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little bug in tensor_regression_layer_pytorch.ipynb #13
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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:
These also support tensor hooks such as tensor dropout or rank regularization (lasso). Feel free to re-open if you still have the issue! |
Awesome, trying it now ,thanks!
Would you recommend to use rank='same' or idk (512,3,3,output_cls) for the
rank for networks like VGG or resnet18?
…On Wed, Jan 27, 2021 at 5:47 AM Jean Kossaifi ***@***.***> wrote:
Closed #13 <#13>.
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Based on the tuple you show for rank, I'm assuming this is for Tucker. I would say Depending on the problem, with fine-tuning/retraining, you should be able to reach |
Hi Jean,
just wanted to point out the in the example tensor_regression_layer_pytorch.ipynb
in the TRL layer forward pass this line
regression_weights = tl.tucker_to_tensor(self.core, self.factors)
should instead be
regression_weights = tl.tucker_to_tensor((self.core, self.factors))
otherwise you get an error.This happened to me while working on my code using latest version
Let me know if you get it too running the example
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