【https://blog.csdn.net/u013921430 转载】
slim是一种轻量级的tensorflow库，可以使模型的构建，训练，测试都变得更加简单。在slim库中对很多常用的函数进行了定义，slim.arg_scope（）是slim库中经常用到的函数之一。函数的定义如下；
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@tf_contextlib.contextmanager
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def arg_scope(list_ops_or_scope, **kwargs):
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"""Stores the default arguments for the given set of list_ops.
-
-
For usage, please see examples at top of the file.
-
-
Args:
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list_ops_or_scope: List or tuple of operations to set argument scope for or
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a dictionary containing the current scope. When list_ops_or_scope is a
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dict, kwargs must be empty. When list_ops_or_scope is a list or tuple,
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then every op in it need to be decorated with @add_arg_scope to work.
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**kwargs: keyword=value that will define the defaults for each op in
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list_ops. All the ops need to accept the given set of arguments.
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Yields:
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the current_scope, which is a dictionary of {op: {arg: value}}
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Raises:
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TypeError: if list_ops is not a list or a tuple.
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ValueError: if any op in list_ops has not be decorated with @add_arg_scope.
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"""
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if isinstance(list_ops_or_scope, dict):
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# Assumes that list_ops_or_scope is a scope that is being reused.
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if kwargs:
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raise ValueError(
'When attempting to re-use a scope by suppling a'
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'dictionary, kwargs must be empty.')
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current_scope = list_ops_or_scope.copy()
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try:
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_get_arg_stack().append(current_scope)
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yield current_scope
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finally:
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_get_arg_stack().pop()
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else:
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# Assumes that list_ops_or_scope is a list/tuple of ops with kwargs.
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if
not isinstance(list_ops_or_scope, (list, tuple)):
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raise TypeError(
'list_ops_or_scope must either be a list/tuple or reused'
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'scope (i.e. dict)')
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try:
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current_scope = current_arg_scope().copy()
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for op
in list_ops_or_scope:
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key_op = _key_op(op)
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if
not has_arg_scope(op):
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raise ValueError(
'%s is not decorated with @add_arg_scope',
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_name_op(op))
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if key_op
in current_scope:
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current_kwargs = current_scope[key_op].copy()
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current_kwargs.update(kwargs)
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current_scope[key_op] = current_kwargs
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else:
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current_scope[key_op] = kwargs.copy()
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_get_arg_stack().append(current_scope)
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yield current_scope
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finally:
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_get_arg_stack().pop()
如注释中所说，这个函数的作用是给list_ops中的内容设置默认值。但是每个list_ops中的每个成员需要用@add_arg_scope修饰才行。所以使用slim.arg_scope（）有两个步骤：
- 使用@slim.add_arg_scope修饰目标函数
- 用 slim.arg_scope（）为目标函数设置默认参数.
例如如下代码；首先用@slim.add_arg_scope修饰目标函数fun1（），然后利用slim.arg_scope（）为它设置默认参数。
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import tensorflow
as tf
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slim =tf.contrib.slim
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@slim.add_arg_scope
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def fun1(a=0,b=0):
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return (a+b)
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with slim.arg_scope([fun1],a=
10):
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x=fun1(b=
30)
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print(x)
运行结果为：
40
平常所用到的slim.conv2d( ),slim.fully_connected( ),slim.max_pool2d( )等函数在他被定义的时候就已经添加了@add_arg_scope。以slim.conv2d( )为例；
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@
add_arg_scope
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def convolution(inputs,
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num_outputs,
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kernel_size,
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stride=1,
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padding='SAME',
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data_format=None,
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rate=1,
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activation_fn=nn.relu,
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normalizer_fn=None,
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normalizer_params=None,
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weights_initializer=initializers.xavier_initializer(),
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weights_regularizer=None,
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biases_initializer=init_ops.zeros_initializer(),
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biases_regularizer=None,
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reuse=None,
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variables_collections=None,
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outputs_collections=None,
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trainable=True,
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scope=None):
所以，在使用过程中可以直接slim.conv2d( )等函数设置默认参数。例如在下面的代码中，不做单独声明的情况下，slim.conv2d, slim.max_pool2d, slim.avg_pool2d三个函数默认的步长都设为1，padding模式都是'VALID'的。但是也可以在调用时进行单独声明。这种参数设置方式在构建网络模型时，尤其是较深的网络时，可以节省时间。
-
with slim.arg_scope(
-
[slim.conv2d, slim.max_pool2d, slim.avg_pool2d],stride =
1, padding =
'VALID'):
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net = slim.conv2d(inputs,
32, [
3,
3], stride =
2, scope =
'Conv2d_1a_3x3')
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net = slim.conv2d(net,
32, [
3,
3], scope =
'Conv2d_2a_3x3')
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net = slim.conv2d(net,
64, [
3,
3], padding =
'SAME', scope =
'Conv2d_2b_3x3')
@修饰符
其实这种用法是python中常用到的。在python中@修饰符放在函数定义的上方，它将被修饰的函数作为参数，并返回修饰后的同名函数。形式如下；
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@fun_a #等价于fun_a(fun_b)
-
def fun_b():
这在本质上讲跟直接调用被修饰的函数没什么区别，但是有时候也有用处，例如在调用被修饰函数前需要输出时间信息，我们可以在@后方的函数中添加输出时间信息的语句，这样每次我们只需要调用@后方的函数即可。
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def funs(fun,factor=20):
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x=fun()
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print(factor*x)
-
-
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@funs #等价funs(add(),fator=20)
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def add(a=10,b=20):
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return(a+b)