尝试使用TensorFlow2.0中的tensorflow.plugins.hparams来创建一组不同的优化器

import tensorflow as tf
from tensorflow.plugins.hparams import api as hp

HP_OPTIMIZER = hp.HParam('optimizer', hp.Discrete(['sgd', 'adam', 'adagrad']))

HPs = [
    HP_OPTIMIZER,
    # 其他实验参数...
]

def run_experiment(hparams):
    # 根据超参数创建相应的优化器
    if hparams[HP_OPTIMIZER] == 'sgd':
        optimizer = tf.keras.optimizers.SGD()
    elif hparams[HP_OPTIMIZER] == 'adam':
        optimizer = tf.keras.optimizers.Adam()
    elif hparams[HP_OPTIMIZER] == 'adagrad':
        optimizer = tf.keras.optimizers.Adagrad()
    else:
        raise ValueError("Unsupported optimizer")

    # 其他模型构建和训练的代码...

    # 使用优化器进行模型参数更新...

    # 返回模型性能指标...

session_num = 0
for hps in hp.hparams_combinations(HPs):
    run_name = "run-%d" % session_num
    print('--- Starting trial: %s' % run_name)
    run_experiment(hps)
    session_num += 1