def testJacobianConsistent(self):
bijector = tfb.IteratedSigmoidCentered()
x = tf.constant((60 * np.random.rand(10) - 30).reshape(5, 2))
jacobian_matrix = batch_jacobian(bijector.forward, x)
# In our case, y[-1] is determined by all the other y, so we can drop it
# for the jacobian calculation.
jacobian_matrix = jacobian_matrix[..., :-1, :]
self.assertAllClose(
tf.linalg.slogdet(jacobian_matrix).log_abs_determinant,
bijector.forward_log_det_jacobian(x, event_ndims=1),
atol=0.,
rtol=1e-7)
def testShapeGetters(self):
x = tf.TensorShape([4])
y = tf.TensorShape([5])
bijector = tfb.IteratedSigmoidCentered(validate_args=True)
self.assertAllEqual(y, bijector.forward_event_shape(x))
self.assertAllEqual(
y.as_list(),
self.evaluate(bijector.forward_event_shape_tensor(x.as_list())))
self.assertAllEqual(x, bijector.inverse_event_shape(y))
self.assertAllEqual(
x.as_list(),
self.evaluate(bijector.inverse_event_shape_tensor(y.as_list())))
def testBijectiveAndFinite(self):
iterated_sigmoid = tfb.IteratedSigmoidCentered()
# Grid of points in [-30, 30] x [-30, 30].
x = np.mgrid[-30:30:0.5, -30:30:0.5].reshape(2, -1).T # pylint: disable=invalid-slice-index
# Make y values on the simplex with a wide range.
y_0 = np.ones(x.shape[0], dtype=self.dtype)
y_1 = self.dtype(1e-3 * np.random.rand(x.shape[0]))
y_2 = self.dtype(1e1 * np.random.rand(x.shape[0]))
y = np.array([y_0, y_1, y_2])
y /= y.sum(axis=0)
y = y.T
bijector_test_util.assert_bijective_and_finite(
iterated_sigmoid, x, y, eval_func=self.evaluate, event_ndims=1)
def testJacobianConsistent(self):
with tf.GradientTape(persistent=True) as g:
x = tf.constant((60 * np.random.rand(10) - 30).reshape(5, 2))
g.watch(x)
bijector = tfb.IteratedSigmoidCentered()
y = bijector.forward(x)
jacobian_matrix = g.batch_jacobian(y, x, experimental_use_pfor=False)
# In our case, y[-1] is determined by all the other y, so we can drop it
# for the jacobian calculation.
jacobian_matrix = jacobian_matrix[..., :-1, :]
self.assertAllClose(tf.linalg.slogdet(jacobian_matrix)[1],
bijector.forward_log_det_jacobian(x,
event_ndims=1),
atol=0.,
rtol=1e-7)
def testBijectorVector(self):
iterated_sigmoid = tfb.IteratedSigmoidCentered()
self.assertStartsWith(iterated_sigmoid.name, "iterated_sigmoid")
x = self.dtype([[0., 0., 0.], -np.log([1 / 3., 1 / 2., 1.])])
y = self.dtype([[0.25, 0.25, 0.25, 0.25], [0.5, 0.25, 0.125, 0.125]])
self.assertAllClose(y, self.evaluate(iterated_sigmoid.forward(x)))
self.assertAllClose(x, self.evaluate(iterated_sigmoid.inverse(y)))
self.assertAllClose(-np.sum(np.log(y), axis=1),
self.evaluate(
iterated_sigmoid.inverse_log_det_jacobian(
y, event_ndims=1)),
atol=0.,
rtol=1e-7)
self.assertAllClose(self.evaluate(
-iterated_sigmoid.inverse_log_det_jacobian(y, event_ndims=1)),
self.evaluate(
iterated_sigmoid.forward_log_det_jacobian(
x, event_ndims=1)),
atol=0.,
rtol=1e-7)
def testBijectorUnknownShape(self):
iterated_sigmoid = tfb.IteratedSigmoidCentered()
self.assertStartsWith(iterated_sigmoid.name, "iterated_sigmoid")
x_ = self.dtype([[0., 0., 0.], -np.log([1 / 3., 1 / 2., 1.])])
y_ = self.dtype([[0.25, 0.25, 0.25, 0.25], [0.5, 0.25, 0.125, 0.125]])
x = tf1.placeholder_with_default(x_, shape=[2, None])
y = tf1.placeholder_with_default(y_, shape=[2, None])
self.assertAllClose(y_, self.evaluate(iterated_sigmoid.forward(x)))
self.assertAllClose(x_, self.evaluate(iterated_sigmoid.inverse(y)))
self.assertAllClose(-np.sum(np.log(y_), axis=1),
self.evaluate(
iterated_sigmoid.inverse_log_det_jacobian(
y, event_ndims=1)),
atol=0.,
rtol=1e-7)
self.assertAllClose(-self.evaluate(
iterated_sigmoid.inverse_log_det_jacobian(y, event_ndims=1)),
self.evaluate(
iterated_sigmoid.forward_log_det_jacobian(
x, event_ndims=1)),
atol=0.,
rtol=1e-7)
