Comments (7)
Check out my answer on #13, I believe it is due to uninitialized bias parameters.
It works for me now, great!
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I have further dived into code and came to know that outputs from TemporalAttentionLayer are nan always, and from FeatureAttentionLayer some come out as nans.
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If you haven't already, I suggest you ensure that
- input data has the correct format
- data itself does not contain nan and are scaled properly before input to the model.
Regards,
Axel
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I have further dived into code and came to know that outputs from TemporalAttentionLayer are nan always, and from FeatureAttentionLayer some come out as nans.
Have you solved this problem yet? I also have this problem, During training, the losses are all nan,and there are many zeros in the data. And I don't understand why the num_values in labeled_anomalies.csv and the shape in the .npy file in the train folder are different. For example, C-1 in labeled_anomalies.csv is 2264, but C-1.npy is 2158. '2264' and '2158' don't match.
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Same problem with the SMD dataset, using the default hyperparameters.
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Setting use_gatv2 to False can produce normal loss.
from mtad-gat-pytorch.
Check out my answer on #13, I believe it is due to uninitialized bias parameters.
from mtad-gat-pytorch.
Related Issues (20)
- Loss and some slice of the output tensors become NAN HOT 1
- About data cleaning HOT 1
- Problems encountered in calculating anomaly scores on the SMAP dataset HOT 1
- FileNotFoundError: [Errno 2] No such file or directory: 'datasets/ServerMachineDataset/processed\\machine-1-1_train.pkl' HOT 1
- Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same HOT 1
- The reason why use shuffle in time-series data HOT 1
- about gat_layer
- My understanding of mtad _ gat.py does not reflect the ' Multivariate Time-series ' in the title of the paper. HOT 2
- Computational resource HOT 1
- The issue with the dataset HOT 1
- msl and smap dataset preprocess gives error HOT 2
- Pot results on the SMD dataset
- Multiple inconsistent training results HOT 1
- about the example output,please!!! HOT 1
- Embedding vector dimension issue in the paper HOT 1
- about adjust_predicts() ,please!!! HOT 2
- The setting about feat_gat_embed_dim and time_gat_embed_dim
- Evaluation Code
- Versions of Ptyhon and Pytorch
- Running it on custome dataset with 20 features
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