我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用tensorflow.SessionLog()。
def testSessionLogStartMessageDiscardsExpiredEvents(self): """Test that SessionLog.START message discards expired events. This discard logic is preferred over the out-of-order step discard logic, but this logic can only be used for event protos which have the SessionLog enum, which was introduced to event.proto for file_version >= brain.Event:2. """ gen = _EventGenerator(self) acc = ea.EventAccumulator(gen) gen.AddEvent(tf.Event(wall_time=0, step=1, file_version='brain.Event:2')) gen.AddScalarTensor('s1', wall_time=1, step=100, value=20) gen.AddScalarTensor('s1', wall_time=1, step=200, value=20) gen.AddScalarTensor('s1', wall_time=1, step=300, value=20) gen.AddScalarTensor('s1', wall_time=1, step=400, value=20) gen.AddScalarTensor('s2', wall_time=1, step=202, value=20) gen.AddScalarTensor('s2', wall_time=1, step=203, value=20) slog = tf.SessionLog(status=tf.SessionLog.START) gen.AddEvent(tf.Event(wall_time=2, step=201, session_log=slog)) acc.Reload() self.assertEqual([x.step for x in acc.Tensors('s1')], [100, 200]) self.assertEqual([x.step for x in acc.Tensors('s2')], [])
def testSessionLogStartMessageDiscardsExpiredEvents(self): """Test that SessionLog.START message discards expired events. This discard logic is preferred over the out-of-order step discard logic, but this logic can only be used for event protos which have the SessionLog enum, which was introduced to event.proto for file_version >= brain.Event:2. """ gen = _EventGenerator(self) acc = ea.EventAccumulator(gen) gen.AddEvent(tf.Event(wall_time=0, step=1, file_version='brain.Event:2')) gen.AddScalar('s1', wall_time=1, step=100, value=20) gen.AddScalar('s1', wall_time=1, step=200, value=20) gen.AddScalar('s1', wall_time=1, step=300, value=20) gen.AddScalar('s1', wall_time=1, step=400, value=20) gen.AddScalar('s2', wall_time=1, step=202, value=20) gen.AddScalar('s2', wall_time=1, step=203, value=20) slog = tf.SessionLog(status=tf.SessionLog.START) gen.AddEvent(tf.Event(wall_time=2, step=201, session_log=slog)) acc.Reload() self.assertEqual([x.step for x in acc.Scalars('s1')], [100, 200]) self.assertEqual([x.step for x in acc.Scalars('s2')], [])
def initialize_tf_variables(self): """ Initialize tensorflow variables (either initializes them from scratch or restores from checkpoint). :return: updated TeLL session """ session = self.tf_session checkpoint = self.workspace.get_checkpoint() # # Initialize or load variables # with Timer(name="Initializing variables"): session.run(tf.global_variables_initializer()) session.run(tf.local_variables_initializer()) if checkpoint is not None: # restore from checkpoint self.tf_saver.restore(session, checkpoint) # get step number from checkpoint step = session.run(self.__global_step_placeholder) + 1 self.global_step = step # reopen summaries for _, summary in self.tf_summaries.items(): summary.reopen() summary.add_session_log(tf.SessionLog(status=tf.SessionLog.START), global_step=step) print("Resuming from checkpoint '{}' at iteration {}".format(checkpoint, step)) else: for _, summary in self.tf_summaries.items(): summary.add_graph(session.graph) return self
def testExpiredDataDiscardedAfterRestartForFileVersionLessThan2(self): """Tests that events are discarded after a restart is detected. If a step value is observed to be lower than what was previously seen, this should force a discard of all previous items with the same tag that are outdated. Only file versions < 2 use this out-of-order discard logic. Later versions discard events based on the step value of SessionLog.START. """ warnings = [] self.stubs.Set(tf.logging, 'warn', warnings.append) gen = _EventGenerator(self) acc = ea.EventAccumulator(gen) gen.AddEvent(tf.Event(wall_time=0, step=0, file_version='brain.Event:1')) gen.AddScalarTensor('s1', wall_time=1, step=100, value=20) gen.AddScalarTensor('s1', wall_time=1, step=200, value=20) gen.AddScalarTensor('s1', wall_time=1, step=300, value=20) acc.Reload() ## Check that number of items are what they should be self.assertEqual([x.step for x in acc.Tensors('s1')], [100, 200, 300]) gen.AddScalarTensor('s1', wall_time=1, step=101, value=20) gen.AddScalarTensor('s1', wall_time=1, step=201, value=20) gen.AddScalarTensor('s1', wall_time=1, step=301, value=20) acc.Reload() ## Check that we have discarded 200 and 300 from s1 self.assertEqual([x.step for x in acc.Tensors('s1')], [100, 101, 201, 301])
def testEventsDiscardedPerTagAfterRestartForFileVersionLessThan2(self): """Tests that event discards after restart, only affect the misordered tag. If a step value is observed to be lower than what was previously seen, this should force a discard of all previous items that are outdated, but only for the out of order tag. Other tags should remain unaffected. Only file versions < 2 use this out-of-order discard logic. Later versions discard events based on the step value of SessionLog.START. """ warnings = [] self.stubs.Set(tf.logging, 'warn', warnings.append) gen = _EventGenerator(self) acc = ea.EventAccumulator(gen) gen.AddEvent(tf.Event(wall_time=0, step=0, file_version='brain.Event:1')) gen.AddScalarTensor('s1', wall_time=1, step=100, value=20) gen.AddScalarTensor('s2', wall_time=1, step=101, value=20) gen.AddScalarTensor('s1', wall_time=1, step=200, value=20) gen.AddScalarTensor('s2', wall_time=1, step=201, value=20) gen.AddScalarTensor('s1', wall_time=1, step=300, value=20) gen.AddScalarTensor('s2', wall_time=1, step=301, value=20) gen.AddScalarTensor('s1', wall_time=1, step=101, value=20) gen.AddScalarTensor('s3', wall_time=1, step=101, value=20) gen.AddScalarTensor('s1', wall_time=1, step=201, value=20) gen.AddScalarTensor('s1', wall_time=1, step=301, value=20) acc.Reload() ## Check that we have discarded 200 and 300 for s1 self.assertEqual([x.step for x in acc.Tensors('s1')], [100, 101, 201, 301]) ## Check that s1 discards do not affect s2 (written before out-of-order) ## or s3 (written after out-of-order). ## i.e. check that only events from the out of order tag are discarded self.assertEqual([x.step for x in acc.Tensors('s2')], [101, 201, 301]) self.assertEqual([x.step for x in acc.Tensors('s3')], [101])
def testSessionLogSummaries(self): data = [ { 'session_log': tf.SessionLog(status=tf.SessionLog.START), 'step': 0 }, { 'session_log': tf.SessionLog(status=tf.SessionLog.CHECKPOINT), 'step': 1 }, { 'session_log': tf.SessionLog(status=tf.SessionLog.CHECKPOINT), 'step': 2 }, { 'session_log': tf.SessionLog(status=tf.SessionLog.CHECKPOINT), 'step': 3 }, { 'session_log': tf.SessionLog(status=tf.SessionLog.STOP), 'step': 4 }, { 'session_log': tf.SessionLog(status=tf.SessionLog.START), 'step': 5 }, { 'session_log': tf.SessionLog(status=tf.SessionLog.STOP), 'step': 6 }, ] self._WriteScalarSummaries(data) units = efi.get_inspection_units(self.logdir) self.assertEqual(1, len(units)) printable = efi.get_dict_to_print(units[0].field_to_obs) self.assertEqual(printable['sessionlog:start']['steps'], [0, 5]) self.assertEqual(printable['sessionlog:stop']['steps'], [4, 6]) self.assertEqual(printable['sessionlog:checkpoint']['num_steps'], 3)
def testExpiredDataDiscardedAfterRestartForFileVersionLessThan2(self): """Tests that events are discarded after a restart is detected. If a step value is observed to be lower than what was previously seen, this should force a discard of all previous items with the same tag that are outdated. Only file versions < 2 use this out-of-order discard logic. Later versions discard events based on the step value of SessionLog.START. """ warnings = [] self.stubs.Set(tf.logging, 'warn', warnings.append) gen = _EventGenerator(self) acc = ea.EventAccumulator(gen) gen.AddEvent(tf.Event(wall_time=0, step=0, file_version='brain.Event:1')) gen.AddScalar('s1', wall_time=1, step=100, value=20) gen.AddScalar('s1', wall_time=1, step=200, value=20) gen.AddScalar('s1', wall_time=1, step=300, value=20) acc.Reload() ## Check that number of items are what they should be self.assertEqual([x.step for x in acc.Scalars('s1')], [100, 200, 300]) gen.AddScalar('s1', wall_time=1, step=101, value=20) gen.AddScalar('s1', wall_time=1, step=201, value=20) gen.AddScalar('s1', wall_time=1, step=301, value=20) acc.Reload() ## Check that we have discarded 200 and 300 from s1 self.assertEqual([x.step for x in acc.Scalars('s1')], [100, 101, 201, 301])
def train(): """ """ ckpt_source_path = tf.train.latest_checkpoint(FLAGS.ckpt_dir_path) xx_real = build_image_batch_reader( FLAGS.x_images_dir_path, FLAGS.batch_size) yy_real = build_image_batch_reader( FLAGS.y_images_dir_path, FLAGS.batch_size) image_pool = {} model = build_cycle_gan(xx_real, yy_real, '') summaries = build_summaries(model) reporter = tf.summary.FileWriter(FLAGS.logs_dir_path) with tf.Session() as session: session.run(tf.global_variables_initializer()) session.run(tf.local_variables_initializer()) if ckpt_source_path is not None: tf.train.Saver().restore(session, ckpt_source_path) # give up overlapped old data step = session.run(model['step']) reporter.add_session_log( tf.SessionLog(status=tf.SessionLog.START), global_step=step) # make dataset reader work coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) while train_one_step(model, summaries, image_pool, reporter): pass coord.request_stop() coord.join(threads)
