4.3共享变量

最新推荐文章于 2024-07-24 19:13:29 发布

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最新推荐文章于 2024-07-24 19:13:29 发布

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分类专栏：深度学习之TensorFlow入门、原理与进阶实战

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博客主要介绍了get_variable和Variable的区别，还展示了get_variable配合variable_scope的使用，包括不同情况下的程序及结果。此外，探讨了共享变量的作用域与初始化，以及作用域与操作符的受限范围，演示了variable_scope的as用法。

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4-9 get_variable和Variable的区别
程序：

import tensorflow as tf

tf.reset_default_graph()

var1 = tf.Variable(1.0, name='firstvar')
print("var1:", var1.name)
var1 = tf.Variable(2.0, name='firstvar')
print("var1:", var1.name)
var2 = tf.Variable(3.0)
print("var2:", var2.name)
var2 = tf.Variable(4.0)
print("var1:", var2.name)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print("var1=", var1.eval())
    print("var2=", var2.eval())

get_var1 = tf.get_variable("firstvar", [1], initializer=tf.constant_initializer(0.3))
print("get_var1:", get_var1.name)

get_var1 = tf.get_variable("firstvar1", [1], initializer=tf.constant_initializer(0.4))
print("get_var1:", get_var1.name)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print("get_var1=", get_var1.eval())

结果：

var1: firstvar:0
var1: firstvar_1:0
var2: Variable:0
var1: Variable_1:0

var1= 2.0
var2= 4.0
get_var1: firstvar_2:0
get_var1: firstvar1:0
get_var1= [0.4]

4-10 get_variable配合variable_scope
程序：

import tensorflow as tf

tf.reset_default_graph()

with tf.variable_scope("test1", ):#定义一个作用域test1
    var1 = tf.get_variable("firstvar", shape=[2], dtype=tf.float32)

with tf.variable_scope("test2"):#使用variable_scope隔开
    var2 = tf.get_variable("firstvar", shape=[2], dtype=tf.float32)

print("var1:", var1.name)
print("var2:", var2.name)

结果：

var1: test1/firstvar:0
var2: test2/firstvar:0

4-11 get_variable配合variable_scope1
程序：

import tensorflow as tf

tf.reset_default_graph()

with tf.variable_scope("test1", reuse=tf.AUTO_REUSE):
    var1 = tf.get_variable("firstvar", shape=[2], dtype=tf.float32)

    with tf.variable_scope("test2"):
        var2 = tf.get_variable("firstvar", shape=[2], dtype=tf.float32)

print("var1:", var1.name)
print("var2:", var2.name)

with tf.variable_scope("test1", reuse=tf.AUTO_REUSE):
    var3 = tf.get_variable("firstvar", shape=[2], dtype=tf.float32)
    with tf.variable_scope("test2"):
        var4 = tf.get_variable("firstvar", shape=[2], dtype=tf.float32)

print("var3:", var3.name)
print("var4:", var4.name)

结果：

var1: test1/firstvar:0
var2: test1/test2/firstvar:0
var3: test1/firstvar:0
var4: test1/test2/firstvar:0

4-12 共享变量的作用域与初始化
程序：

import tensorflow as tf

with tf.variable_scope("test1", initializer=tf.constant_initializer(0.4)):#初始化test1作用域
    var1 = tf.get_variable("firstvar", shape=[2], dtype=tf.float32)

    with tf.variable_scope("test2"):
        var2 = tf.get_variable("firstvar", shape=[2], dtype=tf.float32)
        var3 = tf.get_variable("var3", shape=[2], initializer=tf.constant_initializer(0.3))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print("var1=", var1.eval())
    print("var2=", var2.eval())
    print("var3=", var3.eval())

结果：

var1= [0.4 0.4]
var2= [0.4 0.4]
var3= [0.3 0.3]

4-13 作用域与操作符的受限范围
演示variable_scope的as用法，以及对应的作用域
程序：

import tensorflow as tf

tf.reset_default_graph()

with tf.variable_scope("scope1") as sp:
    var1 = tf.get_variable("v", [1])

print("sp:", sp.name)#作用域名称
print("var1:", var1.name)

with tf.variable_scope("scope2"):
    var2 = tf.get_variable("v", [1])

    with tf.variable_scope(sp) as sp1:
        var3 = tf.get_variable("v3", [1])

        with tf.variable_scope(""):
            var4 = tf.get_variable("v4", [1])

print("sp1:", sp1.name)
print("var2:", var2.name)
print("var3:", var3.name)
print("var4:", var4.name)
with tf.variable_scope("scope"):
    with tf.name_scope("bar"):
        v = tf.get_variable("v", [1])
        x = 1.0 + v
        with tf.name_scope(""):
            y = 1.0 + v
print("v:", v.name)
print("x.op:", x.op.name)
print("y.op:", y.op.name)

结果：

sp: scope1
var1: scope1/v:0
sp1: scope1
var2: scope2/v:0
var3: scope1/v3:0
var4: scope1//v4:0
v: scope/v:0
x.op: scope/bar/add
y.op: add