tf.contrib.metrics.f1_score

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Computes the approximately best F1-score across different thresholds.

tf.contrib.metrics.f1_score(
    labels, predictions, weights=None, num_thresholds=200, metrics_collections=None,
    updates_collections=None, name=None
)

The f1_score function applies a range of thresholds to the predictions to convert them from [0, 1] to bool. Precision and recall are computed by comparing them to the labels. The F1-Score is then defined as 2 * precision * recall / (precision + recall). The best one across the thresholds is returned.

Disclaimer: In practice it may be desirable to choose the best threshold on the validation set and evaluate the F1 score with this threshold on a separate test set. Or it may be desirable to use a fixed threshold (e.g. 0.5).

This function internally creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the pairs of recall and precision values for a linearly spaced set of thresholds from which the best f1-score is derived.

This value is ultimately returned as f1-score, an idempotent operation that computes the F1-score (computed using the aforementioned variables). The num_thresholds variable controls the degree of discretization with larger numbers of thresholds more closely approximating the true best F1-score.

For estimation of the metric over a stream of data, the function creates an update_op operation that updates these variables and returns the F1-score.

Example usage with a custom estimator: def model_fn(features, labels, mode): predictions = make_predictions(features) loss = make_loss(predictions, labels) train_op = tf.contrib.training.create_train_op( total_loss=loss, optimizer='Adam') eval_metric_ops = {'f1': f1_score(labels, predictions)} return tf.estimator.EstimatorSpec( mode=mode, predictions=predictions, loss=loss, train_op=train_op, eval_metric_ops=eval_metric_ops, export_outputs=export_outputs) estimator = tf.estimator.Estimator(model_fn=model_fn)

If weights is None, weights default to 1. Use weights of 0 to mask values.