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

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)


tensorflow实现Variable,Tensor,Numpy之间的互相转换
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tensorflow实现Variable,Tensor,Numpy之间的互相转换
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image.png 【tensorflow实现Variable,Tensor,Numpy之间的互相转换】输出


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