tensorflow2.0——可训练变量

import tensorflow as tf
import numpy as np

###############     tf.Variable(initial value,dtype)    ###############

print(‘############数字为参数###########‘)
a = tf.Variable(3)
print(‘数字为参数a:‘,a)
print(‘############列表为参数###########‘)
a = tf.Variable([1,6])
print(‘列表为参数a:‘,a)
print(‘############np数组为参数###########‘)
a = tf.Variable(np.array([3,6.0]))
print(‘np数组为参数a:‘,a)
print(‘############张量为参数###########‘)
a = tf.Variable(tf.constant([[1,1],[2,2],[2,3]]))
print(‘张量为参数a:‘,a)
print(‘a.trainable:‘,a.trainable)           #   该变量是否可以被训练
print(‘type(a):‘,type(a))
print()
###############     对象名.assign()    ###############
a = tf.Variable([1,2,3])
print(‘原可训练变量a：‘,a)
a.assign([4,2,3])                       #   将可训练变量改变
print(‘改变后的a：‘,a)
a.assign_add([4,0,5])                   #   将变量相加
print(‘相加后的变量a:‘,a)
a.assign_sub([8,8,8])                   #   将变量相减
print(‘相减后的变量a:‘,a)
print()
###############     isinstance()    ###############
a = tf.constant(5)
b = tf.Variable(5)
print(‘a:{}\nb{}‘.format(a,b))
print("isinstance(a,tf.Tensor):{},isinstance(a,tf.Variable):{}".format(isinstance(a,tf.Tensor),isinstance(a,tf.Variable)))
print("isinstance(b,tf.Tensor):{},isinstance(b,tf.Variable):{}".format(isinstance(b,tf.Tensor),isinstance(b,tf.Variable)))