BaseUnet.py 文件源码

def _get_loss(self,labels):

        with tf.name_scope("Loss"):
            """
            with tf.name_scope("logloss"):
                logit = tf.squeeze(tf.nn.sigmoid(self.logit))
                self.loss = tf.reduce_mean(self._logloss(labels, logit))
            """
            with tf.name_scope("L2_loss"):
                if self.flags.lambdax:
                    lambdax = self.flags.lambdax
                else:
                    lambdax = 0
                self.l2loss = lambdax*tf.add_n(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))

            with tf.name_scope("dice_coef"):
                #yp_label = tf.cast(logit>self.flags.threshold, tf.float32)
                logit = tf.squeeze(self.logit)
                self.acc = tf.reduce_mean(self._dice_coef(labels,logit))
                self.metric = "dice_coef"
                self.loss = -self.acc

        with tf.name_scope("summary"):
            if self.flags.visualize:
                tf.summary.scalar(name='dice coef', tensor=self.acc, collections=[tf.GraphKeys.SCALARS])