在TensorFlow中使用矩阵

单位矩阵

import tensorflow as tf

I_matrix=tf.eye(5)


with tf.Session() as sess:
  print(sess.run(I_matrix))

 variable矩阵

import tensorflow as tf

X=tf.Variable(tf.eye(10))
A=tf.Variable(tf.random_normal([5,10]))



with tf.Session() as sess:
  sess.run(X.initializer)
  print(sess.run(X))
  sess.run(A.initializer)
  print(sess.run(A))

矩阵乘法

import tensorflow as tf

X=tf.Variable(tf.eye(10))
A=tf.Variable(tf.random_normal([5,10]))
product=tf.matmul(A,X)


with tf.Session() as sess:
  sess.run(X.initializer)
  #print(sess.run(X))
  sess.run(A.initializer)
  #print(sess.run(A))
  print(sess.run(product))

创建一个只含有0,1的矩阵

import tensorflow as tf


b=tf.Variable(tf.random_uniform([5,10],0,2,tf.int32))


with tf.Session() as sess:
  sess.run(b.initializer)
  print(sess.run(b))

矩阵加法

import tensorflow as tf



A=tf.Variable(tf.random_normal([5,10]))
b=tf.Variable(tf.random_uniform([5,10],0,2,tf.int32))
b_new=tf.cast(b,tf.float32)
add=tf.add(A,b_new)
sub=A-b_new

with tf.Session() as sess:
  sess.run(A.initializer)
  sess.run(b.initializer)
  print(sess.run(A))
  print(sess.run(b))
  print(sess.run(add))
  print(sess.run(sub))

 

剩下还有许多，如a和b是同类型矩阵时：

按元素相乘A=a*b

按元素相除A=tf.div(a,b)

按元素取余数A=tf.mod(a.b)

乘以一个标量A=tf.scalar_mul(2,a)