tensorflow自定义的损失函数 focal_loss出现inf，在训练过程中出现inf

Question

def focal_loss(alpha=0.25, gamma=2.):
    """ focal loss used for train positive/negative samples rate out 
    of balance, improve train performance
    """
    def focal_loss_calc(y_true, y_pred):
        positive = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
        negative = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
        return -(alpha*K.pow(1.-positive, gamma)*K.log(positive) +
                       (1-alpha)*K.pow(negative, gamma)*K.log(1.-negative))
    return focal_loss_calc

self.keras_model.compile(optimizer=optimizer, loss=dice_focal_loss, metrics=[ mean_iou, dice_loss, focal_loss()])

上面的focal loss 开始还是挺正常的，随着训练过程逐渐减小大0.025左右，然后就突然变成inf。何解

Answer 1

解决方法

def focal_loss_calc(alpha=0.25, gamma=2., epsilon=1e-6):
    """ focal loss used for train positive/negative samples rate out 
    of balance, improve train performance
    """
    def focal_loss(y_true, y_pred):
        positive = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
        negative = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
        return -alpha*K.pow(1.-positive, gamma)*K.log(positive+epsilon) - \
            (1-alpha)*K.pow(negative, gamma)*K.log(1.-negative+epsilon)
    return focal_loss