如何在tensorflow中实现优化功能？ [英] How do I implement the optimization function in tensorflow?

问题描述

minΣ（|| xi-X ci || ^ 2 +λ || ci ||），

minΣ(||xi-Xci||^2+ λ||ci||),

st cii = 0 ，

s.t cii = 0,

其中X是d * n形状的矩阵，C是n * n形状的矩阵，xi和ci分别表示X和C的列。

where X is a matrix of shape d * n and C is of the shape n * n, xi and ci means a column of X and C separately.

X在这里是已知的，并且基于X我们想找到C。

X is known here and based on X we want to find C.

推荐答案

通常，像这样的损失需要向量化，而不是使用列：

Usually with a loss like that you need to vectorize it, instead of working with columns:

loss = X - tf.matmul(X, C)
loss = tf.reduce_sum(tf.square(loss))

reg_loss = tf.reduce_sum(tf.square(C), 0)  # L2 loss for each column
reg_loss = tf.reduce_sum(tf.sqrt(reg_loss))

total_loss = loss + lambd * reg_loss

---

要对C的对角线实施零约束，最好的方法是用另一个常数<$将它添加到损失中c $ c> lambd2 ：

reg_loss2 = tf.trace(tf.square(C))
total_loss = total_loss + lambd2 * reg_loss2

这篇关于如何在tensorflow中实现优化功能？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！