dice损失函数

def dice_coef(y_true, y_pred, axis=(1, 2), smooth=1.):
    intersection = tf.reduce_sum(y_true * y_pred, axis=axis)
    union = tf.reduce_sum(y_true + y_pred, axis=axis)
    numerator = tf.constant(2.) * intersection + smooth
    denominator = union + smooth
    coef = numerator / denominator

    return tf.reduce_mean(coef)


def dice_coef_loss(target, prediction, axis=(1, 2), smooth=1.):
    """
    """
    intersection = tf.reduce_sum(prediction * target, axis=axis)
    p = tf.reduce_sum(prediction, axis=axis)
    t = tf.reduce_sum(target, axis=axis)
    numerator = tf.reduce_mean(intersection + smooth)
    denominator = tf.reduce_mean(t + p + smooth)
    dice_loss = -tf.log(2.*numerator) + tf.log(denominator)

    return dice_loss


def combined_dice_ce_loss(y_true, y_pred, axis=(1, 2), smooth=1.,
                          weight=args.weight_dice_loss):
    """
    Combined Dice and Binary Cross Entropy Loss
    """
    return weight*dice_coef_loss(y_true, y_pred, axis, smooth) + \
        (1-weight)*K.losses.binary_crossentropy(y_true, y_pred)