在tensorflow估计器中创建自定义指标

import tensorflow as tf

# 定义自定义指标函数
def custom_metric_fn(labels, predictions):
    mae = tf.reduce_mean(tf.abs(predictions - labels))
    return mae

# 创建自定义指标对象
custom_metric = tf.metrics.regression.mean_absolute_error(labels, predictions)

# 创建估计器
estimator = tf.estimator.Estimator(
    model_fn=model_fn,
    config=config,
    params=params
)

# 在估计器的配置中指定自定义指标
eval_metric_ops = {
    'custom_metric': custom_metric
}

# 创建估计器规范
eval_spec = tf.estimator.EvalSpec(
    input_fn=eval_input_fn,
    steps=eval_steps,
    throttle_secs=throttle_secs,
    exporters=exporters,
    start_delay_secs=start_delay_secs,
    throttle_secs=throttle_secs,
    hooks=hooks,
    name=name,
    eval_metric_ops=eval_metric_ops
)

# 运行估计器
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)