def test_natural_gradient(self):
"""
Test random natural gradient cases.
"""
with tf.Graph().as_default():
with tf.Session() as sess:
for size in range(3, 9):
dist = NaturalSoftmax(size, epsilon=0)
softmax = CategoricalSoftmax(size)
param_row = tf.constant(np.random.normal(size=(size, )),
dtype=tf.float64)
params = tf.stack([param_row])
one_hot = np.zeros((1, size))
one_hot[0, 1] = 1
samples = tf.constant(one_hot, dtype=tf.float64)
kl_div = softmax.kl_divergence(tf.stop_gradient(params),
params)
hessian = sess.run(tf.hessians(kl_div, param_row)[0])
gradient = sess.run(
tf.gradients(softmax.log_prob(params, samples),
params)[0][0])
expected = np.matmul(np.array([gradient]),
np.linalg.pinv(hessian))[0]
actual = sess.run(
tf.gradients(dist.log_prob(params, samples),
params)[0][0])
self.assertTrue(np.allclose(actual, expected))
def test_cat_softmax_generic():
"""
Run generic tests for CategoricalSoftmax.
"""
dist = CategoricalSoftmax(7, low=2)
tester = DistributionTester(dist)
tester.test_all()
def test_generic(self):
"""
Run generic tests with DistributionTester.
"""
dist = CategoricalSoftmax(7, low=2)
tester = DistributionTester(self, dist)
tester.test_all()
def test_nat_softmax_log_prob():
"""
Test log probabilities of NaturalSoftmax.
"""
with tf.Graph().as_default():
with tf.Session() as sess:
dist = NaturalSoftmax(7)
params = tf.constant(np.random.normal(size=(15, 7)), dtype=tf.float64)
sampled = tf.one_hot([random.randrange(7) for _ in range(15)], 7,
dtype=tf.float64)
actual = sess.run(dist.log_prob(params, sampled))
expected = sess.run(CategoricalSoftmax(7).log_prob(params, sampled))
assert np.allclose(actual, expected)