metrics.py 文件源码

def dice_enhance_mod(y_true, y_pred):
    """
    Computes the Sorensen-Dice metric, where P come from class 1,2,3,4,5
                    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_true = y_true[:,:,:,:,:3]

    y_pred_decision = tf.floor((y_pred  + K.epsilon()) / K.max(y_pred, axis=4, keepdims=True))
    # y_pred_decision = tf.where(tf.is_nan(y_pred_decision), tf.zeros_like(y_pred_decision), y_pred_decision)



    mask_true = y_true[:, :, :, :, 2]
    mask_pred = y_pred_decision[:, :, :, :, 2] * K.sum(y_true, axis=4)

    y_sum = K.sum(mask_true * mask_pred)

    return (2. * y_sum + K.epsilon()) / (K.sum(mask_true) + K.sum(mask_pred) + K.epsilon())