Comments (1)
close because of main refactoring.
Now metrics are implemented by the user.
from main_loop_tf.
Related Issues (17)
- Add runtime statistics
- Introduce callbacks to allow for custom code to be executed HOT 1
- Passing the crop size as an argument HOT 2
- training on gpu HOT 5
- [Feature Request] Fix random seed HOT 1
- Expose useful placeholders HOT 1
- Swap sess and unhookedsess
- Fix wiggle lines in TB when experiment is resumed
- MultiGPU training is slower than single gpu
- Checkpoints saving best/last model HOT 1
- Validation does not run every epoch with val_every=1 HOT 2
- Add image summaries to training
- [Feature Request] Use tf.supervisor HOT 1
- [Feature Request] Refactor input pipeline HOT 2
- [Feature Request] Add more options to the optimizer HOT 4
- [Feature request] Log stdout in tensorboard HOT 1
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from main_loop_tf.