Comments (2)
You can understand --episodes-per-batch as something equivalent to batch size. Gradient descent is sensitive to the choice of batch size. Instead of reducing --episodes-per-batch, you might want try reducing --train-shot.
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Hi,
Thanks for your excellent work. Due to the limitation of my GPUs, I couldn't set --episodes-per-batch" to 8 as you did in your paper, instead I set it to 2 and used only one GPU to run your code. However, the result I achieved for miniImagenet 5 way 1 shot accuracy is 59%, which is much lower than your reported result. Could you please tell why "--episodes-per-batch" can influence the result quite significantly?
Thanks
Hello, I am also reproducing this experiment at present. May I ask that I encountered "TypeError: btrisolve() takes 3 positional arguments but 4 were given" during the operation under the situation of Metaoptnet-RR training on the CIfar-FS。May I ask where this parameter is given more? I am looking forward to your reply. Thank you!
from metaoptnet.
Related Issues (20)
- Some questions about MetaOptNetHead_Ridge HOT 2
- About accuracy in CIFAR_FS 5-way 5-shot and how to implement MetaOptNet-SVM-trainval HOT 2
- Keep-rate scheduling of DropBlock in a multi-GPU environment HOT 2
- Why the accuracy of the Prototypical Network is higher than the reported version in paper? HOT 1
- Where is the parameter gamma HOT 1
- Does the performance of different SVM heads vary largely? HOT 1
- Meta gradient Computation HOT 3
- About"TypeError: btrisolve() takes 3 positional arguments but 4 were given"
- what is the difference between novel categories and base category? HOT 1
- Question about meta-validation and meta-testing HOT 4
- Protonet re-implementation details HOT 3
- Parameters for ProtoNet using ResNet12 as backbone
- the parameters config for the cifarfs,the accuracy is only 63% HOT 1
- could you tell me the link which about the miniImageNet_category_split_train_phase_train.pickle ?
- Thanks and some questions
- Overlapping between meta-training classes and meta-testing classes HOT 1
- How do I know what the real category of tieredImagenet is? HOT 1
- Pretrained model
- Project dependencies may have API risk issues
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from metaoptnet.