Comments (5)
Hi Devraj,
Here are my understanding/answers:
(1) using 1/(n_j + 1)
can avoid case when n_j=0
, leading to 1/n_j=1/0
, i.e. nan
.
(2) both losses are defined for labelled v.s. unlabelled data.
(3) updating every iteration converges to more reliable distribution, i.e. reflect the up-to-date distribution.
(4) we currently can only provide code in tensorflow. sorry for the inconvenience.
from semi-memory.
Hi,
It is a parameter to control the proportion of labelled data in each mini-batch.
from semi-memory.
Hi @yanbeic
Thanks for your prompt reply!
I see in your code that you have used a parameter called label-ratio
? Why is that needed can you specify?
Thanks
from semi-memory.
Hi
Thanks again for your response.
I am curious about one thing.
Since the unlabeled portion of the data is much larger than the labeled portion of the data how is the data loading part working ? for example you have taken around 10000
labeled and 40000
unlabeled samples. Now when we are selecting a batch-size of 32
obviously the labeled portion of the data will get exhausted earlier.
Does it mean we need to repeat the labeled portion of the data multiple times?
Thanks again for all your help!
from semi-memory.
In this implementation yes.
But one can also use annealing to decrease this portion during training.
from semi-memory.
Related Issues (7)
- Only working with python2.X? HOT 1
- Test error always around 0% accuracy HOT 7
- model_checkpoint_path = ckpt.model_checkpoint_path AttributeError: 'NoneType' object has no attribute 'model_checkpoint_path' HOT 4
- Why not use the memory module during test? HOT 1
- Have you run on cifar10 with fewer labeled data?
- data argumentation HOT 1
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from semi-memory.