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变量是用来存储和更新参数的，也就是网络中的W或b。变量会被放在内存中。当模型训练结束后，他们需要被存在硬盘上，以便将来使用或分析模型。

一.变量

创建和初始化

　　当创建一个变量的时候，需要将一个Tensor作为初始值传入构造函数Variable()。这个初始值可以是随机值也可以是常量。Tensor的初始值需要指定shape，这个shape通常都是固定的，但是也可以通过一些高级方法重新调整。

　　只是创建了变量还是不够的，需要在定义一个初始化的操作，并且在使用任何变量之前，运行初始化的操作。例：

  1 import tensorflow as tf
  2 
  3 #Create two variables
  4 weights = tf.Variable(tf.random_normal([20,10], stddev=0.35), name="weights")
  5 biases = tf.Variable(tf.zeros([10]), name="biases")
  6 
  7 #Add other net structure...
  8 #...
  9 
 10 #Add an op to initialize the variables.
 11 init = tf.initialize_all_variables()
 12 
 13 #Later, when launching the model
 14 with tf.Session() as sess:
 15     sess.run(init)
 16     print weights.eval()
 17     print biases.eval()

输出：weights是[20,10]的矩阵，biases是[10]的向量。

注意：

　　tf.initialize_all_variables()是并行的初始化所有变量，所以如果需要用一个变量的值给另一个变量初始化的时候，一定要小心。虽然直接初始化不一定会出现问题，但是如果出现问题是很难找到这个原因的。

　　这时应该用如下方式进行初始化：

# Create a variable with a random value.
weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
                      name="weights")
# Create another variable with the same value as ‘weights‘.
w2 = tf.Variable(weights.initialized_value(), name="w2")
# Create another variable with twice the value of ‘weights‘
w_twice = tf.Variable(weights.initialized_value() * 0.2, name="w_twice")

另外还有自定义初始化，因为目前还没用到，先挖个坑，以后再填，详见Variables Documentation

变量可以被初始化成常量，或者是随机数，这个跟初始化的策略有关，具体什么情况下使用什么方法进行初始化也挖个坑，以后学到了再讲。

初始化成常量的方法：

tf.zeros(shape, dtype=tf.float32, name=None)　　

　　全部初始化为0

tf.zeros_like(tensor, dtype=None, name=None, optimize=True) 

　　创建一个shape和指定tensor相同的变量，但全部元素都为零。例如‘tensor’ =[[1,2,3], [4,5,6]]，那么tf.zeros_like(tensor) ==>[[0,0,0],[0,0,0]]

tf.ones(shape, dtype=tf.float32, name=None)

　　全部初始化为1

tf.ones_like()同上

tf.fill(dims, value, name=None)

　　对指定好的shape初始化为value值。tf.fill([2,3], 9) ==> [[9,9,9]　[9,9,9,]]　

tf.constant(value, dtype=None, shape=None, name=‘Const‘, verify_shape=False)

　　For example:

　　```python # Constant 1-D Tensor populated with value list. tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7]

　　# Constant 2-D tensor populated with scalar value -1. tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.] [-1. -1. -1.]] ```

初始化成序列

tf.linspace()

tf.range()

初始化为随机数

tf.random_normal(shape, mean=0.0, stddev=1.0. dtype=tf.float32,  seed=None, name=None)

　　按照正太分布初始化

tf.truncated_normal()

　　同上，但超过两个标准差的数据被丢弃，并重新随机选择数据。即产生的数据都在正太分布的两个标准差内。

保存和加载

　　得到了训练好的模型之后，我们需要将这个模型保存下来，之后可以再次读取这个模型进行进一步的使用。最简单的方法是使用托tf.train.Saver。下面是例子：

首先是保存变量：

import tensorflow as tf

#Create some variables.
v1 = tf.Variable([1,2,3,4,5], name="v1")
v2 = tf.Variable([11,12,13,14], name="v2")

#Add an op to initialize the variables
init = tf.initialize_all_variables()

#Add an op to save and restore all the variables
saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(init)
    #Do something with the model

    print v1.eval()
    print v2.eval()
    #save the variables to disk
    save_path = saver.save(sess,"model/model.ckpt")
    print "Model saved in file:", save_path

接下来是加载：

import tensorflow as tf

v3 = tf.Variable([0,0,0,0,0], name=‘v1‘)
v4 = tf.Variable([0,0,0,0], name=‘v2‘)

saver = tf.train.Saver()

with tf.Session() as sess:
    saver.restore(sess,"model/model.ckpt")
    print "Model restored."
    print v3.eval()
    print v4.eval()

在从文件中恢复变量时，不需要事先进行初始化。注意：在回复变量时，tf.Variable()里的name参数一定要与原来的变量名称一致，这样才能恢复到对应的变量。

变量的函数

__init__(initial_value=None, trainable=True, collections=None, validate_shape=True, caching_device=None, name=None, variable_def=None, dtype=None, expected_shape=None, import_scope=None)

Creates a new variable with value initial_value.

The new variable is added to the graph collections listed in collections, which defaults to [GraphKeys.GLOBAL_VARIABLES].

If trainable is True the variable is also added to the graph collectionGraphKeys.TRAINABLE_VARIABLES.

This constructor creates both a variable Op and an assign Op to set the variable to its initial value.

Args:

• initial_value: A Tensor, or Python object convertible to a Tensor, which is the initial value for the Variable. The initial value must have a shape specified unless validate_shape is set to False. Can also be a callable with no argument that returns the initial value when called. In that case, dtype must be specified. (Note that initializer functions from init_ops.py must first be bound to a shape before being used here.)
• trainable: If True, the default, also adds the variable to the graph collection GraphKeys.TRAINABLE_VARIABLES. This collection is used as the default list of variables to use by the Optimizer classes.应该是会在做最优化的时候使用得到
• collections: List of graph collections keys. The new variable is added to these collections. Defaults to [GraphKeys.GLOBAL_VARIABLES].
• validate_shape: If False, allows the variable to be initialized with a value of unknown shape. If True, the default, the shape ofinitial_value must be known.
• caching_device: Optional device string describing where the Variable should be cached for reading. Defaults to the Variable‘s device. If not None, caches on another device. Typical use is to cache on the device where the Ops using the Variable reside, to deduplicate copying through Switch and other conditional statements.
• name: Optional name for the variable. Defaults to ‘Variable‘ and gets uniquified automatically.
• variable_def: VariableDef protocol buffer. If not None, recreates the Variable object with its contents. variable_def and the other arguments are mutually exclusive.
• dtype: If set, initial_value will be converted to the given type. If None, either the datatype will be kept (if initial_value is a Tensor), or convert_to_tensor will decide.
• expected_shape: A TensorShape. If set, initial_value is expected to have this shape.
• import_scope: Optional string. Name scope to add to the Variable.Only used when initializing from protocol buffer.

Raises:

• ValueError: If both variable_def and initial_value are specified.
• ValueError: If the initial value is not specified, or does not have a shape and validate_shape is True.

eval(session=None)

In a session, computes and returns the value of this variable.

This is not a graph construction method, it does not add ops to the graph.

This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used. See tf.Session for more information on launching a graph and on sessions.

 

v = tf.Variable([1, 2])
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    # Usage passing the session explicitly.
    print(v.eval(sess))
    # Usage with the default session.  The ‘with‘ block
    # above makes ‘sess‘ the default session.
    print(v.eval())

Args:

• session: The session to use to evaluate this variable. If none, the default session is used.

Returns:

A numpy ndarray with a copy of the value of this variable.

二.常见类

Tensor

　　Tensor类是最核心的数据结构。Tensor是一个处理操作输出的符号，它并不保存操作输出的值，但是提供了在Session中计算这些值的方法。

This class has two primary purposes:

1. A Tensor can be passed as an input to another Operation. This builds a dataflow connection between operations, which enables TensorFlow to execute an entire Graph that represents a large, multi-step computation.

2. After the graph has been launched in a session, the value of the Tensor can be computed by passing it toSession.run(). t.eval() is a shortcut for calling tf.get_default_session().run(t).

 Operation

Operation是tensorflow中的节点，使用Tensor作为输入，并输出一个Tensor。其实就是运算操作，例如tf.matmul(a,b），就是a×b。当图在session中启动之后，operation拒可以通过tf.Session.run()这种操作执行，或者op.run()。

Tensorflow学习笔记（2）：变量，常用类和一些操作

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原文地址：http://www.cnblogs.com/claude-gyh/p/6554322.html