Comments (2)
I agree it is problematic and it needs to be changed. The simplest solution I can think of is to do an external for loop and evaluate the unwrapped model. Something like the following:
model = .....
wrapped = ImportanceTraining(model)
for i in range(epochs):
wrapped.fit(...)
model.evaluate(...)
from importance-sampling.
This is problematic - it makes validation completely impossible in some cases, eg when using generators that load heavy images! Is there any way to get around this?
from importance-sampling.
Related Issues (20)
- Addition of RAIS in the implemented methods
- The result seems okay, but there are a few confusing things HOT 2
- Fail when metric given as function HOT 9
- About the complete importance sampling code HOT 9
- Add initial_epoch to ImportanceTraining classes
- Failed when using ModelCheckpoint callback HOT 3
- Port to tensorflow 2 HOT 20
- Support of networks with multiple outputs HOT 10
- How do I get the serial Numbers of important samples HOT 1
- High GPU memory usage HOT 4
- Memory leak? HOT 2
- Question regarding Eq. 29 HOT 1
- Question regarding maximum variance reduction & speedup HOT 4
- RuntimeError: The layer has never been called and thus has no defined output shape. HOT 6
- Adjust Learning rate
- Compatibility with tensorflow = 2.5.0 and Keras = 2.4 HOT 4
- tensorflow and keras versions HOT 2
- Some confusion about the fast grads calculation when converting to Pytorch. HOT 3
- Would it be possible to calculate b from tau_threshold?
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from importance-sampling.