def testChainIldjWithPlaceholder(self):
chain = Chain((Exp(), Exp()))
samples = array_ops.placeholder(
dtype=np.float32, shape=[None, 10], name="samples")
ildj = chain.inverse_log_det_jacobian(samples, event_ndims=0)
self.assertTrue(ildj is not None)
with self.test_session():
ildj.eval({samples: np.zeros([2, 10], np.float32)})
def testChainIldjWithPlaceholder(self):
chain = Chain((Exp(), Exp()))
samples = array_ops.placeholder(dtype=np.float32,
shape=[None, 10],
name="samples")
ildj = chain.inverse_log_det_jacobian(samples, event_ndims=0)
self.assertTrue(ildj is not None)
with self.cached_session():
ildj.eval({samples: np.zeros([2, 10], np.float32)})
def testBijectorIdentity(self):
with self.test_session():
chain = Chain()
self.assertEqual("identity", chain.name)
x = np.asarray([[[1., 2.],
[2., 3.]]])
self.assertAllClose(x, chain.forward(x).eval())
self.assertAllClose(x, chain.inverse(x).eval())
self.assertAllClose(0., chain.inverse_log_det_jacobian(x).eval())
self.assertAllClose(0., chain.forward_log_det_jacobian(x).eval())
def testBijector(self):
with self.test_session():
chain = Chain((Exp(event_ndims=1), Softplus(event_ndims=1)))
self.assertEqual("chain_of_exp_of_softplus", chain.name)
x = np.asarray([[[1., 2.],
[2., 3.]]])
self.assertAllClose(1. + np.exp(x), chain.forward(x).eval())
self.assertAllClose(np.log(x - 1.), chain.inverse(x).eval())
self.assertAllClose(
-np.sum(np.log(x - 1.), axis=2),
chain.inverse_log_det_jacobian(x).eval())
self.assertAllClose(
np.sum(x, axis=2), chain.forward_log_det_jacobian(x).eval())
def testChainAffineExp(self):
scale_diag = np.array([1., 2., 3.], dtype=np.float32)
chain = Chain([Affine(scale_diag=scale_diag), Exp()])
x = [0., np.log(2., dtype=np.float32), np.log(3., dtype=np.float32)]
y = [1., 4., 9.]
self.assertAllClose(y, self.evaluate(chain.forward(x)))
self.assertAllClose(x, self.evaluate(chain.inverse(y)))
self.assertAllClose(
np.log(6, dtype=np.float32) + np.sum(x),
self.evaluate(chain.forward_log_det_jacobian(x, event_ndims=1)))
self.assertAllClose(
-np.log(6, dtype=np.float32) - np.sum(x),
self.evaluate(chain.inverse_log_det_jacobian(y, event_ndims=1)))
def testShapeGetters(self):
with self.test_session():
chain = Chain([
SoftmaxCentered(validate_args=True),
SoftmaxCentered(validate_args=True),
])
x = tensor_shape.TensorShape([1])
y = tensor_shape.TensorShape([2 + 1])
self.assertAllEqual(y, chain.forward_event_shape(x))
self.assertAllEqual(
y.as_list(),
chain.forward_event_shape_tensor(x.as_list()).eval())
self.assertAllEqual(x, chain.inverse_event_shape(y))
self.assertAllEqual(
x.as_list(),
chain.inverse_event_shape_tensor(y.as_list()).eval())
def testScalarCongruency(self):
with self.cached_session():
chain = Chain((Exp(), Softplus()))
assert_scalar_congruency(chain,
lower_x=1e-3,
upper_x=1.5,
rtol=0.05)
def testMinEventNdimsShapeChangingAddRemoveDims(self):
chain = Chain(
[ShapeChanging(2, 1),
ShapeChanging(3, 0),
ShapeChanging(1, 2)])
self.assertEqual(4, chain.forward_min_event_ndims)
self.assertEqual(1, chain.inverse_min_event_ndims)
def testMinEventNdimsShapeChangingRemoveDims(self):
chain = Chain([ShapeChanging(3, 0)])
self.assertEqual(3, chain.forward_min_event_ndims)
self.assertEqual(0, chain.inverse_min_event_ndims)
chain = Chain([ShapeChanging(3, 0), Affine()])
self.assertEqual(3, chain.forward_min_event_ndims)
self.assertEqual(0, chain.inverse_min_event_ndims)
chain = Chain([Affine(), ShapeChanging(3, 0)])
self.assertEqual(4, chain.forward_min_event_ndims)
self.assertEqual(1, chain.inverse_min_event_ndims)
chain = Chain([ShapeChanging(3, 0), ShapeChanging(3, 0)])
self.assertEqual(6, chain.forward_min_event_ndims)
self.assertEqual(0, chain.inverse_min_event_ndims)
def testScalarCongruency(self):
with self.test_session():
bijector = Chain((Exp(), Softplus()))
assert_scalar_congruency(bijector,
lower_x=1e-3,
upper_x=1.5,
rtol=0.05)
def testShapeGetters(self):
with self.test_session():
bijector = Chain([
SoftmaxCentered(
event_ndims=1, validate_args=True),
SoftmaxCentered(
event_ndims=0, validate_args=True)
])
x = tensor_shape.TensorShape([])
y = tensor_shape.TensorShape([2 + 1])
self.assertAllEqual(y, bijector.forward_event_shape(x))
self.assertAllEqual(
y.as_list(),
bijector.forward_event_shape_tensor(x.as_list()).eval())
self.assertAllEqual(x, bijector.inverse_event_shape(y))
self.assertAllEqual(
x.as_list(),
bijector.inverse_event_shape_tensor(y.as_list()).eval())
def testMinEventNdimsChain(self):
chain = Chain([Exp(), Exp(), Exp()])
self.assertEqual(0, chain.forward_min_event_ndims)
self.assertEqual(0, chain.inverse_min_event_ndims)
chain = Chain([Affine(), Affine(), Affine()])
self.assertEqual(1, chain.forward_min_event_ndims)
self.assertEqual(1, chain.inverse_min_event_ndims)
chain = Chain([Exp(), Affine()])
self.assertEqual(1, chain.forward_min_event_ndims)
self.assertEqual(1, chain.inverse_min_event_ndims)
chain = Chain([Affine(), Exp()])
self.assertEqual(1, chain.forward_min_event_ndims)
self.assertEqual(1, chain.inverse_min_event_ndims)
chain = Chain([Affine(), Exp(), Softplus(), Affine()])
self.assertEqual(1, chain.forward_min_event_ndims)
self.assertEqual(1, chain.inverse_min_event_ndims)
def testBijectorIdentity(self):
with self.test_session():
chain = Chain()
self.assertEqual("identity", chain.name)
x = np.asarray([[[1., 2.], [2., 3.]]])
self.assertAllClose(x, chain.forward(x).eval())
self.assertAllClose(x, chain.inverse(x).eval())
self.assertAllClose(0., chain.inverse_log_det_jacobian(x).eval())
self.assertAllClose(0., chain.forward_log_det_jacobian(x).eval())
def testBijector(self):
with self.test_session():
chain = Chain((Exp(event_ndims=1), Softplus(event_ndims=1)))
self.assertEqual("chain_of_exp_of_softplus", chain.name)
x = np.asarray([[[1., 2.], [2., 3.]]])
self.assertAllClose(1. + np.exp(x), chain.forward(x).eval())
self.assertAllClose(np.log(x - 1.), chain.inverse(x).eval())
self.assertAllClose(-np.sum(np.log(x - 1.), axis=2),
chain.inverse_log_det_jacobian(x).eval())
self.assertAllClose(np.sum(x, axis=2),
chain.forward_log_det_jacobian(x).eval())
def testChainAffineExp(self):
scale_diag = np.array([1., 2., 3.], dtype=np.float32)
chain = Chain([Affine(scale_diag=scale_diag), Exp()])
x = [0., np.log(2., dtype=np.float32), np.log(3., dtype=np.float32)]
y = [1., 4., 9.]
self.assertAllClose(y, self.evaluate(chain.forward(x)))
self.assertAllClose(x, self.evaluate(chain.inverse(y)))
self.assertAllClose(
np.log(6, dtype=np.float32) + np.sum(x),
self.evaluate(chain.forward_log_det_jacobian(x, event_ndims=1)))
self.assertAllClose(
-np.log(6, dtype=np.float32) - np.sum(x),
self.evaluate(chain.inverse_log_det_jacobian(y, event_ndims=1)))
def testShapeGetters(self):
with self.cached_session():
chain = Chain([
SoftmaxCentered(validate_args=True),
SoftmaxCentered(validate_args=True),
])
x = tensor_shape.TensorShape([1])
y = tensor_shape.TensorShape([2 + 1])
self.assertAllEqual(y, chain.forward_event_shape(x))
self.assertAllEqual(
y.as_list(),
chain.forward_event_shape_tensor(x.as_list()).eval())
self.assertAllEqual(x, chain.inverse_event_shape(y))
self.assertAllEqual(
x.as_list(),
chain.inverse_event_shape_tensor(y.as_list()).eval())
def testShapeGetters(self):
with self.test_session():
bijector = Chain([
SoftmaxCentered(event_ndims=1, validate_args=True),
SoftmaxCentered(event_ndims=0, validate_args=True)
])
x = tensor_shape.TensorShape([])
y = tensor_shape.TensorShape([2 + 1])
self.assertAllEqual(y, bijector.forward_event_shape(x))
self.assertAllEqual(
y.as_list(),
bijector.forward_event_shape_tensor(x.as_list()).eval())
self.assertAllEqual(x, bijector.inverse_event_shape(y))
self.assertAllEqual(
x.as_list(),
bijector.inverse_event_shape_tensor(y.as_list()).eval())