本文整理汇总了Python中tensorflow.python.ops.linalg_ops.matrix_triangular_solve方法的典型用法代码示例。如果您正苦于以下问题：Python linalg_ops.matrix_triangular_solve方法的具体用法？Python linalg_ops.matrix_triangular_solve怎么用？Python linalg_ops.matrix_triangular_solve使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.ops.linalg_ops的用法示例。
在下文中一共展示了linalg_ops.matrix_triangular_solve方法的10个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _MatrixTriangularSolveGrad
# 需要导入模块: from tensorflow.python.ops import linalg_ops [as 别名]
# 或者: from tensorflow.python.ops.linalg_ops import matrix_triangular_solve [as 别名]
def _MatrixTriangularSolveGrad(op, grad):
"""Gradient for MatrixTriangularSolve."""
a = op.inputs[0]
adjoint_a = op.get_attr("adjoint")
lower_a = op.get_attr("lower")
c = op.outputs[0]
grad_b = linalg_ops.matrix_triangular_solve(
a, grad, lower=lower_a, adjoint=not adjoint_a)
if adjoint_a:
grad_a = -math_ops.matmul(c, grad_b, adjoint_b=True)
else:
grad_a = -math_ops.matmul(grad_b, c, adjoint_b=True)
if lower_a:
grad_a = array_ops.matrix_band_part(grad_a, -1, 0)
else:
grad_a = array_ops.matrix_band_part(grad_a, 0, -1)
return (grad_a, grad_b) 示例2: _define_full_covariance_probs
# 需要导入模块: from tensorflow.python.ops import linalg_ops [as 别名]
# 或者: from tensorflow.python.ops.linalg_ops import matrix_triangular_solve [as 别名]
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) 示例3: sqrt_solve
# 需要导入模块: from tensorflow.python.ops import linalg_ops [as 别名]
# 或者: from tensorflow.python.ops.linalg_ops import matrix_triangular_solve [as 别名]
def sqrt_solve(self, x):
"""Computes `solve(self, x)`.
Doesn't actually do the sqrt! Named as such to agree with API.
To compute (M + V D V.T), we use the the Woodbury matrix identity:
inv(M + V D V.T) = inv(M) - inv(M) V inv(C) V.T inv(M)
where,
C = inv(D) + V.T inv(M) V.
See: https://en.wikipedia.org/wiki/Woodbury_matrix_identity
Args:
x: `Tensor`
Returns:
inv_of_self_times_x: `Tensor`
"""
minv_x = linalg_ops.matrix_triangular_solve(self._m, x)
vt_minv_x = math_ops.matmul(self._v, minv_x, transpose_a=True)
cinv_vt_minv_x = linalg_ops.matrix_solve(
self._woodbury_sandwiched_term(), vt_minv_x)
v_cinv_vt_minv_x = math_ops.matmul(self._v, cinv_vt_minv_x)
minv_v_cinv_vt_minv_x = linalg_ops.matrix_triangular_solve(
self._m, v_cinv_vt_minv_x)
return minv_x - minv_v_cinv_vt_minv_x 示例4: _woodbury_sandwiched_term
# 需要导入模块: from tensorflow.python.ops import linalg_ops [as 别名]
# 或者: from tensorflow.python.ops.linalg_ops import matrix_triangular_solve [as 别名]
def _woodbury_sandwiched_term(self):
"""Computes the sandwiched term in the Woodbury identity.
Computes the "`C`" in the the identity:
inv(M + V D V.T) = inv(M) - inv(M) V inv(C) V.T inv(M)
where,
C = inv(D) + V.T inv(M) V.
See: https://en.wikipedia.org/wiki/Woodbury_matrix_identity
Returns:
woodbury_sandwich_term: A `Tensor` to be used like `C`, above.
"""
minv_v = linalg_ops.matrix_triangular_solve(self._m, self._v)
vt_minv_v = math_ops.matmul(self._v, minv_v, adjoint_a=True)
return self._d_inv.add_to_tensor(vt_minv_v) 示例5: _MatrixTriangularSolveGrad
# 需要导入模块: from tensorflow.python.ops import linalg_ops [as 别名]
# 或者: from tensorflow.python.ops.linalg_ops import matrix_triangular_solve [as 别名]
def _MatrixTriangularSolveGrad(op, grad):
"""Gradient for MatrixTriangularSolve."""
a = op.inputs[0]
adjoint_a = op.get_attr("adjoint")
lower_a = op.get_attr("lower")
c = op.outputs[0]
grad_b = linalg_ops.matrix_triangular_solve(
a, grad, lower=lower_a, adjoint=not adjoint_a)
if adjoint_a:
grad_a = -math_ops.batch_matmul(c, grad_b, adj_y=True)
else:
grad_a = -math_ops.batch_matmul(grad_b, c, adj_y=True)
if lower_a:
grad_a = array_ops.matrix_band_part(grad_a, -1, 0)
else:
grad_a = array_ops.matrix_band_part(grad_a, 0, -1)
return (grad_a, grad_b) 示例6: _CholeskyGrad
# 需要导入模块: from tensorflow.python.ops import linalg_ops [as 别名]
# 或者: from tensorflow.python.ops.linalg_ops import matrix_triangular_solve [as 别名]
def _CholeskyGrad(op, grad):
"""Gradient for Cholesky."""
# Gradient is l^{-H} @ ((l^{H} @ grad) * (tril(ones)-1/2*eye)) @ l^{-1}
l = op.outputs[0]
num_rows = array_ops.shape(l)[-1]
batch_shape = array_ops.shape(l)[:-2]
l_inverse = linalg_ops.matrix_triangular_solve(l,
linalg_ops.eye(
num_rows,
batch_shape=batch_shape,
dtype=l.dtype))
middle = math_ops.matmul(l, grad, adjoint_a=True)
middle = array_ops.matrix_set_diag(middle,
0.5 * array_ops.matrix_diag_part(middle))
middle = array_ops.matrix_band_part(middle, -1, 0)
grad_a = math_ops.matmul(
math_ops.matmul(l_inverse, middle, adjoint_a=True), l_inverse)
grad_a += math_ops.conj(array_ops.matrix_transpose(grad_a))
return grad_a * 0.5 开发者ID:PacktPublishing，项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代码行数:25，代码来源:linalg_grad.py
示例7: _batch_sqrt_solve
# 需要导入模块: from tensorflow.python.ops import linalg_ops [as 别名]
# 或者: from tensorflow.python.ops.linalg_ops import matrix_triangular_solve [as 别名]
def _batch_sqrt_solve(self, rhs):
return linalg_ops.matrix_triangular_solve(self._chol, rhs, lower=True) 示例8: _solve
# 需要导入模块: from tensorflow.python.ops import linalg_ops [as 别名]
# 或者: from tensorflow.python.ops.linalg_ops import matrix_triangular_solve [as 别名]
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
rhs = linear_operator_util.matrix_adjoint(rhs) if adjoint_arg else rhs
return linalg_ops.matrix_triangular_solve(
self._tril, rhs, lower=True, adjoint=adjoint) 示例9: _solve
# 需要导入模块: from tensorflow.python.ops import linalg_ops [as 别名]
# 或者: from tensorflow.python.ops.linalg_ops import matrix_triangular_solve [as 别名]
def _solve(self, rhs, adjoint=False):
return linalg_ops.matrix_triangular_solve(
self._tril, rhs, lower=True, adjoint=adjoint) 示例10: _inverse
# 需要导入模块: from tensorflow.python.ops import linalg_ops [as 别名]
# 或者: from tensorflow.python.ops.linalg_ops import matrix_triangular_solve [as 别名]
def _inverse(self, y):
x = y - self.shift
x, sample_shape = self.shaper.make_batch_of_event_sample_matrices(x)
x = linalg_ops.matrix_triangular_solve(self.scale, x)
x = self.shaper.undo_make_batch_of_event_sample_matrices(x, sample_shape)
return x