tf.estimator.add_metrics

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Creates a new tf.estimator.Estimator which has given metrics.

tf.estimator.add_metrics(
    estimator, metric_fn
)

Example:

def my_auc(labels, predictions):
    auc_metric = tf.keras.metrics.AUC(name="my_auc")
    auc_metric.update_state(y_true=labels, y_pred=predictions['logistic'])
    return {'auc': auc_metric}

  estimator = tf.estimator.DNNClassifier(...)
  estimator = tf.estimator.add_metrics(estimator, my_auc)
  estimator.train(...)
  estimator.evaluate(...)

Example usage of custom metric which uses features:

def my_auc(labels, predictions, features):
    auc_metric = tf.keras.metrics.AUC(name="my_auc")
    auc_metric.update_state(y_true=labels, y_pred=predictions['logistic'],
                            sample_weight=features['weight'])
    return {'auc': auc_metric}

  estimator = tf.estimator.DNNClassifier(...)
  estimator = tf.estimator.add_metrics(estimator, my_auc)
  estimator.train(...)
  estimator.evaluate(...)

Args:

• estimator: A tf.estimator.Estimator object.
• metric_fn: A function which should obey the following signature:
  • Args: can only have following four arguments in any order:
  • predictions: Predictions Tensor or dict of Tensor created by given estimator.
  • features: Input dict of Tensor objects created by input_fn which is given to estimator.evaluate as an argument.
  • labels: Labels Tensor or dict of Tensor created by input_fn which is given to estimator.evaluate as an argument.
  • config: config attribute of the estimator.
  • Returns: Dict of metric results keyed by name. Final metrics are a union of this and estimator's existing metrics. If there is a name conflict between this and estimators existing metrics, this will override the existing one. The values of the dict are the results of calling a metric function, namely a (metric_tensor, update_op) tuple.

Returns:

A new tf.estimator.Estimator which has a union of original metrics with given ones.