本文整理汇总了Python中tensorflow.python.ops.variable_scope.VariableScope方法的典型用法代码示例。如果您正苦于以下问题:Python variable_scope.VariableScope方法的具体用法?Python variable_scope.VariableScope怎么用?Python variable_scope.VariableScope使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.ops.variable_scope
的用法示例。
在下文中一共展示了variable_scope.VariableScope方法的5个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_variables
# 需要导入模块: from tensorflow.python.ops import variable_scope [as 别名]
# 或者: from tensorflow.python.ops.variable_scope import VariableScope [as 别名]
def get_variables(scope=None,
suffix=None,
collection=ops.GraphKeys.GLOBAL_VARIABLES):
"""Gets the list of variables, filtered by scope and/or suffix.
Args:
scope: an optional scope for filtering the variables to return. Can be a
variable scope or a string.
suffix: an optional suffix for filtering the variables to return.
collection: in which collection search for. Defaults to
`GraphKeys.GLOBAL_VARIABLES`.
Returns:
a list of variables in collection with scope and suffix.
"""
if isinstance(scope, variable_scope.VariableScope):
scope = scope.name
if suffix is not None:
if ':' not in suffix:
suffix += ':'
scope = (scope or '') + '.*' + suffix
return ops.get_collection(collection, scope)
示例2: get_variables
# 需要导入模块: from tensorflow.python.ops import variable_scope [as 别名]
# 或者: from tensorflow.python.ops.variable_scope import VariableScope [as 别名]
def get_variables(scope=None, suffix=None,
collection=ops.GraphKeys.GLOBAL_VARIABLES):
"""Gets the list of variables, filtered by scope and/or suffix.
Args:
scope: an optional scope for filtering the variables to return. Can be a
variable scope or a string.
suffix: an optional suffix for filtering the variables to return.
collection: in which collection search for. Defaults to
`GraphKeys.GLOBAL_VARIABLES`.
Returns:
a list of variables in collection with scope and suffix.
"""
if isinstance(scope, variable_scope.VariableScope):
scope = scope.name
if suffix is not None:
if ':' not in suffix:
suffix += ':'
scope = (scope or '') + '.*' + suffix
return ops.get_collection(collection, scope)
示例3: get_variables
# 需要导入模块: from tensorflow.python.ops import variable_scope [as 别名]
# 或者: from tensorflow.python.ops.variable_scope import VariableScope [as 别名]
def get_variables(scope=None,
suffix=None,
collection=ops.GraphKeys.GLOBAL_VARIABLES):
"""Gets the list of variables, filtered by scope and/or suffix.
Args:
scope: an optional scope for filtering the variables to return. Can be a
variable scope or a string.
suffix: an optional suffix for filtering the variables to return.
collection: in which collection search for. Defaults to
`GraphKeys.GLOBAL_VARIABLES`.
Returns:
a list of variables in collection with scope and suffix.
"""
if scope and isinstance(scope, variable_scope.VariableScope):
scope = scope.name
if suffix is not None:
if ':' not in suffix:
suffix += ':'
scope = (scope or '') + '.*' + suffix
return ops.get_collection(collection, scope)
示例4: __init__
# 需要导入模块: from tensorflow.python.ops import variable_scope [as 别名]
# 或者: from tensorflow.python.ops.variable_scope import VariableScope [as 别名]
def __init__(self, subnet, name=None, scope=None):
"""Create the Shared operator.
Use this as:
f = Shared(Cr(100, 3))
g = f | f | f
Ordinarily, you do not need to provide either a name or a scope.
Providing a name is useful if you want a well-defined namespace
for the variables (e.g., for saving a subnet).
Args:
subnet: Definition of the shared network.
name: Optional name for the shared context.
scope: Optional shared scope (must be a Scope, not a string).
Raises:
ValueError: Scope is not of type tf.Scope, name is not
of type string, or both scope and name are given together.
"""
if scope is not None and not isinstance(scope,
variable_scope.VariableScope):
raise ValueError("scope must be None or a VariableScope")
if name is not None and not isinstance(scope, str):
raise ValueError("name must be None or a string")
if scope is not None and name is not None:
raise ValueError("cannot provide both a name and a scope")
if name is None:
name = "Shared_%d" % Shared.shared_number
Shared.shared_number += 1
self.subnet = subnet
self.name = name
self.scope = scope
示例5: __init__
# 需要导入模块: from tensorflow.python.ops import variable_scope [as 别名]
# 或者: from tensorflow.python.ops.variable_scope import VariableScope [as 别名]
def __init__(self, trainable=True, name=None,
dtype=dtypes.float32, **kwargs):
# We use a kwargs dict here because these kwargs only exist
# for compatibility reasons.
# The list of kwargs is subject to changes in the future.
# We do not want to commit to it or to expose the list to users at all.
# Note this is exactly as safe as defining kwargs in the function signature,
# the only difference being that the list of valid kwargs is defined
# below rather rather in the signature, and default values are defined
# in calls to kwargs.get().
allowed_kwargs = {
'_scope',
'_reuse',
}
for kwarg in kwargs:
if kwarg not in allowed_kwargs:
raise TypeError('Keyword argument not understood:', kwarg)
self.trainable = trainable
self.built = False
self._trainable_weights = []
self._non_trainable_weights = []
self._updates = []
self._losses = []
self._reuse = kwargs.get('_reuse')
self._graph = ops.get_default_graph()
self._per_input_losses = {}
self._per_input_updates = {}
self.dtype = dtypes.as_dtype(dtype).name
self.input_spec = None
# Determine layer name (non-unique).
if isinstance(name, vs.VariableScope):
base_name = name.name
else:
base_name = name
self.name = name
if not name:
base_name = _to_snake_case(self.__class__.__name__)
self.name = _unique_layer_name(base_name)
self._base_name = base_name
# Determine variable scope.
scope = kwargs.get('_scope')
if scope:
self._scope = next(vs.variable_scope(scope).gen)
else:
self._scope = None