import tensorflowas tf;
diagonal=[1,1,1,1]
with tf.Session() as sess:
  print(sess.run(tf.diag(diagonal))) 

 #输出的结果为[[1 0 0 0]
    [0 1 0 0]
    [0 0 1 0]
    [0 0 0 1]]

import tensorflow as tf;
diagonal =tf.constant([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]])
with tf.Session() as sess:
 print(sess.run(tf.diag_part(diagonal)))

#输出结果为[1,1,1,1]

import tensorflow as tf;
diagonal =tf.constant([[1,0,0,3],[0,1,2,0],[0,1,1,0],[1,0,0,1]])
with tf.Session() as sess:
 print(sess.run(tf.trace(diagonal)))#输出结果为4

import tensorflow as tf;
diagonal =tf.constant([[1,0,0,3],[0,1,2,0],[0,1,1,0],[1,0,0,1]])
with tf.Session() as sess:
 print(sess.run(tf.transpose(diagonal))) #输出结果为[[1 0 0 1]
                             [0 1 1 0]
                             [0 2 1 0]
                             [3 0 0 1]]

import tensorflow as tf;
A =tf.constant([1,0,0,3],shape=[2,2])
B =tf.constant([2,1,0,2],shape=[2,2])
with tf.Session() as sess:
 print(sess.run(tf.matmul(A,B)))

#输出结果为[[2 1]
   [0 6]]

import tensorflow as tf;
A =tf.constant([1,0,0,3],shape=[2,2],dtype=tf.float32)
with tf.Session() as sess:
 print(sess.run(tf.matrix_determinant(A))) 

#输出结果为3.0

import tensorflow as tf;
A =tf.constant([1,0,0,2],shape=[2,2],dtype=tf.float64)
with tf.Session() as sess:
 print(sess.run(tf.matrix_inverse(A)))

#输出结果为[[ 1. 0. ]
   [ 0. 0.5]]

import tensorflow as tf;
A =tf.constant([1,0,0,2],shape=[2,2],dtype=tf.float64)
with tf.Session() as sess:
 print(sess.run(tf.cholesky(A)))

#输出结果为[[ 1.   0.  ]
   [ 0.   1.41421356]]