Saranraj Nambusubramaniyan

Thanks for reporting the issue. I suspect possible compatibility issues between the OS and CUDA/cudnn versions: I ran the `example.py` under the env: OS: Windows 10 Python : 3.6 TF:2.4...

Hi @kormoczi . Thanks for the update. I updated CUDA and cudnn and found the `example.py` ran successfully with GPU. Logs: `(tfgpu) N:\fer>python example.py 2021-09-02 17:16:47.723348: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened...

@kormoczi Also, 5. Restart your system

@kormoczi Great that it works through docker. cuda 11.x is not packaged with cublas. cudnn provides this functionality for ml frameworks like tf, pytorch or keras to generate (initialize) any...

Thanks for the issue again and hope you enjoy ferr'ing :)

Apart from what mentioned above, `dataset.py` at PR #26 contains additional functions to prepare the data loaders. It burrows several utility functions from `datasets.utils` to extract and preprocess the data....

We haven't added the functions for post-training predictions/inferences yet. I will update Trainer to return the network prediction on the test dataset as traja data frame.

@WolfByttner I am preparing the UML diagram for traja commit #26 . That might easily guide collaborators

Yes, self connections/time-delayed connections are not used in the current latent visualization. Also, note this visualizer support only recurrent networks. At the moment, latent models in traja are feedforward. However,...

I am using "black". Yes, it is an older version and is not compatible with the recent trainer as well due to the complexity compared to AE and VAEs. It...