Python variable_scope.VariableScope方法代码示例

# 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) 

# 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) 

# 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) 

# 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 

# 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