# 3.7    常量与变量

**1.4.1** **常量 Constant**

常量指在运行过程中不会改变的值，在TensorFlow中无需进行初始化操作。

创建语句：

Constant\_name = tf.constant(value)

常量在TensorFlow中一般被用于设置训练步数、训练步长和训练轮数等超参数，此类参数在程序执行过程中一般不需要被改变，所以一般被设置为常量。

**1.4.2** **变量 Variable**

变量是指在运行过程中会改变的值，在TensorFlow中需要进行初始化操作。

创建语句：

name\_variable = tf.Variable(value, name)

**注意：V是大写字母**

个别变量初始化：

init\_op = name\_variable.initializer()

使用TensorFlow编写一个简单的神经网络一般会用到几十个变量，若编写大型的神经网络，往往会使用到成千上万个变量。若每个变量定义完都要初始化未免太过繁琐，所以TensorFlow有提供所有变量初始化的语句。\
   所有变量初始化：

init\_op = tf.global\_variables\_initializer()

![图3-10 变量的初始化](/files/-L_XdzQ3Gg2X4V4MBGeg)

如图10所示，初始化函数也需要调用会话进行执行。TensorFlow中定义的每个变量都要进行初始化，不然会报错。

## > 示例代码 <a href="#shi-li-dai-ma" id="shi-li-dai-ma"></a>

{% code title="1.py" %}

```python
node1 = tf.Variable(3.0,tf.float32,name="node1")
node2 = tf.Variable(4.0,tf.float32,name="node2")
result = tf.add(node1, node2, name='add')

sess = tf.Session()

#变量初始化
init = tf.global_variables_initializer()
sess.run(init)

print(sess.run(result))
```

{% endcode %}


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