如何为 TensorFlow 变量赋值? [英] How to assign a value to a TensorFlow variable?

问题描述

我正在尝试为 python 中的 tensorflow 变量分配一个新值.

 将 tensorflow 导入为 tf将 numpy 导入为 npx = tf.Variable(0)init = tf.initialize_all_variables()sess = tf.InteractiveSession()sess.run(初始化)打印(x.eval())x.assign(1)打印(x.eval())

但我得到的输出是

<预><代码>00

所以值没有改变.我错过了什么?

解决方案

在 TF1 中，声明 x.assign(1) 实际上并没有将值 1 分配给 x，而是创建一个 tf.Operation 您必须显式运行以更新变量.* 调用 Operation.run() 或 Session.run() 可以用来运行操作:

assign_op = x.assign(1)sess.run(assign_op) # 或 `assign_op.op.run()`打印(x.eval())# ==>1

(* 实际上，它返回一个tf.Tensor，对应于变量的更新值，以便于链式赋值.)

然而，在 TF2 x.assign(1) 现在会急切地分配值:

x.assign(1)打印(x.numpy())# ==>1

I am trying to assign a new value to a tensorflow variable in python.

import tensorflow as tf
import numpy as np

x = tf.Variable(0)
init = tf.initialize_all_variables()
sess = tf.InteractiveSession()
sess.run(init)

print(x.eval())

x.assign(1)
print(x.eval())

But the output I get is

0
0

So the value has not changed. What am I missing?

解决方案

In TF1, the statement x.assign(1) does not actually assign the value 1 to x, but rather creates a tf.Operation that you have to explicitly run to update the variable.* A call to Operation.run() or Session.run() can be used to run the operation:

assign_op = x.assign(1)
sess.run(assign_op)  # or `assign_op.op.run()`
print(x.eval())
# ==> 1

(* In fact, it returns a tf.Tensor, corresponding to the updated value of the variable, to make it easier to chain assignments.)

However, in TF2 x.assign(1) will now assign the value eagerly:

x.assign(1)
print(x.numpy())
# ==> 1

这篇关于如何为 TensorFlow 变量赋值?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！