TensorFlow 变量和常量 [英] TensorFlow Variables and Constants

问题描述

我是 tensorflow 的新手，我无法理解变量和常量的区别，我的想法是我们将变量用于方程，常量用于直接值，但是为什么代码 #1 只有效，为什么代码不可用#2 和 #3，请解释在哪些情况下我们必须先运行我们的图 (a) 然后我们的变量 (b) 即

 (a) session.run(model)(b) 打印(session.run(y))

在这种情况下我可以直接执行这个命令即

print(session.run(y))

代码#1:

x = tf.constant(35, name='x')y = tf.Variable(x + 5, name='y')模型 = tf.global_variables_initializer()使用 tf.Session() 作为会话:session.run(模型)打印(会话.运行(y))

代码#2:

x = tf.Variable(35, name='x')y = tf.Variable(x + 5, name='y')模型 = tf.global_variables_initializer()使用 tf.Session() 作为会话:session.run(模型)打印(会话.运行(y))

代码#3:

x = tf.constant(35, name='x')y = tf.constant(x + 5, name='y')模型 = tf.global_variables_initializer()使用 tf.Session() 作为会话:session.run(模型)打印(会话.运行(y))

解决方案

在 TensorFlow 中，常量和变量之间的区别在于，当您声明一些 constant，它的值在以后不能改变(同样初始化应该带值，不带操作).

然而，当您声明一个变量时，您可以在tf.assign() 方法的未来(初始化可以通过值或操作).

函数 tf.global_variables_initializer() 用作为参数传递的值，但它在异步模式下工作，因此当变量之间存在依赖关系时无法正常工作.

您的第一个代码 (#1) 工作正常，因为不依赖于变量初始化，并且常量是用一个值构造的.

由于 tf.global_variables_initializer() 的异步行为，第二个代码 (#2) 不起作用.您可以使用 tf.variables_initializer() 修复它，如下所示:

x = tf.Variable(35, name='x')model_x = tf.variables_initializer([x])y = tf.Variable(x + 5, name='y')model_y = tf.variables_initializer([y])使用 tf.Session() 作为会话:session.run(model_x)session.run(model_y)打印(会话.运行(y))

第三个代码 (#3) 不能正常工作，因为您试图用一个操作初始化一个常量，这是不可能的.要解决它，适当的策略是 (#1).

关于你的最后一个问题.当你的计算图中有变量(b) print(session.run(y))时，你需要运行(a) session.run(model).>

I am new to tensorflow , I am not able to understand the difference of variable and constant, I get the idea that we use variables for equations and constants for direct values , but why code #1 works only and why not code#2 and #3, and please explain in which cases we have to run our graph first(a) and then our variable(b) i.e

 (a) session.run(model)
 (b) print(session.run(y))

and in which case I can directly execute this command i.e

print(session.run(y))

Code #1 :

x = tf.constant(35, name='x')
y = tf.Variable(x + 5, name='y')

model = tf.global_variables_initializer() 

with tf.Session() as session:
    session.run(model)
    print(session.run(y))

Code #2 :

x = tf.Variable(35, name='x')
y = tf.Variable(x + 5, name='y')

model = tf.global_variables_initializer() 

with tf.Session() as session:
    session.run(model)
    print(session.run(y))

Code #3 :

x = tf.constant(35, name='x')
y = tf.constant(x + 5, name='y')

model = tf.global_variables_initializer() 

with tf.Session() as session:
    session.run(model)
    print(session.run(y))

解决方案

In TensorFlow the differences between constants and variables are that when you declare some constant, its value can't be changed in the future (also the initialization should be with a value, not with operation).

Nevertheless, when you declare a Variable, you can change its value in the future with tf.assign() method (and the initialization can be achieved with a value or operation).

The function tf.global_variables_initializer() initialises all variables in your code with the value passed as parameter, but it works in async mode, so doesn't work properly when dependencies exists between variables.

Your first code (#1) works properly because there is no dependencies on variable initialization and the constant is constructed with a value.

The second code (#2) doesn't work because of the async behavior of tf.global_variables_initializer(). You can fix it using tf.variables_initializer() as follows:

x = tf.Variable(35, name='x')
model_x = tf.variables_initializer([x])

y = tf.Variable(x + 5, name='y')
model_y = tf.variables_initializer([y])


with tf.Session() as session:
   session.run(model_x)
   session.run(model_y)
   print(session.run(y))

The third code (#3) doesn't work properly because you are trying to initialize a constant with an operation, that isn't possible. To solve it, an appropriate strategy is (#1).

Regarding to your last question. You need to run (a) session.run(model) when there are variables in your calculation graph (b) print(session.run(y)).

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