Comments (4)
I would like to know what this method(compute_accuracy) does? centralized learning?Model warm up?
from moon.
Hi @18811449050 ,
This method computes the prediction accuracy of the input model on the input dataset. There is no training on the model inside compute_accuracy
.
from moon.
If it is a regression task, how to change the loss?
from moon.
Currently, MOON is designed for the classification task. For regression task, you may need to change the cross-entropy loss (e.g., line 130 of main.py) to loss for regression (e.g., MSELoss). You also need to change the model architecture.
from moon.
Related Issues (20)
- Quesiton for code HOT 1
- Questions about SCAFFOLD code HOT 2
- The code seems inconsistent with the algoritm in paper HOT 1
- Questions about settings of negative samples HOT 2
- About the contrastive loss HOT 1
- About the model Resnet50 HOT 3
- About w_i^t in paper. HOT 1
- Why does the program keep showing“Files already downloaded and verified” HOT 6
- Question about dirichlet non-iid HOT 2
- Processing of Datasets HOT 2
- Question about FedAvg code HOT 2
- Questions about the reported test accuracy. HOT 2
- For tinyimagenet, the test acc is very low, 0.009 HOT 4
- Some questions about the metrics. HOT 5
- why we need to requires_grad=True for batch input, which (not sure) may lead to out of memory error. HOT 1
- Time for Training on CIFAR-100 and Tiny-ImageNet HOT 2
- Questions about T-SNE HOT 3
- Same label for positive and negative cases HOT 1
- L2 norm code issue HOT 2
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from moon.