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cifar10-scan.out
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[31m{'setup': 'scan', 'criterion': 'scan', 'criterion_kwargs': {'entropy_weight': 5.0}, 'update_cluster_head_only': False, 'num_heads': 1, 'backbone': 'resnet18', 'train_db_name': 'cifar-10', 'val_db_name': 'cifar-10', 'num_classes': 10, 'num_neighbors': 20, 'augmentation_strategy': 'ours', 'augmentation_kwargs': {'crop_size': 32, 'normalize': {'mean': [0.4914, 0.4822, 0.4465], 'std': [0.2023, 0.1994, 0.201]}, 'num_strong_augs': 4, 'cutout_kwargs': {'n_holes': 1, 'length': 16, 'random': True}}, 'transformation_kwargs': {'crop_size': 32, 'normalize': {'mean': [0.4914, 0.4822, 0.4465], 'std': [0.2023, 0.1994, 0.201]}}, 'optimizer': 'adam', 'optimizer_kwargs': {'lr': 0.0001, 'weight_decay': 0.0001}, 'epochs': 50, 'batch_size': 128, 'num_workers': 8, 'scheduler': 'constant', 'pretext_dir': 'output/cifar-10/pretext', 'pretext_checkpoint': 'output/cifar-10/pretext/checkpoint.pth.tar', 'pretext_model': 'output/cifar-10/pretext/model.pth.tar', 'topk_neighbors_train_path': 'output/cifar-10/pretext/topk-train-neighbors.npy', 'topk_neighbors_val_path': 'output/cifar-10/pretext/topk-val-neighbors.npy', 'scan_dir': 'output/cifar-10/scan', 'scan_checkpoint': 'output/cifar-10/scan/checkpoint.pth.tar', 'scan_model': 'output/cifar-10/scan/model.pth.tar', 'selflabel_dir': 'output/cifar-10/selflabel', 'selflabel_checkpoint': 'output/cifar-10/selflabel/checkpoint.pth.tar', 'selflabel_model': 'output/cifar-10/selflabel/model.pth.tar'}[0m
[34mGet dataset and dataloaders[0m
Files already downloaded and verified
Files already downloaded and verified
Train transforms: Compose(
RandomHorizontalFlip(p=0.5)
RandomCrop(size=(32, 32), padding=None)
<data.augment.Augment object at 0x7f3729b1d090>
ToTensor()
Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.201])
<data.augment.Cutout object at 0x7f3729b1d150>
)
Validation transforms: Compose(
CenterCrop(size=(32, 32))
ToTensor()
Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.201])
)
Train samples 50000 - Val samples 10000
[34mGet model[0m
ClusteringModel(
(backbone): ResNet(
(conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
)
(cluster_head): ModuleList(
(0): Linear(in_features=512, out_features=10, bias=True)
)
)
[34mGet optimizer[0m
Adam (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
eps: 1e-08
lr: 0.0001
weight_decay: 0.0001
)
[34mGet loss[0m
SCANLoss(
(softmax): Softmax(dim=1)
(bce): BCELoss()
)
[34mNo checkpoint file at output/cifar-10/scan/checkpoint.pth.tar[0m
[34mStarting main loop[0m
[33mEpoch 1/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [0][ 0/390] Total Loss -9.2091e+00 (-9.2091e+00) Consistency Loss 2.3010e+00 (2.3010e+00) Entropy 2.3020e+00 (2.3020e+00)
Epoch: [0][ 25/390] Total Loss -9.2365e+00 (-9.2300e+00) Consistency Loss 2.2599e+00 (2.2787e+00) Entropy 2.2993e+00 (2.3017e+00)
Epoch: [0][ 50/390] Total Loss -9.3234e+00 (-9.2651e+00) Consistency Loss 2.1619e+00 (2.2361e+00) Entropy 2.2971e+00 (2.3003e+00)
Epoch: [0][ 75/390] Total Loss -9.4234e+00 (-9.3042e+00) Consistency Loss 2.0418e+00 (2.1906e+00) Entropy 2.2930e+00 (2.2989e+00)
Epoch: [0][100/390] Total Loss -9.5830e+00 (-9.3510e+00) Consistency Loss 1.8760e+00 (2.1366e+00) Entropy 2.2918e+00 (2.2975e+00)
Epoch: [0][125/390] Total Loss -9.6682e+00 (-9.4024e+00) Consistency Loss 1.7978e+00 (2.0797e+00) Entropy 2.2932e+00 (2.2964e+00)
Epoch: [0][150/390] Total Loss -9.7416e+00 (-9.4587e+00) Consistency Loss 1.7088e+00 (2.0174e+00) Entropy 2.2901e+00 (2.2952e+00)
Epoch: [0][175/390] Total Loss -9.8720e+00 (-9.5078e+00) Consistency Loss 1.5764e+00 (1.9651e+00) Entropy 2.2897e+00 (2.2946e+00)
Epoch: [0][200/390] Total Loss -9.9728e+00 (-9.5557e+00) Consistency Loss 1.4771e+00 (1.9155e+00) Entropy 2.2900e+00 (2.2942e+00)
Epoch: [0][225/390] Total Loss -9.9742e+00 (-9.5974e+00) Consistency Loss 1.4903e+00 (1.8731e+00) Entropy 2.2929e+00 (2.2941e+00)
Epoch: [0][250/390] Total Loss -1.0104e+01 (-9.6401e+00) Consistency Loss 1.3787e+00 (1.8304e+00) Entropy 2.2966e+00 (2.2941e+00)
Epoch: [0][275/390] Total Loss -1.0062e+01 (-9.6806e+00) Consistency Loss 1.4357e+00 (1.7911e+00) Entropy 2.2994e+00 (2.2943e+00)
Epoch: [0][300/390] Total Loss -1.0160e+01 (-9.7223e+00) Consistency Loss 1.3434e+00 (1.7507e+00) Entropy 2.3006e+00 (2.2946e+00)
Epoch: [0][325/390] Total Loss -1.0388e+01 (-9.7611e+00) Consistency Loss 1.1112e+00 (1.7134e+00) Entropy 2.2999e+00 (2.2949e+00)
Epoch: [0][350/390] Total Loss -1.0289e+01 (-9.7957e+00) Consistency Loss 1.2058e+00 (1.6801e+00) Entropy 2.2990e+00 (2.2952e+00)
Epoch: [0][375/390] Total Loss -1.0338e+01 (-9.8289e+00) Consistency Loss 1.1651e+00 (1.6479e+00) Entropy 2.3006e+00 (2.2954e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.2953457832336426, 'consistency': 0.7460132837295532, 'total_loss': -1.5493324995040894}], 'lowest_loss_head': 0, 'lowest_loss': -1.5493324995040894}
New lowest loss on validation set: 10000.0000 -> -1.5493
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.6734, 'ARI': 0.4852559410646432, 'NMI': 0.5704833782563389, 'ACC Top-5': 0.9629, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 2/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [1][ 0/390] Total Loss -1.0368e+01 (-1.0368e+01) Consistency Loss 1.1331e+00 (1.1331e+00) Entropy 2.3002e+00 (2.3002e+00)
Epoch: [1][ 25/390] Total Loss -1.0453e+01 (-1.0358e+01) Consistency Loss 1.0479e+00 (1.1319e+00) Entropy 2.3002e+00 (2.2979e+00)
Epoch: [1][ 50/390] Total Loss -1.0364e+01 (-1.0365e+01) Consistency Loss 1.1242e+00 (1.1255e+00) Entropy 2.2976e+00 (2.2981e+00)
Epoch: [1][ 75/390] Total Loss -1.0505e+01 (-1.0374e+01) Consistency Loss 9.8960e-01 (1.1142e+00) Entropy 2.2990e+00 (2.2977e+00)
Epoch: [1][100/390] Total Loss -1.0568e+01 (-1.0390e+01) Consistency Loss 9.3188e-01 (1.0989e+00) Entropy 2.2999e+00 (2.2977e+00)
Epoch: [1][125/390] Total Loss -1.0521e+01 (-1.0402e+01) Consistency Loss 9.7874e-01 (1.0858e+00) Entropy 2.3000e+00 (2.2976e+00)
Epoch: [1][150/390] Total Loss -1.0459e+01 (-1.0415e+01) Consistency Loss 1.0312e+00 (1.0722e+00) Entropy 2.2980e+00 (2.2974e+00)
Epoch: [1][175/390] Total Loss -1.0425e+01 (-1.0425e+01) Consistency Loss 1.0546e+00 (1.0610e+00) Entropy 2.2959e+00 (2.2973e+00)
Epoch: [1][200/390] Total Loss -1.0592e+01 (-1.0435e+01) Consistency Loss 8.9419e-01 (1.0508e+00) Entropy 2.2972e+00 (2.2972e+00)
Epoch: [1][225/390] Total Loss -1.0547e+01 (-1.0442e+01) Consistency Loss 9.4310e-01 (1.0436e+00) Entropy 2.2981e+00 (2.2970e+00)
Epoch: [1][250/390] Total Loss -1.0429e+01 (-1.0449e+01) Consistency Loss 1.0627e+00 (1.0363e+00) Entropy 2.2984e+00 (2.2970e+00)
Epoch: [1][275/390] Total Loss -1.0273e+01 (-1.0456e+01) Consistency Loss 1.1860e+00 (1.0287e+00) Entropy 2.2918e+00 (2.2968e+00)
Epoch: [1][300/390] Total Loss -1.0630e+01 (-1.0462e+01) Consistency Loss 8.4540e-01 (1.0219e+00) Entropy 2.2952e+00 (2.2968e+00)
Epoch: [1][325/390] Total Loss -1.0703e+01 (-1.0466e+01) Consistency Loss 7.6992e-01 (1.0172e+00) Entropy 2.2946e+00 (2.2967e+00)
Epoch: [1][350/390] Total Loss -1.0451e+01 (-1.0472e+01) Consistency Loss 1.0065e+00 (1.0114e+00) Entropy 2.2915e+00 (2.2966e+00)
Epoch: [1][375/390] Total Loss -1.0532e+01 (-1.0477e+01) Consistency Loss 9.4879e-01 (1.0058e+00) Entropy 2.2962e+00 (2.2966e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.299379348754883, 'consistency': 0.5679693818092346, 'total_loss': -1.7314099669456482}], 'lowest_loss_head': 0, 'lowest_loss': -1.7314099669456482}
New lowest loss on validation set: -1.5493 -> -1.7314
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7501, 'ARI': 0.5773280717385402, 'NMI': 0.6470488871442667, 'ACC Top-5': 0.9762, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 3/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [2][ 0/390] Total Loss -1.0542e+01 (-1.0542e+01) Consistency Loss 9.0469e-01 (9.0469e-01) Entropy 2.2893e+00 (2.2893e+00)
Epoch: [2][ 25/390] Total Loss -1.0663e+01 (-1.0527e+01) Consistency Loss 8.2170e-01 (9.4948e-01) Entropy 2.2969e+00 (2.2953e+00)
Epoch: [2][ 50/390] Total Loss -1.0626e+01 (-1.0537e+01) Consistency Loss 8.5409e-01 (9.3891e-01) Entropy 2.2961e+00 (2.2953e+00)
Epoch: [2][ 75/390] Total Loss -1.0566e+01 (-1.0546e+01) Consistency Loss 8.9068e-01 (9.3007e-01) Entropy 2.2914e+00 (2.2952e+00)
Epoch: [2][100/390] Total Loss -1.0633e+01 (-1.0554e+01) Consistency Loss 8.4855e-01 (9.2170e-01) Entropy 2.2964e+00 (2.2951e+00)
Epoch: [2][125/390] Total Loss -1.0560e+01 (-1.0555e+01) Consistency Loss 9.2711e-01 (9.1982e-01) Entropy 2.2975e+00 (2.2949e+00)
Epoch: [2][150/390] Total Loss -1.0602e+01 (-1.0557e+01) Consistency Loss 8.6302e-01 (9.1825e-01) Entropy 2.2931e+00 (2.2950e+00)
Epoch: [2][175/390] Total Loss -1.0681e+01 (-1.0565e+01) Consistency Loss 8.0229e-01 (9.0939e-01) Entropy 2.2966e+00 (2.2950e+00)
Epoch: [2][200/390] Total Loss -1.0425e+01 (-1.0565e+01) Consistency Loss 1.0586e+00 (9.0951e-01) Entropy 2.2967e+00 (2.2950e+00)
Epoch: [2][225/390] Total Loss -1.0635e+01 (-1.0569e+01) Consistency Loss 8.1037e-01 (9.0541e-01) Entropy 2.2891e+00 (2.2949e+00)
Epoch: [2][250/390] Total Loss -1.0523e+01 (-1.0572e+01) Consistency Loss 9.6836e-01 (9.0229e-01) Entropy 2.2983e+00 (2.2948e+00)
Epoch: [2][275/390] Total Loss -1.0561e+01 (-1.0577e+01) Consistency Loss 9.0365e-01 (8.9697e-01) Entropy 2.2928e+00 (2.2947e+00)
Epoch: [2][300/390] Total Loss -1.0529e+01 (-1.0577e+01) Consistency Loss 9.3099e-01 (8.9647e-01) Entropy 2.2920e+00 (2.2947e+00)
Epoch: [2][325/390] Total Loss -1.0601e+01 (-1.0578e+01) Consistency Loss 8.6048e-01 (8.9520e-01) Entropy 2.2923e+00 (2.2946e+00)
Epoch: [2][350/390] Total Loss -1.0674e+01 (-1.0581e+01) Consistency Loss 8.2132e-01 (8.9171e-01) Entropy 2.2991e+00 (2.2946e+00)
Epoch: [2][375/390] Total Loss -1.0555e+01 (-1.0583e+01) Consistency Loss 8.9124e-01 (8.9053e-01) Entropy 2.2893e+00 (2.2946e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.297792911529541, 'consistency': 0.5209192633628845, 'total_loss': -1.7768736481666565}], 'lowest_loss_head': 0, 'lowest_loss': -1.7768736481666565}
New lowest loss on validation set: -1.7314 -> -1.7769
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7768, 'ARI': 0.6201921929737384, 'NMI': 0.679270862407767, 'ACC Top-5': 0.9847, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 4/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [3][ 0/390] Total Loss -1.0716e+01 (-1.0716e+01) Consistency Loss 7.7694e-01 (7.7694e-01) Entropy 2.2985e+00 (2.2985e+00)
Epoch: [3][ 25/390] Total Loss -1.0566e+01 (-1.0610e+01) Consistency Loss 9.0590e-01 (8.6355e-01) Entropy 2.2944e+00 (2.2947e+00)
Epoch: [3][ 50/390] Total Loss -1.0503e+01 (-1.0612e+01) Consistency Loss 9.6277e-01 (8.6357e-01) Entropy 2.2931e+00 (2.2950e+00)
Epoch: [3][ 75/390] Total Loss -1.0686e+01 (-1.0607e+01) Consistency Loss 7.7992e-01 (8.6610e-01) Entropy 2.2932e+00 (2.2946e+00)
Epoch: [3][100/390] Total Loss -1.0735e+01 (-1.0620e+01) Consistency Loss 7.5652e-01 (8.5350e-01) Entropy 2.2982e+00 (2.2947e+00)
Epoch: [3][125/390] Total Loss -1.0487e+01 (-1.0618e+01) Consistency Loss 9.6175e-01 (8.5408e-01) Entropy 2.2898e+00 (2.2944e+00)
Epoch: [3][150/390] Total Loss -1.0566e+01 (-1.0620e+01) Consistency Loss 9.0748e-01 (8.5148e-01) Entropy 2.2948e+00 (2.2943e+00)
Epoch: [3][175/390] Total Loss -1.0676e+01 (-1.0622e+01) Consistency Loss 8.1407e-01 (8.4952e-01) Entropy 2.2981e+00 (2.2942e+00)
Epoch: [3][200/390] Total Loss -1.0704e+01 (-1.0621e+01) Consistency Loss 7.7925e-01 (8.5067e-01) Entropy 2.2967e+00 (2.2943e+00)
Epoch: [3][225/390] Total Loss -1.0618e+01 (-1.0622e+01) Consistency Loss 8.1374e-01 (8.4878e-01) Entropy 2.2864e+00 (2.2943e+00)
Epoch: [3][250/390] Total Loss -1.0631e+01 (-1.0625e+01) Consistency Loss 8.4950e-01 (8.4695e-01) Entropy 2.2961e+00 (2.2944e+00)
Epoch: [3][275/390] Total Loss -1.0692e+01 (-1.0627e+01) Consistency Loss 7.8073e-01 (8.4508e-01) Entropy 2.2946e+00 (2.2944e+00)
Epoch: [3][300/390] Total Loss -1.0704e+01 (-1.0626e+01) Consistency Loss 7.7712e-01 (8.4565e-01) Entropy 2.2963e+00 (2.2944e+00)
Epoch: [3][325/390] Total Loss -1.0674e+01 (-1.0626e+01) Consistency Loss 8.1407e-01 (8.4504e-01) Entropy 2.2976e+00 (2.2942e+00)
Epoch: [3][350/390] Total Loss -1.0560e+01 (-1.0628e+01) Consistency Loss 9.4674e-01 (8.4297e-01) Entropy 2.3013e+00 (2.2943e+00)
Epoch: [3][375/390] Total Loss -1.0728e+01 (-1.0631e+01) Consistency Loss 7.4986e-01 (8.4035e-01) Entropy 2.2956e+00 (2.2943e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.3005802631378174, 'consistency': 0.5144975781440735, 'total_loss': -1.786082684993744}], 'lowest_loss_head': 0, 'lowest_loss': -1.786082684993744}
New lowest loss on validation set: -1.7769 -> -1.7861
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7826, 'ARI': 0.6191348708950829, 'NMI': 0.6773847333121971, 'ACC Top-5': 0.9867, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 5/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [4][ 0/390] Total Loss -1.0559e+01 (-1.0559e+01) Consistency Loss 8.8780e-01 (8.8780e-01) Entropy 2.2894e+00 (2.2894e+00)
Epoch: [4][ 25/390] Total Loss -1.0672e+01 (-1.0622e+01) Consistency Loss 7.7622e-01 (8.3725e-01) Entropy 2.2897e+00 (2.2918e+00)
Epoch: [4][ 50/390] Total Loss -1.0598e+01 (-1.0633e+01) Consistency Loss 8.7098e-01 (8.2891e-01) Entropy 2.2938e+00 (2.2923e+00)
Epoch: [4][ 75/390] Total Loss -1.0730e+01 (-1.0643e+01) Consistency Loss 7.4860e-01 (8.1863e-01) Entropy 2.2957e+00 (2.2923e+00)
Epoch: [4][100/390] Total Loss -1.0526e+01 (-1.0645e+01) Consistency Loss 9.6390e-01 (8.1774e-01) Entropy 2.2980e+00 (2.2926e+00)
Epoch: [4][125/390] Total Loss -1.0654e+01 (-1.0651e+01) Consistency Loss 7.9450e-01 (8.1445e-01) Entropy 2.2897e+00 (2.2930e+00)
Epoch: [4][150/390] Total Loss -1.0630e+01 (-1.0651e+01) Consistency Loss 8.5886e-01 (8.1461e-01) Entropy 2.2978e+00 (2.2931e+00)
Epoch: [4][175/390] Total Loss -1.0736e+01 (-1.0655e+01) Consistency Loss 7.4228e-01 (8.1029e-01) Entropy 2.2957e+00 (2.2931e+00)
Epoch: [4][200/390] Total Loss -1.0536e+01 (-1.0657e+01) Consistency Loss 9.2431e-01 (8.0868e-01) Entropy 2.2921e+00 (2.2932e+00)
Epoch: [4][225/390] Total Loss -1.0643e+01 (-1.0660e+01) Consistency Loss 7.8964e-01 (8.0645e-01) Entropy 2.2866e+00 (2.2932e+00)
Epoch: [4][250/390] Total Loss -1.0729e+01 (-1.0660e+01) Consistency Loss 7.1420e-01 (8.0596e-01) Entropy 2.2886e+00 (2.2932e+00)
Epoch: [4][275/390] Total Loss -1.0664e+01 (-1.0662e+01) Consistency Loss 7.6902e-01 (8.0366e-01) Entropy 2.2865e+00 (2.2931e+00)
Epoch: [4][300/390] Total Loss -1.0497e+01 (-1.0662e+01) Consistency Loss 9.3165e-01 (8.0335e-01) Entropy 2.2858e+00 (2.2931e+00)
Epoch: [4][325/390] Total Loss -1.0634e+01 (-1.0662e+01) Consistency Loss 7.9721e-01 (8.0392e-01) Entropy 2.2862e+00 (2.2931e+00)
Epoch: [4][350/390] Total Loss -1.0679e+01 (-1.0665e+01) Consistency Loss 8.0427e-01 (8.0106e-01) Entropy 2.2966e+00 (2.2932e+00)
Epoch: [4][375/390] Total Loss -1.0476e+01 (-1.0662e+01) Consistency Loss 9.4756e-01 (8.0376e-01) Entropy 2.2847e+00 (2.2932e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.294863700866699, 'consistency': 0.5052458643913269, 'total_loss': -1.7896178364753723}], 'lowest_loss_head': 0, 'lowest_loss': -1.7896178364753723}
New lowest loss on validation set: -1.7861 -> -1.7896
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7767, 'ARI': 0.618905135261492, 'NMI': 0.6793248854036256, 'ACC Top-5': 0.9836, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 6/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [5][ 0/390] Total Loss -1.0746e+01 (-1.0746e+01) Consistency Loss 7.1348e-01 (7.1348e-01) Entropy 2.2920e+00 (2.2920e+00)
Epoch: [5][ 25/390] Total Loss -1.0699e+01 (-1.0679e+01) Consistency Loss 7.6475e-01 (7.8052e-01) Entropy 2.2928e+00 (2.2920e+00)
Epoch: [5][ 50/390] Total Loss -1.0683e+01 (-1.0666e+01) Consistency Loss 7.8677e-01 (7.9457e-01) Entropy 2.2939e+00 (2.2922e+00)
Epoch: [5][ 75/390] Total Loss -1.0633e+01 (-1.0677e+01) Consistency Loss 8.5315e-01 (7.8762e-01) Entropy 2.2973e+00 (2.2929e+00)
Epoch: [5][100/390] Total Loss -1.0670e+01 (-1.0674e+01) Consistency Loss 7.9513e-01 (7.8979e-01) Entropy 2.2930e+00 (2.2928e+00)
Epoch: [5][125/390] Total Loss -1.0656e+01 (-1.0675e+01) Consistency Loss 8.2184e-01 (7.8825e-01) Entropy 2.2956e+00 (2.2926e+00)
Epoch: [5][150/390] Total Loss -1.0718e+01 (-1.0681e+01) Consistency Loss 7.3416e-01 (7.8219e-01) Entropy 2.2904e+00 (2.2927e+00)
Epoch: [5][175/390] Total Loss -1.0600e+01 (-1.0682e+01) Consistency Loss 8.3860e-01 (7.8243e-01) Entropy 2.2877e+00 (2.2928e+00)
Epoch: [5][200/390] Total Loss -1.0654e+01 (-1.0683e+01) Consistency Loss 8.3065e-01 (7.8153e-01) Entropy 2.2969e+00 (2.2929e+00)
Epoch: [5][225/390] Total Loss -1.0709e+01 (-1.0686e+01) Consistency Loss 7.2999e-01 (7.7928e-01) Entropy 2.2878e+00 (2.2930e+00)
Epoch: [5][250/390] Total Loss -1.0686e+01 (-1.0685e+01) Consistency Loss 7.9269e-01 (7.7936e-01) Entropy 2.2958e+00 (2.2929e+00)
Epoch: [5][275/390] Total Loss -1.0671e+01 (-1.0687e+01) Consistency Loss 7.6133e-01 (7.7805e-01) Entropy 2.2864e+00 (2.2930e+00)
Epoch: [5][300/390] Total Loss -1.0531e+01 (-1.0684e+01) Consistency Loss 8.7511e-01 (7.8092e-01) Entropy 2.2813e+00 (2.2930e+00)
Epoch: [5][325/390] Total Loss -1.0632e+01 (-1.0684e+01) Consistency Loss 8.6413e-01 (7.8174e-01) Entropy 2.2992e+00 (2.2931e+00)
Epoch: [5][350/390] Total Loss -1.0824e+01 (-1.0686e+01) Consistency Loss 6.4883e-01 (7.7984e-01) Entropy 2.2945e+00 (2.2932e+00)
Epoch: [5][375/390] Total Loss -1.0759e+01 (-1.0687e+01) Consistency Loss 7.0369e-01 (7.7863e-01) Entropy 2.2925e+00 (2.2931e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.299506902694702, 'consistency': 0.49412935972213745, 'total_loss': -1.8053775429725647}], 'lowest_loss_head': 0, 'lowest_loss': -1.8053775429725647}
New lowest loss on validation set: -1.7896 -> -1.8054
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7891, 'ARI': 0.6293359832911842, 'NMI': 0.6860164187088658, 'ACC Top-5': 0.9863, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 7/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [6][ 0/390] Total Loss -1.0770e+01 (-1.0770e+01) Consistency Loss 7.0404e-01 (7.0404e-01) Entropy 2.2948e+00 (2.2948e+00)
Epoch: [6][ 25/390] Total Loss -1.0685e+01 (-1.0684e+01) Consistency Loss 7.5179e-01 (7.7889e-01) Entropy 2.2873e+00 (2.2926e+00)
Epoch: [6][ 50/390] Total Loss -1.0584e+01 (-1.0691e+01) Consistency Loss 8.6788e-01 (7.7137e-01) Entropy 2.2904e+00 (2.2925e+00)
Epoch: [6][ 75/390] Total Loss -1.0720e+01 (-1.0685e+01) Consistency Loss 7.0132e-01 (7.8035e-01) Entropy 2.2843e+00 (2.2931e+00)
Epoch: [6][100/390] Total Loss -1.0623e+01 (-1.0685e+01) Consistency Loss 8.4427e-01 (7.8025e-01) Entropy 2.2934e+00 (2.2931e+00)
Epoch: [6][125/390] Total Loss -1.0629e+01 (-1.0685e+01) Consistency Loss 8.3044e-01 (7.8068e-01) Entropy 2.2919e+00 (2.2931e+00)
Epoch: [6][150/390] Total Loss -1.0712e+01 (-1.0687e+01) Consistency Loss 7.6143e-01 (7.7838e-01) Entropy 2.2946e+00 (2.2932e+00)
Epoch: [6][175/390] Total Loss -1.0706e+01 (-1.0690e+01) Consistency Loss 7.7673e-01 (7.7614e-01) Entropy 2.2966e+00 (2.2933e+00)
Epoch: [6][200/390] Total Loss -1.0737e+01 (-1.0693e+01) Consistency Loss 7.2377e-01 (7.7329e-01) Entropy 2.2921e+00 (2.2934e+00)
Epoch: [6][225/390] Total Loss -1.0734e+01 (-1.0696e+01) Consistency Loss 7.3656e-01 (7.7085e-01) Entropy 2.2941e+00 (2.2934e+00)
Epoch: [6][250/390] Total Loss -1.0619e+01 (-1.0696e+01) Consistency Loss 8.5397e-01 (7.7130e-01) Entropy 2.2946e+00 (2.2934e+00)
Epoch: [6][275/390] Total Loss -1.0664e+01 (-1.0697e+01) Consistency Loss 7.9624e-01 (7.6997e-01) Entropy 2.2920e+00 (2.2935e+00)
Epoch: [6][300/390] Total Loss -1.0645e+01 (-1.0696e+01) Consistency Loss 8.4163e-01 (7.7171e-01) Entropy 2.2972e+00 (2.2935e+00)
Epoch: [6][325/390] Total Loss -1.0495e+01 (-1.0697e+01) Consistency Loss 9.9059e-01 (7.7073e-01) Entropy 2.2970e+00 (2.2935e+00)
Epoch: [6][350/390] Total Loss -1.0609e+01 (-1.0697e+01) Consistency Loss 8.2964e-01 (7.7063e-01) Entropy 2.2878e+00 (2.2935e+00)
Epoch: [6][375/390] Total Loss -1.0691e+01 (-1.0698e+01) Consistency Loss 7.7368e-01 (7.6948e-01) Entropy 2.2929e+00 (2.2934e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.2982938289642334, 'consistency': 0.4854818880558014, 'total_loss': -1.812811940908432}], 'lowest_loss_head': 0, 'lowest_loss': -1.812811940908432}
New lowest loss on validation set: -1.8054 -> -1.8128
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7944, 'ARI': 0.6367037392144357, 'NMI': 0.695479406721181, 'ACC Top-5': 0.9853, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 8/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [7][ 0/390] Total Loss -1.0755e+01 (-1.0755e+01) Consistency Loss 7.3530e-01 (7.3530e-01) Entropy 2.2981e+00 (2.2981e+00)
Epoch: [7][ 25/390] Total Loss -1.0679e+01 (-1.0701e+01) Consistency Loss 7.8428e-01 (7.6920e-01) Entropy 2.2926e+00 (2.2941e+00)
Epoch: [7][ 50/390] Total Loss -1.0742e+01 (-1.0707e+01) Consistency Loss 7.2747e-01 (7.6112e-01) Entropy 2.2940e+00 (2.2935e+00)
Epoch: [7][ 75/390] Total Loss -1.0708e+01 (-1.0697e+01) Consistency Loss 7.2826e-01 (7.6785e-01) Entropy 2.2873e+00 (2.2930e+00)
Epoch: [7][100/390] Total Loss -1.0656e+01 (-1.0690e+01) Consistency Loss 7.9430e-01 (7.7487e-01) Entropy 2.2900e+00 (2.2929e+00)
Epoch: [7][125/390] Total Loss -1.0713e+01 (-1.0691e+01) Consistency Loss 7.6302e-01 (7.7255e-01) Entropy 2.2952e+00 (2.2928e+00)
Epoch: [7][150/390] Total Loss -1.0862e+01 (-1.0696e+01) Consistency Loss 6.3291e-01 (7.6840e-01) Entropy 2.2990e+00 (2.2929e+00)
Epoch: [7][175/390] Total Loss -1.0681e+01 (-1.0696e+01) Consistency Loss 8.0577e-01 (7.6693e-01) Entropy 2.2974e+00 (2.2927e+00)
Epoch: [7][200/390] Total Loss -1.0634e+01 (-1.0697e+01) Consistency Loss 7.9197e-01 (7.6666e-01) Entropy 2.2852e+00 (2.2927e+00)
Epoch: [7][225/390] Total Loss -1.0652e+01 (-1.0693e+01) Consistency Loss 8.3203e-01 (7.7111e-01) Entropy 2.2969e+00 (2.2928e+00)
Epoch: [7][250/390] Total Loss -1.0766e+01 (-1.0694e+01) Consistency Loss 7.2655e-01 (7.7054e-01) Entropy 2.2985e+00 (2.2929e+00)
Epoch: [7][275/390] Total Loss -1.0659e+01 (-1.0698e+01) Consistency Loss 8.0631e-01 (7.6643e-01) Entropy 2.2930e+00 (2.2929e+00)
Epoch: [7][300/390] Total Loss -1.0815e+01 (-1.0700e+01) Consistency Loss 6.5789e-01 (7.6444e-01) Entropy 2.2945e+00 (2.2928e+00)
Epoch: [7][325/390] Total Loss -1.0691e+01 (-1.0703e+01) Consistency Loss 8.0004e-01 (7.6135e-01) Entropy 2.2982e+00 (2.2929e+00)
Epoch: [7][350/390] Total Loss -1.0567e+01 (-1.0705e+01) Consistency Loss 8.1583e-01 (7.5988e-01) Entropy 2.2766e+00 (2.2929e+00)
Epoch: [7][375/390] Total Loss -1.0827e+01 (-1.0705e+01) Consistency Loss 6.1639e-01 (7.5863e-01) Entropy 2.2886e+00 (2.2927e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.300848960876465, 'consistency': 0.49269822239875793, 'total_loss': -1.808150738477707}], 'lowest_loss_head': 0, 'lowest_loss': -1.808150738477707}
No new lowest loss on validation set: -1.8128 -> -1.8082
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7814, 'ARI': 0.6206229851257016, 'NMI': 0.6830435138079863, 'ACC Top-5': 0.9873, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 9/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [8][ 0/390] Total Loss -1.0763e+01 (-1.0763e+01) Consistency Loss 7.0657e-01 (7.0657e-01) Entropy 2.2939e+00 (2.2939e+00)
Epoch: [8][ 25/390] Total Loss -1.0695e+01 (-1.0719e+01) Consistency Loss 7.3786e-01 (7.3830e-01) Entropy 2.2865e+00 (2.2915e+00)
Epoch: [8][ 50/390] Total Loss -1.0869e+01 (-1.0717e+01) Consistency Loss 6.1107e-01 (7.3777e-01) Entropy 2.2959e+00 (2.2910e+00)
Epoch: [8][ 75/390] Total Loss -1.0555e+01 (-1.0722e+01) Consistency Loss 8.9966e-01 (7.3565e-01) Entropy 2.2910e+00 (2.2916e+00)
Epoch: [8][100/390] Total Loss -1.0592e+01 (-1.0718e+01) Consistency Loss 8.0154e-01 (7.4160e-01) Entropy 2.2786e+00 (2.2920e+00)
Epoch: [8][125/390] Total Loss -1.0785e+01 (-1.0720e+01) Consistency Loss 7.0917e-01 (7.4063e-01) Entropy 2.2989e+00 (2.2921e+00)
Epoch: [8][150/390] Total Loss -1.0830e+01 (-1.0718e+01) Consistency Loss 6.3273e-01 (7.4246e-01) Entropy 2.2925e+00 (2.2921e+00)
Epoch: [8][175/390] Total Loss -1.0603e+01 (-1.0718e+01) Consistency Loss 7.8070e-01 (7.4340e-01) Entropy 2.2768e+00 (2.2923e+00)
Epoch: [8][200/390] Total Loss -1.0655e+01 (-1.0715e+01) Consistency Loss 7.9921e-01 (7.4734e-01) Entropy 2.2909e+00 (2.2924e+00)
Epoch: [8][225/390] Total Loss -1.0648e+01 (-1.0716e+01) Consistency Loss 8.1350e-01 (7.4622e-01) Entropy 2.2923e+00 (2.2924e+00)
Epoch: [8][250/390] Total Loss -1.0706e+01 (-1.0719e+01) Consistency Loss 7.9202e-01 (7.4401e-01) Entropy 2.2997e+00 (2.2927e+00)
Epoch: [8][275/390] Total Loss -1.0732e+01 (-1.0719e+01) Consistency Loss 7.4481e-01 (7.4430e-01) Entropy 2.2953e+00 (2.2927e+00)
Epoch: [8][300/390] Total Loss -1.0737e+01 (-1.0717e+01) Consistency Loss 7.2020e-01 (7.4618e-01) Entropy 2.2915e+00 (2.2927e+00)
Epoch: [8][325/390] Total Loss -1.0510e+01 (-1.0718e+01) Consistency Loss 9.0670e-01 (7.4472e-01) Entropy 2.2834e+00 (2.2926e+00)
Epoch: [8][350/390] Total Loss -1.0772e+01 (-1.0717e+01) Consistency Loss 6.9257e-01 (7.4596e-01) Entropy 2.2929e+00 (2.2925e+00)
Epoch: [8][375/390] Total Loss -1.0663e+01 (-1.0719e+01) Consistency Loss 7.9024e-01 (7.4432e-01) Entropy 2.2906e+00 (2.2926e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.2989888191223145, 'consistency': 0.4796825051307678, 'total_loss': -1.8193063139915466}], 'lowest_loss_head': 0, 'lowest_loss': -1.8193063139915466}
New lowest loss on validation set: -1.8128 -> -1.8193
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7941, 'ARI': 0.6370481884869236, 'NMI': 0.6950698612149353, 'ACC Top-5': 0.9856, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 10/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [9][ 0/390] Total Loss -1.0719e+01 (-1.0719e+01) Consistency Loss 7.2159e-01 (7.2159e-01) Entropy 2.2882e+00 (2.2882e+00)
Epoch: [9][ 25/390] Total Loss -1.0751e+01 (-1.0729e+01) Consistency Loss 7.0578e-01 (7.3034e-01) Entropy 2.2913e+00 (2.2919e+00)
Epoch: [9][ 50/390] Total Loss -1.0780e+01 (-1.0739e+01) Consistency Loss 6.5552e-01 (7.1972e-01) Entropy 2.2871e+00 (2.2918e+00)
Epoch: [9][ 75/390] Total Loss -1.0806e+01 (-1.0743e+01) Consistency Loss 6.6047e-01 (7.1786e-01) Entropy 2.2932e+00 (2.2922e+00)
Epoch: [9][100/390] Total Loss -1.0837e+01 (-1.0737e+01) Consistency Loss 6.3853e-01 (7.2458e-01) Entropy 2.2951e+00 (2.2923e+00)
Epoch: [9][125/390] Total Loss -1.0658e+01 (-1.0737e+01) Consistency Loss 8.0077e-01 (7.2563e-01) Entropy 2.2918e+00 (2.2925e+00)
Epoch: [9][150/390] Total Loss -1.0660e+01 (-1.0740e+01) Consistency Loss 7.8488e-01 (7.2382e-01) Entropy 2.2890e+00 (2.2927e+00)
Epoch: [9][175/390] Total Loss -1.0664e+01 (-1.0737e+01) Consistency Loss 7.7960e-01 (7.2685e-01) Entropy 2.2886e+00 (2.2927e+00)
Epoch: [9][200/390] Total Loss -1.0819e+01 (-1.0735e+01) Consistency Loss 6.5275e-01 (7.2711e-01) Entropy 2.2944e+00 (2.2924e+00)
Epoch: [9][225/390] Total Loss -1.0740e+01 (-1.0732e+01) Consistency Loss 7.4100e-01 (7.3062e-01) Entropy 2.2962e+00 (2.2925e+00)
Epoch: [9][250/390] Total Loss -1.0884e+01 (-1.0732e+01) Consistency Loss 6.1929e-01 (7.3039e-01) Entropy 2.3007e+00 (2.2924e+00)
Epoch: [9][275/390] Total Loss -1.0781e+01 (-1.0737e+01) Consistency Loss 6.7498e-01 (7.2550e-01) Entropy 2.2911e+00 (2.2925e+00)
Epoch: [9][300/390] Total Loss -1.0694e+01 (-1.0738e+01) Consistency Loss 7.5985e-01 (7.2448e-01) Entropy 2.2907e+00 (2.2925e+00)
Epoch: [9][325/390] Total Loss -1.0866e+01 (-1.0739e+01) Consistency Loss 6.0939e-01 (7.2351e-01) Entropy 2.2950e+00 (2.2924e+00)
Epoch: [9][350/390] Total Loss -1.0854e+01 (-1.0737e+01) Consistency Loss 6.3095e-01 (7.2544e-01) Entropy 2.2969e+00 (2.2925e+00)
Epoch: [9][375/390] Total Loss -1.0786e+01 (-1.0737e+01) Consistency Loss 6.7881e-01 (7.2576e-01) Entropy 2.2930e+00 (2.2925e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.298827886581421, 'consistency': 0.4758132994174957, 'total_loss': -1.8230145871639252}], 'lowest_loss_head': 0, 'lowest_loss': -1.8230145871639252}
New lowest loss on validation set: -1.8193 -> -1.8230
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7915, 'ARI': 0.6319935392743845, 'NMI': 0.6923441383926485, 'ACC Top-5': 0.9871, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 11/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [10][ 0/390] Total Loss -1.0670e+01 (-1.0670e+01) Consistency Loss 7.9668e-01 (7.9668e-01) Entropy 2.2933e+00 (2.2933e+00)
Epoch: [10][ 25/390] Total Loss -1.0733e+01 (-1.0741e+01) Consistency Loss 6.8666e-01 (7.2565e-01) Entropy 2.2840e+00 (2.2932e+00)
Epoch: [10][ 50/390] Total Loss -1.0791e+01 (-1.0733e+01) Consistency Loss 6.7952e-01 (7.2653e-01) Entropy 2.2942e+00 (2.2920e+00)
Epoch: [10][ 75/390] Total Loss -1.0887e+01 (-1.0733e+01) Consistency Loss 5.6374e-01 (7.2585e-01) Entropy 2.2901e+00 (2.2918e+00)
Epoch: [10][100/390] Total Loss -1.0721e+01 (-1.0740e+01) Consistency Loss 7.4915e-01 (7.2066e-01) Entropy 2.2940e+00 (2.2921e+00)
Epoch: [10][125/390] Total Loss -1.0725e+01 (-1.0736e+01) Consistency Loss 7.2993e-01 (7.2700e-01) Entropy 2.2909e+00 (2.2927e+00)
Epoch: [10][150/390] Total Loss -1.0735e+01 (-1.0740e+01) Consistency Loss 7.1467e-01 (7.2372e-01) Entropy 2.2900e+00 (2.2927e+00)
Epoch: [10][175/390] Total Loss -1.0722e+01 (-1.0737e+01) Consistency Loss 7.4931e-01 (7.2672e-01) Entropy 2.2943e+00 (2.2928e+00)
Epoch: [10][200/390] Total Loss -1.0826e+01 (-1.0736e+01) Consistency Loss 6.4310e-01 (7.2793e-01) Entropy 2.2938e+00 (2.2928e+00)
Epoch: [10][225/390] Total Loss -1.0918e+01 (-1.0738e+01) Consistency Loss 5.6801e-01 (7.2668e-01) Entropy 2.2972e+00 (2.2928e+00)
Epoch: [10][250/390] Total Loss -1.0794e+01 (-1.0738e+01) Consistency Loss 6.9587e-01 (7.2656e-01) Entropy 2.2980e+00 (2.2929e+00)
Epoch: [10][275/390] Total Loss -1.0829e+01 (-1.0738e+01) Consistency Loss 6.3773e-01 (7.2613e-01) Entropy 2.2933e+00 (2.2929e+00)
Epoch: [10][300/390] Total Loss -1.0829e+01 (-1.0738e+01) Consistency Loss 6.4614e-01 (7.2718e-01) Entropy 2.2951e+00 (2.2930e+00)
Epoch: [10][325/390] Total Loss -1.0762e+01 (-1.0736e+01) Consistency Loss 7.3097e-01 (7.2856e-01) Entropy 2.2986e+00 (2.2929e+00)
Epoch: [10][350/390] Total Loss -1.0665e+01 (-1.0736e+01) Consistency Loss 8.1685e-01 (7.2830e-01) Entropy 2.2963e+00 (2.2929e+00)
Epoch: [10][375/390] Total Loss -1.0744e+01 (-1.0737e+01) Consistency Loss 7.0497e-01 (7.2718e-01) Entropy 2.2899e+00 (2.2928e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.30145001411438, 'consistency': 0.47600337862968445, 'total_loss': -1.8254466354846954}], 'lowest_loss_head': 0, 'lowest_loss': -1.8254466354846954}
New lowest loss on validation set: -1.8230 -> -1.8254
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7952, 'ARI': 0.6397277413036837, 'NMI': 0.6916876567419737, 'ACC Top-5': 0.9863, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 12/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [11][ 0/390] Total Loss -1.0818e+01 (-1.0818e+01) Consistency Loss 6.6878e-01 (6.6878e-01) Entropy 2.2974e+00 (2.2974e+00)
Epoch: [11][ 25/390] Total Loss -1.0877e+01 (-1.0719e+01) Consistency Loss 5.8177e-01 (7.5253e-01) Entropy 2.2917e+00 (2.2943e+00)
Epoch: [11][ 50/390] Total Loss -1.0694e+01 (-1.0723e+01) Consistency Loss 7.8038e-01 (7.4540e-01) Entropy 2.2948e+00 (2.2937e+00)
Epoch: [11][ 75/390] Total Loss -1.0753e+01 (-1.0725e+01) Consistency Loss 6.9556e-01 (7.4027e-01) Entropy 2.2898e+00 (2.2931e+00)
Epoch: [11][100/390] Total Loss -1.0743e+01 (-1.0728e+01) Consistency Loss 6.8927e-01 (7.3497e-01) Entropy 2.2865e+00 (2.2926e+00)
Epoch: [11][125/390] Total Loss -1.0870e+01 (-1.0733e+01) Consistency Loss 6.1412e-01 (7.3048e-01) Entropy 2.2968e+00 (2.2927e+00)
Epoch: [11][150/390] Total Loss -1.0691e+01 (-1.0734e+01) Consistency Loss 7.4997e-01 (7.2934e-01) Entropy 2.2881e+00 (2.2926e+00)
Epoch: [11][175/390] Total Loss -1.0858e+01 (-1.0736e+01) Consistency Loss 6.1668e-01 (7.2726e-01) Entropy 2.2950e+00 (2.2926e+00)
Epoch: [11][200/390] Total Loss -1.0640e+01 (-1.0738e+01) Consistency Loss 8.0160e-01 (7.2356e-01) Entropy 2.2884e+00 (2.2924e+00)
Epoch: [11][225/390] Total Loss -1.0865e+01 (-1.0742e+01) Consistency Loss 6.1691e-01 (7.2036e-01) Entropy 2.2964e+00 (2.2925e+00)
Epoch: [11][250/390] Total Loss -1.0896e+01 (-1.0743e+01) Consistency Loss 6.0406e-01 (7.1943e-01) Entropy 2.3001e+00 (2.2926e+00)
Epoch: [11][275/390] Total Loss -1.0820e+01 (-1.0745e+01) Consistency Loss 6.3819e-01 (7.1769e-01) Entropy 2.2916e+00 (2.2926e+00)
Epoch: [11][300/390] Total Loss -1.0698e+01 (-1.0744e+01) Consistency Loss 7.6988e-01 (7.1937e-01) Entropy 2.2935e+00 (2.2926e+00)
Epoch: [11][325/390] Total Loss -1.0705e+01 (-1.0743e+01) Consistency Loss 7.4476e-01 (7.2014e-01) Entropy 2.2900e+00 (2.2926e+00)
Epoch: [11][350/390] Total Loss -1.0752e+01 (-1.0746e+01) Consistency Loss 6.6084e-01 (7.1736e-01) Entropy 2.2825e+00 (2.2927e+00)
Epoch: [11][375/390] Total Loss -1.0885e+01 (-1.0743e+01) Consistency Loss 6.0511e-01 (7.1941e-01) Entropy 2.2981e+00 (2.2926e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.2992024421691895, 'consistency': 0.4749070405960083, 'total_loss': -1.8242954015731812}], 'lowest_loss_head': 0, 'lowest_loss': -1.8242954015731812}
No new lowest loss on validation set: -1.8254 -> -1.8243
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7905, 'ARI': 0.6333655137477372, 'NMI': 0.6889326089705496, 'ACC Top-5': 0.9863, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 13/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [12][ 0/390] Total Loss -1.0575e+01 (-1.0575e+01) Consistency Loss 8.8771e-01 (8.8771e-01) Entropy 2.2925e+00 (2.2925e+00)
Epoch: [12][ 25/390] Total Loss -1.0690e+01 (-1.0722e+01) Consistency Loss 7.2790e-01 (7.3587e-01) Entropy 2.2835e+00 (2.2916e+00)
Epoch: [12][ 50/390] Total Loss -1.0717e+01 (-1.0732e+01) Consistency Loss 7.6351e-01 (7.3128e-01) Entropy 2.2961e+00 (2.2926e+00)
Epoch: [12][ 75/390] Total Loss -1.0743e+01 (-1.0735e+01) Consistency Loss 7.4316e-01 (7.2778e-01) Entropy 2.2973e+00 (2.2926e+00)
Epoch: [12][100/390] Total Loss -1.0743e+01 (-1.0739e+01) Consistency Loss 7.5532e-01 (7.2525e-01) Entropy 2.2996e+00 (2.2928e+00)
Epoch: [12][125/390] Total Loss -1.0694e+01 (-1.0744e+01) Consistency Loss 7.6191e-01 (7.1862e-01) Entropy 2.2912e+00 (2.2926e+00)
Epoch: [12][150/390] Total Loss -1.0592e+01 (-1.0748e+01) Consistency Loss 8.6541e-01 (7.1492e-01) Entropy 2.2914e+00 (2.2926e+00)
Epoch: [12][175/390] Total Loss -1.0856e+01 (-1.0751e+01) Consistency Loss 5.8674e-01 (7.1251e-01) Entropy 2.2885e+00 (2.2927e+00)
Epoch: [12][200/390] Total Loss -1.0776e+01 (-1.0750e+01) Consistency Loss 6.8318e-01 (7.1295e-01) Entropy 2.2919e+00 (2.2927e+00)
Epoch: [12][225/390] Total Loss -1.0723e+01 (-1.0751e+01) Consistency Loss 7.7483e-01 (7.1252e-01) Entropy 2.2996e+00 (2.2927e+00)
Epoch: [12][250/390] Total Loss -1.0775e+01 (-1.0755e+01) Consistency Loss 7.0603e-01 (7.0845e-01) Entropy 2.2962e+00 (2.2928e+00)
Epoch: [12][275/390] Total Loss -1.0533e+01 (-1.0754e+01) Consistency Loss 8.8133e-01 (7.0959e-01) Entropy 2.2829e+00 (2.2927e+00)
Epoch: [12][300/390] Total Loss -1.0643e+01 (-1.0752e+01) Consistency Loss 8.2669e-01 (7.1168e-01) Entropy 2.2940e+00 (2.2926e+00)
Epoch: [12][325/390] Total Loss -1.0813e+01 (-1.0750e+01) Consistency Loss 6.5663e-01 (7.1269e-01) Entropy 2.2940e+00 (2.2926e+00)
Epoch: [12][350/390] Total Loss -1.0622e+01 (-1.0750e+01) Consistency Loss 8.5857e-01 (7.1212e-01) Entropy 2.2960e+00 (2.2925e+00)
Epoch: [12][375/390] Total Loss -1.0731e+01 (-1.0750e+01) Consistency Loss 7.4780e-01 (7.1151e-01) Entropy 2.2958e+00 (2.2924e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.300323009490967, 'consistency': 0.47021371126174927, 'total_loss': -1.8301092982292175}], 'lowest_loss_head': 0, 'lowest_loss': -1.8301092982292175}
New lowest loss on validation set: -1.8254 -> -1.8301
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.8019, 'ARI': 0.6471468129541601, 'NMI': 0.7009438573802915, 'ACC Top-5': 0.9887, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 14/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [13][ 0/390] Total Loss -1.0809e+01 (-1.0809e+01) Consistency Loss 6.7066e-01 (6.7066e-01) Entropy 2.2959e+00 (2.2959e+00)
Epoch: [13][ 25/390] Total Loss -1.0843e+01 (-1.0762e+01) Consistency Loss 6.3552e-01 (6.9819e-01) Entropy 2.2957e+00 (2.2921e+00)
Epoch: [13][ 50/390] Total Loss -1.0716e+01 (-1.0762e+01) Consistency Loss 7.5399e-01 (7.0146e-01) Entropy 2.2940e+00 (2.2926e+00)
Epoch: [13][ 75/390] Total Loss -1.0658e+01 (-1.0767e+01) Consistency Loss 7.9690e-01 (6.9329e-01) Entropy 2.2910e+00 (2.2921e+00)
Epoch: [13][100/390] Total Loss -1.0762e+01 (-1.0757e+01) Consistency Loss 7.3613e-01 (7.0398e-01) Entropy 2.2996e+00 (2.2923e+00)
Epoch: [13][125/390] Total Loss -1.0742e+01 (-1.0756e+01) Consistency Loss 7.3036e-01 (7.0634e-01) Entropy 2.2944e+00 (2.2925e+00)
Epoch: [13][150/390] Total Loss -1.0781e+01 (-1.0756e+01) Consistency Loss 7.0447e-01 (7.0746e-01) Entropy 2.2971e+00 (2.2928e+00)
Epoch: [13][175/390] Total Loss -1.0690e+01 (-1.0756e+01) Consistency Loss 7.8578e-01 (7.0665e-01) Entropy 2.2952e+00 (2.2926e+00)
Epoch: [13][200/390] Total Loss -1.0809e+01 (-1.0758e+01) Consistency Loss 6.5623e-01 (7.0504e-01) Entropy 2.2931e+00 (2.2927e+00)
Epoch: [13][225/390] Total Loss -1.0750e+01 (-1.0756e+01) Consistency Loss 7.2160e-01 (7.0788e-01) Entropy 2.2943e+00 (2.2927e+00)
Epoch: [13][250/390] Total Loss -1.0844e+01 (-1.0754e+01) Consistency Loss 6.0661e-01 (7.1001e-01) Entropy 2.2902e+00 (2.2928e+00)
Epoch: [13][275/390] Total Loss -1.0775e+01 (-1.0758e+01) Consistency Loss 7.1762e-01 (7.0615e-01) Entropy 2.2986e+00 (2.2928e+00)
Epoch: [13][300/390] Total Loss -1.0755e+01 (-1.0758e+01) Consistency Loss 7.2387e-01 (7.0553e-01) Entropy 2.2957e+00 (2.2927e+00)
Epoch: [13][325/390] Total Loss -1.0746e+01 (-1.0760e+01) Consistency Loss 7.1979e-01 (7.0440e-01) Entropy 2.2931e+00 (2.2928e+00)
Epoch: [13][350/390] Total Loss -1.0896e+01 (-1.0759e+01) Consistency Loss 5.6829e-01 (7.0560e-01) Entropy 2.2928e+00 (2.2929e+00)
Epoch: [13][375/390] Total Loss -1.0774e+01 (-1.0760e+01) Consistency Loss 6.7206e-01 (7.0458e-01) Entropy 2.2892e+00 (2.2928e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.301344871520996, 'consistency': 0.48116156458854675, 'total_loss': -1.8201833069324493}], 'lowest_loss_head': 0, 'lowest_loss': -1.8201833069324493}
No new lowest loss on validation set: -1.8301 -> -1.8202
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7925, 'ARI': 0.6366538920084186, 'NMI': 0.6958766548470646, 'ACC Top-5': 0.9902, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 15/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [14][ 0/390] Total Loss -1.0582e+01 (-1.0582e+01) Consistency Loss 8.5931e-01 (8.5931e-01) Entropy 2.2882e+00 (2.2882e+00)
Epoch: [14][ 25/390] Total Loss -1.0700e+01 (-1.0749e+01) Consistency Loss 7.3491e-01 (7.1062e-01) Entropy 2.2870e+00 (2.2919e+00)
Epoch: [14][ 50/390] Total Loss -1.0702e+01 (-1.0745e+01) Consistency Loss 7.8987e-01 (7.1469e-01) Entropy 2.2985e+00 (2.2919e+00)
Epoch: [14][ 75/390] Total Loss -1.0735e+01 (-1.0747e+01) Consistency Loss 7.4121e-01 (7.1198e-01) Entropy 2.2952e+00 (2.2919e+00)
Epoch: [14][100/390] Total Loss -1.0835e+01 (-1.0758e+01) Consistency Loss 6.5924e-01 (7.0437e-01) Entropy 2.2989e+00 (2.2924e+00)
Epoch: [14][125/390] Total Loss -1.0837e+01 (-1.0762e+01) Consistency Loss 6.3918e-01 (7.0083e-01) Entropy 2.2952e+00 (2.2925e+00)
Epoch: [14][150/390] Total Loss -1.0754e+01 (-1.0765e+01) Consistency Loss 7.0540e-01 (6.9805e-01) Entropy 2.2919e+00 (2.2926e+00)
Epoch: [14][175/390] Total Loss -1.0805e+01 (-1.0765e+01) Consistency Loss 6.6900e-01 (6.9785e-01) Entropy 2.2948e+00 (2.2926e+00)
Epoch: [14][200/390] Total Loss -1.0642e+01 (-1.0761e+01) Consistency Loss 8.2390e-01 (7.0118e-01) Entropy 2.2931e+00 (2.2924e+00)
Epoch: [14][225/390] Total Loss -1.0873e+01 (-1.0764e+01) Consistency Loss 6.2341e-01 (6.9827e-01) Entropy 2.2992e+00 (2.2924e+00)
Epoch: [14][250/390] Total Loss -1.0666e+01 (-1.0763e+01) Consistency Loss 8.1500e-01 (6.9798e-01) Entropy 2.2962e+00 (2.2923e+00)
Epoch: [14][275/390] Total Loss -1.0773e+01 (-1.0761e+01) Consistency Loss 7.0872e-01 (6.9989e-01) Entropy 2.2963e+00 (2.2923e+00)
Epoch: [14][300/390] Total Loss -1.0935e+01 (-1.0759e+01) Consistency Loss 5.6449e-01 (7.0223e-01) Entropy 2.2999e+00 (2.2923e+00)
Epoch: [14][325/390] Total Loss -1.0838e+01 (-1.0763e+01) Consistency Loss 5.6684e-01 (6.9867e-01) Entropy 2.2810e+00 (2.2924e+00)
Epoch: [14][350/390] Total Loss -1.0699e+01 (-1.0764e+01) Consistency Loss 7.3970e-01 (6.9856e-01) Entropy 2.2878e+00 (2.2924e+00)
Epoch: [14][375/390] Total Loss -1.0789e+01 (-1.0764e+01) Consistency Loss 6.7754e-01 (6.9821e-01) Entropy 2.2933e+00 (2.2925e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.298499822616577, 'consistency': 0.4639408588409424, 'total_loss': -1.8345589637756348}], 'lowest_loss_head': 0, 'lowest_loss': -1.8345589637756348}
New lowest loss on validation set: -1.8301 -> -1.8346
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7972, 'ARI': 0.6396275937721003, 'NMI': 0.6974175472113853, 'ACC Top-5': 0.9891, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 16/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [15][ 0/390] Total Loss -1.0777e+01 (-1.0777e+01) Consistency Loss 6.8919e-01 (6.8919e-01) Entropy 2.2932e+00 (2.2932e+00)
Epoch: [15][ 25/390] Total Loss -1.0773e+01 (-1.0789e+01) Consistency Loss 6.9469e-01 (6.7677e-01) Entropy 2.2935e+00 (2.2932e+00)
Epoch: [15][ 50/390] Total Loss -1.0759e+01 (-1.0770e+01) Consistency Loss 7.2027e-01 (6.9299e-01) Entropy 2.2958e+00 (2.2926e+00)
Epoch: [15][ 75/390] Total Loss -1.0801e+01 (-1.0776e+01) Consistency Loss 6.8505e-01 (6.8594e-01) Entropy 2.2973e+00 (2.2925e+00)
Epoch: [15][100/390] Total Loss -1.0791e+01 (-1.0778e+01) Consistency Loss 6.7171e-01 (6.8335e-01) Entropy 2.2926e+00 (2.2922e+00)
Epoch: [15][125/390] Total Loss -1.0831e+01 (-1.0776e+01) Consistency Loss 6.2723e-01 (6.8469e-01) Entropy 2.2917e+00 (2.2921e+00)
Epoch: [15][150/390] Total Loss -1.0618e+01 (-1.0771e+01) Consistency Loss 8.1488e-01 (6.8886e-01) Entropy 2.2866e+00 (2.2920e+00)
Epoch: [15][175/390] Total Loss -1.0867e+01 (-1.0771e+01) Consistency Loss 5.9835e-01 (6.8867e-01) Entropy 2.2931e+00 (2.2919e+00)
Epoch: [15][200/390] Total Loss -1.0853e+01 (-1.0768e+01) Consistency Loss 6.2904e-01 (6.9222e-01) Entropy 2.2964e+00 (2.2920e+00)
Epoch: [15][225/390] Total Loss -1.0825e+01 (-1.0767e+01) Consistency Loss 6.2517e-01 (6.9408e-01) Entropy 2.2900e+00 (2.2922e+00)
Epoch: [15][250/390] Total Loss -1.0854e+01 (-1.0769e+01) Consistency Loss 6.0030e-01 (6.9207e-01) Entropy 2.2908e+00 (2.2922e+00)
Epoch: [15][275/390] Total Loss -1.0827e+01 (-1.0769e+01) Consistency Loss 6.4540e-01 (6.9264e-01) Entropy 2.2946e+00 (2.2923e+00)
Epoch: [15][300/390] Total Loss -1.0918e+01 (-1.0772e+01) Consistency Loss 5.6284e-01 (6.8947e-01) Entropy 2.2961e+00 (2.2923e+00)
Epoch: [15][325/390] Total Loss -1.0702e+01 (-1.0772e+01) Consistency Loss 7.3759e-01 (6.8947e-01) Entropy 2.2879e+00 (2.2922e+00)
Epoch: [15][350/390] Total Loss -1.0775e+01 (-1.0772e+01) Consistency Loss 7.2546e-01 (6.8907e-01) Entropy 2.3001e+00 (2.2923e+00)
Epoch: [15][375/390] Total Loss -1.0852e+01 (-1.0773e+01) Consistency Loss 6.0764e-01 (6.8914e-01) Entropy 2.2920e+00 (2.2924e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.299570083618164, 'consistency': 0.463140606880188, 'total_loss': -1.836429476737976}], 'lowest_loss_head': 0, 'lowest_loss': -1.836429476737976}
New lowest loss on validation set: -1.8346 -> -1.8364
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7941, 'ARI': 0.63989025442952, 'NMI': 0.6990034877575929, 'ACC Top-5': 0.9885, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 17/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [16][ 0/390] Total Loss -1.0822e+01 (-1.0822e+01) Consistency Loss 6.4036e-01 (6.4036e-01) Entropy 2.2924e+00 (2.2924e+00)
Epoch: [16][ 25/390] Total Loss -1.0736e+01 (-1.0779e+01) Consistency Loss 7.2460e-01 (6.7926e-01) Entropy 2.2921e+00 (2.2916e+00)
Epoch: [16][ 50/390] Total Loss -1.0666e+01 (-1.0777e+01) Consistency Loss 8.1449e-01 (6.8493e-01) Entropy 2.2961e+00 (2.2924e+00)
Epoch: [16][ 75/390] Total Loss -1.0747e+01 (-1.0772e+01) Consistency Loss 7.0264e-01 (6.9281e-01) Entropy 2.2900e+00 (2.2930e+00)
Epoch: [16][100/390] Total Loss -1.0722e+01 (-1.0773e+01) Consistency Loss 7.4070e-01 (6.9268e-01) Entropy 2.2926e+00 (2.2930e+00)
Epoch: [16][125/390] Total Loss -1.0779e+01 (-1.0772e+01) Consistency Loss 7.0722e-01 (6.9306e-01) Entropy 2.2972e+00 (2.2930e+00)
Epoch: [16][150/390] Total Loss -1.0778e+01 (-1.0777e+01) Consistency Loss 6.7692e-01 (6.8888e-01) Entropy 2.2910e+00 (2.2931e+00)
Epoch: [16][175/390] Total Loss -1.0653e+01 (-1.0776e+01) Consistency Loss 8.0170e-01 (6.8896e-01) Entropy 2.2909e+00 (2.2929e+00)
Epoch: [16][200/390] Total Loss -1.0738e+01 (-1.0775e+01) Consistency Loss 7.4466e-01 (6.8940e-01) Entropy 2.2966e+00 (2.2929e+00)
Epoch: [16][225/390] Total Loss -1.0683e+01 (-1.0772e+01) Consistency Loss 7.5915e-01 (6.9214e-01) Entropy 2.2885e+00 (2.2929e+00)
Epoch: [16][250/390] Total Loss -1.0696e+01 (-1.0770e+01) Consistency Loss 7.4535e-01 (6.9404e-01) Entropy 2.2883e+00 (2.2928e+00)
Epoch: [16][275/390] Total Loss -1.0779e+01 (-1.0770e+01) Consistency Loss 6.9895e-01 (6.9509e-01) Entropy 2.2955e+00 (2.2929e+00)
Epoch: [16][300/390] Total Loss -1.0788e+01 (-1.0771e+01) Consistency Loss 6.6091e-01 (6.9312e-01) Entropy 2.2899e+00 (2.2929e+00)
Epoch: [16][325/390] Total Loss -1.0777e+01 (-1.0774e+01) Consistency Loss 6.4917e-01 (6.9009e-01) Entropy 2.2853e+00 (2.2929e+00)
Epoch: [16][350/390] Total Loss -1.0795e+01 (-1.0774e+01) Consistency Loss 6.5124e-01 (6.8997e-01) Entropy 2.2893e+00 (2.2928e+00)
Epoch: [16][375/390] Total Loss -1.0696e+01 (-1.0774e+01) Consistency Loss 7.5657e-01 (6.8951e-01) Entropy 2.2906e+00 (2.2926e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.2985317707061768, 'consistency': 0.46424776315689087, 'total_loss': -1.8342840075492859}], 'lowest_loss_head': 0, 'lowest_loss': -1.8342840075492859}
No new lowest loss on validation set: -1.8364 -> -1.8343
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.794, 'ARI': 0.6436506125287315, 'NMI': 0.70094212763094, 'ACC Top-5': 0.9892, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 18/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [17][ 0/390] Total Loss -1.0706e+01 (-1.0706e+01) Consistency Loss 7.8034e-01 (7.8034e-01) Entropy 2.2973e+00 (2.2973e+00)
Epoch: [17][ 25/390] Total Loss -1.0750e+01 (-1.0777e+01) Consistency Loss 7.2776e-01 (6.8840e-01) Entropy 2.2956e+00 (2.2931e+00)
Epoch: [17][ 50/390] Total Loss -1.0807e+01 (-1.0791e+01) Consistency Loss 6.5463e-01 (6.7814e-01) Entropy 2.2923e+00 (2.2939e+00)
Epoch: [17][ 75/390] Total Loss -1.0712e+01 (-1.0795e+01) Consistency Loss 7.5179e-01 (6.7406e-01) Entropy 2.2928e+00 (2.2937e+00)
Epoch: [17][100/390] Total Loss -1.0736e+01 (-1.0796e+01) Consistency Loss 7.3241e-01 (6.7101e-01) Entropy 2.2937e+00 (2.2935e+00)
Epoch: [17][125/390] Total Loss -1.0746e+01 (-1.0793e+01) Consistency Loss 7.3211e-01 (6.7376e-01) Entropy 2.2956e+00 (2.2934e+00)
Epoch: [17][150/390] Total Loss -1.0842e+01 (-1.0802e+01) Consistency Loss 5.8779e-01 (6.6445e-01) Entropy 2.2859e+00 (2.2933e+00)
Epoch: [17][175/390] Total Loss -1.0764e+01 (-1.0798e+01) Consistency Loss 6.7873e-01 (6.6843e-01) Entropy 2.2886e+00 (2.2933e+00)
Epoch: [17][200/390] Total Loss -1.0750e+01 (-1.0795e+01) Consistency Loss 7.2257e-01 (6.7075e-01) Entropy 2.2945e+00 (2.2931e+00)
Epoch: [17][225/390] Total Loss -1.0751e+01 (-1.0792e+01) Consistency Loss 6.7897e-01 (6.7316e-01) Entropy 2.2861e+00 (2.2930e+00)
Epoch: [17][250/390] Total Loss -1.0722e+01 (-1.0793e+01) Consistency Loss 7.4321e-01 (6.7186e-01) Entropy 2.2930e+00 (2.2930e+00)
Epoch: [17][275/390] Total Loss -1.0625e+01 (-1.0792e+01) Consistency Loss 8.1987e-01 (6.7344e-01) Entropy 2.2890e+00 (2.2931e+00)
Epoch: [17][300/390] Total Loss -1.0684e+01 (-1.0787e+01) Consistency Loss 7.2241e-01 (6.7761e-01) Entropy 2.2812e+00 (2.2929e+00)
Epoch: [17][325/390] Total Loss -1.0785e+01 (-1.0789e+01) Consistency Loss 6.8880e-01 (6.7565e-01) Entropy 2.2947e+00 (2.2928e+00)
Epoch: [17][350/390] Total Loss -1.0949e+01 (-1.0789e+01) Consistency Loss 5.2545e-01 (6.7483e-01) Entropy 2.2950e+00 (2.2928e+00)
Epoch: [17][375/390] Total Loss -1.0799e+01 (-1.0789e+01) Consistency Loss 6.7705e-01 (6.7480e-01) Entropy 2.2951e+00 (2.2928e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.3008251190185547, 'consistency': 0.46275895833969116, 'total_loss': -1.8380661606788635}], 'lowest_loss_head': 0, 'lowest_loss': -1.8380661606788635}
New lowest loss on validation set: -1.8364 -> -1.8381
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7914, 'ARI': 0.6350824940130828, 'NMI': 0.6934124299488079, 'ACC Top-5': 0.9893, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 19/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [18][ 0/390] Total Loss -1.0786e+01 (-1.0786e+01) Consistency Loss 7.0746e-01 (7.0746e-01) Entropy 2.2988e+00 (2.2988e+00)
Epoch: [18][ 25/390] Total Loss -1.0785e+01 (-1.0767e+01) Consistency Loss 6.7652e-01 (6.9516e-01) Entropy 2.2923e+00 (2.2924e+00)
Epoch: [18][ 50/390] Total Loss -1.0768e+01 (-1.0774e+01) Consistency Loss 6.9052e-01 (6.8676e-01) Entropy 2.2917e+00 (2.2922e+00)
Epoch: [18][ 75/390] Total Loss -1.0802e+01 (-1.0779e+01) Consistency Loss 6.8838e-01 (6.8224e-01) Entropy 2.2981e+00 (2.2922e+00)
Epoch: [18][100/390] Total Loss -1.0698e+01 (-1.0783e+01) Consistency Loss 7.3544e-01 (6.7956e-01) Entropy 2.2867e+00 (2.2924e+00)
Epoch: [18][125/390] Total Loss -1.0747e+01 (-1.0786e+01) Consistency Loss 7.3873e-01 (6.7699e-01) Entropy 2.2971e+00 (2.2927e+00)
Epoch: [18][150/390] Total Loss -1.0910e+01 (-1.0792e+01) Consistency Loss 5.4975e-01 (6.7156e-01) Entropy 2.2919e+00 (2.2927e+00)
Epoch: [18][175/390] Total Loss -1.0727e+01 (-1.0791e+01) Consistency Loss 7.5826e-01 (6.7335e-01) Entropy 2.2971e+00 (2.2928e+00)
Epoch: [18][200/390] Total Loss -1.0783e+01 (-1.0791e+01) Consistency Loss 6.8440e-01 (6.7341e-01) Entropy 2.2936e+00 (2.2930e+00)
Epoch: [18][225/390] Total Loss -1.0956e+01 (-1.0792e+01) Consistency Loss 5.2633e-01 (6.7336e-01) Entropy 2.2964e+00 (2.2930e+00)
Epoch: [18][250/390] Total Loss -1.0792e+01 (-1.0789e+01) Consistency Loss 6.5929e-01 (6.7539e-01) Entropy 2.2903e+00 (2.2929e+00)
Epoch: [18][275/390] Total Loss -1.0737e+01 (-1.0789e+01) Consistency Loss 6.4584e-01 (6.7455e-01) Entropy 2.2766e+00 (2.2927e+00)
Epoch: [18][300/390] Total Loss -1.0926e+01 (-1.0790e+01) Consistency Loss 5.3109e-01 (6.7301e-01) Entropy 2.2914e+00 (2.2926e+00)
Epoch: [18][325/390] Total Loss -1.0725e+01 (-1.0791e+01) Consistency Loss 7.3506e-01 (6.7215e-01) Entropy 2.2921e+00 (2.2927e+00)
Epoch: [18][350/390] Total Loss -1.0749e+01 (-1.0791e+01) Consistency Loss 7.3074e-01 (6.7248e-01) Entropy 2.2960e+00 (2.2926e+00)
Epoch: [18][375/390] Total Loss -1.0734e+01 (-1.0790e+01) Consistency Loss 7.4644e-01 (6.7352e-01) Entropy 2.2960e+00 (2.2927e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.2993202209472656, 'consistency': 0.46360835433006287, 'total_loss': -1.8357118666172028}], 'lowest_loss_head': 0, 'lowest_loss': -1.8357118666172028}
No new lowest loss on validation set: -1.8381 -> -1.8357
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.8006, 'ARI': 0.6481557154594619, 'NMI': 0.7072533670477569, 'ACC Top-5': 0.9855, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 20/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [19][ 0/390] Total Loss -1.0883e+01 (-1.0883e+01) Consistency Loss 5.9270e-01 (5.9270e-01) Entropy 2.2952e+00 (2.2952e+00)
Epoch: [19][ 25/390] Total Loss -1.0702e+01 (-1.0803e+01) Consistency Loss 7.8553e-01 (6.7300e-01) Entropy 2.2976e+00 (2.2951e+00)
Epoch: [19][ 50/390] Total Loss -1.0836e+01 (-1.0804e+01) Consistency Loss 6.5751e-01 (6.6673e-01) Entropy 2.2987e+00 (2.2942e+00)
Epoch: [19][ 75/390] Total Loss -1.0921e+01 (-1.0800e+01) Consistency Loss 5.5922e-01 (6.6746e-01) Entropy 2.2961e+00 (2.2935e+00)
Epoch: [19][100/390] Total Loss -1.0907e+01 (-1.0801e+01) Consistency Loss 5.6941e-01 (6.6471e-01) Entropy 2.2953e+00 (2.2931e+00)
Epoch: [19][125/390] Total Loss -1.0764e+01 (-1.0796e+01) Consistency Loss 7.0464e-01 (6.6929e-01) Entropy 2.2938e+00 (2.2930e+00)
Epoch: [19][150/390] Total Loss -1.0797e+01 (-1.0797e+01) Consistency Loss 6.3635e-01 (6.6750e-01) Entropy 2.2867e+00 (2.2929e+00)
Epoch: [19][175/390] Total Loss -1.0681e+01 (-1.0798e+01) Consistency Loss 7.6599e-01 (6.6599e-01) Entropy 2.2895e+00 (2.2927e+00)
Epoch: [19][200/390] Total Loss -1.0753e+01 (-1.0796e+01) Consistency Loss 7.1146e-01 (6.6710e-01) Entropy 2.2930e+00 (2.2927e+00)
Epoch: [19][225/390] Total Loss -1.0706e+01 (-1.0795e+01) Consistency Loss 7.8861e-01 (6.6837e-01) Entropy 2.2990e+00 (2.2928e+00)
Epoch: [19][250/390] Total Loss -1.0789e+01 (-1.0794e+01) Consistency Loss 6.4223e-01 (6.6885e-01) Entropy 2.2863e+00 (2.2926e+00)
Epoch: [19][275/390] Total Loss -1.0490e+01 (-1.0794e+01) Consistency Loss 9.8309e-01 (6.6857e-01) Entropy 2.2947e+00 (2.2926e+00)
Epoch: [19][300/390] Total Loss -1.0890e+01 (-1.0795e+01) Consistency Loss 5.2071e-01 (6.6813e-01) Entropy 2.2822e+00 (2.2926e+00)
Epoch: [19][325/390] Total Loss -1.0773e+01 (-1.0795e+01) Consistency Loss 6.5744e-01 (6.6796e-01) Entropy 2.2861e+00 (2.2926e+00)
Epoch: [19][350/390] Total Loss -1.0784e+01 (-1.0796e+01) Consistency Loss 6.7377e-01 (6.6731e-01) Entropy 2.2915e+00 (2.2926e+00)
Epoch: [19][375/390] Total Loss -1.0833e+01 (-1.0795e+01) Consistency Loss 6.5756e-01 (6.6828e-01) Entropy 2.2980e+00 (2.2926e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.3002066612243652, 'consistency': 0.46762555837631226, 'total_loss': -1.832581102848053}], 'lowest_loss_head': 0, 'lowest_loss': -1.832581102848053}
No new lowest loss on validation set: -1.8381 -> -1.8326
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7964, 'ARI': 0.6462774830588149, 'NMI': 0.7023728183905777, 'ACC Top-5': 0.9875, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 21/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [20][ 0/390] Total Loss -1.0920e+01 (-1.0920e+01) Consistency Loss 5.6766e-01 (5.6766e-01) Entropy 2.2975e+00 (2.2975e+00)
Epoch: [20][ 25/390] Total Loss -1.0892e+01 (-1.0795e+01) Consistency Loss 5.4790e-01 (6.6531e-01) Entropy 2.2880e+00 (2.2920e+00)
Epoch: [20][ 50/390] Total Loss -1.0822e+01 (-1.0791e+01) Consistency Loss 6.2973e-01 (6.6695e-01) Entropy 2.2904e+00 (2.2915e+00)
Epoch: [20][ 75/390] Total Loss -1.0897e+01 (-1.0798e+01) Consistency Loss 5.7967e-01 (6.6160e-01) Entropy 2.2954e+00 (2.2918e+00)
Epoch: [20][100/390] Total Loss -1.0755e+01 (-1.0799e+01) Consistency Loss 7.3419e-01 (6.6162e-01) Entropy 2.2979e+00 (2.2922e+00)
Epoch: [20][125/390] Total Loss -1.0682e+01 (-1.0794e+01) Consistency Loss 7.5735e-01 (6.6758e-01) Entropy 2.2880e+00 (2.2924e+00)
Epoch: [20][150/390] Total Loss -1.0642e+01 (-1.0793e+01) Consistency Loss 7.7865e-01 (6.6812e-01) Entropy 2.2842e+00 (2.2923e+00)
Epoch: [20][175/390] Total Loss -1.0823e+01 (-1.0795e+01) Consistency Loss 6.4253e-01 (6.6741e-01) Entropy 2.2931e+00 (2.2924e+00)
Epoch: [20][200/390] Total Loss -1.0920e+01 (-1.0798e+01) Consistency Loss 5.4439e-01 (6.6448e-01) Entropy 2.2929e+00 (2.2925e+00)
Epoch: [20][225/390] Total Loss -1.0773e+01 (-1.0799e+01) Consistency Loss 6.9290e-01 (6.6252e-01) Entropy 2.2932e+00 (2.2923e+00)
Epoch: [20][250/390] Total Loss -1.0821e+01 (-1.0798e+01) Consistency Loss 6.3613e-01 (6.6269e-01) Entropy 2.2915e+00 (2.2922e+00)
Epoch: [20][275/390] Total Loss -1.0800e+01 (-1.0796e+01) Consistency Loss 6.8166e-01 (6.6493e-01) Entropy 2.2963e+00 (2.2922e+00)
Epoch: [20][300/390] Total Loss -1.0945e+01 (-1.0794e+01) Consistency Loss 5.2820e-01 (6.6691e-01) Entropy 2.2945e+00 (2.2923e+00)
Epoch: [20][325/390] Total Loss -1.0841e+01 (-1.0795e+01) Consistency Loss 5.8820e-01 (6.6567e-01) Entropy 2.2858e+00 (2.2921e+00)
Epoch: [20][350/390] Total Loss -1.0809e+01 (-1.0794e+01) Consistency Loss 6.7665e-01 (6.6692e-01) Entropy 2.2971e+00 (2.2922e+00)
Epoch: [20][375/390] Total Loss -1.0782e+01 (-1.0793e+01) Consistency Loss 6.4399e-01 (6.6803e-01) Entropy 2.2852e+00 (2.2922e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.3010685443878174, 'consistency': 0.46095940470695496, 'total_loss': -1.8401091396808624}], 'lowest_loss_head': 0, 'lowest_loss': -1.8401091396808624}
New lowest loss on validation set: -1.8381 -> -1.8401
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.8014, 'ARI': 0.6506684352308094, 'NMI': 0.7048562350488508, 'ACC Top-5': 0.9884, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 22/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [21][ 0/390] Total Loss -1.0768e+01 (-1.0768e+01) Consistency Loss 6.7962e-01 (6.7962e-01) Entropy 2.2896e+00 (2.2896e+00)
Epoch: [21][ 25/390] Total Loss -1.0814e+01 (-1.0783e+01) Consistency Loss 6.1883e-01 (6.6824e-01) Entropy 2.2866e+00 (2.2903e+00)
Epoch: [21][ 50/390] Total Loss -1.0819e+01 (-1.0798e+01) Consistency Loss 6.5400e-01 (6.6054e-01) Entropy 2.2947e+00 (2.2918e+00)
Epoch: [21][ 75/390] Total Loss -1.0846e+01 (-1.0803e+01) Consistency Loss 5.9403e-01 (6.5543e-01) Entropy 2.2880e+00 (2.2917e+00)
Epoch: [21][100/390] Total Loss -1.0750e+01 (-1.0794e+01) Consistency Loss 7.3305e-01 (6.6645e-01) Entropy 2.2965e+00 (2.2922e+00)
Epoch: [21][125/390] Total Loss -1.0786e+01 (-1.0791e+01) Consistency Loss 6.9990e-01 (6.7211e-01) Entropy 2.2973e+00 (2.2926e+00)
Epoch: [21][150/390] Total Loss -1.0785e+01 (-1.0792e+01) Consistency Loss 6.5301e-01 (6.7140e-01) Entropy 2.2876e+00 (2.2927e+00)
Epoch: [21][175/390] Total Loss -1.0781e+01 (-1.0795e+01) Consistency Loss 6.6690e-01 (6.6899e-01) Entropy 2.2895e+00 (2.2928e+00)
Epoch: [21][200/390] Total Loss -1.0795e+01 (-1.0793e+01) Consistency Loss 6.6351e-01 (6.7091e-01) Entropy 2.2918e+00 (2.2929e+00)
Epoch: [21][225/390] Total Loss -1.0806e+01 (-1.0794e+01) Consistency Loss 6.7296e-01 (6.7058e-01) Entropy 2.2959e+00 (2.2930e+00)
Epoch: [21][250/390] Total Loss -1.0899e+01 (-1.0795e+01) Consistency Loss 5.8268e-01 (6.7048e-01) Entropy 2.2963e+00 (2.2930e+00)
Epoch: [21][275/390] Total Loss -1.0812e+01 (-1.0795e+01) Consistency Loss 6.6210e-01 (6.7057e-01) Entropy 2.2948e+00 (2.2930e+00)
Epoch: [21][300/390] Total Loss -1.0756e+01 (-1.0796e+01) Consistency Loss 6.8912e-01 (6.6908e-01) Entropy 2.2890e+00 (2.2931e+00)
Epoch: [21][325/390] Total Loss -1.0895e+01 (-1.0798e+01) Consistency Loss 5.7218e-01 (6.6737e-01) Entropy 2.2933e+00 (2.2930e+00)
Epoch: [21][350/390] Total Loss -1.0855e+01 (-1.0796e+01) Consistency Loss 6.0106e-01 (6.6949e-01) Entropy 2.2911e+00 (2.2930e+00)
Epoch: [21][375/390] Total Loss -1.0889e+01 (-1.0796e+01) Consistency Loss 5.9881e-01 (6.6888e-01) Entropy 2.2976e+00 (2.2930e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.300426483154297, 'consistency': 0.46264979243278503, 'total_loss': -1.8377766907215118}], 'lowest_loss_head': 0, 'lowest_loss': -1.8377766907215118}
No new lowest loss on validation set: -1.8401 -> -1.8378
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.801, 'ARI': 0.6490557934061598, 'NMI': 0.7028585239233969, 'ACC Top-5': 0.9892, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 23/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [22][ 0/390] Total Loss -1.0848e+01 (-1.0848e+01) Consistency Loss 6.3484e-01 (6.3484e-01) Entropy 2.2966e+00 (2.2966e+00)
Epoch: [22][ 25/390] Total Loss -1.0728e+01 (-1.0807e+01) Consistency Loss 7.6023e-01 (6.6592e-01) Entropy 2.2977e+00 (2.2946e+00)
Epoch: [22][ 50/390] Total Loss -1.0799e+01 (-1.0805e+01) Consistency Loss 6.2195e-01 (6.6601e-01) Entropy 2.2841e+00 (2.2942e+00)
Epoch: [22][ 75/390] Total Loss -1.0775e+01 (-1.0801e+01) Consistency Loss 7.0413e-01 (6.6839e-01) Entropy 2.2959e+00 (2.2938e+00)
Epoch: [22][100/390] Total Loss -1.0906e+01 (-1.0798e+01) Consistency Loss 5.3265e-01 (6.6939e-01) Entropy 2.2877e+00 (2.2935e+00)
Epoch: [22][125/390] Total Loss -1.0831e+01 (-1.0796e+01) Consistency Loss 6.6175e-01 (6.6887e-01) Entropy 2.2986e+00 (2.2929e+00)
Epoch: [22][150/390] Total Loss -1.0809e+01 (-1.0800e+01) Consistency Loss 6.5258e-01 (6.6405e-01) Entropy 2.2924e+00 (2.2929e+00)
Epoch: [22][175/390] Total Loss -1.0728e+01 (-1.0801e+01) Consistency Loss 7.2607e-01 (6.6315e-01) Entropy 2.2907e+00 (2.2927e+00)
Epoch: [22][200/390] Total Loss -1.0751e+01 (-1.0803e+01) Consistency Loss 7.3483e-01 (6.6022e-01) Entropy 2.2972e+00 (2.2927e+00)
Epoch: [22][225/390] Total Loss -1.0834e+01 (-1.0802e+01) Consistency Loss 6.1761e-01 (6.6027e-01) Entropy 2.2903e+00 (2.2926e+00)
Epoch: [22][250/390] Total Loss -1.0872e+01 (-1.0802e+01) Consistency Loss 6.2329e-01 (6.6010e-01) Entropy 2.2990e+00 (2.2925e+00)
Epoch: [22][275/390] Total Loss -1.0889e+01 (-1.0804e+01) Consistency Loss 6.1564e-01 (6.5907e-01) Entropy 2.3008e+00 (2.2925e+00)
Epoch: [22][300/390] Total Loss -1.0780e+01 (-1.0800e+01) Consistency Loss 6.6243e-01 (6.6233e-01) Entropy 2.2884e+00 (2.2925e+00)
Epoch: [22][325/390] Total Loss -1.0838e+01 (-1.0802e+01) Consistency Loss 6.3488e-01 (6.6108e-01) Entropy 2.2946e+00 (2.2926e+00)
Epoch: [22][350/390] Total Loss -1.0769e+01 (-1.0800e+01) Consistency Loss 6.8164e-01 (6.6233e-01) Entropy 2.2901e+00 (2.2925e+00)
Epoch: [22][375/390] Total Loss -1.0761e+01 (-1.0801e+01) Consistency Loss 7.2471e-01 (6.6170e-01) Entropy 2.2972e+00 (2.2925e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.301325798034668, 'consistency': 0.4649856686592102, 'total_loss': -1.8363401293754578}], 'lowest_loss_head': 0, 'lowest_loss': -1.8363401293754578}
No new lowest loss on validation set: -1.8401 -> -1.8363
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7966, 'ARI': 0.6428702784149132, 'NMI': 0.6983649399781615, 'ACC Top-5': 0.9891, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 24/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [23][ 0/390] Total Loss -1.0869e+01 (-1.0869e+01) Consistency Loss 5.8129e-01 (5.8129e-01) Entropy 2.2901e+00 (2.2901e+00)
Epoch: [23][ 25/390] Total Loss -1.0829e+01 (-1.0822e+01) Consistency Loss 6.5890e-01 (6.3797e-01) Entropy 2.2976e+00 (2.2920e+00)
Epoch: [23][ 50/390] Total Loss -1.0882e+01 (-1.0798e+01) Consistency Loss 5.9630e-01 (6.6234e-01) Entropy 2.2957e+00 (2.2920e+00)
Epoch: [23][ 75/390] Total Loss -1.0832e+01 (-1.0801e+01) Consistency Loss 6.4228e-01 (6.5989e-01) Entropy 2.2948e+00 (2.2923e+00)
Epoch: [23][100/390] Total Loss -1.0853e+01 (-1.0800e+01) Consistency Loss 6.3039e-01 (6.6259e-01) Entropy 2.2968e+00 (2.2925e+00)
Epoch: [23][125/390] Total Loss -1.0832e+01 (-1.0796e+01) Consistency Loss 6.5521e-01 (6.6704e-01) Entropy 2.2973e+00 (2.2925e+00)
Epoch: [23][150/390] Total Loss -1.0792e+01 (-1.0798e+01) Consistency Loss 6.9715e-01 (6.6435e-01) Entropy 2.2979e+00 (2.2925e+00)
Epoch: [23][175/390] Total Loss -1.0948e+01 (-1.0800e+01) Consistency Loss 5.4620e-01 (6.6369e-01) Entropy 2.2989e+00 (2.2926e+00)
Epoch: [23][200/390] Total Loss -1.0997e+01 (-1.0800e+01) Consistency Loss 4.9240e-01 (6.6264e-01) Entropy 2.2979e+00 (2.2926e+00)
Epoch: [23][225/390] Total Loss -1.0862e+01 (-1.0800e+01) Consistency Loss 6.2565e-01 (6.6384e-01) Entropy 2.2975e+00 (2.2928e+00)
Epoch: [23][250/390] Total Loss -1.0859e+01 (-1.0803e+01) Consistency Loss 6.1520e-01 (6.6165e-01) Entropy 2.2949e+00 (2.2929e+00)
Epoch: [23][275/390] Total Loss -1.0727e+01 (-1.0805e+01) Consistency Loss 7.3290e-01 (6.5999e-01) Entropy 2.2921e+00 (2.2929e+00)
Epoch: [23][300/390] Total Loss -1.0866e+01 (-1.0807e+01) Consistency Loss 6.1560e-01 (6.5707e-01) Entropy 2.2964e+00 (2.2929e+00)
Epoch: [23][325/390] Total Loss -1.0794e+01 (-1.0807e+01) Consistency Loss 6.9323e-01 (6.5686e-01) Entropy 2.2975e+00 (2.2929e+00)
Epoch: [23][350/390] Total Loss -1.0730e+01 (-1.0806e+01) Consistency Loss 7.3719e-01 (6.5903e-01) Entropy 2.2934e+00 (2.2929e+00)
Epoch: [23][375/390] Total Loss -1.0913e+01 (-1.0806e+01) Consistency Loss 5.8139e-01 (6.5876e-01) Entropy 2.2988e+00 (2.2929e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.298536777496338, 'consistency': 0.46370723843574524, 'total_loss': -1.8348295390605927}], 'lowest_loss_head': 0, 'lowest_loss': -1.8348295390605927}
No new lowest loss on validation set: -1.8401 -> -1.8348
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7919, 'ARI': 0.6336882603989457, 'NMI': 0.6968271919253887, 'ACC Top-5': 0.9872, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 25/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [24][ 0/390] Total Loss -1.0802e+01 (-1.0802e+01) Consistency Loss 6.6996e-01 (6.6996e-01) Entropy 2.2945e+00 (2.2945e+00)
Epoch: [24][ 25/390] Total Loss -1.0747e+01 (-1.0818e+01) Consistency Loss 6.9499e-01 (6.5096e-01) Entropy 2.2884e+00 (2.2938e+00)
Epoch: [24][ 50/390] Total Loss -1.0855e+01 (-1.0801e+01) Consistency Loss 6.1583e-01 (6.6069e-01) Entropy 2.2942e+00 (2.2924e+00)
Epoch: [24][ 75/390] Total Loss -1.0749e+01 (-1.0795e+01) Consistency Loss 7.4982e-01 (6.6514e-01) Entropy 2.2997e+00 (2.2920e+00)
Epoch: [24][100/390] Total Loss -1.0833e+01 (-1.0795e+01) Consistency Loss 6.1472e-01 (6.6378e-01) Entropy 2.2895e+00 (2.2918e+00)
Epoch: [24][125/390] Total Loss -1.0775e+01 (-1.0799e+01) Consistency Loss 7.1293e-01 (6.6135e-01) Entropy 2.2975e+00 (2.2921e+00)
Epoch: [24][150/390] Total Loss -1.0840e+01 (-1.0798e+01) Consistency Loss 5.7456e-01 (6.6350e-01) Entropy 2.2828e+00 (2.2922e+00)
Epoch: [24][175/390] Total Loss -1.0783e+01 (-1.0798e+01) Consistency Loss 6.6841e-01 (6.6375e-01) Entropy 2.2903e+00 (2.2924e+00)
Epoch: [24][200/390] Total Loss -1.0765e+01 (-1.0799e+01) Consistency Loss 7.1079e-01 (6.6260e-01) Entropy 2.2952e+00 (2.2923e+00)
Epoch: [24][225/390] Total Loss -1.0877e+01 (-1.0797e+01) Consistency Loss 6.2386e-01 (6.6490e-01) Entropy 2.3001e+00 (2.2923e+00)
Epoch: [24][250/390] Total Loss -1.0906e+01 (-1.0797e+01) Consistency Loss 5.8726e-01 (6.6490e-01) Entropy 2.2986e+00 (2.2924e+00)
Epoch: [24][275/390] Total Loss -1.0770e+01 (-1.0795e+01) Consistency Loss 7.1722e-01 (6.6668e-01) Entropy 2.2975e+00 (2.2924e+00)
Epoch: [24][300/390] Total Loss -1.0782e+01 (-1.0797e+01) Consistency Loss 6.7738e-01 (6.6458e-01) Entropy 2.2918e+00 (2.2924e+00)
Epoch: [24][325/390] Total Loss -1.0851e+01 (-1.0798e+01) Consistency Loss 6.3384e-01 (6.6383e-01) Entropy 2.2970e+00 (2.2923e+00)
Epoch: [24][350/390] Total Loss -1.0824e+01 (-1.0797e+01) Consistency Loss 6.7359e-01 (6.6448e-01) Entropy 2.2996e+00 (2.2924e+00)
Epoch: [24][375/390] Total Loss -1.0764e+01 (-1.0799e+01) Consistency Loss 6.9643e-01 (6.6328e-01) Entropy 2.2920e+00 (2.2924e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.299321413040161, 'consistency': 0.4527387022972107, 'total_loss': -1.8465827107429504}], 'lowest_loss_head': 0, 'lowest_loss': -1.8465827107429504}
New lowest loss on validation set: -1.8401 -> -1.8466
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7996, 'ARI': 0.6481087552674129, 'NMI': 0.7043014178656127, 'ACC Top-5': 0.9881, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 26/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [25][ 0/390] Total Loss -1.0629e+01 (-1.0629e+01) Consistency Loss 7.7881e-01 (7.7881e-01) Entropy 2.2815e+00 (2.2815e+00)
Epoch: [25][ 25/390] Total Loss -1.0819e+01 (-1.0775e+01) Consistency Loss 6.5627e-01 (6.9112e-01) Entropy 2.2951e+00 (2.2933e+00)
Epoch: [25][ 50/390] Total Loss -1.0724e+01 (-1.0807e+01) Consistency Loss 7.0170e-01 (6.6063e-01) Entropy 2.2852e+00 (2.2935e+00)
Epoch: [25][ 75/390] Total Loss -1.0869e+01 (-1.0806e+01) Consistency Loss 6.1665e-01 (6.6045e-01) Entropy 2.2972e+00 (2.2932e+00)
Epoch: [25][100/390] Total Loss -1.0856e+01 (-1.0802e+01) Consistency Loss 5.6273e-01 (6.6158e-01) Entropy 2.2837e+00 (2.2927e+00)
Epoch: [25][125/390] Total Loss -1.0646e+01 (-1.0797e+01) Consistency Loss 7.9306e-01 (6.6596e-01) Entropy 2.2878e+00 (2.2927e+00)
Epoch: [25][150/390] Total Loss -1.0822e+01 (-1.0801e+01) Consistency Loss 6.7146e-01 (6.6284e-01) Entropy 2.2987e+00 (2.2928e+00)
Epoch: [25][175/390] Total Loss -1.0925e+01 (-1.0805e+01) Consistency Loss 5.4272e-01 (6.5974e-01) Entropy 2.2936e+00 (2.2929e+00)
Epoch: [25][200/390] Total Loss -1.0892e+01 (-1.0803e+01) Consistency Loss 5.8435e-01 (6.6083e-01) Entropy 2.2952e+00 (2.2928e+00)
Epoch: [25][225/390] Total Loss -1.0783e+01 (-1.0806e+01) Consistency Loss 6.1388e-01 (6.5767e-01) Entropy 2.2793e+00 (2.2927e+00)
Epoch: [25][250/390] Total Loss -1.0709e+01 (-1.0804e+01) Consistency Loss 7.5329e-01 (6.5914e-01) Entropy 2.2925e+00 (2.2926e+00)
Epoch: [25][275/390] Total Loss -1.0927e+01 (-1.0809e+01) Consistency Loss 5.6572e-01 (6.5411e-01) Entropy 2.2985e+00 (2.2927e+00)
Epoch: [25][300/390] Total Loss -1.0684e+01 (-1.0808e+01) Consistency Loss 7.4977e-01 (6.5539e-01) Entropy 2.2868e+00 (2.2927e+00)
Epoch: [25][325/390] Total Loss -1.0808e+01 (-1.0808e+01) Consistency Loss 6.8913e-01 (6.5589e-01) Entropy 2.2994e+00 (2.2927e+00)
Epoch: [25][350/390] Total Loss -1.0795e+01 (-1.0808e+01) Consistency Loss 6.9906e-01 (6.5561e-01) Entropy 2.2989e+00 (2.2928e+00)
Epoch: [25][375/390] Total Loss -1.0831e+01 (-1.0807e+01) Consistency Loss 6.6994e-01 (6.5640e-01) Entropy 2.3003e+00 (2.2928e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.298746347427368, 'consistency': 0.4560682475566864, 'total_loss': -1.8426780998706818}], 'lowest_loss_head': 0, 'lowest_loss': -1.8426780998706818}
No new lowest loss on validation set: -1.8466 -> -1.8427
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.804, 'ARI': 0.6513644114828019, 'NMI': 0.7065161684724975, 'ACC Top-5': 0.9889, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 27/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [26][ 0/390] Total Loss -1.0714e+01 (-1.0714e+01) Consistency Loss 7.5773e-01 (7.5773e-01) Entropy 2.2944e+00 (2.2944e+00)
Epoch: [26][ 25/390] Total Loss -1.0840e+01 (-1.0822e+01) Consistency Loss 6.2908e-01 (6.4020e-01) Entropy 2.2939e+00 (2.2924e+00)
Epoch: [26][ 50/390] Total Loss -1.0769e+01 (-1.0827e+01) Consistency Loss 7.0141e-01 (6.3826e-01) Entropy 2.2940e+00 (2.2931e+00)
Epoch: [26][ 75/390] Total Loss -1.0887e+01 (-1.0824e+01) Consistency Loss 5.6099e-01 (6.4211e-01) Entropy 2.2895e+00 (2.2932e+00)
Epoch: [26][100/390] Total Loss -1.0780e+01 (-1.0817e+01) Consistency Loss 6.4041e-01 (6.4725e-01) Entropy 2.2840e+00 (2.2928e+00)
Epoch: [26][125/390] Total Loss -1.0927e+01 (-1.0810e+01) Consistency Loss 5.2515e-01 (6.5293e-01) Entropy 2.2905e+00 (2.2925e+00)
Epoch: [26][150/390] Total Loss -1.0804e+01 (-1.0814e+01) Consistency Loss 6.8831e-01 (6.4825e-01) Entropy 2.2985e+00 (2.2925e+00)
Epoch: [26][175/390] Total Loss -1.0598e+01 (-1.0809e+01) Consistency Loss 8.7631e-01 (6.5283e-01) Entropy 2.2949e+00 (2.2924e+00)
Epoch: [26][200/390] Total Loss -1.0850e+01 (-1.0808e+01) Consistency Loss 6.1455e-01 (6.5473e-01) Entropy 2.2930e+00 (2.2926e+00)
Epoch: [26][225/390] Total Loss -1.0781e+01 (-1.0810e+01) Consistency Loss 6.7724e-01 (6.5285e-01) Entropy 2.2916e+00 (2.2926e+00)
Epoch: [26][250/390] Total Loss -1.0934e+01 (-1.0811e+01) Consistency Loss 5.4865e-01 (6.5290e-01) Entropy 2.2966e+00 (2.2927e+00)
Epoch: [26][275/390] Total Loss -1.0785e+01 (-1.0809e+01) Consistency Loss 6.7570e-01 (6.5468e-01) Entropy 2.2922e+00 (2.2927e+00)
Epoch: [26][300/390] Total Loss -1.0921e+01 (-1.0812e+01) Consistency Loss 5.4859e-01 (6.5153e-01) Entropy 2.2940e+00 (2.2928e+00)
Epoch: [26][325/390] Total Loss -1.0821e+01 (-1.0813e+01) Consistency Loss 6.1241e-01 (6.5001e-01) Entropy 2.2868e+00 (2.2927e+00)
Epoch: [26][350/390] Total Loss -1.0715e+01 (-1.0814e+01) Consistency Loss 7.4654e-01 (6.4945e-01) Entropy 2.2923e+00 (2.2928e+00)
Epoch: [26][375/390] Total Loss -1.0836e+01 (-1.0814e+01) Consistency Loss 6.4226e-01 (6.4923e-01) Entropy 2.2957e+00 (2.2927e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.2991912364959717, 'consistency': 0.4635506570339203, 'total_loss': -1.8356405794620514}], 'lowest_loss_head': 0, 'lowest_loss': -1.8356405794620514}
No new lowest loss on validation set: -1.8466 -> -1.8356
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7975, 'ARI': 0.6439804519069783, 'NMI': 0.7026080583996989, 'ACC Top-5': 0.9853, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 28/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [27][ 0/390] Total Loss -1.0721e+01 (-1.0721e+01) Consistency Loss 7.4409e-01 (7.4409e-01) Entropy 2.2930e+00 (2.2930e+00)
Epoch: [27][ 25/390] Total Loss -1.0776e+01 (-1.0812e+01) Consistency Loss 7.0401e-01 (6.4932e-01) Entropy 2.2961e+00 (2.2923e+00)
Epoch: [27][ 50/390] Total Loss -1.0943e+01 (-1.0817e+01) Consistency Loss 5.5178e-01 (6.4282e-01) Entropy 2.2990e+00 (2.2919e+00)
Epoch: [27][ 75/390] Total Loss -1.0829e+01 (-1.0807e+01) Consistency Loss 6.4772e-01 (6.5408e-01) Entropy 2.2953e+00 (2.2923e+00)
Epoch: [27][100/390] Total Loss -1.0871e+01 (-1.0810e+01) Consistency Loss 6.0434e-01 (6.5035e-01) Entropy 2.2951e+00 (2.2921e+00)
Epoch: [27][125/390] Total Loss -1.0815e+01 (-1.0813e+01) Consistency Loss 6.5829e-01 (6.4813e-01) Entropy 2.2947e+00 (2.2921e+00)
Epoch: [27][150/390] Total Loss -1.0849e+01 (-1.0813e+01) Consistency Loss 6.1415e-01 (6.4816e-01) Entropy 2.2926e+00 (2.2921e+00)
Epoch: [27][175/390] Total Loss -1.0881e+01 (-1.0816e+01) Consistency Loss 5.9566e-01 (6.4550e-01) Entropy 2.2953e+00 (2.2923e+00)
Epoch: [27][200/390] Total Loss -1.0786e+01 (-1.0814e+01) Consistency Loss 6.9075e-01 (6.4860e-01) Entropy 2.2953e+00 (2.2925e+00)
Epoch: [27][225/390] Total Loss -1.0847e+01 (-1.0810e+01) Consistency Loss 6.3274e-01 (6.5178e-01) Entropy 2.2959e+00 (2.2923e+00)
Epoch: [27][250/390] Total Loss -1.0782e+01 (-1.0811e+01) Consistency Loss 6.7390e-01 (6.5026e-01) Entropy 2.2912e+00 (2.2923e+00)
Epoch: [27][275/390] Total Loss -1.0649e+01 (-1.0812e+01) Consistency Loss 7.8821e-01 (6.4958e-01) Entropy 2.2875e+00 (2.2923e+00)
Epoch: [27][300/390] Total Loss -1.0836e+01 (-1.0811e+01) Consistency Loss 6.2318e-01 (6.5045e-01) Entropy 2.2919e+00 (2.2924e+00)
Epoch: [27][325/390] Total Loss -1.0786e+01 (-1.0813e+01) Consistency Loss 6.4356e-01 (6.4848e-01) Entropy 2.2858e+00 (2.2924e+00)
Epoch: [27][350/390] Total Loss -1.0750e+01 (-1.0814e+01) Consistency Loss 6.9669e-01 (6.4820e-01) Entropy 2.2894e+00 (2.2923e+00)
Epoch: [27][375/390] Total Loss -1.0906e+01 (-1.0815e+01) Consistency Loss 5.8621e-01 (6.4755e-01) Entropy 2.2985e+00 (2.2925e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.3012022972106934, 'consistency': 0.45590877532958984, 'total_loss': -1.8452935218811035}], 'lowest_loss_head': 0, 'lowest_loss': -1.8452935218811035}
No new lowest loss on validation set: -1.8466 -> -1.8453
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.801, 'ARI': 0.6496636704216281, 'NMI': 0.7040393958418842, 'ACC Top-5': 0.9865, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 29/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [28][ 0/390] Total Loss -1.0965e+01 (-1.0965e+01) Consistency Loss 5.2321e-01 (5.2321e-01) Entropy 2.2977e+00 (2.2977e+00)
Epoch: [28][ 25/390] Total Loss -1.0785e+01 (-1.0811e+01) Consistency Loss 7.0456e-01 (6.5848e-01) Entropy 2.2980e+00 (2.2939e+00)
Epoch: [28][ 50/390] Total Loss -1.0853e+01 (-1.0810e+01) Consistency Loss 6.2952e-01 (6.5840e-01) Entropy 2.2966e+00 (2.2937e+00)
Epoch: [28][ 75/390] Total Loss -1.0867e+01 (-1.0812e+01) Consistency Loss 6.0224e-01 (6.5585e-01) Entropy 2.2938e+00 (2.2936e+00)
Epoch: [28][100/390] Total Loss -1.0772e+01 (-1.0815e+01) Consistency Loss 7.0152e-01 (6.5403e-01) Entropy 2.2947e+00 (2.2938e+00)
Epoch: [28][125/390] Total Loss -1.0732e+01 (-1.0808e+01) Consistency Loss 7.5815e-01 (6.5819e-01) Entropy 2.2980e+00 (2.2933e+00)
Epoch: [28][150/390] Total Loss -1.0857e+01 (-1.0810e+01) Consistency Loss 6.4354e-01 (6.5649e-01) Entropy 2.3002e+00 (2.2933e+00)
Epoch: [28][175/390] Total Loss -1.0841e+01 (-1.0811e+01) Consistency Loss 6.2386e-01 (6.5540e-01) Entropy 2.2930e+00 (2.2933e+00)
Epoch: [28][200/390] Total Loss -1.0765e+01 (-1.0812e+01) Consistency Loss 6.7200e-01 (6.5313e-01) Entropy 2.2874e+00 (2.2930e+00)
Epoch: [28][225/390] Total Loss -1.0880e+01 (-1.0813e+01) Consistency Loss 5.9041e-01 (6.5136e-01) Entropy 2.2940e+00 (2.2929e+00)
Epoch: [28][250/390] Total Loss -1.0948e+01 (-1.0813e+01) Consistency Loss 4.9423e-01 (6.5202e-01) Entropy 2.2884e+00 (2.2929e+00)
Epoch: [28][275/390] Total Loss -1.0777e+01 (-1.0811e+01) Consistency Loss 6.2311e-01 (6.5304e-01) Entropy 2.2801e+00 (2.2929e+00)
Epoch: [28][300/390] Total Loss -1.1032e+01 (-1.0813e+01) Consistency Loss 4.5430e-01 (6.5231e-01) Entropy 2.2973e+00 (2.2930e+00)
Epoch: [28][325/390] Total Loss -1.0934e+01 (-1.0814e+01) Consistency Loss 5.6618e-01 (6.5093e-01) Entropy 2.3001e+00 (2.2930e+00)
Epoch: [28][350/390] Total Loss -1.0776e+01 (-1.0816e+01) Consistency Loss 6.5683e-01 (6.4899e-01) Entropy 2.2866e+00 (2.2930e+00)
Epoch: [28][375/390] Total Loss -1.0863e+01 (-1.0818e+01) Consistency Loss 6.0182e-01 (6.4706e-01) Entropy 2.2929e+00 (2.2929e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.299301862716675, 'consistency': 0.45537662506103516, 'total_loss': -1.8439252376556396}], 'lowest_loss_head': 0, 'lowest_loss': -1.8439252376556396}
No new lowest loss on validation set: -1.8466 -> -1.8439
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.805, 'ARI': 0.6480245639625398, 'NMI': 0.7056716079815214, 'ACC Top-5': 0.9872, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 30/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [29][ 0/390] Total Loss -1.0894e+01 (-1.0894e+01) Consistency Loss 5.9806e-01 (5.9806e-01) Entropy 2.2984e+00 (2.2984e+00)
Epoch: [29][ 25/390] Total Loss -1.0796e+01 (-1.0787e+01) Consistency Loss 6.6324e-01 (6.7326e-01) Entropy 2.2918e+00 (2.2920e+00)
Epoch: [29][ 50/390] Total Loss -1.0801e+01 (-1.0798e+01) Consistency Loss 6.6035e-01 (6.6623e-01) Entropy 2.2922e+00 (2.2929e+00)
Epoch: [29][ 75/390] Total Loss -1.0655e+01 (-1.0808e+01) Consistency Loss 7.9907e-01 (6.5539e-01) Entropy 2.2909e+00 (2.2928e+00)
Epoch: [29][100/390] Total Loss -1.0706e+01 (-1.0818e+01) Consistency Loss 7.2077e-01 (6.4624e-01) Entropy 2.2853e+00 (2.2929e+00)
Epoch: [29][125/390] Total Loss -1.0806e+01 (-1.0815e+01) Consistency Loss 6.2550e-01 (6.4811e-01) Entropy 2.2863e+00 (2.2926e+00)
Epoch: [29][150/390] Total Loss -1.0788e+01 (-1.0816e+01) Consistency Loss 6.3536e-01 (6.4733e-01) Entropy 2.2847e+00 (2.2926e+00)
Epoch: [29][175/390] Total Loss -1.0791e+01 (-1.0814e+01) Consistency Loss 6.8025e-01 (6.4822e-01) Entropy 2.2943e+00 (2.2925e+00)
Epoch: [29][200/390] Total Loss -1.0906e+01 (-1.0818e+01) Consistency Loss 5.9142e-01 (6.4598e-01) Entropy 2.2995e+00 (2.2927e+00)
Epoch: [29][225/390] Total Loss -1.0767e+01 (-1.0819e+01) Consistency Loss 6.7126e-01 (6.4461e-01) Entropy 2.2877e+00 (2.2928e+00)
Epoch: [29][250/390] Total Loss -1.1039e+01 (-1.0821e+01) Consistency Loss 4.3143e-01 (6.4245e-01) Entropy 2.2940e+00 (2.2927e+00)
Epoch: [29][275/390] Total Loss -1.0810e+01 (-1.0818e+01) Consistency Loss 6.9129e-01 (6.4567e-01) Entropy 2.3003e+00 (2.2928e+00)
Epoch: [29][300/390] Total Loss -1.0937e+01 (-1.0818e+01) Consistency Loss 5.4923e-01 (6.4595e-01) Entropy 2.2973e+00 (2.2927e+00)
Epoch: [29][325/390] Total Loss -1.0741e+01 (-1.0817e+01) Consistency Loss 7.1756e-01 (6.4691e-01) Entropy 2.2917e+00 (2.2927e+00)
Epoch: [29][350/390] Total Loss -1.0740e+01 (-1.0817e+01) Consistency Loss 7.1354e-01 (6.4688e-01) Entropy 2.2908e+00 (2.2928e+00)
Epoch: [29][375/390] Total Loss -1.0977e+01 (-1.0819e+01) Consistency Loss 5.0623e-01 (6.4557e-01) Entropy 2.2966e+00 (2.2929e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.301542282104492, 'consistency': 0.4602453112602234, 'total_loss': -1.8412969708442688}], 'lowest_loss_head': 0, 'lowest_loss': -1.8412969708442688}
No new lowest loss on validation set: -1.8466 -> -1.8413
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.8029, 'ARI': 0.6496890324667234, 'NMI': 0.7032387625562958, 'ACC Top-5': 0.9875, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 31/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [30][ 0/390] Total Loss -1.0745e+01 (-1.0745e+01) Consistency Loss 7.1256e-01 (7.1256e-01) Entropy 2.2915e+00 (2.2915e+00)
Epoch: [30][ 25/390] Total Loss -1.0922e+01 (-1.0831e+01) Consistency Loss 5.6670e-01 (6.3188e-01) Entropy 2.2978e+00 (2.2926e+00)
Epoch: [30][ 50/390] Total Loss -1.0891e+01 (-1.0826e+01) Consistency Loss 5.8315e-01 (6.3916e-01) Entropy 2.2947e+00 (2.2930e+00)
Epoch: [30][ 75/390] Total Loss -1.0586e+01 (-1.0819e+01) Consistency Loss 8.1753e-01 (6.4673e-01) Entropy 2.2807e+00 (2.2931e+00)
Epoch: [30][100/390] Total Loss -1.0923e+01 (-1.0828e+01) Consistency Loss 5.4344e-01 (6.3807e-01) Entropy 2.2933e+00 (2.2932e+00)
Epoch: [30][125/390] Total Loss -1.0799e+01 (-1.0821e+01) Consistency Loss 6.4159e-01 (6.4487e-01) Entropy 2.2881e+00 (2.2932e+00)
Epoch: [30][150/390] Total Loss -1.0767e+01 (-1.0819e+01) Consistency Loss 7.0592e-01 (6.4677e-01) Entropy 2.2945e+00 (2.2932e+00)
Epoch: [30][175/390] Total Loss -1.0955e+01 (-1.0820e+01) Consistency Loss 5.3304e-01 (6.4491e-01) Entropy 2.2976e+00 (2.2930e+00)
Epoch: [30][200/390] Total Loss -1.0844e+01 (-1.0821e+01) Consistency Loss 5.9310e-01 (6.4341e-01) Entropy 2.2874e+00 (2.2928e+00)
Epoch: [30][225/390] Total Loss -1.0874e+01 (-1.0820e+01) Consistency Loss 5.4726e-01 (6.4314e-01) Entropy 2.2842e+00 (2.2927e+00)
Epoch: [30][250/390] Total Loss -1.0988e+01 (-1.0822e+01) Consistency Loss 4.7918e-01 (6.4070e-01) Entropy 2.2934e+00 (2.2926e+00)
Epoch: [30][275/390] Total Loss -1.0849e+01 (-1.0824e+01) Consistency Loss 6.2607e-01 (6.3950e-01) Entropy 2.2950e+00 (2.2927e+00)
Epoch: [30][300/390] Total Loss -1.0914e+01 (-1.0821e+01) Consistency Loss 5.5483e-01 (6.4279e-01) Entropy 2.2938e+00 (2.2927e+00)
Epoch: [30][325/390] Total Loss -1.0781e+01 (-1.0821e+01) Consistency Loss 7.0994e-01 (6.4328e-01) Entropy 2.2981e+00 (2.2928e+00)
Epoch: [30][350/390] Total Loss -1.0922e+01 (-1.0821e+01) Consistency Loss 5.5792e-01 (6.4296e-01) Entropy 2.2960e+00 (2.2928e+00)
Epoch: [30][375/390] Total Loss -1.0866e+01 (-1.0822e+01) Consistency Loss 6.0220e-01 (6.4248e-01) Entropy 2.2937e+00 (2.2928e+00)
Make prediction on validation set ...
Evaluate based on SCAN loss ...
{'scan': [{'entropy': 2.3007802963256836, 'consistency': 0.4614773988723755, 'total_loss': -1.839302897453308}], 'lowest_loss_head': 0, 'lowest_loss': -1.839302897453308}
No new lowest loss on validation set: -1.8466 -> -1.8393
Lowest loss head is 0
Evaluate with hungarian matching algorithm ...
{'ACC': 0.7911, 'ARI': 0.6347409416483302, 'NMI': 0.6965653056341379, 'ACC Top-5': 0.9878, 'hungarian_match': [(0, 7), (1, 5), (2, 2), (3, 8), (4, 1), (5, 4), (6, 3), (7, 6), (8, 0), (9, 9)]}
Checkpoint ...
[33mEpoch 32/50[0m
[33m---------------[0m
Adjusted learning rate to 0.00010
Train ...
Epoch: [31][ 0/390] Total Loss -1.0786e+01 (-1.0786e+01) Consistency Loss 6.3912e-01 (6.3912e-01) Entropy 2.2851e+00 (2.2851e+00)
Epoch: [31][ 25/390] Total Loss -1.0844e+01 (-1.0829e+01) Consistency Loss 5.7172e-01 (6.3727e-01) Entropy 2.2832e+00 (2.2933e+00)
Epoch: [31][ 50/390] Total Loss -1.0816e+01 (-1.0821e+01) Consistency Loss 6.7470e-01 (6.4317e-01) Entropy 2.2982e+00 (2.2928e+00)
Epoch: [31][ 75/390] Total Loss -1.0856e+01 (-1.0824e+01) Consistency Loss 5.7592e-01 (6.4062e-01) Entropy 2.2863e+00 (2.2929e+00)
Epoch: [31][100/390] Total Loss -1.0955e+01 (-1.0831e+01) Consistency Loss 5.4138e-01 (6.3449e-01) Entropy 2.2993e+00 (2.2930e+00)
Epoch: [31][125/390] Total Loss -1.0931e+01 (-1.0830e+01) Consistency Loss 5.4992e-01 (6.3414e-01) Entropy 2.2961e+00 (2.2928e+00)
Epoch: [31][150/390] Total Loss -1.0926e+01 (-1.0827e+01) Consistency Loss 5.2150e-01 (6.3816e-01) Entropy 2.2896e+00 (2.2931e+00)
Epoch: [31][175/390] Total Loss -1.0763e+01 (-1.0830e+01) Consistency Loss 7.1603e-01 (6.3599e-01) Entropy 2.2959e+00 (2.2931e+00)
Epoch: [31][200/390] Total Loss -1.0827e+01 (-1.0827e+01) Consistency Loss 6.6162e-01 (6.3842e-01) Entropy 2.2978e+00 (2.2930e+00)
Epoch: [31][225/390] Total Loss -1.0884e+01 (-1.0826e+01) Consistency Loss 5.5295e-01 (6.3987e-01) Entropy 2.2874e+00 (2.2931e+00)