Python variable_scope.variable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 variable [as 别名]
def _create_slots(self, var_list):
        first_var = min(var_list, key=lambda x: x.name)

        create_new = self._beta1_power is None
        if not create_new and context.in_graph_mode():
            create_new = (self._beta1_power.graph is not first_var.graph)

        if create_new:
            with ops.colocate_with(first_var):
                self._beta1_power = variable_scope.variable(
                    self._beta1, name="beta1_power", trainable=False)
                self._beta2_power = variable_scope.variable(
                    self._beta2, name="beta2_power", trainable=False)
        # Create slots for the first and second moments.
        for v in var_list:
            self._zeros_slot(v, "m", self._name)
            self._zeros_slot(v, "v", self._name)
            self._zeros_slot(v, "vhat", self._name) 

# 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 variable [as 别名]
def local_variable(initial_value,
                   validate_shape=True,
                   name=None,
                   use_resource=None):
  """Create a variable with a value and add it to `GraphKeys.LOCAL_VARIABLES`.

  Args:
    initial_value: See variables.Variable.__init__.
    validate_shape: See variables.Variable.__init__.
    name: See variables.Variable.__init__.
    use_resource: If `True` use a ResourceVariable instead of a Variable.

  Returns:
    New variable.
  """
  return variable_scope.variable(
      initial_value,
      trainable=False,
      collections=[ops.GraphKeys.LOCAL_VARIABLES],
      validate_shape=validate_shape,
      use_resource=use_resource,
      name=name) 

# 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 variable [as 别名]
def global_variable(initial_value,
                    validate_shape=True,
                    name=None,
                    use_resource=None):
  """Create a variable with a value and add it to `GraphKeys.GLOBAL_VARIABLES`.

  Args:
    initial_value: See variables.Variable.__init__.
    validate_shape: See variables.Variable.__init__.
    name: See variables.Variable.__init__.
    use_resource: If `True` use a ResourceVariable instead of a Variable.

  Returns:
    New variable.
  """
  return variable_scope.variable(
      initial_value,
      trainable=False,
      collections=[ops.GraphKeys.GLOBAL_VARIABLES],
      validate_shape=validate_shape,
      use_resource=use_resource,
      name=name) 

# 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 variable [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 variable [as 别名]
def get_variable_full_name(var):
  """Returns the full name of a variable.

  For normal Variables, this is the same as the var.op.name.  For
  sliced or PartitionedVariables, this name is the same for all the
  slices/partitions. In both cases, this is normally the name used in
  a checkpoint file.

  Args:
    var: A `Variable` object.

  Returns:
    A string that is the full name.
  """
  if var._save_slice_info:
    return var._save_slice_info.full_name
  else:
    return var.op.name


# TODO(nsilberman): add flag to load exponential moving averages instead
#
# TODO(sguada): Update docs in slim/g3doc/index.md to describe
# the new feature where the var_list dictionary can have values that
# are each a list of 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 variable [as 别名]
def _create_local(name, shape, collections=None, validate_shape=True,
                  dtype=dtypes.float32):
  """Creates a new local variable.

  Args:
    name: The name of the new or existing variable.
    shape: Shape of the new or existing variable.
    collections: A list of collection names to which the Variable will be added.
    validate_shape: Whether to validate the shape of the variable.
    dtype: Data type of the variables.

  Returns:
    The created variable.
  """
  # Make sure local variables are added to tf.GraphKeys.LOCAL_VARIABLES
  collections = list(collections or [])
  collections += [ops.GraphKeys.LOCAL_VARIABLES]
  return variable_scope.variable(
      array_ops.zeros(shape, dtype=dtype),
      name=name,
      trainable=False,
      collections=collections,
      validate_shape=validate_shape) 

# 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 variable [as 别名]
def compute_gradients(self, *args, **kwargs):
    """Compute gradients of "loss" for the variables in "var_list".

    This simply wraps the compute_gradients() from the real optimizer. The
    gradients will be aggregated in the apply_gradients() so that user can
    modify the gradients like clipping with per replica global norm if needed.
    The global norm with aggregated gradients can be bad as one replica's huge
    gradients can hurt the gradients from other replicas.

    Args:
      *args: Arguments for compute_gradients().
      **kwargs: Keyword arguments for compute_gradients().

    Returns:
      A list of (gradient, variable) pairs.
    """
    return self._opt.compute_gradients(*args, **kwargs) 

# 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 variable [as 别名]
def _create_slots(self, var_list):
    # Create the beta1 and beta2 accumulators on the same device as the first
    # variable.
    if (self._beta1_power is None or
        self._beta1_power.graph is not var_list[0].graph):
      with ops.colocate_with(var_list[0]):
        self._beta1_power = variable_scope.variable(self._beta1,
                                                    name="beta1_power",
                                                    trainable=False)
        self._beta2_power = variable_scope.variable(self._beta2,
                                                    name="beta2_power",
                                                    trainable=False)
    # Create slots for the first and second moments.
    for v in var_list:
      self._zeros_slot(v, "m", self._name)
      self._zeros_slot(v, "v", self._name) 

# 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 variable [as 别名]
def aggregate_metrics(*value_update_tuples):
  """Aggregates the metric value tensors and update ops into two lists.

  Args:
    *value_update_tuples: a variable number of tuples, each of which contain the
      pair of (value_tensor, update_op) from a streaming metric.

  Returns:
    A list of value `Tensor` objects and a list of update ops.

  Raises:
    ValueError: if `value_update_tuples` is empty.
  """
  if not value_update_tuples:
    raise ValueError('Expected at least one value_tensor/update_op pair')
  value_ops, update_ops = zip(*value_update_tuples)
  return list(value_ops), list(update_ops) 

# 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 variable [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 variable [as 别名]
def get_unique_variable(var_op_name):
  """Gets the variable uniquely identified by that var_op_name.

  Args:
    var_op_name: the full name of the variable op, including the scope.

  Returns:
    a tensorflow variable.

  Raises:
    ValueError: if no variable uniquely identified by the name exists.
  """
  candidates = get_variables(scope=var_op_name)
  if not candidates:
    raise ValueError('Couldnt find variable %s' % var_op_name)

  for candidate in candidates:
    if candidate.op.name == var_op_name:
      return candidate
  raise ValueError('Variable %s does not uniquely identify a variable',
                   var_op_name) 

# 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 variable [as 别名]
def assign_from_values_fn(var_names_to_values):
  """Returns a function that assigns specific variables from the given values.

  This function provides a mechanism for performing assignment of variables
  to values in a way that does not fill the graph with large assignment values.

  Args:
    var_names_to_values: A map from variable names to values.

  Returns:
    A function that takes a single argument, a `tf.Session`, that applies the
    assignment operation.

  Raises:
    ValueError: if any of the given variable names were not found.
  """
  assign_op, feed_dict = assign_from_values(var_names_to_values)
  def callback(session):
    return session.run(assign_op, feed_dict)
  return callback


# pylint: disable=protected-access
# Currently variable_scope doesn't provide very good APIs to access
# all variables under scope and retrieve and check existing scopes. 

# 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 variable [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 variable [as 别名]
def get_variable_full_name(var):
  """Returns the full name of a variable.

  For normal Variables, this is the same as the var.op.name.  For
  sliced or PartitionedVariables, this name is the same for all the
  slices/partitions. In both cases, this is normally the name used in
  a checkpoint file.

  Args:
    var: A `Variable` object.

  Returns:
    A string that is the full name.
  """
  if var._save_slice_info:
    return var._save_slice_info.full_name
  else:
    return var.op.name 

# 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 variable [as 别名]
def _create_slots(self, var_list):
        first_var = min(var_list, key=lambda x: x.name)

        create_new = self._beta1_power is None
        if not create_new and context.in_graph_mode():
            create_new = (self._beta1_power.graph is not first_var.graph)

        if create_new:
            with ops.colocate_with(first_var):
                self._beta1_power = variable_scope.variable(self._beta1, name="beta1_power", trainable=False)
                self._beta2_power = variable_scope.variable(self._beta2, name="beta2_power", trainable=False)
        # Create slots for the first and second moments.
        for v in var_list :
            self._zeros_slot(v, "m", self._name)
            self._zeros_slot(v, "v", self._name)
            self._zeros_slot(v, "vhat", self._name)