tensorflow笔记4：函数：tf.assign()、tf.assign_add()、tf.identity()、tf.control_dependencies()

函数原型：

tf.assign(ref, value, validate_shape=None, use_locking=None, name=None)

  Defined in tensorflow/python/ops/state_ops.py.

  将　value 赋值给　ref，并输出 ref，即 ref = value；

  这使得需要使用复位值的连续操作变简单

  Defined in tensorflow/python/framework/tensor_shape.py.

Args	Annotations
ref	A mutable Tensor. Should be from a Variable node. May be uninitialized.
value	A Tensor. Must have the same type as ref. The value to be assigned to the variable.
validate_shape	An optional bool. Defaults to True. If true, the operation will validate that the shape of ‘value’ matches the shape of the Tensor being assigned to. If false, ‘ref’ will take on the shape of ‘value’.
use_locking	An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
name	A name for the operation (optional).

---

Returns :

  Same as “ref”. Returned as a convenience for operations that want to use the new value after the variable has been reset.

函数原型：

tf.assign_add(ref,value,use_locking=None,name=None)

Defined in tensorflow/python/ops/state_ops.py.

See the guide: Variables > Variable helper functions

Update 'ref' by adding 'value' to it.

更新ref的值，通过增加value，即：ref = ref + value；

This operation outputs "ref" after the update is done. This makes it easier to chain operations that need to use the reset value.

函数原型：tf.identity

tf.identity(input,name=None)

Return a tensor with the same shape and contents as input.

返回一个具有相同形状张量和内容作为输入；

Args:

• input: A Tensor.
• name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as input.

函数原型：tf.control_dependencies

tf.control_dependencies(control_inputs)

tf.control_dependencies()设计是用来控制计算流图的，给图中的某些计算指定顺序。比如：我们想要获取参数更新后的值，那么我们可以这么组织我们的代码。自己的理解：如果不是tf的tensor，并且没有加入到整个图中，则不会执行；

Defined in tensorflow/python/framework/ops.py.

See the guide: Building Graphs > Utility functions

Wrapper for Graph.control_dependencies() using the default graph.

See tf.Graph.control_dependencies for more details.

举个例子：

下面程序要做的是，5次循环，每次循环给x加1，赋值给y，然后打印出来,

x = tf.Variable(0.0)
#返回一个op，表示给变量x加1的操作
x_plus_1 = tf.assign_add(x, 1)

#control_dependencies的意义是，在执行with包含的内容（在这里就是 y = x）前
#先执行control_dependencies中的内容（在这里就是 x_plus_1）
with tf.control_dependencies([x_plus_1]):
    y = x
init = tf.initialize_all_variables()

with tf.Session() as session:
    init.run()
    for i in xrange(5):
        print(y.eval())

由于control_dependencies的所以执行print前都会先执行x_plus_1。

这个打印的是0，0，0，0，0 ,也就是说没有达到我们预期的效果，这是因为此时的y是一个复制了x变量的变量，并未和图上的节点相联系不接受流程控制函数的调遣，

改成如下，

import tensorflow as tf
x = tf.Variable(0.0)
print(x)
x_plus_1 = tf.assign_add(x, 1)
with tf.control_dependencies([x_plus_1]):
    y = x + 0.0
    print(y) #z=tf.identity(x,name='x')
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    for i in range(5):
        print(sess.run(y))

<tf.Variable 'Variable:0' shape=() dtype=float32_ref>

Tensor("add:0", shape=(), dtype=float32)

1.0  2.0  3.0  4.0  5.0

可以看到当y定义为节点的输出后，就可以顺利执行操作了，此时y成为节点的输出，可以被图识别。

如果改成这样：

x = tf.Variable(0.0)
x_plus_1 = tf.assign_add(x, 1)

with tf.control_dependencies([x_plus_1]):
    y = tf.identity(x)#修改部分
init = tf.initialize_all_variables()

with tf.Session() as session:
    init.run()
    for i in range(5):
        print(y.eval())
This works: it prints 1, 2, 3, 4, 5.

这时候打印的是1，2，3，4，5

解释：

查询y为：Tensor("Identity_1:0", shape=(), dtype=float32)，和节点联系起来了。
tf.identity是返回了一个一模一样新的tensor，再control_dependencies的作用块下，需要增加一个新节点到gragh中。