Comments (3)
Im getting the same issue.. Is there a way to solve this?
from itaml.
Im getting the same issue.. Is there a way to solve this?
No, I tried a lot of methods and finally did not solve this problem
Im getting the same issue.. Is there a way to solve this?
from itaml.
The session log files contain accuracy of the base model, not the meta-model. There is meta_test
method of learner object, which trains the meta-model using the base model and memory buffer, then the accuracy is as presented in the paper (the accuracy is printed to console after training meta tasks
step). I'm not sure what's the point of the session files though, as they don't give information about the meta-model at all, at first it was confusing for me as well.
Edit: I was running train_mnist.py
, maybe on other datasets it's different
from itaml.
Related Issues (20)
- a few question about the implementation HOT 8
- AttributeError: type object 'args' has no attribute 'overflow' HOT 2
- something wrong happends in training cifar10 HOT 3
- Where to check task accuracy and class accuracy? HOT 2
- Fair comparison HOT 2
- sys.argv[1] is out of range HOT 2
- train_mnist loss go to nan at Sess 3
- about adding parameters HOT 5
- about adding classes HOT 2
- Nan Loss during training MNIST dataset HOT 2
- Why BCE is used instead of CE with Softmax? HOT 1
- Something strange in the Algorithm 1.. HOT 4
- Something strange about the update of theta and psi in the inner loop HOT 6
- What is the difference between two output of model? -->outputs2, outputs = model(inputs) HOT 1
- Training script for ImageNet-100 HOT 2
- Running without CUDA HOT 2
- Code for other methods
- A question about theta and task-specific phi HOT 1
- Task prediction
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