Skip to content
This repository has been archived by the owner on Sep 16, 2024. It is now read-only.

visualize and unsupervised loss #11

Open
huangyuan2020 opened this issue Jul 28, 2020 · 1 comment
Open

visualize and unsupervised loss #11

huangyuan2020 opened this issue Jul 28, 2020 · 1 comment

Comments

@huangyuan2020
Copy link

Thank you very much for your work, I find your examples are supervised, I wonder if I can use some custom unsupervised loss function to train, or do you have any suggestions?
In addition, I found that the training process was encapsulated in dt.engine, so could Tensorboardx be used to visualize the intermediate results of the training process?

@DrSleep
Copy link
Owner

DrSleep commented Jul 29, 2020

  1. You can specify which loss per each task to use. If you implement your own loss function to train, supervised or unsupervised, you can specify it in this example here. By default all the loss functions accept the tensor with predictions and the tensor with ground truth during the forward pass (you can see some examples here). For the unsupervised case, you can provide some dummy ground truth and then implement the logic however you want.
  2. At the moment you cannot visualise intermediate results of the training (or validation) process. As you rightly pointed out, the training process is hidden within dt.engine.train and neither inputs nor predictions are being used outside it. There is some WIP that adds an option of visualisation callback, but it is not a priority right now

Sign up for free to subscribe to this conversation on GitHub. Already have an account? Sign in.
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants