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.
|
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TensorFlow 1 version
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