utils.py 文件源码

def summANDsave(self,training = True):

        with tf.name_scope('saversANDsummaries'):

            if training:
                #saver
                self.saver_for_train = tf.train.Saver(keep_checkpoint_every_n_hours=2,
                                                      max_to_keep=1)  # will save all the tf graph vars!!!
                self.saver_for_play = tf.train.Saver(tf.trainable_variables(), keep_checkpoint_every_n_hours=2,
                                                     max_to_keep=2)  # used after training
                self.train_writer = tf.summary.FileWriter(self.trainSummaryDir)

                self.latest_checkpoint = tf.train.latest_checkpoint(self.trainDir)

                #summaries
                # loss
                self.lossTotalSummaryHolder = tf.placeholder(dtype = tf.float16)
                self.lossTotalSummary = tf.summary.scalar('total Loss per episode', self.lossTotalSummaryHolder)
                self.lossAvgSummaryHolder = tf.placeholder(dtype = tf.float16)
                self.lossAvgSummary = tf.summary.scalar('Avg.Loss per episode', self.lossAvgSummaryHolder)
                self.episodeUpdatesHolder = tf.placeholder(dtype = tf.uint16)
                self.episodeUpdates = tf.summary.scalar('Episode updates', self.episodeUpdatesHolder)

            else:

                self.latest_checkpoint = tf.train.latest_checkpoint(self.playDir)

            # reward
            self.rewardTotalSummaryHolder = tf.placeholder(dtype=tf.float16)
            self.rewardTotalSummary = tf.summary.scalar('total Reward per episode', self.rewardTotalSummaryHolder)
            self.rewardAvgSummaryHolder = tf.placeholder(dtype=tf.float16)
            self.rewardAvgSummary = tf.summary.scalar('Avg.Reward per episode', self.rewardAvgSummaryHolder)
            self.episodeDurSummaryHolder = tf.placeholder(dtype=tf.float16)
            self.episodeDurSummary = tf.summary.scalar('Episode duration', self.episodeDurSummaryHolder)

            #savers
            self.play_writer = tf.summary.FileWriter(self.playSummaryDir)
            #merger
            self.summary_merger = tf.summary.merge_all()