estimator

estimator同keras是tensorflow的高级API。在tensorflow1.13以上，estimator已经作为一个单独的package从tensorflow分离出来了。
estimator抽象了tensorflow底层的api, 同keras一样，他分离了model和data, 不同于keras这个不得不认养的儿子，estimator作为tensorflow的亲儿子，天生具有分布式的基因，更容易在生产环境里面使用

tensorflow官方文档提供了比较详细的estimator程序的构建过程:
https://www.tensorflow.org/guide#estimators

tensorflow model提供了estimator构建的mnist程序:
https://github.com/tensorflow/models/blob/master/official/mnist/mnist.py

estimator模型由model_fn决定:
官方文档:

其中features, labels是必需的。model, params, config参数是可选的
如下是estiamtor定义的一个模型:

def my_model(features, labels, mode, params):
    """DNN with three hidden layers and learning_rate=0.1."""
    # Create three fully connected layers.
    net = tf.feature_column.input_layer(features, params['feature_columns'])
    for units in params['hidden_units']:
        net = tf.layers.dense(net, units=units, activation=tf.nn.relu)

    # Compute logits (1 per class).
    logits = tf.layers.dense(net, params['n_classes'], activation=None)

    # Compute predictions.
    predicted_classes = tf.argmax(logits, 1)
    if mode == tf.estimator.ModeKeys.PREDICT:
        predictions = {
            'class_ids': predicted_classes[:, tf.newaxis],
            'probabilities': tf.nn.softmax(logits),
            'logits': logits,
        }
        return tf.estimator.EstimatorSpec(mode, predictions=predictions)

    # Compute loss.
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)

    # Compute evaluation metrics.
    accuracy = tf.metrics.accuracy(labels=labels,
                                   predictions=predicted_classes,
                                   name='acc_op')
    metrics = {'accuracy': accuracy}
    tf.summary.scalar('accuracy', accuracy[1])

    if mode == tf.estimator.ModeKeys.EVAL:
        return tf.estimator.EstimatorSpec(
            mode, loss=loss, eval_metric_ops=metrics)

    # Create training op.
    assert mode == tf.estimator.ModeKeys.TRAIN

    optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)

可以看见features, labels分别是模型的input,output。所以是必须的。但是在有的模型里面。比如bert预训练的模型里面，我们不需要训练，所以只用到features。