random_accuracy_df[['loss', 'accuracy']].plot()
plt.title('Loss & Accuracy Per EPOCH For Random Model')
plt.xlabel('EPOCH')
plt.ylabel('Accruacy')
plt.show()
bayesian_tuner = BayesianOptimization(hypermodel,
objective='accuracy',
max_trials=10,
seed=10,
project_name='divorce test')
bayesian_tuner.search(X_train.values,
y_train.values.flatten(),
epochs=10,
validation_data=(X_test.values, y_test.values.flatten()))
bayesian_params = bayesian_tuner.get_best_hyperparameters()[0]
bayesian_model = bayesian_tuner.hypermodel.build(bayesian_params)
bayesian_model.fit(X.values, y.values.flatten(), epochs=15)
bayesian_accuracy_df = pd.DataFrame(bayesian_model.history.history)
bayesian_accuracy_df[['loss', 'accuracy']].plot()
plt.title('Loss & Accuracy Per EPOCH For Bayesian Optimisation Model')
plt.xlabel('EPOCH')
plt.ylabel('Accruacy')
plt.show()
max_trials=TOTAL_TRIALS,
objective=kerastuner.Objective("val_auc", direction="max"),
executions_per_trial=EXECUTION_PER_TRIAL,
directory=base_dir,
project_name=exp_name
)
history = tuner.search(train_gen,
epochs=EPOCHS,
validation_data=val_gen,
callbacks = [es, cp],
verbose =2,
use_multiprocessing=False)
# Save best model and weight
best_model = tuner.get_best_models()[0]
best_config = best_model.optimizer.get_config()
best_hyperparameters = tuner.get_best_hyperparameters()[0].get_config()
best_hyperparameters_values = tuner.get_best_hyperparameters()[0].values
best_model.save(model_dir)
best_model.save_weights(weight_dir)
with open(os.path.join(param_dir, 'hyperparameters.txt'), "w") as text_file:
text_file.write(str(best_hyperparameters))
pickle.dump(best_hyperparameters_values, open(os.path.join(param_dir,'hyperparameters.pickle'), 'wb'))
print('Done')