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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.
Args | |
---|---|
labels
|
A Tensor whose shape matches predictions . Will be cast to
bool .
|
predictions
|
A floating point Tensor of arbitrary shape and whose values
are in the range [0, 1] .
|
weights
|
Optional Tensor whose rank is either 0, or the same rank as
labels , and must be broadcastable to labels (i.e., all dimensions must
be either 1 , or the same as the corresponding labels dimension).
|
num_thresholds
|
The number of thresholds to use when discretizing the roc curve. |
metrics_collections
|
An optional list of collections that f1_score should
be added to.
|
updates_collections
|
An optional list of collections that update_op should
be added to.
|
name
|
An optional variable_scope name. |
Returns | |
---|---|
f1_score
|
A scalar Tensor representing the current best f1-score across
different thresholds.
|
update_op
|
An operation that increments the true_positives ,
true_negatives , false_positives and false_negatives variables
appropriately and whose value matches the f1_score .
|
Raises | |
---|---|
ValueError
|
If predictions and labels have mismatched shapes, or if
weights is not None and its shape doesn't match predictions , or if
either metrics_collections or updates_collections are not a list or
tuple.
|