tfg.nn.metric.fscore.evaluate

Computes the fscore metric for the given ground truth and predicted labels.

tfg.nn.metric.fscore.evaluate(
    ground_truth: type_alias.TensorLike,
    prediction: type_alias.TensorLike,
    precision_function: Callable[..., Any] = tfg.nn.metric.precision.evaluate,
    recall_function: Callable[..., Any] = tfg.nn.metric.recall.evaluate,
    name: str = 'fscore_evaluate'
) -> tf.Tensor

The fscore is calculated as 2 * (precision * recall) / (precision + recall) where the precision and recall are evaluated by the given function parameters. The precision and recall functions default to their definition for boolean labels (see https://en.wikipedia.org/wiki/Precision_and_recall for more details).

Note
In the following, A1 to An are optional batch dimensions, which must be broadcast compatible.

Args
ground_truth	A tensor of shape [A1, ..., An, N], where the last axis represents the ground truth values.
prediction	A tensor of shape [A1, ..., An, N], where the last axis represents the predicted values.
precision_function	The function to use for evaluating the precision. Defaults to the precision evaluation for binary ground-truth and predictions.
recall_function	The function to use for evaluating the recall. Defaults to the recall evaluation for binary ground-truth and prediction.
name	A name for this op. Defaults to "fscore_evaluate".

Returns
A tensor of shape [A1, ..., An] that stores the fscore metric for the given ground truth labels and predictions.

Raises
ValueError	if the shape of ground_truth, prediction is not supported.