TrainsTunerLogger class provides the required binding for Trains automatic logging. Instantiate a
TrainsTunerLogger object and assign it as the logger for a Keras Tuner tuner.
This is demonstrated in the keras_tuner_cifar.py example, which uses Keras Tuner's
Hyperband tuner. It finds the best hyperparameters to train a network on a CIFAR10 dataset.
Hyperband object is created, instantiate a
TrainsTunerLogger object and assign it to the
tuner = kt.Hyperband( build_model, project_name='kt examples', logger=TrainsTunerLogger(), objective='val_accuracy', max_epochs=10, hyperband_iterations=6)
When the script runs, it logs a tabular summary of hyperparameters tested and their metrics by trial Id, and a scalar plot showing metrics for all runs, and summary plot, as well as the output model with its configuration and snapshot location.
Trains logs the scalars from training each network. They appear in RESULTS > SCALARS.
Summary of hyperparameter optimization
Trains automatically logs the parameters of each experiment run in the hyperparameter search. They appear in tabular form in RESULTS > PLOTS.
Trains automatically stores the output model. It appears in ARTIFACTS > Output Model.
Model details include snap locations. Model details appear in the MODELS tab.
The model configuration is stored with the model.
Trains automatically logs the TensorFlow DEFINEs. Trains automatically logs the TensorFlow DEFINEs. They appear in RESULTS > CONFIGURATION > HYPER PARAMETERS.
The Task configuration appears in RESULTS > CONFIGURATION > General.