Comments (1)
Sorry for the mess. It is simply because of the complexity and compatibility of tensorflow and keras. I am removing the requirements of keras and tensorflow in PyOD and let users to make sure they have the library if they want to use neural net based models.
As for the broken TF, what you need is to uninstall tensorflow and keras and then reinstall them with either pip or conda. The root cause is incompatibility with your local version and the required version.
Let me know if that fixes your problems.
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Related Issues (20)
- Update contamination without retraining HOT 4
- ValueError: node array from the pickle has an incompatible dtype HOT 1
- ABOD missing support for kernels
- ABOD weighting schemes / not matching the definition? HOT 1
- ROD models: RuntimeWarning: invalid value encountered in divide
- Which algorithms support ‘Semi-supervised Novelty Detection’?
- Support SHAP for model explanation
- Default parameter inconsistent with docs
- different results depending on time-span HOT 2
- saved model size
- Model prediction in Autoencoder does not support adjusting batch size
- False positive warning when manipulating pandas dataframes
- Grateful
- issues about scikit-learn HOT 3
- TOS selection methods and parameter
- Add wheels to PyPI?
- TypeError: SUOD.__init__() got an unexpected keyword argument 'cost_forecast_loc_fit' HOT 2
- DIF model: duplicate normalization
- How will the effectiveness of the model be evaluated, and does the library provide the appropriate methodology? HOT 1
- Quasi-Monte Carlo Discrepancy always predicts an outlier HOT 1
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