def test_phases_feed(self):
score = tf.placeholder(tf.float32, [])
loop = tools.Loop(None)
loop.add_phase('phase_1',
done=True,
score=score,
summary='',
steps=1,
report_every=1,
log_every=None,
checkpoint_every=None,
feed={score: 1})
loop.add_phase('phase_2',
done=True,
score=score,
summary='',
steps=3,
report_every=1,
log_every=None,
checkpoint_every=None,
feed={score: 2})
loop.add_phase('phase_3',
done=True,
score=score,
summary='',
steps=2,
report_every=1,
log_every=None,
checkpoint_every=None,
feed={score: 3})
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
scores = list(loop.run(sess, saver=None, max_step=15))
self.assertAllEqual([1, 2, 2, 2, 3, 3, 1, 2, 2, 2, 3, 3, 1, 2, 2],
scores)
def initialize_variables(sess, saver, logdir, checkpoint=None, resume=None):
"""Initialize or restore variables from a checkpoint if available.
Args:
sess: Session to initialize variables in.
saver: Saver to restore variables.
logdir: Directory to search for checkpoints.
checkpoint: Specify what checkpoint name to use; defaults to most recent.
resume: Whether to expect recovering a checkpoint or starting a new run.
Raises:
ValueError: If resume expected but no log directory specified.
RuntimeError: If no resume expected but a checkpoint was found.
"""
sess.run(
tf.group(tf.local_variables_initializer(),
tf.global_variables_initializer()))
if resume and not (logdir or checkpoint):
raise ValueError('Need to specify logdir to resume a checkpoint.')
if logdir:
state = tf.train.get_checkpoint_state(logdir)
if checkpoint:
checkpoint = os.path.join(logdir, checkpoint)
if not checkpoint and state and state.model_checkpoint_path:
checkpoint = state.model_checkpoint_path
if checkpoint and resume is False:
message = 'Found unexpected checkpoint when starting a new run.'
raise RuntimeError(message)
if checkpoint:
saver.restore(sess, checkpoint)
def test_done_automatic(self):
batch_env = self._create_test_batch_env((1, 2, 3, 4))
algo = tools.MockAlgorithm(batch_env)
done, _, _ = tools.simulate(batch_env, algo, log=False, reset=False)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
self.assertAllEqual([True, False, False, False], sess.run(done))
self.assertAllEqual([True, True, False, False], sess.run(done))
self.assertAllEqual([True, False, True, False], sess.run(done))
self.assertAllEqual([True, True, False, True], sess.run(done))
def test_reset_automatic(self):
batch_env = self._create_test_batch_env((1, 2, 3, 4))
algo = tools.MockAlgorithm(batch_env)
done, _, _ = tools.simulate(batch_env, algo, log=False, reset=False)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(10):
sess.run(done)
self.assertAllEqual([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], batch_env[0].steps)
self.assertAllEqual([2, 2, 2, 2, 2], batch_env[1].steps)
self.assertAllEqual([3, 3, 3, 1], batch_env[2].steps)
self.assertAllEqual([4, 4, 2], batch_env[3].steps)
def test_done_forced(self):
reset = tf.placeholder_with_default(False, ())
batch_env = self._create_test_batch_env((2, 4))
algo = tools.MockAlgorithm(batch_env)
done, _, _ = tools.simulate(batch_env, algo, False, reset)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
self.assertAllEqual([False, False], sess.run(done))
self.assertAllEqual([False, False], sess.run(done, {reset: True}))
self.assertAllEqual([True, False], sess.run(done))
self.assertAllEqual([False, False], sess.run(done, {reset: True}))
self.assertAllEqual([True, False], sess.run(done))
self.assertAllEqual([False, False], sess.run(done))
self.assertAllEqual([True, True], sess.run(done))
def test_not_done(self):
step = tf.Variable(0, False, dtype=tf.int32, name='step')
done = tf.equal((step + 1) % 2, 0)
score = tf.cast(step, tf.float32)
loop = tools.Loop(None, step)
loop.add_phase('phase_1',
done,
score,
summary='',
steps=1,
report_every=3)
# Score: 0 1 2 3 4 5 6 7 8
# Done: x x x x
# Report: x x x
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
scores = list(loop.run(sess, saver=None, max_step=9))
self.assertAllEqual([1, 4, 7], scores)
def test_report_every_step(self):
step = tf.Variable(0, False, dtype=tf.int32, name='step')
loop = tools.Loop(None, step)
loop.add_phase('phase_1',
done=True,
score=0,
summary='',
steps=1,
report_every=3)
# Step: 0 1 2 3 4 5 6 7 8
# Report: x x x
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
scores = loop.run(sess, saver=None, max_step=9)
next(scores)
self.assertEqual(3, sess.run(step))
next(scores)
self.assertEqual(6, sess.run(step))
next(scores)
self.assertEqual(9, sess.run(step))
def test_average_score_over_phases(self):
loop = tools.Loop(None)
loop.add_phase('phase_1',
done=True,
score=1,
summary='',
steps=1,
report_every=2)
loop.add_phase('phase_2',
done=True,
score=2,
summary='',
steps=2,
report_every=5)
# Score: 1 2 2 1 2 2 1 2 2 1 2 2 1 2 2 1 2
# Report 1: x x x
# Report 2: x x
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
scores = list(loop.run(sess, saver=None, max_step=17))
self.assertAllEqual([1, 2, 1, 2, 1], scores)
def test_not_done_batch(self):
step = tf.Variable(0, False, dtype=tf.int32, name='step')
done = tf.equal([step % 3, step % 4], 0)
score = tf.cast([step, step**2], tf.float32)
loop = tools.Loop(None, step)
loop.add_phase('phase_1',
done,
score,
summary='',
steps=1,
report_every=8)
# Step: 0 2 4 6
# Score 1: 0 2 4 6
# Done 1: x x
# Score 2: 0 4 16 32
# Done 2: x x
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
scores = list(loop.run(sess, saver=None, max_step=8))
self.assertEqual(8, sess.run(step))
self.assertAllEqual([(0 + 0 + 16 + 6) / 4], scores)
def _initialize_vars(self):
self.sess.run(tf.global_variables_initializer())
return