diff options
Diffstat (limited to 'tensorflow/contrib/learn/python/learn/graph_actions.py')
| -rw-r--r-- | tensorflow/contrib/learn/python/learn/graph_actions.py | 13 |
1 files changed, 12 insertions, 1 deletions
diff --git a/tensorflow/contrib/learn/python/learn/graph_actions.py b/tensorflow/contrib/learn/python/learn/graph_actions.py index 43e99a2f17..5cae1e9a4a 100644 --- a/tensorflow/contrib/learn/python/learn/graph_actions.py +++ b/tensorflow/contrib/learn/python/learn/graph_actions.py @@ -128,6 +128,7 @@ def _monitored_train(graph, supervisor_save_model_secs=600, supervisor_save_model_steps=None, keep_checkpoint_max=5, + keep_checkpoint_every_n_hours=10000.0, supervisor_save_summaries_secs=None, supervisor_save_summaries_steps=100, feed_fn=None, @@ -176,6 +177,13 @@ def _monitored_train(graph, keep. As new files are created, older files are deleted. If None or 0, all checkpoint files are kept. This is simply passed as the max_to_keep arg to `tf.Saver` constructor. + keep_checkpoint_every_n_hours: In addition to keeping the most recent + `keep_checkpoint_max` checkpoint files, you might want to keep one checkpoint file + for every N hours of training. This can be useful if you want to later + analyze how a model progressed during a long training session. For + example, passing `keep_checkpoint_every_n_hours=2` ensures that you keep + one checkpoint file for every 2 hours of training. The default value of + 10,000 hours effectively disables the feature. supervisor_save_summaries_secs: Save summaries every `supervisor_save_summaries_secs` seconds when training. supervisor_save_summaries_steps: Save summaries every @@ -248,7 +256,10 @@ def _monitored_train(graph, def make_saver(): return tf_saver.Saver( - sharded=True, max_to_keep=keep_checkpoint_max, defer_build=True) + sharded=True, + max_to_keep=keep_checkpoint_max, + keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours, + defer_build=True) scaffold = monitored_session.Scaffold( init_op=init_op, |