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)