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View Code? Open in Web Editor NEWSource code of ICLR2020 paper 'Towards Fast Adaptation of Neural Architectures with Meta Learning'
Source code of ICLR2020 paper 'Towards Fast Adaptation of Neural Architectures with Meta Learning'
line 128
train_accs_theta, train_accs_w = meta_train(train_loader, maml, device, epoch, writer)
However,the function "meta_train" returns only one value named "accs", so is there any solutions? Look forward to your reply.
As title.
# batch_size here means total episode number
mini = MiniImagenet(args.data_path, mode='train', n_way=args.n_way, k_shot=args.k_spt,
k_query=args.k_qry,
batch_size=args.batch_size, resize=args.img_size)
mini_valid = MiniImagenet(args.data_path, mode='train', n_way=args.n_way, k_shot=args.k_spt,
k_query=args.k_qry,
batch_size=args.test_batch_size, resize=args.img_size)
train_loader = DataLoader(mini, args.meta_batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True)
valid_loader = DataLoader(mini_valid, args.meta_test_batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True)
mini and mini_valid are sampled from train dataset, but mini is used for meta_train and mini_valid is used for meta_test. So if mini_valid is a part of mini, it is unfair. And I divide the train dataset into 80% and 20%. mini is sampled from 80% and mini_valid is sampled from 20%. Finally acc by running train_tnas.sh is 0.57 but the original acc by runing train_tnas.sh is 0.60.
Dear Dongze Lian:
Sorry to be a bother. I am a Grade-One graduate student from NWPU, your excellent work has inspired me a lot. Thank you! However, I have met some problems when I reappeared your results.
i ) For part of AutoMAML, my results is 49.44% for 1-shot and 61.96% for 5-shot, which is about 2 percent below your result, can you give me some suggestions about the super-parameters' value?
ii ) For part of 'train_tnas_from_scrach', there lost some file(such as 'MiniImagenet_task.py' and 'meta_nas_train.py'), can you send for me? Thank you, my email address is [email protected]
I will be appreciated for your reply, thank you very much!
Gaoyitao
hi, i have viewed the code, but i have some questions about the settings of the training for the task-specific architecture. Since the model have seen specific the test task(like 5 test classes) T while updating the Theta, why the model still use the whole meta test tasks (only 20 test classes) set for evaluation? I think it is a data leak. Could you explain about it?
[https://github.com/dongzelian/T-NAS/blob/master/train_tnas_miniimagenet.py](https://github.com/dongzelian/T-NAS/blob/master/train_tnas_miniimagenet.py
cant find learner.py file in dir,so that the project does not work properly,
can you give me this file by email([email protected]) ,thanks?
It's a nice work. Looking forward your source code!
Hi, thanks for your inspiring work
I have one question about the automl algorithm in the appendix:
while in meta train, there are two repeat inner weights update procedures. Do these two update procedures sever different purposes: the first one for outer weights $\widetilde{w} $ update and the second one for outer architecture
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