python - Confused about tensorflow sessions -

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im working tensorflow , used tensorflow tutorial training cifar-10 network. training using monitoredsession. 

with tf.train.monitoredtrainingsession(         checkpoint_dir=model_path,         hooks=[tf.train.stopatstephook(last_step=max_batches),                tf.train.nantensorhook(cross_entropy),                _loggerhook()]) mon_sess:     while not mon_sess.should_stop():         mon_sess.run(train_step)

in last line there run(train_step). , whats nice here is, function used until max_batches reached. put own code arround this.

but want use normal session. because want use own concept of checkpoint saving , implementing validation-set.

if use normal session like

with tf.session() sess:     sess.run(initialiser..)     while(whatever):         sess.run(train_step)

it doesnt work in monitored session. steps sess.run() not repeadly. monitored different? , how can solve that.

thanks :)





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