Comments (3)
Hi Christina,
Thanks for reporting, you are completely right! The issue should now have been fixed in 6af351c.
As a side note, we now provide well tested PyTorch Tensor Regression Layers in TensorLy-Torch:
- API: http://tensorly.org/torch/dev/modules/api.html#tensor-regression-layers
- Example: http://tensorly.org/torch/dev/user_guide/trl.html
These also support tensor hooks such as tensor dropout or rank regularization (lasso).
Feel free to re-open if you still have the issue!
from tensorly-notebooks.
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Based on the tuple you show for rank, I'm assuming this is for Tucker. I would say rank='same'
is a good start. Alternatively, rank=kernel.shape
should always work well.
Ideally you want to reduce the rank to benefit from the low-rank regularization (e.g. rank=0.75
).
Depending on the problem, with fine-tuning/retraining, you should be able to reach rank=0.5
without loss of performance (the low-rank regularization may even help generalize better with longer fine-tuning/retraining).
Let me know of any feedback you may have! :)
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