tensorflow中生成variable有兩個函數：tf.Variable和tf.get_variable。

tf.Variable定義如下

class Variable(object)  
def __init__(self, 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 collection GraphKeys.TRAINABLE_VARIABLES.
    This constructor creates both a variable Op and an assign Op to set the variable to its initial value.
  
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.
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 of initial_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.

tf.get_variable定義如下

def get_variable(name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True, use_resource=None, custom_getter=None)

tf.Variable函數會返回一個variable，如果給出的name已經存在，會自動修改name，生成個新的。

tf.get_variable函數擁有一個變量檢查機制，會檢測已經存在的變量是否設置為共享變量，如果已經存在的變量沒有設置為共享變量，TensorFlow 運行到第二個擁有相同名字的變量的時候，就會報錯。

tf.get_variable一般和tf.variable_scope配合使用，用於在同一個的變量域中共享同一個變量，或在不同變量域中使用同名變量。

測試代碼

import tensorflow as tf

w1 = tf.Variable(1, name=‘w1‘)
w2 = tf.Variable(2, name=‘w1‘)

print(‘w1.name=‘,w1.name)
print(‘w2.name=‘,w2.name)

# w3 = tf.get_variable(name=‘w3‘,initializer=1)
# w4 = tf.get_variable(name=‘w3‘,initializer=2)
w3 = tf.get_variable(name=‘w3‘,initializer=2)
print(‘w3.name=‘,w3.name)

with tf.variable_scope(‘scope1‘) as scope1:
    w1scope1 = tf.Variable(1, name=‘w1‘)
    print(‘w1scope1.name=‘, w1scope1.name)
    w4 = tf.get_variable(name=‘w4‘, initializer=1.0)
    print(‘w4.name=‘, w4.name)
    # scope1.reuse_variables() 或 tf.get_variable_scope().reuse_variables()
    tf.get_variable_scope().reuse_variables()
    w5 = tf.get_variable(name=‘w4‘, initializer=2.0)
    print(‘w5.name=‘, w5.name)

    # 報錯，生成失敗
    # w8 = tf.get_variable(name=‘w8‘, initializer=1.0)
print(‘w1.name=‘, w1.name)

with tf.variable_scope(‘scope2‘, reuse=None) as scope2:
    w6 = tf.get_variable(name=‘w6‘, initializer=1.0)
    print(‘w6.name=‘, w6.name)
    w1 = tf.Variable(1, name=‘w1‘)
    print(‘w1.name=‘, w1.name)

print(‘w1.name=‘, w1.name)


with tf.variable_scope(‘scope2‘, reuse=True):
    w7 = tf.get_variable(name=‘w6‘, initializer=2.0)
    print(‘w7.name=‘, w7.name)

結果

w1.name= w1:0
w2.name= w1_1:0
w3.name= w3:0
w1scope1.name= scope1/w1:0
w4.name= scope1/w4:0
w5.name= scope1/w4:0
w1.name= w1:0
w6.name= scope2/w6:0
w1.name= scope2/w1:0
w1.name= scope2/w1:0
w7.name= scope2/w6:0

測試結果可以看出

1、tf.Variable如果有重復的name，那麽新生成的variable名字自動加後綴_1 _2

2、在variable_scope內使用同一個name的variable和全局的同name或其他variable_scope的同name完全是不同的variable。

3、在一個variable_scope下申請variable必須設置variable_scope的reuse為None。

4、同一個variable_scope下想重用variable需要執行 域名字.reuse_variables() 或 tf.get_variable_scope().reuse_variables() 而且之後不能再申請除了重用外的變量。

5、想重用某個variable_scope下的variable，重設reuse=True就可以了。

TF-variable生成方法區別