soochan-lee / cn-dpm Goto Github PK
View Code? Open in Web Editor NEWOfficial code for ICLR 2020 paper "A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning."
License: MIT License
Official code for ICLR 2020 paper "A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning."
License: MIT License
When I change the ckpt_step from 1000000000 to 10000 in cifar10-cndpm.yaml, it ave the following error:
Saving checkpoint... Traceback (most recent call last):
File "main.py", line 92, in <module>
main()
File "main.py", line 88, in main
train_model(config, model, data_scheduler, writer)
File "./CN-DPM/train.py", line 76, in train_model
pickle.dump(model, f)
AttributeError: Can't pickle local object 'CnnSharingVae.__init__.<locals>.<lambda>'
The original code works well on cifar100, however after I add normalization to cifar100 it gets very low accuracy. Is there any way to make it work on normalized dataset?
Firstly thank you so much for sharing the codebase. In the function nll(self, x, y, step=None)
, you calculate the chilled_log_softmax. I am wondering what's the meaning of it and why do you want to use it. Thank you in advance.
# Classifier chilling
chilled_log_softmax = F.log_softmax(
log_softmax / self.config['classifier_chill'], dim=1)
chilled_loss_pred = self.ce_loss(chilled_log_softmax, y)
# Value with chill & gradient without chill
loss_pred = loss_pred - loss_pred.detach() \
+ chilled_loss_pred.detach()
Hi,
We are trying to apply CN-DPM on another dataset called CORE50 and the result is quite bad. Since CNDPM has so many hyper-parameters, except for log-alpha and classifier-chill, what hyper-parameters do you think are crucial to tune? Thank you so much and I look forward to hearing back from you.
pip install -r requirements.txt
gives the following error:
ERROR: Could not find a version that satisfies the requirement pytorch==1.0.1 (from -r requirements.txt (line 9)) (from versions: 0.1.2, 1.0.2)
ERROR: No matching distribution found for pytorch==1.0.1 (from -r requirements.txt (line 9))
It seems conda install pytorch==1.0.1 torchvision==0.2.2 -c pytorch
works.
Thank you very much for the code which is well written and easy to launch for training. I could train the generative model version however if I have troubles understanding how to run inference on external dataset from the saved model, could you provide some script to run testing?
For instance for reconstruction, do you always use the last expert, let say at the end of split-mnist if I want to retest on 0/1?
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