fitting for hyperas.py

fitting.py

    best_run, best_model = optim.minimize(model=create_model,
                                            data=data,
                                            algo=tpe.suggest,
                                            max_evals=5,
                                            trials=Trials(),
                                            notebook_name='gala_recog')
    
    # Evaluate the model on the test data using `evaluate`
    print('\n# Evaluate on test data')
    results = best_model.evaluate(X_test, y_test, batch_size=128)
    print('test loss, test acc:', results)

    # Generate predictions (probabilities -- the output of the last layer)
    # on new data using `predict`
    print('\n# Generate predictions for 3 samples')
    predictions = best_model.predict(X_test[:3])
    print('predictions shape:', predictions.shape)

    print(best_run)