在Tensorflow BoostedTreesEstimator中实现自定义损失函数

def custom_loss(y_true, y_pred):
    return tf.square(y_true - y_pred)

estimator = tf.estimator.BoostedTreesEstimator(
    n_batches_per_layer=100,
    model_dir='model_dir',
    config=tf.estimator.RunConfig().replace(save_summary_steps=10)
)

def model_fn(features, labels, mode):
    # 定义模型结构和训练过程
    # ...

    # 使用自定义损失函数
    loss = custom_loss(labels, predictions)

    # 创建EstimatorSpec对象
    estimator_spec = tf.estimator.EstimatorSpec(
        mode=mode,
        loss=loss,
        train_op=train_op,
        eval_metric_ops=eval_metric_ops
    )

    return estimator_spec

estimator._model_fn = model_fn

train_input_fn = tf.estimator.inputs.numpy_input_fn(
    x=train_x,
    y=train_y,
    batch_size=batch_size,
    num_epochs=None,
    shuffle=True
)

estimator.train(input_fn=train_input_fn, steps=num_steps)

eval_input_fn = tf.estimator.inputs.numpy_input_fn(
    x=eval_x,
    y=eval_y,
    batch_size=batch_size,
    num_epochs=1,
    shuffle=False
)

estimator.evaluate(input_fn=eval_input_fn)