def test_random_normal(self, mock_stateless_random_normal):
_ = dynamics.random_normal(shape=[3, 1], i=41 / 5, key=9)
_, call_args = mock_stateless_random_normal.call_args
assert_ops = [
tf.assert_equal(tf.stack([9, 8]), call_args['seed']),
tf.assert_equal([3, 1], call_args['shape'])
]
with self.session() as sess:
sess.run(assert_ops)
def test_antithetic_normal_lowers_variance(self):
shape = [512]
num_trials = 128
key_ph = tf.placeholder(shape=(), dtype=tf.int32)
normal_samples = dynamics.random_normal(shape, key=key_ph)
antithetic_normal_samples = dynamics.random_antithetic_normal(
shape, key=key_ph)
mean_estimator = tf.reduce_mean(normal_samples)
antithetic_mean_estimator = tf.reduce_mean(antithetic_normal_samples)
mean_estimates = []
antithetic_mean_estimates = []
with self.session() as session:
for i in range(num_trials):
mean_estimates.append(
session.run(mean_estimator, feed_dict={key_ph: i}))
antithetic_mean_estimates.append(
session.run(antithetic_mean_estimator, feed_dict={key_ph: i}))
self.assertLessEqual(
np.std(antithetic_mean_estimates), np.std(mean_estimates))
def _dynamics_op(unused_s, t, dt):
return dynamics.random_normal([num_samples], t, dt)
def test_random_normal(self, mock_stateless_random_normal):
_ = dynamics.random_normal(shape=[3, 1], i=41 / 5, key=9)
_, call_args = mock_stateless_random_normal.call_args
tf.assert_equal(tf.stack([9, 8]), call_args['seed']).mark_used()
tf.assert_equal([3, 1], call_args['shape']).mark_used()