tensorflow实现Variable，Tensor，Numpy之间的互相转换 tensorflow实现Variable，Tensor，N

import tensorflow.compat.v1 as tf tf.disable_v2_behavior() import numpy as np weight = tf.get_variable(name='weights',initializer=tf.random_normal([5,2], stddev=0.01)) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) print('------------------打印出已经初始化之后的Variable的值------------------------------') print(sess.run(weight)) print('----------weight的类型------------') print(type(weight)) # Variable转换为Tensor # Variable类型转换为tensor类型(无论是numpy转换为Tensor还是Variable转换为Tensor都可以使用tf.convert_to_tensor) data_tensor = tf.convert_to_tensor(weight) # 打印出Tensor的值（由Variable转化而来） print('------------------Variable转化为Tensor，打印出Tensor的值--------------------------') print(sess.run(data_tensor)) # tensor转化为numpy print('-------------------tensor转换为numpy，打印出numpy的值-----------------') data_numpy = data_tensor.eval() print(data_numpy) print('------------------numpy转换为Tensor---------------------------') ten = tf.convert_to_tensor(data_numpy) print(ten) print(sess.run(ten)) # tensor转化为Variable（其实是Variable继承Tensor的结构，但是没有值 print('---------------------tensor转换为Variable(需要重新进行初始化)----------------------') v = tf.Variable(data_tensor) # 此时Variable继承的是Tensor的结构，至于Variable的值，需要重新进行initialize sess.run(tf.global_variables_initializer()) print(sess.run(weight)) # 此时输出的weight和v的结构是相同的，但是值是不同的。 print(sess.run(v)) # Variable转换为numpy（也是使用eval） print('---------------Variable转换为numpy（也是使用eval）--------------------') data_numpy2 = weight.eval() print(data_numpy2)