本文整理汇总了Python中tensorflow.cholesky_solve方法的典型用法代码示例。如果您正苦于以下问题：Python tensorflow.cholesky_solve方法的具体用法？Python tensorflow.cholesky_solve怎么用？Python tensorflow.cholesky_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的用法示例。
在下文中一共展示了tensorflow.cholesky_solve方法的4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: build_backward_variance
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import cholesky_solve [as 别名]
def build_backward_variance(self, Yvar):
"""
Additional method for scaling variance backward (used in :class:`.Normalizer`). Can process both the diagonal
variances returned by predict_f, as well as full covariance matrices.
:param Yvar: size N x N x P or size N x P
:return: Yvar scaled, same rank and size as input
"""
rank = tf.rank(Yvar)
# Because TensorFlow evaluates both fn1 and fn2, the transpose can't be in the same line. If a full cov
# matrix is provided fn1 turns it into a rank 4, then tries to transpose it as a rank 3.
# Splitting it in two steps however works fine.
Yvar = tf.cond(tf.equal(rank, 2), lambda: tf.matrix_diag(tf.transpose(Yvar)), lambda: Yvar)
Yvar = tf.cond(tf.equal(rank, 2), lambda: tf.transpose(Yvar, perm=[1, 2, 0]), lambda: Yvar)
N = tf.shape(Yvar)[0]
D = tf.shape(Yvar)[2]
L = tf.cholesky(tf.square(tf.transpose(self.A)))
Yvar = tf.reshape(Yvar, [N * N, D])
scaled_var = tf.reshape(tf.transpose(tf.cholesky_solve(L, tf.transpose(Yvar))), [N, N, D])
return tf.cond(tf.equal(rank, 2), lambda: tf.reduce_sum(scaled_var, axis=1), lambda: scaled_var) 示例2: build_backward
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import cholesky_solve [as 别名]
def build_backward(self, Y):
"""
TensorFlow implementation of the inverse mapping
"""
L = tf.cholesky(tf.transpose(self.A))
XT = tf.cholesky_solve(L, tf.transpose(Y-self.b))
return tf.transpose(XT) 示例3: test_works_with_five_different_random_pos_def_matrices
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import cholesky_solve [as 别名]
def test_works_with_five_different_random_pos_def_matrices(self):
with self.test_session():
for n in range(1, 6):
for np_type, atol in [(np.float32, 0.05), (np.float64, 1e-5)]:
# Create 2 x n x n matrix
array = np.array(
[_random_pd_matrix(n, self.rng), _random_pd_matrix(n, self.rng)]
).astype(np_type)
chol = tf.cholesky(array)
for k in range(1, 3):
rhs = self.rng.randn(2, n, k).astype(np_type)
x = tf.cholesky_solve(chol, rhs)
self.assertAllClose(
rhs, tf.batch_matmul(array, x).eval(), atol=atol) 示例4: KL
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import cholesky_solve [as 别名]
def KL(self):
"""
The KL divergence from the variational distribution to the prior
:return: KL divergence from N(q_mu, q_sqrt) to N(0, I), independently for each GP
"""
# if self.white:
# return gauss_kl(self.q_mu, self.q_sqrt)
# else:
# return gauss_kl(self.q_mu, self.q_sqrt, self.Ku)
self.build_cholesky_if_needed()
KL = -0.5 * self.num_outputs * self.num_inducing
KL -= 0.5 * tf.reduce_sum(tf.log(tf.matrix_diag_part(self.q_sqrt) ** 2))
if not self.white:
KL += tf.reduce_sum(tf.log(tf.matrix_diag_part(self.Lu))) * self.num_outputs
KL += 0.5 * tf.reduce_sum(tf.square(tf.matrix_triangular_solve(self.Lu_tiled, self.q_sqrt, lower=True)))
Kinv_m = tf.cholesky_solve(self.Lu, self.q_mu)
KL += 0.5 * tf.reduce_sum(self.q_mu * Kinv_m)
else:
KL += 0.5 * tf.reduce_sum(tf.square(self.q_sqrt))
KL += 0.5 * tf.reduce_sum(self.q_mu**2)
return KL