使用带自定义损失函数的tensorboard.plugins.hparams应用编程接口进行超参数调整

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

# 定义超参数搜索空间
HP_NUM_UNITS = hp.HParam('num_units', hp.Discrete([16, 32, 64]))
HP_LEARNING_RATE = hp.HParam('learning_rate', hp.RealInterval(0.001, 0.01))

# 自定义损失函数
def custom_loss(y_true, y_pred):
    return tf.reduce_mean(tf.square(y_true - y_pred)) + tf.reduce_sum(tf.abs(y_pred))

# 构建模型
model = tf.keras.Sequential([
    tf.keras.layers.Dense(hp.get('num_units'), activation='relu'),
    tf.keras.layers.Dense(1)
])

# 编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(hp.get('learning_rate')),
              loss=custom_loss,
              metrics=['mse'])

# 训练模型并记录超参数
with tf.summary.create_file_writer('logs/hparam_tuning').as_default():
    hp.hparams_config(
        hparams=[HP_NUM_UNITS, HP_LEARNING_RATE],
        metrics=[tf.metrics.MeanSquaredError()]
    )
    for num_units in HP_NUM_UNITS.domain.values:
        for learning_rate in (HP_LEARNING_RATE.domain.min_value,
                              HP_LEARNING_RATE.domain.max_value):
            model.set_weights(initial_weights)  # 重置模型权重
            model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate),
                          loss=custom_loss,
                          metrics=['mse'])
            model.fit(x_train, y_train, epochs=1, validation_data=(x_val, y_val))
            hp.hparams({HP_NUM_UNITS: num_units, HP_LEARNING_RATE: learning_rate})
            tf.summary.scalar('mse', metrics['mse'].result(), step=1)