Comments (4)
LZ's policy is to wait until progress stalls, only then lower. Since LZ has regular promotion matches there is an easy way to define stalling. Minigo could keep the always promote policy but add some matches as a way to measure this. Or use CGOS results.
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Learning rate of .1/.01/.001 for 200k/400k/800k steps (per AGZ, 2048 batch) equates to 400M/800M/1.6B positions
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(for my later reference) On training batch size of 32, that's 12.8M steps / 25.6M steps / 51.2 M steps.
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We still don't really know the right answer here. We're using the rates in paper 2 but it seems to be based more off of the l2_cost.
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Related Issues (20)
- run concurrent selfplay without bazel HOT 1
- Running minigo with Sabaki GUI HOT 2
- Problem while building tpu-image HOT 3
- Problem in features.stone_features HOT 1
- Onscreen buttons in lw_demo don't toggle (work)
- Minigo not working on Coral accelerator HOT 4
- Add Edge TPU support to C++ engine HOT 1
- Decouple the conv data format from the input feature layout HOT 8
- How strong is the model in kyu/dan? HOT 7
- 000990-cormorant: stderr thread died HOT 1
- Wrong argument passed in minigui/fetch-and-run.sh HOT 1
- How to communicate with engine easily outside stdin HOT 2
- Support for sending board state to the engine via GTP HOT 6
- Looking for 9x9 model files in .minigo file format HOT 7
- Error on Minigo v15(990)
- tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found. (0) Invalid argument: Assign requires shapes of both tensors to match HOT 3
- The setting of num_readouts to get strongest of minigo
- train.sh in cloud tpu
- Minigo training using Coral Dev Board HOT 1
- ./cc/configure_tensorflow.sh HOT 1
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