network.py 文件源码

def cal_loss(self):
        expand_annotations = tf.expand_dims(
            self.annotations, -1, name='annotations/expand_dims')
        one_hot_annotations = tf.squeeze(
            expand_annotations, axis=[self.channel_axis],
            name='annotations/squeeze')
        one_hot_annotations = tf.one_hot(
            one_hot_annotations, depth=self.conf.class_num,
            axis=self.channel_axis, name='annotations/one_hot')
        losses = tf.losses.softmax_cross_entropy(
            one_hot_annotations, self.predictions, scope='loss/losses')
        self.loss_op = tf.reduce_mean(losses, name='loss/loss_op')
        self.decoded_predictions = tf.argmax(
            self.predictions, self.channel_axis, name='accuracy/decode_pred')
        self.dice_accuracy_op, self.sub_dice_list = ops.dice_accuracy(self.decoded_predictions,\
                                self.annotations,self.conf.class_num)
        correct_prediction = tf.equal(
            self.annotations, self.decoded_predictions,
            name='accuracy/correct_pred')
        self.accuracy_op = tf.reduce_mean(
            tf.cast(correct_prediction, tf.float32, name='accuracy/cast'),
            name='accuracy/accuracy_op')