tfg.nn.metric.precision.evaluate

Computes the precision metric for the given ground truth and predictions.

tfg.nn.metric.precision.evaluate(
    ground_truth: type_alias.TensorLike,
    prediction: type_alias.TensorLike,
    classes: Optional[Union[int, List[int], Tuple[int]]] = None,
    reduce_average: bool = True,
    prediction_to_category_function: Callable[..., Any] = _cast_to_int,
    name: str = 'precision_evaluate'
) -> tf.Tensor

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

ground_truth A tensor of shape [A1, ..., An, N], where the last axis represents the ground truth labels. Will be cast to int32.
prediction A tensor of shape [A1, ..., An, N], where the last axis represents the predictions (which can be continuous).
classes An integer or a list/tuple of integers representing the classes for which the precision will be evaluated. In case 'classes' is 'None', the number of classes will be inferred from the given labels and the precision will be calculated for each of the classes. Defaults to 'None'.
reduce_average Whether to calculate the average of the precision for each class and return a single precision value. Defaults to true.
prediction_to_category_function A function to associate a prediction to a category. Defaults to rounding down the value of the prediction to the nearest integer value.
name A name for this op. Defaults to "precision_evaluate".

A tensor of shape [A1, ..., An, C], where the last axis represents the precision calculated for each of the requested classes.

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