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lunwentest_cifar10_DPFC_400.log
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nohup: ignoring input
cuda:5
Namespace(batch_size=150, bn_sparsity=0.9, classes_per_user=10, clip_bound=2, 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=10, epsilon=8, feature_dim=128, global_lr=8, image_size=224, instance_project_lr=0.03, instance_temperature=0.5, kl_threshold=0.7, learning_rate=0.04, linear_sparsity=0.75, local_epoch=5, loss_KL=0.5, mini_bs=150, miu=0.05, miuh=0.5, miuw=0, model_path='save/Cifar-10-DPFL-ResNet18-noiid-classes_per_user6-noper', momentum=0.3, n_clients=400, 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: 1.6455078125
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H.weight 600000 torch.Size([60000, 10])
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Round: 0 User: 383 Train Loss: 966.468
Norm: tensor(19.8220, device='cuda:5')
Round: 0 User: 384 Train Loss: 1008.675
Norm: tensor(19.7110, device='cuda:5')
Round: 0 User: 385 Train Loss: 1000.909
Norm: tensor(18.9067, device='cuda:5')
Round: 0 User: 386 Train Loss: 989.437
Norm: tensor(20.3818, device='cuda:5')
Round: 0 User: 387 Train Loss: 983.192
Norm: tensor(18.6788, device='cuda:5')
Round: 0 User: 388 Train Loss: 1006.453
Norm: tensor(19.1554, device='cuda:5')
Round: 0 User: 389 Train Loss: 1003.848
Norm: tensor(19.7461, device='cuda:5')
Round: 0 User: 390 Train Loss: 1003.634
Norm: tensor(19.5806, device='cuda:5')
Round: 0 User: 391 Train Loss: 1018.630
Norm: tensor(18.8906, device='cuda:5')
Round: 0 User: 392 Train Loss: 990.441
Norm: tensor(17.8298, device='cuda:5')
Round: 0 User: 393 Train Loss: 972.121
Norm: tensor(19.0519, device='cuda:5')
Round: 0 User: 394 Train Loss: 980.042
Norm: tensor(20.7533, device='cuda:5')
Round: 0 User: 395 Train Loss: 1028.357
Norm: tensor(20.0795, device='cuda:5')
Round: 0 User: 396 Train Loss: 998.877
Norm: tensor(18.6263, device='cuda:5')
Round: 0 User: 397 Train Loss: 986.215
Norm: tensor(18.5756, device='cuda:5')
Round: 0 User: 398 Train Loss: 1000.101
Norm: tensor(19.5638, device='cuda:5')
Round: 0 User: 399 Train Loss: 996.653
count 400
updated norm: tensor(1.9806, device='cuda:5')
tensor([8, 8, 8, ..., 8, 8, 3]) [1 3 9 ... 0 5 9]
### Creating features from model ###
Global NMI = 0.0153 ARI = 0.0022 F = 0.1910 ACC = 0.1234
Norm: tensor(7.9135, device='cuda:5')
Round: 1 User: 0 Train Loss: 446.563
Norm: tensor(7.4751, device='cuda:5')
Round: 1 User: 1 Train Loss: 432.566
Norm: tensor(7.4424, device='cuda:5')
Round: 1 User: 2 Train Loss: 428.233
Norm: tensor(7.6519, device='cuda:5')
Round: 1 User: 3 Train Loss: 431.590
Norm: tensor(7.3644, device='cuda:5')
Round: 1 User: 4 Train Loss: 416.561
Norm: tensor(7.6126, device='cuda:5')
Round: 1 User: 5 Train Loss: 435.596
Norm: tensor(7.6384, device='cuda:5')
Round: 1 User: 6 Train Loss: 439.834
Norm: tensor(7.7059, device='cuda:5')
Round: 1 User: 7 Train Loss: 437.096
Norm: tensor(7.6746, device='cuda:5')
Round: 1 User: 8 Train Loss: 432.958
Norm: tensor(7.4647, device='cuda:5')
Round: 1 User: 9 Train Loss: 432.812
Norm: tensor(7.9470, device='cuda:5')
Round: 1 User: 10 Train Loss: 450.612
Norm: tensor(7.6694, device='cuda:5')
Round: 1 User: 11 Train Loss: 436.429
Norm: tensor(7.2428, device='cuda:5')
Round: 1 User: 12 Train Loss: 430.897
Norm: tensor(7.3491, device='cuda:5')
Round: 1 User: 13 Train Loss: 433.182
Norm: tensor(8.1001, device='cuda:5')
Round: 1 User: 14 Train Loss: 447.457
Norm: tensor(7.4639, device='cuda:5')
Round: 1 User: 15 Train Loss: 433.126
Norm: tensor(7.3082, device='cuda:5')
Round: 1 User: 16 Train Loss: 429.139
Norm: tensor(7.5846, device='cuda:5')
Round: 1 User: 17 Train Loss: 442.412
Norm: tensor(7.6538, device='cuda:5')
Round: 1 User: 18 Train Loss: 434.010
Norm: tensor(7.7434, device='cuda:5')
Round: 1 User: 19 Train Loss: 438.802
Norm: tensor(7.7426, device='cuda:5')
Round: 1 User: 20 Train Loss: 442.498
Norm: tensor(7.9488, device='cuda:5')
Round: 1 User: 21 Train Loss: 454.454
Norm: tensor(7.7060, device='cuda:5')
Round: 1 User: 22 Train Loss: 442.650
Norm: tensor(7.4656, device='cuda:5')
Round: 1 User: 23 Train Loss: 426.584
Norm: tensor(7.5325, device='cuda:5')
Round: 1 User: 24 Train Loss: 436.269
Norm: tensor(7.6579, device='cuda:5')
Round: 1 User: 25 Train Loss: 437.994
Norm: tensor(7.5443, device='cuda:5')
Round: 1 User: 26 Train Loss: 436.935
Norm: tensor(7.4474, device='cuda:5')
Round: 1 User: 27 Train Loss: 433.301
Norm: tensor(7.5020, device='cuda:5')
Round: 1 User: 28 Train Loss: 427.243
Norm: tensor(7.9375, device='cuda:5')
Round: 1 User: 29 Train Loss: 445.879
Norm: tensor(7.2451, device='cuda:5')
Round: 1 User: 30 Train Loss: 425.087
Norm: tensor(7.6584, device='cuda:5')
Round: 1 User: 31 Train Loss: 437.464
Norm: tensor(7.8385, device='cuda:5')
Round: 1 User: 32 Train Loss: 452.107
Norm: tensor(7.6581, device='cuda:5')
Round: 1 User: 33 Train Loss: 441.798
Norm: tensor(7.7318, device='cuda:5')
Round: 1 User: 34 Train Loss: 440.986
Norm: tensor(7.6501, device='cuda:5')
Round: 1 User: 35 Train Loss: 441.522
Norm: tensor(7.7228, device='cuda:5')
Round: 1 User: 36 Train Loss: 442.908
Norm: tensor(7.6181, device='cuda:5')
Round: 1 User: 37 Train Loss: 443.724
Norm: tensor(7.5323, device='cuda:5')
Round: 1 User: 38 Train Loss: 425.105
Norm: tensor(7.5660, device='cuda:5')
Round: 1 User: 39 Train Loss: 429.378
Norm: tensor(7.7453, device='cuda:5')
Round: 1 User: 40 Train Loss: 436.249
Norm: tensor(7.6089, device='cuda:5')
Round: 1 User: 41 Train Loss: 437.062
Norm: tensor(7.6200, device='cuda:5')
Round: 1 User: 42 Train Loss: 430.418
Norm: tensor(8.0714, device='cuda:5')
Round: 1 User: 43 Train Loss: 457.048
Norm: tensor(7.7261, device='cuda:5')
Round: 1 User: 44 Train Loss: 440.345
Norm: tensor(7.9186, device='cuda:5')
Round: 1 User: 45 Train Loss: 441.023
Norm: tensor(8.0441, device='cuda:5')
Round: 1 User: 46 Train Loss: 443.284
Norm: tensor(7.4299, device='cuda:5')
Round: 1 User: 47 Train Loss: 427.873
Norm: tensor(7.7637, device='cuda:5')
Round: 1 User: 48 Train Loss: 438.734
Norm: tensor(7.5110, device='cuda:5')
Round: 1 User: 49 Train Loss: 434.846
Norm: tensor(7.6899, device='cuda:5')
Round: 1 User: 50 Train Loss: 440.679
Norm: tensor(7.6386, device='cuda:5')
Round: 1 User: 51 Train Loss: 439.185
Norm: tensor(7.6921, device='cuda:5')
Round: 1 User: 52 Train Loss: 438.006
Norm: tensor(7.7334, device='cuda:5')
Round: 1 User: 53 Train Loss: 443.915
Norm: tensor(7.2932, device='cuda:5')
Round: 1 User: 54 Train Loss: 418.480
Norm: tensor(7.8525, device='cuda:5')
Round: 1 User: 55 Train Loss: 444.679
Norm: tensor(7.9337, device='cuda:5')
Round: 1 User: 56 Train Loss: 444.525
Norm: tensor(7.7525, device='cuda:5')
Round: 1 User: 57 Train Loss: 435.732
Norm: tensor(7.5383, device='cuda:5')
Round: 1 User: 58 Train Loss: 437.249
Norm: tensor(7.3639, device='cuda:5')
Round: 1 User: 59 Train Loss: 425.993
Norm: tensor(7.5109, device='cuda:5')
Round: 1 User: 60 Train Loss: 428.158
Norm: tensor(7.7728, device='cuda:5')
Round: 1 User: 61 Train Loss: 441.578
Norm: tensor(7.2791, device='cuda:5')
Round: 1 User: 62 Train Loss: 416.018
Norm: tensor(7.7936, device='cuda:5')
Round: 1 User: 63 Train Loss: 452.742
Norm: tensor(7.4697, device='cuda:5')
Round: 1 User: 64 Train Loss: 428.568
Norm: tensor(7.4134, device='cuda:5')
Round: 1 User: 65 Train Loss: 421.693
Norm: tensor(7.9158, device='cuda:5')
Round: 1 User: 66 Train Loss: 447.567
Norm: tensor(7.4663, device='cuda:5')
Round: 1 User: 67 Train Loss: 426.081
Norm: tensor(7.2816, device='cuda:5')
Round: 1 User: 68 Train Loss: 419.041
Norm: tensor(7.6172, device='cuda:5')
Round: 1 User: 69 Train Loss: 428.154
Norm: tensor(7.6358, device='cuda:5')
Round: 1 User: 70 Train Loss: 433.823
Norm: tensor(7.5731, device='cuda:5')
Round: 1 User: 71 Train Loss: 435.586
Norm: tensor(7.4319, device='cuda:5')
Round: 1 User: 72 Train Loss: 424.781
Norm: tensor(7.1780, device='cuda:5')
Round: 1 User: 73 Train Loss: 423.921
Norm: tensor(7.6364, device='cuda:5')
Round: 1 User: 74 Train Loss: 439.573
Norm: tensor(7.9751, device='cuda:5')
Round: 1 User: 75 Train Loss: 454.779
Norm: tensor(7.2624, device='cuda:5')
Round: 1 User: 76 Train Loss: 423.773
Norm: tensor(7.5453, device='cuda:5')
Round: 1 User: 77 Train Loss: 434.610
Norm: tensor(7.5922, device='cuda:5')
Round: 1 User: 78 Train Loss: 442.584
Norm: tensor(7.7097, device='cuda:5')
Round: 1 User: 79 Train Loss: 445.622
Norm: tensor(7.3143, device='cuda:5')
Round: 1 User: 80 Train Loss: 424.670
Norm: tensor(7.4618, device='cuda:5')
Round: 1 User: 81 Train Loss: 422.337
Norm: tensor(7.5497, device='cuda:5')
Round: 1 User: 82 Train Loss: 427.234
Norm: tensor(7.7424, device='cuda:5')
Round: 1 User: 83 Train Loss: 434.739
Norm: tensor(7.8997, device='cuda:5')
Round: 1 User: 84 Train Loss: 445.954
Norm: tensor(7.5181, device='cuda:5')
Round: 1 User: 85 Train Loss: 428.486
Norm: tensor(7.4926, device='cuda:5')
Round: 1 User: 86 Train Loss: 429.168
Norm: tensor(7.4909, device='cuda:5')
Round: 1 User: 87 Train Loss: 433.055
Norm: tensor(7.6103, device='cuda:5')
Round: 1 User: 88 Train Loss: 433.173
Norm: tensor(7.5280, device='cuda:5')
Round: 1 User: 89 Train Loss: 447.885
Norm: tensor(7.4010, device='cuda:5')
Round: 1 User: 90 Train Loss: 426.074
Norm: tensor(7.8739, device='cuda:5')
Round: 1 User: 91 Train Loss: 448.578
Norm: tensor(7.3999, device='cuda:5')
Round: 1 User: 92 Train Loss: 430.193
Norm: tensor(7.7464, device='cuda:5')
Round: 1 User: 93 Train Loss: 434.022
Norm: tensor(7.6754, device='cuda:5')