如何迫使张量流张量对称? [英] How to force tensorflow tensors to be symmetric?

问题描述

我在图形中有一组MxM 对称矩阵变量，我想对其值进行优化.

I have a set of MxM symmetric matrix Variables in a graph whose values I'd like to optimize.

有没有办法执行对称条件?

我曾考虑过要在损失函数中添加一个术语以强制执行，但这似乎很尴尬和round回.我希望的是类似tf.matmul(A,B,symmA=True)的东西 其中仅会使用和学习A的三角形部分.也许像tf.upperTriangularToFull(A)这样的东西会从三角形部分创建密集的矩阵.

I've thought about adding a term to the loss function to enforce it, but this seems awkward and roundabout. What I'd hoped for is something like tf.matmul(A,B,symmA=True) where only a triangular portion of A would be used and learned. Or maybe something like tf.upperTriangularToFull(A) which would create a dense matrix from a triangular part.

推荐答案

如果您symA = 0.5 * (A + tf.transpose(A))怎么办?它效率低下，但至少是对称的.

What if you do symA = 0.5 * (A + tf.transpose(A))? It is inefficient but at least it's symmetric.

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