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 test_average_score_over_phases3(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)
loop.add_phase('phase_2',
done=True,
score=3,
summary='',
steps=1,
report_every=5)
# Score: 1 2 2 3 1 2 2 3 1 2 2 3 1 2 2 1 2 2 1 2
# Report 1: x x x
# Report 2: x x
# Report 3:
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], scores)
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)
# Step: 0 1 2 3 4 5 6 7 8 9
# Score: 0 1 2 3 4 5 6 7 8 9 10 11
# Done: x x x x x x
# Report: x x x x
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
scores = list(loop.run(sess, saver=None, max_step=12))
self.assertAllEqual([1, 4, 7, 10], scores)
def test_report_every_step(self):
step = tf.Variable(0, trainable=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_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 8
# Score 1: 0 2 4 6
# Done 1: x x
# Score 2: 0 4 16 32
# Done 2: x x
# Report : 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)