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lunwen_cifar10_DPFC3.log
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nohup: ignoring input
cuda:2
Namespace(batch_size=3000, bn_sparsity=0.9, classes_per_user=10, clip_bound=1.8, clipping_style='all-layer', cluster_project_lr=0.03, cluster_temperature=1.0, dataset='CIFAR-10', dataset_dir='/home/chenyannan/fast-differential-privacy-main/examples/image_classification/data', downsample_lr=0.04, epochs=50, epsilon=8, feature_dim=128, global_lr=1.06, image_size=224, instance_project_lr=0.03, instance_temperature=0.5, kl_threshold=0.7, learning_rate=0.05, linear_sparsity=0.75, local_epoch=2, loss_KL=0.5, mini_bs=300, miu=0.05, miuh=0, miuw=0, model_path='save/Cifar-10-DPFL-ResNet18-noiid-classes_per_user6-noper', momentum=0.3, n_clients=20, num_class=10, r_conv=6, r_proj=16, reload=False, resnet='ResNet18_lora', resnet_lr=0.13, sample_ratio=1, seed=17, smooth_K=6, smooth_loss_radius=2, smooth_step=0, start_epoch=0, test_image_size=256, thou=0.1, trans_lr=0.02, weight_decay=1e-05, workers=8)
sigma: 3.67919921875
W.weight 5120 torch.Size([10, 512])
H.weight 600000 torch.Size([60000, 10])
tensor([5, 3, 1, ..., 5, 0, 8]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.0003 ARI = 0.0000 F = 0.1076 ACC = 0.1051
Round: 0 User: 0 Train Loss: 720.222
Round: 0 User: 1 Train Loss: 715.316
Round: 0 User: 2 Train Loss: 728.515
Round: 0 User: 3 Train Loss: 715.362
Round: 0 User: 4 Train Loss: 721.847
Round: 0 User: 5 Train Loss: 713.868
Round: 0 User: 6 Train Loss: 728.911
Round: 0 User: 7 Train Loss: 725.861
Round: 0 User: 8 Train Loss: 720.375
Round: 0 User: 9 Train Loss: 717.100
Round: 0 User: 10 Train Loss: 716.897
Round: 0 User: 11 Train Loss: 719.683
Round: 0 User: 12 Train Loss: 712.025
Round: 0 User: 13 Train Loss: 725.413
Round: 0 User: 14 Train Loss: 720.750
Round: 0 User: 15 Train Loss: 717.071
Round: 0 User: 16 Train Loss: 723.054
Round: 0 User: 17 Train Loss: 714.815
Round: 0 User: 18 Train Loss: 723.678
Round: 0 User: 19 Train Loss: 722.764
count 20
updated norm: tensor(73.1054, device='cuda:2')
tensor([8, 8, 8, ..., 9, 0, 8]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.0055 ARI = 0.0022 F = 0.1141 ACC = 0.1209
Round: 1 User: 0 Train Loss: 566.060
Round: 1 User: 1 Train Loss: 558.592
Round: 1 User: 2 Train Loss: 573.483
Round: 1 User: 3 Train Loss: 553.079
Round: 1 User: 4 Train Loss: 565.636
Round: 1 User: 5 Train Loss: 552.514
Round: 1 User: 6 Train Loss: 570.936
Round: 1 User: 7 Train Loss: 558.530
Round: 1 User: 8 Train Loss: 565.559
Round: 1 User: 9 Train Loss: 557.198
Round: 1 User: 10 Train Loss: 567.962
Round: 1 User: 11 Train Loss: 564.034
Round: 1 User: 12 Train Loss: 551.085
Round: 1 User: 13 Train Loss: 583.658
Round: 1 User: 14 Train Loss: 568.617
Round: 1 User: 15 Train Loss: 559.828
Round: 1 User: 16 Train Loss: 570.638
Round: 1 User: 17 Train Loss: 572.218
Round: 1 User: 18 Train Loss: 563.013
Round: 1 User: 19 Train Loss: 574.404
count 20
updated norm: tensor(33.5959, device='cuda:2')
tensor([8, 8, 8, ..., 9, 8, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.0155 ARI = 0.0014 F = 0.1773 ACC = 0.1217
Round: 2 User: 0 Train Loss: 440.277
Round: 2 User: 1 Train Loss: 444.370
Round: 2 User: 2 Train Loss: 442.090
Round: 2 User: 3 Train Loss: 437.307
Round: 2 User: 4 Train Loss: 441.888
Round: 2 User: 5 Train Loss: 444.591
Round: 2 User: 6 Train Loss: 442.328
Round: 2 User: 7 Train Loss: 444.447
Round: 2 User: 8 Train Loss: 438.928
Round: 2 User: 9 Train Loss: 442.183
Round: 2 User: 10 Train Loss: 439.857
Round: 2 User: 11 Train Loss: 443.224
Round: 2 User: 12 Train Loss: 440.644
Round: 2 User: 13 Train Loss: 442.315
Round: 2 User: 14 Train Loss: 438.763
Round: 2 User: 15 Train Loss: 440.017
Round: 2 User: 16 Train Loss: 442.026
Round: 2 User: 17 Train Loss: 444.135
Round: 2 User: 18 Train Loss: 441.911
Round: 2 User: 19 Train Loss: 441.857
count 20
updated norm: tensor(34.5165, device='cuda:2')
tensor([8, 8, 8, ..., 8, 8, 8]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.0145 ARI = 0.0002 F = 0.3075 ACC = 0.1085
Round: 3 User: 0 Train Loss: 384.467
Round: 3 User: 1 Train Loss: 392.031
Round: 3 User: 2 Train Loss: 392.385
Round: 3 User: 3 Train Loss: 381.693
Round: 3 User: 4 Train Loss: 385.881
Round: 3 User: 5 Train Loss: 390.014
Round: 3 User: 6 Train Loss: 388.766
Round: 3 User: 7 Train Loss: 389.849
Round: 3 User: 8 Train Loss: 388.998
Round: 3 User: 9 Train Loss: 384.541
Round: 3 User: 10 Train Loss: 384.427
Round: 3 User: 11 Train Loss: 388.330
Round: 3 User: 12 Train Loss: 381.796
Round: 3 User: 13 Train Loss: 393.989
Round: 3 User: 14 Train Loss: 381.829
Round: 3 User: 15 Train Loss: 386.079
Round: 3 User: 16 Train Loss: 385.109
Round: 3 User: 17 Train Loss: 392.157
Round: 3 User: 18 Train Loss: 383.027
Round: 3 User: 19 Train Loss: 385.389
count 20
updated norm: tensor(19.0170, device='cuda:2')
tensor([8, 8, 8, ..., 0, 8, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.0364 ARI = 0.0044 F = 0.1793 ACC = 0.1368
Round: 4 User: 0 Train Loss: 391.434
Round: 4 User: 1 Train Loss: 395.476
Round: 4 User: 2 Train Loss: 398.530
Round: 4 User: 3 Train Loss: 387.686
Round: 4 User: 4 Train Loss: 391.841
Round: 4 User: 5 Train Loss: 392.144
Round: 4 User: 6 Train Loss: 396.423
Round: 4 User: 7 Train Loss: 397.653
Round: 4 User: 8 Train Loss: 395.784
Round: 4 User: 9 Train Loss: 389.910
Round: 4 User: 10 Train Loss: 388.374
Round: 4 User: 11 Train Loss: 391.668
Round: 4 User: 12 Train Loss: 387.020
Round: 4 User: 13 Train Loss: 397.384
Round: 4 User: 14 Train Loss: 390.980
Round: 4 User: 15 Train Loss: 392.793
Round: 4 User: 16 Train Loss: 392.126
Round: 4 User: 17 Train Loss: 394.957
Round: 4 User: 18 Train Loss: 389.541
Round: 4 User: 19 Train Loss: 390.865
count 20
updated norm: tensor(32.4245, device='cuda:2')
tensor([8, 8, 8, ..., 8, 8, 8]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.0072 ARI = 0.0000 F = 0.3130 ACC = 0.1040
Round: 5 User: 0 Train Loss: 286.764
Round: 5 User: 1 Train Loss: 289.356
Round: 5 User: 2 Train Loss: 291.359
Round: 5 User: 3 Train Loss: 283.795
Round: 5 User: 4 Train Loss: 287.218
Round: 5 User: 5 Train Loss: 287.622
Round: 5 User: 6 Train Loss: 290.993
Round: 5 User: 7 Train Loss: 292.238
Round: 5 User: 8 Train Loss: 289.949
Round: 5 User: 9 Train Loss: 285.911
Round: 5 User: 10 Train Loss: 284.193
Round: 5 User: 11 Train Loss: 286.389
Round: 5 User: 12 Train Loss: 284.146
Round: 5 User: 13 Train Loss: 290.081
Round: 5 User: 14 Train Loss: 286.993
Round: 5 User: 15 Train Loss: 287.643
Round: 5 User: 16 Train Loss: 287.436
Round: 5 User: 17 Train Loss: 288.629
Round: 5 User: 18 Train Loss: 286.736
Round: 5 User: 19 Train Loss: 286.776
count 20
updated norm: tensor(6.2817, device='cuda:2')
tensor([2, 0, 3, ..., 8, 8, 8]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.0557 ARI = 0.0052 F = 0.2259 ACC = 0.1432
Round: 6 User: 0 Train Loss: 290.947
Round: 6 User: 1 Train Loss: 292.993
Round: 6 User: 2 Train Loss: 294.145
Round: 6 User: 3 Train Loss: 288.290
Round: 6 User: 4 Train Loss: 291.846
Round: 6 User: 5 Train Loss: 292.343
Round: 6 User: 6 Train Loss: 294.740
Round: 6 User: 7 Train Loss: 296.048
Round: 6 User: 8 Train Loss: 293.428
Round: 6 User: 9 Train Loss: 290.618
Round: 6 User: 10 Train Loss: 288.829
Round: 6 User: 11 Train Loss: 290.682
Round: 6 User: 12 Train Loss: 289.331
Round: 6 User: 13 Train Loss: 292.889
Round: 6 User: 14 Train Loss: 291.616
Round: 6 User: 15 Train Loss: 291.601
Round: 6 User: 16 Train Loss: 291.845
Round: 6 User: 17 Train Loss: 292.284
Round: 6 User: 18 Train Loss: 292.228
Round: 6 User: 19 Train Loss: 291.628
count 20
updated norm: tensor(9.9633, device='cuda:2')
tensor([8, 8, 8, ..., 8, 8, 8]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.0098 ARI = 0.0001 F = 0.3133 ACC = 0.1052
Round: 7 User: 0 Train Loss: 209.246
Round: 7 User: 1 Train Loss: 210.825
Round: 7 User: 2 Train Loss: 211.627
Round: 7 User: 3 Train Loss: 206.800
Round: 7 User: 4 Train Loss: 209.482
Round: 7 User: 5 Train Loss: 209.153
Round: 7 User: 6 Train Loss: 211.308
Round: 7 User: 7 Train Loss: 212.894
Round: 7 User: 8 Train Loss: 211.482
Round: 7 User: 9 Train Loss: 208.245
Round: 7 User: 10 Train Loss: 207.343
Round: 7 User: 11 Train Loss: 208.548
Round: 7 User: 12 Train Loss: 207.081
Round: 7 User: 13 Train Loss: 211.367
Round: 7 User: 14 Train Loss: 208.476
Round: 7 User: 15 Train Loss: 209.706
Round: 7 User: 16 Train Loss: 209.052
Round: 7 User: 17 Train Loss: 209.511
Round: 7 User: 18 Train Loss: 208.888
Round: 7 User: 19 Train Loss: 209.119
count 20
updated norm: tensor(4.9881, device='cuda:2')
tensor([8, 8, 8, ..., 8, 8, 8]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.0303 ARI = 0.0007 F = 0.3069 ACC = 0.1153
Round: 8 User: 0 Train Loss: 203.353
Round: 8 User: 1 Train Loss: 204.484
Round: 8 User: 2 Train Loss: 204.789
Round: 8 User: 3 Train Loss: 200.934
Round: 8 User: 4 Train Loss: 203.752
Round: 8 User: 5 Train Loss: 203.213
Round: 8 User: 6 Train Loss: 204.970
Round: 8 User: 7 Train Loss: 206.239
Round: 8 User: 8 Train Loss: 205.084
Round: 8 User: 9 Train Loss: 202.148
Round: 8 User: 10 Train Loss: 201.507
Round: 8 User: 11 Train Loss: 202.533
Round: 8 User: 12 Train Loss: 201.559
Round: 8 User: 13 Train Loss: 205.146
Round: 8 User: 14 Train Loss: 202.554
Round: 8 User: 15 Train Loss: 203.569
Round: 8 User: 16 Train Loss: 203.384
Round: 8 User: 17 Train Loss: 203.492
Round: 8 User: 18 Train Loss: 203.165
Round: 8 User: 19 Train Loss: 203.519
count 20
updated norm: tensor(5.5903, device='cuda:2')
tensor([8, 8, 8, ..., 8, 8, 8]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.0547 ARI = 0.0029 F = 0.3002 ACC = 0.1282
Round: 9 User: 0 Train Loss: 213.929
Round: 9 User: 1 Train Loss: 216.317
Round: 9 User: 2 Train Loss: 217.660
Round: 9 User: 3 Train Loss: 211.386
Round: 9 User: 4 Train Loss: 214.427
Round: 9 User: 5 Train Loss: 214.408
Round: 9 User: 6 Train Loss: 216.768
Round: 9 User: 7 Train Loss: 218.447
Round: 9 User: 8 Train Loss: 217.229
Round: 9 User: 9 Train Loss: 213.451
Round: 9 User: 10 Train Loss: 212.144
Round: 9 User: 11 Train Loss: 213.384
Round: 9 User: 12 Train Loss: 211.361
Round: 9 User: 13 Train Loss: 216.505
Round: 9 User: 14 Train Loss: 213.474
Round: 9 User: 15 Train Loss: 214.710
Round: 9 User: 16 Train Loss: 213.517
Round: 9 User: 17 Train Loss: 214.808
Round: 9 User: 18 Train Loss: 213.495
Round: 9 User: 19 Train Loss: 213.315
count 20
updated norm: tensor(6.6468, device='cuda:2')
tensor([8, 8, 8, ..., 8, 8, 8]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.1018 ARI = 0.0127 F = 0.2928 ACC = 0.1532
Round: 10 User: 0 Train Loss: 210.452
Round: 10 User: 1 Train Loss: 211.008
Round: 10 User: 2 Train Loss: 211.624
Round: 10 User: 3 Train Loss: 208.104
Round: 10 User: 4 Train Loss: 210.444
Round: 10 User: 5 Train Loss: 210.157
Round: 10 User: 6 Train Loss: 211.723
Round: 10 User: 7 Train Loss: 213.459
Round: 10 User: 8 Train Loss: 211.548
Round: 10 User: 9 Train Loss: 209.688
Round: 10 User: 10 Train Loss: 208.698
Round: 10 User: 11 Train Loss: 209.668
Round: 10 User: 12 Train Loss: 208.562
Round: 10 User: 13 Train Loss: 211.938
Round: 10 User: 14 Train Loss: 208.981
Round: 10 User: 15 Train Loss: 210.811
Round: 10 User: 16 Train Loss: 209.909
Round: 10 User: 17 Train Loss: 210.104
Round: 10 User: 18 Train Loss: 209.961
Round: 10 User: 19 Train Loss: 210.538
count 20
updated norm: tensor(9.1076, device='cuda:2')
tensor([8, 8, 8, ..., 8, 8, 8]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.1447 ARI = 0.0304 F = 0.2915 ACC = 0.1783
Round: 11 User: 0 Train Loss: 240.534
Round: 11 User: 1 Train Loss: 241.734
Round: 11 User: 2 Train Loss: 243.030
Round: 11 User: 3 Train Loss: 238.018
Round: 11 User: 4 Train Loss: 240.871
Round: 11 User: 5 Train Loss: 240.779
Round: 11 User: 6 Train Loss: 242.732
Round: 11 User: 7 Train Loss: 244.493
Round: 11 User: 8 Train Loss: 242.362
Round: 11 User: 9 Train Loss: 240.293
Round: 11 User: 10 Train Loss: 238.659
Round: 11 User: 11 Train Loss: 239.968
Round: 11 User: 12 Train Loss: 238.540
Round: 11 User: 13 Train Loss: 242.382
Round: 11 User: 14 Train Loss: 239.254
Round: 11 User: 15 Train Loss: 240.920
Round: 11 User: 16 Train Loss: 239.996
Round: 11 User: 17 Train Loss: 240.774
Round: 11 User: 18 Train Loss: 240.358
Round: 11 User: 19 Train Loss: 240.453
count 20
updated norm: tensor(12.0488, device='cuda:2')
tensor([8, 8, 8, ..., 0, 8, 8]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.1779 ARI = 0.0520 F = 0.2928 ACC = 0.1956
Round: 12 User: 0 Train Loss: 263.137
Round: 12 User: 1 Train Loss: 263.828
Round: 12 User: 2 Train Loss: 264.555
Round: 12 User: 3 Train Loss: 260.686
Round: 12 User: 4 Train Loss: 263.515
Round: 12 User: 5 Train Loss: 263.367
Round: 12 User: 6 Train Loss: 264.803
Round: 12 User: 7 Train Loss: 266.414
Round: 12 User: 8 Train Loss: 264.011
Round: 12 User: 9 Train Loss: 263.178
Round: 12 User: 10 Train Loss: 261.617
Round: 12 User: 11 Train Loss: 262.503
Round: 12 User: 12 Train Loss: 261.726
Round: 12 User: 13 Train Loss: 264.667
Round: 12 User: 14 Train Loss: 261.509
Round: 12 User: 15 Train Loss: 263.373
Round: 12 User: 16 Train Loss: 262.470
Round: 12 User: 17 Train Loss: 263.211
Round: 12 User: 18 Train Loss: 263.226
Round: 12 User: 19 Train Loss: 263.597
count 20
updated norm: tensor(14.9729, device='cuda:2')
tensor([8, 8, 8, ..., 0, 8, 2]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.1999 ARI = 0.0699 F = 0.2963 ACC = 0.2049
Round: 13 User: 0 Train Loss: 281.874
Round: 13 User: 1 Train Loss: 282.413
Round: 13 User: 2 Train Loss: 282.966
Round: 13 User: 3 Train Loss: 279.144
Round: 13 User: 4 Train Loss: 282.283
Round: 13 User: 5 Train Loss: 282.002
Round: 13 User: 6 Train Loss: 283.831
Round: 13 User: 7 Train Loss: 284.945
Round: 13 User: 8 Train Loss: 282.633
Round: 13 User: 9 Train Loss: 281.631
Round: 13 User: 10 Train Loss: 280.104
Round: 13 User: 11 Train Loss: 280.876
Round: 13 User: 12 Train Loss: 280.419
Round: 13 User: 13 Train Loss: 283.539
Round: 13 User: 14 Train Loss: 280.220
Round: 13 User: 15 Train Loss: 281.788
Round: 13 User: 16 Train Loss: 281.412
Round: 13 User: 17 Train Loss: 282.109
Round: 13 User: 18 Train Loss: 282.201
Round: 13 User: 19 Train Loss: 282.316
count 20
updated norm: tensor(18.0306, device='cuda:2')
tensor([8, 8, 2, ..., 0, 8, 2]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2198 ARI = 0.0925 F = 0.2966 ACC = 0.2163
Round: 14 User: 0 Train Loss: 270.589
Round: 14 User: 1 Train Loss: 272.195
Round: 14 User: 2 Train Loss: 272.928
Round: 14 User: 3 Train Loss: 267.523
Round: 14 User: 4 Train Loss: 271.117
Round: 14 User: 5 Train Loss: 271.004
Round: 14 User: 6 Train Loss: 273.707
Round: 14 User: 7 Train Loss: 274.531
Round: 14 User: 8 Train Loss: 272.910
Round: 14 User: 9 Train Loss: 270.379
Round: 14 User: 10 Train Loss: 268.457
Round: 14 User: 11 Train Loss: 269.519
Round: 14 User: 12 Train Loss: 268.905
Round: 14 User: 13 Train Loss: 272.864
Round: 14 User: 14 Train Loss: 269.922
Round: 14 User: 15 Train Loss: 270.858
Round: 14 User: 16 Train Loss: 270.548
Round: 14 User: 17 Train Loss: 271.835
Round: 14 User: 18 Train Loss: 270.925
Round: 14 User: 19 Train Loss: 270.774
count 20
updated norm: tensor(18.3600, device='cuda:2')
tensor([8, 8, 2, ..., 3, 4, 2]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2293 ARI = 0.1068 F = 0.2957 ACC = 0.2258
Round: 15 User: 0 Train Loss: 272.227
Round: 15 User: 1 Train Loss: 271.853
Round: 15 User: 2 Train Loss: 272.548
Round: 15 User: 3 Train Loss: 269.943
Round: 15 User: 4 Train Loss: 272.288
Round: 15 User: 5 Train Loss: 272.076
Round: 15 User: 6 Train Loss: 273.099
Round: 15 User: 7 Train Loss: 274.654
Round: 15 User: 8 Train Loss: 271.856
Round: 15 User: 9 Train Loss: 272.149
Round: 15 User: 10 Train Loss: 270.846
Round: 15 User: 11 Train Loss: 271.533
Round: 15 User: 12 Train Loss: 270.811
Round: 15 User: 13 Train Loss: 273.313
Round: 15 User: 14 Train Loss: 269.771
Round: 15 User: 15 Train Loss: 272.339
Round: 15 User: 16 Train Loss: 271.079
Round: 15 User: 17 Train Loss: 271.496
Round: 15 User: 18 Train Loss: 272.177
Round: 15 User: 19 Train Loss: 272.748
count 20
updated norm: tensor(16.7533, device='cuda:2')
tensor([8, 8, 2, ..., 3, 4, 2]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2313 ARI = 0.1078 F = 0.2954 ACC = 0.2270
Round: 16 User: 0 Train Loss: 268.001
Round: 16 User: 1 Train Loss: 268.519
Round: 16 User: 2 Train Loss: 269.138
Round: 16 User: 3 Train Loss: 265.087
Round: 16 User: 4 Train Loss: 268.360
Round: 16 User: 5 Train Loss: 268.033
Round: 16 User: 6 Train Loss: 270.374
Round: 16 User: 7 Train Loss: 270.720
Round: 16 User: 8 Train Loss: 269.044
Round: 16 User: 9 Train Loss: 267.741
Round: 16 User: 10 Train Loss: 266.230
Round: 16 User: 11 Train Loss: 266.898
Round: 16 User: 12 Train Loss: 266.560
Round: 16 User: 13 Train Loss: 269.885
Round: 16 User: 14 Train Loss: 266.207
Round: 16 User: 15 Train Loss: 267.801
Round: 16 User: 16 Train Loss: 267.234
Round: 16 User: 17 Train Loss: 268.270
Round: 16 User: 18 Train Loss: 268.518
Round: 16 User: 19 Train Loss: 268.235
count 20
updated norm: tensor(16.8001, device='cuda:2')
tensor([2, 8, 2, ..., 3, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2377 ARI = 0.1249 F = 0.2911 ACC = 0.2400
Round: 17 User: 0 Train Loss: 230.279
Round: 17 User: 1 Train Loss: 229.652
Round: 17 User: 2 Train Loss: 230.275
Round: 17 User: 3 Train Loss: 228.079
Round: 17 User: 4 Train Loss: 229.638
Round: 17 User: 5 Train Loss: 229.462
Round: 17 User: 6 Train Loss: 230.282
Round: 17 User: 7 Train Loss: 232.626
Round: 17 User: 8 Train Loss: 230.081
Round: 17 User: 9 Train Loss: 229.453
Round: 17 User: 10 Train Loss: 228.429
Round: 17 User: 11 Train Loss: 229.333
Round: 17 User: 12 Train Loss: 228.250
Round: 17 User: 13 Train Loss: 230.708
Round: 17 User: 14 Train Loss: 227.398
Round: 17 User: 15 Train Loss: 230.493
Round: 17 User: 16 Train Loss: 228.896
Round: 17 User: 17 Train Loss: 229.171
Round: 17 User: 18 Train Loss: 229.417
Round: 17 User: 19 Train Loss: 230.115
count 20
updated norm: tensor(11.8175, device='cuda:2')
tensor([2, 8, 2, ..., 3, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2370 ARI = 0.1234 F = 0.2883 ACC = 0.2425
Round: 18 User: 0 Train Loss: 239.196
Round: 18 User: 1 Train Loss: 240.315
Round: 18 User: 2 Train Loss: 241.318
Round: 18 User: 3 Train Loss: 235.964
Round: 18 User: 4 Train Loss: 239.615
Round: 18 User: 5 Train Loss: 239.855
Round: 18 User: 6 Train Loss: 242.333
Round: 18 User: 7 Train Loss: 242.727
Round: 18 User: 8 Train Loss: 240.998
Round: 18 User: 9 Train Loss: 239.344
Round: 18 User: 10 Train Loss: 237.226
Round: 18 User: 11 Train Loss: 238.258
Round: 18 User: 12 Train Loss: 237.454
Round: 18 User: 13 Train Loss: 241.238
Round: 18 User: 14 Train Loss: 238.023
Round: 18 User: 15 Train Loss: 239.208
Round: 18 User: 16 Train Loss: 238.603
Round: 18 User: 17 Train Loss: 240.043
Round: 18 User: 18 Train Loss: 239.930
Round: 18 User: 19 Train Loss: 239.382
count 20
updated norm: tensor(11.0700, device='cuda:2')
tensor([2, 8, 2, ..., 3, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2386 ARI = 0.1289 F = 0.2851 ACC = 0.2491
Round: 19 User: 0 Train Loss: 207.505
Round: 19 User: 1 Train Loss: 207.592
Round: 19 User: 2 Train Loss: 208.187
Round: 19 User: 3 Train Loss: 204.623
Round: 19 User: 4 Train Loss: 207.691
Round: 19 User: 5 Train Loss: 207.448
Round: 19 User: 6 Train Loss: 209.269
Round: 19 User: 7 Train Loss: 209.709
Round: 19 User: 8 Train Loss: 208.487
Round: 19 User: 9 Train Loss: 206.773
Round: 19 User: 10 Train Loss: 205.421
Round: 19 User: 11 Train Loss: 206.379
Round: 19 User: 12 Train Loss: 205.847
Round: 19 User: 13 Train Loss: 208.741
Round: 19 User: 14 Train Loss: 205.909
Round: 19 User: 15 Train Loss: 207.285
Round: 19 User: 16 Train Loss: 206.708
Round: 19 User: 17 Train Loss: 207.491
Round: 19 User: 18 Train Loss: 207.765
Round: 19 User: 19 Train Loss: 207.637
count 20
updated norm: tensor(7.4024, device='cuda:2')
tensor([2, 8, 2, ..., 3, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2372 ARI = 0.1311 F = 0.2796 ACC = 0.2561
Round: 20 User: 0 Train Loss: 203.487
Round: 20 User: 1 Train Loss: 203.919
Round: 20 User: 2 Train Loss: 204.917
Round: 20 User: 3 Train Loss: 200.750
Round: 20 User: 4 Train Loss: 203.150
Round: 20 User: 5 Train Loss: 203.404
Round: 20 User: 6 Train Loss: 205.156
Round: 20 User: 7 Train Loss: 206.659
Round: 20 User: 8 Train Loss: 204.391
Round: 20 User: 9 Train Loss: 203.165
Round: 20 User: 10 Train Loss: 201.399
Round: 20 User: 11 Train Loss: 202.739
Round: 20 User: 12 Train Loss: 201.547
Round: 20 User: 13 Train Loss: 204.420
Round: 20 User: 14 Train Loss: 201.216
Round: 20 User: 15 Train Loss: 203.701
Round: 20 User: 16 Train Loss: 202.232
Round: 20 User: 17 Train Loss: 203.221
Round: 20 User: 18 Train Loss: 203.361
Round: 20 User: 19 Train Loss: 203.403
count 20
updated norm: tensor(7.2088, device='cuda:2')
tensor([2, 1, 2, ..., 3, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2352 ARI = 0.1312 F = 0.2750 ACC = 0.2609
Round: 21 User: 0 Train Loss: 182.191
Round: 21 User: 1 Train Loss: 181.981
Round: 21 User: 2 Train Loss: 182.620
Round: 21 User: 3 Train Loss: 179.667
Round: 21 User: 4 Train Loss: 182.028
Round: 21 User: 5 Train Loss: 181.898
Round: 21 User: 6 Train Loss: 183.211
Round: 21 User: 7 Train Loss: 184.165
Round: 21 User: 8 Train Loss: 182.791
Round: 21 User: 9 Train Loss: 181.182
Round: 21 User: 10 Train Loss: 180.099
Round: 21 User: 11 Train Loss: 181.171
Round: 21 User: 12 Train Loss: 180.505
Round: 21 User: 13 Train Loss: 182.782
Round: 21 User: 14 Train Loss: 180.274
Round: 21 User: 15 Train Loss: 182.336
Round: 21 User: 16 Train Loss: 181.157
Round: 21 User: 17 Train Loss: 181.619
Round: 21 User: 18 Train Loss: 181.937
Round: 21 User: 19 Train Loss: 182.268
count 20
updated norm: tensor(6.3004, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2346 ARI = 0.1327 F = 0.2705 ACC = 0.2677
Round: 22 User: 0 Train Loss: 176.676
Round: 22 User: 1 Train Loss: 176.743
Round: 22 User: 2 Train Loss: 177.448
Round: 22 User: 3 Train Loss: 174.139
Round: 22 User: 4 Train Loss: 176.443
Round: 22 User: 5 Train Loss: 176.436
Round: 22 User: 6 Train Loss: 177.953
Round: 22 User: 7 Train Loss: 179.097
Round: 22 User: 8 Train Loss: 177.388
Round: 22 User: 9 Train Loss: 175.962
Round: 22 User: 10 Train Loss: 174.637
Round: 22 User: 11 Train Loss: 175.786
Round: 22 User: 12 Train Loss: 174.911
Round: 22 User: 13 Train Loss: 177.339
Round: 22 User: 14 Train Loss: 174.596
Round: 22 User: 15 Train Loss: 176.869
Round: 22 User: 16 Train Loss: 175.461
Round: 22 User: 17 Train Loss: 176.246
Round: 22 User: 18 Train Loss: 176.582
Round: 22 User: 19 Train Loss: 176.755
count 20
updated norm: tensor(6.7169, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2354 ARI = 0.1354 F = 0.2670 ACC = 0.2754
Round: 23 User: 0 Train Loss: 165.329
Round: 23 User: 1 Train Loss: 165.253
Round: 23 User: 2 Train Loss: 165.770
Round: 23 User: 3 Train Loss: 162.949
Round: 23 User: 4 Train Loss: 165.189
Round: 23 User: 5 Train Loss: 164.925
Round: 23 User: 6 Train Loss: 166.299
Round: 23 User: 7 Train Loss: 167.282
Round: 23 User: 8 Train Loss: 165.956
Round: 23 User: 9 Train Loss: 164.362
Round: 23 User: 10 Train Loss: 163.326
Round: 23 User: 11 Train Loss: 164.335
Round: 23 User: 12 Train Loss: 163.739
Round: 23 User: 13 Train Loss: 165.893
Round: 23 User: 14 Train Loss: 163.431
Round: 23 User: 15 Train Loss: 165.474
Round: 23 User: 16 Train Loss: 164.207
Round: 23 User: 17 Train Loss: 164.859
Round: 23 User: 18 Train Loss: 165.208
Round: 23 User: 19 Train Loss: 165.486
count 20
updated norm: tensor(7.1337, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2363 ARI = 0.1386 F = 0.2633 ACC = 0.2821
Round: 24 User: 0 Train Loss: 159.496
Round: 24 User: 1 Train Loss: 159.477
Round: 24 User: 2 Train Loss: 159.876
Round: 24 User: 3 Train Loss: 157.167
Round: 24 User: 4 Train Loss: 159.467
Round: 24 User: 5 Train Loss: 159.090
Round: 24 User: 6 Train Loss: 160.526
Round: 24 User: 7 Train Loss: 161.317
Round: 24 User: 8 Train Loss: 160.134
Round: 24 User: 9 Train Loss: 158.374
Round: 24 User: 10 Train Loss: 157.499
Round: 24 User: 11 Train Loss: 158.484
Round: 24 User: 12 Train Loss: 158.021
Round: 24 User: 13 Train Loss: 160.152
Round: 24 User: 14 Train Loss: 157.778
Round: 24 User: 15 Train Loss: 159.632
Round: 24 User: 16 Train Loss: 158.564
Round: 24 User: 17 Train Loss: 159.195
Round: 24 User: 18 Train Loss: 159.427
Round: 24 User: 19 Train Loss: 159.710
count 20
updated norm: tensor(7.6458, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2388 ARI = 0.1442 F = 0.2614 ACC = 0.2890
Round: 25 User: 0 Train Loss: 164.766
Round: 25 User: 1 Train Loss: 165.773
Round: 25 User: 2 Train Loss: 166.156
Round: 25 User: 3 Train Loss: 162.474
Round: 25 User: 4 Train Loss: 165.415
Round: 25 User: 5 Train Loss: 164.948
Round: 25 User: 6 Train Loss: 166.990
Round: 25 User: 7 Train Loss: 167.421
Round: 25 User: 8 Train Loss: 165.917
Round: 25 User: 9 Train Loss: 163.790
Round: 25 User: 10 Train Loss: 163.007
Round: 25 User: 11 Train Loss: 164.046
Round: 25 User: 12 Train Loss: 163.572
Round: 25 User: 13 Train Loss: 165.953
Round: 25 User: 14 Train Loss: 163.716
Round: 25 User: 15 Train Loss: 165.284
Round: 25 User: 16 Train Loss: 164.405
Round: 25 User: 17 Train Loss: 165.704
Round: 25 User: 18 Train Loss: 165.316
Round: 25 User: 19 Train Loss: 165.275
count 20
updated norm: tensor(8.1347, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2423 ARI = 0.1507 F = 0.2613 ACC = 0.3003
Round: 26 User: 0 Train Loss: 183.795
Round: 26 User: 1 Train Loss: 186.114
Round: 26 User: 2 Train Loss: 187.156
Round: 26 User: 3 Train Loss: 181.652
Round: 26 User: 4 Train Loss: 185.196
Round: 26 User: 5 Train Loss: 185.244
Round: 26 User: 6 Train Loss: 187.811
Round: 26 User: 7 Train Loss: 187.999
Round: 26 User: 8 Train Loss: 185.550
Round: 26 User: 9 Train Loss: 183.555
Round: 26 User: 10 Train Loss: 182.579
Round: 26 User: 11 Train Loss: 183.655
Round: 26 User: 12 Train Loss: 182.776
Round: 26 User: 13 Train Loss: 185.512
Round: 26 User: 14 Train Loss: 183.550
Round: 26 User: 15 Train Loss: 185.198
Round: 26 User: 16 Train Loss: 183.844
Round: 26 User: 17 Train Loss: 186.345
Round: 26 User: 18 Train Loss: 185.516
Round: 26 User: 19 Train Loss: 184.883
count 20
updated norm: tensor(8.6558, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2447 ARI = 0.1552 F = 0.2628 ACC = 0.3067
Round: 27 User: 0 Train Loss: 193.943
Round: 27 User: 1 Train Loss: 194.261
Round: 27 User: 2 Train Loss: 196.171
Round: 27 User: 3 Train Loss: 191.744
Round: 27 User: 4 Train Loss: 193.948
Round: 27 User: 5 Train Loss: 194.750
Round: 27 User: 6 Train Loss: 196.242
Round: 27 User: 7 Train Loss: 197.117
Round: 27 User: 8 Train Loss: 194.420
Round: 27 User: 9 Train Loss: 193.743
Round: 27 User: 10 Train Loss: 192.135
Round: 27 User: 11 Train Loss: 193.503
Round: 27 User: 12 Train Loss: 192.204
Round: 27 User: 13 Train Loss: 194.484
Round: 27 User: 14 Train Loss: 192.397
Round: 27 User: 15 Train Loss: 194.921
Round: 27 User: 16 Train Loss: 193.017
Round: 27 User: 17 Train Loss: 194.468
Round: 27 User: 18 Train Loss: 194.742
Round: 27 User: 19 Train Loss: 194.481
count 20
updated norm: tensor(8.2858, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2494 ARI = 0.1586 F = 0.2669 ACC = 0.3098
Round: 28 User: 0 Train Loss: 174.421
Round: 28 User: 1 Train Loss: 173.192
Round: 28 User: 2 Train Loss: 174.265
Round: 28 User: 3 Train Loss: 172.227
Round: 28 User: 4 Train Loss: 173.588
Round: 28 User: 5 Train Loss: 173.923
Round: 28 User: 6 Train Loss: 174.271
Round: 28 User: 7 Train Loss: 175.692
Round: 28 User: 8 Train Loss: 174.152
Round: 28 User: 9 Train Loss: 173.502
Round: 28 User: 10 Train Loss: 172.588
Round: 28 User: 11 Train Loss: 173.639
Round: 28 User: 12 Train Loss: 172.702
Round: 28 User: 13 Train Loss: 174.345
Round: 28 User: 14 Train Loss: 172.240
Round: 28 User: 15 Train Loss: 174.723
Round: 28 User: 16 Train Loss: 172.950
Round: 28 User: 17 Train Loss: 173.046
Round: 28 User: 18 Train Loss: 173.651
Round: 28 User: 19 Train Loss: 174.283
count 20
updated norm: tensor(7.1707, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2530 ARI = 0.1619 F = 0.2697 ACC = 0.3121
Round: 29 User: 0 Train Loss: 163.474
Round: 29 User: 1 Train Loss: 163.109
Round: 29 User: 2 Train Loss: 163.660
Round: 29 User: 3 Train Loss: 161.230
Round: 29 User: 4 Train Loss: 163.566
Round: 29 User: 5 Train Loss: 162.857
Round: 29 User: 6 Train Loss: 163.807
Round: 29 User: 7 Train Loss: 164.884
Round: 29 User: 8 Train Loss: 163.662
Round: 29 User: 9 Train Loss: 162.530
Round: 29 User: 10 Train Loss: 161.870
Round: 29 User: 11 Train Loss: 162.916
Round: 29 User: 12 Train Loss: 162.287
Round: 29 User: 13 Train Loss: 163.818
Round: 29 User: 14 Train Loss: 161.983
Round: 29 User: 15 Train Loss: 163.797
Round: 29 User: 16 Train Loss: 162.566
Round: 29 User: 17 Train Loss: 163.175
Round: 29 User: 18 Train Loss: 162.954
Round: 29 User: 19 Train Loss: 163.757
count 20
updated norm: tensor(6.2897, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2536 ARI = 0.1643 F = 0.2699 ACC = 0.3147
Round: 30 User: 0 Train Loss: 165.363
Round: 30 User: 1 Train Loss: 165.760
Round: 30 User: 2 Train Loss: 166.553
Round: 30 User: 3 Train Loss: 163.262
Round: 30 User: 4 Train Loss: 165.860
Round: 30 User: 5 Train Loss: 165.291
Round: 30 User: 6 Train Loss: 166.657
Round: 30 User: 7 Train Loss: 167.468
Round: 30 User: 8 Train Loss: 165.939
Round: 30 User: 9 Train Loss: 164.605
Round: 30 User: 10 Train Loss: 163.946
Round: 30 User: 11 Train Loss: 164.864
Round: 30 User: 12 Train Loss: 164.144
Round: 30 User: 13 Train Loss: 166.093
Round: 30 User: 14 Train Loss: 164.288
Round: 30 User: 15 Train Loss: 166.043
Round: 30 User: 16 Train Loss: 164.669
Round: 30 User: 17 Train Loss: 165.938
Round: 30 User: 18 Train Loss: 165.569
Round: 30 User: 19 Train Loss: 165.844
count 20
updated norm: tensor(5.5272, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2537 ARI = 0.1657 F = 0.2699 ACC = 0.3166
Round: 31 User: 0 Train Loss: 168.590
Round: 31 User: 1 Train Loss: 168.868
Round: 31 User: 2 Train Loss: 170.021
Round: 31 User: 3 Train Loss: 166.552
Round: 31 User: 4 Train Loss: 168.855
Round: 31 User: 5 Train Loss: 168.613
Round: 31 User: 6 Train Loss: 170.092
Round: 31 User: 7 Train Loss: 170.967
Round: 31 User: 8 Train Loss: 169.103
Round: 31 User: 9 Train Loss: 167.943
Round: 31 User: 10 Train Loss: 167.092
Round: 31 User: 11 Train Loss: 168.253
Round: 31 User: 12 Train Loss: 167.327
Round: 31 User: 13 Train Loss: 169.330
Round: 31 User: 14 Train Loss: 167.343
Round: 31 User: 15 Train Loss: 169.268
Round: 31 User: 16 Train Loss: 167.980
Round: 31 User: 17 Train Loss: 169.246
Round: 31 User: 18 Train Loss: 168.807
Round: 31 User: 19 Train Loss: 169.122
count 20
updated norm: tensor(4.8670, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2530 ARI = 0.1664 F = 0.2699 ACC = 0.3176
Round: 32 User: 0 Train Loss: 166.353
Round: 32 User: 1 Train Loss: 166.580
Round: 32 User: 2 Train Loss: 167.731
Round: 32 User: 3 Train Loss: 164.404
Round: 32 User: 4 Train Loss: 166.567
Round: 32 User: 5 Train Loss: 166.475
Round: 32 User: 6 Train Loss: 167.742
Round: 32 User: 7 Train Loss: 168.692
Round: 32 User: 8 Train Loss: 166.825
Round: 32 User: 9 Train Loss: 165.654
Round: 32 User: 10 Train Loss: 164.969
Round: 32 User: 11 Train Loss: 165.991
Round: 32 User: 12 Train Loss: 164.946
Round: 32 User: 13 Train Loss: 167.080
Round: 32 User: 14 Train Loss: 165.114
Round: 32 User: 15 Train Loss: 167.126
Round: 32 User: 16 Train Loss: 165.631
Round: 32 User: 17 Train Loss: 166.798
Round: 32 User: 18 Train Loss: 166.584
Round: 32 User: 19 Train Loss: 166.787
count 20
updated norm: tensor(4.4788, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2536 ARI = 0.1679 F = 0.2710 ACC = 0.3184
Round: 33 User: 0 Train Loss: 165.784
Round: 33 User: 1 Train Loss: 165.936
Round: 33 User: 2 Train Loss: 167.151
Round: 33 User: 3 Train Loss: 163.764
Round: 33 User: 4 Train Loss: 165.916
Round: 33 User: 5 Train Loss: 165.720
Round: 33 User: 6 Train Loss: 167.153
Round: 33 User: 7 Train Loss: 168.129
Round: 33 User: 8 Train Loss: 166.141
Round: 33 User: 9 Train Loss: 165.090
Round: 33 User: 10 Train Loss: 164.304
Round: 33 User: 11 Train Loss: 165.544
Round: 33 User: 12 Train Loss: 164.456
Round: 33 User: 13 Train Loss: 166.465
Round: 33 User: 14 Train Loss: 164.418
Round: 33 User: 15 Train Loss: 166.449
Round: 33 User: 16 Train Loss: 165.113
Round: 33 User: 17 Train Loss: 166.340
Round: 33 User: 18 Train Loss: 165.860
Round: 33 User: 19 Train Loss: 166.268
count 20
updated norm: tensor(4.2169, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2526 ARI = 0.1676 F = 0.2705 ACC = 0.3176
Round: 34 User: 0 Train Loss: 162.936
Round: 34 User: 1 Train Loss: 163.143
Round: 34 User: 2 Train Loss: 164.235
Round: 34 User: 3 Train Loss: 161.033
Round: 34 User: 4 Train Loss: 163.179
Round: 34 User: 5 Train Loss: 162.967
Round: 34 User: 6 Train Loss: 164.189
Round: 34 User: 7 Train Loss: 165.194
Round: 34 User: 8 Train Loss: 163.321
Round: 34 User: 9 Train Loss: 162.174
Round: 34 User: 10 Train Loss: 161.627
Round: 34 User: 11 Train Loss: 162.594
Round: 34 User: 12 Train Loss: 161.511
Round: 34 User: 13 Train Loss: 163.672
Round: 34 User: 14 Train Loss: 161.664
Round: 34 User: 15 Train Loss: 163.710
Round: 34 User: 16 Train Loss: 162.169
Round: 34 User: 17 Train Loss: 163.352
Round: 34 User: 18 Train Loss: 163.080
Round: 34 User: 19 Train Loss: 163.341
count 20
updated norm: tensor(4.2490, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2521 ARI = 0.1677 F = 0.2701 ACC = 0.3170
Round: 35 User: 0 Train Loss: 163.534
Round: 35 User: 1 Train Loss: 163.752
Round: 35 User: 2 Train Loss: 165.026
Round: 35 User: 3 Train Loss: 161.547
Round: 35 User: 4 Train Loss: 163.718
Round: 35 User: 5 Train Loss: 163.484
Round: 35 User: 6 Train Loss: 164.994
Round: 35 User: 7 Train Loss: 165.956
Round: 35 User: 8 Train Loss: 163.848
Round: 35 User: 9 Train Loss: 162.846
Round: 35 User: 10 Train Loss: 162.099
Round: 35 User: 11 Train Loss: 163.362
Round: 35 User: 12 Train Loss: 162.211
Round: 35 User: 13 Train Loss: 164.270
Round: 35 User: 14 Train Loss: 162.186
Round: 35 User: 15 Train Loss: 164.226
Round: 35 User: 16 Train Loss: 162.893
Round: 35 User: 17 Train Loss: 164.258
Round: 35 User: 18 Train Loss: 163.593
Round: 35 User: 19 Train Loss: 164.065
count 20
updated norm: tensor(4.2599, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2512 ARI = 0.1670 F = 0.2689 ACC = 0.3162
Round: 36 User: 0 Train Loss: 161.015
Round: 36 User: 1 Train Loss: 161.250
Round: 36 User: 2 Train Loss: 162.368
Round: 36 User: 3 Train Loss: 159.180
Round: 36 User: 4 Train Loss: 161.314
Round: 36 User: 5 Train Loss: 161.092
Round: 36 User: 6 Train Loss: 162.295
Round: 36 User: 7 Train Loss: 163.302
Round: 36 User: 8 Train Loss: 161.393
Round: 36 User: 9 Train Loss: 160.245
Round: 36 User: 10 Train Loss: 159.770
Round: 36 User: 11 Train Loss: 160.711
Round: 36 User: 12 Train Loss: 159.582
Round: 36 User: 13 Train Loss: 161.816
Round: 36 User: 14 Train Loss: 159.762
Round: 36 User: 15 Train Loss: 161.801
Round: 36 User: 16 Train Loss: 160.275
Round: 36 User: 17 Train Loss: 161.472
Round: 36 User: 18 Train Loss: 161.170
Round: 36 User: 19 Train Loss: 161.452
count 20
updated norm: tensor(4.4383, device='cuda:2')
tensor([2, 0, 2, ..., 0, 3, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2498 ARI = 0.1668 F = 0.2683 ACC = 0.3150
Round: 37 User: 0 Train Loss: 162.144
Round: 37 User: 1 Train Loss: 162.401
Round: 37 User: 2 Train Loss: 163.765
Round: 37 User: 3 Train Loss: 160.176
Round: 37 User: 4 Train Loss: 162.354
Round: 37 User: 5 Train Loss: 162.139
Round: 37 User: 6 Train Loss: 163.691
Round: 37 User: 7 Train Loss: 164.637
Round: 37 User: 8 Train Loss: 162.422
Round: 37 User: 9 Train Loss: 161.472
Round: 37 User: 10 Train Loss: 160.728
Round: 37 User: 11 Train Loss: 162.045
Round: 37 User: 12 Train Loss: 160.811
Round: 37 User: 13 Train Loss: 162.925
Round: 37 User: 14 Train Loss: 160.826
Round: 37 User: 15 Train Loss: 162.834
Round: 37 User: 16 Train Loss: 161.552
Round: 37 User: 17 Train Loss: 162.984
Round: 37 User: 18 Train Loss: 162.190
Round: 37 User: 19 Train Loss: 162.715
count 20
updated norm: tensor(4.4542, device='cuda:2')
tensor([2, 0, 2, ..., 0, 0, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2495 ARI = 0.1671 F = 0.2680 ACC = 0.3145
Round: 38 User: 0 Train Loss: 159.277
Round: 38 User: 1 Train Loss: 159.514
Round: 38 User: 2 Train Loss: 160.627
Round: 38 User: 3 Train Loss: 157.483
Round: 38 User: 4 Train Loss: 159.611
Round: 38 User: 5 Train Loss: 159.358
Round: 38 User: 6 Train Loss: 160.525
Round: 38 User: 7 Train Loss: 161.527
Round: 38 User: 8 Train Loss: 159.635
Round: 38 User: 9 Train Loss: 158.484
Round: 38 User: 10 Train Loss: 158.075
Round: 38 User: 11 Train Loss: 158.986
Round: 38 User: 12 Train Loss: 157.826
Round: 38 User: 13 Train Loss: 160.118
Round: 38 User: 14 Train Loss: 158.042
Round: 38 User: 15 Train Loss: 160.040
Round: 38 User: 16 Train Loss: 158.546
Round: 38 User: 17 Train Loss: 159.722
Round: 38 User: 18 Train Loss: 159.425
Round: 38 User: 19 Train Loss: 159.713
count 20
updated norm: tensor(4.5385, device='cuda:2')
tensor([2, 0, 2, ..., 0, 0, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.2495 ARI = 0.1676 F = 0.2680 ACC = 0.3137
Round: 39 User: 0 Train Loss: 160.326
Round: 39 User: 1 Train Loss: 160.613
Round: 39 User: 2 Train Loss: 162.035
Round: 39 User: 3 Train Loss: 158.348
Round: 39 User: 4 Train Loss: 160.558
Round: 39 User: 5 Train Loss: 160.314
Round: 39 User: 6 Train Loss: 161.903
Round: 39 User: 7 Train Loss: 162.823
Round: 39 User: 8 Train Loss: 160.543
Round: 39 User: 9 Train Loss: 159.662
Round: 39 User: 10 Train Loss: 158.909
Round: 39 User: 11 Train Loss: 160.262
Round: 39 User: 12 Train Loss: 158.984
Round: 39 User: 13 Train Loss: 161.123
Round: 39 User: 14 Train Loss: 159.022
Round: 39 User: 15 Train Loss: 160.993