This repository was archived by the owner on Mar 11, 2021. It is now read-only.
This repository was archived by the owner on Mar 11, 2021. It is now read-only.
Dataset Tensorboard metrics #898
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We have a number of metrics that measure a bunch of things
- metrics about the model weights
- l2_cost
- model accuracy on constant data
- value_cost on pro holdout
- model accuracy on recent RL data
- , policy_cost, policy_entropy, value_cost (diff between target and output)
- value_confidence (what was the average value output)
- RL data
- policy target top 1 (what percents of readouts went to the top move?)
- average_winrate_observed
I think we should add
- value_cost_bias = value_cost(target_value, average_winrate_observed)
- average_move = average move number of the position (requires adding to the tf examples)
- search_q_error = |target_value - search_q| (requires adding to the tf example)
Anything else that would be interesting?
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