Comments (6)
Actually,your test set will be finetuned (train 10 steps for each support set) after every 500 steps training on training set. So you can see the acc growth from 0,2615 to 0.796 via 10 steps finetune.
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Thank you. Are the support and query sets automatically split from the test set?
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They were randomly selected from each task test dataset.
from maml-pytorch.
Hello, where did you get those csv files in your miniimagenet dataset?
from maml-pytorch.
Hello, where did you get those csv files in your miniimagenet dataset?
Sorry, I didn't use the miniimagenet dataset, so I'm not very familiar with it. I'm using my own dataset, where the images for each category are placed in their respective folders. So, I wrote a simple Python program to generate the csv files.
from maml-pytorch.
Ok, thank you very much for your reply.
from maml-pytorch.
Related Issues (20)
- why use custom grad clip function?
- omniglot dataset download error HOT 1
- Does the hessian really gets computed? HOT 1
- Can you please add a 1-d CNN model to the learner.
- Incorrect losses_q HOT 3
- about dataset spiltting
- create_graph parameter is False hence first-order MAML? HOT 1
- Asking about inner and outer loop
- 您好,对于代码有两个问题,请教您一下,谢谢 HOT 3
- About training and testing HOT 6
- can you offer us your requirements of environment?
- Why is `for epoch in range(args.epoch // 10000):` HOT 2
- Why the code for Learner is so complicated? HOT 1
- 请问模型权值文件在哪里进行保存?代码在哪里? HOT 1
- debug
- Using the Learner object for my project, Loss not behaving at its best
- What is the backup file for and what is the reference navie5 in navie5?
- 准确率不变 HOT 4
- 训练自己的数据集
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