def testEventNdimsLargerThanOneRaises(self): with self.test_session(): mu = [1., -1] with self.assertRaisesRegexp( ValueError, (r"event_ndims\(2\) was not 0 or 1")): # Scale corresponds to 2x2 identity matrix. bijector = Affine(shift=mu, event_ndims=2, validate_args=True) bijector.forward([1., 1.]).eval()
def testNoBatchMultivariateRaisesWhenSingular(self): with self.test_session(): mu = [1., -1] bijector = Affine( shift=mu, # Has zero on the diagonal. scale_diag=[0., 1], validate_args=True) with self.assertRaisesOpError("Condition x > 0"): bijector.forward([1., 1.]).eval()
def testNoBatchMultivariateRaisesWhenSingular(self): with self.test_session(): mu = [1., -1] bijector = Affine( shift=mu, # Has zero on the diagonal. scale_diag=[0., 1], validate_args=True) with self.assertRaisesOpError("diagonal part must be non-zero"): bijector.forward([1., 1.]).eval()
def testScaleZeroScalarRaises(self): with self.test_session(): mu = -1. # Check Identity matrix with zero scaling. bijector = Affine( shift=mu, scale_identity_multiplier=0., event_ndims=0, validate_args=True) with self.assertRaisesOpError("identity_multiplier should be non-zero"): bijector.forward(1.).eval()
def testBatchMultivariateFullDynamic(self): with self.test_session() as sess: x = array_ops.placeholder(dtypes.float32, name="x") mu = array_ops.placeholder(dtypes.float32, name="mu") scale_diag = array_ops.placeholder(dtypes.float32, name="scale_diag") event_ndims = array_ops.placeholder(dtypes.int32, name="event_ndims") x_value = np.array([[[1., 1]]], dtype=np.float32) mu_value = np.array([[1., -1]], dtype=np.float32) scale_diag_value = np.array([[2., 2]], dtype=np.float32) event_ndims_value = 1 feed_dict = { x: x_value, mu: mu_value, scale_diag: scale_diag_value, event_ndims: event_ndims_value } bijector = Affine(shift=mu, scale_diag=scale_diag, event_ndims=event_ndims) self.assertEqual(1, sess.run(bijector.event_ndims, feed_dict)) self.assertAllClose([[[3., 1]]], sess.run(bijector.forward(x), feed_dict)) self.assertAllClose([[[0., 1]]], sess.run(bijector.inverse(x), feed_dict)) self.assertAllClose([-np.log(4)], sess.run(bijector.inverse_log_det_jacobian(x), feed_dict))
def testCompareToBijector(self): """Demonstrates equivalence between TD, Bijector approach and AR dist.""" sample_shape = np.int32([4, 5]) batch_shape = np.int32([]) event_size = np.int32(2) with self.cached_session() as sess: batch_event_shape = np.concatenate([batch_shape, [event_size]], axis=0) sample0 = array_ops.zeros(batch_event_shape) affine = Affine(scale_tril=self._random_scale_tril(event_size)) ar = autoregressive_lib.Autoregressive(self._normal_fn(affine), sample0, validate_args=True) ar_flow = MaskedAutoregressiveFlow(is_constant_jacobian=True, shift_and_log_scale_fn=lambda x: [None, affine.forward(x)], validate_args=True) td = transformed_distribution_lib.TransformedDistribution( distribution=normal_lib.Normal(loc=0., scale=1.), bijector=ar_flow, event_shape=[event_size], batch_shape=batch_shape, validate_args=True) x_shape = np.concatenate([sample_shape, batch_shape, [event_size]], axis=0) x = 2. * self._rng.random_sample(x_shape).astype(np.float32) - 1. td_log_prob_, ar_log_prob_ = sess.run( [td.log_prob(x), ar.log_prob(x)]) self.assertAllClose(td_log_prob_, ar_log_prob_, atol=0., rtol=1e-6)
def testNoBatchMultivariateFullDynamic(self): with self.cached_session() as sess: x = array_ops.placeholder(dtypes.float32, name="x") mu = array_ops.placeholder(dtypes.float32, name="mu") scale_diag = array_ops.placeholder(dtypes.float32, name="scale_diag") x_value = np.array([[1., 1]], dtype=np.float32) mu_value = np.array([1., -1], dtype=np.float32) scale_diag_value = np.array([2., 2], dtype=np.float32) feed_dict = { x: x_value, mu: mu_value, scale_diag: scale_diag_value, } bijector = Affine(shift=mu, scale_diag=scale_diag) self.assertAllClose([[3., 1]], sess.run(bijector.forward(x), feed_dict)) self.assertAllClose([[0., 1]], sess.run(bijector.inverse(x), feed_dict)) self.assertAllClose( -np.log(4), sess.run(bijector.inverse_log_det_jacobian(x, event_ndims=1), feed_dict))
def testBatchMultivariateFullDynamic(self): with self.test_session() as sess: x = array_ops.placeholder(dtypes.float32, name="x") mu = array_ops.placeholder(dtypes.float32, name="mu") scale_diag = array_ops.placeholder(dtypes.float32, name="scale_diag") event_ndims = array_ops.placeholder(dtypes.int32, name="event_ndims") x_value = np.array([[[1., 1]]], dtype=np.float32) mu_value = np.array([[1., -1]], dtype=np.float32) scale_diag_value = np.array([[2., 2]], dtype=np.float32) event_ndims_value = 1 feed_dict = { x: x_value, mu: mu_value, scale_diag: scale_diag_value, event_ndims: event_ndims_value } bijector = Affine( shift=mu, scale_diag=scale_diag, event_ndims=event_ndims) self.assertEqual(1, sess.run(bijector.event_ndims, feed_dict)) self.assertAllClose([[[3., 1]]], sess.run(bijector.forward(x), feed_dict)) self.assertAllClose([[[0., 1]]], sess.run(bijector.inverse(x), feed_dict)) self.assertAllClose([-np.log(4)], sess.run( bijector.inverse_log_det_jacobian(x), feed_dict))
def testCompareToBijector(self): """Demonstrates equivalence between TD, Bijector approach and AR dist.""" sample_shape = np.int32([4, 5]) batch_shape = np.int32([]) event_size = np.int32(2) with self.test_session() as sess: batch_event_shape = np.concatenate([batch_shape, [event_size]], axis=0) sample0 = array_ops.zeros(batch_event_shape) affine = Affine(scale_tril=self._random_scale_tril(event_size)) ar = autoregressive_lib.Autoregressive( self._normal_fn(affine), sample0, validate_args=True) ar_flow = MaskedAutoregressiveFlow( is_constant_jacobian=True, shift_and_log_scale_fn=lambda x: [None, affine.forward(x)], validate_args=True) td = transformed_distribution_lib.TransformedDistribution( distribution=normal_lib.Normal(loc=0., scale=1.), bijector=ar_flow, event_shape=[event_size], batch_shape=batch_shape, validate_args=True) x_shape = np.concatenate( [sample_shape, batch_shape, [event_size]], axis=0) x = 2. * self._rng.random_sample(x_shape).astype(np.float32) - 1. td_log_prob_, ar_log_prob_ = sess.run([td.log_prob(x), ar.log_prob(x)]) self.assertAllClose(td_log_prob_, ar_log_prob_, atol=0., rtol=1e-6)
def testScaleZeroScalarRaises(self): with self.test_session(): mu = -1. # Check Identity matrix with zero scaling. bijector = Affine( shift=mu, scale_identity_multiplier=0.0, event_ndims=0, validate_args=True) with self.assertRaisesOpError("Condition x > 0"): bijector.forward(1.).eval() # Check Diag matrix with zero scaling. bijector = Affine( shift=mu, scale_diag=[0.0], event_ndims=0, validate_args=True) with self.assertRaisesOpError("Condition x > 0"): bijector.forward(1.).eval()
def testScaleZeroScalarRaises(self): with self.test_session(): mu = -1. # Check Identity matrix with zero scaling. bijector = Affine(shift=mu, scale_identity_multiplier=0.0, event_ndims=0, validate_args=True) with self.assertRaisesOpError("Condition x > 0"): bijector.forward(1.).eval() # Check Diag matrix with zero scaling. bijector = Affine(shift=mu, scale_diag=[0.0], event_ndims=0, validate_args=True) with self.assertRaisesOpError("Condition x > 0"): bijector.forward(1.).eval()
def _testLegalInputs(self, shift=None, scale_params=None, x=None): def _powerset(x): s = list(x) return itertools.chain.from_iterable( itertools.combinations(s, r) for r in range(len(s) + 1)) for args in _powerset(scale_params.items()): with self.test_session(): args = dict(args) scale_args = dict({"x": x}, **args) scale = self._makeScale(**scale_args) # We haven't specified enough information for the scale. if scale is None: with self.assertRaisesRegexp(ValueError, ("must be specified.")): bijector = Affine(shift=shift, **args) else: bijector = Affine(shift=shift, **args) np_x = x # For the case a vector is passed in, we need to make the shape # match the matrix for matmul to work. if x.ndim == scale.ndim - 1: np_x = np.expand_dims(x, axis=-1) forward = np.matmul(scale, np_x) + shift if x.ndim == scale.ndim - 1: forward = np.squeeze(forward, axis=-1) self.assertAllClose(forward, bijector.forward(x).eval()) backward = np.linalg.solve(scale, np_x - shift) if x.ndim == scale.ndim - 1: backward = np.squeeze(backward, axis=-1) self.assertAllClose(backward, bijector.inverse(x).eval()) scale *= np.ones(shape=x.shape[:-1], dtype=scale.dtype) ildj = -np.log(np.abs(np.linalg.det(scale))) # TODO (jvdillon): We need to make it so the scale_identity_multiplier id:1102 # https://github.com/imdone/tensorflow/issues/1103 # case does not deviate in expected shape. Fixing this will get rid of # these special cases. if (ildj.ndim > 0 and (len(scale_args) == 1 or (len(scale_args) == 2 and scale_args.get( "scale_identity_multiplier", None) is not None))): ildj = np.squeeze(ildj[0]) elif ildj.ndim < scale.ndim - 2: ildj = np.reshape(ildj, scale.shape[0:-2]) self.assertAllClose( ildj, bijector.inverse_log_det_jacobian( x, event_ndims=1).eval())
def _testLegalInputs(self, shift=None, scale_params=None, x=None): def _powerset(x): s = list(x) return itertools.chain.from_iterable( itertools.combinations(s, r) for r in range(len(s) + 1)) for args in _powerset(scale_params.items()): with self.test_session(): args = dict(args) scale_args = dict({"x": x}, **args) scale = self._makeScale(**scale_args) bijector_args = dict({"event_ndims": 1}, **args) # We haven't specified enough information for the scale. if scale is None: with self.assertRaisesRegexp(ValueError, ("must be specified.")): bijector = Affine(shift=shift, **bijector_args) else: bijector = Affine(shift=shift, **bijector_args) np_x = x # For the case a vector is passed in, we need to make the shape # match the matrix for matmul to work. if x.ndim == scale.ndim - 1: np_x = np.expand_dims(x, axis=-1) forward = np.matmul(scale, np_x) + shift if x.ndim == scale.ndim - 1: forward = np.squeeze(forward, axis=-1) self.assertAllClose(forward, bijector.forward(x).eval()) backward = np.linalg.solve(scale, np_x - shift) if x.ndim == scale.ndim - 1: backward = np.squeeze(backward, axis=-1) self.assertAllClose(backward, bijector.inverse(x).eval()) ildj = -np.log(np.abs(np.linalg.det(scale))) # TODO(jvdillon): We need to make it so the scale_identity_multiplier # case does not deviate in expected shape. Fixing this will get rid of # these special cases. if (ildj.ndim > 0 and (len(scale_args) == 1 or ( len(scale_args) == 2 and scale_args.get("scale_identity_multiplier", None) is not None))): ildj = np.squeeze(ildj[0]) elif ildj.ndim < scale.ndim - 2: ildj = np.reshape(ildj, scale.shape[0:-2]) self.assertAllClose(ildj, bijector.inverse_log_det_jacobian(x).eval())
def testNoBatchMultivariateFullDynamic(self): with self.test_session() as sess: x = array_ops.placeholder(dtypes.float32, name="x") mu = array_ops.placeholder(dtypes.float32, name="mu") scale_diag = array_ops.placeholder(dtypes.float32, name="scale_diag") x_value = np.array([[1., 1]], dtype=np.float32) mu_value = np.array([1., -1], dtype=np.float32) scale_diag_value = np.array([2., 2], dtype=np.float32) feed_dict = { x: x_value, mu: mu_value, scale_diag: scale_diag_value, } bijector = Affine(shift=mu, scale_diag=scale_diag) self.assertAllClose([[3., 1]], sess.run(bijector.forward(x), feed_dict)) self.assertAllClose([[0., 1]], sess.run(bijector.inverse(x), feed_dict)) self.assertAllClose( -np.log(4), sess.run(bijector.inverse_log_det_jacobian(x), feed_dict))
def testEventNdimsLargerThanOneRaises(self): with self.test_session(): mu = [1., -1] # Scale corresponds to 2x2 identity matrix. bijector = Affine(shift=mu, event_ndims=2, validate_args=True) bijector.forward([1., 1.]).eval()
def testEventNdimsLargerThanOneRaises(self): with self.test_session(): mu = [1., -1] # Scale corresponds to 2x2 identity matrix. bijector = Affine(shift=mu, event_ndims=2, validate_args=True) bijector.forward([1., 1.]).eval()