blocks|key|4168480|text|您可以通过在名称后面添加'/‘来强制重用作用域，例如：tf.variable_scope("foo/",+reuse=True):|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4168481|然而，这并不能解决你的问题。|4168482|对于变量，调用tf.Variable将始终创建一个新变量，而调用tf.get_variable将重用该变量(如果该变量已经存在)。|4168483|但是有了占位符就没有tf.get_placeholder了。|4168484|您可以做的是在foo之外定义您的占位符，只定义一次，并在需要时使用tf.get_default_graph().get_tensor_by_name(name)或直接使用python变量按名称获取它们。|4168485|使用get_tensor_by_name的示例|4168486|import+tensorflow+as+tf

with+tf.name_scope("scope"):
++++tf.placeholder(tf.float32,name="a")
++++tf.placeholder(tf.float32,name="b")

def+foo():
++++a+=+tf.get_default_graph().get_tensor_by_name("scope/a:0")
++++b+=+tf.get_default_graph().get_tensor_by_name("scope/b:0")

++++return+a,b

a,b+=+foo()

print(a)
print(b)|code-block|syntax|javascript|4168487|请注意，与变量不同，占位符不维护可重用或不可重用的状态。它们只是一个指向稍后要输入的张量的“指针”。它们不应该是模型的一部分，而应该是模型的输入，所以无论如何都不应该多次创建它们。|4168488|entityMap^0|R|12|0|0|7|B|W|F|0|A|I|0|X|1B|0|2|I|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|11|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|12|8|@$9|13|A|14|B|C]|$9|15|A|16|B|C]]|D|@]|E|$]]|$1|J|3|K|5|6|7|17|8|@$9|18|A|19|B|C]]|D|@]|E|$]]|$1|L|3|M|5|6|7|1A|8|@$9|1B|A|1C|B|C]]|D|@]|E|$]]|$1|N|3|O|5|6|7|1D|8|@$9|1E|A|1F|B|C]]|D|@]|E|$]]|$1|P|3|Q|5|R|7|1G|8|@]|D|@]|E|$S|T]]|$1|U|3|V|5|6|7|1H|8|@]|D|@]|E|$]]|$1|W|3|-4|5|6|7|1I|8|@]|D|@]|E|$]]]|X|$]]

You can force the reuse of a scope by adding a '/' after the name i.e.: <code>tf.variable_scope("foo/", reuse=True):</code>

However that won't solve your problem.

In the case of variables, calling <code>tf.Variable</code> will always create a new variable, whereas calling <code>tf.get_variable</code> will reuse it if it already exists.

But with Placeholders there is no <code>tf.get_placeholder</code>.

What you can do is define your placeholders outside of foo, only once, and get them by name using <code>tf.get_default_graph().get_tensor_by_name(name)</code> or directly using the python variable whenever you need them.

example with <code>get_tensor_by_name</code>:

<pre><code>import tensorflow as tf

with tf.name_scope("scope"):
 tf.placeholder(tf.float32,name="a")
 tf.placeholder(tf.float32,name="b")

def foo():
 a = tf.get_default_graph().get_tensor_by_name("scope/a:0")
 b = tf.get_default_graph().get_tensor_by_name("scope/b:0")

 return a,b

a,b = foo()

print(a)
print(b)
</code></pre>

Note that placeholders, unlike variables, do not maintain a state that can be reused or not. They are merely a "pointer" to a tensor which will be fed later. They should not be part of your model, but an input to it, so you should not be creating them several times anyway.

blocks|key|4169038|text|对于在Jupyter笔记本中运行代码两次的情况，请确保笔记本的内核没有重用变量。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4169039|相反，如果在默认图形的范围内多次调用函数foo()，则始终会得到相同的结果：|offset|length|style|CODE|4169040|def+foo():
++with+tf.variable_scope("foo",+reuse=True):
++++a+=+tf.placeholder(tf.float32,name="a")
++++b+=+tf.placeholder(tf.float32,name="b")
++return+a,b

with+tf.Graph().as_default():
++a,b++=+foo()

++print(a)+#+gives+variable+name+'foo/a'
++print(b)+#+gives+variable+name+'foo/b'|code-block|syntax|javascript|4169041|但这与用例不同，在用例中，重复调用该函数以创建更多的占位符。|4169042|c,d+=+foo()|4169043|在这种情况下，上面的f4答案是推荐的解决方案。|4169044|entityMap^0|0|K|5|0|0|0|0|A|2|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|V|8|@$D|W|E|X|F|G]]|9|@]|A|$]]|$1|H|3|I|5|J|7|Y|8|@]|9|@]|A|$K|L]]|$1|M|3|N|5|6|7|Z|8|@]|9|@]|A|$]]|$1|O|3|P|5|J|7|10|8|@]|9|@]|A|$K|L]]|$1|Q|3|R|5|6|7|11|8|@$D|12|E|13|F|G]]|9|@]|A|$]]|$1|S|3|-4|5|6|7|14|8|@]|9|@]|A|$]]]|T|$]]

Just for the case that you ran the code in a Jupyter notebook twice make sure that the notebook's kernel is not reusing the variables. 

If you, instead, call the function <code>foo()</code> multiple times within the scope of the default graph you always get the same result:

<pre><code>def foo():
 with tf.variable_scope("foo", reuse=True):
 a = tf.placeholder(tf.float32,name="a")
 b = tf.placeholder(tf.float32,name="b")
 return a,b

with tf.Graph().as_default():
 a,b = foo()

 print(a) # gives variable name 'foo/a'
 print(b) # gives variable name 'foo/b'
</code></pre>

But that is different from the use case, where the function is called repeatedly to create more placeholders.

<pre><code>c,d = foo()
</code></pre>

In this case the answer above of <code>f4</code> is the recommended solution.

I just tried to get an understanding of the TensorFlow naming behavior and I still need some clarification. 
I worked on a project where I went into trouble with the naming of tensor, because they were predefined in a function, which was called later.

So I have the following example here:

<pre><code>import tensorflow as tf


 def foo():

 with tf.variable_scope("foo", reuse=True):

 a = tf.placeholder(tf.float32,name="a")
 b = tf.placeholder(tf.float32,name="b")

 return a,b
 ##

 a,b = foo()

 print(a)
 print(b)
</code></pre>

I get the output: 

<pre><code>Tensor("foo/a:0", dtype=float32)
Tensor("foo/b:0", dtype=float32)
</code></pre>

When I call it again though, I get the output:

<pre><code>Tensor("foo_1/a:0", dtype=float32)
Tensor("foo_1/b:0", dtype=float32)
</code></pre>

Why is this the case? I set reuse on true, so I expect the tensors to be in the same variable_scope "foo" again or that the program throws an error like "tensors already defined". 

So, I tried a workaround with tf.get_variable:

<pre><code> def foo():
 with tf.variable_scope("foo", reuse=True):

 a = tf.get_variable("v", [1])


 return a
##

a1 = foo()
print(a1)

graph = tf.get_default_graph()
#call tensors by name in tensorflow to avoid confusion with the naming
graph.get_tensor_by_name("foo/v:0")
</code></pre>

Here, i get always the same output:

<pre><code>&lt;tf.Variable 'foo/v:0' shape=(1,) dtype=float32_ref&gt;
</code></pre>

Unfortunately, I cannot work with variables because you cannot define a dynamic shape for them. You need placeholders to define variable shapes.
Can someone explain me why the program continues to create new variable_scopes for placeholders but not when I call tf.get_variable() ? 

Thank you!

Tensorflow variable_scope: tf.placeholder and tf.get_variable within a function

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我只是想了解一下TensorFlow的命名行为，我仍然需要一些澄清。我在一个项目中遇到了张量命名的麻烦，因为它们是在一个函数中预定义的，这个函数后来被调用。因此，我这里有以下示例：import tensorflow as tf    def foo():        with tf.variable_scope("f...

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