tf.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:

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def (features, labels, mode, params): """DNN with three hidden layers and learning_rate=0.1.""" net = tf.feature_column.input_layer(features, params['feature_columns']) for units in params['hidden_units']: net = t 大专栏  tf.estimatorf.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。