics for L_infinity dist model on test set
Epoch 194:  clean acc 0.1156   certified acc 0.0668
scalar:  1.1831
Epoch 195:  train loss 0.5829   train acc 0.6198   worst 0.3475   lr 0.0284   p 11.92   eps 0.7807   mix 0.0598   time 27.66
scalar:  1.1858
Epoch 196:  train loss 0.5855   train acc 0.6169   worst 0.3442   lr 0.0283   p 11.97   eps 0.7807   mix 0.0595   time 27.50
scalar:  1.1933
Epoch 197:  train loss 0.5853   train acc 0.6181   worst 0.3447   lr 0.0283   p 12.02   eps 0.7807   mix 0.0592   time 26.95
scalar:  1.176
Epoch 198:  train loss 0.5838   train acc 0.6190   worst 0.3456   lr 0.0283   p 12.07   eps 0.7807   mix 0.0589   time 27.09
scalar:  1.193
Epoch 199:  train loss 0.5870   train acc 0.6166   worst 0.3404   lr 0.0283   p 12.12   eps 0.7807   mix 0.0586   time 27.33
Epoch 199:  test acc 0.5846   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 199:  clean acc 0.1061   certified acc 0.0808
Calculating metrics for L_infinity dist model on test set
Epoch 199:  clean acc 0.1056   certified acc 0.0824
scalar:  1.1925
Epoch 200:  train loss 0.5858   train acc 0.6167   worst 0.3421   lr 0.0283   p 12.17   eps 0.7807   mix 0.0583   time 27.71
scalar:  1.2072
Epoch 201:  train loss 0.5863   train acc 0.6165   worst 0.3428   lr 0.0283   p 12.23   eps 0.7807   mix 0.0579   time 27.28
scalar:  1.1929
Epoch 202:  train loss 0.5882   train acc 0.6147   worst 0.3382   lr 0.0282   p 12.28   eps 0.7807   mix 0.0576   time 27.27
scalar:  1.207
Epoch 203:  train loss 0.5860   train acc 0.6183   worst 0.3371   lr 0.0282   p 12.33   eps 0.7807   mix 0.0573   time 27.59
scalar:  1.2324
Epoch 204:  train loss 0.5884   train acc 0.6156   worst 0.3358   lr 0.0282   p 12.38   eps 0.7807   mix 0.0570   time 27.28
Epoch 204:  test acc 0.5967   time 2.51
Calculating metrics for L_infinity dist model on training set
Epoch 204:  clean acc 0.1044   certified acc 0.0816
Calculating metrics for L_infinity dist model on test set
Epoch 204:  clean acc 0.1051   certified acc 0.0820
scalar:  1.233
Epoch 205:  train loss 0.5908   train acc 0.6128   worst 0.3348   lr 0.0282   p 12.43   eps 0.7807   mix 0.0567   time 27.86
scalar:  1.2323
Epoch 206:  train loss 0.5904   train acc 0.6148   worst 0.3326   lr 0.0282   p 12.48   eps 0.7807   mix 0.0564   time 26.73
scalar:  1.2556
Epoch 207:  train loss 0.5888   train acc 0.6152   worst 0.3324   lr 0.0282   p 12.54   eps 0.7807   mix 0.0561   time 27.08
scalar:  1.2759
Epoch 208:  train loss 0.5908   train acc 0.6152   worst 0.3298   lr 0.0281   p 12.59   eps 0.7807   mix 0.0558   time 27.47
scalar:  1.2337
Epoch 209:  train loss 0.5915   train acc 0.6140   worst 0.3270   lr 0.0281   p 12.64   eps 0.7807   mix 0.0555   time 27.15
Epoch 209:  test acc 0.5926   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 209:  clean acc 0.1271   certified acc 0.0543
Calculating metrics for L_infinity dist model on test set
Epoch 209:  clean acc 0.1312   certified acc 0.0571
scalar:  1.2681
Epoch 210:  train loss 0.5909   train acc 0.6158   worst 0.3268   lr 0.0281   p 12.70   eps 0.7807   mix 0.0552   time 27.94
scalar:  1.2919
Epoch 211:  train loss 0.5910   train acc 0.6150   worst 0.3248   lr 0.0281   p 12.75   eps 0.7807   mix 0.0549   time 27.44
scalar:  1.2463
Epoch 212:  train loss 0.5927   train acc 0.6151   worst 0.3237   lr 0.0281   p 12.80   eps 0.7807   mix 0.0546   time 27.16
scalar:  1.2587
Epoch 213:  train loss 0.5936   train acc 0.6128   worst 0.3223   lr 0.0281   p 12.86   eps 0.7807   mix 0.0543   time 27.46
scalar:  1.2739
Epoch 214:  train loss 0.5933   train acc 0.6126   worst 0.3237   lr 0.0280   p 12.91   eps 0.7807   mix 0.0540   time 27.46
Epoch 214:  test acc 0.5830   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 214:  clean acc 0.1293   certified acc 0.0259
Calculating metrics for L_infinity dist model on test set
Epoch 214:  clean acc 0.1358   certified acc 0.0282
scalar:  1.276
Epoch 215:  train loss 0.5942   train acc 0.6126   worst 0.3228   lr 0.0280   p 12.97   eps 0.7807   mix 0.0537   time 27.54
scalar:  1.2776
Epoch 216:  train loss 0.5937   train acc 0.6121   worst 0.3207   lr 0.0280   p 13.02   eps 0.7807   mix 0.0534   time 27.07
scalar:  1.3156
Epoch 217:  train loss 0.5960   train acc 0.6107   worst 0.3172   lr 0.0280   p 13.07   eps 0.7807   mix 0.0531   time 27.00
scalar:  1.2919
Epoch 218:  train loss 0.5953   train acc 0.6113   worst 0.3170   lr 0.0280   p 13.13   eps 0.7807   mix 0.0529   time 27.20
scalar:  1.2809
Epoch 219:  train loss 0.5968   train acc 0.6116   worst 0.3141   lr 0.0279   p 13.18   eps 0.7807   mix 0.0526   time 27.20
Epoch 219:  test acc 0.5866   time 2.48
Calculating metrics for L_infinity dist model on training set
Epoch 219:  clean acc 0.1190   certified acc 0.0197
Calculating metrics for L_infinity dist model on test set
Epoch 219:  clean acc 0.1238   certified acc 0.0226
scalar:  1.3132
Epoch 220:  train loss 0.5965   train acc 0.6081   worst 0.3165   lr 0.0279   p 13.24   eps 0.7807   mix 0.0523   time 27.68
scalar:  1.319
Epoch 221:  train loss 0.5980   train acc 0.6097   worst 0.3145   lr 0.0279   p 13.30   eps 0.7807   mix 0.0520   time 27.23
scalar:  1.3285
Epoch 222:  train loss 0.5961   train acc 0.6115   worst 0.3125   lr 0.0279   p 13.35   eps 0.7807   mix 0.0517   time 26.91
scalar:  1.3246
Epoch 223:  train loss 0.5997   train acc 0.6071   worst 0.3100   lr 0.0279   p 13.41   eps 0.7807   mix 0.0514   time 26.97
scalar:  1.3225
Epoch 224:  train loss 0.5988   train acc 0.6075   worst 0.3105   lr 0.0279   p 13.46   eps 0.7807   mix 0.0512   time 27.79
Epoch 224:  test acc 0.5845   time 2.51
Calculating metrics for L_infinity dist model on training set
Epoch 224:  clean acc 0.1328   certified acc 0.0268
Calculating metrics for L_infinity dist model on test set
Epoch 224:  clean acc 0.1334   certified acc 0.0303
scalar:  1.3214
Epoch 225:  train loss 0.6000   train acc 0.6088   worst 0.3089   lr 0.0278   p 13.52   eps 0.7807   mix 0.0509   time 27.76
scalar:  1.3441
Epoch 226:  train loss 0.5994   train acc 0.6068   worst 0.3083   lr 0.0278   p 13.58   eps 0.7807   mix 0.0506   time 27.31
scalar:  1.3279
Epoch 227:  train loss 0.6016   train acc 0.6058   worst 0.3062   lr 0.0278   p 13.64   eps 0.7807   mix 0.0503   time 26.93
scalar:  1.3368
Epoch 228:  train loss 0.6018   train acc 0.6061   worst 0.3035   lr 0.0278   p 13.69   eps 0.7807   mix 0.0501   time 27.16
scalar:  1.3506
Epoch 229:  train loss 0.6025   train acc 0.6039   worst 0.3031   lr 0.0278   p 13.75   eps 0.7807   mix 0.0498   time 27.83
Epoch 229:  test acc 0.5832   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 229:  clean acc 0.1288   certified acc 0.0301
Calculating metrics for L_infinity dist model on test set
Epoch 229:  clean acc 0.1293   certified acc 0.0307
scalar:  1.3124
Epoch 230:  train loss 0.6036   train acc 0.6044   worst 0.3031   lr 0.0277   p 13.81   eps 0.7807   mix 0.0495   time 27.80
scalar:  1.3575
Epoch 231:  train loss 0.6025   train acc 0.6038   worst 0.3021   lr 0.0277   p 13.87   eps 0.7807   mix 0.0493   time 27.37
scalar:  1.3686
Epoch 232:  train loss 0.6039   train acc 0.6069   worst 0.3007   lr 0.0277   p 13.92   eps 0.7807   mix 0.0490   time 27.04
scalar:  1.3572
Epoch 233:  train loss 0.6050   train acc 0.6021   worst 0.2990   lr 0.0277   p 13.98   eps 0.7807   mix 0.0487   time 27.30
scalar:  1.356
Epoch 234:  train loss 0.6046   train acc 0.6033   worst 0.2996   lr 0.0277   p 14.04   eps 0.7807   mix 0.0485   time 27.48
Epoch 234:  test acc 0.5838   time 2.51
Calculating metrics for L_infinity dist model on training set
Epoch 234:  clean acc 0.1482   certified acc 0.0153
Calculating metrics for L_infinity dist model on test set
Epoch 234:  clean acc 0.1576   certified acc 0.0175
scalar:  1.3734
Epoch 235:  train loss 0.6046   train acc 0.6053   worst 0.2974   lr 0.0276   p 14.10   eps 0.7807   mix 0.0482   time 27.84
scalar:  1.3809
Epoch 236:  train loss 0.6066   train acc 0.6004   worst 0.2986   lr 0.0276   p 14.16   eps 0.7807   mix 0.0480   time 27.68
scalar:  1.3718
Epoch 237:  train loss 0.6063   train acc 0.6027   worst 0.2944   lr 0.0276   p 14.22   eps 0.7807   mix 0.0477   time 26.79
scalar:  1.3807
Epoch 238:  train loss 0.6079   train acc 0.6013   worst 0.2923   lr 0.0276   p 14.28   eps 0.7807   mix 0.0474   time 27.40
scalar:  1.388
Epoch 239:  train loss 0.6091   train acc 0.5986   worst 0.2939   lr 0.0276   p 14.34   eps 0.7807   mix 0.0472   time 27.57
Epoch 239:  test acc 0.5764   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 239:  clean acc 0.1666   certified acc 0.0267
Calculating metrics for L_infinity dist model on test set
Epoch 239:  clean acc 0.1753   certified acc 0.0277
scalar:  1.4015
Epoch 240:  train loss 0.6075   train acc 0.6006   worst 0.2920   lr 0.0275   p 14.40   eps 0.7807   mix 0.0469   time 27.71
scalar:  1.4188
Epoch 241:  train loss 0.6074   train acc 0.6008   worst 0.2907   lr 0.0275   p 14.46   eps 0.7807   mix 0.0467   time 27.28
scalar:  1.4119
Epoch 242:  train loss 0.6079   train acc 0.5997   worst 0.2915   lr 0.0275   p 14.52   eps 0.7807   mix 0.0464   time 26.93
scalar:  1.424
Epoch 243:  train loss 0.6083   train acc 0.5988   worst 0.2912   lr 0.0275   p 14.58   eps 0.7807   mix 0.0462   time 26.97
scalar:  1.4317
Epoch 244:  train loss 0.6092   train acc 0.5993   worst 0.2892   lr 0.0275   p 14.64   eps 0.7807   mix 0.0459   time 28.21
Epoch 244:  test acc 0.5771   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 244:  clean acc 0.1583   certified acc 0.0245
Calculating metrics for L_infinity dist model on test set
Epoch 244:  clean acc 0.1697   certified acc 0.0311
scalar:  1.417
Epoch 245:  train loss 0.6103   train acc 0.6004   worst 0.2867   lr 0.0274   p 14.71   eps 0.7807   mix 0.0457   time 27.89
scalar:  1.4641
Epoch 246:  train loss 0.6116   train acc 0.5991   worst 0.2846   lr 0.0274   p 14.77   eps 0.7807   mix 0.0454   time 27.23
scalar:  1.4322
Epoch 247:  train loss 0.6112   train acc 0.5985   worst 0.2843   lr 0.0274   p 14.83   eps 0.7807   mix 0.0452   time 27.06
scalar:  1.4338
Epoch 248:  train loss 0.6136   train acc 0.5979   worst 0.2820   lr 0.0274   p 14.89   eps 0.7807   mix 0.0449   time 27.06
scalar:  1.4577
Epoch 249:  train loss 0.6138   train acc 0.5952   worst 0.2849   lr 0.0274   p 14.95   eps 0.7807   mix 0.0447   time 27.79
Epoch 249:  test acc 0.5708   time 2.50
Calculating metrics for L_infinity dist model on training set
Epoch 249:  clean acc 0.1651   certified acc 0.0234
Calculating metrics for L_infinity dist model on test set
Epoch 249:  clean acc 0.1758   certified acc 0.0287
scalar:  1.4232
Epoch 250:  train loss 0.6117   train acc 0.5969   worst 0.2834   lr 0.0273   p 15.02   eps 0.7807   mix 0.0445   time 27.82
scalar:  1.4469
Epoch 251:  train loss 0.6139   train acc 0.5981   worst 0.2804   lr 0.0273   p 15.08   eps 0.7807   mix 0.0442   time 27.23
scalar:  1.4753
Epoch 252:  train loss 0.6130   train acc 0.5949   worst 0.2814   lr 0.0273   p 15.14   eps 0.7807   mix 0.0440   time 27.22
scalar:  1.4577
Epoch 253:  train loss 0.6137   train acc 0.5980   worst 0.2795   lr 0.0273   p 15.21   eps 0.7807   mix 0.0437   time 27.14
scalar:  1.4722
Epoch 254:  train loss 0.6150   train acc 0.5970   worst 0.2766   lr 0.0273   p 15.27   eps 0.7807   mix 0.0435   time 27.58
Epoch 254:  test acc 0.5747   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 254:  clean acc 0.1646   certified acc 0.0220
Calculating metrics for L_infinity dist model on test set
Epoch 254:  clean acc 0.1719   certified acc 0.0236
scalar:  1.4829
Epoch 255:  train loss 0.6164   train acc 0.5931   worst 0.2756   lr 0.0272   p 15.34   eps 0.7807   mix 0.0433   time 28.05
scalar:  1.4624
Epoch 256:  train loss 0.6158   train acc 0.5944   worst 0.2733   lr 0.0272   p 15.40   eps 0.7807   mix 0.0430   time 27.36
scalar:  1.4679
Epoch 257:  train loss 0.6172   train acc 0.5935   worst 0.2733   lr 0.0272   p 15.47   eps 0.7807   mix 0.0428   time 27.18
scalar:  1.4629
Epoch 258:  train loss 0.6169   train acc 0.5948   worst 0.2721   lr 0.0272   p 15.53   eps 0.7807   mix 0.0426   time 27.49
scalar:  1.513
Epoch 259:  train loss 0.6178   train acc 0.5930   worst 0.2718   lr 0.0272   p 15.60   eps 0.7807   mix 0.0423   time 27.57
Epoch 259:  test acc 0.5731   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 259:  clean acc 0.1720   certified acc 0.0245
Calculating metrics for L_infinity dist model on test set
Epoch 259:  clean acc 0.1771   certified acc 0.0286
scalar:  1.505
Epoch 260:  train loss 0.6174   train acc 0.5957   worst 0.2712   lr 0.0271   p 15.66   eps 0.7807   mix 0.0421   time 27.85
scalar:  1.4956
Epoch 261:  train loss 0.6209   train acc 0.5911   worst 0.2680   lr 0.0271   p 15.73   eps 0.7807   mix 0.0419   time 27.30
scalar:  1.4987
Epoch 262:  train loss 0.6199   train acc 0.5919   worst 0.2683   lr 0.0271   p 15.79   eps 0.7807   mix 0.0417   time 27.30
scalar:  1.4686
Epoch 263:  train loss 0.6182   train acc 0.5939   worst 0.2696   lr 0.0271   p 15.86   eps 0.7807   mix 0.0414   time 27.25
scalar:  1.503
Epoch 264:  train loss 0.6209   train acc 0.5935   worst 0.2652   lr 0.0270   p 15.93   eps 0.7807   mix 0.0412   time 27.63
Epoch 264:  test acc 0.5739   time 2.51
Calculating metrics for L_infinity dist model on training set
Epoch 264:  clean acc 0.1716   certified acc 0.0204
Calculating metrics for L_infinity dist model on test set
Epoch 264:  clean acc 0.1764   certified acc 0.0236
scalar:  1.5285
Epoch 265:  train loss 0.6216   train acc 0.5888   worst 0.2650   lr 0.0270   p 15.99   eps 0.7807   mix 0.0410   time 28.00
scalar:  1.5053
Epoch 266:  train loss 0.6207   train acc 0.5917   worst 0.2653   lr 0.0270   p 16.06   eps 0.7807   mix 0.0408   time 27.39
scalar:  1.5273
Epoch 267:  train loss 0.6214   train acc 0.5884   worst 0.2660   lr 0.0270   p 16.13   eps 0.7807   mix 0.0406   time 27.08
scalar:  1.5302
Epoch 268:  train loss 0.6222   train acc 0.5903   worst 0.2616   lr 0.0270   p 16.20   eps 0.7807   mix 0.0403   time 27.17
scalar:  1.5343
Epoch 269:  train loss 0.6246   train acc 0.5866   worst 0.2638   lr 0.0269   p 16.26   eps 0.7807   mix 0.0401   time 27.31
Epoch 269:  test acc 0.5690   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 269:  clean acc 0.1667   certified acc 0.0245
Calculating metrics for L_infinity dist model on test set
Epoch 269:  clean acc 0.1769   certified acc 0.0298
scalar:  1.5327
Epoch 270:  train loss 0.6250   train acc 0.5858   worst 0.2622   lr 0.0269   p 16.33   eps 0.7807   mix 0.0399   time 28.45
scalar:  1.5034
Epoch 271:  train loss 0.6236   train acc 0.5902   worst 0.2621   lr 0.0269   p 16.40   eps 0.7807   mix 0.0397   time 27.57
scalar:  1.5393
Epoch 272:  train loss 0.6243   train acc 0.5874   worst 0.2606   lr 0.0269   p 16.47   eps 0.7807   mix 0.0395   time 27.13
scalar:  1.5499
Epoch 273:  train loss 0.6231   train acc 0.5881   worst 0.2594   lr 0.0269   p 16.54   eps 0.7807   mix 0.0393   time 27.44
scalar:  1.525
Epoch 274:  train loss 0.6264   train acc 0.5862   worst 0.2589   lr 0.0268   p 16.61   eps 0.7807   mix 0.0391   time 27.26
Epoch 274:  test acc 0.5605   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 274:  clean acc 0.1582   certified acc 0.0444
Calculating metrics for L_infinity dist model on test set
Epoch 274:  clean acc 0.1606   certified acc 0.0487
scalar:  1.548
Epoch 275:  train loss 0.6276   train acc 0.5840   worst 0.2568   lr 0.0268   p 16.68   eps 0.7807   mix 0.0388   time 27.70
scalar:  1.5281
Epoch 276:  train loss 0.6277   train acc 0.5832   worst 0.2567   lr 0.0268   p 16.75   eps 0.7807   mix 0.0386   time 27.32
scalar:  1.5487
Epoch 277:  train loss 0.6261   train acc 0.5876   worst 0.2552   lr 0.0268   p 16.82   eps 0.7807   mix 0.0384   time 27.09
scalar:  1.5631
Epoch 278:  train loss 0.6268   train acc 0.5847   worst 0.2542   lr 0.0267   p 16.89   eps 0.7807   mix 0.0382   time 27.22
scalar:  1.5418
Epoch 279:  train loss 0.6275   train acc 0.5832   worst 0.2534   lr 0.0267   p 16.96   eps 0.7807   mix 0.0380   time 27.04
Epoch 279:  test acc 0.5699   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 279:  clean acc 0.1636   certified acc 0.0239
Calculating metrics for L_infinity dist model on test set
Epoch 279:  clean acc 0.1713   certified acc 0.0270
scalar:  1.5429
Epoch 280:  train loss 0.6265   train acc 0.5847   worst 0.2540   lr 0.0267   p 17.03   eps 0.7807   mix 0.0378   time 27.95
scalar:  1.5565
Epoch 281:  train loss 0.6293   train acc 0.5837   worst 0.2511   lr 0.0267   p 17.11   eps 0.7807   mix 0.0376   time 27.78
scalar:  1.5829
Epoch 282:  train loss 0.6276   train acc 0.5834   worst 0.2530   lr 0.0266   p 17.18   eps 0.7807   mix 0.0374   time 27.21
scalar:  1.5754
Epoch 283:  train loss 0.6292   train acc 0.5839   worst 0.2516   lr 0.0266   p 17.25   eps 0.7807   mix 0.0372   time 27.04
scalar:  1.6037
Epoch 284:  train loss 0.6301   train acc 0.5826   worst 0.2487   lr 0.0266   p 17.32   eps 0.7807   mix 0.0370   time 27.14
Epoch 284:  test acc 0.5618   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 284:  clean acc 0.1709   certified acc 0.0302
Calculating metrics for L_infinity dist model on test set
Epoch 284:  clean acc 0.1755   certified acc 0.0306
scalar:  1.5454
Epoch 285:  train loss 0.6304   train acc 0.5807   worst 0.2486   lr 0.0266   p 17.39   eps 0.7807   mix 0.0368   time 28.10
scalar:  1.5731
Epoch 286:  train loss 0.6320   train acc 0.5811   worst 0.2489   lr 0.0266   p 17.47   eps 0.7807   mix 0.0366   time 27.54
scalar:  1.5845
Epoch 287:  train loss 0.6321   train acc 0.5824   worst 0.2456   lr 0.0265   p 17.54   eps 0.7807   mix 0.0364   time 27.16
scalar:  1.6125
Epoch 288:  train loss 0.6323   train acc 0.5838   worst 0.2440   lr 0.0265   p 17.62   eps 0.7807   mix 0.0362   time 27.35
scalar:  1.5868
Epoch 289:  train loss 0.6319   train acc 0.5805   worst 0.2458   lr 0.0265   p 17.69   eps 0.7807   mix 0.0360   time 27.33
Epoch 289:  test acc 0.5661   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 289:  clean acc 0.1675   certified acc 0.0417
Calculating metrics for L_infinity dist model on test set
Epoch 289:  clean acc 0.1778   certified acc 0.0504
scalar:  1.5982
Epoch 290:  train loss 0.6326   train acc 0.5804   worst 0.2430   lr 0.0265   p 17.76   eps 0.7807   mix 0.0358   time 27.88
scalar:  1.6106
Epoch 291:  train loss 0.6342   train acc 0.5773   worst 0.2417   lr 0.0264   p 17.84   eps 0.7807   mix 0.0356   time 27.20
scalar:  1.5984
Epoch 292:  train loss 0.6354   train acc 0.5782   worst 0.2421   lr 0.0264   p 17.91   eps 0.7807   mix 0.0354   time 27.33
scalar:  1.626
Epoch 293:  train loss 0.6335   train acc 0.5808   worst 0.2418   lr 0.0264   p 17.99   eps 0.7807   mix 0.0352   time 27.11
scalar:  1.6251
Epoch 294:  train loss 0.6373   train acc 0.5742   worst 0.2388   lr 0.0264   p 18.06   eps 0.7807   mix 0.0351   time 27.07
Epoch 294:  test acc 0.5622   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 294:  clean acc 0.1709   certified acc 0.0246
Calculating metrics for L_infinity dist model on test set
Epoch 294:  clean acc 0.1802   certified acc 0.0282
scalar:  1.5968
Epoch 295:  train loss 0.6384   train acc 0.5748   worst 0.2387   lr 0.0263   p 18.14   eps 0.7807   mix 0.0349   time 27.92
scalar:  1.6215
Epoch 296:  train loss 0.6371   train acc 0.5760   worst 0.2398   lr 0.0263   p 18.22   eps 0.7807   mix 0.0347   time 27.01
scalar:  1.6007
Epoch 297:  train loss 0.6358   train acc 0.5751   worst 0.2394   lr 0.0263   p 18.29   eps 0.7807   mix 0.0345   time 27.35
scalar:  1.5855
Epoch 298:  train loss 0.6408   train acc 0.5718   worst 0.2346   lr 0.0263   p 18.37   eps 0.7807   mix 0.0343   time 27.56
scalar:  1.6286
Epoch 299:  train loss 0.6387   train acc 0.5738   worst 0.2351   lr 0.0263   p 18.45   eps 0.7807   mix 0.0341   time 27.52
Epoch 299:  test acc 0.5620   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 299:  clean acc 0.1761   certified acc 0.0400
Calculating metrics for L_infinity dist model on test set
Epoch 299:  clean acc 0.1849   certified acc 0.0459
scalar:  1.6218
Epoch 300:  train loss 0.6405   train acc 0.5715   worst 0.2356   lr 0.0262   p 18.53   eps 0.7807   mix 0.0339   time 28.09
scalar:  1.6045
Epoch 301:  train loss 0.6391   train acc 0.5739   worst 0.2355   lr 0.0262   p 18.60   eps 0.7807   mix 0.0337   time 27.14
scalar:  1.6205
Epoch 302:  train loss 0.6407   train acc 0.5728   worst 0.2322   lr 0.0262   p 18.68   eps 0.7807   mix 0.0336   time 27.07
scalar:  1.599
Epoch 303:  train loss 0.6413   train acc 0.5716   worst 0.2317   lr 0.0262   p 18.76   eps 0.7807   mix 0.0334   time 27.17
scalar:  1.6339
Epoch 304:  train loss 0.6407   train acc 0.5715   worst 0.2330   lr 0.0261   p 18.84   eps 0.7807   mix 0.0332   time 27.20
Epoch 304:  test acc 0.5502   time 2.51
Calculating metrics for L_infinity dist model on training set
Epoch 304:  clean acc 0.1728   certified acc 0.0436
Calculating metrics for L_infinity dist model on test set
Epoch 304:  clean acc 0.1849   certified acc 0.0502
scalar:  1.6544
Epoch 305:  train loss 0.6403   train acc 0.5736   worst 0.2308   lr 0.0261   p 18.92   eps 0.7807   mix 0.0330   time 28.07
scalar:  1.6697
Epoch 306:  train loss 0.6412   train acc 0.5744   worst 0.2301   lr 0.0261   p 19.00   eps 0.7807   mix 0.0328   time 27.26
scalar:  1.6515
Epoch 307:  train loss 0.6393   train acc 0.5753   worst 0.2314   lr 0.0261   p 19.08   eps 0.7807   mix 0.0327   time 27.63
scalar:  1.6454
Epoch 308:  train loss 0.6443   train acc 0.5706   worst 0.2269   lr 0.0260   p 19.16   eps 0.7807   mix 0.0325   time 27.49
scalar:  1.6515
Epoch 309:  train loss 0.6435   train acc 0.5714   worst 0.2280   lr 0.0260   p 19.24   eps 0.7807   mix 0.0323   time 27.30
Epoch 309:  test acc 0.5580   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 309:  clean acc 0.1593   certified acc 0.0362
Calculating metrics for L_infinity dist model on test set
Epoch 309:  clean acc 0.1680   certified acc 0.0434
scalar:  1.6516
Epoch 310:  train loss 0.6437   train acc 0.5678   worst 0.2278   lr 0.0260   p 19.32   eps 0.7807   mix 0.0321   time 28.10
scalar:  1.6393
Epoch 311:  train loss 0.6431   train acc 0.5708   worst 0.2261   lr 0.0260   p 19.40   eps 0.7807   mix 0.0320   time 26.93
scalar:  1.6825
Epoch 312:  train loss 0.6451   train acc 0.5682   worst 0.2255   lr 0.0259   p 19.48   eps 0.7807   mix 0.0318   time 27.21
scalar:  1.6613
Epoch 313:  train loss 0.6441   train acc 0.5682   worst 0.2251   lr 0.0259   p 19.56   eps 0.7807   mix 0.0316   time 27.24
scalar:  1.6421
Epoch 314:  train loss 0.6454   train acc 0.5678   worst 0.2240   lr 0.0259   p 19.65   eps 0.7807   mix 0.0315   time 27.50
Epoch 314:  test acc 0.5488   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 314:  clean acc 0.1785   certified acc 0.0314
Calculating metrics for L_infinity dist model on test set
Epoch 314:  clean acc 0.1805   certified acc 0.0358
scalar:  1.6763
Epoch 315:  train loss 0.6466   train acc 0.5660   worst 0.2225   lr 0.0259   p 19.73   eps 0.7807   mix 0.0313   time 28.46
scalar:  1.6318
Epoch 316:  train loss 0.6470   train acc 0.5646   worst 0.2241   lr 0.0258   p 19.81   eps 0.7807   mix 0.0311   time 26.99
scalar:  1.6841
Epoch 317:  train loss 0.6456   train acc 0.5664   worst 0.2232   lr 0.0258   p 19.90   eps 0.7807   mix 0.0310   time 27.31
scalar:  1.6531
Epoch 318:  train loss 0.6472   train acc 0.5662   worst 0.2204   lr 0.0258   p 19.98   eps 0.7807   mix 0.0308   time 27.00
scalar:  1.6877
Epoch 319:  train loss 0.6488   train acc 0.5666   worst 0.2207   lr 0.0258   p 20.06   eps 0.7807   mix 0.0306   time 27.64
Epoch 319:  test acc 0.5501   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 319:  clean acc 0.1646   certified acc 0.0347
Calculating metrics for L_infinity dist model on test set
Epoch 319:  clean acc 0.1685   certified acc 0.0398
scalar:  1.6855
Epoch 320:  train loss 0.6501   train acc 0.5633   worst 0.2189   lr 0.0257   p 20.15   eps 0.7807   mix 0.0305   time 28.05
scalar:  1.6845
Epoch 321:  train loss 0.6482   train acc 0.5636   worst 0.2174   lr 0.0257   p 20.23   eps 0.7807   mix 0.0303   time 27.13
scalar:  1.682
Epoch 322:  train loss 0.6503   train acc 0.5632   worst 0.2182   lr 0.0257   p 20.32   eps 0.7807   mix 0.0301   time 27.33
scalar:  1.6521
Epoch 323:  train loss 0.6514   train acc 0.5627   worst 0.2139   lr 0.0257   p 20.40   eps 0.7807   mix 0.0300   time 27.47
scalar:  1.6932
Epoch 324:  train loss 0.6508   train acc 0.5649   worst 0.2140   lr 0.0256   p 20.49   eps 0.7807   mix 0.0298   time 27.40
Epoch 324:  test acc 0.5444   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 324:  clean acc 0.1688   certified acc 0.0210
Calculating metrics for L_infinity dist model on test set
Epoch 324:  clean acc 0.1793   certified acc 0.0254
scalar:  1.673
Epoch 325:  train loss 0.6505   train acc 0.5634   worst 0.2179   lr 0.0256   p 20.58   eps 0.7807   mix 0.0296   time 27.86
scalar:  1.7
Epoch 326:  train loss 0.6509   train acc 0.5630   worst 0.2150   lr 0.0256   p 20.66   eps 0.7807   mix 0.0295   time 27.30
scalar:  1.6829
Epoch 327:  train loss 0.6527   train acc 0.5619   worst 0.2141   lr 0.0256   p 20.75   eps 0.7807   mix 0.0293   time 27.34
scalar:  1.6764
Epoch 328:  train loss 0.6525   train acc 0.5624   worst 0.2120   lr 0.0255   p 20.84   eps 0.7807   mix 0.0292   time 27.41
scalar:  1.6857
Epoch 329:  train loss 0.6509   train acc 0.5617   worst 0.2127   lr 0.0255   p 20.92   eps 0.7807   mix 0.0290   time 27.30
Epoch 329:  test acc 0.5455   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 329:  clean acc 0.1756   certified acc 0.0224
Calculating metrics for L_infinity dist model on test set
Epoch 329:  clean acc 0.1842   certified acc 0.0276
scalar:  1.7032
Epoch 330:  train loss 0.6525   train acc 0.5612   worst 0.2114   lr 0.0255   p 21.01   eps 0.7807   mix 0.0289   time 28.00
scalar:  1.7054
Epoch 331:  train loss 0.6538   train acc 0.5613   worst 0.2100   lr 0.0255   p 21.10   eps 0.7807   mix 0.0287   time 27.72
scalar:  1.6778
Epoch 332:  train loss 0.6538   train acc 0.5609   worst 0.2118   lr 0.0254   p 21.19   eps 0.7807   mix 0.0285   time 27.16
scalar:  1.6958
Epoch 333:  train loss 0.6539   train acc 0.5587   worst 0.2096   lr 0.0254   p 21.28   eps 0.7807   mix 0.0284   time 27.14
scalar:  1.6839
Epoch 334:  train loss 0.6560   train acc 0.5572   worst 0.2084   lr 0.0254   p 21.37   eps 0.7807   mix 0.0282   time 27.43
Epoch 334:  test acc 0.5456   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 334:  clean acc 0.1710   certified acc 0.0327
Calculating metrics for L_infinity dist model on test set
Epoch 334:  clean acc 0.1795   certified acc 0.0407
scalar:  1.6788
Epoch 335:  train loss 0.6538   train acc 0.5582   worst 0.2105   lr 0.0253   p 21.46   eps 0.7807   mix 0.0281   time 27.94
scalar:  1.7115
Epoch 336:  train loss 0.6549   train acc 0.5581   worst 0.2064   lr 0.0253   p 21.55   eps 0.7807   mix 0.0279   time 27.41
scalar:  1.6965
Epoch 337:  train loss 0.6560   train acc 0.5593   worst 0.2066   lr 0.0253   p 21.64   eps 0.7807   mix 0.0278   time 27.38
scalar:  1.7305
Epoch 338:  train loss 0.6565   train acc 0.5569   worst 0.2063   lr 0.0253   p 21.73   eps 0.7807   mix 0.0276   time 27.34
scalar:  1.7157
Epoch 339:  train loss 0.6575   train acc 0.5570   worst 0.2051   lr 0.0252   p 21.82   eps 0.7807   mix 0.0275   time 27.32
Epoch 339:  test acc 0.5473   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 339:  clean acc 0.1845   certified acc 0.0281
Calculating metrics for L_infinity dist model on test set
Epoch 339:  clean acc 0.1912   certified acc 0.0368
scalar:  1.7071
Epoch 340:  train loss 0.6571   train acc 0.5573   worst 0.2050   lr 0.0252   p 21.91   eps 0.7807   mix 0.0273   time 27.79
scalar:  1.7359
Epoch 341:  train loss 0.6576   train acc 0.5546   worst 0.2058   lr 0.0252   p 22.01   eps 0.7807   mix 0.0272   time 27.54
scalar:  1.7273
Epoch 342:  train loss 0.6584   train acc 0.5549   worst 0.2044   lr 0.0252   p 22.10   eps 0.7807   mix 0.0270   time 26.99
scalar:  1.7479
Epoch 343:  train loss 0.6576   train acc 0.5548   worst 0.2056   lr 0.0251   p 22.19   eps 0.7807   mix 0.0269   time 27.61
scalar:  1.6958
Epoch 344:  train loss 0.6597   train acc 0.5535   worst 0.2020   lr 0.0251   p 22.28   eps 0.7807   mix 0.0268   time 27.75
Epoch 344:  test acc 0.5426   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 344:  clean acc 0.1739   certified acc 0.0321
Calculating metrics for L_infinity dist model on test set
Epoch 344:  clean acc 0.1768   certified acc 0.0361
scalar:  1.7275
Epoch 345:  train loss 0.6605   train acc 0.5517   worst 0.2014   lr 0.0251   p 22.38   eps 0.7807   mix 0.0266   time 27.91
scalar:  1.7281
Epoch 346:  train loss 0.6577   train acc 0.5561   worst 0.2021   lr 0.0251   p 22.47   eps 0.7807   mix 0.0265   time 27.48
scalar:  1.734
Epoch 347:  train loss 0.6607   train acc 0.5518   worst 0.1990   lr 0.0250   p 22.57   eps 0.7807   mix 0.0263   time 27.20
scalar:  1.733
Epoch 348:  train loss 0.6598   train acc 0.5527   worst 0.1990   lr 0.0250   p 22.66   eps 0.7807   mix 0.0262   time 27.27
scalar:  1.7421
Epoch 349:  train loss 0.6615   train acc 0.5495   worst 0.1993   lr 0.0250   p 22.76   eps 0.7807   mix 0.0260   time 27.91
Epoch 349:  test acc 0.5398   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 349:  clean acc 0.1597   certified acc 0.0269
Calculating metrics for L_infinity dist model on test set
Epoch 349:  clean acc 0.1663   certified acc 0.0302
scalar:  1.7251
Epoch 350:  train loss 0.6612   train acc 0.5517   worst 0.1988   lr 0.0249   p 22.85   eps 0.7807   mix 0.0259   time 27.86
scalar:  1.7451
Epoch 351:  train loss 0.6614   train acc 0.5521   worst 0.1988   lr 0.0249   p 22.95   eps 0.7807   mix 0.0258   time 27.36
scalar:  1.7555
Epoch 352:  train loss 0.6613   train acc 0.5508   worst 0.1970   lr 0.0249   p 23.05   eps 0.7807   mix 0.0256   time 27.15
scalar:  1.7471
Epoch 353:  train loss 0.6620   train acc 0.5494   worst 0.1972   lr 0.0249   p 23.14   eps 0.7807   mix 0.0255   time 27.03
scalar:  1.7438
Epoch 354:  train loss 0.6616   train acc 0.5525   worst 0.1960   lr 0.0248   p 23.24   eps 0.7807   mix 0.0253   time 27.91
Epoch 354:  test acc 0.5463   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 354:  clean acc 0.1757   certified acc 0.0184
Calculating metrics for L_infinity dist model on test set
Epoch 354:  clean acc 0.1836   certified acc 0.0220
scalar:  1.7849
Epoch 355:  train loss 0.6635   train acc 0.5517   worst 0.1955   lr 0.0248   p 23.34   eps 0.7807   mix 0.0252   time 28.03
scalar:  1.7492
Epoch 356:  train loss 0.6657   train acc 0.5470   worst 0.1931   lr 0.0248   p 23.44   eps 0.7807   mix 0.0251   time 27.30
scalar:  1.7349
Epoch 357:  train loss 0.6646   train acc 0.5518   worst 0.1933   lr 0.0248   p 23.53   eps 0.7807   mix 0.0249   time 27.22
scalar:  1.7642
Epoch 358:  train loss 0.6645   train acc 0.5487   worst 0.1937   lr 0.0247   p 23.63   eps 0.7807   mix 0.0248   time 27.16
scalar:  1.7979
Epoch 359:  train loss 0.6650   train acc 0.5475   worst 0.1943   lr 0.0247   p 23.73   eps 0.7807   mix 0.0247   time 27.56
Epoch 359:  test acc 0.5372   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 359:  clean acc 0.1726   certified acc 0.0194
Calculating metrics for L_infinity dist model on test set
Epoch 359:  clean acc 0.1786   certified acc 0.0222
scalar:  1.7579
Epoch 360:  train loss 0.6649   train acc 0.5487   worst 0.1918   lr 0.0247   p 23.83   eps 0.7807   mix 0.0245   time 27.70
scalar:  1.7782
Epoch 361:  train loss 0.6654   train acc 0.5497   worst 0.1901   lr 0.0246   p 23.93   eps 0.7807   mix 0.0244   time 27.59
scalar:  1.7751
Epoch 362:  train loss 0.6661   train acc 0.5479   worst 0.1916   lr 0.0246   p 24.03   eps 0.7807   mix 0.0243   time 27.09
scalar:  1.7724
Epoch 363:  train loss 0.6679   train acc 0.5480   worst 0.1894   lr 0.0246   p 24.13   eps 0.7807   mix 0.0241   time 27.36
scalar:  1.7517
Epoch 364:  train loss 0.6668   train acc 0.5453   worst 0.1924   lr 0.0246   p 24.24   eps 0.7807   mix 0.0240   time 27.75
Epoch 364:  test acc 0.5384   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 364:  clean acc 0.1730   certified acc 0.0217
Calculating metrics for L_infinity dist model on test set
Epoch 364:  clean acc 0.1839   certified acc 0.0269
scalar:  1.7651
Epoch 365:  train loss 0.6671   train acc 0.5457   worst 0.1892   lr 0.0245   p 24.34   eps 0.7807   mix 0.0239   time 27.33
scalar:  1.7482
Epoch 366:  train loss 0.6668   train acc 0.5462   worst 0.1898   lr 0.0245   p 24.44   eps 0.7807   mix 0.0238   time 27.88
scalar:  1.7561
Epoch 367:  train loss 0.6686   train acc 0.5412   worst 0.1886   lr 0.0245   p 24.54   eps 0.7807   mix 0.0236   time 27.15
scalar:  1.7803
Epoch 368:  train loss 0.6697   train acc 0.5445   worst 0.1884   lr 0.0244   p 24.65   eps 0.7807   mix 0.0235   time 26.91
scalar:  1.7816
Epoch 369:  train loss 0.6673   train acc 0.5464   worst 0.1887   lr 0.0244   p 24.75   eps 0.7807   mix 0.0234   time 27.77
Epoch 369:  test acc 0.5365   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 369:  clean acc 0.1825   certified acc 0.0188
Calculating metrics for L_infinity dist model on test set
Epoch 369:  clean acc 0.1890   certified acc 0.0245
scalar:  1.8047
Epoch 370:  train loss 0.6693   train acc 0.5440   worst 0.1877   lr 0.0244   p 24.85   eps 0.7807   mix 0.0232   time 27.48
scalar:  1.7644
Epoch 371:  train loss 0.6685   train acc 0.5453   worst 0.1845   lr 0.0244   p 24.96   eps 0.7807   mix 0.0231   time 27.66
scalar:  1.7963
Epoch 372:  train loss 0.6701   train acc 0.5439   worst 0.1839   lr 0.0243   p 25.06   eps 0.7807   mix 0.0230   time 27.23
scalar:  1.7956
Epoch 373:  train loss 0.6694   train acc 0.5455   worst 0.1832   lr 0.0243   p 25.17   eps 0.7807   mix 0.0229   time 27.53
scalar:  1.7857
Epoch 374:  train loss 0.6699   train acc 0.5436   worst 0.1837   lr 0.0243   p 25.28   eps 0.7807   mix 0.0227   time 27.95
Epoch 374:  test acc 0.5392   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 374:  clean acc 0.1708   certified acc 0.0224
Calculating metrics for L_infinity dist model on test set
Epoch 374:  clean acc 0.1783   certified acc 0.0250
scalar:  1.7894
Epoch 375:  train loss 0.6716   train acc 0.5427   worst 0.1828   lr 0.0243   p 25.38   eps 0.7807   mix 0.0226   time 27.32
scalar:  1.7881
Epoch 376:  train loss 0.6704   train acc 0.5412   worst 0.1836   lr 0.0242   p 25.49   eps 0.7807   mix 0.0225   time 28.01
scalar:  1.7893
Epoch 377:  train loss 0.6716   train acc 0.5412   worst 0.1819   lr 0.0242   p 25.60   eps 0.7807   mix 0.0224   time 27.45
scalar:  1.7902
Epoch 378:  train loss 0.6707   train acc 0.5436   worst 0.1824   lr 0.0242   p 25.70   eps 0.7807   mix 0.0223   time 27.44
scalar:  1.8302
Epoch 379:  train loss 0.6739   train acc 0.5403   worst 0.1790   lr 0.0241   p 25.81   eps 0.7807   mix 0.0221   time 27.52
Epoch 379:  test acc 0.5298   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 379:  clean acc 0.1662   certified acc 0.0191
Calculating metrics for L_infinity dist model on test set
Epoch 379:  clean acc 0.1736   certified acc 0.0222
scalar:  1.7977
Epoch 380:  train loss 0.6715   train acc 0.5423   worst 0.1820   lr 0.0241   p 25.92   eps 0.7807   mix 0.0220   time 27.62
scalar:  1.8001
Epoch 381:  train loss 0.6718   train acc 0.5425   worst 0.1806   lr 0.0241   p 26.03   eps 0.7807   mix 0.0219   time 27.55
scalar:  1.7892
Epoch 382:  train loss 0.6731   train acc 0.5392   worst 0.1790   lr 0.0240   p 26.14   eps 0.7807   mix 0.0218   time 27.24
scalar:  1.8105
Epoch 383:  train loss 0.6744   train acc 0.5379   worst 0.1796   lr 0.0240   p 26.25   eps 0.7807   mix 0.0217   time 26.91
scalar:  1.7919
Epoch 384:  train loss 0.6729   train acc 0.5407   worst 0.1805   lr 0.0240   p 26.36   eps 0.7807   mix 0.0216   time 28.00
Epoch 384:  test acc 0.5311   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 384:  clean acc 0.1770   certified acc 0.0277
Calculating metrics for L_infinity dist model on test set
Epoch 384:  clean acc 0.1804   certified acc 0.0294
scalar:  1.7839
Epoch 385:  train loss 0.6734   train acc 0.5408   worst 0.1760   lr 0.0240   p 26.47   eps 0.7807   mix 0.0214   time 27.49
scalar:  1.8379
Epoch 386:  train loss 0.6749   train acc 0.5376   worst 0.1795   lr 0.0239   p 26.58   eps 0.7807   mix 0.0213   time 27.64
scalar:  1.8136
Epoch 387:  train loss 0.6754   train acc 0.5361   worst 0.1787   lr 0.0239   p 26.69   eps 0.7807   mix 0.0212   time 27.15
scalar:  1.8124
Epoch 388:  train loss 0.6738   train acc 0.5385   worst 0.1781   lr 0.0239   p 26.81   eps 0.7807   mix 0.0211   time 27.20
scalar:  1.807
Epoch 389:  train loss 0.6747   train acc 0.5402   worst 0.1759   lr 0.0238   p 26.92   eps 0.7807   mix 0.0210   time 27.68
Epoch 389:  test acc 0.5290   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 389:  clean acc 0.1898   certified acc 0.0198
Calculating metrics for L_infinity dist model on test set
Epoch 389:  clean acc 0.1981   certified acc 0.0244
scalar:  1.8508
Epoch 390:  train loss 0.6750   train acc 0.5410   worst 0.1748   lr 0.0238   p 27.03   eps 0.7807   mix 0.0209   time 27.45
scalar:  1.8347
Epoch 391:  train loss 0.6753   train acc 0.5392   worst 0.1736   lr 0.0238   p 27.15   eps 0.7807   mix 0.0208   time 27.71
scalar:  1.8257
Epoch 392:  train loss 0.6760   train acc 0.5386   worst 0.1752   lr 0.0238   p 27.26   eps 0.7807   mix 0.0206   time 27.31
scalar:  1.8216
Epoch 393:  train loss 0.6772   train acc 0.5361   worst 0.1743   lr 0.0237   p 27.37   eps 0.7807   mix 0.0205   time 27.24
scalar:  1.8435
Epoch 394:  train loss 0.6771   train acc 0.5339   worst 0.1734   lr 0.0237   p 27.49   eps 0.7807   mix 0.0204   time 27.82
Epoch 394:  test acc 0.5332   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 394:  clean acc 0.1863   certified acc 0.0165
Calculating metrics for L_infinity dist model on test set
Epoch 394:  clean acc 0.1884   certified acc 0.0179
scalar:  1.8227
Epoch 395:  train loss 0.6765   train acc 0.5368   worst 0.1724   lr 0.0237   p 27.61   eps 0.7807   mix 0.0203   time 27.89
scalar:  1.827
Epoch 396:  train loss 0.6795   train acc 0.5359   worst 0.1718   lr 0.0236   p 27.72   eps 0.7807   mix 0.0202   time 27.60
scalar:  1.8479
Epoch 397:  train loss 0.6789   train acc 0.5368   worst 0.1711   lr 0.0236   p 27.84   eps 0.7807   mix 0.0201   time 27.39
scalar:  1.8346
Epoch 398:  train loss 0.6786   train acc 0.5340   worst 0.1716   lr 0.0236   p 27.96   eps 0.7807   mix 0.0200   time 27.11
scalar:  1.7939
Epoch 399:  train loss 0.6765   train acc 0.5355   worst 0.1729   lr 0.0236   p 28.07   eps 0.7807   mix 0.0199   time 27.85
Epoch 399:  test acc 0.5235   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 399:  clean acc 0.1804   certified acc 0.0203
Calculating metrics for L_infinity dist model on test set
Epoch 399:  clean acc 0.1847   certified acc 0.0225
scalar:  1.8301
Epoch 400:  train loss 0.6778   train acc 0.5351   worst 0.1709   lr 0.0235   p 28.19   eps 0.7807   mix 0.0198   time 27.82
scalar:  1.833
Epoch 401:  train loss 0.6802   train acc 0.5338   worst 0.1696   lr 0.0235   p 28.31   eps 0.7807   mix 0.0197   time 27.64
scalar:  1.8308
Epoch 402:  train loss 0.6804   train acc 0.5338   worst 0.1679   lr 0.0235   p 28.43   eps 0.7807   mix 0.0196   time 27.74
scalar:  1.8623
Epoch 403:  train loss 0.6811   train acc 0.5334   worst 0.1696   lr 0.0234   p 28.55   eps 0.7807   mix 0.0194   time 27.32
scalar:  1.8487
Epoch 404:  train loss 0.6808   train acc 0.5336   worst 0.1673   lr 0.0234   p 28.67   eps 0.7807   mix 0.0193   time 28.01
Epoch 404:  test acc 0.5281   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 404:  clean acc 0.1852   certified acc 0.0251
Calculating metrics for L_infinity dist model on test set
Epoch 404:  clean acc 0.1871   certified acc 0.0287
scalar:  1.8477
Epoch 405:  train loss 0.6811   train acc 0.5352   worst 0.1675   lr 0.0234   p 28.79   eps 0.7807   mix 0.0192   time 27.45
scalar:  1.882
Epoch 406:  train loss 0.6815   train acc 0.5340   worst 0.1658   lr 0.0233   p 28.91   eps 0.7807   mix 0.0191   time 27.49
scalar:  1.8737
Epoch 407:  train loss 0.6823   train acc 0.5297   worst 0.1661   lr 0.0233   p 29.03   eps 0.7807   mix 0.0190   time 27.31
scalar:  1.8384
Epoch 408:  train loss 0.6818   train acc 0.5325   worst 0.1669   lr 0.0233   p 29.15   eps 0.7807   mix 0.0189   time 27.50
scalar:  1.8407
Epoch 409:  train loss 0.6810   train acc 0.5332   worst 0.1670   lr 0.0233   p 29.28   eps 0.7807   mix 0.0188   time 27.46
Epoch 409:  test acc 0.5236   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 409:  clean acc 0.2133   certified acc 0.0213
Calculating metrics for L_infinity dist model on test set
Epoch 409:  clean acc 0.2204   certified acc 0.0258
scalar:  1.8384
Epoch 410:  train loss 0.6819   train acc 0.5317   worst 0.1657   lr 0.0232   p 29.40   eps 0.7807   mix 0.0187   time 27.25
scalar:  1.8759
Epoch 411:  train loss 0.6812   train acc 0.5332   worst 0.1651   lr 0.0232   p 29.52   eps 0.7807   mix 0.0186   time 27.49
scalar:  1.8838
Epoch 412:  train loss 0.6822   train acc 0.5316   worst 0.1650   lr 0.0232   p 29.65   eps 0.7807   mix 0.0185   time 27.80
scalar:  1.891
Epoch 413:  train loss 0.6831   train acc 0.5324   worst 0.1620   lr 0.0231   p 29.77   eps 0.7807   mix 0.0184   time 27.47
scalar:  1.8769
Epoch 414:  train loss 0.6835   train acc 0.5312   worst 0.1634   lr 0.0231   p 29.90   eps 0.7807   mix 0.0183   time 27.65
Epoch 414:  test acc 0.5260   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 414:  clean acc 0.1817   certified acc 0.0238
Calculating metrics for L_infinity dist model on test set
Epoch 414:  clean acc 0.1892   certified acc 0.0262
scalar:  1.8249
Epoch 415:  train loss 0.6849   train acc 0.5312   worst 0.1606   lr 0.0231   p 30.02   eps 0.7807   mix 0.0182   time 27.54
scalar:  1.8251
Epoch 416:  train loss 0.6832   train acc 0.5305   worst 0.1624   lr 0.0230   p 30.15   eps 0.7807   mix 0.0181   time 27.33
scalar:  1.8485
Epoch 417:  train loss 0.6850   train acc 0.5280   worst 0.1623   lr 0.0230   p 30.28   eps 0.7807   mix 0.0180   time 27.50
scalar:  1.8707
Epoch 418:  train loss 0.6851   train acc 0.5305   worst 0.1608   lr 0.0230   p 30.40   eps 0.7807   mix 0.0179   time 27.31
scalar:  1.8624
Epoch 419:  train loss 0.6857   train acc 0.5305   worst 0.1590   lr 0.0229   p 30.53   eps 0.7807   mix 0.0178   time 27.48
Epoch 419:  test acc 0.5174   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 419:  clean acc 0.1955   certified acc 0.0298
Calculating metrics for L_infinity dist model on test set
Epoch 419:  clean acc 0.1954   certified acc 0.0331
scalar:  1.852
Epoch 420:  train loss 0.6836   train acc 0.5264   worst 0.1618   lr 0.0229   p 30.66   eps 0.7807   mix 0.0177   time 27.76
scalar:  1.8283
Epoch 421:  train loss 0.6853   train acc 0.5261   worst 0.1606   lr 0.0229   p 30.79   eps 0.7807   mix 0.0176   time 27.42
scalar:  1.8695
Epoch 422:  train loss 0.6842   train acc 0.5261   worst 0.1623   lr 0.0229   p 30.92   eps 0.7807   mix 0.0176   time 27.74
scalar:  1.8612
Epoch 423:  train loss 0.6862   train acc 0.5287   worst 0.1579   lr 0.0228   p 31.05   eps 0.7807   mix 0.0175   time 27.32
scalar:  1.872
Epoch 424:  train loss 0.6864   train acc 0.5279   worst 0.1590   lr 0.0228   p 31.18   eps 0.7807   mix 0.0174   time 27.24
Epoch 424:  test acc 0.5200   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 424:  clean acc 0.2079   certified acc 0.0247
Calculating metrics for L_infinity dist model on test set
Epoch 424:  clean acc 0.2153   certified acc 0.0268
scalar:  1.8348
Epoch 425:  train loss 0.6864   train acc 0.5250   worst 0.1589   lr 0.0228   p 31.31   eps 0.7807   mix 0.0173   time 27.65
scalar:  1.8635
Epoch 426:  train loss 0.6853   train acc 0.5302   worst 0.1591   lr 0.0227   p 31.44   eps 0.7807   mix 0.0172   time 26.87
scalar:  1.8996
Epoch 427:  train loss 0.6877   train acc 0.5242   worst 0.1572   lr 0.0227   p 31.57   eps 0.7807   mix 0.0171   time 27.92
scalar:  1.8884
Epoch 428:  train loss 0.6870   train acc 0.5258   worst 0.1581   lr 0.0227   p 31.71   eps 0.7807   mix 0.0170   time 27.30
scalar:  1.8653
Epoch 429:  train loss 0.6876   train acc 0.5250   worst 0.1562   lr 0.0226   p 31.84   eps 0.7807   mix 0.0169   time 27.62
Epoch 429:  test acc 0.5244   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 429:  clean acc 0.2039   certified acc 0.0238
Calculating metrics for L_infinity dist model on test set
Epoch 429:  clean acc 0.2091   certified acc 0.0265
scalar:  1.8641
Epoch 430:  train loss 0.6885   train acc 0.5268   worst 0.1553   lr 0.0226   p 31.98   eps 0.7807   mix 0.0168   time 27.86
scalar:  1.8983
Epoch 431:  train loss 0.6875   train acc 0.5256   worst 0.1550   lr 0.0226   p 32.11   eps 0.7807   mix 0.0167   time 27.34
scalar:  1.8691
Epoch 432:  train loss 0.6890   train acc 0.5267   worst 0.1541   lr 0.0225   p 32.24   eps 0.7807   mix 0.0166   time 27.66
scalar:  1.8995
Epoch 433:  train loss 0.6896   train acc 0.5236   worst 0.1545   lr 0.0225   p 32.38   eps 0.7807   mix 0.0165   time 27.09
scalar:  1.9032
Epoch 434:  train loss 0.6911   train acc 0.5219   worst 0.1545   lr 0.0225   p 32.52   eps 0.7807   mix 0.0164   time 27.42
Epoch 434:  test acc 0.5193   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 434:  clean acc 0.1888   certified acc 0.0265
Calculating metrics for L_infinity dist model on test set
Epoch 434:  clean acc 0.1901   certified acc 0.0252
scalar:  1.8919
Epoch 435:  train loss 0.6896   train acc 0.5244   worst 0.1531   lr 0.0224   p 32.65   eps 0.7807   mix 0.0164   time 27.49
scalar:  1.886
Epoch 436:  train loss 0.6887   train acc 0.5237   worst 0.1545   lr 0.0224   p 32.79   eps 0.7807   mix 0.0163   time 27.06
scalar:  1.8841
Epoch 437:  train loss 0.6899   train acc 0.5227   worst 0.1521   lr 0.0224   p 32.93   eps 0.7807   mix 0.0162   time 27.42
scalar:  1.8915
Epoch 438:  train loss 0.6893   train acc 0.5246   worst 0.1537   lr 0.0224   p 33.07   eps 0.7807   mix 0.0161   time 27.56
scalar:  1.8963
Epoch 439:  train loss 0.6922   train acc 0.5215   worst 0.1511   lr 0.0223   p 33.21   eps 0.7807   mix 0.0160   time 27.63
Epoch 439:  test acc 0.5176   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 439:  clean acc 0.2096   certified acc 0.0224
Calculating metrics for L_infinity dist model on test set
Epoch 439:  clean acc 0.2128   certified acc 0.0242
scalar:  1.8593
Epoch 440:  train loss 0.6909   train acc 0.5214   worst 0.1524   lr 0.0223   p 33.35   eps 0.7807   mix 0.0159   time 27.66
scalar:  1.8886
Epoch 441:  train loss 0.6912   train acc 0.5242   worst 0.1497   lr 0.0223   p 33.49   eps 0.7807   mix 0.0158   time 27.35
scalar:  1.8754
Epoch 442:  train loss 0.6911   train acc 0.5225   worst 0.1505   lr 0.0222   p 33.63   eps 0.7807   mix 0.0158   time 27.43
scalar:  1.8846
Epoch 443:  train loss 0.6913   train acc 0.5221   worst 0.1521   lr 0.0222   p 33.77   eps 0.7807   mix 0.0157   time 27.62
scalar:  1.8805
Epoch 444:  train loss 0.6909   train acc 0.5239   worst 0.1500   lr 0.0222   p 33.91   eps 0.7807   mix 0.0156   time 27.64
Epoch 444:  test acc 0.5180   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 444:  clean acc 0.2025   certified acc 0.0368
Calculating metrics for L_infinity dist model on test set
Epoch 444:  clean acc 0.2052   certified acc 0.0382
scalar:  1.8801
Epoch 445:  train loss 0.6914   train acc 0.5241   worst 0.1478   lr 0.0221   p 34.05   eps 0.7807   mix 0.0155   time 28.01
scalar:  1.9317
Epoch 446:  train loss 0.6914   train acc 0.5238   worst 0.1487   lr 0.0221   p 34.20   eps 0.7807   mix 0.0154   time 27.34
scalar:  1.9127
Epoch 447:  train loss 0.6930   train acc 0.5206   worst 0.1485   lr 0.0221   p 34.34   eps 0.7807   mix 0.0153   time 27.45
scalar:  1.9099
Epoch 448:  train loss 0.6937   train acc 0.5198   worst 0.1476   lr 0.0220   p 34.49   eps 0.7807   mix 0.0152   time 27.62
scalar:  1.9253
Epoch 449:  train loss 0.6935   train acc 0.5202   worst 0.1471   lr 0.0220   p 34.63   eps 0.7807   mix 0.0152   time 27.48
Epoch 449:  test acc 0.5138   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 449:  clean acc 0.2168   certified acc 0.0320
Calculating metrics for L_infinity dist model on test set
Epoch 449:  clean acc 0.2158   certified acc 0.0329
scalar:  1.8903
Epoch 450:  train loss 0.6925   train acc 0.5210   worst 0.1478   lr 0.0220   p 34.78   eps 0.7807   mix 0.0151   time 27.89
scalar:  1.9098
Epoch 451:  train loss 0.6948   train acc 0.5216   worst 0.1467   lr 0.0219   p 34.92   eps 0.7807   mix 0.0150   time 27.16
scalar:  1.9097
Epoch 452:  train loss 0.6933   train acc 0.5193   worst 0.1466   lr 0.0219   p 35.07   eps 0.7807   mix 0.0149   time 27.47
scalar:  1.8887
Epoch 453:  train loss 0.6946   train acc 0.5194   worst 0.1468   lr 0.0219   p 35.22   eps 0.7807   mix 0.0148   time 27.84
scalar:  1.8947
Epoch 454:  train loss 0.6929   train acc 0.5202   worst 0.1453   lr 0.0218   p 35.36   eps 0.7807   mix 0.0148   time 27.49
Epoch 454:  test acc 0.5126   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 454:  clean acc 0.2236   certified acc 0.0319
Calculating metrics for L_infinity dist model on test set
Epoch 454:  clean acc 0.2235   certified acc 0.0313
scalar:  1.9241
Epoch 455:  train loss 0.6947   train acc 0.5224   worst 0.1446   lr 0.0218   p 35.51   eps 0.7807   mix 0.0147   time 27.62
scalar:  1.9236
Epoch 456:  train loss 0.6937   train acc 0.5215   worst 0.1446   lr 0.0218   p 35.66   eps 0.7807   mix 0.0146   time 27.24
scalar:  1.9151
Epoch 457:  train loss 0.6951   train acc 0.5185   worst 0.1458   lr 0.0217   p 35.81   eps 0.7807   mix 0.0145   time 27.35
scalar:  1.8963
Epoch 458:  train loss 0.6951   train acc 0.5180   worst 0.1450   lr 0.0217   p 35.96   eps 0.7807   mix 0.0144   time 27.67
scalar:  1.8713
Epoch 459:  train loss 0.6968   train acc 0.5176   worst 0.1438   lr 0.0217   p 36.12   eps 0.7807   mix 0.0144   time 27.54
Epoch 459:  test acc 0.5109   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 459:  clean acc 0.2228   certified acc 0.0306
Calculating metrics for L_infinity dist model on test set
Epoch 459:  clean acc 0.2207   certified acc 0.0337
scalar:  1.8954
Epoch 460:  train loss 0.6949   train acc 0.5200   worst 0.1432   lr 0.0216   p 36.27   eps 0.7807   mix 0.0143   time 27.66
scalar:  1.9206
Epoch 461:  train loss 0.6970   train acc 0.5184   worst 0.1401   lr 0.0216   p 36.42   eps 0.7807   mix 0.0142   time 27.39
scalar:  1.9304
Epoch 462:  train loss 0.6955   train acc 0.5198   worst 0.1431   lr 0.0216   p 36.57   eps 0.7807   mix 0.0141   time 27.38
scalar:  1.9213
Epoch 463:  train loss 0.6973   train acc 0.5200   worst 0.1402   lr 0.0216   p 36.73   eps 0.7807   mix 0.0141   time 27.64
scalar:  1.9388
Epoch 464:  train loss 0.6984   train acc 0.5168   worst 0.1416   lr 0.0215   p 36.88   eps 0.7807   mix 0.0140   time 27.79
Epoch 464:  test acc 0.5133   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 464:  clean acc 0.2358   certified acc 0.0404
Calculating metrics for L_infinity dist model on test set
Epoch 464:  clean acc 0.2391   certified acc 0.0426
scalar:  1.9203
Epoch 465:  train loss 0.6984   train acc 0.5155   worst 0.1398   lr 0.0215   p 37.04   eps 0.7807   mix 0.0139   time 27.65
scalar:  1.899
Epoch 466:  train loss 0.6970   train acc 0.5158   worst 0.1419   lr 0.0215   p 37.19   eps 0.7807   mix 0.0138   time 27.30
scalar:  1.9166
Epoch 467:  train loss 0.6988   train acc 0.5155   worst 0.1390   lr 0.0214   p 37.35   eps 0.7807   mix 0.0138   time 27.27
scalar:  1.9153
Epoch 468:  train loss 0.6985   train acc 0.5176   worst 0.1387   lr 0.0214   p 37.51   eps 0.7807   mix 0.0137   time 27.65
scalar:  1.949
Epoch 469:  train loss 0.6974   train acc 0.5211   worst 0.1386   lr 0.0214   p 37.66   eps 0.7807   mix 0.0136   time 27.44
Epoch 469:  test acc 0.5099   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 469:  clean acc 0.2746   certified acc 0.0370
Calculating metrics for L_infinity dist model on test set
Epoch 469:  clean acc 0.2794   certified acc 0.0402
scalar:  1.9548
Epoch 470:  train loss 0.6985   train acc 0.5171   worst 0.1403   lr 0.0213   p 37.82   eps 0.7807   mix 0.0135   time 27.59
scalar:  1.9398
Epoch 471:  train loss 0.6984   train acc 0.5176   worst 0.1396   lr 0.0213   p 37.98   eps 0.7807   mix 0.0135   time 27.13
scalar:  1.9025
Epoch 472:  train loss 0.6980   train acc 0.5183   worst 0.1374   lr 0.0213   p 38.14   eps 0.7807   mix 0.0134   time 27.66
scalar:  1.9347
Epoch 473:  train loss 0.6984   train acc 0.5180   worst 0.1391   lr 0.0212   p 38.30   eps 0.7807   mix 0.0133   time 27.97
scalar:  1.9607
Epoch 474:  train loss 0.6985   train acc 0.5161   worst 0.1376   lr 0.0212   p 38.46   eps 0.7807   mix 0.0133   time 27.54
Epoch 474:  test acc 0.5112   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 474:  clean acc 0.2793   certified acc 0.0385
Calculating metrics for L_infinity dist model on test set
Epoch 474:  clean acc 0.2728   certified acc 0.0391
scalar:  1.9429
Epoch 475:  train loss 0.6981   train acc 0.5162   worst 0.1401   lr 0.0212   p 38.62   eps 0.7807   mix 0.0132   time 27.57
scalar:  1.932
Epoch 476:  train loss 0.6992   train acc 0.5162   worst 0.1367   lr 0.0211   p 38.79   eps 0.7807   mix 0.0131   time 27.24
scalar:  1.9771
Epoch 477:  train loss 0.6992   train acc 0.5140   worst 0.1365   lr 0.0211   p 38.95   eps 0.7807   mix 0.0130   time 27.37
scalar:  1.9238
Epoch 478:  train loss 0.7004   train acc 0.5131   worst 0.1364   lr 0.0211   p 39.11   eps 0.7807   mix 0.0130   time 27.50
scalar:  1.9396
Epoch 479:  train loss 0.7004   train acc 0.5146   worst 0.1367   lr 0.0210   p 39.28   eps 0.7807   mix 0.0129   time 27.92
Epoch 479:  test acc 0.5090   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 479:  clean acc 0.2646   certified acc 0.0397
Calculating metrics for L_infinity dist model on test set
Epoch 479:  clean acc 0.2648   certified acc 0.0409
scalar:  1.9143
Epoch 480:  train loss 0.7009   train acc 0.5130   worst 0.1358   lr 0.0210   p 39.44   eps 0.7807   mix 0.0128   time 27.43
scalar:  1.9535
Epoch 481:  train loss 0.6999   train acc 0.5146   worst 0.1362   lr 0.0210   p 39.61   eps 0.7807   mix 0.0128   time 27.08
scalar:  1.9484
Epoch 482:  train loss 0.7003   train acc 0.5164   worst 0.1339   lr 0.0209   p 39.78   eps 0.7807   mix 0.0127   time 27.43
scalar:  1.9536
Epoch 483:  train loss 0.6998   train acc 0.5137   worst 0.1353   lr 0.0209   p 39.94   eps 0.7807   mix 0.0126   time 27.90
scalar:  1.9664
Epoch 484:  train loss 0.7014   train acc 0.5129   worst 0.1346   lr 0.0209   p 40.11   eps 0.7807   mix 0.0126   time 28.02
Epoch 484:  test acc 0.5082   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 484:  clean acc 0.2785   certified acc 0.0436
Calculating metrics for L_infinity dist model on test set
Epoch 484:  clean acc 0.2818   certified acc 0.0412
scalar:  1.9441
Epoch 485:  train loss 0.7018   train acc 0.5172   worst 0.1320   lr 0.0208   p 40.28   eps 0.7807   mix 0.0125   time 27.51
scalar:  1.9623
Epoch 486:  train loss 0.7020   train acc 0.5140   worst 0.1331   lr 0.0208   p 40.45   eps 0.7807   mix 0.0124   time 27.14
scalar:  1.9739
Epoch 487:  train loss 0.7008   train acc 0.5136   worst 0.1344   lr 0.0208   p 40.62   eps 0.7807   mix 0.0124   time 27.20
scalar:  1.9871
Epoch 488:  train loss 0.7015   train acc 0.5128   worst 0.1334   lr 0.0207   p 40.79   eps 0.7807   mix 0.0123   time 27.49
scalar:  1.9411
Epoch 489:  train loss 0.7018   train acc 0.5128   worst 0.1346   lr 0.0207   p 40.96   eps 0.7807   mix 0.0122   time 27.87
Epoch 489:  test acc 0.5082   time 2.63
Calculating metrics for L_infinity dist model on training set
Epoch 489:  clean acc 0.2838   certified acc 0.0446
Calculating metrics for L_infinity dist model on test set
Epoch 489:  clean acc 0.2814   certified acc 0.0435
scalar:  1.949
Epoch 490:  train loss 0.7007   train acc 0.5166   worst 0.1319   lr 0.0207   p 41.14   eps 0.7807   mix 0.0122   time 27.50
scalar:  1.9722
Epoch 491:  train loss 0.7009   train acc 0.5155   worst 0.1325   lr 0.0206   p 41.31   eps 0.7807   mix 0.0121   time 27.39
scalar:  1.979
Epoch 492:  train loss 0.7024   train acc 0.5126   worst 0.1327   lr 0.0206   p 41.48   eps 0.7807   mix 0.0120   time 27.48
scalar:  1.9593
Epoch 493:  train loss 0.7040   train acc 0.5114   worst 0.1315   lr 0.0206   p 41.66   eps 0.7807   mix 0.0120   time 27.59
scalar:  1.9294
Epoch 494:  train loss 0.7031   train acc 0.5131   worst 0.1310   lr 0.0205   p 41.83   eps 0.7807   mix 0.0119   time 27.88
Epoch 494:  test acc 0.5121   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 494:  clean acc 0.2741   certified acc 0.0500
Calculating metrics for L_infinity dist model on test set
Epoch 494:  clean acc 0.2777   certified acc 0.0475
scalar:  1.962
Epoch 495:  train loss 0.7035   train acc 0.5145   worst 0.1288   lr 0.0205   p 42.01   eps 0.7807   mix 0.0118   time 27.10
scalar:  1.9764
Epoch 496:  train loss 0.7035   train acc 0.5118   worst 0.1290   lr 0.0205   p 42.18   eps 0.7807   mix 0.0118   time 27.18
scalar:  1.9991
Epoch 497:  train loss 0.7029   train acc 0.5135   worst 0.1295   lr 0.0204   p 42.36   eps 0.7807   mix 0.0117   time 27.11
scalar:  1.9938
Epoch 498:  train loss 0.7044   train acc 0.5129   worst 0.1305   lr 0.0204   p 42.54   eps 0.7807   mix 0.0116   time 27.33
scalar:  1.9426
Epoch 499:  train loss 0.7026   train acc 0.5121   worst 0.1307   lr 0.0204   p 42.72   eps 0.7807   mix 0.0116   time 27.95
Epoch 499:  test acc 0.5036   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 499:  clean acc 0.2860   certified acc 0.0479
Calculating metrics for L_infinity dist model on test set
Epoch 499:  clean acc 0.2866   certified acc 0.0474
scalar:  1.9757
Epoch 500:  train loss 0.7050   train acc 0.5122   worst 0.1265   lr 0.0203   p 42.90   eps 0.7807   mix 0.0115   time 27.51
scalar:  2.0097
Epoch 501:  train loss 0.7036   train acc 0.5111   worst 0.1304   lr 0.0203   p 43.08   eps 0.7807   mix 0.0115   time 27.25
scalar:  1.9801
Epoch 502:  train loss 0.7053   train acc 0.5092   worst 0.1301   lr 0.0203   p 43.26   eps 0.7807   mix 0.0114   time 27.33
scalar:  1.9629
Epoch 503:  train loss 0.7053   train acc 0.5113   worst 0.1279   lr 0.0202   p 43.44   eps 0.7807   mix 0.0113   time 27.37
scalar:  1.9986
Epoch 504:  train loss 0.7057   train acc 0.5113   worst 0.1251   lr 0.0202   p 43.63   eps 0.7807   mix 0.0113   time 28.17
Epoch 504:  test acc 0.5064   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 504:  clean acc 0.2845   certified acc 0.0592
Calculating metrics for L_infinity dist model on test set
Epoch 504:  clean acc 0.2836   certified acc 0.0562
scalar:  2.013
Epoch 505:  train loss 0.7046   train acc 0.5100   worst 0.1294   lr 0.0201   p 43.81   eps 0.7807   mix 0.0112   time 27.58
scalar:  1.984
Epoch 506:  train loss 0.7058   train acc 0.5114   worst 0.1275   lr 0.0201   p 43.99   eps 0.7807   mix 0.0111   time 27.11
scalar:  2.0076
Epoch 507:  train loss 0.7040   train acc 0.5131   worst 0.1272   lr 0.0201   p 44.18   eps 0.7807   mix 0.0111   time 27.17
scalar:  1.9666
Epoch 508:  train loss 0.7057   train acc 0.5106   worst 0.1266   lr 0.0200   p 44.36   eps 0.7807   mix 0.0110   time 27.36
scalar:  2.0015
Epoch 509:  train loss 0.7077   train acc 0.5089   worst 0.1275   lr 0.0200   p 44.55   eps 0.7807   mix 0.0110   time 27.90
Epoch 509:  test acc 0.5046   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 509:  clean acc 0.2953   certified acc 0.0574
Calculating metrics for L_infinity dist model on test set
Epoch 509:  clean acc 0.2980   certified acc 0.0571
scalar:  1.9726
Epoch 510:  train loss 0.7074   train acc 0.5114   worst 0.1237   lr 0.0200   p 44.74   eps 0.7807   mix 0.0109   time 27.36
scalar:  1.9638
Epoch 511:  train loss 0.7063   train acc 0.5109   worst 0.1257   lr 0.0199   p 44.93   eps 0.7807   mix 0.0108   time 27.29
scalar:  2.0079
Epoch 512:  train loss 0.7057   train acc 0.5114   worst 0.1255   lr 0.0199   p 45.12   eps 0.7807   mix 0.0108   time 26.95
scalar:  2.0014
Epoch 513:  train loss 0.7070   train acc 0.5103   worst 0.1260   lr 0.0199   p 45.31   eps 0.7807   mix 0.0107   time 27.61
scalar:  1.9945
Epoch 514:  train loss 0.7062   train acc 0.5105   worst 0.1257   lr 0.0198   p 45.50   eps 0.7807   mix 0.0107   time 27.65
Epoch 514:  test acc 0.5028   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 514:  clean acc 0.3180   certified acc 0.0684
Calculating metrics for L_infinity dist model on test set
Epoch 514:  clean acc 0.3184   certified acc 0.0694
scalar:  2.0212
Epoch 515:  train loss 0.7068   train acc 0.5115   worst 0.1248   lr 0.0198   p 45.69   eps 0.7807   mix 0.0106   time 27.52
scalar:  2.0237
Epoch 516:  train loss 0.7079   train acc 0.5094   worst 0.1241   lr 0.0198   p 45.88   eps 0.7807   mix 0.0106   time 27.03
scalar:  1.9789
Epoch 517:  train loss 0.7075   train acc 0.5089   worst 0.1252   lr 0.0197   p 46.07   eps 0.7807   mix 0.0105   time 27.02
scalar:  1.9543
Epoch 518:  train loss 0.7089   train acc 0.5078   worst 0.1236   lr 0.0197   p 46.27   eps 0.7807   mix 0.0104   time 27.49
scalar:  1.9912
Epoch 519:  train loss 0.7072   train acc 0.5083   worst 0.1239   lr 0.0197   p 46.46   eps 0.7807   mix 0.0104   time 27.54
Epoch 519:  test acc 0.5065   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 519:  clean acc 0.3089   certified acc 0.0751
Calculating metrics for L_infinity dist model on test set
Epoch 519:  clean acc 0.3089   certified acc 0.0755
scalar:  1.9848
Epoch 520:  train loss 0.7084   train acc 0.5093   worst 0.1237   lr 0.0196   p 46.66   eps 0.7807   mix 0.0103   time 27.36
scalar:  2.0023
Epoch 521:  train loss 0.7076   train acc 0.5087   worst 0.1219   lr 0.0196   p 46.85   eps 0.7807   mix 0.0103   time 27.37
scalar:  2.0031
Epoch 522:  train loss 0.7084   train acc 0.5085   worst 0.1219   lr 0.0196   p 47.05   eps 0.7807   mix 0.0102   time 27.39
scalar:  2.0121
Epoch 523:  train loss 0.7097   train acc 0.5084   worst 0.1197   lr 0.0195   p 47.25   eps 0.7807   mix 0.0102   time 27.56
scalar:  2.0164
Epoch 524:  train loss 0.7073   train acc 0.5095   worst 0.1235   lr 0.0195   p 47.45   eps 0.7807   mix 0.0101   time 27.55
Epoch 524:  test acc 0.5034   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 524:  clean acc 0.3321   certified acc 0.0739
Calculating metrics for L_infinity dist model on test set
Epoch 524:  clean acc 0.3288   certified acc 0.0729
scalar:  2.0209
Epoch 525:  train loss 0.7053   train acc 0.5103   worst 0.1240   lr 0.0195   p 47.65   eps 0.7807   mix 0.0101   time 27.43
scalar:  2.0287
Epoch 526:  train loss 0.7086   train acc 0.5098   worst 0.1213   lr 0.0194   p 47.85   eps 0.7807   mix 0.0100   time 27.18
scalar:  2.0205
Epoch 527:  train loss 0.7094   train acc 0.5089   worst 0.1204   lr 0.0194   p 48.05   eps 0.7807   mix 0.0100   time 27.12
scalar:  2.0236
Epoch 528:  train loss 0.7091   train acc 0.5064   worst 0.1223   lr 0.0194   p 48.25   eps 0.7807   mix 0.0099   time 27.42
scalar:  2.0015
Epoch 529:  train loss 0.7081   train acc 0.5095   worst 0.1211   lr 0.0193   p 48.45   eps 0.7807   mix 0.0098   time 27.73
Epoch 529:  test acc 0.5011   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 529:  clean acc 0.3248   certified acc 0.0811
Calculating metrics for L_infinity dist model on test set
Epoch 529:  clean acc 0.3228   certified acc 0.0783
scalar:  1.9797
Epoch 530:  train loss 0.7088   train acc 0.5078   worst 0.1208   lr 0.0193   p 48.66   eps 0.7807   mix 0.0098   time 27.70
scalar:  2.0391
Epoch 531:  train loss 0.7102   train acc 0.5055   worst 0.1205   lr 0.0193   p 48.86   eps 0.7807   mix 0.0097   time 27.16
scalar:  1.9944
Epoch 532:  train loss 0.7095   train acc 0.5090   worst 0.1186   lr 0.0192   p 49.07   eps 0.7807   mix 0.0097   time 26.87
scalar:  2.0348
Epoch 533:  train loss 0.7116   train acc 0.5065   worst 0.1187   lr 0.0192   p 49.27   eps 0.7807   mix 0.0096   time 27.67
scalar:  2.0414
Epoch 534:  train loss 0.7105   train acc 0.5066   worst 0.1187   lr 0.0192   p 49.48   eps 0.7807   mix 0.0096   time 27.45
Epoch 534:  test acc 0.5065   time 2.64
Calculating metrics for L_infinity dist model on training set
Epoch 534:  clean acc 0.3189   certified acc 0.0824
Calculating metrics for L_infinity dist model on test set
Epoch 534:  clean acc 0.3168   certified acc 0.0858
scalar:  2.0332
Epoch 535:  train loss 0.7096   train acc 0.5094   worst 0.1184   lr 0.0191   p 49.69   eps 0.7807   mix 0.0095   time 27.95
scalar:  2.0404
Epoch 536:  train loss 0.7113   train acc 0.5066   worst 0.1188   lr 0.0191   p 49.90   eps 0.7807   mix 0.0095   time 27.08
scalar:  2.0125
Epoch 537:  train loss 0.7109   train acc 0.5085   worst 0.1167   lr 0.0190   p 50.11   eps 0.7807   mix 0.0094   time 27.01
scalar:  2.0147
Epoch 538:  train loss 0.7108   train acc 0.5079   worst 0.1184   lr 0.0190   p 50.32   eps 0.7807   mix 0.0094   time 27.24
scalar:  2.0205
Epoch 539:  train loss 0.7104   train acc 0.5079   worst 0.1186   lr 0.0190   p 50.53   eps 0.7807   mix 0.0093   time 27.24
Epoch 539:  test acc 0.5001   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 539:  clean acc 0.3455   certified acc 0.0947
Calculating metrics for L_infinity dist model on test set
Epoch 539:  clean acc 0.3451   certified acc 0.0970
scalar:  2.0217
Epoch 540:  train loss 0.7121   train acc 0.5054   worst 0.1164   lr 0.0189   p 50.74   eps 0.7807   mix 0.0093   time 27.82
scalar:  2.0283
Epoch 541:  train loss 0.7113   train acc 0.5043   worst 0.1185   lr 0.0189   p 50.96   eps 0.7807   mix 0.0092   time 26.89
scalar:  2.0348
Epoch 542:  train loss 0.7117   train acc 0.5074   worst 0.1175   lr 0.0189   p 51.17   eps 0.7807   mix 0.0092   time 27.01
scalar:  2.0328
Epoch 543:  train loss 0.7136   train acc 0.5054   worst 0.1150   lr 0.0188   p 51.39   eps 0.7807   mix 0.0091   time 27.65
scalar:  2.0395
Epoch 544:  train loss 0.7110   train acc 0.5041   worst 0.1168   lr 0.0188   p 51.60   eps 0.7807   mix 0.0091   time 27.67
Epoch 544:  test acc 0.4981   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 544:  clean acc 0.3333   certified acc 0.0919
Calculating metrics for L_infinity dist model on test set
Epoch 544:  clean acc 0.3342   certified acc 0.0898
scalar:  2.0184
Epoch 545:  train loss 0.7112   train acc 0.5078   worst 0.1162   lr 0.0188   p 51.82   eps 0.7807   mix 0.0090   time 27.75
scalar:  2.0453
Epoch 546:  train loss 0.7116   train acc 0.5054   worst 0.1177   lr 0.0187   p 52.04   eps 0.7807   mix 0.0090   time 26.93
scalar:  2.0442
Epoch 547:  train loss 0.7116   train acc 0.5042   worst 0.1161   lr 0.0187   p 52.26   eps 0.7807   mix 0.0089   time 27.27
scalar:  2.0377
Epoch 548:  train loss 0.7122   train acc 0.5051   worst 0.1158   lr 0.0187   p 52.48   eps 0.7807   mix 0.0089   time 27.20
scalar:  2.0407
Epoch 549:  train loss 0.7133   train acc 0.5063   worst 0.1137   lr 0.0186   p 52.70   eps 0.7807   mix 0.0088   time 27.40
Epoch 549:  test acc 0.4977   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 549:  clean acc 0.3367   certified acc 0.1014
Calculating metrics for L_infinity dist model on test set
Epoch 549:  clean acc 0.3445   certified acc 0.0997
scalar:  2.0498
Epoch 550:  train loss 0.7137   train acc 0.5050   worst 0.1139   lr 0.0186   p 52.92   eps 0.7807   mix 0.0088   time 27.72
scalar:  2.0525
Epoch 551:  train loss 0.7123   train acc 0.5055   worst 0.1165   lr 0.0186   p 53.14   eps 0.7807   mix 0.0087   time 27.18
scalar:  2.0376
Epoch 552:  train loss 0.7131   train acc 0.5064   worst 0.1152   lr 0.0185   p 53.37   eps 0.7807   mix 0.0087   time 27.06
scalar:  2.0446
Epoch 553:  train loss 0.7133   train acc 0.5032   worst 0.1139   lr 0.0185   p 53.59   eps 0.7807   mix 0.0086   time 27.42
scalar:  2.0272
Epoch 554:  train loss 0.7132   train acc 0.5053   worst 0.1162   lr 0.0184   p 53.82   eps 0.7807   mix 0.0086   time 27.33
Epoch 554:  test acc 0.4981   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 554:  clean acc 0.3454   certified acc 0.0997
Calculating metrics for L_infinity dist model on test set
Epoch 554:  clean acc 0.3458   certified acc 0.1012
scalar:  2.0355
Epoch 555:  train loss 0.7142   train acc 0.5032   worst 0.1144   lr 0.0184   p 54.04   eps 0.7807   mix 0.0086   time 27.60
scalar:  2.0522
Epoch 556:  train loss 0.7132   train acc 0.5058   worst 0.1140   lr 0.0184   p 54.27   eps 0.7807   mix 0.0085   time 27.12
scalar:  2.0473
Epoch 557:  train loss 0.7147   train acc 0.5037   worst 0.1149   lr 0.0183   p 54.50   eps 0.7807   mix 0.0085   time 27.20
scalar:  2.0545
Epoch 558:  train loss 0.7131   train acc 0.5067   worst 0.1141   lr 0.0183   p 54.73   eps 0.7807   mix 0.0084   time 27.17
scalar:  2.0449
Epoch 559:  train loss 0.7142   train acc 0.5037   worst 0.1134   lr 0.0183   p 54.96   eps 0.7807   mix 0.0084   time 27.68
Epoch 559:  test acc 0.4965   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 559:  clean acc 0.3556   certified acc 0.1128
Calculating metrics for L_infinity dist model on test set
Epoch 559:  clean acc 0.3559   certified acc 0.1145
scalar:  2.0528
Epoch 560:  train loss 0.7145   train acc 0.5057   worst 0.1129   lr 0.0182   p 55.19   eps 0.7807   mix 0.0083   time 27.95
scalar:  2.043
Epoch 561:  train loss 0.7143   train acc 0.5064   worst 0.1108   lr 0.0182   p 55.42   eps 0.7807   mix 0.0083   time 27.04
scalar:  2.0726
Epoch 562:  train loss 0.7119   train acc 0.5062   worst 0.1160   lr 0.0182   p 55.65   eps 0.7807   mix 0.0082   time 27.10
scalar:  2.0226
Epoch 563:  train loss 0.7139   train acc 0.5054   worst 0.1125   lr 0.0181   p 55.89   eps 0.7807   mix 0.0082   time 27.16
scalar:  2.064
Epoch 564:  train loss 0.7146   train acc 0.5047   worst 0.1116   lr 0.0181   p 56.12   eps 0.7807   mix 0.0081   time 27.44
Epoch 564:  test acc 0.4983   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 564:  clean acc 0.3719   certified acc 0.1187
Calculating metrics for L_infinity dist model on test set
Epoch 564:  clean acc 0.3761   certified acc 0.1194
scalar:  2.0299
Epoch 565:  train loss 0.7153   train acc 0.5031   worst 0.1105   lr 0.0181   p 56.36   eps 0.7807   mix 0.0081   time 27.96
scalar:  2.0649
Epoch 566:  train loss 0.7139   train acc 0.5050   worst 0.1121   lr 0.0180   p 56.60   eps 0.7807   mix 0.0081   time 27.04
scalar:  2.0356
Epoch 567:  train loss 0.7152   train acc 0.5030   worst 0.1119   lr 0.0180   p 56.84   eps 0.7807   mix 0.0080   time 27.13
scalar:  2.0693
Epoch 568:  train loss 0.7146   train acc 0.5061   worst 0.1110   lr 0.0180   p 57.07   eps 0.7807   mix 0.0080   time 27.48
scalar:  2.0654
Epoch 569:  train loss 0.7177   train acc 0.5032   worst 0.1100   lr 0.0179   p 57.31   eps 0.7807   mix 0.0079   time 27.89
Epoch 569:  test acc 0.5035   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 569:  clean acc 0.3636   certified acc 0.1181
Calculating metrics for L_infinity dist model on test set
Epoch 569:  clean acc 0.3640   certified acc 0.1199
scalar:  2.0788
Epoch 570:  train loss 0.7166   train acc 0.5023   worst 0.1101   lr 0.0179   p 57.56   eps 0.7807   mix 0.0079   time 27.37
scalar:  2.0644
Epoch 571:  train loss 0.7155   train acc 0.5052   worst 0.1103   lr 0.0178   p 57.80   eps 0.7807   mix 0.0078   time 27.26
scalar:  2.08
Epoch 572:  train loss 0.7163   train acc 0.5032   worst 0.1089   lr 0.0178   p 58.04   eps 0.7807   mix 0.0078   time 26.84
scalar:  2.0613
Epoch 573:  train loss 0.7143   train acc 0.5064   worst 0.1089   lr 0.0178   p 58.29   eps 0.7807   mix 0.0078   time 27.11
scalar:  2.0646
Epoch 574:  train loss 0.7157   train acc 0.5012   worst 0.1110   lr 0.0177   p 58.53   eps 0.7807   mix 0.0077   time 27.52
Epoch 574:  test acc 0.4952   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 574:  clean acc 0.3755   certified acc 0.1190
Calculating metrics for L_infinity dist model on test set
Epoch 574:  clean acc 0.3744   certified acc 0.1173
scalar:  2.0624
Epoch 575:  train loss 0.7148   train acc 0.5043   worst 0.1111   lr 0.0177   p 58.78   eps 0.7807   mix 0.0077   time 27.33
scalar:  2.0833
Epoch 576:  train loss 0.7170   train acc 0.5032   worst 0.1074   lr 0.0177   p 59.02   eps 0.7807   mix 0.0076   time 27.73
scalar:  2.0997
Epoch 577:  train loss 0.7151   train acc 0.5035   worst 0.1104   lr 0.0176   p 59.27   eps 0.7807   mix 0.0076   time 27.11
scalar:  2.0501
Epoch 578:  train loss 0.7169   train acc 0.5024   worst 0.1083   lr 0.0176   p 59.52   eps 0.7807   mix 0.0076   time 27.30
scalar:  2.0728
Epoch 579:  train loss 0.7157   train acc 0.5007   worst 0.1107   lr 0.0176   p 59.77   eps 0.7807   mix 0.0075   time 27.83
Epoch 579:  test acc 0.4950   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 579:  clean acc 0.3809   certified acc 0.1212
Calculating metrics for L_infinity dist model on test set
Epoch 579:  clean acc 0.3804   certified acc 0.1212
scalar:  2.0575
Epoch 580:  train loss 0.7167   train acc 0.5027   worst 0.1084   lr 0.0175   p 60.02   eps 0.7807   mix 0.0075   time 27.27
scalar:  2.1032
Epoch 581:  train loss 0.7160   train acc 0.5029   worst 0.1091   lr 0.0175   p 60.28   eps 0.7807   mix 0.0074   time 27.68
scalar:  2.0957
Epoch 582:  train loss 0.7186   train acc 0.4989   worst 0.1069   lr 0.0175   p 60.53   eps 0.7807   mix 0.0074   time 27.07
scalar:  2.0342
Epoch 583:  train loss 0.7180   train acc 0.5019   worst 0.1068   lr 0.0174   p 60.78   eps 0.7807   mix 0.0074   time 27.36
scalar:  2.0794
Epoch 584:  train loss 0.7170   train acc 0.5039   worst 0.1065   lr 0.0174   p 61.04   eps 0.7807   mix 0.0073   time 27.51
Epoch 584:  test acc 0.4937   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 584:  clean acc 0.3786   certified acc 0.1236
Calculating metrics for L_infinity dist model on test set
Epoch 584:  clean acc 0.3780   certified acc 0.1260
scalar:  2.0804
Epoch 585:  train loss 0.7169   train acc 0.5033   worst 0.1077   lr 0.0173   p 61.30   eps 0.7807   mix 0.0073   time 26.89
scalar:  2.0886
Epoch 586:  train loss 0.7167   train acc 0.5013   worst 0.1070   lr 0.0173   p 61.55   eps 0.7807   mix 0.0072   time 27.92
scalar:  2.0607
Epoch 587:  train loss 0.7165   train acc 0.5024   worst 0.1089   lr 0.0173   p 61.81   eps 0.7807   mix 0.0072   time 27.19
scalar:  2.0767
Epoch 588:  train loss 0.7177   train acc 0.5012   worst 0.1083   lr 0.0172   p 62.07   eps 0.7807   mix 0.0072   time 26.95
scalar:  2.0589
Epoch 589:  train loss 0.7178   train acc 0.4998   worst 0.1070   lr 0.0172   p 62.34   eps 0.7807   mix 0.0071   time 27.72
Epoch 589:  test acc 0.4956   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 589:  clean acc 0.3809   certified acc 0.1290
Calculating metrics for L_infinity dist model on test set
Epoch 589:  clean acc 0.3857   certified acc 0.1281
scalar:  2.0856
Epoch 590:  train loss 0.7177   train acc 0.5003   worst 0.1063   lr 0.0172   p 62.60   eps 0.7807   mix 0.0071   time 26.92
scalar:  2.0815
Epoch 591:  train loss 0.7181   train acc 0.4992   worst 0.1064   lr 0.0171   p 62.86   eps 0.7807   mix 0.0070   time 27.79
scalar:  2.0851
Epoch 592:  train loss 0.7190   train acc 0.5025   worst 0.1051   lr 0.0171   p 63.13   eps 0.7807   mix 0.0070   time 26.98
scalar:  2.1127
Epoch 593:  train loss 0.7174   train acc 0.5017   worst 0.1063   lr 0.0171   p 63.39   eps 0.7807   mix 0.0070   time 27.24
scalar:  2.1056
Epoch 594:  train loss 0.7187   train acc 0.5032   worst 0.1051   lr 0.0170   p 63.66   eps 0.7807   mix 0.0069   time 27.81
Epoch 594:  test acc 0.4932   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 594:  clean acc 0.3969   certified acc 0.1359
Calculating metrics for L_infinity dist model on test set
Epoch 594:  clean acc 0.3974   certified acc 0.1359
scalar:  2.1138
Epoch 595:  train loss 0.7183   train acc 0.5021   worst 0.1048   lr 0.0170   p 63.93   eps 0.7807   mix 0.0069   time 27.25
scalar:  2.0833
Epoch 596:  train loss 0.7185   train acc 0.4995   worst 0.1069   lr 0.0170   p 64.19   eps 0.7807   mix 0.0069   time 27.81
scalar:  2.0567
Epoch 597:  train loss 0.7198   train acc 0.5010   worst 0.1063   lr 0.0169   p 64.46   eps 0.7807   mix 0.0068   time 27.04
scalar:  2.0514
Epoch 598:  train loss 0.7172   train acc 0.5045   worst 0.1040   lr 0.0169   p 64.74   eps 0.7807   mix 0.0068   time 26.86
scalar:  2.105
Epoch 599:  train loss 0.7194   train acc 0.4993   worst 0.1053   lr 0.0168   p 65.01   eps 0.7807   mix 0.0067   time 27.79
Epoch 599:  test acc 0.4877   time 2.61
Calculating metrics for L_infinity dist model on training set
Epoch 599:  clean acc 0.4012   certified acc 0.1408
Calculating metrics for L_infinity dist model on test set
Epoch 599:  clean acc 0.3999   certified acc 0.1424
scalar:  2.0779
Epoch 600:  train loss 0.7206   train acc 0.5005   worst 0.1031   lr 0.0168   p 65.28   eps 0.7807   mix 0.0067   time 26.90
scalar:  2.0958
Epoch 601:  train loss 0.7192   train acc 0.5025   worst 0.1034   lr 0.0168   p 65.56   eps 0.7807   mix 0.0067   time 27.98
scalar:  2.103
Epoch 602:  train loss 0.7200   train acc 0.5034   worst 0.1013   lr 0.0167   p 65.83   eps 0.7807   mix 0.0066   time 26.96
scalar:  2.1253
Epoch 603:  train loss 0.7198   train acc 0.5007   worst 0.1035   lr 0.0167   p 66.11   eps 0.7807   mix 0.0066   time 27.13
scalar:  2.0952
Epoch 604:  train loss 0.7181   train acc 0.5039   worst 0.1030   lr 0.0167   p 66.39   eps 0.7807   mix 0.0066   time 27.73
Epoch 604:  test acc 0.4930   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 604:  clean acc 0.4030   certified acc 0.1383
Calculating metrics for L_infinity dist model on test set
Epoch 604:  clean acc 0.4024   certified acc 0.1401
scalar:  2.0948
Epoch 605:  train loss 0.7189   train acc 0.5014   worst 0.1037   lr 0.0166   p 66.67   eps 0.7807   mix 0.0065   time 27.02
scalar:  2.1118
Epoch 606:  train loss 0.7190   train acc 0.4999   worst 0.1027   lr 0.0166   p 66.95   eps 0.7807   mix 0.0065   time 27.70
scalar:  2.0848
Epoch 607:  train loss 0.7202   train acc 0.4965   worst 0.1033   lr 0.0166   p 67.23   eps 0.7807   mix 0.0065   time 27.18
scalar:  2.0641
Epoch 608:  train loss 0.7200   train acc 0.4998   worst 0.1042   lr 0.0165   p 67.51   eps 0.7807   mix 0.0064   time 27.21
scalar:  2.0932
Epoch 609:  train loss 0.7205   train acc 0.4974   worst 0.1031   lr 0.0165   p 67.80   eps 0.7807   mix 0.0064   time 27.35
Epoch 609:  test acc 0.4899   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 609:  clean acc 0.4138   certified acc 0.1533
Calculating metrics for L_infinity dist model on test set
Epoch 609:  clean acc 0.4146   certified acc 0.1496
scalar:  2.0771
Epoch 610:  train loss 0.7212   train acc 0.5001   worst 0.1023   lr 0.0164   p 68.08   eps 0.7807   mix 0.0064   time 27.20
scalar:  2.0955
Epoch 611:  train loss 0.7216   train acc 0.4971   worst 0.1040   lr 0.0164   p 68.37   eps 0.7807   mix 0.0063   time 27.62
scalar:  2.0881
Epoch 612:  train loss 0.7200   train acc 0.4994   worst 0.1019   lr 0.0164   p 68.65   eps 0.7807   mix 0.0063   time 26.96
scalar:  2.0947
Epoch 613:  train loss 0.7201   train acc 0.4976   worst 0.1033   lr 0.0163   p 68.94   eps 0.7807   mix 0.0063   time 27.15
scalar:  2.0828
Epoch 614:  train loss 0.7213   train acc 0.4979   worst 0.1026   lr 0.0163   p 69.23   eps 0.7807   mix 0.0062   time 27.01
Epoch 614:  test acc 0.4924   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 614:  clean acc 0.4189   certified acc 0.1502
Calculating metrics for L_infinity dist model on test set
Epoch 614:  clean acc 0.4154   certified acc 0.1470
scalar:  2.0668
Epoch 615:  train loss 0.7207   train acc 0.4981   worst 0.1030   lr 0.0163   p 69.52   eps 0.7807   mix 0.0062   time 27.26
scalar:  2.0739
Epoch 616:  train loss 0.7215   train acc 0.4989   worst 0.1011   lr 0.0162   p 69.82   eps 0.7807   mix 0.0062   time 27.48
scalar:  2.0755
Epoch 617:  train loss 0.7208   train acc 0.4984   worst 0.1023   lr 0.0162   p 70.11   eps 0.7807   mix 0.0061   time 27.21
scalar:  2.0962
Epoch 618:  train loss 0.7216   train acc 0.4961   worst 0.1023   lr 0.0162   p 70.41   eps 0.7807   mix 0.0061   time 27.25
scalar:  2.0818
Epoch 619:  train loss 0.7206   train acc 0.5002   worst 0.1008   lr 0.0161   p 70.70   eps 0.7807   mix 0.0061   time 27.11
Epoch 619:  test acc 0.4974   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 619:  clean acc 0.4205   certified acc 0.1573
Calculating metrics for L_infinity dist model on test set
Epoch 619:  clean acc 0.4211   certified acc 0.1526
scalar:  2.1028
Epoch 620:  train loss 0.7204   train acc 0.4975   worst 0.1016   lr 0.0161   p 71.00   eps 0.7807   mix 0.0060   time 27.14
scalar:  2.0908
Epoch 621:  train loss 0.7217   train acc 0.5008   worst 0.1017   lr 0.0161   p 71.30   eps 0.7807   mix 0.0060   time 27.36
scalar:  2.1291
Epoch 622:  train loss 0.7215   train acc 0.5012   worst 0.1004   lr 0.0160   p 71.60   eps 0.7807   mix 0.0060   time 27.67
scalar:  2.1111
Epoch 623:  train loss 0.7219   train acc 0.4984   worst 0.0997   lr 0.0160   p 71.90   eps 0.7807   mix 0.0059   time 27.05
scalar:  2.1017
Epoch 624:  train loss 0.7207   train acc 0.5009   worst 0.1006   lr 0.0159   p 72.20   eps 0.7807   mix 0.0059   time 27.16
Epoch 624:  test acc 0.4891   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 624:  clean acc 0.4220   certified acc 0.1556
Calculating metrics for L_infinity dist model on test set
Epoch 624:  clean acc 0.4148   certified acc 0.1531
scalar:  2.117
Epoch 625:  train loss 0.7208   train acc 0.4985   worst 0.1012   lr 0.0159   p 72.51   eps 0.7807   mix 0.0059   time 27.18
scalar:  2.1115
Epoch 626:  train loss 0.7225   train acc 0.4965   worst 0.0999   lr 0.0159   p 72.81   eps 0.7807   mix 0.0058   time 27.48
scalar:  2.0912
Epoch 627:  train loss 0.7218   train acc 0.4992   worst 0.1011   lr 0.0158   p 73.12   eps 0.7807   mix 0.0058   time 27.59
scalar:  2.103
Epoch 628:  train loss 0.7209   train acc 0.4984   worst 0.0999   lr 0.0158   p 73.43   eps 0.7807   mix 0.0058   time 27.09
scalar:  2.1146
Epoch 629:  train loss 0.7228   train acc 0.4984   worst 0.0985   lr 0.0158   p 73.73   eps 0.7807   mix 0.0057   time 26.85
Epoch 629:  test acc 0.4899   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 629:  clean acc 0.4200   certified acc 0.1607
Calculating metrics for L_infinity dist model on test set
Epoch 629:  clean acc 0.4175   certified acc 0.1551
scalar:  2.1034
Epoch 630:  train loss 0.7225   train acc 0.4975   worst 0.1013   lr 0.0157   p 74.04   eps 0.7807   mix 0.0057   time 27.50
scalar:  2.1182
Epoch 631:  train loss 0.7219   train acc 0.4985   worst 0.0989   lr 0.0157   p 74.36   eps 0.7807   mix 0.0057   time 27.11
scalar:  2.1116
Epoch 632:  train loss 0.7224   train acc 0.4996   worst 0.0981   lr 0.0157   p 74.67   eps 0.7807   mix 0.0056   time 27.30
scalar:  2.1267
Epoch 633:  train loss 0.7218   train acc 0.4972   worst 0.0994   lr 0.0156   p 74.98   eps 0.7807   mix 0.0056   time 27.26
scalar:  2.1323
Epoch 634:  train loss 0.7232   train acc 0.4996   worst 0.0965   lr 0.0156   p 75.30   eps 0.7807   mix 0.0056   time 26.95
Epoch 634:  test acc 0.4879   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 634:  clean acc 0.4298   certified acc 0.1732
Calculating metrics for L_infinity dist model on test set
Epoch 634:  clean acc 0.4273   certified acc 0.1705
scalar:  2.1148
Epoch 635:  train loss 0.7224   train acc 0.4992   worst 0.0981   lr 0.0155   p 75.62   eps 0.7807   mix 0.0056   time 27.42
scalar:  2.1147
Epoch 636:  train loss 0.7222   train acc 0.4987   worst 0.0998   lr 0.0155   p 75.93   eps 0.7807   mix 0.0055   time 27.04
scalar:  2.097
Epoch 637:  train loss 0.7214   train acc 0.4990   worst 0.0996   lr 0.0155   p 76.25   eps 0.7807   mix 0.0055   time 27.44
scalar:  2.117
Epoch 638:  train loss 0.7229   train acc 0.4974   worst 0.0983   lr 0.0154   p 76.57   eps 0.7807   mix 0.0055   time 27.57
scalar:  2.1013
Epoch 639:  train loss 0.7210   train acc 0.4994   worst 0.0979   lr 0.0154   p 76.90   eps 0.7807   mix 0.0054   time 27.33
Epoch 639:  test acc 0.4830   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 639:  clean acc 0.4338   certified acc 0.1704
Calculating metrics for L_infinity dist model on test set
Epoch 639:  clean acc 0.4307   certified acc 0.1689
scalar:  2.1261
Epoch 640:  train loss 0.7232   train acc 0.4957   worst 0.0988   lr 0.0154   p 77.22   eps 0.7807   mix 0.0054   time 27.38
scalar:  2.1267
Epoch 641:  train loss 0.7240   train acc 0.4975   worst 0.0970   lr 0.0153   p 77.54   eps 0.7807   mix 0.0054   time 27.44
scalar:  2.1411
Epoch 642:  train loss 0.7226   train acc 0.4999   worst 0.0983   lr 0.0153   p 77.87   eps 0.7807   mix 0.0053   time 27.37
scalar:  2.1372
Epoch 643:  train loss 0.7240   train acc 0.4959   worst 0.0972   lr 0.0153   p 78.20   eps 0.7807   mix 0.0053   time 26.96
scalar:  2.1099
Epoch 644:  train loss 0.7233   train acc 0.4988   worst 0.0961   lr 0.0152   p 78.53   eps 0.7807   mix 0.0053   time 27.28
Epoch 644:  test acc 0.4839   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 644:  clean acc 0.4503   certified acc 0.1787
Calculating metrics for L_infinity dist model on test set
Epoch 644:  clean acc 0.4476   certified acc 0.1759
scalar:  2.1456
Epoch 645:  train loss 0.7240   train acc 0.4974   worst 0.0969   lr 0.0152   p 78.86   eps 0.7807   mix 0.0053   time 27.48
scalar:  2.1049
Epoch 646:  train loss 0.7236   train acc 0.4980   worst 0.0966   lr 0.0151   p 79.19   eps 0.7807   mix 0.0052   time 27.25
scalar:  2.136
Epoch 647:  train loss 0.7247   train acc 0.4936   worst 0.0964   lr 0.0151   p 79.52   eps 0.7807   mix 0.0052   time 27.10
scalar:  2.0929
Epoch 648:  train loss 0.7233   train acc 0.4982   worst 0.0959   lr 0.0151   p 79.86   eps 0.7807   mix 0.0052   time 27.52
scalar:  2.1107
Epoch 649:  train loss 0.7239   train acc 0.4989   worst 0.0962   lr 0.0150   p 80.19   eps 0.7807   mix 0.0051   time 27.01
Epoch 649:  test acc 0.4881   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 649:  clean acc 0.4554   certified acc 0.1850
Calculating metrics for L_infinity dist model on test set
Epoch 649:  clean acc 0.4565   certified acc 0.1786
scalar:  2.1311
Epoch 650:  train loss 0.7216   train acc 0.4980   worst 0.0980   lr 0.0150   p 80.53   eps 0.7807   mix 0.0051   time 27.45
scalar:  2.1273
Epoch 651:  train loss 0.7226   train acc 0.4968   worst 0.0976   lr 0.0150   p 80.87   eps 0.7807   mix 0.0051   time 27.25
scalar:  2.1085
Epoch 652:  train loss 0.7234   train acc 0.4976   worst 0.0970   lr 0.0149   p 81.21   eps 0.7807   mix 0.0051   time 26.92
scalar:  2.1234
Epoch 653:  train loss 0.7247   train acc 0.4963   worst 0.0965   lr 0.0149   p 81.55   eps 0.7807   mix 0.0050   time 27.56
scalar:  2.1232
Epoch 654:  train loss 0.7252   train acc 0.4957   worst 0.0948   lr 0.0149   p 81.89   eps 0.7807   mix 0.0050   time 27.10
Epoch 654:  test acc 0.4854   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 654:  clean acc 0.4548   certified acc 0.1867
Calculating metrics for L_infinity dist model on test set
Epoch 654:  clean acc 0.4539   certified acc 0.1848
scalar:  2.1105
Epoch 655:  train loss 0.7244   train acc 0.4973   worst 0.0964   lr 0.0148   p 82.24   eps 0.7807   mix 0.0050   time 27.21
scalar:  2.1156
Epoch 656:  train loss 0.7253   train acc 0.4973   worst 0.0955   lr 0.0148   p 82.58   eps 0.7807   mix 0.0050   time 27.09
scalar:  2.1235
Epoch 657:  train loss 0.7239   train acc 0.4947   worst 0.0959   lr 0.0147   p 82.93   eps 0.7807   mix 0.0049   time 26.93
scalar:  2.1071
Epoch 658:  train loss 0.7242   train acc 0.4990   worst 0.0945   lr 0.0147   p 83.28   eps 0.7807   mix 0.0049   time 27.33
scalar:  2.1422
Epoch 659:  train loss 0.7256   train acc 0.4962   worst 0.0944   lr 0.0147   p 83.63   eps 0.7807   mix 0.0049   time 27.18
Epoch 659:  test acc 0.4899   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 659:  clean acc 0.4676   certified acc 0.1919
Calculating metrics for L_infinity dist model on test set
Epoch 659:  clean acc 0.4613   certified acc 0.1889
scalar:  2.1173
Epoch 660:  train loss 0.7240   train acc 0.4990   worst 0.0945   lr 0.0146   p 83.98   eps 0.7807   mix 0.0048   time 27.61
scalar:  2.1388
Epoch 661:  train loss 0.7257   train acc 0.4975   worst 0.0943   lr 0.0146   p 84.34   eps 0.7807   mix 0.0048   time 27.03
scalar:  2.1343
Epoch 662:  train loss 0.7250   train acc 0.4971   worst 0.0934   lr 0.0146   p 84.69   eps 0.7807   mix 0.0048   time 26.69
scalar:  2.1233
Epoch 663:  train loss 0.7255   train acc 0.4941   worst 0.0953   lr 0.0145   p 85.05   eps 0.7807   mix 0.0048   time 27.05
scalar:  2.1187
Epoch 664:  train loss 0.7232   train acc 0.4956   worst 0.0962   lr 0.0145   p 85.41   eps 0.7807   mix 0.0047   time 27.17
Epoch 664:  test acc 0.4898   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 664:  clean acc 0.4744   certified acc 0.1995
Calculating metrics for L_infinity dist model on test set
Epoch 664:  clean acc 0.4692   certified acc 0.1950
scalar:  2.121
Epoch 665:  train loss 0.7262   train acc 0.4962   worst 0.0944   lr 0.0145   p 85.77   eps 0.7807   mix 0.0047   time 27.41
scalar:  2.1503
Epoch 666:  train loss 0.7264   train acc 0.4926   worst 0.0942   lr 0.0144   p 86.13   eps 0.7807   mix 0.0047   time 26.86
scalar:  2.1207
Epoch 667:  train loss 0.7261   train acc 0.4936   worst 0.0951   lr 0.0144   p 86.49   eps 0.7807   mix 0.0047   time 26.88
scalar:  2.1045
Epoch 668:  train loss 0.7267   train acc 0.4938   worst 0.0947   lr 0.0143   p 86.85   eps 0.7807   mix 0.0046   time 27.69
scalar:  2.1173
Epoch 669:  train loss 0.7240   train acc 0.4951   worst 0.0958   lr 0.0143   p 87.22   eps 0.7807   mix 0.0046   time 27.37
Epoch 669:  test acc 0.4866   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 669:  clean acc 0.4733   certified acc 0.2093
Calculating metrics for L_infinity dist model on test set
Epoch 669:  clean acc 0.4683   certified acc 0.2038
scalar:  2.1387
Epoch 670:  train loss 0.7251   train acc 0.4966   worst 0.0939   lr 0.0143   p 87.58   eps 0.7807   mix 0.0046   time 27.75
scalar:  2.112
Epoch 671:  train loss 0.7243   train acc 0.4956   worst 0.0951   lr 0.0142   p 87.95   eps 0.7807   mix 0.0046   time 26.99
scalar:  2.1335
Epoch 672:  train loss 0.7256   train acc 0.4965   worst 0.0939   lr 0.0142   p 88.32   eps 0.7807   mix 0.0045   time 26.89
scalar:  2.132
Epoch 673:  train loss 0.7237   train acc 0.4966   worst 0.0944   lr 0.0142   p 88.69   eps 0.7807   mix 0.0045   time 27.07
scalar:  2.1392
Epoch 674:  train loss 0.7254   train acc 0.4922   worst 0.0946   lr 0.0141   p 89.07   eps 0.7807   mix 0.0045   time 27.54
Epoch 674:  test acc 0.4891   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 674:  clean acc 0.4591   certified acc 0.2027
Calculating metrics for L_infinity dist model on test set
Epoch 674:  clean acc 0.4542   certified acc 0.1943
scalar:  2.1196
Epoch 675:  train loss 0.7261   train acc 0.4934   worst 0.0943   lr 0.0141   p 89.44   eps 0.7807   mix 0.0045   time 27.42
scalar:  2.1364
Epoch 676:  train loss 0.7259   train acc 0.4959   worst 0.0930   lr 0.0141   p 89.82   eps 0.7807   mix 0.0044   time 27.03
scalar:  2.1338
Epoch 677:  train loss 0.7273   train acc 0.4955   worst 0.0929   lr 0.0140   p 90.20   eps 0.7807   mix 0.0044   time 26.68
scalar:  2.1355
Epoch 678:  train loss 0.7250   train acc 0.4974   worst 0.0932   lr 0.0140   p 90.58   eps 0.7807   mix 0.0044   time 26.94
scalar:  2.1514
Epoch 679:  train loss 0.7266   train acc 0.4939   worst 0.0927   lr 0.0139   p 90.96   eps 0.7807   mix 0.0044   time 27.49
Epoch 679:  test acc 0.4910   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 679:  clean acc 0.4702   certified acc 0.2020
Calculating metrics for L_infinity dist model on test set
Epoch 679:  clean acc 0.4636   certified acc 0.1925
scalar:  2.1416
Epoch 680:  train loss 0.7278   train acc 0.4942   worst 0.0921   lr 0.0139   p 91.34   eps 0.7807   mix 0.0044   time 27.41
scalar:  2.1377
Epoch 681:  train loss 0.7254   train acc 0.4954   worst 0.0933   lr 0.0139   p 91.72   eps 0.7807   mix 0.0043   time 27.33
scalar:  2.1493
Epoch 682:  train loss 0.7264   train acc 0.4974   worst 0.0918   lr 0.0138   p 92.11   eps 0.7807   mix 0.0043   time 26.76
scalar:  2.1435
Epoch 683:  train loss 0.7266   train acc 0.4944   worst 0.0934   lr 0.0138   p 92.50   eps 0.7807   mix 0.0043   time 26.96
scalar:  2.1472
Epoch 684:  train loss 0.7263   train acc 0.4959   worst 0.0933   lr 0.0138   p 92.89   eps 0.7807   mix 0.0043   time 27.36
Epoch 684:  test acc 0.4848   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 684:  clean acc 0.4636   certified acc 0.2012
Calculating metrics for L_infinity dist model on test set
Epoch 684:  clean acc 0.4568   certified acc 0.1917
scalar:  2.1475
Epoch 685:  train loss 0.7251   train acc 0.4960   worst 0.0915   lr 0.0137   p 93.28   eps 0.7807   mix 0.0042   time 27.04
scalar:  2.1306
Epoch 686:  train loss 0.7283   train acc 0.4946   worst 0.0905   lr 0.0137   p 93.67   eps 0.7807   mix 0.0042   time 27.22
scalar:  2.1332
Epoch 687:  train loss 0.7270   train acc 0.4946   worst 0.0919   lr 0.0137   p 94.06   eps 0.7807   mix 0.0042   time 27.02
scalar:  2.1457
Epoch 688:  train loss 0.7266   train acc 0.4970   worst 0.0904   lr 0.0136   p 94.46   eps 0.7807   mix 0.0042   time 26.51
scalar:  2.1498
Epoch 689:  train loss 0.7277   train acc 0.4946   worst 0.0919   lr 0.0136   p 94.86   eps 0.7807   mix 0.0041   time 27.29
Epoch 689:  test acc 0.4855   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 689:  clean acc 0.4760   certified acc 0.2100
Calculating metrics for L_infinity dist model on test set
Epoch 689:  clean acc 0.4726   certified acc 0.2042
scalar:  2.1404
Epoch 690:  train loss 0.7273   train acc 0.4957   worst 0.0902   lr 0.0136   p 95.26   eps 0.7807   mix 0.0041   time 27.03
scalar:  2.1377
Epoch 691:  train loss 0.7257   train acc 0.4966   worst 0.0920   lr 0.0135   p 95.66   eps 0.7807   mix 0.0041   time 27.30
scalar:  2.1439
Epoch 692:  train loss 0.7271   train acc 0.4952   worst 0.0906   lr 0.0135   p 96.06   eps 0.7807   mix 0.0041   time 26.99
scalar:  2.1361
Epoch 693:  train loss 0.7266   train acc 0.4955   worst 0.0892   lr 0.0134   p 96.46   eps 0.7807   mix 0.0041   time 26.72
scalar:  2.148
Epoch 694:  train loss 0.7262   train acc 0.4966   worst 0.0917   lr 0.0134   p 96.87   eps 0.7807   mix 0.0040   time 27.75
Epoch 694:  test acc 0.4836   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 694:  clean acc 0.4798   certified acc 0.2162
Calculating metrics for L_infinity dist model on test set
Epoch 694:  clean acc 0.4770   certified acc 0.2149
scalar:  2.1552
Epoch 695:  train loss 0.7268   train acc 0.4943   worst 0.0897   lr 0.0134   p 97.28   eps 0.7807   mix 0.0040   time 27.49
scalar:  2.1491
Epoch 696:  train loss 0.7278   train acc 0.4957   worst 0.0913   lr 0.0133   p 97.69   eps 0.7807   mix 0.0040   time 27.25
scalar:  2.1538
Epoch 697:  train loss 0.7278   train acc 0.4963   worst 0.0906   lr 0.0133   p 98.10   eps 0.7807   mix 0.0040   time 26.85
scalar:  2.1436
Epoch 698:  train loss 0.7271   train acc 0.4973   worst 0.0899   lr 0.0133   p 98.51   eps 0.7807   mix 0.0039   time 26.60
scalar:  2.1574
Epoch 699:  train loss 0.7272   train acc 0.4913   worst 0.0903   lr 0.0132   p 98.93   eps 0.7807   mix 0.0039   time 27.45
Epoch 699:  test acc 0.4852   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 699:  clean acc 0.4779   certified acc 0.2201
Calculating metrics for L_infinity dist model on test set
Epoch 699:  clean acc 0.4752   certified acc 0.2145
scalar:  2.1364
Epoch 700:  train loss 0.7272   train acc 0.4923   worst 0.0919   lr 0.0132   p 99.34   eps 0.7807   mix 0.0039   time 27.15
scalar:  2.1269
Epoch 701:  train loss 0.7275   train acc 0.4953   worst 0.0900   lr 0.0132   p 99.76   eps 0.7807   mix 0.0039   time 27.08
scalar:  2.1563
Epoch 702:  train loss 0.7277   train acc 0.4944   worst 0.0908   lr 0.0131   p 100.18   eps 0.7807   mix 0.0039   time 27.03
scalar:  2.1569
Epoch 703:  train loss 0.7282   train acc 0.4953   worst 0.0895   lr 0.0131   p 100.60   eps 0.7807   mix 0.0038   time 26.84
scalar:  2.1618
Epoch 704:  train loss 0.7276   train acc 0.4948   worst 0.0905   lr 0.0130   p 101.02   eps 0.7807   mix 0.0038   time 27.55
Epoch 704:  test acc 0.4826   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 704:  clean acc 0.4797   certified acc 0.2227
Calculating metrics for L_infinity dist model on test set
Epoch 704:  clean acc 0.4723   certified acc 0.2134
scalar:  2.1492
Epoch 705:  train loss 0.7266   train acc 0.4960   worst 0.0896   lr 0.0130   p 101.45   eps 0.7807   mix 0.0038   time 27.26
scalar:  2.1404
Epoch 706:  train loss 0.7288   train acc 0.4939   worst 0.0900   lr 0.0130   p 101.88   eps 0.7807   mix 0.0038   time 26.94
scalar:  2.16
Epoch 707:  train loss 0.7274   train acc 0.4955   worst 0.0901   lr 0.0129   p 102.30   eps 0.7807   mix 0.0038   time 27.25
scalar:  2.1552
Epoch 708:  train loss 0.7295   train acc 0.4920   worst 0.0887   lr 0.0129   p 102.73   eps 0.7807   mix 0.0037   time 26.76
scalar:  2.1484
Epoch 709:  train loss 0.7282   train acc 0.4913   worst 0.0894   lr 0.0129   p 103.17   eps 0.7807   mix 0.0037   time 27.52
Epoch 709:  test acc 0.4829   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 709:  clean acc 0.4793   certified acc 0.2320
Calculating metrics for L_infinity dist model on test set
Epoch 709:  clean acc 0.4759   certified acc 0.2254
scalar:  2.1318
Epoch 710:  train loss 0.7287   train acc 0.4917   worst 0.0895   lr 0.0128   p 103.60   eps 0.7807   mix 0.0037   time 27.24
scalar:  2.1383
Epoch 711:  train loss 0.7283   train acc 0.4924   worst 0.0893   lr 0.0128   p 104.04   eps 0.7807   mix 0.0037   time 27.23
scalar:  2.1452
Epoch 712:  train loss 0.7291   train acc 0.4907   worst 0.0900   lr 0.0128   p 104.47   eps 0.7807   mix 0.0037   time 27.11
scalar:  2.1456
Epoch 713:  train loss 0.7280   train acc 0.4916   worst 0.0895   lr 0.0127   p 104.91   eps 0.7807   mix 0.0036   time 27.02
scalar:  2.1367
Epoch 714:  train loss 0.7275   train acc 0.4942   worst 0.0905   lr 0.0127   p 105.36   eps 0.7807   mix 0.0036   time 27.09
Epoch 714:  test acc 0.4816   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 714:  clean acc 0.4855   certified acc 0.2260
Calculating metrics for L_infinity dist model on test set
Epoch 714:  clean acc 0.4800   certified acc 0.2224
scalar:  2.1511
Epoch 715:  train loss 0.7286   train acc 0.4948   worst 0.0882   lr 0.0127   p 105.80   eps 0.7807   mix 0.0036   time 27.11
scalar:  2.1632
Epoch 716:  train loss 0.7283   train acc 0.4924   worst 0.0903   lr 0.0126   p 106.24   eps 0.7807   mix 0.0036   time 26.98
scalar:  2.1422
Epoch 717:  train loss 0.7289   train acc 0.4927   worst 0.0894   lr 0.0126   p 106.69   eps 0.7807   mix 0.0036   time 26.94
scalar:  2.152
Epoch 718:  train loss 0.7287   train acc 0.4943   worst 0.0894   lr 0.0125   p 107.14   eps 0.7807   mix 0.0035   time 26.95
scalar:  2.148
Epoch 719:  train loss 0.7297   train acc 0.4919   worst 0.0884   lr 0.0125   p 107.59   eps 0.7807   mix 0.0035   time 27.27
Epoch 719:  test acc 0.4854   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 719:  clean acc 0.4801   certified acc 0.2222
Calculating metrics for L_infinity dist model on test set
Epoch 719:  clean acc 0.4745   certified acc 0.2161
scalar:  2.1539
Epoch 720:  train loss 0.7282   train acc 0.4938   worst 0.0888   lr 0.0125   p 108.04   eps 0.7807   mix 0.0035   time 27.28
scalar:  2.1462
Epoch 721:  train loss 0.7293   train acc 0.4907   worst 0.0885   lr 0.0124   p 108.50   eps 0.7807   mix 0.0035   time 27.05
scalar:  2.1475
Epoch 722:  train loss 0.7285   train acc 0.4935   worst 0.0888   lr 0.0124   p 108.95   eps 0.7807   mix 0.0035   time 27.08
scalar:  2.1599
Epoch 723:  train loss 0.7286   train acc 0.4906   worst 0.0875   lr 0.0124   p 109.41   eps 0.7807   mix 0.0035   time 27.10
scalar:  2.165
Epoch 724:  train loss 0.7275   train acc 0.4922   worst 0.0890   lr 0.0123   p 109.87   eps 0.7807   mix 0.0034   time 27.15
Epoch 724:  test acc 0.4829   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 724:  clean acc 0.4875   certified acc 0.2358
Calculating metrics for L_infinity dist model on test set
Epoch 724:  clean acc 0.4808   certified acc 0.2340
scalar:  2.1652
Epoch 725:  train loss 0.7282   train acc 0.4930   worst 0.0895   lr 0.0123   p 110.34   eps 0.7807   mix 0.0034   time 27.35
scalar:  2.1628
Epoch 726:  train loss 0.7286   train acc 0.4936   worst 0.0892   lr 0.0123   p 110.80   eps 0.7807   mix 0.0034   time 27.02
scalar:  2.1618
Epoch 727:  train loss 0.7291   train acc 0.4937   worst 0.0885   lr 0.0122   p 111.27   eps 0.7807   mix 0.0034   time 26.96
scalar:  2.1533
Epoch 728:  train loss 0.7270   train acc 0.4935   worst 0.0887   lr 0.0122   p 111.73   eps 0.7807   mix 0.0034   time 26.79
scalar:  2.1588
Epoch 729:  train loss 0.7288   train acc 0.4939   worst 0.0879   lr 0.0122   p 112.20   eps 0.7807   mix 0.0033   time 27.14
Epoch 729:  test acc 0.4840   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 729:  clean acc 0.4852   certified acc 0.2364
Calculating metrics for L_infinity dist model on test set
Epoch 729:  clean acc 0.4813   certified acc 0.2307
scalar:  2.1516
Epoch 730:  train loss 0.7304   train acc 0.4927   worst 0.0867   lr 0.0121   p 112.68   eps 0.7807   mix 0.0033   time 27.23
scalar:  2.1639
Epoch 731:  train loss 0.7293   train acc 0.4945   worst 0.0874   lr 0.0121   p 113.15   eps 0.7807   mix 0.0033   time 26.97
scalar:  2.1809
Epoch 732:  train loss 0.7287   train acc 0.4949   worst 0.0870   lr 0.0120   p 113.63   eps 0.7807   mix 0.0033   time 27.29
scalar:  2.1744
Epoch 733:  train loss 0.7299   train acc 0.4933   worst 0.0867   lr 0.0120   p 114.10   eps 0.7807   mix 0.0033   time 26.93
scalar:  2.1705
Epoch 734:  train loss 0.7290   train acc 0.4915   worst 0.0868   lr 0.0120   p 114.58   eps 0.7807   mix 0.0033   time 27.01
Epoch 734:  test acc 0.4846   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 734:  clean acc 0.4825   certified acc 0.2408
Calculating metrics for L_infinity dist model on test set
Epoch 734:  clean acc 0.4789   certified acc 0.2361
scalar:  2.1731
Epoch 735:  train loss 0.7282   train acc 0.4943   worst 0.0883   lr 0.0119   p 115.07   eps 0.7807   mix 0.0032   time 27.42
scalar:  2.1668
Epoch 736:  train loss 0.7296   train acc 0.4911   worst 0.0864   lr 0.0119   p 115.55   eps 0.7807   mix 0.0032   time 26.92
scalar:  2.1703
Epoch 737:  train loss 0.7287   train acc 0.4928   worst 0.0877   lr 0.0119   p 116.04   eps 0.7807   mix 0.0032   time 27.40
scalar:  2.1708
Epoch 738:  train loss 0.7313   train acc 0.4916   worst 0.0861   lr 0.0118   p 116.53   eps 0.7807   mix 0.0032   time 26.83
scalar:  2.1627
Epoch 739:  train loss 0.7286   train acc 0.4897   worst 0.0893   lr 0.0118   p 117.02   eps 0.7807   mix 0.0032   time 27.30
Epoch 739:  test acc 0.4827   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 739:  clean acc 0.4793   certified acc 0.2490
Calculating metrics for L_infinity dist model on test set
Epoch 739:  clean acc 0.4792   certified acc 0.2430
scalar:  2.1351
Epoch 740:  train loss 0.7299   train acc 0.4921   worst 0.0867   lr 0.0118   p 117.51   eps 0.7807   mix 0.0031   time 27.53
scalar:  2.1665
Epoch 741:  train loss 0.7294   train acc 0.4934   worst 0.0876   lr 0.0117   p 118.00   eps 0.7807   mix 0.0031   time 26.76
scalar:  2.1779
Epoch 742:  train loss 0.7297   train acc 0.4951   worst 0.0859   lr 0.0117   p 118.50   eps 0.7807   mix 0.0031   time 27.06
scalar:  2.171
Epoch 743:  train loss 0.7299   train acc 0.4921   worst 0.0871   lr 0.0117   p 119.00   eps 0.7807   mix 0.0031   time 26.74
scalar:  2.175
Epoch 744:  train loss 0.7310   train acc 0.4910   worst 0.0867   lr 0.0116   p 119.50   eps 0.7807   mix 0.0031   time 27.10
Epoch 744:  test acc 0.4853   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 744:  clean acc 0.4872   certified acc 0.2434
Calculating metrics for L_infinity dist model on test set
Epoch 744:  clean acc 0.4814   certified acc 0.2358
scalar:  2.1566
Epoch 745:  train loss 0.7299   train acc 0.4910   worst 0.0861   lr 0.0116   p 120.00   eps 0.7807   mix 0.0031   time 27.55
scalar:  2.153
Epoch 746:  train loss 0.7296   train acc 0.4890   worst 0.0881   lr 0.0116   p 120.51   eps 0.7807   mix 0.0030   time 27.05
scalar:  2.1447
Epoch 747:  train loss 0.7307   train acc 0.4867   worst 0.0871   lr 0.0115   p 121.01   eps 0.7807   mix 0.0030   time 27.02
scalar:  2.1486
Epoch 748:  train loss 0.7308   train acc 0.4898   worst 0.0859   lr 0.0115   p 121.52   eps 0.7807   mix 0.0030   time 26.78
scalar:  2.1429
Epoch 749:  train loss 0.7302   train acc 0.4902   worst 0.0860   lr 0.0114   p 122.03   eps 0.7807   mix 0.0030   time 27.08
Epoch 749:  test acc 0.4842   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 749:  clean acc 0.4900   certified acc 0.2492
Calculating metrics for L_infinity dist model on test set
Epoch 749:  clean acc 0.4872   certified acc 0.2426
scalar:  2.1484
Epoch 750:  train loss 0.7301   train acc 0.4921   worst 0.0873   lr 0.0114   p 122.55   eps 0.7807   mix 0.0030   time 27.34
scalar:  2.1574
Epoch 751:  train loss 0.7297   train acc 0.4899   worst 0.0873   lr 0.0114   p 123.06   eps 0.7807   mix 0.0030   time 26.89
scalar:  2.1489
Epoch 752:  train loss 0.7303   train acc 0.4910   worst 0.0862   lr 0.0113   p 123.58   eps 0.7807   mix 0.0029   time 26.89
scalar:  2.1473
Epoch 753:  train loss 0.7299   train acc 0.4912   worst 0.0866   lr 0.0113   p 124.10   eps 0.7807   mix 0.0029   time 26.87
scalar:  2.1497
Epoch 754:  train loss 0.7293   train acc 0.4936   worst 0.0863   lr 0.0113   p 124.62   eps 0.7807   mix 0.0029   time 26.81
Epoch 754:  test acc 0.4826   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 754:  clean acc 0.4913   certified acc 0.2545
Calculating metrics for L_infinity dist model on test set
Epoch 754:  clean acc 0.4861   certified acc 0.2494
scalar:  2.1778
Epoch 755:  train loss 0.7313   train acc 0.4908   worst 0.0854   lr 0.0112   p 125.15   eps 0.7807   mix 0.0029   time 27.20
scalar:  2.1486
Epoch 756:  train loss 0.7297   train acc 0.4914   worst 0.0862   lr 0.0112   p 125.67   eps 0.7807   mix 0.0029   time 26.91
scalar:  2.1766
Epoch 757:  train loss 0.7309   train acc 0.4887   worst 0.0866   lr 0.0112   p 126.20   eps 0.7807   mix 0.0029   time 27.09
scalar:  2.1498
Epoch 758:  train loss 0.7307   train acc 0.4937   worst 0.0849   lr 0.0111   p 126.73   eps 0.7807   mix 0.0029   time 26.80
scalar:  2.1761
Epoch 759:  train loss 0.7292   train acc 0.4927   worst 0.0869   lr 0.0111   p 127.27   eps 0.7807   mix 0.0028   time 27.07
Epoch 759:  test acc 0.4794   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 759:  clean acc 0.4845   certified acc 0.2543
Calculating metrics for L_infinity dist model on test set
Epoch 759:  clean acc 0.4815   certified acc 0.2512
scalar:  2.1554
Epoch 760:  train loss 0.7307   train acc 0.4887   worst 0.0870   lr 0.0111   p 127.80   eps 0.7807   mix 0.0028   time 27.06
scalar:  2.1561
Epoch 761:  train loss 0.7316   train acc 0.4867   worst 0.0864   lr 0.0110   p 128.34   eps 0.7807   mix 0.0028   time 27.02
scalar:  2.1592
Epoch 762:  train loss 0.7313   train acc 0.4897   worst 0.0873   lr 0.0110   p 128.88   eps 0.7807   mix 0.0028   time 27.45
scalar:  2.1576
Epoch 763:  train loss 0.7321   train acc 0.4904   worst 0.0843   lr 0.0110   p 129.42   eps 0.7807   mix 0.0028   time 26.66
scalar:  2.1739
Epoch 764:  train loss 0.7310   train acc 0.4908   worst 0.0845   lr 0.0109   p 129.97   eps 0.7807   mix 0.0028   time 27.24
Epoch 764:  test acc 0.4818   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 764:  clean acc 0.4923   certified acc 0.2570
Calculating metrics for L_infinity dist model on test set
Epoch 764:  clean acc 0.4801   certified acc 0.2481
scalar:  2.1822
Epoch 765:  train loss 0.7318   train acc 0.4910   worst 0.0830   lr 0.0109   p 130.51   eps 0.7807   mix 0.0027   time 27.18
scalar:  2.1656
Epoch 766:  train loss 0.7321   train acc 0.4909   worst 0.0847   lr 0.0108   p 131.06   eps 0.7807   mix 0.0027   time 26.97
scalar:  2.186
Epoch 767:  train loss 0.7293   train acc 0.4919   worst 0.0850   lr 0.0108   p 131.61   eps 0.7807   mix 0.0027   time 27.11
scalar:  2.146
Epoch 768:  train loss 0.7308   train acc 0.4896   worst 0.0864   lr 0.0108   p 132.17   eps 0.7807   mix 0.0027   time 26.66
scalar:  2.1621
Epoch 769:  train loss 0.7307   train acc 0.4914   worst 0.0836   lr 0.0107   p 132.72   eps 0.7807   mix 0.0027   time 27.01
Epoch 769:  test acc 0.4812   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 769:  clean acc 0.4862   certified acc 0.2587
Calculating metrics for L_infinity dist model on test set
Epoch 769:  clean acc 0.4777   certified acc 0.2509
scalar:  2.1733
Epoch 770:  train loss 0.7314   train acc 0.4906   worst 0.0847   lr 0.0107   p 133.28   eps 0.7807   mix 0.0027   time 27.56
scalar:  2.165
Epoch 771:  train loss 0.7309   train acc 0.4910   worst 0.0852   lr 0.0107   p 133.84   eps 0.7807   mix 0.0027   time 26.97
scalar:  2.1599
Epoch 772:  train loss 0.7302   train acc 0.4919   worst 0.0851   lr 0.0106   p 134.40   eps 0.7807   mix 0.0026   time 27.16
scalar:  2.1616
Epoch 773:  train loss 0.7306   train acc 0.4919   worst 0.0831   lr 0.0106   p 134.97   eps 0.7807   mix 0.0026   time 26.69
scalar:  2.1777
Epoch 774:  train loss 0.7313   train acc 0.4897   worst 0.0854   lr 0.0106   p 135.54   eps 0.7807   mix 0.0026   time 26.93
Epoch 774:  test acc 0.4829   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 774:  clean acc 0.4852   certified acc 0.2603
Calculating metrics for L_infinity dist model on test set
Epoch 774:  clean acc 0.4827   certified acc 0.2527
scalar:  2.1504
Epoch 775:  train loss 0.7317   train acc 0.4894   worst 0.0849   lr 0.0105   p 136.11   eps 0.7807   mix 0.0026   time 27.21
scalar:  2.1629
Epoch 776:  train loss 0.7316   train acc 0.4879   worst 0.0835   lr 0.0105   p 136.68   eps 0.7807   mix 0.0026   time 27.03
scalar:  2.1644
Epoch 777:  train loss 0.7319   train acc 0.4922   worst 0.0829   lr 0.0105   p 137.26   eps 0.7807   mix 0.0026   time 27.16
scalar:  2.1732
Epoch 778:  train loss 0.7308   train acc 0.4904   worst 0.0844   lr 0.0104   p 137.83   eps 0.7807   mix 0.0026   time 26.93
scalar:  2.174
Epoch 779:  train loss 0.7306   train acc 0.4918   worst 0.0849   lr 0.0104   p 138.41   eps 0.7807   mix 0.0025   time 26.68
Epoch 779:  test acc 0.4824   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 779:  clean acc 0.4873   certified acc 0.2618
Calculating metrics for L_infinity dist model on test set
Epoch 779:  clean acc 0.4789   certified acc 0.2541
scalar:  2.1597
Epoch 780:  train loss 0.7318   train acc 0.4905   worst 0.0853   lr 0.0104   p 139.00   eps 0.7807   mix 0.0025   time 27.15
scalar:  2.1782
Epoch 781:  train loss 0.7322   train acc 0.4924   worst 0.0832   lr 0.0103   p 139.58   eps 0.7807   mix 0.0025   time 26.93
scalar:  2.1616
Epoch 782:  train loss 0.7320   train acc 0.4915   worst 0.0835   lr 0.0103   p 140.17   eps 0.7807   mix 0.0025   time 27.06
scalar:  2.1886
Epoch 783:  train loss 0.7312   train acc 0.4885   worst 0.0839   lr 0.0103   p 140.76   eps 0.7807   mix 0.0025   time 26.41
scalar:  2.1467
Epoch 784:  train loss 0.7311   train acc 0.4890   worst 0.0852   lr 0.0102   p 141.35   eps 0.7807   mix 0.0025   time 26.88
Epoch 784:  test acc 0.4792   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 784:  clean acc 0.4871   certified acc 0.2639
Calculating metrics for L_infinity dist model on test set
Epoch 784:  clean acc 0.4827   certified acc 0.2595
scalar:  2.1637
Epoch 785:  train loss 0.7314   train acc 0.4903   worst 0.0852   lr 0.0102   p 141.94   eps 0.7807   mix 0.0025   time 26.91
scalar:  2.1652
Epoch 786:  train loss 0.7316   train acc 0.4924   worst 0.0829   lr 0.0102   p 142.54   eps 0.7807   mix 0.0025   time 27.92
scalar:  2.1816
Epoch 787:  train loss 0.7323   train acc 0.4913   worst 0.0821   lr 0.0101   p 143.14   eps 0.7807   mix 0.0024   time 27.01
scalar:  2.1818
Epoch 788:  train loss 0.7327   train acc 0.4903   worst 0.0826   lr 0.0101   p 143.74   eps 0.7807   mix 0.0024   time 26.85
scalar:  2.1868
Epoch 789:  train loss 0.7314   train acc 0.4919   worst 0.0842   lr 0.0101   p 144.35   eps 0.7807   mix 0.0024   time 27.01
Epoch 789:  test acc 0.4826   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 789:  clean acc 0.4897   certified acc 0.2644
Calculating metrics for L_infinity dist model on test set
Epoch 789:  clean acc 0.4823   certified acc 0.2597
scalar:  2.2015
Epoch 790:  train loss 0.7307   train acc 0.4921   worst 0.0842   lr 0.0100   p 144.96   eps 0.7807   mix 0.0024   time 26.62
scalar:  2.164
Epoch 791:  train loss 0.7313   train acc 0.4926   worst 0.0842   lr 0.0100   p 145.57   eps 0.7807   mix 0.0024   time 27.62
scalar:  2.1874
Epoch 792:  train loss 0.7322   train acc 0.4916   worst 0.0822   lr 0.0100   p 146.18   eps 0.7807   mix 0.0024   time 27.42
scalar:  2.1928
Epoch 793:  train loss 0.7318   train acc 0.4928   worst 0.0822   lr 0.0099   p 146.79   eps 0.7807   mix 0.0024   time 27.05
scalar:  2.2031
Epoch 794:  train loss 0.7322   train acc 0.4937   worst 0.0816   lr 0.0099   p 147.41   eps 0.7807   mix 0.0024   time 26.82
Epoch 794:  test acc 0.4808   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 794:  clean acc 0.4877   certified acc 0.2661
Calculating metrics for L_infinity dist model on test set
Epoch 794:  clean acc 0.4757   certified acc 0.2565
scalar:  2.2041
Epoch 795:  train loss 0.7335   train acc 0.4884   worst 0.0827   lr 0.0099   p 148.03   eps 0.7807   mix 0.0023   time 26.76
scalar:  2.1785
Epoch 796:  train loss 0.7304   train acc 0.4925   worst 0.0832   lr 0.0098   p 148.65   eps 0.7807   mix 0.0023   time 27.75
scalar:  2.1708
Epoch 797:  train loss 0.7313   train acc 0.4928   worst 0.0822   lr 0.0098   p 149.28   eps 0.7807   mix 0.0023   time 26.91
scalar:  2.175
Epoch 798:  train loss 0.7312   train acc 0.4922   worst 0.0833   lr 0.0097   p 149.91   eps 0.7807   mix 0.0023   time 27.01
scalar:  2.1835
Epoch 799:  train loss 0.7330   train acc 0.4883   worst 0.0836   lr 0.0097   p 150.54   eps 0.7807   mix 0.0023   time 26.78
Epoch 799:  test acc 0.4811   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 799:  clean acc 0.4887   certified acc 0.2684
Calculating metrics for L_infinity dist model on test set
Epoch 799:  clean acc 0.4799   certified acc 0.2623
scalar:  2.187
Epoch 800:  train loss 0.7313   train acc 0.4907   worst 0.0826   lr 0.0097   p 151.17   eps 0.7807   mix 0.0023   time 26.77
scalar:  2.1794
Epoch 801:  train loss 0.7313   train acc 0.4914   worst 0.0825   lr 0.0096   p 151.81   eps 0.7807   mix 0.0023   time 27.28
scalar:  2.1878
Epoch 802:  train loss 0.7313   train acc 0.4931   worst 0.0836   lr 0.0096   p 152.45   eps 0.7807   mix 0.0023   time 27.09
scalar:  2.1824
Epoch 803:  train loss 0.7321   train acc 0.4917   worst 0.0825   lr 0.0096   p 153.09   eps 0.7807   mix 0.0022   time 27.50
scalar:  2.1904
Epoch 804:  train loss 0.7312   train acc 0.4909   worst 0.0811   lr 0.0095   p 153.73   eps 0.7807   mix 0.0022   time 26.90
Epoch 804:  test acc 0.4794   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 804:  clean acc 0.4885   certified acc 0.2725
Calculating metrics for L_infinity dist model on test set
Epoch 804:  clean acc 0.4810   certified acc 0.2665
scalar:  2.1929
Epoch 805:  train loss 0.7314   train acc 0.4941   worst 0.0818   lr 0.0095   p 154.38   eps 0.7807   mix 0.0022   time 26.57
scalar:  2.1908
Epoch 806:  train loss 0.7327   train acc 0.4909   worst 0.0828   lr 0.0095   p 155.03   eps 0.7807   mix 0.0022   time 27.21
scalar:  2.1797
Epoch 807:  train loss 0.7329   train acc 0.4912   worst 0.0822   lr 0.0094   p 155.68   eps 0.7807   mix 0.0022   time 26.69
scalar:  2.1892
Epoch 808:  train loss 0.7325   train acc 0.4916   worst 0.0812   lr 0.0094   p 156.34   eps 0.7807   mix 0.0022   time 27.54
scalar:  2.2032
Epoch 809:  train loss 0.7319   train acc 0.4891   worst 0.0817   lr 0.0094   p 156.99   eps 0.7807   mix 0.0022   time 26.98
Epoch 809:  test acc 0.4794   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 809:  clean acc 0.4884   certified acc 0.2710
Calculating metrics for L_infinity dist model on test set
Epoch 809:  clean acc 0.4780   certified acc 0.2657
scalar:  2.1766
Epoch 810:  train loss 0.7325   train acc 0.4913   worst 0.0810   lr 0.0093   p 157.65   eps 0.7807   mix 0.0022   time 26.50
scalar:  2.1988
Epoch 811:  train loss 0.7304   train acc 0.4938   worst 0.0815   lr 0.0093   p 158.32   eps 0.7807   mix 0.0021   time 27.12
scalar:  2.2023
Epoch 812:  train loss 0.7322   train acc 0.4920   worst 0.0810   lr 0.0093   p 158.98   eps 0.7807   mix 0.0021   time 26.87
scalar:  2.2073
Epoch 813:  train loss 0.7325   train acc 0.4918   worst 0.0819   lr 0.0092   p 159.65   eps 0.7807   mix 0.0021   time 27.25
scalar:  2.1963
Epoch 814:  train loss 0.7325   train acc 0.4921   worst 0.0817   lr 0.0092   p 160.32   eps 0.7807   mix 0.0021   time 26.59
Epoch 814:  test acc 0.4796   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 814:  clean acc 0.4781   certified acc 0.2705
Calculating metrics for L_infinity dist model on test set
Epoch 814:  clean acc 0.4714   certified acc 0.2631
scalar:  2.2016
Epoch 815:  train loss 0.7323   train acc 0.4880   worst 0.0823   lr 0.0092   p 161.00   eps 0.7807   mix 0.0021   time 26.69
scalar:  2.1824
Epoch 816:  train loss 0.7324   train acc 0.4908   worst 0.0806   lr 0.0091   p 161.68   eps 0.7807   mix 0.0021   time 27.60
scalar:  2.1818
Epoch 817:  train loss 0.7315   train acc 0.4920   worst 0.0820   lr 0.0091   p 162.36   eps 0.7807   mix 0.0021   time 26.98
scalar:  2.2008
Epoch 818:  train loss 0.7325   train acc 0.4906   worst 0.0818   lr 0.0091   p 163.04   eps 0.7807   mix 0.0021   time 27.31
scalar:  2.192
Epoch 819:  train loss 0.7331   train acc 0.4929   worst 0.0815   lr 0.0090   p 163.72   eps 0.7807   mix 0.0021   time 26.94
Epoch 819:  test acc 0.4796   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 819:  clean acc 0.4812   certified acc 0.2711
Calculating metrics for L_infinity dist model on test set
Epoch 819:  clean acc 0.4722   certified acc 0.2604
scalar:  2.1924
Epoch 820:  train loss 0.7313   train acc 0.4943   worst 0.0819   lr 0.0090   p 164.41   eps 0.7807   mix 0.0020   time 26.70
scalar:  2.1987
Epoch 821:  train loss 0.7321   train acc 0.4916   worst 0.0811   lr 0.0090   p 165.11   eps 0.7807   mix 0.0020   time 27.59
scalar:  2.1873
Epoch 822:  train loss 0.7329   train acc 0.4921   worst 0.0803   lr 0.0089   p 165.80   eps 0.7807   mix 0.0020   time 27.02
scalar:  2.1975
Epoch 823:  train loss 0.7318   train acc 0.4913   worst 0.0810   lr 0.0089   p 166.50   eps 0.7807   mix 0.0020   time 27.25
scalar:  2.1749
Epoch 824:  train loss 0.7336   train acc 0.4909   worst 0.0816   lr 0.0089   p 167.20   eps 0.7807   mix 0.0020   time 26.85
Epoch 824:  test acc 0.4810   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 824:  clean acc 0.4851   certified acc 0.2756
Calculating metrics for L_infinity dist model on test set
Epoch 824:  clean acc 0.4750   certified acc 0.2684
scalar:  2.1923
Epoch 825:  train loss 0.7311   train acc 0.4928   worst 0.0820   lr 0.0088   p 167.90   eps 0.7807   mix 0.0020   time 26.64
scalar:  2.1995
Epoch 826:  train loss 0.7328   train acc 0.4922   worst 0.0807   lr 0.0088   p 168.61   eps 0.7807   mix 0.0020   time 27.41
scalar:  2.2054
Epoch 827:  train loss 0.7320   train acc 0.4939   worst 0.0803   lr 0.0088   p 169.32   eps 0.7807   mix 0.0020   time 26.94
scalar:  2.2072
Epoch 828:  train loss 0.7319   train acc 0.4904   worst 0.0807   lr 0.0087   p 170.03   eps 0.7807   mix 0.0020   time 27.43
scalar:  2.2107
Epoch 829:  train loss 0.7318   train acc 0.4910   worst 0.0817   lr 0.0087   p 170.75   eps 0.7807   mix 0.0019   time 27.00
Epoch 829:  test acc 0.4787   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 829:  clean acc 0.4869   certified acc 0.2734
Calculating metrics for L_infinity dist model on test set
Epoch 829:  clean acc 0.4788   certified acc 0.2661
scalar:  2.1943
Epoch 830:  train loss 0.7312   train acc 0.4919   worst 0.0808   lr 0.0087   p 171.46   eps 0.7807   mix 0.0019   time 26.82
scalar:  2.1995
Epoch 831:  train loss 0.7328   train acc 0.4925   worst 0.0811   lr 0.0086   p 172.19   eps 0.7807   mix 0.0019   time 27.23
scalar:  2.1986
Epoch 832:  train loss 0.7329   train acc 0.4897   worst 0.0799   lr 0.0086   p 172.91   eps 0.7807   mix 0.0019   time 27.13
scalar:  2.2005
Epoch 833:  train loss 0.7322   train acc 0.4908   worst 0.0801   lr 0.0086   p 173.64   eps 0.7807   mix 0.0019   time 27.71
scalar:  2.1977
Epoch 834:  train loss 0.7324   train acc 0.4913   worst 0.0811   lr 0.0085   p 174.37   eps 0.7807   mix 0.0019   time 27.06
Epoch 834:  test acc 0.4771   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 834:  clean acc 0.4873   certified acc 0.2749
Calculating metrics for L_infinity dist model on test set
Epoch 834:  clean acc 0.4765   certified acc 0.2653
scalar:  2.1986
Epoch 835:  train loss 0.7330   train acc 0.4877   worst 0.0798   lr 0.0085   p 175.10   eps 0.7807   mix 0.0019   time 26.67
scalar:  2.1917
Epoch 836:  train loss 0.7311   train acc 0.4947   worst 0.0806   lr 0.0085   p 175.84   eps 0.7807   mix 0.0019   time 26.87
scalar:  2.2123
Epoch 837:  train loss 0.7337   train acc 0.4895   worst 0.0816   lr 0.0084   p 176.58   eps 0.7807   mix 0.0019   time 27.36
scalar:  2.1898
Epoch 838:  train loss 0.7332   train acc 0.4905   worst 0.0810   lr 0.0084   p 177.32   eps 0.7807   mix 0.0019   time 27.20
scalar:  2.1952
Epoch 839:  train loss 0.7318   train acc 0.4928   worst 0.0804   lr 0.0084   p 178.07   eps 0.7807   mix 0.0018   time 27.14
Epoch 839:  test acc 0.4803   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 839:  clean acc 0.4836   certified acc 0.2776
Calculating metrics for L_infinity dist model on test set
Epoch 839:  clean acc 0.4770   certified acc 0.2705
scalar:  2.1993
Epoch 840:  train loss 0.7322   train acc 0.4924   worst 0.0803   lr 0.0084   p 178.82   eps 0.7807   mix 0.0018   time 26.75
scalar:  2.2006
Epoch 841:  train loss 0.7319   train acc 0.4922   worst 0.0802   lr 0.0083   p 179.57   eps 0.7807   mix 0.0018   time 27.18
scalar:  2.2061
Epoch 842:  train loss 0.7318   train acc 0.4917   worst 0.0806   lr 0.0083   p 180.32   eps 0.7807   mix 0.0018   time 27.17
scalar:  2.2165
Epoch 843:  train loss 0.7332   train acc 0.4896   worst 0.0800   lr 0.0083   p 181.08   eps 0.7807   mix 0.0018   time 27.30
scalar:  2.2004
Epoch 844:  train loss 0.7334   train acc 0.4891   worst 0.0793   lr 0.0082   p 181.84   eps 0.7807   mix 0.0018   time 27.21
Epoch 844:  test acc 0.4811   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 844:  clean acc 0.4852   certified acc 0.2784
Calculating metrics for L_infinity dist model on test set
Epoch 844:  clean acc 0.4778   certified acc 0.2687
scalar:  2.2031
Epoch 845:  train loss 0.7326   train acc 0.4913   worst 0.0813   lr 0.0082   p 182.61   eps 0.7807   mix 0.0018   time 26.77
scalar:  2.213
Epoch 846:  train loss 0.7318   train acc 0.4919   worst 0.0798   lr 0.0082   p 183.38   eps 0.7807   mix 0.0018   time 26.63
scalar:  2.2144
Epoch 847:  train loss 0.7312   train acc 0.4925   worst 0.0820   lr 0.0081   p 184.15   eps 0.7807   mix 0.0018   time 27.19
scalar:  2.2091
Epoch 848:  train loss 0.7335   train acc 0.4907   worst 0.0806   lr 0.0081   p 184.92   eps 0.7807   mix 0.0018   time 27.19
scalar:  2.2041
Epoch 849:  train loss 0.7319   train acc 0.4924   worst 0.0805   lr 0.0081   p 185.70   eps 0.7807   mix 0.0017   time 27.12
Epoch 849:  test acc 0.4820   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 849:  clean acc 0.4864   certified acc 0.2790
Calculating metrics for L_infinity dist model on test set
Epoch 849:  clean acc 0.4778   certified acc 0.2708
scalar:  2.2123
Epoch 850:  train loss 0.7339   train acc 0.4893   worst 0.0803   lr 0.0080   p 186.48   eps 0.7807   mix 0.0017   time 26.66
scalar:  2.2021
Epoch 851:  train loss 0.7324   train acc 0.4900   worst 0.0811   lr 0.0080   p 187.27   eps 0.7807   mix 0.0017   time 26.95
scalar:  2.2084
Epoch 852:  train loss 0.7336   train acc 0.4909   worst 0.0783   lr 0.0080   p 188.06   eps 0.7807   mix 0.0017   time 27.11
scalar:  2.2138
Epoch 853:  train loss 0.7329   train acc 0.4908   worst 0.0798   lr 0.0079   p 188.85   eps 0.7807   mix 0.0017   time 27.02
scalar:  2.2087
Epoch 854:  train loss 0.7334   train acc 0.4895   worst 0.0806   lr 0.0079   p 189.64   eps 0.7807   mix 0.0017   time 27.01
Epoch 854:  test acc 0.4796   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 854:  clean acc 0.4880   certified acc 0.2803
Calculating metrics for L_infinity dist model on test set
Epoch 854:  clean acc 0.4773   certified acc 0.2721
scalar:  2.2086
Epoch 855:  train loss 0.7320   train acc 0.4922   worst 0.0798   lr 0.0079   p 190.44   eps 0.7807   mix 0.0017   time 26.77
scalar:  2.2238
Epoch 856:  train loss 0.7329   train acc 0.4941   worst 0.0793   lr 0.0078   p 191.24   eps 0.7807   mix 0.0017   time 26.69
scalar:  2.2355
Epoch 857:  train loss 0.7320   train acc 0.4921   worst 0.0798   lr 0.0078   p 192.05   eps 0.7807   mix 0.0017   time 27.36
scalar:  2.2221
Epoch 858:  train loss 0.7330   train acc 0.4911   worst 0.0803   lr 0.0078   p 192.85   eps 0.7807   mix 0.0017   time 27.45
scalar:  2.2224
Epoch 859:  train loss 0.7317   train acc 0.4932   worst 0.0802   lr 0.0077   p 193.66   eps 0.7807   mix 0.0017   time 26.95
Epoch 859:  test acc 0.4780   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 859:  clean acc 0.4842   certified acc 0.2802
Calculating metrics for L_infinity dist model on test set
Epoch 859:  clean acc 0.4755   certified acc 0.2738
scalar:  2.2211
Epoch 860:  train loss 0.7318   train acc 0.4936   worst 0.0800   lr 0.0077   p 194.48   eps 0.7807   mix 0.0016   time 26.87
scalar:  2.2295
Epoch 861:  train loss 0.7325   train acc 0.4925   worst 0.0800   lr 0.0077   p 195.30   eps 0.7807   mix 0.0016   time 26.69
scalar:  2.225
Epoch 862:  train loss 0.7321   train acc 0.4908   worst 0.0797   lr 0.0076   p 196.12   eps 0.7807   mix 0.0016   time 27.28
scalar:  2.2316
Epoch 863:  train loss 0.7323   train acc 0.4916   worst 0.0791   lr 0.0076   p 196.94   eps 0.7807   mix 0.0016   time 27.03
scalar:  2.2164
Epoch 864:  train loss 0.7313   train acc 0.4920   worst 0.0812   lr 0.0076   p 197.77   eps 0.7807   mix 0.0016   time 27.04
Epoch 864:  test acc 0.4819   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 864:  clean acc 0.4886   certified acc 0.2791
Calculating metrics for L_infinity dist model on test set
Epoch 864:  clean acc 0.4780   certified acc 0.2702
scalar:  2.2091
Epoch 865:  train loss 0.7328   train acc 0.4903   worst 0.0805   lr 0.0076   p 198.61   eps 0.7807   mix 0.0016   time 26.77
scalar:  2.1987
Epoch 866:  train loss 0.7324   train acc 0.4904   worst 0.0805   lr 0.0075   p 199.44   eps 0.7807   mix 0.0016   time 26.75
scalar:  2.2087
Epoch 867:  train loss 0.7317   train acc 0.4932   worst 0.0773   lr 0.0075   p 200.28   eps 0.7807   mix 0.0016   time 27.29
scalar:  2.2154
Epoch 868:  train loss 0.7332   train acc 0.4892   worst 0.0797   lr 0.0075   p 201.12   eps 0.7807   mix 0.0016   time 27.38
scalar:  2.2039
Epoch 869:  train loss 0.7326   train acc 0.4915   worst 0.0798   lr 0.0074   p 201.97   eps 0.7807   mix 0.0016   time 27.04
Epoch 869:  test acc 0.4778   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 869:  clean acc 0.4876   certified acc 0.2836
Calculating metrics for L_infinity dist model on test set
Epoch 869:  clean acc 0.4773   certified acc 0.2715
scalar:  2.1973
Epoch 870:  train loss 0.7324   train acc 0.4919   worst 0.0795   lr 0.0074   p 202.82   eps 0.7807   mix 0.0016   time 26.89
scalar:  2.21
Epoch 871:  train loss 0.7329   train acc 0.4900   worst 0.0794   lr 0.0074   p 203.67   eps 0.7807   mix 0.0016   time 26.94
scalar:  2.2097
Epoch 872:  train loss 0.7333   train acc 0.4895   worst 0.0805   lr 0.0073   p 204.53   eps 0.7807   mix 0.0015   time 27.35
scalar:  2.1993
Epoch 873:  train loss 0.7316   train acc 0.4937   worst 0.0802   lr 0.0073   p 205.39   eps 0.7807   mix 0.0015   time 27.45
scalar:  2.2215
Epoch 874:  train loss 0.7319   train acc 0.4940   worst 0.0784   lr 0.0073   p 206.25   eps 0.7807   mix 0.0015   time 27.07
Epoch 874:  test acc 0.4824   time 2.62
Calculating metrics for L_infinity dist model on training set
Epoch 874:  clean acc 0.4871   certified acc 0.2820
Calculating metrics for L_infinity dist model on test set
Epoch 874:  clean acc 0.4774   certified acc 0.2731
scalar:  2.2405
Epoch 875:  train loss 0.7325   train acc 0.4889   worst 0.0802   lr 0.0072   p 207.12   eps 0.7807   mix 0.0015   time 27.07
scalar:  2.2174
Epoch 876:  train loss 0.7333   train acc 0.4908   worst 0.0784   lr 0.0072   p 207.99   eps 0.7807   mix 0.0015   time 26.59
scalar:  2.2171
Epoch 877:  train loss 0.7324   train acc 0.4912   worst 0.0789   lr 0.0072   p 208.87   eps 0.7807   mix 0.0015   time 27.45
scalar:  2.202
Epoch 878:  train loss 0.7320   train acc 0.4937   worst 0.0786   lr 0.0071   p 209.75   eps 0.7807   mix 0.0015   time 27.20
scalar:  2.2306
Epoch 879:  train loss 0.7338   train acc 0.4902   worst 0.0784   lr 0.0071   p 210.63   eps 0.7807   mix 0.0015   time 27.16
Epoch 879:  test acc 0.4814   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 879:  clean acc 0.4872   certified acc 0.2843
Calculating metrics for L_infinity dist model on test set
Epoch 879:  clean acc 0.4786   certified acc 0.2795
scalar:  2.217
Epoch 880:  train loss 0.7329   train acc 0.4888   worst 0.0784   lr 0.0071   p 211.52   eps 0.7807   mix 0.0015   time 26.62
scalar:  2.2209
Epoch 881:  train loss 0.7323   train acc 0.4929   worst 0.0792   lr 0.0071   p 212.41   eps 0.7807   mix 0.0015   time 26.89
scalar:  2.2278
Epoch 882:  train loss 0.7326   train acc 0.4918   worst 0.0782   lr 0.0070   p 213.30   eps 0.7807   mix 0.0015   time 27.25
scalar:  2.2166
Epoch 883:  train loss 0.7329   train acc 0.4919   worst 0.0778   lr 0.0070   p 214.20   eps 0.7807   mix 0.0015   time 27.13
scalar:  2.233
Epoch 884:  train loss 0.7330   train acc 0.4907   worst 0.0776   lr 0.0070   p 215.10   eps 0.7807   mix 0.0014   time 27.16
Epoch 884:  test acc 0.4807   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 884:  clean acc 0.4860   certified acc 0.2831
Calculating metrics for L_infinity dist model on test set
Epoch 884:  clean acc 0.4753   certified acc 0.2753
scalar:  2.2206
Epoch 885:  train loss 0.7321   train acc 0.4943   worst 0.0795   lr 0.0069   p 216.00   eps 0.7807   mix 0.0014   time 26.82
scalar:  2.2266
Epoch 886:  train loss 0.7336   train acc 0.4893   worst 0.0805   lr 0.0069   p 216.91   eps 0.7807   mix 0.0014   time 26.64
scalar:  2.1987
Epoch 887:  train loss 0.7339   train acc 0.4910   worst 0.0800   lr 0.0069   p 217.82   eps 0.7807   mix 0.0014   time 26.90
scalar:  2.2216
Epoch 888:  train loss 0.7326   train acc 0.4902   worst 0.0800   lr 0.0068   p 218.74   eps 0.7807   mix 0.0014   time 27.20
scalar:  2.2156
Epoch 889:  train loss 0.7340   train acc 0.4926   worst 0.0791   lr 0.0068   p 219.66   eps 0.7807   mix 0.0014   time 27.08
Epoch 889:  test acc 0.4797   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 889:  clean acc 0.4824   certified acc 0.2838
Calculating metrics for L_infinity dist model on test set
Epoch 889:  clean acc 0.4732   certified acc 0.2775
scalar:  2.2266
Epoch 890:  train loss 0.7330   train acc 0.4893   worst 0.0788   lr 0.0068   p 220.59   eps 0.7807   mix 0.0014   time 26.92
scalar:  2.2092
Epoch 891:  train loss 0.7324   train acc 0.4921   worst 0.0779   lr 0.0067   p 221.51   eps 0.7807   mix 0.0014   time 26.71
scalar:  2.2189
Epoch 892:  train loss 0.7328   train acc 0.4928   worst 0.0798   lr 0.0067   p 222.45   eps 0.7807   mix 0.0014   time 27.32
scalar:  2.2224
Epoch 893:  train loss 0.7328   train acc 0.4915   worst 0.0777   lr 0.0067   p 223.38   eps 0.7807   mix 0.0014   time 27.36
scalar:  2.2375
Epoch 894:  train loss 0.7326   train acc 0.4936   worst 0.0778   lr 0.0067   p 224.32   eps 0.7807   mix 0.0014   time 27.23
Epoch 894:  test acc 0.4803   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 894:  clean acc 0.4889   certified acc 0.2847
Calculating metrics for L_infinity dist model on test set
Epoch 894:  clean acc 0.4781   certified acc 0.2770
scalar:  2.2395
Epoch 895:  train loss 0.7333   train acc 0.4915   worst 0.0791   lr 0.0066   p 225.26   eps 0.7807   mix 0.0014   time 26.47
scalar:  2.2344
Epoch 896:  train loss 0.7332   train acc 0.4911   worst 0.0782   lr 0.0066   p 226.21   eps 0.7807   mix 0.0014   time 26.92
scalar:  2.2237
Epoch 897:  train loss 0.7327   train acc 0.4930   worst 0.0794   lr 0.0066   p 227.16   eps 0.7807   mix 0.0013   time 27.40
scalar:  2.2311
Epoch 898:  train loss 0.7329   train acc 0.4937   worst 0.0792   lr 0.0065   p 228.12   eps 0.7807   mix 0.0013   time 26.96
scalar:  2.231
Epoch 899:  train loss 0.7320   train acc 0.4935   worst 0.0782   lr 0.0065   p 229.08   eps 0.7807   mix 0.0013   time 26.99
Epoch 899:  test acc 0.4782   time 2.50
Calculating metrics for L_infinity dist model on training set
Epoch 899:  clean acc 0.4895   certified acc 0.2874
Calculating metrics for L_infinity dist model on test set
Epoch 899:  clean acc 0.4766   certified acc 0.2747
scalar:  2.2344
Epoch 900:  train loss 0.7332   train acc 0.4928   worst 0.0767   lr 0.0065   p 230.04   eps 0.7807   mix 0.0013   time 26.95
scalar:  2.2405
Epoch 901:  train loss 0.7325   train acc 0.4917   worst 0.0787   lr 0.0064   p 231.01   eps 0.7807   mix 0.0013   time 26.88
scalar:  2.2343
Epoch 902:  train loss 0.7329   train acc 0.4932   worst 0.0780   lr 0.0064   p 231.98   eps 0.7807   mix 0.0013   time 26.85
scalar:  2.2344
Epoch 903:  train loss 0.7321   train acc 0.4933   worst 0.0783   lr 0.0064   p 232.96   eps 0.7807   mix 0.0013   time 26.88
scalar:  2.2368
Epoch 904:  train loss 0.7317   train acc 0.4946   worst 0.0801   lr 0.0064   p 233.94   eps 0.7807   mix 0.0013   time 27.25
Epoch 904:  test acc 0.4812   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 904:  clean acc 0.4914   certified acc 0.2895
Calculating metrics for L_infinity dist model on test set
Epoch 904:  clean acc 0.4818   certified acc 0.2781
scalar:  2.2404
Epoch 905:  train loss 0.7326   train acc 0.4905   worst 0.0781   lr 0.0063   p 234.92   eps 0.7807   mix 0.0013   time 26.84
scalar:  2.2262
Epoch 906:  train loss 0.7328   train acc 0.4926   worst 0.0792   lr 0.0063   p 235.91   eps 0.7807   mix 0.0013   time 26.81
scalar:  2.2374
Epoch 907:  train loss 0.7330   train acc 0.4913   worst 0.0785   lr 0.0063   p 236.90   eps 0.7807   mix 0.0013   time 26.96
scalar:  2.2353
Epoch 908:  train loss 0.7335   train acc 0.4880   worst 0.0799   lr 0.0062   p 237.90   eps 0.7807   mix 0.0013   time 26.99
scalar:  2.2178
Epoch 909:  train loss 0.7342   train acc 0.4931   worst 0.0774   lr 0.0062   p 238.90   eps 0.7807   mix 0.0013   time 27.02
Epoch 909:  test acc 0.4830   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 909:  clean acc 0.4903   certified acc 0.2896
Calculating metrics for L_infinity dist model on test set
Epoch 909:  clean acc 0.4793   certified acc 0.2788
scalar:  2.2303
Epoch 910:  train loss 0.7329   train acc 0.4925   worst 0.0764   lr 0.0062   p 239.91   eps 0.7807   mix 0.0013   time 26.54
scalar:  2.2398
Epoch 911:  train loss 0.7323   train acc 0.4915   worst 0.0775   lr 0.0062   p 240.92   eps 0.7807   mix 0.0012   time 26.58
scalar:  2.237
Epoch 912:  train loss 0.7318   train acc 0.4945   worst 0.0777   lr 0.0061   p 241.93   eps 0.7807   mix 0.0012   time 27.29
scalar:  2.2439
Epoch 913:  train loss 0.7332   train acc 0.4893   worst 0.0783   lr 0.0061   p 242.95   eps 0.7807   mix 0.0012   time 26.57
scalar:  2.2293
Epoch 914:  train loss 0.7325   train acc 0.4913   worst 0.0776   lr 0.0061   p 243.97   eps 0.7807   mix 0.0012   time 27.02
Epoch 914:  test acc 0.4800   time 2.51
Calculating metrics for L_infinity dist model on training set
Epoch 914:  clean acc 0.4912   certified acc 0.2915
Calculating metrics for L_infinity dist model on test set
Epoch 914:  clean acc 0.4766   certified acc 0.2794
scalar:  2.235
Epoch 915:  train loss 0.7329   train acc 0.4908   worst 0.0773   lr 0.0060   p 245.00   eps 0.7807   mix 0.0012   time 26.72
scalar:  2.2349
Epoch 916:  train loss 0.7338   train acc 0.4910   worst 0.0756   lr 0.0060   p 246.03   eps 0.7807   mix 0.0012   time 26.77
scalar:  2.2316
Epoch 917:  train loss 0.7321   train acc 0.4923   worst 0.0785   lr 0.0060   p 247.06   eps 0.7807   mix 0.0012   time 27.39
scalar:  2.2489
Epoch 918:  train loss 0.7316   train acc 0.4926   worst 0.0784   lr 0.0060   p 248.10   eps 0.7807   mix 0.0012   time 26.96
scalar:  2.2375
Epoch 919:  train loss 0.7333   train acc 0.4939   worst 0.0766   lr 0.0059   p 249.15   eps 0.7807   mix 0.0012   time 27.43
Epoch 919:  test acc 0.4805   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 919:  clean acc 0.4894   certified acc 0.2913
Calculating metrics for L_infinity dist model on test set
Epoch 919:  clean acc 0.4790   certified acc 0.2782
scalar:  2.2402
Epoch 920:  train loss 0.7332   train acc 0.4917   worst 0.0768   lr 0.0059   p 250.19   eps 0.7807   mix 0.0012   time 26.60
scalar:  2.243
Epoch 921:  train loss 0.7308   train acc 0.4926   worst 0.0779   lr 0.0059   p 251.25   eps 0.7807   mix 0.0012   time 27.17
scalar:  2.2364
Epoch 922:  train loss 0.7331   train acc 0.4929   worst 0.0785   lr 0.0058   p 252.30   eps 0.7807   mix 0.0012   time 26.95
scalar:  2.2416
Epoch 923:  train loss 0.7331   train acc 0.4911   worst 0.0763   lr 0.0058   p 253.37   eps 0.7807   mix 0.0012   time 26.95
scalar:  2.2379
Epoch 924:  train loss 0.7329   train acc 0.4928   worst 0.0769   lr 0.0058   p 254.43   eps 0.7807   mix 0.0012   time 27.12
Epoch 924:  test acc 0.4829   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 924:  clean acc 0.4919   certified acc 0.2895
Calculating metrics for L_infinity dist model on test set
Epoch 924:  clean acc 0.4808   certified acc 0.2774
scalar:  2.2456
Epoch 925:  train loss 0.7333   train acc 0.4925   worst 0.0765   lr 0.0057   p 255.50   eps 0.7807   mix 0.0012   time 26.46
scalar:  2.2501
Epoch 926:  train loss 0.7319   train acc 0.4913   worst 0.0780   lr 0.0057   p 256.58   eps 0.7807   mix 0.0012   time 26.95
scalar:  2.2465
Epoch 927:  train loss 0.7319   train acc 0.4936   worst 0.0771   lr 0.0057   p 257.66   eps 0.7807   mix 0.0011   time 26.78
scalar:  2.2509
Epoch 928:  train loss 0.7325   train acc 0.4944   worst 0.0777   lr 0.0057   p 258.74   eps 0.7807   mix 0.0011   time 26.96
scalar:  2.2654
Epoch 929:  train loss 0.7325   train acc 0.4924   worst 0.0758   lr 0.0056   p 259.83   eps 0.7807   mix 0.0011   time 26.68
Epoch 929:  test acc 0.4773   time 2.51
Calculating metrics for L_infinity dist model on training set
Epoch 929:  clean acc 0.4887   certified acc 0.2901
Calculating metrics for L_infinity dist model on test set
Epoch 929:  clean acc 0.4768   certified acc 0.2793
scalar:  2.2557
Epoch 930:  train loss 0.7328   train acc 0.4929   worst 0.0766   lr 0.0056   p 260.92   eps 0.7807   mix 0.0011   time 26.75
scalar:  2.2541
Epoch 931:  train loss 0.7320   train acc 0.4957   worst 0.0768   lr 0.0056   p 262.02   eps 0.7807   mix 0.0011   time 27.07
scalar:  2.256
Epoch 932:  train loss 0.7323   train acc 0.4934   worst 0.0760   lr 0.0056   p 263.12   eps 0.7807   mix 0.0011   time 26.76
scalar:  2.2543
Epoch 933:  train loss 0.7328   train acc 0.4924   worst 0.0784   lr 0.0055   p 264.23   eps 0.7807   mix 0.0011   time 26.99
scalar:  2.2462
Epoch 934:  train loss 0.7335   train acc 0.4906   worst 0.0767   lr 0.0055   p 265.34   eps 0.7807   mix 0.0011   time 26.78
Epoch 934:  test acc 0.4839   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 934:  clean acc 0.4908   certified acc 0.2910
Calculating metrics for L_infinity dist model on test set
Epoch 934:  clean acc 0.4800   certified acc 0.2821
scalar:  2.2371
Epoch 935:  train loss 0.7308   train acc 0.4951   worst 0.0782   lr 0.0055   p 266.46   eps 0.7807   mix 0.0011   time 26.75
scalar:  2.2508
Epoch 936:  train loss 0.7321   train acc 0.4922   worst 0.0770   lr 0.0054   p 267.58   eps 0.7807   mix 0.0011   time 27.06
scalar:  2.25
Epoch 937:  train loss 0.7332   train acc 0.4916   worst 0.0772   lr 0.0054   p 268.71   eps 0.7807   mix 0.0011   time 26.71
scalar:  2.2446
Epoch 938:  train loss 0.7320   train acc 0.4923   worst 0.0754   lr 0.0054   p 269.84   eps 0.7807   mix 0.0011   time 26.87
scalar:  2.2464
Epoch 939:  train loss 0.7321   train acc 0.4913   worst 0.0770   lr 0.0054   p 270.97   eps 0.7807   mix 0.0011   time 26.91
Epoch 939:  test acc 0.4791   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 939:  clean acc 0.4914   certified acc 0.2933
Calculating metrics for L_infinity dist model on test set
Epoch 939:  clean acc 0.4764   certified acc 0.2801
scalar:  2.2487
Epoch 940:  train loss 0.7327   train acc 0.4941   worst 0.0765   lr 0.0053   p 272.11   eps 0.7807   mix 0.0011   time 26.79
scalar:  2.2513
Epoch 941:  train loss 0.7322   train acc 0.4965   worst 0.0761   lr 0.0053   p 273.26   eps 0.7807   mix 0.0011   time 27.25
scalar:  2.2644
Epoch 942:  train loss 0.7333   train acc 0.4897   worst 0.0766   lr 0.0053   p 274.41   eps 0.7807   mix 0.0011   time 26.87
scalar:  2.2436
Epoch 943:  train loss 0.7328   train acc 0.4906   worst 0.0773   lr 0.0052   p 275.56   eps 0.7807   mix 0.0011   time 26.83
scalar:  2.2441
Epoch 944:  train loss 0.7323   train acc 0.4915   worst 0.0777   lr 0.0052   p 276.72   eps 0.7807   mix 0.0010   time 27.03
Epoch 944:  test acc 0.4782   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 944:  clean acc 0.4909   certified acc 0.2938
Calculating metrics for L_infinity dist model on test set
Epoch 944:  clean acc 0.4804   certified acc 0.2827
scalar:  2.2471
Epoch 945:  train loss 0.7326   train acc 0.4917   worst 0.0762   lr 0.0052   p 277.88   eps 0.7807   mix 0.0010   time 27.38
scalar:  2.2498
Epoch 946:  train loss 0.7320   train acc 0.4916   worst 0.0768   lr 0.0052   p 279.05   eps 0.7807   mix 0.0010   time 27.22
scalar:  2.2471
Epoch 947:  train loss 0.7325   train acc 0.4923   worst 0.0775   lr 0.0051   p 280.23   eps 0.7807   mix 0.0010   time 26.56
scalar:  2.2485
Epoch 948:  train loss 0.7337   train acc 0.4921   worst 0.0764   lr 0.0051   p 281.41   eps 0.7807   mix 0.0010   time 26.83
scalar:  2.2521
Epoch 949:  train loss 0.7318   train acc 0.4948   worst 0.0760   lr 0.0051   p 282.59   eps 0.7807   mix 0.0010   time 26.87
Epoch 949:  test acc 0.4817   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 949:  clean acc 0.4938   certified acc 0.2940
Calculating metrics for L_infinity dist model on test set
Epoch 949:  clean acc 0.4819   certified acc 0.2819
scalar:  2.2594
Epoch 950:  train loss 0.7330   train acc 0.4922   worst 0.0758   lr 0.0051   p 283.78   eps 0.7807   mix 0.0010   time 27.34
scalar:  2.2564
Epoch 951:  train loss 0.7315   train acc 0.4944   worst 0.0760   lr 0.0050   p 284.97   eps 0.7807   mix 0.0010   time 26.95
scalar:  2.2609
Epoch 952:  train loss 0.7321   train acc 0.4924   worst 0.0768   lr 0.0050   p 286.17   eps 0.7807   mix 0.0010   time 26.75
scalar:  2.2534
Epoch 953:  train loss 0.7331   train acc 0.4942   worst 0.0762   lr 0.0050   p 287.38   eps 0.7807   mix 0.0010   time 26.95
scalar:  2.2688
Epoch 954:  train loss 0.7325   train acc 0.4922   worst 0.0765   lr 0.0049   p 288.58   eps 0.7807   mix 0.0010   time 26.82
Epoch 954:  test acc 0.4809   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 954:  clean acc 0.4926   certified acc 0.2955
Calculating metrics for L_infinity dist model on test set
Epoch 954:  clean acc 0.4816   certified acc 0.2842
scalar:  2.264
Epoch 955:  train loss 0.7322   train acc 0.4943   worst 0.0771   lr 0.0049   p 289.80   eps 0.7807   mix 0.0010   time 27.33
scalar:  2.2631
Epoch 956:  train loss 0.7316   train acc 0.4951   worst 0.0775   lr 0.0049   p 291.02   eps 0.7807   mix 0.0010   time 26.93
scalar:  2.259
Epoch 957:  train loss 0.7325   train acc 0.4925   worst 0.0759   lr 0.0049   p 292.24   eps 0.7807   mix 0.0010   time 26.64
scalar:  2.2577
Epoch 958:  train loss 0.7313   train acc 0.4927   worst 0.0778   lr 0.0048   p 293.47   eps 0.7807   mix 0.0010   time 26.69
scalar:  2.264
Epoch 959:  train loss 0.7315   train acc 0.4927   worst 0.0763   lr 0.0048   p 294.71   eps 0.7807   mix 0.0010   time 26.67
Epoch 959:  test acc 0.4822   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 959:  clean acc 0.4937   certified acc 0.2959
Calculating metrics for L_infinity dist model on test set
Epoch 959:  clean acc 0.4801   certified acc 0.2813
scalar:  2.2668
Epoch 960:  train loss 0.7313   train acc 0.4942   worst 0.0768   lr 0.0048   p 295.95   eps 0.7807   mix 0.0010   time 26.96
scalar:  2.2662
Epoch 961:  train loss 0.7324   train acc 0.4918   worst 0.0766   lr 0.0048   p 297.19   eps 0.7807   mix 0.0010   time 27.14
scalar:  2.2638
Epoch 962:  train loss 0.7320   train acc 0.4922   worst 0.0772   lr 0.0047   p 298.44   eps 0.7807   mix 0.0009   time 26.79
scalar:  2.2536
Epoch 963:  train loss 0.7317   train acc 0.4923   worst 0.0762   lr 0.0047   p 299.70   eps 0.7807   mix 0.0009   time 26.60
scalar:  2.2589
Epoch 964:  train loss 0.7322   train acc 0.4940   worst 0.0760   lr 0.0047   p 300.96   eps 0.7807   mix 0.0009   time 26.66
Epoch 964:  test acc 0.4789   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 964:  clean acc 0.4924   certified acc 0.2959
Calculating metrics for L_infinity dist model on test set
Epoch 964:  clean acc 0.4794   certified acc 0.2831
scalar:  2.2604
Epoch 965:  train loss 0.7313   train acc 0.4949   worst 0.0768   lr 0.0047   p 302.23   eps 0.7807   mix 0.0009   time 27.27
scalar:  2.2634
Epoch 966:  train loss 0.7325   train acc 0.4933   worst 0.0776   lr 0.0046   p 303.50   eps 0.7807   mix 0.0009   time 27.12
scalar:  2.2694
Epoch 967:  train loss 0.7317   train acc 0.4955   worst 0.0755   lr 0.0046   p 304.77   eps 0.7807   mix 0.0009   time 26.69
scalar:  2.2762
Epoch 968:  train loss 0.7316   train acc 0.4926   worst 0.0760   lr 0.0046   p 306.06   eps 0.7807   mix 0.0009   time 26.88
scalar:  2.2639
Epoch 969:  train loss 0.7325   train acc 0.4952   worst 0.0763   lr 0.0045   p 307.34   eps 0.7807   mix 0.0009   time 26.47
Epoch 969:  test acc 0.4830   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 969:  clean acc 0.4911   certified acc 0.2970
Calculating metrics for L_infinity dist model on test set
Epoch 969:  clean acc 0.4818   certified acc 0.2836
scalar:  2.27
Epoch 970:  train loss 0.7329   train acc 0.4924   worst 0.0751   lr 0.0045   p 308.64   eps 0.7807   mix 0.0009   time 27.36
scalar:  2.2625
Epoch 971:  train loss 0.7315   train acc 0.4918   worst 0.0769   lr 0.0045   p 309.94   eps 0.7807   mix 0.0009   time 27.20
scalar:  2.2596
Epoch 972:  train loss 0.7311   train acc 0.4969   worst 0.0759   lr 0.0045   p 311.24   eps 0.7807   mix 0.0009   time 26.74
scalar:  2.2702
Epoch 973:  train loss 0.7322   train acc 0.4939   worst 0.0776   lr 0.0044   p 312.55   eps 0.7807   mix 0.0009   time 26.97
scalar:  2.2635
Epoch 974:  train loss 0.7323   train acc 0.4942   worst 0.0767   lr 0.0044   p 313.86   eps 0.7807   mix 0.0009   time 26.75
Epoch 974:  test acc 0.4831   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 974:  clean acc 0.4914   certified acc 0.2955
Calculating metrics for L_infinity dist model on test set
Epoch 974:  clean acc 0.4803   certified acc 0.2833
scalar:  2.2727
Epoch 975:  train loss 0.7318   train acc 0.4914   worst 0.0784   lr 0.0044   p 315.18   eps 0.7807   mix 0.0009   time 27.36
scalar:  2.2686
Epoch 976:  train loss 0.7321   train acc 0.4947   worst 0.0754   lr 0.0044   p 316.51   eps 0.7807   mix 0.0009   time 27.22
scalar:  2.2693
Epoch 977:  train loss 0.7316   train acc 0.4964   worst 0.0753   lr 0.0043   p 317.84   eps 0.7807   mix 0.0009   time 26.60
scalar:  2.2784
Epoch 978:  train loss 0.7318   train acc 0.4936   worst 0.0759   lr 0.0043   p 319.18   eps 0.7807   mix 0.0009   time 26.75
scalar:  2.2715
Epoch 979:  train loss 0.7317   train acc 0.4935   worst 0.0765   lr 0.0043   p 320.52   eps 0.7807   mix 0.0009   time 26.84
Epoch 979:  test acc 0.4807   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 979:  clean acc 0.4935   certified acc 0.2994
Calculating metrics for L_infinity dist model on test set
Epoch 979:  clean acc 0.4825   certified acc 0.2838
scalar:  2.2723
Epoch 980:  train loss 0.7311   train acc 0.4955   worst 0.0775   lr 0.0043   p 321.87   eps 0.7807   mix 0.0009   time 27.55
scalar:  2.267
Epoch 981:  train loss 0.7311   train acc 0.4941   worst 0.0765   lr 0.0042   p 323.23   eps 0.7807   mix 0.0009   time 27.25
scalar:  2.2693
Epoch 982:  train loss 0.7319   train acc 0.4942   worst 0.0760   lr 0.0042   p 324.59   eps 0.7807   mix 0.0009   time 26.69
scalar:  2.2705
Epoch 983:  train loss 0.7323   train acc 0.4941   worst 0.0767   lr 0.0042   p 325.95   eps 0.7807   mix 0.0008   time 26.97
scalar:  2.2731
Epoch 984:  train loss 0.7324   train acc 0.4944   worst 0.0758   lr 0.0042   p 327.32   eps 0.7807   mix 0.0008   time 26.86
Epoch 984:  test acc 0.4847   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 984:  clean acc 0.4961   certified acc 0.2993
Calculating metrics for L_infinity dist model on test set
Epoch 984:  clean acc 0.4820   certified acc 0.2842
scalar:  2.28
Epoch 985:  train loss 0.7311   train acc 0.4954   worst 0.0759   lr 0.0041   p 328.70   eps 0.7807   mix 0.0008   time 26.93
scalar:  2.2766
Epoch 986:  train loss 0.7330   train acc 0.4925   worst 0.0752   lr 0.0041   p 330.08   eps 0.7807   mix 0.0008   time 27.49
scalar:  2.2746
Epoch 987:  train loss 0.7311   train acc 0.4962   worst 0.0756   lr 0.0041   p 331.47   eps 0.7807   mix 0.0008   time 27.10
scalar:  2.2797
Epoch 988:  train loss 0.7326   train acc 0.4939   worst 0.0759   lr 0.0041   p 332.87   eps 0.7807   mix 0.0008   time 26.82
scalar:  2.2808
Epoch 989:  train loss 0.7324   train acc 0.4962   worst 0.0753   lr 0.0040   p 334.27   eps 0.7807   mix 0.0008   time 26.64
Epoch 989:  test acc 0.4811   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 989:  clean acc 0.4967   certified acc 0.2972
Calculating metrics for L_infinity dist model on test set
Epoch 989:  clean acc 0.4841   certified acc 0.2830
scalar:  2.2851
Epoch 990:  train loss 0.7306   train acc 0.4941   worst 0.0764   lr 0.0040   p 335.67   eps 0.7807   mix 0.0008   time 27.03
scalar:  2.2758
Epoch 991:  train loss 0.7324   train acc 0.4918   worst 0.0759   lr 0.0040   p 337.08   eps 0.7807   mix 0.0008   time 27.23
scalar:  2.2723
Epoch 992:  train loss 0.7307   train acc 0.4967   worst 0.0774   lr 0.0040   p 338.50   eps 0.7807   mix 0.0008   time 26.72
scalar:  2.2736
Epoch 993:  train loss 0.7306   train acc 0.4964   worst 0.0769   lr 0.0039   p 339.93   eps 0.7807   mix 0.0008   time 26.94
scalar:  2.2848
Epoch 994:  train loss 0.7311   train acc 0.4950   worst 0.0758   lr 0.0039   p 341.36   eps 0.7807   mix 0.0008   time 26.61
Epoch 994:  test acc 0.4843   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 994:  clean acc 0.4948   certified acc 0.2995
Calculating metrics for L_infinity dist model on test set
Epoch 994:  clean acc 0.4824   certified acc 0.2833
scalar:  2.2821
Epoch 995:  train loss 0.7319   train acc 0.4930   worst 0.0745   lr 0.0039   p 342.79   eps 0.7807   mix 0.0008   time 27.04
scalar:  2.2783
Epoch 996:  train loss 0.7320   train acc 0.4952   worst 0.0736   lr 0.0039   p 344.24   eps 0.7807   mix 0.0008   time 27.58
scalar:  2.2872
Epoch 997:  train loss 0.7322   train acc 0.4943   worst 0.0755   lr 0.0038   p 345.68   eps 0.7807   mix 0.0008   time 26.60
scalar:  2.2805
Epoch 998:  train loss 0.7316   train acc 0.4956   worst 0.0757   lr 0.0038   p 347.14   eps 0.7807   mix 0.0008   time 26.88
scalar:  2.2883
Epoch 999:  train loss 0.7324   train acc 0.4933   worst 0.0754   lr 0.0038   p 348.60   eps 0.7807   mix 0.0008   time 26.71
Epoch 999:  test acc 0.4834   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 999:  clean acc 0.4938   certified acc 0.2985
Calculating metrics for L_infinity dist model on test set
Epoch 999:  clean acc 0.4816   certified acc 0.2836
scalar:  2.2792
Epoch 1000:  train loss 0.7309   train acc 0.4942   worst 0.0766   lr 0.0038   p 350.07   eps 0.7807   mix 0.0008   time 27.18
scalar:  2.2793
Epoch 1001:  train loss 0.7312   train acc 0.4970   worst 0.0758   lr 0.0037   p 351.54   eps 0.7807   mix 0.0008   time 27.29
scalar:  2.2864
Epoch 1002:  train loss 0.7319   train acc 0.4954   worst 0.0759   lr 0.0037   p 353.02   eps 0.7807   mix 0.0008   time 26.62
scalar:  2.2884
Epoch 1003:  train loss 0.7327   train acc 0.4942   worst 0.0743   lr 0.0037   p 354.50   eps 0.7807   mix 0.0008   time 26.97
scalar:  2.288
Epoch 1004:  train loss 0.7319   train acc 0.4937   worst 0.0751   lr 0.0037   p 355.99   eps 0.7807   mix 0.0008   time 26.62
Epoch 1004:  test acc 0.4828   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1004:  clean acc 0.4980   certified acc 0.3003
Calculating metrics for L_infinity dist model on test set
Epoch 1004:  clean acc 0.4860   certified acc 0.2846
scalar:  2.2869
Epoch 1005:  train loss 0.7310   train acc 0.4951   worst 0.0753   lr 0.0037   p 357.49   eps 0.7807   mix 0.0008   time 27.39
scalar:  2.2871
Epoch 1006:  train loss 0.7322   train acc 0.4956   worst 0.0762   lr 0.0036   p 359.00   eps 0.7807   mix 0.0007   time 27.63
scalar:  2.2872
Epoch 1007:  train loss 0.7314   train acc 0.4966   worst 0.0755   lr 0.0036   p 360.51   eps 0.7807   mix 0.0007   time 26.69
scalar:  2.2897
Epoch 1008:  train loss 0.7305   train acc 0.4951   worst 0.0758   lr 0.0036   p 362.02   eps 0.7807   mix 0.0007   time 26.83
scalar:  2.2871
Epoch 1009:  train loss 0.7300   train acc 0.4973   worst 0.0764   lr 0.0036   p 363.55   eps 0.7807   mix 0.0007   time 26.64
Epoch 1009:  test acc 0.4818   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1009:  clean acc 0.4990   certified acc 0.2987
Calculating metrics for L_infinity dist model on test set
Epoch 1009:  clean acc 0.4828   certified acc 0.2862
scalar:  2.2889
Epoch 1010:  train loss 0.7321   train acc 0.4940   worst 0.0759   lr 0.0035   p 365.08   eps 0.7807   mix 0.0007   time 27.14
scalar:  2.2825
Epoch 1011:  train loss 0.7309   train acc 0.4959   worst 0.0755   lr 0.0035   p 366.61   eps 0.7807   mix 0.0007   time 27.59
scalar:  2.2837
Epoch 1012:  train loss 0.7314   train acc 0.4943   worst 0.0763   lr 0.0035   p 368.16   eps 0.7807   mix 0.0007   time 26.62
scalar:  2.2815
Epoch 1013:  train loss 0.7312   train acc 0.4934   worst 0.0755   lr 0.0035   p 369.70   eps 0.7807   mix 0.0007   time 27.22
scalar:  2.2844
Epoch 1014:  train loss 0.7312   train acc 0.4945   worst 0.0750   lr 0.0034   p 371.26   eps 0.7807   mix 0.0007   time 26.82
Epoch 1014:  test acc 0.4814   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 1014:  clean acc 0.4970   certified acc 0.2991
Calculating metrics for L_infinity dist model on test set
Epoch 1014:  clean acc 0.4840   certified acc 0.2861
scalar:  2.286
Epoch 1015:  train loss 0.7311   train acc 0.4936   worst 0.0766   lr 0.0034   p 372.82   eps 0.7807   mix 0.0007   time 27.25
scalar:  2.2834
Epoch 1016:  train loss 0.7313   train acc 0.4956   worst 0.0757   lr 0.0034   p 374.39   eps 0.7807   mix 0.0007   time 27.53
scalar:  2.2879
Epoch 1017:  train loss 0.7317   train acc 0.4957   worst 0.0746   lr 0.0034   p 375.97   eps 0.7807   mix 0.0007   time 26.68
scalar:  2.2896
Epoch 1018:  train loss 0.7309   train acc 0.4967   worst 0.0750   lr 0.0034   p 377.55   eps 0.7807   mix 0.0007   time 26.78
scalar:  2.29
Epoch 1019:  train loss 0.7299   train acc 0.4992   worst 0.0763   lr 0.0033   p 379.14   eps 0.7807   mix 0.0007   time 26.61
Epoch 1019:  test acc 0.4825   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1019:  clean acc 0.4993   certified acc 0.3001
Calculating metrics for L_infinity dist model on test set
Epoch 1019:  clean acc 0.4858   certified acc 0.2869
scalar:  2.301
Epoch 1020:  train loss 0.7303   train acc 0.4947   worst 0.0761   lr 0.0033   p 380.73   eps 0.7807   mix 0.0007   time 27.46
scalar:  2.2923
Epoch 1021:  train loss 0.7306   train acc 0.4972   worst 0.0748   lr 0.0033   p 382.33   eps 0.7807   mix 0.0007   time 27.44
scalar:  2.2953
Epoch 1022:  train loss 0.7314   train acc 0.4942   worst 0.0744   lr 0.0033   p 383.94   eps 0.7807   mix 0.0007   time 27.18
scalar:  2.2904
Epoch 1023:  train loss 0.7314   train acc 0.4961   worst 0.0744   lr 0.0032   p 385.56   eps 0.7807   mix 0.0007   time 27.13
scalar:  2.2969
Epoch 1024:  train loss 0.7311   train acc 0.4942   worst 0.0765   lr 0.0032   p 387.18   eps 0.7807   mix 0.0007   time 26.61
Epoch 1024:  test acc 0.4841   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1024:  clean acc 0.5018   certified acc 0.3019
Calculating metrics for L_infinity dist model on test set
Epoch 1024:  clean acc 0.4860   certified acc 0.2861
scalar:  2.2922
Epoch 1025:  train loss 0.7317   train acc 0.4946   worst 0.0759   lr 0.0032   p 388.81   eps 0.7807   mix 0.0007   time 27.40
scalar:  2.2933
Epoch 1026:  train loss 0.7312   train acc 0.4952   worst 0.0761   lr 0.0032   p 390.44   eps 0.7807   mix 0.0007   time 27.33
scalar:  2.2938
Epoch 1027:  train loss 0.7314   train acc 0.4932   worst 0.0762   lr 0.0031   p 392.09   eps 0.7807   mix 0.0007   time 27.08
scalar:  2.288
Epoch 1028:  train loss 0.7309   train acc 0.4971   worst 0.0753   lr 0.0031   p 393.74   eps 0.7807   mix 0.0007   time 26.90
scalar:  2.2925
Epoch 1029:  train loss 0.7299   train acc 0.4975   worst 0.0762   lr 0.0031   p 395.39   eps 0.7807   mix 0.0007   time 26.82
Epoch 1029:  test acc 0.4848   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1029:  clean acc 0.4998   certified acc 0.3010
Calculating metrics for L_infinity dist model on test set
Epoch 1029:  clean acc 0.4858   certified acc 0.2879
scalar:  2.2958
Epoch 1030:  train loss 0.7310   train acc 0.4955   worst 0.0755   lr 0.0031   p 397.06   eps 0.7807   mix 0.0007   time 27.19
scalar:  2.2995
Epoch 1031:  train loss 0.7312   train acc 0.4974   worst 0.0740   lr 0.0031   p 398.73   eps 0.7807   mix 0.0007   time 27.37
scalar:  2.3012
Epoch 1032:  train loss 0.7288   train acc 0.5006   worst 0.0762   lr 0.0030   p 400.40   eps 0.7807   mix 0.0006   time 26.97
scalar:  2.3069
Epoch 1033:  train loss 0.7303   train acc 0.4956   worst 0.0760   lr 0.0030   p 402.09   eps 0.7807   mix 0.0006   time 27.08
scalar:  2.2954
Epoch 1034:  train loss 0.7322   train acc 0.4933   worst 0.0745   lr 0.0030   p 403.78   eps 0.7807   mix 0.0006   time 26.79
Epoch 1034:  test acc 0.4796   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1034:  clean acc 0.4990   certified acc 0.3022
Calculating metrics for L_infinity dist model on test set
Epoch 1034:  clean acc 0.4836   certified acc 0.2868
scalar:  2.2926
Epoch 1035:  train loss 0.7309   train acc 0.4963   worst 0.0751   lr 0.0030   p 405.48   eps 0.7807   mix 0.0006   time 26.93
scalar:  2.2957
Epoch 1036:  train loss 0.7304   train acc 0.4957   worst 0.0754   lr 0.0030   p 407.19   eps 0.7807   mix 0.0006   time 27.49
scalar:  2.2953
Epoch 1037:  train loss 0.7299   train acc 0.4954   worst 0.0750   lr 0.0029   p 408.90   eps 0.7807   mix 0.0006   time 26.92
scalar:  2.2957
Epoch 1038:  train loss 0.7293   train acc 0.4980   worst 0.0756   lr 0.0029   p 410.62   eps 0.7807   mix 0.0006   time 27.09
scalar:  2.3
Epoch 1039:  train loss 0.7301   train acc 0.4954   worst 0.0761   lr 0.0029   p 412.35   eps 0.7807   mix 0.0006   time 26.79
Epoch 1039:  test acc 0.4829   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 1039:  clean acc 0.4991   certified acc 0.3021
Calculating metrics for L_infinity dist model on test set
Epoch 1039:  clean acc 0.4838   certified acc 0.2866
scalar:  2.3025
Epoch 1040:  train loss 0.7306   train acc 0.4944   worst 0.0766   lr 0.0029   p 414.08   eps 0.7807   mix 0.0006   time 27.05
scalar:  2.2972
Epoch 1041:  train loss 0.7301   train acc 0.4974   worst 0.0741   lr 0.0028   p 415.82   eps 0.7807   mix 0.0006   time 27.71
scalar:  2.2986
Epoch 1042:  train loss 0.7298   train acc 0.4957   worst 0.0746   lr 0.0028   p 417.57   eps 0.7807   mix 0.0006   time 27.06
scalar:  2.2964
Epoch 1043:  train loss 0.7303   train acc 0.4960   worst 0.0754   lr 0.0028   p 419.33   eps 0.7807   mix 0.0006   time 26.74
scalar:  2.3008
Epoch 1044:  train loss 0.7304   train acc 0.4960   worst 0.0750   lr 0.0028   p 421.09   eps 0.7807   mix 0.0006   time 26.70
Epoch 1044:  test acc 0.4859   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 1044:  clean acc 0.5001   certified acc 0.3026
Calculating metrics for L_infinity dist model on test set
Epoch 1044:  clean acc 0.4874   certified acc 0.2862
scalar:  2.3008
Epoch 1045:  train loss 0.7310   train acc 0.4944   worst 0.0739   lr 0.0028   p 422.87   eps 0.7807   mix 0.0006   time 26.92
scalar:  2.2942
Epoch 1046:  train loss 0.7301   train acc 0.4967   worst 0.0746   lr 0.0027   p 424.65   eps 0.7807   mix 0.0006   time 27.44
scalar:  2.2939
Epoch 1047:  train loss 0.7311   train acc 0.4961   worst 0.0756   lr 0.0027   p 426.43   eps 0.7807   mix 0.0006   time 27.23
scalar:  2.2984
Epoch 1048:  train loss 0.7290   train acc 0.4982   worst 0.0758   lr 0.0027   p 428.23   eps 0.7807   mix 0.0006   time 27.01
scalar:  2.2966
Epoch 1049:  train loss 0.7303   train acc 0.4968   worst 0.0756   lr 0.0027   p 430.03   eps 0.7807   mix 0.0006   time 26.86
Epoch 1049:  test acc 0.4850   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1049:  clean acc 0.5018   certified acc 0.3031
Calculating metrics for L_infinity dist model on test set
Epoch 1049:  clean acc 0.4871   certified acc 0.2866
scalar:  2.2993
Epoch 1050:  train loss 0.7311   train acc 0.4956   worst 0.0741   lr 0.0027   p 431.84   eps 0.7807   mix 0.0006   time 26.91
scalar:  2.2994
Epoch 1051:  train loss 0.7296   train acc 0.4960   worst 0.0756   lr 0.0026   p 433.65   eps 0.7807   mix 0.0006   time 27.42
scalar:  2.2978
Epoch 1052:  train loss 0.7303   train acc 0.4951   worst 0.0753   lr 0.0026   p 435.48   eps 0.7807   mix 0.0006   time 27.38
scalar:  2.299
Epoch 1053:  train loss 0.7294   train acc 0.4977   worst 0.0752   lr 0.0026   p 437.31   eps 0.7807   mix 0.0006   time 27.01
scalar:  2.3025
Epoch 1054:  train loss 0.7303   train acc 0.4958   worst 0.0767   lr 0.0026   p 439.15   eps 0.7807   mix 0.0006   time 26.79
Epoch 1054:  test acc 0.4827   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 1054:  clean acc 0.5016   certified acc 0.3029
Calculating metrics for L_infinity dist model on test set
Epoch 1054:  clean acc 0.4846   certified acc 0.2847
scalar:  2.3003
Epoch 1055:  train loss 0.7295   train acc 0.4968   worst 0.0769   lr 0.0026   p 441.00   eps 0.7807   mix 0.0006   time 27.06
scalar:  2.3031
Epoch 1056:  train loss 0.7298   train acc 0.4964   worst 0.0753   lr 0.0025   p 442.85   eps 0.7807   mix 0.0006   time 27.17
scalar:  2.3016
Epoch 1057:  train loss 0.7300   train acc 0.4971   worst 0.0748   lr 0.0025   p 444.72   eps 0.7807   mix 0.0006   time 27.41
scalar:  2.3022
Epoch 1058:  train loss 0.7296   train acc 0.4964   worst 0.0758   lr 0.0025   p 446.59   eps 0.7807   mix 0.0006   time 26.86
scalar:  2.3026
Epoch 1059:  train loss 0.7310   train acc 0.4953   worst 0.0748   lr 0.0025   p 448.47   eps 0.7807   mix 0.0006   time 26.77
Epoch 1059:  test acc 0.4842   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1059:  clean acc 0.5002   certified acc 0.3036
Calculating metrics for L_infinity dist model on test set
Epoch 1059:  clean acc 0.4860   certified acc 0.2852
scalar:  2.3053
Epoch 1060:  train loss 0.7297   train acc 0.4971   worst 0.0747   lr 0.0025   p 450.35   eps 0.7807   mix 0.0006   time 27.07
scalar:  2.3051
Epoch 1061:  train loss 0.7302   train acc 0.4978   worst 0.0764   lr 0.0024   p 452.25   eps 0.7807   mix 0.0006   time 26.99
scalar:  2.3045
Epoch 1062:  train loss 0.7291   train acc 0.4965   worst 0.0747   lr 0.0024   p 454.15   eps 0.7807   mix 0.0006   time 27.30
scalar:  2.3043
Epoch 1063:  train loss 0.7305   train acc 0.4974   worst 0.0743   lr 0.0024   p 456.06   eps 0.7807   mix 0.0005   time 27.01
scalar:  2.3053
Epoch 1064:  train loss 0.7290   train acc 0.4975   worst 0.0766   lr 0.0024   p 457.98   eps 0.7807   mix 0.0005   time 26.70
Epoch 1064:  test acc 0.4833   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1064:  clean acc 0.5024   certified acc 0.3030
Calculating metrics for L_infinity dist model on test set
Epoch 1064:  clean acc 0.4877   certified acc 0.2874
scalar:  2.3074
Epoch 1065:  train loss 0.7295   train acc 0.4993   worst 0.0742   lr 0.0024   p 459.91   eps 0.7807   mix 0.0005   time 26.83
scalar:  2.3092
Epoch 1066:  train loss 0.7311   train acc 0.4964   worst 0.0747   lr 0.0023   p 461.84   eps 0.7807   mix 0.0005   time 26.90
scalar:  2.311
Epoch 1067:  train loss 0.7289   train acc 0.4971   worst 0.0752   lr 0.0023   p 463.79   eps 0.7807   mix 0.0005   time 27.20
scalar:  2.3041
Epoch 1068:  train loss 0.7280   train acc 0.4994   worst 0.0752   lr 0.0023   p 465.74   eps 0.7807   mix 0.0005   time 27.10
scalar:  2.3101
Epoch 1069:  train loss 0.7292   train acc 0.4977   worst 0.0748   lr 0.0023   p 467.70   eps 0.7807   mix 0.0005   time 26.80
Epoch 1069:  test acc 0.4838   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 1069:  clean acc 0.5035   certified acc 0.3043
Calculating metrics for L_infinity dist model on test set
Epoch 1069:  clean acc 0.4876   certified acc 0.2847
scalar:  2.3084
Epoch 1070:  train loss 0.7296   train acc 0.4984   worst 0.0747   lr 0.0023   p 469.66   eps 0.7807   mix 0.0005   time 26.93
scalar:  2.3067
Epoch 1071:  train loss 0.7303   train acc 0.4972   worst 0.0747   lr 0.0022   p 471.64   eps 0.7807   mix 0.0005   time 27.07
scalar:  2.3072
Epoch 1072:  train loss 0.7308   train acc 0.4952   worst 0.0743   lr 0.0022   p 473.63   eps 0.7807   mix 0.0005   time 27.21
scalar:  2.3056
Epoch 1073:  train loss 0.7286   train acc 0.4985   worst 0.0770   lr 0.0022   p 475.62   eps 0.7807   mix 0.0005   time 27.15
scalar:  2.3079
Epoch 1074:  train loss 0.7301   train acc 0.4957   worst 0.0756   lr 0.0022   p 477.62   eps 0.7807   mix 0.0005   time 26.80
Epoch 1074:  test acc 0.4832   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1074:  clean acc 0.5034   certified acc 0.3044
Calculating metrics for L_infinity dist model on test set
Epoch 1074:  clean acc 0.4866   certified acc 0.2862
scalar:  2.3062
Epoch 1075:  train loss 0.7305   train acc 0.4978   worst 0.0748   lr 0.0022   p 479.63   eps 0.7807   mix 0.0005   time 27.04
scalar:  2.3087
Epoch 1076:  train loss 0.7300   train acc 0.4970   worst 0.0758   lr 0.0021   p 481.65   eps 0.7807   mix 0.0005   time 27.19
scalar:  2.307
Epoch 1077:  train loss 0.7288   train acc 0.4976   worst 0.0752   lr 0.0021   p 483.67   eps 0.7807   mix 0.0005   time 27.35
scalar:  2.3095
Epoch 1078:  train loss 0.7281   train acc 0.4989   worst 0.0747   lr 0.0021   p 485.71   eps 0.7807   mix 0.0005   time 27.08
scalar:  2.312
Epoch 1079:  train loss 0.7297   train acc 0.4983   worst 0.0751   lr 0.0021   p 487.75   eps 0.7807   mix 0.0005   time 26.60
Epoch 1079:  test acc 0.4842   time 2.52
Calculating metrics for L_infinity dist model on training set
Epoch 1079:  clean acc 0.5023   certified acc 0.3050
Calculating metrics for L_infinity dist model on test set
Epoch 1079:  clean acc 0.4854   certified acc 0.2866
scalar:  2.3112
Epoch 1080:  train loss 0.7283   train acc 0.4967   worst 0.0751   lr 0.0021   p 489.80   eps 0.7807   mix 0.0005   time 27.19
scalar:  2.3087
Epoch 1081:  train loss 0.7293   train acc 0.4968   worst 0.0756   lr 0.0021   p 491.86   eps 0.7807   mix 0.0005   time 26.68
scalar:  2.3075
Epoch 1082:  train loss 0.7300   train acc 0.4974   worst 0.0748   lr 0.0020   p 493.93   eps 0.7807   mix 0.0005   time 27.36
scalar:  2.3105
Epoch 1083:  train loss 0.7293   train acc 0.4963   worst 0.0751   lr 0.0020   p 496.01   eps 0.7807   mix 0.0005   time 27.00
scalar:  2.3084
Epoch 1084:  train loss 0.7285   train acc 0.5002   worst 0.0756   lr 0.0020   p 498.10   eps 0.7807   mix 0.0005   time 26.69
Epoch 1084:  test acc 0.4838   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 1084:  clean acc 0.5062   certified acc 0.3055
Calculating metrics for L_infinity dist model on test set
Epoch 1084:  clean acc 0.4895   certified acc 0.2864
scalar:  2.3145
Epoch 1085:  train loss 0.7289   train acc 0.4977   worst 0.0753   lr 0.0020   p 500.19   eps 0.7807   mix 0.0005   time 26.90
scalar:  2.3132
Epoch 1086:  train loss 0.7299   train acc 0.4971   worst 0.0748   lr 0.0020   p 502.30   eps 0.7807   mix 0.0005   time 26.91
scalar:  2.3096
Epoch 1087:  train loss 0.7287   train acc 0.4977   worst 0.0756   lr 0.0019   p 504.41   eps 0.7807   mix 0.0005   time 26.95
scalar:  2.3092
Epoch 1088:  train loss 0.7286   train acc 0.4974   worst 0.0764   lr 0.0019   p 506.53   eps 0.7807   mix 0.0005   time 27.03
scalar:  2.3087
Epoch 1089:  train loss 0.7297   train acc 0.4960   worst 0.0745   lr 0.0019   p 508.67   eps 0.7807   mix 0.0005   time 26.79
Epoch 1089:  test acc 0.4815   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 1089:  clean acc 0.5026   certified acc 0.3070
Calculating metrics for L_infinity dist model on test set
Epoch 1089:  clean acc 0.4848   certified acc 0.2883
scalar:  2.3067
Epoch 1090:  train loss 0.7298   train acc 0.4977   worst 0.0746   lr 0.0019   p 510.81   eps 0.7807   mix 0.0005   time 26.89
scalar:  2.3096
Epoch 1091:  train loss 0.7293   train acc 0.4977   worst 0.0735   lr 0.0019   p 512.96   eps 0.7807   mix 0.0005   time 26.86
scalar:  2.3104
Epoch 1092:  train loss 0.7289   train acc 0.4995   worst 0.0760   lr 0.0019   p 515.11   eps 0.7807   mix 0.0005   time 27.17
scalar:  2.314
Epoch 1093:  train loss 0.7288   train acc 0.4982   worst 0.0746   lr 0.0018   p 517.28   eps 0.7807   mix 0.0005   time 27.19
scalar:  2.3148
Epoch 1094:  train loss 0.7287   train acc 0.4993   worst 0.0762   lr 0.0018   p 519.46   eps 0.7807   mix 0.0005   time 26.95
Epoch 1094:  test acc 0.4854   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1094:  clean acc 0.5047   certified acc 0.3066
Calculating metrics for L_infinity dist model on test set
Epoch 1094:  clean acc 0.4880   certified acc 0.2863
scalar:  2.3122
Epoch 1095:  train loss 0.7299   train acc 0.4981   worst 0.0740   lr 0.0018   p 521.64   eps 0.7807   mix 0.0005   time 27.12
scalar:  2.3112
Epoch 1096:  train loss 0.7302   train acc 0.4977   worst 0.0749   lr 0.0018   p 523.84   eps 0.7807   mix 0.0005   time 26.60
scalar:  2.3113
Epoch 1097:  train loss 0.7284   train acc 0.4982   worst 0.0752   lr 0.0018   p 526.04   eps 0.7807   mix 0.0005   time 27.17
scalar:  2.3121
Epoch 1098:  train loss 0.7284   train acc 0.4981   worst 0.0750   lr 0.0018   p 528.25   eps 0.7807   mix 0.0005   time 27.18
scalar:  2.313
Epoch 1099:  train loss 0.7283   train acc 0.4987   worst 0.0751   lr 0.0017   p 530.48   eps 0.7807   mix 0.0005   time 27.25
Epoch 1099:  test acc 0.4873   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 1099:  clean acc 0.5026   certified acc 0.3066
Calculating metrics for L_infinity dist model on test set
Epoch 1099:  clean acc 0.4887   certified acc 0.2888
scalar:  2.3129
Epoch 1100:  train loss 0.7281   train acc 0.4987   worst 0.0745   lr 0.0017   p 532.71   eps 0.7807   mix 0.0004   time 26.72
scalar:  2.3144
Epoch 1101:  train loss 0.7289   train acc 0.4982   worst 0.0744   lr 0.0017   p 534.95   eps 0.7807   mix 0.0004   time 27.00
scalar:  2.3161
Epoch 1102:  train loss 0.7290   train acc 0.4980   worst 0.0750   lr 0.0017   p 537.20   eps 0.7807   mix 0.0004   time 27.08
scalar:  2.315
Epoch 1103:  train loss 0.7295   train acc 0.4975   worst 0.0750   lr 0.0017   p 539.46   eps 0.7807   mix 0.0004   time 26.88
scalar:  2.3126
Epoch 1104:  train loss 0.7279   train acc 0.5006   worst 0.0762   lr 0.0017   p 541.73   eps 0.7807   mix 0.0004   time 27.16
Epoch 1104:  test acc 0.4834   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1104:  clean acc 0.5051   certified acc 0.3056
Calculating metrics for L_infinity dist model on test set
Epoch 1104:  clean acc 0.4861   certified acc 0.2885
scalar:  2.3143
Epoch 1105:  train loss 0.7297   train acc 0.4975   worst 0.0735   lr 0.0016   p 544.01   eps 0.7807   mix 0.0004   time 27.16
scalar:  2.3145
Epoch 1106:  train loss 0.7297   train acc 0.4960   worst 0.0766   lr 0.0016   p 546.30   eps 0.7807   mix 0.0004   time 27.06
scalar:  2.3104
Epoch 1107:  train loss 0.7283   train acc 0.4971   worst 0.0752   lr 0.0016   p 548.60   eps 0.7807   mix 0.0004   time 26.80
scalar:  2.3096
Epoch 1108:  train loss 0.7286   train acc 0.4971   worst 0.0749   lr 0.0016   p 550.91   eps 0.7807   mix 0.0004   time 27.51
scalar:  2.3125
Epoch 1109:  train loss 0.7275   train acc 0.4977   worst 0.0767   lr 0.0016   p 553.22   eps 0.7807   mix 0.0004   time 26.91
Epoch 1109:  test acc 0.4854   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1109:  clean acc 0.5029   certified acc 0.3062
Calculating metrics for L_infinity dist model on test set
Epoch 1109:  clean acc 0.4894   certified acc 0.2886
scalar:  2.311
Epoch 1110:  train loss 0.7279   train acc 0.4986   worst 0.0747   lr 0.0016   p 555.55   eps 0.7807   mix 0.0004   time 27.00
scalar:  2.3096
Epoch 1111:  train loss 0.7302   train acc 0.4975   worst 0.0742   lr 0.0015   p 557.89   eps 0.7807   mix 0.0004   time 26.89
scalar:  2.3109
Epoch 1112:  train loss 0.7292   train acc 0.4974   worst 0.0747   lr 0.0015   p 560.24   eps 0.7807   mix 0.0004   time 27.04
scalar:  2.3116
Epoch 1113:  train loss 0.7291   train acc 0.4983   worst 0.0750   lr 0.0015   p 562.59   eps 0.7807   mix 0.0004   time 27.17
scalar:  2.3118
Epoch 1114:  train loss 0.7285   train acc 0.4980   worst 0.0761   lr 0.0015   p 564.96   eps 0.7807   mix 0.0004   time 27.13
Epoch 1114:  test acc 0.4829   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 1114:  clean acc 0.5014   certified acc 0.3055
Calculating metrics for L_infinity dist model on test set
Epoch 1114:  clean acc 0.4855   certified acc 0.2894
scalar:  2.3135
Epoch 1115:  train loss 0.7290   train acc 0.4985   worst 0.0746   lr 0.0015   p 567.34   eps 0.7807   mix 0.0004   time 26.60
scalar:  2.3121
Epoch 1116:  train loss 0.7290   train acc 0.4982   worst 0.0747   lr 0.0015   p 569.72   eps 0.7807   mix 0.0004   time 26.73
scalar:  2.3143
Epoch 1117:  train loss 0.7277   train acc 0.4978   worst 0.0760   lr 0.0014   p 572.12   eps 0.7807   mix 0.0004   time 26.93
scalar:  2.3137
Epoch 1118:  train loss 0.7274   train acc 0.4996   worst 0.0759   lr 0.0014   p 574.53   eps 0.7807   mix 0.0004   time 27.01
scalar:  2.3139
Epoch 1119:  train loss 0.7281   train acc 0.4981   worst 0.0746   lr 0.0014   p 576.95   eps 0.7807   mix 0.0004   time 27.13
Epoch 1119:  test acc 0.4843   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1119:  clean acc 0.5016   certified acc 0.3051
Calculating metrics for L_infinity dist model on test set
Epoch 1119:  clean acc 0.4874   certified acc 0.2866
scalar:  2.3149
Epoch 1120:  train loss 0.7298   train acc 0.4960   worst 0.0748   lr 0.0014   p 579.37   eps 0.7807   mix 0.0004   time 27.24
scalar:  2.3147
Epoch 1121:  train loss 0.7283   train acc 0.4972   worst 0.0750   lr 0.0014   p 581.81   eps 0.7807   mix 0.0004   time 27.10
scalar:  2.3153
Epoch 1122:  train loss 0.7278   train acc 0.4999   worst 0.0761   lr 0.0014   p 584.26   eps 0.7807   mix 0.0004   time 26.98
scalar:  2.3176
Epoch 1123:  train loss 0.7281   train acc 0.4994   worst 0.0744   lr 0.0014   p 586.72   eps 0.7807   mix 0.0004   time 27.43
scalar:  2.3186
Epoch 1124:  train loss 0.7291   train acc 0.4986   worst 0.0752   lr 0.0013   p 589.18   eps 0.7807   mix 0.0004   time 27.26
Epoch 1124:  test acc 0.4853   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1124:  clean acc 0.5004   certified acc 0.3066
Calculating metrics for L_infinity dist model on test set
Epoch 1124:  clean acc 0.4868   certified acc 0.2878
scalar:  2.316
Epoch 1125:  train loss 0.7273   train acc 0.4996   worst 0.0752   lr 0.0013   p 591.66   eps 0.7807   mix 0.0004   time 27.41
scalar:  2.3151
Epoch 1126:  train loss 0.7284   train acc 0.4976   worst 0.0750   lr 0.0013   p 594.15   eps 0.7807   mix 0.0004   time 27.32
scalar:  2.3152
Epoch 1127:  train loss 0.7279   train acc 0.4975   worst 0.0749   lr 0.0013   p 596.65   eps 0.7807   mix 0.0004   time 26.99
scalar:  2.3156
Epoch 1128:  train loss 0.7278   train acc 0.5002   worst 0.0746   lr 0.0013   p 599.16   eps 0.7807   mix 0.0004   time 27.52
scalar:  2.3186
Epoch 1129:  train loss 0.7279   train acc 0.4978   worst 0.0745   lr 0.0013   p 601.68   eps 0.7807   mix 0.0004   time 27.05
Epoch 1129:  test acc 0.4854   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1129:  clean acc 0.5038   certified acc 0.3072
Calculating metrics for L_infinity dist model on test set
Epoch 1129:  clean acc 0.4872   certified acc 0.2882
scalar:  2.3178
Epoch 1130:  train loss 0.7294   train acc 0.4995   worst 0.0750   lr 0.0012   p 604.22   eps 0.7807   mix 0.0004   time 26.87
scalar:  2.3177
Epoch 1131:  train loss 0.7281   train acc 0.4986   worst 0.0750   lr 0.0012   p 606.76   eps 0.7807   mix 0.0004   time 27.23
scalar:  2.319
Epoch 1132:  train loss 0.7287   train acc 0.4983   worst 0.0740   lr 0.0012   p 609.31   eps 0.7807   mix 0.0004   time 27.08
scalar:  2.3193
Epoch 1133:  train loss 0.7296   train acc 0.4965   worst 0.0756   lr 0.0012   p 611.87   eps 0.7807   mix 0.0004   time 27.14
scalar:  2.3171
Epoch 1134:  train loss 0.7285   train acc 0.4966   worst 0.0748   lr 0.0012   p 614.45   eps 0.7807   mix 0.0004   time 27.23
Epoch 1134:  test acc 0.4844   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 1134:  clean acc 0.5030   certified acc 0.3082
Calculating metrics for L_infinity dist model on test set
Epoch 1134:  clean acc 0.4867   certified acc 0.2878
scalar:  2.3174
Epoch 1135:  train loss 0.7271   train acc 0.5002   worst 0.0752   lr 0.0012   p 617.03   eps 0.7807   mix 0.0004   time 27.01
scalar:  2.3181
Epoch 1136:  train loss 0.7277   train acc 0.4988   worst 0.0756   lr 0.0012   p 619.63   eps 0.7807   mix 0.0004   time 27.05
scalar:  2.3175
Epoch 1137:  train loss 0.7281   train acc 0.4993   worst 0.0749   lr 0.0011   p 622.24   eps 0.7807   mix 0.0004   time 26.96
scalar:  2.3181
Epoch 1138:  train loss 0.7275   train acc 0.4989   worst 0.0736   lr 0.0011   p 624.85   eps 0.7807   mix 0.0004   time 27.09
scalar:  2.3182
Epoch 1139:  train loss 0.7279   train acc 0.5001   worst 0.0760   lr 0.0011   p 627.48   eps 0.7807   mix 0.0004   time 27.10
Epoch 1139:  test acc 0.4846   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 1139:  clean acc 0.5038   certified acc 0.3083
Calculating metrics for L_infinity dist model on test set
Epoch 1139:  clean acc 0.4854   certified acc 0.2879
scalar:  2.3175
Epoch 1140:  train loss 0.7288   train acc 0.4969   worst 0.0743   lr 0.0011   p 630.12   eps 0.7807   mix 0.0004   time 27.17
scalar:  2.3181
Epoch 1141:  train loss 0.7275   train acc 0.4994   worst 0.0753   lr 0.0011   p 632.78   eps 0.7807   mix 0.0004   time 27.15
scalar:  2.3188
Epoch 1142:  train loss 0.7278   train acc 0.4998   worst 0.0742   lr 0.0011   p 635.44   eps 0.7807   mix 0.0004   time 26.75
scalar:  2.3212
Epoch 1143:  train loss 0.7287   train acc 0.4998   worst 0.0742   lr 0.0011   p 638.11   eps 0.7807   mix 0.0004   time 27.13
scalar:  2.3228
Epoch 1144:  train loss 0.7284   train acc 0.4966   worst 0.0755   lr 0.0011   p 640.80   eps 0.7807   mix 0.0004   time 26.71
Epoch 1144:  test acc 0.4849   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 1144:  clean acc 0.5032   certified acc 0.3079
Calculating metrics for L_infinity dist model on test set
Epoch 1144:  clean acc 0.4867   certified acc 0.2880
scalar:  2.3223
Epoch 1145:  train loss 0.7277   train acc 0.4980   worst 0.0736   lr 0.0010   p 643.49   eps 0.7807   mix 0.0004   time 26.94
scalar:  2.3236
Epoch 1146:  train loss 0.7283   train acc 0.4980   worst 0.0751   lr 0.0010   p 646.20   eps 0.7807   mix 0.0004   time 27.16
scalar:  2.323
Epoch 1147:  train loss 0.7275   train acc 0.4995   worst 0.0744   lr 0.0010   p 648.92   eps 0.7807   mix 0.0003   time 26.94
scalar:  2.3231
Epoch 1148:  train loss 0.7279   train acc 0.4974   worst 0.0758   lr 0.0010   p 651.65   eps 0.7807   mix 0.0003   time 27.30
scalar:  2.321
Epoch 1149:  train loss 0.7277   train acc 0.4980   worst 0.0757   lr 0.0010   p 654.39   eps 0.7807   mix 0.0003   time 27.11
Epoch 1149:  test acc 0.4839   time 2.59
Calculating metrics for L_infinity dist model on training set
Epoch 1149:  clean acc 0.5022   certified acc 0.3076
Calculating metrics for L_infinity dist model on test set
Epoch 1149:  clean acc 0.4858   certified acc 0.2880
scalar:  2.3199
Epoch 1150:  train loss 0.7282   train acc 0.4973   worst 0.0751   lr 0.0010   p 657.14   eps 0.7807   mix 0.0003   time 26.89
scalar:  2.3199
Epoch 1151:  train loss 0.7281   train acc 0.4972   worst 0.0745   lr 0.0010   p 659.91   eps 0.7807   mix 0.0003   time 26.68
scalar:  2.3201
Epoch 1152:  train loss 0.7285   train acc 0.4963   worst 0.0755   lr 0.0009   p 662.68   eps 0.7807   mix 0.0003   time 27.13
scalar:  2.3191
Epoch 1153:  train loss 0.7276   train acc 0.4985   worst 0.0759   lr 0.0009   p 665.47   eps 0.7807   mix 0.0003   time 26.98
scalar:  2.3191
Epoch 1154:  train loss 0.7271   train acc 0.5003   worst 0.0749   lr 0.0009   p 668.27   eps 0.7807   mix 0.0003   time 27.52
Epoch 1154:  test acc 0.4850   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1154:  clean acc 0.5032   certified acc 0.3109
Calculating metrics for L_infinity dist model on test set
Epoch 1154:  clean acc 0.4844   certified acc 0.2882
scalar:  2.3203
Epoch 1155:  train loss 0.7278   train acc 0.4968   worst 0.0747   lr 0.0009   p 671.08   eps 0.7807   mix 0.0003   time 27.01
scalar:  2.3197
Epoch 1156:  train loss 0.7273   train acc 0.5000   worst 0.0751   lr 0.0009   p 673.91   eps 0.7807   mix 0.0003   time 26.88
scalar:  2.3215
Epoch 1157:  train loss 0.7278   train acc 0.5003   worst 0.0748   lr 0.0009   p 676.74   eps 0.7807   mix 0.0003   time 27.19
scalar:  2.3223
Epoch 1158:  train loss 0.7271   train acc 0.5006   worst 0.0743   lr 0.0009   p 679.59   eps 0.7807   mix 0.0003   time 26.94
scalar:  2.3239
Epoch 1159:  train loss 0.7269   train acc 0.5011   worst 0.0750   lr 0.0009   p 682.45   eps 0.7807   mix 0.0003   time 27.29
Epoch 1159:  test acc 0.4852   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 1159:  clean acc 0.5032   certified acc 0.3060
Calculating metrics for L_infinity dist model on test set
Epoch 1159:  clean acc 0.4865   certified acc 0.2878
scalar:  2.3249
Epoch 1160:  train loss 0.7285   train acc 0.4986   worst 0.0743   lr 0.0009   p 685.32   eps 0.7807   mix 0.0003   time 27.18
scalar:  2.3252
Epoch 1161:  train loss 0.7273   train acc 0.4995   worst 0.0747   lr 0.0008   p 688.20   eps 0.7807   mix 0.0003   time 26.87
scalar:  2.3257
Epoch 1162:  train loss 0.7274   train acc 0.4997   worst 0.0747   lr 0.0008   p 691.10   eps 0.7807   mix 0.0003   time 27.19
scalar:  2.3254
Epoch 1163:  train loss 0.7285   train acc 0.4998   worst 0.0752   lr 0.0008   p 694.01   eps 0.7807   mix 0.0003   time 26.66
scalar:  2.3258
Epoch 1164:  train loss 0.7278   train acc 0.4992   worst 0.0745   lr 0.0008   p 696.93   eps 0.7807   mix 0.0003   time 27.39
Epoch 1164:  test acc 0.4852   time 2.60
Calculating metrics for L_infinity dist model on training set
Epoch 1164:  clean acc 0.5064   certified acc 0.3104
Calculating metrics for L_infinity dist model on test set
Epoch 1164:  clean acc 0.4889   certified acc 0.2896
scalar:  2.3255
Epoch 1165:  train loss 0.7270   train acc 0.5012   worst 0.0752   lr 0.0008   p 699.86   eps 0.7807   mix 0.0003   time 27.22
scalar:  2.3254
Epoch 1166:  train loss 0.7278   train acc 0.4977   worst 0.0751   lr 0.0008   p 702.80   eps 0.7807   mix 0.0003   time 27.16
scalar:  2.3252
Epoch 1167:  train loss 0.7268   train acc 0.4986   worst 0.0734   lr 0.0008   p 705.76   eps 0.7807   mix 0.0003   time 27.02
scalar:  2.326
Epoch 1168:  train loss 0.7273   train acc 0.5008   worst 0.0735   lr 0.0008   p 708.73   eps 0.7807   mix 0.0003   time 26.79
scalar:  2.3262
Epoch 1169:  train loss 0.7280   train acc 0.4976   worst 0.0743   lr 0.0007   p 711.71   eps 0.7807   mix 0.0003   time 27.29
Epoch 1169:  test acc 0.4842   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 1169:  clean acc 0.5027   certified acc 0.3082
Calculating metrics for L_infinity dist model on test set
Epoch 1169:  clean acc 0.4860   certified acc 0.2879
scalar:  2.3258
Epoch 1170:  train loss 0.7280   train acc 0.4997   worst 0.0750   lr 0.0007   p 714.71   eps 0.7807   mix 0.0003   time 27.45
scalar:  2.3254
Epoch 1171:  train loss 0.7276   train acc 0.4990   worst 0.0744   lr 0.0007   p 717.71   eps 0.7807   mix 0.0003   time 27.21
scalar:  2.326
Epoch 1172:  train loss 0.7282   train acc 0.4991   worst 0.0748   lr 0.0007   p 720.73   eps 0.7807   mix 0.0003   time 27.23
scalar:  2.3257
Epoch 1173:  train loss 0.7269   train acc 0.4998   worst 0.0740   lr 0.0007   p 723.77   eps 0.7807   mix 0.0003   time 26.90
scalar:  2.3268
Epoch 1174:  train loss 0.7281   train acc 0.4988   worst 0.0740   lr 0.0007   p 726.81   eps 0.7807   mix 0.0003   time 27.51
Epoch 1174:  test acc 0.4852   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1174:  clean acc 0.5050   certified acc 0.3098
Calculating metrics for L_infinity dist model on test set
Epoch 1174:  clean acc 0.4891   certified acc 0.2880
scalar:  2.3266
Epoch 1175:  train loss 0.7273   train acc 0.5003   worst 0.0755   lr 0.0007   p 729.87   eps 0.7807   mix 0.0003   time 27.19
scalar:  2.3264
Epoch 1176:  train loss 0.7274   train acc 0.5007   worst 0.0745   lr 0.0007   p 732.94   eps 0.7807   mix 0.0003   time 26.43
scalar:  2.3272
Epoch 1177:  train loss 0.7280   train acc 0.5002   worst 0.0749   lr 0.0007   p 736.02   eps 0.7807   mix 0.0003   time 27.01
scalar:  2.327
Epoch 1178:  train loss 0.7277   train acc 0.5008   worst 0.0743   lr 0.0006   p 739.12   eps 0.7807   mix 0.0003   time 26.65
scalar:  2.3288
Epoch 1179:  train loss 0.7264   train acc 0.5015   worst 0.0736   lr 0.0006   p 742.23   eps 0.7807   mix 0.0003   time 27.22
Epoch 1179:  test acc 0.4841   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 1179:  clean acc 0.5036   certified acc 0.3111
Calculating metrics for L_infinity dist model on test set
Epoch 1179:  clean acc 0.4868   certified acc 0.2891
scalar:  2.33
Epoch 1180:  train loss 0.7278   train acc 0.5008   worst 0.0739   lr 0.0006   p 745.35   eps 0.7807   mix 0.0003   time 27.07
scalar:  2.3306
Epoch 1181:  train loss 0.7276   train acc 0.5001   worst 0.0740   lr 0.0006   p 748.49   eps 0.7807   mix 0.0003   time 27.04
scalar:  2.3311
Epoch 1182:  train loss 0.7277   train acc 0.4984   worst 0.0742   lr 0.0006   p 751.64   eps 0.7807   mix 0.0003   time 26.99
scalar:  2.3295
Epoch 1183:  train loss 0.7256   train acc 0.4994   worst 0.0758   lr 0.0006   p 754.80   eps 0.7807   mix 0.0003   time 26.49
scalar:  2.3291
Epoch 1184:  train loss 0.7264   train acc 0.4990   worst 0.0739   lr 0.0006   p 757.98   eps 0.7807   mix 0.0003   time 27.32
Epoch 1184:  test acc 0.4838   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1184:  clean acc 0.5026   certified acc 0.3112
Calculating metrics for L_infinity dist model on test set
Epoch 1184:  clean acc 0.4865   certified acc 0.2883
scalar:  2.3287
Epoch 1185:  train loss 0.7278   train acc 0.4999   worst 0.0743   lr 0.0006   p 761.17   eps 0.7807   mix 0.0003   time 27.14
scalar:  2.3295
Epoch 1186:  train loss 0.7278   train acc 0.4995   worst 0.0741   lr 0.0006   p 764.37   eps 0.7807   mix 0.0003   time 26.91
scalar:  2.3299
Epoch 1187:  train loss 0.7264   train acc 0.4999   worst 0.0752   lr 0.0006   p 767.58   eps 0.7807   mix 0.0003   time 26.72
scalar:  2.33
Epoch 1188:  train loss 0.7282   train acc 0.5005   worst 0.0741   lr 0.0005   p 770.81   eps 0.7807   mix 0.0003   time 26.62
scalar:  2.3307
Epoch 1189:  train loss 0.7280   train acc 0.4975   worst 0.0747   lr 0.0005   p 774.06   eps 0.7807   mix 0.0003   time 27.30
Epoch 1189:  test acc 0.4819   time 2.57
Calculating metrics for L_infinity dist model on training set
Epoch 1189:  clean acc 0.5050   certified acc 0.3127
Calculating metrics for L_infinity dist model on test set
Epoch 1189:  clean acc 0.4858   certified acc 0.2890
scalar:  2.3308
Epoch 1190:  train loss 0.7267   train acc 0.5004   worst 0.0741   lr 0.0005   p 777.31   eps 0.7807   mix 0.0003   time 26.97
scalar:  2.331
Epoch 1191:  train loss 0.7274   train acc 0.4987   worst 0.0745   lr 0.0005   p 780.58   eps 0.7807   mix 0.0003   time 26.75
scalar:  2.3312
Epoch 1192:  train loss 0.7274   train acc 0.5002   worst 0.0754   lr 0.0005   p 783.87   eps 0.7807   mix 0.0003   time 27.41
scalar:  2.3312
Epoch 1193:  train loss 0.7272   train acc 0.5013   worst 0.0735   lr 0.0005   p 787.17   eps 0.7807   mix 0.0003   time 26.59
scalar:  2.3323
Epoch 1194:  train loss 0.7260   train acc 0.5002   worst 0.0752   lr 0.0005   p 790.48   eps 0.7807   mix 0.0003   time 27.21
Epoch 1194:  test acc 0.4850   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1194:  clean acc 0.5057   certified acc 0.3090
Calculating metrics for L_infinity dist model on test set
Epoch 1194:  clean acc 0.4852   certified acc 0.2904
scalar:  2.3321
Epoch 1195:  train loss 0.7262   train acc 0.5010   worst 0.0746   lr 0.0005   p 793.80   eps 0.7807   mix 0.0003   time 26.61
scalar:  2.3322
Epoch 1196:  train loss 0.7266   train acc 0.5019   worst 0.0746   lr 0.0005   p 797.14   eps 0.7807   mix 0.0003   time 27.38
scalar:  2.3327
Epoch 1197:  train loss 0.7262   train acc 0.5018   worst 0.0751   lr 0.0005   p 800.50   eps 0.7807   mix 0.0003   time 26.94
scalar:  2.3333
Epoch 1198:  train loss 0.7281   train acc 0.5006   worst 0.0728   lr 0.0005   p 803.87   eps 0.7807   mix 0.0003   time 27.05
scalar:  2.3339
Epoch 1199:  train loss 0.7261   train acc 0.4998   worst 0.0746   lr 0.0004   p 807.25   eps 0.7807   mix 0.0003   time 26.82
Epoch 1199:  test acc 0.4850   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 1199:  clean acc 0.5059   certified acc 0.3100
Calculating metrics for L_infinity dist model on test set
Epoch 1199:  clean acc 0.4878   certified acc 0.2897
Generate adversarial examples on test dataset
adversarial attack acc 29.2800
scalar:  2.3333
Epoch 1200:  train loss 0.7271   train acc 0.5017   worst 0.0749   lr 0.0004   p 810.64   eps 0.7807   mix 0.0003   time 26.45
scalar:  2.334
Epoch 1201:  train loss 0.7276   train acc 0.4995   worst 0.0749   lr 0.0004   p 814.05   eps 0.7807   mix 0.0003   time 26.90
scalar:  2.3342
Epoch 1202:  train loss 0.7267   train acc 0.4992   worst 0.0748   lr 0.0004   p 817.48   eps 0.7807   mix 0.0003   time 26.90
scalar:  2.3341
Epoch 1203:  train loss 0.7266   train acc 0.5002   worst 0.0731   lr 0.0004   p 820.92   eps 0.7807   mix 0.0003   time 26.63
scalar:  2.3336
Epoch 1204:  train loss 0.7261   train acc 0.5016   worst 0.0740   lr 0.0004   p 824.37   eps 0.7807   mix 0.0003   time 27.13
Epoch 1204:  test acc 0.4846   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 1204:  clean acc 0.5062   certified acc 0.3129
Calculating metrics for L_infinity dist model on test set
Epoch 1204:  clean acc 0.4859   certified acc 0.2892
scalar:  2.3337
Epoch 1205:  train loss 0.7262   train acc 0.5012   worst 0.0739   lr 0.0004   p 827.84   eps 0.7807   mix 0.0003   time 26.59
scalar:  2.3344
Epoch 1206:  train loss 0.7260   train acc 0.5026   worst 0.0752   lr 0.0004   p 831.32   eps 0.7807   mix 0.0003   time 26.90
scalar:  2.3347
Epoch 1207:  train loss 0.7270   train acc 0.4999   worst 0.0742   lr 0.0004   p 834.82   eps 0.7807   mix 0.0003   time 26.49
scalar:  2.3348
Epoch 1208:  train loss 0.7271   train acc 0.4997   worst 0.0743   lr 0.0004   p 838.33   eps 0.7807   mix 0.0003   time 26.29
scalar:  2.3344
Epoch 1209:  train loss 0.7264   train acc 0.5004   worst 0.0756   lr 0.0004   p 841.86   eps 0.7807   mix 0.0002   time 26.33
Epoch 1209:  test acc 0.4837   time 2.54
Calculating metrics for L_infinity dist model on training set
Epoch 1209:  clean acc 0.5035   certified acc 0.3101
Calculating metrics for L_infinity dist model on test set
Epoch 1209:  clean acc 0.4846   certified acc 0.2897
scalar:  2.3343
Epoch 1210:  train loss 0.7259   train acc 0.5031   worst 0.0746   lr 0.0004   p 845.40   eps 0.7807   mix 0.0002   time 26.36
scalar:  2.3353
Epoch 1211:  train loss 0.7256   train acc 0.5015   worst 0.0757   lr 0.0003   p 848.96   eps 0.7807   mix 0.0002   time 26.24
scalar:  2.3361
Epoch 1212:  train loss 0.7273   train acc 0.4976   worst 0.0746   lr 0.0003   p 852.53   eps 0.7807   mix 0.0002   time 26.14
scalar:  2.3351
Epoch 1213:  train loss 0.7264   train acc 0.5008   worst 0.0742   lr 0.0003   p 856.12   eps 0.7807   mix 0.0002   time 26.14
scalar:  2.3356
Epoch 1214:  train loss 0.7273   train acc 0.4999   worst 0.0744   lr 0.0003   p 859.72   eps 0.7807   mix 0.0002   time 26.12
Epoch 1214:  test acc 0.4843   time 2.50
Calculating metrics for L_infinity dist model on training set
Epoch 1214:  clean acc 0.5024   certified acc 0.3118
Calculating metrics for L_infinity dist model on test set
Epoch 1214:  clean acc 0.4859   certified acc 0.2891
scalar:  2.3355
Epoch 1215:  train loss 0.7270   train acc 0.5008   worst 0.0741   lr 0.0003   p 863.34   eps 0.7807   mix 0.0002   time 25.89
scalar:  2.3356
Epoch 1216:  train loss 0.7261   train acc 0.5028   worst 0.0744   lr 0.0003   p 866.97   eps 0.7807   mix 0.0002   time 26.30
scalar:  2.3357
Epoch 1217:  train loss 0.7278   train acc 0.4992   worst 0.0749   lr 0.0003   p 870.62   eps 0.7807   mix 0.0002   time 26.05
scalar:  2.3358
Epoch 1218:  train loss 0.7273   train acc 0.4986   worst 0.0752   lr 0.0003   p 874.28   eps 0.7807   mix 0.0002   time 26.03
scalar:  2.336
Epoch 1219:  train loss 0.7281   train acc 0.5004   worst 0.0745   lr 0.0003   p 877.96   eps 0.7807   mix 0.0002   time 26.07
Epoch 1219:  test acc 0.4866   time 2.55
Calculating metrics for L_infinity dist model on training set
Epoch 1219:  clean acc 0.5043   certified acc 0.3116
Calculating metrics for L_infinity dist model on test set
Epoch 1219:  clean acc 0.4868   certified acc 0.2885
scalar:  2.3361
Epoch 1220:  train loss 0.7277   train acc 0.5006   worst 0.0736   lr 0.0003   p 881.65   eps 0.7807   mix 0.0002   time 26.02
scalar:  2.3363
Epoch 1221:  train loss 0.7275   train acc 0.4993   worst 0.0753   lr 0.0003   p 885.36   eps 0.7807   mix 0.0002   time 26.41
scalar:  2.3363
Epoch 1222:  train loss 0.7275   train acc 0.4995   worst 0.0749   lr 0.0003   p 889.09   eps 0.7807   mix 0.0002   time 26.43
scalar:  2.3359
Epoch 1223:  train loss 0.7270   train acc 0.4991   worst 0.0739   lr 0.0003   p 892.83   eps 0.7807   mix 0.0002   time 26.51
scalar:  2.3357
Epoch 1224:  train loss 0.7270   train acc 0.4991   worst 0.0743   lr 0.0003   p 896.59   eps 0.7807   mix 0.0002   time 26.63
Epoch 1224:  test acc 0.4854   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1224:  clean acc 0.5054   certified acc 0.3118
Calculating metrics for L_infinity dist model on test set
Epoch 1224:  clean acc 0.4864   certified acc 0.2896
scalar:  2.3359
Epoch 1225:  train loss 0.7269   train acc 0.4996   worst 0.0737   lr 0.0002   p 900.36   eps 0.7807   mix 0.0002   time 26.76
scalar:  2.3361
Epoch 1226:  train loss 0.7263   train acc 0.5000   worst 0.0753   lr 0.0002   p 904.15   eps 0.7807   mix 0.0002   time 26.96
scalar:  2.336
Epoch 1227:  train loss 0.7287   train acc 0.4971   worst 0.0738   lr 0.0002   p 907.95   eps 0.7807   mix 0.0002   time 26.82
scalar:  2.3357
Epoch 1228:  train loss 0.7271   train acc 0.4983   worst 0.0745   lr 0.0002   p 911.77   eps 0.7807   mix 0.0002   time 26.65
scalar:  2.3352
Epoch 1229:  train loss 0.7260   train acc 0.5006   worst 0.0751   lr 0.0002   p 915.61   eps 0.7807   mix 0.0002   time 27.03
Epoch 1229:  test acc 0.4861   time 2.58
Calculating metrics for L_infinity dist model on training set
Epoch 1229:  clean acc 0.5039   certified acc 0.3088
Calculating metrics for L_infinity dist model on test set
Epoch 1229:  clean acc 0.4881   certified acc 0.2902
scalar:  2.3352
Epoch 1230:  train loss 0.7263   train acc 0.4991   worst 0.0749   lr 0.0002   p 919.46   eps 0.7807   mix 0.0002   time 26.80
scalar:  2.3355
Epoch 1231:  train loss 0.7279   train acc 0.5007   worst 0.0742   lr 0.0002   p 923.33   eps 0.7807   mix 0.0002   time 27.11
scalar:  2.3354
Epoch 1232:  train loss 0.7270   train acc 0.5016   worst 0.0757   lr 0.0002   p 927.21   eps 0.7807   mix 0.0002   time 26.85
scalar:  2.3359
Epoch 1233:  train loss 0.7270   train acc 0.5003   worst 0.0735   lr 0.0002   p 931.11   eps 0.7807   mix 0.0002   time 26.69
scalar:  2.3358
Epoch 1234:  train loss 0.7256   train acc 0.5000   worst 0.0744   lr 0.0002   p 935.03   eps 0.7807   mix 0.0002   time 27.02
Epoch 1234:  test acc 0.4864   time 2.53
Calculating metrics for L_infinity dist model on training set
Epoch 1234:  clean acc 0.5051   certified acc 0.3109
Calculating metrics for L_infinity dist model on test set
Epoch 1234:  clean acc 0.4893   certified acc 0.2903
scalar:  2.3358
Epoch 1235:  train loss 0.7265   train acc 0.5020   worst 0.0741   lr 0.0002   p 938.96   eps 0.7807   mix 0.0002   time 26.77
scalar:  2.3361
Epoch 1236:  train loss 0.7267   train acc 0.5004   worst 0.0737   lr 0.0002   p 942.91   eps 0.7807   mix 0.0002   time 27.29
scalar:  2.3364
Epoch 1237:  train loss 0.7263   train acc 0.5024   worst 0.0745   lr 0.0002   p 946.88   eps 0.7807   mix 0.0002   time 26.79
scalar:  2.3364
Epoch 1238:  train loss 0.7270   train acc 0.5012   worst 0.0725   lr 0.0002   p 950.87   eps 0.7807   mix 0.0002   time 26.44
scalar:  2.3367
Epoch 1239:  train loss 0.7267   train acc 0.5009   worst 0.0745   lr 0.0002   p 954.87   eps 0.7807   mix 0.0002   time 26.80
Epoch 1239:  test acc 0.4846   time 2.51
Calculating metrics for L_infinity dist model on training set
Epoch 1239:  clean acc 0.5032   certified acc 0.3119
Calculating metrics for L_infinity dist model on test set
Epoch 1239:  clean acc 0.4866   certified acc 0.2900
scalar:  2.3368
Epoch 1240:  train loss 0.7267   train acc 0.5018   worst 0.0740   lr 0.0002   p 958.88   eps 0.7807   mix 0.0002   time 26.68
scalar:  2.3367
Epoch 1241:  train loss 0.7268   train acc 0.5011   worst 0.0734   lr 0.0002   p 962.92   eps 0.7807   mix 0.0002   time 26.86
scalar:  2.337
Epoch 1242:  train loss 0.7270   train acc 0.5028   worst 0.0748   lr 0.0001   p 966.97   eps 0.7807   mix 0.0002   time 27.11
scalar:  2.3374
Epoch 1243:  train loss 0.7276   train acc 0.5017   worst 0.0739   lr 0.0001   p 971.04   eps 0.7807   mix 0.0002   time 26.54
scalar:  2.3374
Epoch 1244:  train loss 0.7282   train acc 0.4991   worst 0.0739   lr 0.0001   p 975.12   eps 0.7807   mix 0.0002   time 27.02
Epoch 1244:  test acc 0.4865   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1244:  clean acc 0.5073   certified acc 0.3120
Calculating metrics for L_infinity dist model on test set
Epoch 1244:  clean acc 0.4874   certified acc 0.2904
scalar:  2.3374
Epoch 1245:  train loss 0.7282   train acc 0.4980   worst 0.0740   lr 0.0001   p 979.23   eps 0.7807   mix 0.0002   time 26.64
scalar:  2.3375
Epoch 1246:  train loss 0.7263   train acc 0.5023   worst 0.0744   lr 0.0001   p 983.35   eps 0.7807   mix 0.0002   time 27.10
scalar:  2.3375
Epoch 1247:  train loss 0.7265   train acc 0.4995   worst 0.0747   lr 0.0001   p 987.48   eps 0.7807   mix 0.0002   time 27.71
scalar:  2.3377
Epoch 1248:  train loss 0.7264   train acc 0.5010   worst 0.0740   lr 0.0001   p 991.64   eps 0.7807   mix 0.0002   time 26.50
scalar:  2.3378
Epoch 1249:  train loss 0.7277   train acc 0.5003   worst 0.0747   lr 0.0001   p 995.81   eps 0.7807   mix 0.0002   time 26.93
Epoch 1249:  test acc 0.4866   time 2.56
Calculating metrics for L_infinity dist model on training set
Epoch 1249:  clean acc 0.5042   certified acc 0.3106
Calculating metrics for L_infinity dist model on test set
Epoch 1249:  clean acc 0.4872   certified acc 0.2896
Generate adversarial examples on test dataset
adversarial attack acc 29.2200
scalar:  2.3379
Epoch 1250:  train loss 0.7273   train acc 0.5027   worst 0.0705   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.16
scalar:  2.3385
Epoch 1251:  train loss 0.7291   train acc 0.5014   worst 0.0699   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.18
scalar:  2.339
Epoch 1252:  train loss 0.7294   train acc 0.5011   worst 0.0700   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.04
scalar:  2.3395
Epoch 1253:  train loss 0.7284   train acc 0.5021   worst 0.0699   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.09
scalar:  2.3401
Epoch 1254:  train loss 0.7290   train acc 0.4998   worst 0.0688   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.45
Epoch 1254:  test acc 0.4870   time 1.01
Calculating metrics for L_infinity dist model on training set
Epoch 1254:  clean acc 0.5023   certified acc 0.3141
Calculating metrics for L_infinity dist model on test set
Epoch 1254:  clean acc 0.4870   certified acc 0.2896
scalar:  2.3403
Epoch 1255:  train loss 0.7293   train acc 0.5013   worst 0.0698   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.11
scalar:  2.3409
Epoch 1256:  train loss 0.7296   train acc 0.5010   worst 0.0680   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.08
scalar:  2.3413
Epoch 1257:  train loss 0.7291   train acc 0.5027   worst 0.0704   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.11
scalar:  2.3419
Epoch 1258:  train loss 0.7284   train acc 0.5026   worst 0.0715   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.09
scalar:  2.3422
Epoch 1259:  train loss 0.7288   train acc 0.5016   worst 0.0700   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.09
Epoch 1259:  test acc 0.4851   time 0.94
Calculating metrics for L_infinity dist model on training set
Epoch 1259:  clean acc 0.5035   certified acc 0.3121
Calculating metrics for L_infinity dist model on test set
Epoch 1259:  clean acc 0.4851   certified acc 0.2908
scalar:  2.3426
Epoch 1260:  train loss 0.7288   train acc 0.5010   worst 0.0699   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.07
scalar:  2.3429
Epoch 1261:  train loss 0.7286   train acc 0.5007   worst 0.0695   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.04
scalar:  2.3432
Epoch 1262:  train loss 0.7282   train acc 0.5018   worst 0.0710   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.04
scalar:  2.3435
Epoch 1263:  train loss 0.7294   train acc 0.5010   worst 0.0697   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.08
scalar:  2.3437
Epoch 1264:  train loss 0.7287   train acc 0.5019   worst 0.0686   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.34
Epoch 1264:  test acc 0.4859   time 1.31
Calculating metrics for L_infinity dist model on training set
Epoch 1264:  clean acc 0.5015   certified acc 0.3121
Calculating metrics for L_infinity dist model on test set
Epoch 1264:  clean acc 0.4859   certified acc 0.2899
scalar:  2.3439
Epoch 1265:  train loss 0.7289   train acc 0.5010   worst 0.0710   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.14
scalar:  2.3442
Epoch 1266:  train loss 0.7277   train acc 0.5015   worst 0.0704   lr 0.0001   p inf   eps 0.7807   mix 0.0002   time 6.12
scalar:  2.3444
Epoch 1267:  train loss 0.7289   train acc 0.5008   worst 0.0695   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.10
scalar:  2.3446
Epoch 1268:  train loss 0.7296   train acc 0.5026   worst 0.0697   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.10
scalar:  2.3449
Epoch 1269:  train loss 0.7302   train acc 0.5005   worst 0.0701   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.10
Epoch 1269:  test acc 0.4868   time 1.09
Calculating metrics for L_infinity dist model on training set
Epoch 1269:  clean acc 0.5022   certified acc 0.3130
Calculating metrics for L_infinity dist model on test set
Epoch 1269:  clean acc 0.4868   certified acc 0.2903
scalar:  2.3451
Epoch 1270:  train loss 0.7295   train acc 0.5000   worst 0.0703   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.08
scalar:  2.3452
Epoch 1271:  train loss 0.7283   train acc 0.5022   worst 0.0713   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.16
scalar:  2.3454
Epoch 1272:  train loss 0.7293   train acc 0.4994   worst 0.0712   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.16
scalar:  2.3455
Epoch 1273:  train loss 0.7277   train acc 0.5012   worst 0.0696   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.21
scalar:  2.3457
Epoch 1274:  train loss 0.7281   train acc 0.5015   worst 0.0703   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.10
Epoch 1274:  test acc 0.4858   time 1.16
Calculating metrics for L_infinity dist model on training set
Epoch 1274:  clean acc 0.5012   certified acc 0.3128
Calculating metrics for L_infinity dist model on test set
Epoch 1274:  clean acc 0.4858   certified acc 0.2899
scalar:  2.3458
Epoch 1275:  train loss 0.7278   train acc 0.5018   worst 0.0697   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.06
scalar:  2.346
Epoch 1276:  train loss 0.7286   train acc 0.5027   worst 0.0696   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.41
scalar:  2.346
Epoch 1277:  train loss 0.7288   train acc 0.5003   worst 0.0719   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.25
scalar:  2.3461
Epoch 1278:  train loss 0.7297   train acc 0.4983   worst 0.0711   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.13
scalar:  2.3462
Epoch 1279:  train loss 0.7292   train acc 0.5006   worst 0.0714   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.23
Epoch 1279:  test acc 0.4865   time 0.84
Calculating metrics for L_infinity dist model on training set
Epoch 1279:  clean acc 0.5013   certified acc 0.3119
Calculating metrics for L_infinity dist model on test set
Epoch 1279:  clean acc 0.4865   certified acc 0.2898
scalar:  2.3463
Epoch 1280:  train loss 0.7287   train acc 0.5002   worst 0.0702   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.15
scalar:  2.3463
Epoch 1281:  train loss 0.7296   train acc 0.5003   worst 0.0715   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.05
scalar:  2.3464
Epoch 1282:  train loss 0.7291   train acc 0.4995   worst 0.0714   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.07
scalar:  2.3464
Epoch 1283:  train loss 0.7296   train acc 0.5008   worst 0.0705   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.16
scalar:  2.3465
Epoch 1284:  train loss 0.7289   train acc 0.5011   worst 0.0697   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.16
Epoch 1284:  test acc 0.4853   time 1.01
Calculating metrics for L_infinity dist model on training set
Epoch 1284:  clean acc 0.5017   certified acc 0.3140
Calculating metrics for L_infinity dist model on test set
Epoch 1284:  clean acc 0.4853   certified acc 0.2906
scalar:  2.3465
Epoch 1285:  train loss 0.7286   train acc 0.5002   worst 0.0700   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.08
scalar:  2.3466
Epoch 1286:  train loss 0.7291   train acc 0.5007   worst 0.0706   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.16
scalar:  2.3466
Epoch 1287:  train loss 0.7288   train acc 0.5016   worst 0.0700   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.08
scalar:  2.3466
Epoch 1288:  train loss 0.7282   train acc 0.5011   worst 0.0720   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.19
scalar:  2.3466
Epoch 1289:  train loss 0.7288   train acc 0.4988   worst 0.0694   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.10
Epoch 1289:  test acc 0.4842   time 1.02
Calculating metrics for L_infinity dist model on training set
Epoch 1289:  clean acc 0.5006   certified acc 0.3128
Calculating metrics for L_infinity dist model on test set
Epoch 1289:  clean acc 0.4842   certified acc 0.2904
scalar:  2.3466
Epoch 1290:  train loss 0.7286   train acc 0.5020   worst 0.0698   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.06
scalar:  2.3467
Epoch 1291:  train loss 0.7291   train acc 0.5009   worst 0.0713   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.09
scalar:  2.3467
Epoch 1292:  train loss 0.7290   train acc 0.5010   worst 0.0704   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.10
scalar:  2.3467
Epoch 1293:  train loss 0.7292   train acc 0.5005   worst 0.0708   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.08
scalar:  2.3467
Epoch 1294:  train loss 0.7286   train acc 0.4993   worst 0.0714   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.08
Epoch 1294:  test acc 0.4844   time 0.92
Calculating metrics for L_infinity dist model on training set
Epoch 1294:  clean acc 0.5042   certified acc 0.3143
Calculating metrics for L_infinity dist model on test set
Epoch 1294:  clean acc 0.4844   certified acc 0.2892
scalar:  2.3467
Epoch 1295:  train loss 0.7291   train acc 0.5013   worst 0.0695   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.14
Epoch 1295:  test acc 0.4869   time 1.00
Calculating metrics for L_infinity dist model on training set
Epoch 1295:  clean acc 0.5042   certified acc 0.3129
Calculating metrics for L_infinity dist model on test set
Epoch 1295:  clean acc 0.4869   certified acc 0.2909
Generate adversarial examples on test dataset
adversarial attack acc 32.8100
scalar:  2.3467
Epoch 1296:  train loss 0.7282   train acc 0.5014   worst 0.0702   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.07
Epoch 1296:  test acc 0.4868   time 0.96
Calculating metrics for L_infinity dist model on training set
Epoch 1296:  clean acc 0.5025   certified acc 0.3143
Calculating metrics for L_infinity dist model on test set
Epoch 1296:  clean acc 0.4868   certified acc 0.2905
Generate adversarial examples on test dataset
adversarial attack acc 32.6000
scalar:  2.3467
Epoch 1297:  train loss 0.7283   train acc 0.5023   worst 0.0704   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.04
Epoch 1297:  test acc 0.4867   time 0.91
Calculating metrics for L_infinity dist model on training set
Epoch 1297:  clean acc 0.5033   certified acc 0.3138
Calculating metrics for L_infinity dist model on test set
Epoch 1297:  clean acc 0.4867   certified acc 0.2894
Generate adversarial examples on test dataset
adversarial attack acc 32.7700
scalar:  2.3467
Epoch 1298:  train loss 0.7302   train acc 0.4991   worst 0.0693   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.01
Epoch 1298:  test acc 0.4872   time 0.96
Calculating metrics for L_infinity dist model on training set
Epoch 1298:  clean acc 0.5013   certified acc 0.3125
Calculating metrics for L_infinity dist model on test set
Epoch 1298:  clean acc 0.4872   certified acc 0.2903
Generate adversarial examples on test dataset
adversarial attack acc 32.6600
scalar:  2.3467
Epoch 1299:  train loss 0.7288   train acc 0.5012   worst 0.0712   lr 0.0000   p inf   eps 0.7807   mix 0.0002   time 6.05
Epoch 1299:  test acc 0.4858   time 0.93
Calculating metrics for L_infinity dist model on training set
Epoch 1299:  clean acc 0.5031   certified acc 0.3130
Calculating metrics for L_infinity dist model on test set
Epoch 1299:  clean acc 0.4858   certified acc 0.2917
Generate adversarial examples on test dataset
adversarial attack acc 32.8600
============Training completes===========
Generate adversarial examples on test dataset
adversarial attack acc 32.9100
Calculating test acc and certified test acc
Epoch 1300:  clean acc 0.4858   certified acc 0.2917
