def test_step_counter_every_n_secs(self):
with tf.Graph().as_default() as g, tf.Session() as sess:
global_step = tf.contrib.framework.get_or_create_global_step()
train_op = tf.assign_add(global_step, 1)
summary_writer = testing.FakeSummaryWriter(self.log_dir, g)
hook = tf.train.StepCounterHook(
summary_writer=summary_writer, every_n_steps=None, every_n_secs=0.1)
hook.begin()
sess.run(tf.global_variables_initializer())
mon_sess = monitored_session._HookedSession(sess, [hook])
mon_sess.run(train_op)
time.sleep(0.2)
mon_sess.run(train_op)
time.sleep(0.2)
mon_sess.run(train_op)
hook.end(sess)
summary_writer.assert_summaries(
test_case=self,
expected_logdir=self.log_dir,
expected_graph=g,
expected_summaries={})
self.assertTrue(summary_writer.summaries, 'No summaries were created.')
self.assertItemsEqual([2, 3], summary_writer.summaries.keys())
for summary in summary_writer.summaries.values():
summary_value = summary[0].value[0]
self.assertEqual('global_step/sec', summary_value.tag)
self.assertGreater(summary_value.simple_value, 0)
def test_global_step_name(self):
with tf.Graph().as_default() as g, tf.Session() as sess:
with tf.variable_scope('bar'):
foo_step = tf.get_variable('foo',
initializer=0,
trainable=False,
collections=[
tf.GraphKeys.GLOBAL_STEP,
tf.GraphKeys.GLOBAL_VARIABLES
])
train_op = tf.assign_add(foo_step, 1)
summary_writer = testing.FakeSummaryWriter(self.log_dir, g)
hook = tf.train.StepCounterHook(summary_writer=summary_writer,
every_n_steps=1,
every_n_secs=None)
hook.begin()
sess.run(tf.global_variables_initializer())
mon_sess = monitored_session._HookedSession(sess, [hook])
mon_sess.run(train_op)
mon_sess.run(train_op)
hook.end(sess)
summary_writer.assert_summaries(test_case=self,
expected_logdir=self.log_dir,
expected_graph=g,
expected_summaries={})
self.assertTrue(summary_writer.summaries,
'No summaries were created.')
self.assertItemsEqual([2], summary_writer.summaries.keys())
summary_value = summary_writer.summaries[2][0].value[0]
self.assertEqual('bar/foo/sec', summary_value.tag)
def setUp(self):
tf.test.TestCase.setUp(self)
self.log_dir = 'log/dir'
self.summary_writer = testing.FakeSummaryWriter(self.log_dir)
var = tf.Variable(0.0)
tensor = tf.assign_add(var, 1.0)
self.summary_op = tf.summary.scalar('my_summary', tensor)
global_step = tf.contrib.framework.get_or_create_global_step()
self.train_op = tf.assign_add(global_step, 1)
def test_summary_saver(self):
with ops.Graph().as_default() as g, self.test_session(g):
log_dir = 'log/dir'
summary_writer = testing.FakeSummaryWriter(log_dir, g)
var = variables.Variable(0.0)
var.initializer.run()
tensor = state_ops.assign_add(var, 1.0)
summary_op = summary.scalar('my_summary', tensor)
self._run_monitor(
learn.monitors.SummarySaver(
summary_op=summary_op,
save_steps=8,
summary_writer=summary_writer),
num_epochs=3,
num_steps_per_epoch=10)
summary_writer.assert_summaries(
test_case=self,
expected_logdir=log_dir,
expected_graph=g,
expected_summaries={
0: {
'my_summary': 1.0
},
1: {
'my_summary': 2.0
},
9: {
'my_summary': 3.0
},
17: {
'my_summary': 4.0
},
25: {
'my_summary': 5.0
},
29: {
'my_summary': 6.0
},
})
def test_step_counter_every_n_steps(self):
with tf.Graph().as_default() as g, tf.Session() as sess:
global_step = tf.contrib.framework.get_or_create_global_step()
train_op = tf.assign_add(global_step, 1)
summary_writer = testing.FakeSummaryWriter(self.log_dir, g)
hook = tf.train.StepCounterHook(summary_writer=summary_writer,
every_n_steps=10)
hook.begin()
sess.run(tf.initialize_all_variables())
mon_sess = monitored_session._HookedSession(sess, [hook])
for _ in range(30):
time.sleep(0.01)
mon_sess.run(train_op)
hook.end(sess)
summary_writer.assert_summaries(test_case=self,
expected_logdir=self.log_dir,
expected_graph=g,
expected_summaries={})
self.assertItemsEqual([11, 21], summary_writer.summaries.keys())
for step in [11, 21]:
summary_value = summary_writer.summaries[step][0].value[0]
self.assertEqual('global_step/sec', summary_value.tag)
self.assertGreater(summary_value.simple_value, 0)
def test_step_counter(self):
with tf.Graph().as_default() as g, tf.Session() as sess:
global_step = tf.contrib.framework.get_or_create_global_step()
train_op = tf.assign_add(global_step, 1)
summary_writer = testing.FakeSummaryWriter(self.log_dir, g)
hook = basic_session_run_hooks.StepCounterHook(
summary_writer=summary_writer, every_n_steps=10)
hook.begin()
sess.run(tf.initialize_all_variables())
mon_sess = monitored_session._HookedSession(sess, [hook])
for _ in range(30):
time.sleep(0.01)
mon_sess.run(train_op)
hook.end(sess)
summary_writer.assert_summaries(test_case=self,
expected_logdir=self.log_dir,
expected_graph=g,
expected_summaries={})
for step in [11, 21]:
summary_value = summary_writer.summaries[step][0].value[0]
self.assertTrue(summary_value.tag, 'global_step/sec')
# check at least 10 steps per sec is recorded.
self.assertGreater(summary_value.simple_value, 10)
def test_summary_saver(self):
with tf.Graph().as_default() as g, tf.Session() as sess:
log_dir = 'log/dir'
summary_writer = testing.FakeSummaryWriter(log_dir, g)
var = tf.Variable(0.0)
tensor = tf.assign_add(var, 1.0)
summary_op = tf.scalar_summary('my_summary', tensor)
global_step = tf.contrib.framework.get_or_create_global_step()
train_op = tf.assign_add(global_step, 1)
hook = basic_session_run_hooks.SummarySaverHook(
summary_op=summary_op,
save_steps=8,
summary_writer=summary_writer)
hook.begin()
sess.run(tf.initialize_all_variables())
mon_sess = monitored_session._HookedSession(sess, [hook])
for i in range(30):
_ = i
mon_sess.run(train_op)
hook.end(sess)
summary_writer.assert_summaries(test_case=self,
expected_logdir=log_dir,
expected_graph=g,
expected_summaries={
1: {
'my_summary': 1.0
},
9: {
'my_summary': 2.0
},
17: {
'my_summary': 3.0
},
25: {
'my_summary': 4.0
},
})
