Separate train an eval for estimator #22
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anj-s
reviewed
Nov 9, 2018
| name='eval_only', default=False, | ||
| help=flags_core.help_wrap('Skip training and only perform evaluation on ' | ||
| 'the latest checkpoint.')) | ||
| name='skip_eval', default=False, |
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Why not add 'skip_eval' instead of modifying an existing flag?
anj-s
reviewed
Nov 9, 2018
| @@ -523,8 +523,8 @@ def input_fn_eval(): | |||
| num_epochs=1, | |||
| dtype=flags_core.get_tf_dtype(flags_obj)) | |||
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| if flags_obj.eval_only or not flags_obj.train_epochs: | |||
| # If --eval_only is set, perform a single loop with zero train epochs. | |||
| if not flags_obj.train_epochs: | |||
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When is this true given we have a default value for train_epochs?
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...by changing the flag "eval_only" to "skip_eval". It is still possible to do eval only, by setting the training epoch to 0, and skip eval to false.
This way, it is possible to do both training only and evaluation only.