make_estimator() supports metrics

      feed_dict[self._training_placeholder] = train_value

      yield feed_dict

  def make_estimator(self, feature_columns, weight_column=None, model_dir=None):

  def make_estimator(self, feature_columns, weight_column=None, model_dir=None, metrics={}):

    """Construct a Tensorflow Estimator from this model.

    tf.estimator.Estimator is the standard Tensorflow API for representing models.

    weight_column: tf.feature_column or None

      if this model includes a Weights layer, this describes the input weights.

      Otherwise, this should be None.

    metrics: map

      metrics that should be computed in calls to evaluate().  For each entry,

      the key is the name to report for the metric, and the value is a function

      of the form f(labels, predictions, weights) that returns the tensors for

      computing the metric.  Any of the functions in tf.metrics can be used, as

      can other functions that satisfy the same interface.

    model_dir: str

      the directory in which the Estimator should save files.  If None, this

      defaults to the model's model_dir.

        return tf.estimator.EstimatorSpec(mode, predictions=predictions)

      if mode == tf.estimator.ModeKeys.EVAL:

        loss = create_tensors(self.loss, tensors, 0)

        return tf.esimator.EstimatorSpec(mode, loss=loss)

        predictions = create_tensors(self.outputs[0], tensors, 0)

        if len(self.task_weights) == 0:

          weights = None

        else:

          weights = tensors[self.task_weights[0]]

        eval_metric_ops = {}

        for name, function in metrics.items():

          eval_metric_ops[name] = function(labels, predictions, weights)

        return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=eval_metric_ops)

      if mode == tf.estimator.ModeKeys.TRAIN:

        loss = create_tensors(self.loss, tensors, 1)

        global_step = tf.train.get_global_step()