python - Confused about tensorflow sessions -
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 :)
wiki
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