There are several options for monitoring the experiment.
# turn off progress bar
Scan(disable_progress_bar=True)
# enable live training plot
from talos import live
out = model.fit(X,
Y,
epochs=20,
callbacks=[live()])
# turn on parameter printing
Scan(print_params=True)
Progress Bar : A round-by-round updating progress bar that shows the remaining rounds, together with a time estimate to completion. Progress bar is on by default.
Live Monitoring : Live monitoring provides an epoch-by-epoch updating line graph that is enabled through the live()
custom callback.
Round Hyperparameters : Displays the hyperparameters for each permutation. Does not work together with live monitoring.
Epoch-by-epoch training data is available during the experiment using the ExperimentLogCallback
:
model.fit(...
callbacks=[talos.utils.ExperimentLogCallback('experiment_name', params)])
Here params
is the params dictionary in the Scan()
input model. Both
experiment_name
and experiment_id
should match with the current experiment,
as otherwise