tf.name_scope()和tf.Variable()，tf.variable_scope()和tf.get_variable()

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Author：Maddock
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① tf.name_scope()和tf.Variable()：

见我将它们俩写一起就知道他们两个总是一起使用，目的是方便参数命名管理，比如在Alexnet—Tensorflow实战中，Alexnet一共有5个卷积层，而每层都有“kernel”，“bias”等参数，所以为了区分每一层的参数命名情况，引入tf.name_scope进行参数命名管理。
代码：

import tensorflow as tf
# 第一个卷积层
with tf.name_scope("cov1") as scope:
    weight1 = tf.Variable([1.0,2.0],name = 'weights')
    bias1 = tf.Variable([0.3],name = 'bias')

# 第二个卷积层
with tf.name_scope('cov2') as scope:
    weight2 = tf.Variable([4.0,2.0],name= 'weights')
    bias2 = tf.Variable([0.33],name = 'bias')

print(weight1.name)
print(weight2.name)

输出：

注意：上述代码执行完毕之后就已经存在了两个conv1和conv2的空间，如果再次执行上述代码会再次生成其他命名空间

如下：

import tensorflow as tf
# 第一个卷积层
with tf.name_scope("cov1") as scope:
    weight1 = tf.Variable([1.0,2.0],name = 'weights')
    bias1 = tf.Variable([0.3],name = 'bias')

# 第二个卷积层
with tf.name_scope('cov2') as scope:
    weight2 = tf.Variable([4.0,2.0],name= 'weights')
    bias2 = tf.Variable([0.33],name = 'bias')

print(weight1.name)
print(weight2.name)

import tensorflow as tf

# 第一个卷积层
with tf.name_scope("cov1") as scope:
    weight1 = tf.Variable([1.0, 2.0], name='weights')
    bias1 = tf.Variable([0.3], name='bias')

# 第二个卷积层
with tf.name_scope('cov2') as scope:
    weight2 = tf.Variable([4.0, 2.0], name='weights')
    bias2 = tf.Variable([0.33], name='bias')

print(weight1.name)
print(weight2.name)

输出：

② tf.variable_scope()和tf.get_variable()

tf.variable_scope() 主要结合 tf.get_variable() 来使用，目的是实现变量共享。

代码：

# 使用tf.Variable_scope()和tf.get_variable()
import  tensorflow as tf
with tf.variable_scope('v_scope') as scope1:
    weights1 = tf.get_variable("Weights",shape=[2,3])
    bias1 = tf.get_variable("bias",shape=[3])

# 下面来共享定义好的变量
# 注意：在下面的 scope 中的变量必须已经定义过了，才能设置 reuse=True，否则会报错
with tf.variable_scope('v_scope',reuse = True) as scope2:
    weights2 = tf.get_variable("Weights")

print(weights1.name)
print(weights2.name)

输出：

end