See the guide: Training > Training Hooks
Saves summaries every N steps.
__init__( save_steps=None, save_secs=None, output_dir=None, summary_writer=None, scaffold=None, summary_op=None )
int, save summaries every N steps. Exactly one of
save_stepsshould be set.
int, save summaries every N seconds.
string, the directory to save the summaries to. Only used if no
output_dirwas passed, one will be created accordingly.
Scaffoldto get summary_op if it's not provided.
stringcontaining the serialized
Summaryprotocol buffer or a list of
Tensor. They are most likely an output by TF summary methods like
tf.summary.merge_all. It can be passed in as one tensor; if more than one, they must be passed in as a list.
ValueError: Exactly one of scaffold or summary_op should be set.
after_create_session( session, coord )
Called when new TensorFlow session is created.
This is called to signal the hooks that a new session has been created. This
has two essential differences with the situation in which
begin is called:
- When this is called, the graph is finalized and ops can no longer be added to the graph.
- This method will also be called as a result of recovering a wrapped session, not only at the beginning of the overall session.
session: A TensorFlow Session that has been created.
coord: A Coordinator object which keeps track of all threads.
after_run( run_context, run_values )