slim.arg_scope()的使用
slim.arg_scope()的使用
slim.arg_scope()函数的使用
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；
@tf_contextlib.contextmanager
def arg_scope(list_ops_or_scope, **kwargs):
"""Stores the default arguments for the given set of list_ops.
For usage, please see examples at top of the file.
Args:
list_ops_or_scope: List or tuple of operations to set argument scope for or
a dictionary containing the current scope. When list_ops_or_scope is a
dict, kwargs must be empty. When list_ops_or_scope is a list or tuple,
then every op in it need to be decorated with @add_arg_scope to work.
**kwargs: keyword=value that will define the defaults for each op in
list_ops. All the ops need to accept the given set of arguments.
Yields:
the current_scope, which is a dictionary of {op: {arg: value}}
Raises:
TypeError: if list_ops is not a list or a tuple.
ValueError: if any op in list_ops has not be decorated with @add_arg_scope.
"""
if isinstance(list_ops_or_scope, dict):
# Assumes that list_ops_or_scope is a scope that is being reused.
if kwargs:
raise ValueError('When attempting to re-use a scope by suppling a'
'dictionary, kwargs must be empty.')
current_scope = list_ops_or_scope.copy()
try:
_get_arg_stack().append(current_scope)
yield current_scope
finally:
_get_arg_stack().pop()
else:
# Assumes that list_ops_or_scope is a list/tuple of ops with kwargs.
if not isinstance(list_ops_or_scope, (list, tuple)):
raise TypeError('list_ops_or_scope must either be a list/tuple or reused'
'scope (i.e. dict)')
try:
current_scope = current_arg_scope().copy()
for op in list_ops_or_scope:
key_op = _key_op(op)
if not has_arg_scope(op):
raise ValueError('%s is not decorated with @add_arg_scope',
_name_op(op))
if key_op in current_scope:
current_kwargs = current_scope[key_op].copy()
current_kwargs.update(kwargs)
current_scope[key_op] = current_kwargs
else:
current_scope[key_op] = kwargs.copy()
_get_arg_stack().append(current_scope)
yield current_scope
finally:
_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。
import tensorflow as tf
slim =tf.contrib.slim
@slim.add_arg_scope
def fun1(a=0,b=0):
return (a+b)
with slim.arg_scope([fun1],a=10):
x=fun1(b=30)
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；
@add_arg_scope
def convolution(inputs,
num_outputs,
kernel_size,
stride=1,
padding='SAME',
data_format=None,
rate=1,
activation_fn=nn.relu,
normalizer_fn=None,
normalizer_params=None,
weights_initializer=initializers.xavier_initializer(),
weights_regularizer=None,
biases_initializer=init_ops.zeros_initializer(),
biases_regularizer=None,
reuse=None,
variables_collections=None,
outputs_collections=None,
trainable=True,
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'):
net = slim.conv2d(inputs, 32, [3, 3], stride = 2, scope = 'Conv2d_1a_3x3')
net = slim.conv2d(net, 32, [3, 3], scope = 'Conv2d_2a_3x3')
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；
@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。
def funs(fun,factor=20):
x=fun()
print(factor*x)
@funs #等价funs(add(),fator=20)
def add(a=10,b=20):
return(a+b)转载地址：https://blog.csdn.net/u013921430/article/details/80915696