tf.diag(diagonal,name=None) #生成對角矩陣

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

 #輸出的結果為[[1 0 0 0]    [0 1 0 0]    [0 0 1 0]    [0 0 0 1]]

tf.diag_part(input,name=None) #功能與tf.diag函數相反,返回對角陣的對角元素

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)))

#輸出結果為[1,1,1,1]

tf.trace(x,name=None) #求一個2維Tensor足跡，即為對角值diagonal之和

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)))#輸出結果為4

tf.transpose(a,perm=None,name='transpose') #調換tensor的維度順序，按照列表perm的維度排列調換tensor的順序

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))) #輸出結果為[[1 0 0 1]                             [0 1 1 0]                             [0 2 1 0]                             [3 0 0 1]]

tf.matmul(a,b,transpose_a=False,transpose_b=False,a_is_sparse=False,b_is_sparse=False,name=None) #矩陣相乘

transpose_a=False,transpose_b=False #運算前是否轉置

a_is_sparse=False,b_is_sparse=False #a,b是否當作系數矩陣進行運算

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)))

#輸出結果為[[2 1]   [0 6]]

tf.matrix_determinant(input,name=None) #計算行列式

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))) 

#輸出結果為3.0

tf.matrix_inverse(input,adjoint=None,name=None)

adjoint決定計算前是否進行轉置

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)))

#輸出結果為[[ 1. 0. ]   [ 0. 0.5]]

tf.cholesky(input,name=None) #對輸入方陣cholesky分解，即為將一個對稱正定矩陣表示成一個下三角矩陣L和其轉置的乘積德分解

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)))

#輸出結果為[[ 1.   0.  ]   [ 0.   1.41421356]]

以上這篇對Tensorflow中的矩陣運算函數詳解就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持武林站長站。