| with tf.train.MonitoredTrainingSession(master=server.target,\ | |
| is_chief=is_chiefing, | |
| checkpoint_dir=arsg['save_dir'],\ | |
| hooks=hooks,\ | |
| save_checkpoint_secs=600.) as mon_sess: | |
| tf_feed = ctx.get_data_feed(train_mode=True) | |
| step = 0 | |
| while not mon_sess.should_stop() and not tf_feed.should_stop() and step < args['steps']: | |
| batch_data, batch_labels = get_next_batch(tf_feed.next_batch(args['batch_size'])) | |
| if len(batch_data) > 0: | |
| feed = {model_input: batch_data, model_labels: batch_labels} | |
| _, logloss, step = mon_sess.run([tf_optimizer, tf_loss,tf_global_step],feed_dict=feed) | |
| if mon_sess.should_stop() or step >= args['steps']: | |
| tf_feed.terminate() | |
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