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TrainMTFL.txt
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=======Arguments=======
/xlearning/pengxin/Softwares/anaconda3/envs/torch1120/bin/python
dataset: MTFL
MnistTrain: 1
batch_size: 512
train_epoch: 300
NetVersion: Shuffle
GroupWiseEncoder: False
GroupWiseDecoder: 1
WithFeatureB: False
FeatureType: GlT_GaussainlizeAndTanh
BatchNormType: 1001
RepresentationType: Normalize
ActivationType: Tanh
representation_dim: 5
RunInManuel: False
VisualFreq: 20
DrawTSNE: False
DrawUmap: False
WarmAll: 20
WarmBalance: 0
WarmConsistency: 0
WarmUpProto: 0
WarmOneHot: 0
SoftAssignmentTemperatureBalance: 0.1
SoftAssignmentTemperatureHot: 0.1
SoftAssignmentTemperatureSelfCons: 0.2
Reconstruction: 1.0
ReconstructionEpoch: 999
GlobalBalanceLoss: 0.0
InfoGlobalLoss: 0.0
InfoBalanceLoss: 0.04
InfoFairLoss: 0.2
GroupWiseBalanceLoss: 0.0
ClusterWiseBalanceLoss: 0.0
BalanceLossType: KL
BalanceLossNoDetach: False
OneHot: 0.04
loss_cons: 0.0
loss_self_cons: 0.0
SelfConsLossType: Assignment
Discriminative: 0.0
DiscriminativeTest: 0
reconstruct_all: 0
Decenc: 0.0
LearnRate: 0.001
LearnRateDecayType: None
WeightDecay: 0
LearnRateWarm: 0
betas_a: 0.9
betas_v: 0.999
seed: 9116
resume: /xlearning/pengxin/FC_Cps/MTFL/Epoch299.checkpoint
/xlearning/pengxin/Softwares/anaconda3/envs/torch1120/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.
warnings.warn(
/xlearning/pengxin/Softwares/anaconda3/envs/torch1120/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet50-0676ba61.pth" to /xlearning/pengxin/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth
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torch.mean(x)==-0.11466330289840698, torch.std(x)==0.5310376286506653
torch.min(x)==-0.7250906825065613, torch.max(x)==1.0
torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True))==tensor([[23.6055],
[25.5792],
[22.9094],
...,
[24.2430],
[24.1685],
[23.2173]])
=> loading checkpoint '/xlearning/pengxin/FC_Cps/MTFL/Epoch299.checkpoint'
=> loaded checkpoint '/xlearning/pengxin/FC_Cps/MTFL/Epoch299.checkpoint' (epoch 299)
Save check point into /xlearning/pengxin/Codes/230520/RunSet0520_RePo5/ --dataset MTFL --resume Epoch299 --seed 9116/Checkpoints/Epoch299.checkpoint
ACC= 70.2|70.150, NMI= 19.1|19.067, Bal= 90.4|90.423, MNCE= 99.8|99.817, Fmeasure= 32.0|32.018
Clu\g 0, 1,
1145: 594, 551,
855: 406, 449,
Typ\g 0, 1,
692: 346, 346,
1308: 654, 654,
t\c 0 1
0: 320 300| 26 46|
1: 274 251|380 403|
Ending...
2023-05-20 16:55:35 Dequeue 2023-05-20_16:55:01_000001.Process