metrics.py 文件源码

def jaccard(y_pred, y_true, n_classes, one_hot=False):

    assert (y_pred.ndim == 2) or (y_pred.ndim == 1)

    # y_pred to indices
    if y_pred.ndim == 2:
        y_pred = T.argmax(y_pred, axis=1)

    if one_hot:
        y_true = T.argmax(y_true, axis=1)

    # Compute confusion matrix
    cm = T.zeros((n_classes, n_classes))
    for i in range(n_classes):
        for j in range(n_classes):
            cm = T.set_subtensor(
                cm[i, j], T.sum(T.eq(y_pred, i) * T.eq(y_true, j)))

    # Compute Jaccard Index
    TP_perclass = T.cast(cm.diagonal(), _FLOATX)
    FP_perclass = cm.sum(1) - TP_perclass
    FN_perclass = cm.sum(0) - TP_perclass

    num = TP_perclass
    denom = TP_perclass + FP_perclass + FN_perclass

    return T.stack([num, denom], axis=0)