metrics.py 文件源码

def dice(y_true, y_pred):
    """
    Computes the Sorensen-Dice metric
                    TP
        Dice = 2 -------
                  T + P
    Parameters
    ----------
    y_true : keras.placeholder
        Placeholder that contains the ground truth labels of the classes
    y_pred : keras.placeholder
        Placeholder that contains the class prediction

    Returns
    -------
    scalar
        Dice metric
    """
    y_pred_decision = tf.floor((y_pred + K.epsilon()) / K.max(y_pred, axis=4, keepdims=True))

    y_sum = K.sum(y_true * y_pred_decision)

    return (2. * y_sum + K.epsilon()) / (K.sum(y_true) + K.sum(y_pred_decision) + K.epsilon())