Keras超参数优化tensorboard

以下是使用TensorBoard进行Keras超参数优化的步骤：

1. 引入必要的库和模块：

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

2. 定义超参数搜索空间：

HP_UNITS = hp.HParam('units', hp.Discrete([16, 32, 64]))
HP_DROPOUT = hp.HParam('dropout', hp.RealInterval(0.1, 0.2))
HP_OPTIMIZER = hp.HParam('optimizer', hp.Discrete(['adam', 'sgd']))

METRIC_ACCURACY = 'accuracy'

3. 定义模型训练函数：

def train_model(hparams):
    model = keras.Sequential([
        keras.layers.Dense(hparams[HP_UNITS], activation='relu'),
        keras.layers.Dropout(hparams[HP_DROPOUT]),
        keras.layers.Dense(10)
    ])

    model.compile(optimizer=hparams[HP_OPTIMIZER],
                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                  metrics=[METRIC_ACCURACY])

    model.fit(x_train, y_train, epochs=10)

    _, accuracy = model.evaluate(x_test, y_test)
    return accuracy

4. 定义TensorBoard回调函数：

def run(run_dir, hparams):
    with tf.summary.create_file_writer(run_dir).as_default():
        hp.hparams(hparams)  # 记录超参数
        accuracy = train_model(hparams)
        tf.summary.scalar(METRIC_ACCURACY, accuracy, step=1)

5. 运行超参数搜索：

session_num = 0

for units in HP_UNITS.domain.values:
    for dropout_rate in (HP_DROPOUT.domain.min_value, HP_DROPOUT.domain.max_value):
        for optimizer in HP_OPTIMIZER.domain.values:
            hparams = {
                HP_UNITS: units,
                HP_DROPOUT: dropout_rate,
                HP_OPTIMIZER: optimizer
            }
            run_name = "run-%d" % session_num
            print('-- Starting trial: %s' % run_name)
            print({h.name: hparams[h] for h in hparams})
            run('logs/hparam_tuning/' + run_name, hparams)
            session_num += 1

6. 启动TensorBoard：

tensorboard --logdir logs/hparam_tuning