# 'random_normal_initializer_stdev': 0.00025797511482927632, # 'rate_of_learning': 0.20172634121590136} # persist the optimized configuration to a file persist_results( optimized_configuration, optimized_config_directory + '/' + model_identifier + '.txt') # optimized_configuration = read_optimal_hyperparameter_values(optimized_config_directory + '/' + model_identifier + '.txt') # get the validation errors for the best hyperparameter configs smape_error, smape_error_list = train_model(optimized_configuration) # print(smape_error_list) # write the final list of validation errors to a file validation_errors_file = model_training_configs.VALIDATION_ERRORS_DIRECTORY + model_identifier + ".csv" with open(validation_errors_file, "w") as output: writer = csv.writer(output, lineterminator='\n') writer.writerow(smape_error_list) print("Optimized configuration: {}".format(optimized_configuration)) print("Optimized Value: {}\n".format(smape_error)) # test the model for i in range(1, 11): args.seed = i testing(args, optimized_configuration) # testing(args, optimized_configuration)
# select the optimizer if optimizer == "cocob": optimizer_fn = cocob_optimizer_fn elif optimizer == "adagrad": optimizer_fn = adagrad_optimizer_fn elif optimizer == "adam": optimizer_fn = adam_optimizer_fn optimized_configuration = { 'num_hidden_layers': optimized_params['num_hidden_layers'], 'cell_dimension': optimized_params['cell_dimension'], 'l2_regularization': optimized_params['l2_regularization'], 'gaussian_noise_stdev': optimized_params['gaussian_noise_stdev'], 'random_normal_initializer_stdev': optimized_params['random_normal_initializer_stdev'], 'minibatch_size': optimized_params['minibatch_size'], 'max_epoch_size': optimized_params['max_epoch_size'], 'max_num_epochs': optimized_params['max_num_epochs'], 'learning_rate': '' } testing(args, optimized_configuration, "validation")
'seed': seed, 'cell_type': cell_type, 'without_stl_decomposition': without_stl_decomposition } model_trainer = StackingModelTrainer(**model_kwargs) # read the initial hyperparamter configurations from the file hyperparameter_values_dic = read_initial_hyperparameter_values( initial_hyperparameter_values_file) optimized_configuration = smac() # persist the optimized configuration to a file persist_results( optimized_configuration, optimized_config_directory + '/' + model_identifier + '.txt') # get the validation errors for the best hyperparameter configs smape_error, smape_error_list = train_model(optimized_configuration) # write the final list of validation errors to a file validation_errors_file = model_training_configs.VALIDATION_ERRORS_DIRECTORY + model_identifier + ".csv" with open(validation_errors_file, "w") as output: writer = csv.writer(output, lineterminator='\n') writer.writerow(smape_error_list) print("Optimized configuration: {}".format(optimized_configuration)) print("Optimized Value: {}\n".format(smape_error)) testing(args, optimized_configuration, "test")