You can find a tutorial here, a little long but you can jump the part of building the network. Or you can read my small summary below, based on my experiance.
First, MonitoredSession should be used instead of normal Session.
A SessionRunHook extends session.run() calls for the MonitoredSession.
Then some common SessionRunHook classes can be found here. A simple one is LoggingTensorHook but you might want to add the following line after your imports for seeing the logs when running:
tf.logging.set_verbosity(tf.logging.INFO)
Or you have option to implement your own SessionRunHook class. A simple one is from cifar10 tutorial
class _LoggerHook(tf.train.SessionRunHook):
"""Logs loss and runtime."""
def begin(self):
self._step = -1
self._start_time = time.time()
def before_run(self, run_context):
self._step += 1
return tf.train.SessionRunArgs(loss) # Asks for loss value.
def after_run(self, run_context, run_values):
if self._step % FLAGS.log_frequency == 0:
current_time = time.time()
duration = current_time - self._start_time
self._start_time = current_time
loss_value = run_values.results
examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
sec_per_batch = float(duration / FLAGS.log_frequency)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), self._step, loss_value,
examples_per_sec, sec_per_batch))
where loss is defined outside the class. This _LoggerHook uses print to print the information while LoggingTensorHook uses tf.logging.INFO.
At last, for better understanding how it works, the execution order is presented by pseudocode with MonitoredSession here:
call hooks.begin()
sess = tf.Session()
call hooks.after_create_session()
while not stop is requested: # py code: while not mon_sess.should_stop():
call hooks.before_run()
try:
results = sess.run(merged_fetches, feed_dict=merged_feeds)
except (errors.OutOfRangeError, StopIteration):
break
call hooks.after_run()
call hooks.end()
sess.close()
Hope this helps.
