转载自: http://blog.csdn.net/txwh0820/article/details/46392293 矩阵的迹求导法则 1. 复杂矩阵问题求导方法:可以从小到大,从scalar到vector再到matrix 2. x is a column vector, A is a matrix d(A∗x)/dx=A d(xT∗A)/dxT=A d(xT∗A)/dx=AT d(xT∗A∗x)/dx=xT(AT+A) 3. Practice: 4. 矩阵求导计算法则 求导公式(撇号为
cr:http://blog.csdn.net/txwh0820/article/details/46392293 一.矩阵的迹求导法则 1. 复杂矩阵问题求导方法:可以从小到大,从scalar到vector再到matrix 2. x is a column vector, A is a matrix d(A∗x)/dx=A d(xT∗A)/dxT=A d(xT∗A)/dx=AT d(xT∗A∗x)/dx=xT(AT+A) 3. Practice: 4. 矩阵求导计算法则 求导公式(撇号为
# coding:utf-8 from __future__ import absolute_import from __future__ import unicode_literals from __future__ import print_function from __future__ import division import tensorflow as tf x = tf.placeholder(dtype=tf.float32) y = tf.placeholder(dtype=