.ipynb.rb

tuner = kt.tuners.BayesianOptimization(
  model_builder(hparams)
  objective='val_accuracy',
  max_trials=50)

tuner.search(img_train, label_train, epochs = 10, validation_data = (img_test, label_test), callbacks = [ClearTrainingOutput()])

# Get the optimal hyperparameters
best_hps = tuner.get_best_hyperparameters(num_trials = 1)[0]