(labels, predictions, num_classes, pos_indices=None, weights=None,
          average='micro', beta=1)
return (fbeta, op)
Multi-class fbeta metric for Tensorflow
    Parameters
    ----------
    labels : Tensor of tf.int32 or tf.int64
        The true labels
    predictions : Tensor of tf.int32 or tf.int64
        The predictions, same shape as labels
    num_classes : int
        The number of classes
    pos_indices : list of int, optional
        The indices of the positive classes, default is all
    weights : Tensor of tf.int32, optional
        Mask, must be of compatible shape with labels
    average : str, optional
        'micro': counts the total number of true positives, false
            positives, and false negatives for the classes in
            `pos_indices` and infer the metric from it.
        'macro': will compute the metric separately for each class in
            `pos_indices` and average. Will not account for class
            imbalance.
        'weighted': will compute the metric separately for each class in
            `pos_indices` and perform a weighted average by the total
            number of true labels for each class.
    beta : int, optional
        Weight of precision in harmonic mean
    Returns
    -------
    tuple of (scalar float Tensor, update_op)
Multi-class fbeta metric for Tensorflow
    Parameters
    ----------
    labels : Tensor of tf.int32 or tf.int64
        The true labels
    predictions : Tensor of tf.int32 or tf.int64
        The predictions, same shape as labels
    num_classes : int
        The number of classes
    pos_indices : list of int, optional
        The indices of the positive classes, default is all
    weights : Tensor of tf.int32, optional
        Mask, must be of compatible shape with labels
    average : str, optional
        'micro': counts the total number of true positives, false
            positives, and false negatives for the classes in
            `pos_indices` and infer the metric from it.
        'macro': will compute the metric separately for each class in
            `pos_indices` and average. Will not account for class
            imbalance.
        'weighted': will compute the metric separately for each class in
            `pos_indices` and perform a weighted average by the total
            number of true labels for each class.
    beta : int, optional
        Weight of precision in harmonic mean
    Returns
    -------
    tuple of (scalar float Tensor, update_op)
Multi - class fbeta metric for Tensorflow Parameters ---------- labels : Tensor of tf . int32 or tf . int64 The true labels predictions : Tensor of tf . int32 or tf . int64 The predictions same shape as labels num_classes : int The number of classes pos_indices : list of int optional The indices of the positive classes default is all weights : Tensor of tf . int32 optional Mask must be of compatible shape with labels average : str optional micro : counts the total number of true positives false positives and false negatives for the classes in pos_indices and infer the metric from it . macro : will compute the metric separately for each class in pos_indices and average . Will not account for class imbalance . weighted : will compute the metric separately for each class in pos_indices and perform a weighted average by the total number of true labels for each class . beta : int optional Weight of precision in harmonic mean Returns ------- tuple of ( scalar float Tensor update_op )
def fbeta(labels, predictions, num_classes, pos_indices=None, weights=None,
          average='micro', beta=1):
    """Multi-class fbeta metric for Tensorflow
    Parameters
    ----------
    labels : Tensor of tf.int32 or tf.int64
        The true labels
    predictions : Tensor of tf.int32 or tf.int64
        The predictions, same shape as labels
    num_classes : int
        The number of classes
    pos_indices : list of int, optional
        The indices of the positive classes, default is all
    weights : Tensor of tf.int32, optional
        Mask, must be of compatible shape with labels
    average : str, optional
        'micro': counts the total number of true positives, false
            positives, and false negatives for the classes in
            `pos_indices` and infer the metric from it.
        'macro': will compute the metric separately for each class in
            `pos_indices` and average. Will not account for class
            imbalance.
        'weighted': will compute the metric separately for each class in
            `pos_indices` and perform a weighted average by the total
            number of true labels for each class.
    beta : int, optional
        Weight of precision in harmonic mean
    Returns
    -------
    tuple of (scalar float Tensor, update_op)
    """
    cm, op = _streaming_confusion_matrix(
        labels, predictions, num_classes, weights)
    _, _, fbeta = metrics_from_confusion_matrix(
        cm, pos_indices, average=average, beta=beta)
    _, _, op = metrics_from_confusion_matrix(
        op, pos_indices, average=average, beta=beta)
    return (fbeta, op)
def fbeta ( labels , predictions , num_classes , pos_indices = None , weights = None , average = 'micro' , beta = 1 ) : cm , op = _streaming_confusion_matrix ( labels , predictions , num_classes , weights ) _ , _ , fbeta = metrics_from_confusion_matrix ( cm , pos_indices , average = average , beta = beta ) _ , _ , op = metrics_from_confusion_matrix ( op , pos_indices , average = average , beta = beta ) return ( fbeta , op )