optimise.py 文件源码

def hyperopt_search(args, data, model, param_grid, max_evals):

    def objective(param_grid):
        args.num_hidden = param_grid['num_hidden']
        args.dropout_output = param_grid['dropout_output']
        args.dropout_input = param_grid['dropout_input']
        args.clip_norm = param_grid['clip_norm']
        args.batch_size = param_grid['batch_size']
        # args.learning_rate = param_grid['learning_rate']
        print(args)
        print()
        scores = run_network(args, data, model, tuning=args.tune)
        test_score, eval_score = scores
        tf.reset_default_graph()
        eval_score = -eval_score[0]
        return {'loss': eval_score, 'params': args, 'status': STATUS_OK}

    trials = Trials()
    results = fmin(
        objective, param_grid, algo=tpe.suggest,
        trials=trials, max_evals=max_evals)

    return results, trials.results