Comments (2)
andreped
commented on May 30, 2024
Finished optimizer unit test in: 7301014
However, it was observed that the results may vary within optimizers, this has probably something to do with how frequently the adaptive learning rates are updated.
Will add disclaimer regarding this in the README, but at least in the unit test, we found there to be quite small difference (to the order of 1e-4) when using GA for different optimizers.
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andreped
commented on May 30, 2024
I have updated the README to mention this minor indiscripancy when using adaptive optimizers with GA here: 707f0f0
from gradientaccumulator.
Related Issues (20)
- Use tf.function on train_step HOT 11
- 0.5.1, tf 2.11 error for accuoptimizer HOT 8
- Replacing AccumBatchNormalization not working as intended HOT 2
- ConvNeXt not compatible with Model wrapper HOT 1
- No mixed precision support with GradientAccumulateOptimizer? HOT 7
- Replacing BN layer with AccumBN layer results in poorer convergence
- pytest-cov with multiprocessing results in occational deadlock HOT 2
- Add documentations on accumulated batch normalization HOT 1
- `gamma` and `beta` in AccumBatchNormalization never updated HOT 2
- Is mixed precision properly handled for all use cases? HOT 2
- Reduce number of unit tests HOT 2
- Unit tests fail on tf >= 2.10 HOT 1
- AccumBN is not compatible with 3D ops e.g. Conv3D HOT 1
- Mixed precision not working as intended with AccumBatchNormalization HOT 8
- Add linting to improve code style HOT 1
- Unit test for optimizer invariance in distributed trainings HOT 2
- Optimizer wrapper not working as intended HOT 2
- Optimizer wrapper not compatible with tf==2.6 HOT 2
- AttributeError using Optimizer wrapper with tf==2.4 HOT 1
- Optimizer wrapper performance is dependent on tensorflow version HOT 3
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