def testNonSquareMatrix(self):
with self.assertRaises(ValueError):
linalg_ops.cholesky(np.array([[1., 2., 3.], [3., 4., 5.]]))
with self.assertRaises(ValueError):
linalg_ops.cholesky(
np.array([[[1., 2., 3.], [3., 4., 5.]], [[1., 2., 3.], [3., 4., 5.]]
]))
def benchmarkCholeskyOp(self):
for shape in self.shapes:
with ops.Graph().as_default(), \
session.Session() as sess, \
ops.device("/cpu:0"):
matrix = variables.Variable(self._GenerateMatrix(shape))
l = linalg_ops.cholesky(matrix)
variables.global_variables_initializer().run()
self.run_op_benchmark(
sess,
control_flow_ops.group(
l,),
min_iters=25,
name="cholesky_cpu_{shape}".format(shape=shape))
if test.is_gpu_available(True):
with ops.Graph().as_default(), \
session.Session() as sess, \
ops.device("/device:GPU:0"):
matrix = variables.Variable(self._GenerateMatrix(shape))
l = linalg_ops.cholesky(matrix)
variables.global_variables_initializer().run()
self.run_op_benchmark(
sess,
control_flow_ops.group(
l,),
min_iters=25,
name="cholesky_gpu_{shape}".format(shape=shape))
def testWrongDimensions(self):
for dtype in self.float_types:
tensor3 = constant_op.constant([1., 2.], dtype=dtype)
with self.assertRaises(ValueError):
linalg_ops.cholesky(tensor3)
with self.assertRaises(ValueError):
linalg_ops.cholesky(tensor3)
def testNonSquareMatrix(self):
for dtype in self.float_types:
with self.assertRaises(ValueError):
linalg_ops.cholesky(np.array([[1., 2., 3.], [3., 4., 5.]], dtype=dtype))
with self.assertRaises(ValueError):
linalg_ops.cholesky(
np.array(
[[[1., 2., 3.], [3., 4., 5.]], [[1., 2., 3.], [3., 4., 5.]]],
dtype=dtype))
def testConcurrentExecutesWithoutError(self):
with self.test_session(use_gpu=True) as sess:
matrix1 = random_ops.random_normal([5, 5], seed=42)
matrix2 = random_ops.random_normal([5, 5], seed=42)
matrix1 = math_ops.matmul(matrix1, matrix1, adjoint_a=True)
matrix2 = math_ops.matmul(matrix2, matrix2, adjoint_a=True)
c1 = linalg_ops.cholesky(matrix1)
c2 = linalg_ops.cholesky(matrix2)
c1_val, c2_val = sess.run([c1, c2])
self.assertAllEqual(c1_val, c2_val)
def _variance(self):
x = math_ops.sqrt(self.df) * self._square_scale_operator()
d = array_ops.expand_dims(array_ops.matrix_diag_part(x), -1)
v = math_ops.square(x) + math_ops.matmul(d, d, adjoint_b=True)
if self.cholesky_input_output_matrices:
return linalg_ops.cholesky(v)
return v
def _overdetermined(op, grad):
"""Gradients for the overdetermined case of MatrixSolveLs.
This is the backprop for the solution to the normal equations of the first
kind:
X = F(A, B) = (A^T * A + lambda * I)^{-1} * A^T * B
which solve the least squares problem
min ||A * X - B||_F^2 + lambda ||X||_F^2.
"""
a = op.inputs[0]
b = op.inputs[1]
l2_regularizer = op.inputs[2]
x = op.outputs[0]
a_shape = array_ops.shape(a)
batch_shape = a_shape[:-2]
n = a_shape[-1]
identity = linalg_ops.eye(n, batch_shape=batch_shape, dtype=a.dtype)
gramian = math_ops.batch_matmul(
a, a, adj_x=True) + l2_regularizer * identity
chol = linalg_ops.cholesky(gramian)
# Temporary z = (A^T * A + lambda * I)^{-1} * grad.
z = linalg_ops.cholesky_solve(chol, grad)
xzt = math_ops.batch_matmul(x, z, adj_y=True)
zx_sym = xzt + array_ops.matrix_transpose(xzt)
grad_a = -math_ops.batch_matmul(a, zx_sym) + math_ops.batch_matmul(
b, z, adj_y=True)
grad_b = math_ops.batch_matmul(a, z)
return (grad_a, grad_b, None)
def _underdetermined(op, grad):
"""Gradients for the underdetermined case of MatrixSolveLs.
This is the backprop for the solution to the normal equations of the second
kind:
X = F(A, B) = A * (A*A^T + lambda*I)^{-1} * B
that (for lambda=0) solve the least squares problem
min ||X||_F subject to A*X = B.
"""
a = op.inputs[0]
b = op.inputs[1]
l2_regularizer = op.inputs[2]
a_shape = array_ops.shape(a)
batch_shape = a_shape[:-2]
m = a_shape[-2]
identity = linalg_ops.eye(m, batch_shape=batch_shape, dtype=a.dtype)
gramian = math_ops.batch_matmul(
a, a, adj_y=True) + l2_regularizer * identity
chol = linalg_ops.cholesky(gramian)
grad_b = linalg_ops.cholesky_solve(chol, math_ops.batch_matmul(a, grad))
# Temporary z = (A * A^T + lambda * I)^{-1} * B.
z = linalg_ops.cholesky_solve(chol, b)
bz = -math_ops.batch_matmul(grad_b, z, adj_y=True)
bz_sym = bz + array_ops.matrix_transpose(bz)
grad_a = math_ops.batch_matmul(bz_sym, a) + math_ops.batch_matmul(z, grad)
return (grad_a, grad_b, None)
def _verifyCholesky(self, x):
# Verify that LL^T == x.
chol = linalg_ops.cholesky(x)
verification = test_util.matmul_without_tf32(chol,
chol,
adjoint_b=True)
self._verifyCholeskyBase(x, chol, verification)
def _underdetermined(op, grad):
"""Gradients for the underdetermined case of MatrixSolveLs.
This is the backprop for the solution to the normal equations of the second
kind:
X = F(A, B) = A * (A*A^T + lambda*I)^{-1} * B
that (for lambda=0) solve the least squares problem
min ||X||_F subject to A*X = B.
"""
a = op.inputs[0]
b = op.inputs[1]
l2_regularizer = math_ops.cast(op.inputs[2], a.dtype.base_dtype)
a_shape = array_ops.shape(a)
batch_shape = a_shape[:-2]
m = a_shape[-2]
identity = linalg_ops.eye(m, batch_shape=batch_shape, dtype=a.dtype)
gramian = math_ops.batch_matmul(a, a,
adj_y=True) + l2_regularizer * identity
chol = linalg_ops.cholesky(gramian)
grad_b = linalg_ops.cholesky_solve(chol,
math_ops.batch_matmul(a, grad))
# Temporary tmp = (A * A^T + lambda * I)^{-1} * B.
tmp = linalg_ops.cholesky_solve(chol, b)
a1 = math_ops.batch_matmul(tmp, a, adj_x=True)
a1 = -math_ops.batch_matmul(grad_b, a1)
a2 = grad - math_ops.batch_matmul(a, grad_b, adj_x=True)
a2 = math_ops.batch_matmul(tmp, a2, adj_y=True)
grad_a = a1 + a2
return (grad_a, grad_b, None)
def _overdetermined(op, grad):
"""Gradients for the overdetermined case of MatrixSolveLs.
This is the backprop for the solution to the normal equations of the first
kind:
X = F(A, B) = (A^T * A + lambda * I)^{-1} * A^T * B
which solve the least squares problem
min ||A * X - B||_F^2 + lambda ||X||_F^2.
"""
a = op.inputs[0]
b = op.inputs[1]
l2_regularizer = math_ops.cast(op.inputs[2], a.dtype.base_dtype)
x = op.outputs[0]
a_shape = array_ops.shape(a)
batch_shape = a_shape[:-2]
n = a_shape[-1]
identity = linalg_ops.eye(n, batch_shape=batch_shape, dtype=a.dtype)
gramian = math_ops.matmul(a, a, adjoint_a=True) + l2_regularizer * identity
chol = linalg_ops.cholesky(gramian)
# Temporary z = (A^T * A + lambda * I)^{-1} * grad.
z = linalg_ops.cholesky_solve(chol, grad)
xzt = math_ops.matmul(x, z, adjoint_b=True)
zx_sym = xzt + array_ops.matrix_transpose(xzt)
grad_a = -math_ops.matmul(a, zx_sym) + math_ops.matmul(b, z, adjoint_b=True)
grad_b = math_ops.matmul(a, z)
return (grad_a, grad_b, None)
def _variance(self):
x = math_ops.sqrt(self.df) * self.scale_operator_pd.to_dense()
d = array_ops.expand_dims(array_ops.matrix_diag_part(x), -1)
v = math_ops.square(x) + math_ops.matmul(d, d, adjoint_b=True)
if self.cholesky_input_output_matrices:
return linalg_ops.cholesky(v)
return v
def _underdetermined(op, grad):
"""Gradients for the underdetermined case of MatrixSolveLs.
This is the backprop for the solution to the normal equations of the second
kind:
X = F(A, B) = A * (A*A^T + lambda*I)^{-1} * B
that (for lambda=0) solve the least squares problem
min ||X||_F subject to A*X = B.
"""
a = op.inputs[0]
b = op.inputs[1]
l2_regularizer = math_ops.cast(op.inputs[2], a.dtype.base_dtype)
a_shape = array_ops.shape(a)
batch_shape = a_shape[:-2]
m = a_shape[-2]
identity = linalg_ops.eye(m, batch_shape=batch_shape, dtype=a.dtype)
gramian = math_ops.matmul(a, a, adjoint_b=True) + l2_regularizer * identity
chol = linalg_ops.cholesky(gramian)
grad_b = linalg_ops.cholesky_solve(chol, math_ops.matmul(a, grad))
# Temporary tmp = (A * A^T + lambda * I)^{-1} * B.
tmp = linalg_ops.cholesky_solve(chol, b)
a1 = math_ops.matmul(tmp, a, adjoint_a=True)
a1 = -math_ops.matmul(grad_b, a1)
a2 = grad - math_ops.matmul(a, grad_b, adjoint_a=True)
a2 = math_ops.matmul(tmp, a2, adjoint_b=True)
grad_a = a1 + a2
return (grad_a, grad_b, None)
def runFiniteDifferences(self, shapes, dtypes=(dtypes_lib.float32, dtypes_lib.float64), scalarTest=False):
with self.test_session(use_gpu=False):
for shape in shapes:
for batch in False, True:
for dtype in dtypes:
if not scalarTest:
x = constant_op.constant(np.random.randn(shape[0], shape[1]), dtype)
tensor = math_ops.matmul(x, array_ops.transpose(x)) / shape[0]
else:
# This is designed to be a faster test for larger matrices.
x = constant_op.constant(np.random.randn(), dtype)
R = constant_op.constant(np.random.randn(shape[0], shape[1]), dtype)
e = math_ops.mul(R, x)
tensor = math_ops.matmul(e, array_ops.transpose(e)) / shape[0]
# Inner-most matrices in tensor are positive definite.
if batch:
tensor = array_ops.tile(array_ops.expand_dims(tensor, 0), [4, 1, 1])
y = linalg_ops.cholesky(tensor)
if scalarTest:
y = math_ops.reduce_mean(y)
error = gradient_checker.compute_gradient_error(x, x._shape_as_list(), y, y._shape_as_list())
tf_logging.info("error = %f", error)
if dtype == dtypes_lib.float64:
self.assertLess(error, 1e-5)
else:
self.assertLess(error, 3e-3)
def _verifyCholesky(self, x):
# Verify that LL^T == x.
# rocBLAS on ROCm stack does not support complex<float> dgemv yet
with self.cached_session(
use_gpu=True and not test.is_built_with_rocm()) as sess:
chol = linalg_ops.cholesky(x)
verification = math_ops.matmul(chol, chol, adjoint_b=True)
self._verifyCholeskyBase(sess, x, chol, verification)
def _verifyCholesky(self, x, atol=1e-6):
# Verify that LL^T == x.
with self.test_session() as sess:
placeholder = array_ops.placeholder(
dtypes.as_dtype(x.dtype), shape=x.shape)
with self.test_scope():
chol = linalg_ops.cholesky(placeholder)
verification = math_ops.matmul(chol, chol, adjoint_b=True)
self._verifyCholeskyBase(sess, placeholder, x, chol, verification, atol)
def _log_abs_determinant(self):
logging.warn(
"Using (possibly slow) default implementation of determinant."
" Requires conversion to a dense matrix and O(N^3) operations.")
if self._can_use_cholesky():
diag = array_ops.matrix_diag_part(linalg_ops.cholesky(self.to_dense()))
return 2 * math_ops.reduce_sum(math_ops.log(diag), axis=[-1])
_, log_abs_det = linalg.slogdet(self.to_dense())
return log_abs_det
def _verifyCholesky(self, x, atol=1e-6):
# Verify that LL^T == x.
with self.session() as sess:
placeholder = array_ops.placeholder(
dtypes.as_dtype(x.dtype), shape=x.shape)
with self.test_scope():
chol = linalg_ops.cholesky(placeholder)
verification = test_util.matmul_without_tf32(chol, chol, adjoint_b=True)
self._verifyCholeskyBase(sess, placeholder, x, chol, verification, atol)
def _log_abs_determinant(self):
logging.warn(
"Using (possibly slow) default implementation of determinant."
" Requires conversion to a dense matrix and O(N^3) operations.")
if self._can_use_cholesky():
diag = array_ops.matrix_diag_part(linalg_ops.cholesky(self.to_dense()))
return 2 * math_ops.reduce_sum(math_ops.log(diag), axis=[-1])
_, log_abs_det = linalg.slogdet(self.to_dense())
return log_abs_det
def test_cholesky(self):
with self.test_session(graph=ops.Graph()) as sess:
sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
operator, mat = self.operator_and_matrix(
shapes_info, dtype, use_placeholder=use_placeholder,
ensure_self_adjoint_and_pd=True)
op_chol = operator.cholesky().to_dense()
mat_chol = linalg_ops.cholesky(mat)
op_chol_v, mat_chol_v = sess.run([op_chol, mat_chol])
self.assertAC(mat_chol_v, op_chol_v)
def test_cholesky(self):
with self.test_session(graph=ops.Graph()) as sess:
sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
operator, mat = self.operator_and_matrix(
shapes_info, dtype, use_placeholder=use_placeholder,
ensure_self_adjoint_and_pd=True)
op_chol = operator.cholesky().to_dense()
mat_chol = linalg_ops.cholesky(mat)
op_chol_v, mat_chol_v = sess.run([op_chol, mat_chol])
self.assertAC(mat_chol_v, op_chol_v)
def _dense_solve(self, rhs, adjoint=False, adjoint_arg=False):
"""Solve by conversion to a dense matrix."""
if self.is_square is False: # pylint: disable=g-bool-id-comparison
raise NotImplementedError(
"Solve is not yet implemented for non-square operators.")
rhs = linalg.adjoint(rhs) if adjoint_arg else rhs
if self._can_use_cholesky():
return linalg_ops.cholesky_solve(
linalg_ops.cholesky(self.to_dense()), rhs)
return linear_operator_util.matrix_solve_with_broadcast(
self.to_dense(), rhs, adjoint=adjoint)
def testNotInvertibleCpu(self):
# Non-invertible inputs result in lower-triangular NaNs.
x = constant_op.constant([[1., -1., 0.], [-1., 1., -1.], [0., -1., 1.]])
chol = linalg_ops.cholesky(x)
# Extract the lower-triangular elements.
lower_mask = array_ops.matrix_band_part(
constant_op.constant(True, shape=x.shape), -1, 0)
chol_lower = array_ops.boolean_mask(chol, lower_mask)
# Assert all NaN.
all_nan = self.evaluate(
math_ops.reduce_all(math_ops.reduce_all(math_ops.is_nan(chol_lower))))
self.assertTrue(all_nan)
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
"""Default implementation of _solve."""
if self.is_square is False:
raise NotImplementedError(
"Solve is not yet implemented for non-square operators.")
logging.warn(
"Using (possibly slow) default implementation of solve."
" Requires conversion to a dense matrix and O(N^3) operations.")
rhs = linalg.adjoint(rhs) if adjoint_arg else rhs
if self._can_use_cholesky():
return linalg_ops.cholesky_solve(
linalg_ops.cholesky(self.to_dense()), rhs)
return linalg_ops.matrix_solve(self.to_dense(), rhs, adjoint=adjoint)
def benchmarkCholeskyOp(self):
for size in self.sizes:
data = self._GenerateData(size)
with ops.Graph().as_default(), \
session.Session() as sess, \
ops.device("/cpu:0"):
l = linalg_ops.cholesky(data)
self.run_op_benchmark(
sess, control_flow_ops.group(l,),
min_iters=25,
name="cholesky_cpu_{size}".format(size=size))
if test.is_gpu_available(True):
with ops.Graph().as_default(), \
session.Session() as sess, \
ops.device("/gpu:0"):
l = linalg_ops.cholesky(data)
self.run_op_benchmark(
sess, l,
min_iters=25,
name="cholesky_gpu_{size}".format(size=size))
def Compute(x):
# Turn the random matrix x into a Hermitian matrix by
# computing the quadratic form x * x^H.
a = test_util.matmul_without_tf32(
x, math_ops.conj(array_ops.matrix_transpose(x))) / shape[0]
if batch:
a = array_ops.tile(array_ops.expand_dims(a, 0), [2, 1, 1])
# Finally take the cholesky decomposition of the Hermitian matrix.
c = linalg_ops.cholesky(a)
if scalar_test:
# Reduce to a single scalar output to speed up test.
c = math_ops.reduce_mean(c)
return c
def testAgainstSpecialized(self):
np.random.seed(0)
data = np.random.randn(33, 33).astype(np.float32)
data = np.matmul(data, data.T)
grad_data = np.random.randn(*data.shape).astype(np.float32)
with ops.Graph().as_default(), self.test_session(use_gpu=False) as s:
x = constant_op.constant(data, dtypes_lib.float32)
chol = linalg_ops.cholesky(x)
composite_grad = gradients_impl.gradients(chol, x, grad_data)[0]
specialized_grad = SpecializedGrad(chol, grad_data)
reference, actual = s.run([specialized_grad, composite_grad])
self.assertAllClose(reference, actual)
def __init__(self,
df,
scale,
cholesky_input_output_matrices=False,
validate_args=False,
allow_nan_stats=True,
name="WishartFull"):
"""Construct Wishart distributions.
Args:
df: `float` or `double` `Tensor`. Degrees of freedom, must be greater than
or equal to dimension of the scale matrix.
scale: `float` or `double` `Tensor`. The symmetric positive definite
scale matrix of the distribution.
cholesky_input_output_matrices: Python `bool`. Any function which whose
input or output is a matrix assumes the input is Cholesky and returns a
Cholesky factored matrix. Example `log_prob` input takes a Cholesky and
`sample_n` returns a Cholesky when
`cholesky_input_output_matrices=True`.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or
more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
"""
parameters = dict(locals())
with ops.name_scope(name) as name:
with ops.name_scope("init", values=[scale]):
scale = ops.convert_to_tensor(scale)
if validate_args:
scale = distribution_util.assert_symmetric(scale)
chol = linalg_ops.cholesky(scale)
chol = control_flow_ops.with_dependencies([
check_ops.assert_positive(array_ops.matrix_diag_part(chol))
] if validate_args else [], chol)
super(WishartFull, self).__init__(
df=df,
scale_operator=linalg.LinearOperatorLowerTriangular(
tril=chol,
is_non_singular=True,
is_positive_definite=True,
is_square=True),
cholesky_input_output_matrices=cholesky_input_output_matrices,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name)
self._parameters = parameters
def __init__(self,
matrix,
is_non_singular=None,
is_self_adjoint=None,
is_positive_definite=None,
name="LinearOperatorMatrix"):
"""Initialize a `LinearOperatorMatrix`.
Args:
matrix: Shape `[B1,...,Bb, M, N]` with `b >= 0`, `M, N >= 0`.
Allowed dtypes: `float32`, `float64`, `complex64`, `complex128`.
is_non_singular: Expect that this operator is non-singular.
is_self_adjoint: Expect that this operator is equal to its hermitian
transpose.
is_positive_definite: Expect that this operator is positive definite,
meaning the real part of all eigenvalues is positive. We do not require
the operator to be self-adjoint to be positive-definite. See:
https://en.wikipedia.org/wiki/Positive-definite_matrix
#Extension_for_non_symmetric_matrices
name: A name for this `LinearOperator`.
Raises:
TypeError: If `diag.dtype` is not an allowed type.
"""
allowed_dtypes = [
dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128
]
with ops.name_scope(name, values=[matrix]):
self._matrix = ops.convert_to_tensor(matrix, name="matrix")
dtype = self._matrix.dtype
if dtype not in allowed_dtypes:
raise TypeError(
"Argument matrix must have dtype in %s. Found: %s" %
(allowed_dtypes, dtype))
# Special treatment for (real) Symmetric Positive Definite.
self._is_spd = ((not dtype.is_complex) and is_self_adjoint
and is_positive_definite)
if self._is_spd:
self._chol = linalg_ops.cholesky(self._matrix)
super(LinearOperatorMatrix,
self).__init__(dtype=self._matrix.dtype,
graph_parents=[self._matrix],
is_non_singular=is_non_singular,
is_self_adjoint=is_self_adjoint,
is_positive_definite=is_positive_definite,
name=name)
def __init__(self,
df,
scale,
cholesky_input_output_matrices=False,
validate_args=False,
allow_nan_stats=True,
name="WishartFull"):
"""Construct Wishart distributions.
Args:
df: `float` or `double` `Tensor`. Degrees of freedom, must be greater than
or equal to dimension of the scale matrix.
scale: `float` or `double` `Tensor`. The symmetric positive definite
scale matrix of the distribution.
cholesky_input_output_matrices: Python `bool`. Any function which whose
input or output is a matrix assumes the input is Cholesky and returns a
Cholesky factored matrix. Example `log_prob` input takes a Cholesky and
`sample_n` returns a Cholesky when
`cholesky_input_output_matrices=True`.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or
more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
"""
parameters = dict(locals())
with ops.name_scope(name) as name:
with ops.name_scope("init", values=[scale]):
scale = ops.convert_to_tensor(scale)
if validate_args:
scale = distribution_util.assert_symmetric(scale)
chol = linalg_ops.cholesky(scale)
chol = control_flow_ops.with_dependencies([
check_ops.assert_positive(array_ops.matrix_diag_part(chol))
] if validate_args else [], chol)
super(WishartFull, self).__init__(
df=df,
scale_operator=linalg.LinearOperatorLowerTriangular(
tril=chol,
is_non_singular=True,
is_positive_definite=True,
is_square=True),
cholesky_input_output_matrices=cholesky_input_output_matrices,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name)
self._parameters = parameters
def runFiniteDifferences(self,
shapes,
dtypes=(dtypes_lib.float32, dtypes_lib.float64,
dtypes_lib.complex64,
dtypes_lib.complex128),
scalarTest=False):
# rocBLAS on ROCm stack does not support complex<float> GEMV yet
with self.session(use_gpu=True and not test.is_built_with_rocm()):
for shape in shapes:
for batch in False, True:
for dtype in dtypes:
if not scalarTest:
data = np.random.randn(shape[0], shape[1])
if dtype.is_complex:
data = data.astype(np.complex64)
data += 1j * np.random.randn(
shape[0], shape[1])
x = constant_op.constant(data, dtype)
tensor = math_ops.matmul(
x, math_ops.conj(
array_ops.transpose(x))) / shape[0]
else:
# This is designed to be a faster test for larger matrices.
data = np.random.randn()
if dtype.is_complex:
data = np.complex64(data)
data += 1j * np.random.randn()
x = constant_op.constant(data, dtype)
R = constant_op.constant(
np.random.randn(shape[0], shape[1]), dtype)
e = math_ops.multiply(R, x)
tensor = math_ops.matmul(
e, math_ops.conj(
array_ops.transpose(e))) / shape[0]
# Inner-most matrices in tensor are positive definite.
if batch:
tensor = array_ops.tile(
array_ops.expand_dims(tensor, 0), [4, 1, 1])
y = linalg_ops.cholesky(tensor)
if scalarTest:
y = math_ops.reduce_mean(y)
error = gradient_checker.compute_gradient_error(
x, x._shape_as_list(), y, y._shape_as_list())
tf_logging.info("error = %f", error)
if dtype == dtypes_lib.float64:
self.assertLess(error, 1e-5)
elif dtype == dtypes_lib.complex128:
self.assertLess(error, 5e-5)
else:
self.assertLess(error, 5e-3)
def __init__(self,
matrix,
is_non_singular=None,
is_self_adjoint=None,
is_positive_definite=None,
name="LinearOperatorMatrix"):
"""Initialize a `LinearOperatorMatrix`.
Args:
matrix: Shape `[B1,...,Bb, M, N]` with `b >= 0`, `M, N >= 0`.
Allowed dtypes: `float32`, `float64`, `complex64`, `complex128`.
is_non_singular: Expect that this operator is non-singular.
is_self_adjoint: Expect that this operator is equal to its hermitian
transpose.
is_positive_definite: Expect that this operator is positive definite,
meaning the real part of all eigenvalues is positive. We do not require
the operator to be self-adjoint to be positive-definite. See:
https://en.wikipedia.org/wiki/Positive-definite_matrix
#Extension_for_non_symmetric_matrices
name: A name for this `LinearOperator`.
Raises:
TypeError: If `diag.dtype` is not an allowed type.
"""
allowed_dtypes = [
dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128]
with ops.name_scope(name, values=[matrix]):
self._matrix = ops.convert_to_tensor(matrix, name="matrix")
dtype = self._matrix.dtype
if dtype not in allowed_dtypes:
raise TypeError(
"Argument matrix must have dtype in %s. Found: %s"
% (allowed_dtypes, dtype))
# Special treatment for (real) Symmetric Positive Definite.
self._is_spd = (
(not dtype.is_complex) and is_self_adjoint and is_positive_definite)
if self._is_spd:
self._chol = linalg_ops.cholesky(self._matrix)
super(LinearOperatorMatrix, self).__init__(
dtype=self._matrix.dtype,
graph_parents=[self._matrix],
is_non_singular=is_non_singular,
is_self_adjoint=is_self_adjoint,
is_positive_definite=is_positive_definite,
name=name)
def test_works_with_five_different_random_pos_def_matrices(self):
for n in range(1, 6):
for np_type, atol in [(np.float32, 0.05), (np.float64, 1e-5)]:
with self.session(use_gpu=True):
# Create 2 x n x n matrix
array = np.array(
[_RandomPDMatrix(n, self.rng),
_RandomPDMatrix(n, self.rng)]).astype(np_type)
chol = linalg_ops.cholesky(array)
for k in range(1, 3):
rhs = self.rng.randn(2, n, k).astype(np_type)
x = linalg_ops.cholesky_solve(chol, rhs)
self.assertAllClose(
rhs, math_ops.matmul(array, x).eval(), atol=atol)
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
"""Default implementation of _solve."""
if self.is_square is False:
raise NotImplementedError(
"Solve is not yet implemented for non-square operators.")
logging.warn(
"Using (possibly slow) default implementation of solve."
" Requires conversion to a dense matrix and O(N^3) operations.")
rhs = linalg.adjoint(rhs) if adjoint_arg else rhs
if self._can_use_cholesky():
return linear_operator_util.cholesky_solve_with_broadcast(
linalg_ops.cholesky(self.to_dense()), rhs)
return linear_operator_util.matrix_solve_with_broadcast(
self.to_dense(), rhs, adjoint=adjoint)
def test_cholesky(self):
self._skip_if_tests_to_skip_contains("cholesky")
for use_placeholder in self._use_placeholder_options:
for build_info in self._operator_build_infos:
for dtype in self._dtypes_to_test:
with self.test_session(graph=ops.Graph()) as sess:
sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
operator, mat = self._operator_and_matrix(
build_info, dtype, use_placeholder=use_placeholder,
ensure_self_adjoint_and_pd=True)
op_chol = operator.cholesky().to_dense()
mat_chol = linalg_ops.cholesky(mat)
op_chol_v, mat_chol_v = sess.run([op_chol, mat_chol])
self.assertAC(mat_chol_v, op_chol_v)
def test_cholesky(self):
self._skip_if_tests_to_skip_contains("cholesky")
for use_placeholder in self._use_placeholder_options:
for build_info in self._operator_build_infos:
for dtype in self._dtypes_to_test:
with self.test_session(graph=ops.Graph()) as sess:
sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
operator, mat = self._operator_and_matrix(
build_info, dtype, use_placeholder=use_placeholder,
ensure_self_adjoint_and_pd=True)
op_chol = operator.cholesky().to_dense()
mat_chol = linalg_ops.cholesky(mat)
op_chol_v, mat_chol_v = sess.run([op_chol, mat_chol])
self.assertAC(mat_chol_v, op_chol_v)
def benchmarkCholeskyOp(self):
for size in self.sizes:
data = self._GenerateData(size)
with ops.Graph().as_default(), \
session.Session() as sess, \
ops.device("/cpu:0"):
l = linalg_ops.cholesky(data)
self.run_op_benchmark(
sess, control_flow_ops.group(l,),
min_iters=25,
name="cholesky_cpu_{size}".format(size=size))
if test.is_gpu_available(True):
with ops.Graph().as_default(), \
session.Session() as sess, \
ops.device("/device:GPU:0"):
l = linalg_ops.cholesky(data)
self.run_op_benchmark(
sess,
control_flow_ops.group(
l,),
min_iters=25,
name="cholesky_gpu_{size}".format(size=size))
def _assert_positive_definite(self):
"""Default implementation of _assert_positive_definite."""
logging.warn(
"Using (possibly slow) default implementation of "
"assert_positive_definite."
" Requires conversion to a dense matrix and O(N^3) operations.")
# If the operator is self-adjoint, then checking that
# Cholesky decomposition succeeds + results in positive diag is necessary
# and sufficient.
if self.is_self_adjoint:
return check_ops.assert_positive(
array_ops.matrix_diag_part(linalg_ops.cholesky(self.to_dense())),
message="Matrix was not positive definite.")
# We have no generic check for positive definite.
raise NotImplementedError("assert_positive_definite is not implemented.")
def _assert_positive_definite(self):
"""Default implementation of _assert_positive_definite."""
logging.warn(
"Using (possibly slow) default implementation of "
"assert_positive_definite."
" Requires conversion to a dense matrix and O(N^3) operations.")
# If the operator is self-adjoint, then checking that
# Cholesky decomposition succeeds + results in positive diag is necessary
# and sufficient.
if self.is_self_adjoint:
return check_ops.assert_positive(
array_ops.matrix_diag_part(linalg_ops.cholesky(self.to_dense())),
message="Matrix was not positive definite.")
# We have no generic check for positive definite.
raise NotImplementedError("assert_positive_definite is not implemented.")
def test_cholesky(self):
with self.test_session(graph=ops.Graph()) as sess:
# This test fails to pass for float32 type by a small margin if we use
# random_seed.DEFAULT_GRAPH_SEED. The correct fix would be relaxing the
# test tolerance but the tolerance in this test is configured universally
# depending on its type. So instead of lowering tolerance for all tests
# or special casing this, just use a seed, +2, that makes this test pass.
sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED + 2
operator, mat = self.operator_and_matrix(
shapes_info, dtype, use_placeholder=use_placeholder,
ensure_self_adjoint_and_pd=True)
op_chol = operator.cholesky().to_dense()
mat_chol = linalg_ops.cholesky(mat)
op_chol_v, mat_chol_v = sess.run([op_chol, mat_chol])
self.assertAC(mat_chol_v, op_chol_v)
def _chol_capacitance(self, batch_mode):
"""Cholesky factorization of the capacitance term."""
# Cholesky factor for (D^{-1} + V^T M^{-1} V), which is sometimes
# known as the "capacitance" matrix.
# self._operator will use batch if need be. Automatically. We cannot force
# that here.
# M^{-1} V
minv_v = self._operator.solve(self._v)
# V^T M^{-1} V
vt_minv_v = math_ops.matmul(self._v, minv_v, adjoint_a=True)
# D^{-1} + V^T M^{-1} V
capacitance = self._diag_inv_operator.add_to_tensor(vt_minv_v)
# Cholesky[D^{-1} + V^T M^{-1} V]
return linalg_ops.cholesky(capacitance)
def _chol_capacitance(self, batch_mode):
"""Cholesky factorization of the capacitance term."""
# Cholesky factor for (D^{-1} + V^T M^{-1} V), which is sometimes
# known as the "capacitance" matrix.
# self._operator will use batch if need be. Automatically. We cannot force
# that here.
# M^{-1} V
minv_v = self._operator.solve(self._v)
# V^T M^{-1} V
vt_minv_v = math_ops.matmul(self._v, minv_v, adjoint_a=True)
# D^{-1} + V^T M^{-1} V
capacitance = self._diag_inv_operator.add_to_tensor(vt_minv_v)
# Cholesky[D^{-1} + V^T M^{-1} V]
return linalg_ops.cholesky(capacitance)
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
if self.base_operator.is_non_singular is False:
raise ValueError(
"Solve not implemented unless this is a perturbation of a "
"non-singular LinearOperator.")
# The Woodbury formula gives:
# https://en.wikipedia.org/wiki/Woodbury_matrix_identity
# (L + UDV^H)^{-1}
# = L^{-1} - L^{-1} U (D^{-1} + V^H L^{-1} U)^{-1} V^H L^{-1}
# = L^{-1} - L^{-1} U C^{-1} V^H L^{-1}
# where C is the capacitance matrix, C := D^{-1} + V^H L^{-1} U
# Note also that, with ^{-H} being the inverse of the adjoint,
# (L + UDV^H)^{-H}
# = L^{-H} - L^{-H} V C^{-H} U^H L^{-H}
l = self.base_operator
if adjoint:
# If adjoint, U and V have flipped roles in the operator.
v, u = self._get_uv_as_tensors()
# Capacitance should still be computed with u=self.u and v=self.v, which
# after the "flip" on the line above means u=v, v=u. I.e. no need to
# "flip" in the capacitance call, since the call to
# matrix_solve_with_broadcast below is done with the `adjoint` argument,
# and this takes care of things.
capacitance = self._make_capacitance(u=v, v=u)
else:
u, v = self._get_uv_as_tensors()
capacitance = self._make_capacitance(u=u, v=v)
# L^{-1} rhs
linv_rhs = l.solve(rhs, adjoint=adjoint, adjoint_arg=adjoint_arg)
# V^H L^{-1} rhs
vh_linv_rhs = math_ops.matmul(v, linv_rhs, adjoint_a=True)
# C^{-1} V^H L^{-1} rhs
if self._use_cholesky:
capinv_vh_linv_rhs = linalg_ops.cholesky_solve(
linalg_ops.cholesky(capacitance), vh_linv_rhs)
else:
capinv_vh_linv_rhs = linear_operator_util.matrix_solve_with_broadcast(
capacitance, vh_linv_rhs, adjoint=adjoint)
# U C^{-1} V^H M^{-1} rhs
u_capinv_vh_linv_rhs = math_ops.matmul(u, capinv_vh_linv_rhs)
# L^{-1} U C^{-1} V^H L^{-1} rhs
linv_u_capinv_vh_linv_rhs = l.solve(u_capinv_vh_linv_rhs,
adjoint=adjoint)
# L^{-1} - L^{-1} U C^{-1} V^H L^{-1}
return linv_rhs - linv_u_capinv_vh_linv_rhs
def create_distribution(batch_size, num_components, num_features):
cat = distributions_py.Categorical(
logits=np.random.randn(batch_size, num_components))
mus = [
variables.Variable(np.random.randn(batch_size, num_features))
for _ in range(num_components)
]
sigmas = [
variables.Variable(
psd(np.random.rand(batch_size, num_features, num_features)))
for _ in range(num_components)
]
components = list(
distributions_py.MultivariateNormalTriL(
loc=mu, scale_tril=linalg_ops.cholesky(sigma))
for (mu, sigma) in zip(mus, sigmas))
return distributions_py.Mixture(cat, components)
def create_distribution(batch_size, num_components, num_features):
cat = ds.Categorical(
logits=np.random.randn(batch_size, num_components))
mus = [
variables.Variable(np.random.randn(batch_size, num_features))
for _ in range(num_components)
]
sigmas = [
variables.Variable(
psd(np.random.rand(batch_size, num_features, num_features)))
for _ in range(num_components)
]
components = list(
ds.MultivariateNormalTriL(
loc=mu, scale_tril=linalg_ops.cholesky(sigma))
for (mu, sigma) in zip(mus, sigmas))
return ds.Mixture(cat, components, use_static_graph=self.use_static_graph)
def __init__(self,
matrix,
is_non_singular=None,
is_self_adjoint=None,
is_positive_definite=None,
name="LinearOperatorFullMatrix"):
r"""Initialize a `LinearOperatorFullMatrix`.
Args:
matrix: Shape `[B1,...,Bb, M, N]` with `b >= 0`, `M, N >= 0`.
Allowed dtypes: `float32`, `float64`, `complex64`, `complex128`.
is_non_singular: Expect that this operator is non-singular.
is_self_adjoint: Expect that this operator is equal to its hermitian
transpose.
is_positive_definite: Expect that this operator is positive definite,
meaning the quadratic form `x^H A x` has positive real part for all
nonzero `x`. Note that we do not require the operator to be
self-adjoint to be positive-definite. See:
https://en.wikipedia.org/wiki/Positive-definite_matrix\
#Extension_for_non_symmetric_matrices
name: A name for this `LinearOperator`.
Raises:
TypeError: If `diag.dtype` is not an allowed type.
"""
with ops.name_scope(name, values=[matrix]):
self._matrix = ops.convert_to_tensor(matrix, name="matrix")
self._check_matrix(self._matrix)
# Special treatment for (real) Symmetric Positive Definite.
self._is_spd = (
(not self._matrix.dtype.is_complex)
and is_self_adjoint and is_positive_definite)
if self._is_spd:
self._chol = linalg_ops.cholesky(self._matrix)
super(LinearOperatorFullMatrix, self).__init__(
dtype=self._matrix.dtype,
graph_parents=[self._matrix],
is_non_singular=is_non_singular,
is_self_adjoint=is_self_adjoint,
is_positive_definite=is_positive_definite,
name=name)
def __init__(self,
matrix,
is_non_singular=None,
is_self_adjoint=None,
is_positive_definite=None,
name="LinearOperatorFullMatrix"):
r"""Initialize a `LinearOperatorFullMatrix`.
Args:
matrix: Shape `[B1,...,Bb, M, N]` with `b >= 0`, `M, N >= 0`.
Allowed dtypes: `float32`, `float64`, `complex64`, `complex128`.
is_non_singular: Expect that this operator is non-singular.
is_self_adjoint: Expect that this operator is equal to its hermitian
transpose.
is_positive_definite: Expect that this operator is positive definite,
meaning the quadratic form `x^H A x` has positive real part for all
nonzero `x`. Note that we do not require the operator to be
self-adjoint to be positive-definite. See:
https://en.wikipedia.org/wiki/Positive-definite_matrix\
#Extension_for_non_symmetric_matrices
name: A name for this `LinearOperator`.
Raises:
TypeError: If `diag.dtype` is not an allowed type.
"""
with ops.name_scope(name, values=[matrix]):
self._matrix = ops.convert_to_tensor(matrix, name="matrix")
self._check_matrix(self._matrix)
# Special treatment for (real) Symmetric Positive Definite.
self._is_spd = (
(not self._matrix.dtype.is_complex)
and is_self_adjoint and is_positive_definite)
if self._is_spd:
self._chol = linalg_ops.cholesky(self._matrix)
super(LinearOperatorFullMatrix, self).__init__(
dtype=self._matrix.dtype,
graph_parents=[self._matrix],
is_non_singular=is_non_singular,
is_self_adjoint=is_self_adjoint,
is_positive_definite=is_positive_definite,
name=name)
def _log_abs_determinant(self):
# Recall
# det(L + UDV^H) = det(D^{-1} + V^H L^{-1} U) det(D) det(L)
# = det(C) det(D) det(L)
log_abs_det_d = self.diag_operator.log_abs_determinant()
log_abs_det_l = self.base_operator.log_abs_determinant()
if self._use_cholesky:
chol_cap_diag = array_ops.matrix_diag_part(
linalg_ops.cholesky(self._make_capacitance()))
log_abs_det_c = 2 * math_ops.reduce_sum(
math_ops.log(chol_cap_diag), axis=[-1])
else:
det_c = linalg_ops.matrix_determinant(self._make_capacitance())
log_abs_det_c = math_ops.log(math_ops.abs(det_c))
if self.dtype.is_complex:
log_abs_det_c = math_ops.cast(log_abs_det_c, dtype=self.dtype)
return log_abs_det_c + log_abs_det_d + log_abs_det_l
def _define_full_covariance_probs(self, shard_id, shard):
"""Defines the full covariance probabilties per example in a class.
Updates a matrix with dimension num_examples X num_classes.
Args:
shard_id: id of the current shard.
shard: current data shard, 1 X num_examples X dimensions.
"""
diff = shard - self._means
cholesky = linalg_ops.cholesky(self._covs + self._min_var)
log_det_covs = 2.0 * math_ops.reduce_sum(
math_ops.log(array_ops.matrix_diag_part(cholesky)), 1)
x_mu_cov = math_ops.square(
linalg_ops.matrix_triangular_solve(cholesky,
array_ops.transpose(
diff, perm=[0, 2, 1]),
lower=True))
diag_m = array_ops.transpose(math_ops.reduce_sum(x_mu_cov, 1))
self._probs[shard_id] = -0.5 * (diag_m + math_ops.to_float(
self._dimensions) * math_ops.log(2 * np.pi) + log_det_covs)
def _chol_capacitance(self, batch_mode):
"""Cholesky factorization of the capacitance term."""
# Cholesky factor for (D^{-1} + V^T M^{-1} V), which is sometimes
# known as the "capacitance" matrix.
# We can do a Cholesky decomposition, since a priori M is a
# positive-definite Hermitian matrix, which causes the "capacitance" to
# also be positive-definite Hermitian, and thus have a Cholesky
# decomposition.
# self._operator will use batch if need be. Automatically. We cannot force
# that here.
# M^{-1} V
minv_v = self._operator.solve(self._v)
# V^T M^{-1} V
vt_minv_v = math_ops.matmul(self._v, minv_v, adjoint_a=True)
# D^{-1} + V^T M^{-1} V
capacitance = self._diag_inv_operator.add_to_tensor(vt_minv_v)
# Cholesky[D^{-1} + V^T M^{-1} V]
return linalg_ops.cholesky(capacitance)
def _chol_capacitance(self, batch_mode):
"""Cholesky factorization of the capacitance term."""
# Cholesky factor for (D^{-1} + V^T M^{-1} V), which is sometimes
# known as the "capacitance" matrix.
# We can do a Cholesky decomposition, since a priori M is a
# positive-definite Hermitian matrix, which causes the "capacitance" to
# also be positive-definite Hermitian, and thus have a Cholesky
# decomposition.
# self._operator will use batch if need be. Automatically. We cannot force
# that here.
# M^{-1} V
minv_v = self._operator.solve(self._v)
# V^T M^{-1} V
vt_minv_v = math_ops.matmul(self._v, minv_v, adjoint_a=True)
# D^{-1} + V^T M^{-1} V
capacitance = self._diag_inv_operator.add_to_tensor(vt_minv_v)
# Cholesky[D^{-1} + V^T M^{-1} V]
return linalg_ops.cholesky(capacitance)
def _define_full_covariance_probs(self, shard_id, shard):
"""Defines the full covariance probabilities per example in a class.
Updates a matrix with dimension num_examples X num_classes.
Args:
shard_id: id of the current shard.
shard: current data shard, 1 X num_examples X dimensions.
"""
diff = shard - self._means
cholesky = linalg_ops.cholesky(self._covs + self._min_var)
log_det_covs = 2.0 * math_ops.reduce_sum(
math_ops.log(array_ops.matrix_diag_part(cholesky)), 1)
x_mu_cov = math_ops.square(
linalg_ops.matrix_triangular_solve(
cholesky, array_ops.transpose(
diff, perm=[0, 2, 1]), lower=True))
diag_m = array_ops.transpose(math_ops.reduce_sum(x_mu_cov, 1))
self._probs[shard_id] = -0.5 * (diag_m + math_ops.to_float(self._dimensions)
* math_ops.log(2 * np.pi) + log_det_covs)
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
if self.base_operator.is_non_singular is False:
raise ValueError(
"Solve not implemented unless this is a perturbation of a "
"non-singular LinearOperator.")
# The Woodbury formula gives:
# https://en.wikipedia.org/wiki/Woodbury_matrix_identity
# (L + UDV^H)^{-1}
# = L^{-1} - L^{-1} U (D^{-1} + V^H L^{-1} U)^{-1} V^H L^{-1}
# = L^{-1} - L^{-1} U C^{-1} V^H L^{-1}
# where C is the capacitance matrix, C := D^{-1} + V^H L^{-1} U
# Note also that, with ^{-H} being the inverse of the adjoint,
# (L + UDV^H)^{-H}
# = L^{-H} - L^{-H} V C^{-H} U^H L^{-H}
l = self.base_operator
if adjoint:
v = self.u
u = self.v
else:
v = self.v
u = self.u
# L^{-1} rhs
linv_rhs = l.solve(rhs, adjoint=adjoint, adjoint_arg=adjoint_arg)
# V^H L^{-1} rhs
vh_linv_rhs = math_ops.matmul(v, linv_rhs, adjoint_a=True)
# C^{-1} V^H L^{-1} rhs
if self._use_cholesky:
capinv_vh_linv_rhs = linalg_ops.cholesky_solve(
linalg_ops.cholesky(self._make_capacitance()), vh_linv_rhs)
else:
capinv_vh_linv_rhs = linear_operator_util.matrix_solve_with_broadcast(
self._make_capacitance(), vh_linv_rhs, adjoint=adjoint)
# U C^{-1} V^H M^{-1} rhs
u_capinv_vh_linv_rhs = math_ops.matmul(u, capinv_vh_linv_rhs)
# L^{-1} U C^{-1} V^H L^{-1} rhs
linv_u_capinv_vh_linv_rhs = l.solve(u_capinv_vh_linv_rhs,
adjoint=adjoint)
# L^{-1} - L^{-1} U C^{-1} V^H L^{-1}
return linv_rhs - linv_u_capinv_vh_linv_rhs
def __init__(self, matrix, verify_pd=True, name="OperatorPDFull"):
"""Initialize an OperatorPDFull.
Args:
matrix: Shape `[N1,...,Nb, k, k]` tensor with `b >= 0`, `k >= 1`. The
last two dimensions should be `k x k` symmetric positive definite
matrices.
verify_pd: Whether to check that `matrix` is symmetric positive definite.
If `verify_pd` is `False`, correct behavior is not guaranteed.
name: A name to prepend to all ops created by this class.
"""
with ops.name_scope(name):
with ops.name_scope("init", values=[matrix]):
matrix = ops.convert_to_tensor(matrix)
# Check symmetric here. Positivity will be verified by checking the
# diagonal of the Cholesky factor inside the parent class. The Cholesky
# factorization linalg_ops.cholesky() does not always fail for non PSD
# matrices, so don't rely on that.
if verify_pd:
matrix = distribution_util.assert_symmetric(matrix)
chol = linalg_ops.cholesky(matrix)
super(OperatorPDFull, self).__init__(chol, verify_pd=verify_pd)
def _verifyCholesky(self, x):
# Verify that LL^T == x.
with self.test_session(use_gpu=True) as sess:
chol = linalg_ops.cholesky(x)
verification = math_ops.matmul(chol, chol, adjoint_b=True)
self._verifyCholeskyBase(sess, x, chol, verification)
def _inverse(self, y):
return (math_ops.sqrt(y) if self._static_event_ndims == 0
else linalg_ops.cholesky(y))
def testWrongDimensions(self):
tensor3 = constant_op.constant([1., 2.])
with self.assertRaises(ValueError):
linalg_ops.cholesky(tensor3)
with self.assertRaises(ValueError):
linalg_ops.cholesky(tensor3)
def _get_cached_chol(self):
if not hasattr(self, "_cached_chol"):
self._cached_chol = linalg_ops.cholesky(self._get_cached_dense_matrix())
return self._cached_chol