| 571 | print("Training completed.") |
| 572 | |
| 573 | def eval(sess_config, input_hooks, model, data_init_op, steps, checkpoint_dir): |
| 574 | model.is_training = False |
| 575 | hooks = [] |
| 576 | hooks.extend(input_hooks) |
| 577 | |
| 578 | scaffold = tf.train.Scaffold( |
| 579 | local_init_op=tf.group(tf.local_variables_initializer(), data_init_op)) |
| 580 | session_creator = tf.train.ChiefSessionCreator( |
| 581 | scaffold=scaffold, checkpoint_dir=checkpoint_dir, config=sess_config) |
| 582 | writer = tf.summary.FileWriter(os.path.join(checkpoint_dir, 'eval')) |
| 583 | merged = tf.summary.merge_all() |
| 584 | |
| 585 | with tf.train.MonitoredSession(session_creator=session_creator, |
| 586 | hooks=hooks) as sess: |
| 587 | for _in in range(1, steps + 1): |
| 588 | if (_in != steps): |
| 589 | sess.run([model.acc_op, model.auc_op]) |
| 590 | if (_in % 1000 == 0): |
| 591 | print("Evaluation complete:[{}/{}]".format(_in, steps)) |
| 592 | else: |
| 593 | eval_acc, eval_auc, events = sess.run( |
| 594 | [model.acc_op, model.auc_op, merged]) |
| 595 | writer.add_summary(events, _in) |
| 596 | print("Evaluation complete:[{}/{}]".format(_in, steps)) |
| 597 | print("ACC = {}\nAUC = {}".format(eval_acc, eval_auc)) |
| 598 | |
| 599 | def main(tf_config=None, server=None): |
| 600 | # check dataset and count data set size |