Python gen_resource_variable_ops.assign_variable_op函数代码示例

  def assign(self, value, use_locking=None, name=None, read_value=True):
    """Assigns a new value to this variable.

    Args:
      value: A `Tensor`. The new value for this variable.
      use_locking: If `True`, use locking during the assignment.
      name: The name to use for the assignment.
      read_value: A `bool`. Whether to read and return the new value of the
          variable or not.

    Returns:
      If `read_value` is `True`, this method will return the new value of the
      variable after the assignment has completed. Otherwise, when in graph mode
      it will return the `Operation` that does the assignment, and when in eager
      mode it will return `None`.
    """
    # Note: not depending on the cached value here since this can used to
    # initialize the variable.
    with _handle_graph(self.handle):
      value_tensor = ops.convert_to_tensor(value, dtype=self.dtype)
      self._shape.assert_is_compatible_with(value_tensor.shape)
      assign_op = gen_resource_variable_ops.assign_variable_op(
          self.handle, value_tensor, name=name)
      if read_value:
        return self._lazy_read(assign_op)
    return assign_op

def shape_safe_assign_variable_handle(handle, shape, value, name=None):
  """Helper that checks shape compatibility and assigns variable."""
  value_tensor = ops.convert_to_tensor(value)
  shape.assert_is_compatible_with(value_tensor.shape)
  return gen_resource_variable_ops.assign_variable_op(handle,
                                                      value_tensor,
                                                      name=name)

  def assign(self, value, use_locking=None, name=None, read_value=False):
    """Assign `value` to all replicas.

    Outside of the tpu.rewrite context, assign explicitly to all replicas.
    Inside of the tpu.rewrite context, assigns to the local replica.

    Arguments:
      value: Tensor to assign
      use_locking: ignored
      name: ignored
      read_value: return the value from the assignment
    Returns:
      Assignment operation, or new value of the variable if `read_value` is True
    """
    del use_locking
    if _enclosing_tpu_context() is None:
      assign_ops = []
      with self._assign_dependencies():
        for var in self._vars:
          assign_ops.append(var.assign(value, use_locking=None, name=name))

        if read_value:
          with ops.control_dependencies(assign_ops):
            return self.read_value()
        else:
          return control_flow_ops.group(assign_ops)

    with _handle_graph(self.handle), self._assign_dependencies():
      value_tensor = ops.convert_to_tensor(value, dtype=self.dtype)
      assign_op = gen_resource_variable_ops.assign_variable_op(
          self.handle, value_tensor, name=name)
    if read_value:
      return self._read_variable_op()
    return assign_op

 def assign(self, value, use_locking=None, name=None):
   value_tensor = ops.convert_to_tensor(value, dtype=self.dtype)
   self._shape.assert_is_compatible_with(value_tensor.shape)
   return self._lazy_read(
       gen_resource_variable_ops.assign_variable_op(
           self.handle,
           value_tensor,
           name=name))

 def assign(self, value, use_locking=None, name=None):
   with ops.control_dependencies([
       gen_resource_variable_ops.assign_variable_op(
           self.handle,
           ops.convert_to_tensor(value, dtype=self.dtype),
           name=name)
   ]):
     return self.read_value()

 def assign(self, value, use_locking=None, name=None):
   value_tensor = ops.convert_to_tensor(value, dtype=self.dtype)
   self._shape.assert_is_compatible_with(value_tensor.shape)
   with ops.control_dependencies([
       gen_resource_variable_ops.assign_variable_op(
           self.handle,
           value_tensor,
           name=name)
   ]):
     return self.read_value()

#.........这里部分代码省略.........
            with ops.name_scope("Initializer"):
              initial_value = ops.convert_to_tensor(
                  initial_value, name="initial_value", dtype=dtype)
            self._handle = _eager_safe_variable_handle(
                shape=initial_value.get_shape(),
                dtype=initial_value.dtype.base_dtype,
                shared_name=handle_name,
                name=name,
                graph_mode=False)
            self._handle_device = (
                self._handle.device if self._in_graph_mode else
                context.get_default_context().device_name)
            self._shape = initial_value.get_shape()
        # pylint: enable=protected-access

        # Or get the initial value from a Tensor or Python object.
        else:
          with ops.name_scope("Initializer"):
            initial_value = ops.convert_to_tensor(
                initial_value, name="initial_value", dtype=dtype)
          # pylint: disable=protected-access
          if (self._in_graph_mode and initial_value is not None and
              initial_value.op._get_control_flow_context() is not None):
            raise ValueError(
                "Initializer for variable %s is from inside a control-flow "
                "construct, such as a loop or conditional. When creating a "
                "variable inside a loop or conditional, use a lambda as the "
                "initializer." % name)
          # pylint: enable=protected-access
          self._handle = _eager_safe_variable_handle(
              shape=initial_value.get_shape(),
              dtype=initial_value.dtype.base_dtype,
              shared_name=handle_name,
              name=name,
              graph_mode=self._in_graph_mode)
          self._handle_device = (self._handle.device if self._in_graph_mode else
                                 context.get_default_context().device_name)
          self._shape = initial_value.get_shape()

        self._initial_value = initial_value if self._in_graph_mode else None
        self._handle_name = handle_name + ":0"
        self._dtype = initial_value.dtype.base_dtype
        self._constraint = constraint

        if self._in_graph_mode:
          with ops.name_scope("IsInitialized"):
            self._is_initialized_op = (
                gen_resource_variable_ops.var_is_initialized_op(self._handle))
          if initial_value is not None:
            with ops.name_scope("Assign") as n, ops.colocate_with(self._handle):
              self._initializer_op = (
                  gen_resource_variable_ops.assign_variable_op(
                      self._handle,
                      self._try_guard_against_uninitialized_dependencies(
                          initial_value),
                      name=n))
          with ops.name_scope("Read"), ops.colocate_with(self._handle):
            # Manually assign reads to the handle's device to avoid log
            # messages.
            with ops.device(self._handle_device):
              value = self._read_variable_op()
            self._graph_element = value
            if caching_device is not None:
              # Variables may be created in a tf.device() or ops.colocate_with()
              # context. At the same time, users would expect caching device to
              # be independent of this context, and/or would not expect the
              # current device context to be merged with the caching device
              # spec.  Therefore we reset the colocation stack before creating
              # the cached value. Note that resetting the colocation stack will
              # also reset the device stack.
              with ops.colocate_with(None, ignore_existing=True):
                with ops.device(caching_device):
                  self._cached_value = array_ops.identity(value)
            else:
              self._cached_value = None
        else:
          gen_resource_variable_ops.assign_variable_op(self._handle,
                                                       initial_value)
          self._is_initialized_op = None
          self._initializer_op = None
          self._graph_element = None
          if caching_device:
            with ops.device(caching_device):
              self._cached_value = self._read_variable_op()
          else:
            self._cached_value = None
        if context.in_graph_mode():
          ops.add_to_collections(collections, self)
        elif ops.GraphKeys.GLOBAL_STEP in collections:
          ops.add_to_collections(ops.GraphKeys.GLOBAL_STEP, self)

    if not self._in_graph_mode:
      # After the handle has been created, set up a way to clean it up when
      # executing eagerly. We'll hold the only reference to the deleter, so that
      # when this object is garbage collected the deleter will be too. This
      # means ResourceVariables can be part of reference cycles without those
      # cycles being uncollectable, and means that no __del__ will be defined at
      # all in graph mode.
      self._handle_deleter = EagerResourceDeleter(
          handle=self._handle, handle_device=self._handle_device)

  def _init_from_args(self,
                      initial_value=None,
                      trainable=True,
                      collections=None,
                      validate_shape=True,
                      caching_device=None,
                      name=None,
                      dtype=None):

    """Creates a variable.

    Args:
      initial_value: A `Tensor`, or Python object convertible to a `Tensor`,
        which is the initial value for the Variable. The initial value must have
        a shape specified unless `validate_shape` is set to False. Can also be a
        callable with no argument that returns the initial value when called.
        (Note that initializer functions from init_ops.py must first be bound
         to a shape before being used here.)
      trainable: If `True`, the default, also adds the variable to the graph
        collection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as
        the default list of variables to use by the `Optimizer` classes.
      collections: List of graph collections keys. The new variable is added to
        these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
      validate_shape: Ignored. Provided for compatibility with tf.Variable.
      caching_device: Optional device string or function describing where the
        Variable should be cached for reading.  Defaults to the Variable's
        device.  If not `None`, caches on another device.  Typical use is to
        cache on the device where the Ops using the Variable reside, to
        deduplicate copying through `Switch` and other conditional statements.
      name: Optional name for the variable. Defaults to `'Variable'` and gets
        uniquified automatically.
      dtype: If set, initial_value will be converted to the given type.
        If None, either the datatype will be kept (if initial_value is
       a Tensor) or float32 will be used (if it is a Python object convertible
       to a Tensor).

    Raises:
      ValueError: If the initial value is not specified, or does not have a
        shape and `validate_shape` is `True`.
    """
    if initial_value is None:
      raise ValueError("initial_value must be specified.")
    init_from_fn = callable(initial_value)

    if collections is None:
      collections = [ops.GraphKeys.GLOBAL_VARIABLES]
    if not isinstance(collections, (list, tuple, set)):
      raise ValueError(
          "collections argument to Variable constructor must be a list, tuple, "
          "or set. Got %s of type %s" % (collections, type(collections)))
    if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections:
      collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES]
    self._save_slice_info = None
    with ops.control_dependencies(None):
      with ops.name_scope(name, "Variable", [] if init_from_fn else
                          [initial_value]) as name:
        # pylint: disable=protected-access
        true_name = ops._name_from_scope_name(name)
        if init_from_fn:
          # Use attr_scope and device(None) to simulate the behavior of
          # colocate_with when the variable we want to colocate with doesn't
          # yet exist.
          attr = attr_value_pb2.AttrValue(
              list=attr_value_pb2.AttrValue.ListValue(
                  s=[compat.as_bytes("loc:@%s" % true_name)]))
          with ops.get_default_graph()._attr_scope({"_class": attr}):
            with ops.name_scope("Initializer"), ops.device(None):
              self._initial_value = ops.convert_to_tensor(
                  initial_value(), name="initial_value", dtype=dtype)
            self._handle = gen_resource_variable_ops.var_handle_op(
                shape=self._initial_value.get_shape(),
                dtype=self._initial_value.dtype.base_dtype,
                shared_name=true_name, name=name)
        # pylint: enable=protected-access

        # Or get the initial value from a Tensor or Python object.
        else:
          self._initial_value = ops.convert_to_tensor(
              initial_value, name="initial_value", dtype=dtype)
          self._handle = gen_resource_variable_ops.var_handle_op(
              shape=self._initial_value.get_shape(),
              dtype=self._initial_value.dtype.base_dtype,
              shared_name=true_name, name=name)

        self._dtype = self._initial_value.dtype.base_dtype

        with ops.name_scope("IsInitialized"):
          self._is_initialized_op = (
              gen_resource_variable_ops.var_is_initialized_op(self._handle))
        if initial_value is not None:
          with ops.name_scope("Assign") as n, ops.colocate_with(self._handle):
            self._initialize_op = gen_resource_variable_ops.assign_variable_op(
                self._handle, self._initial_value, name=n)
        with ops.name_scope("Read"), ops.colocate_with(self._handle):
          # Manually assign reads to the handle's device to avoid log messages.
          with ops.device(self._handle.device):
            value = gen_resource_variable_ops.read_variable_op(
                self._handle, dtype=self._dtype)
          self._graph_element = value
          if caching_device is not None:
#.........这里部分代码省略.........

  def __init__(self,
               initial_value=None,
               name=None,
               caching_device=None,
               trainable=True,
               collections=None,
               dtype=None,
               shape=None):

    """Creates a variable.

    Args:
      initial_value: A `Tensor` or Python object convertible to a `Tensor`
        representing the initial value of this variable.
      name: The name of this variable. Automatically uniquified.
      caching_device: device where the variable value's read by default.
      trainable: Whether the global read of this variable will be used for
        training.
      collections: Additional collections to which the `read` operation for
        this variable is to be added. Defaults to [].
      dtype: The type of this variable. Can be omitted if it can be deduced
        from the initial_value. If different from the type of the initial
        value it will be cast to this type.
      shape: The shape of this variable. Only specify if there is no initial
        value but shape inference is desired.
    """
    if initial_value is not None:
      if callable(initial_value):
        initial_value = initial_value()
      initial_value = ops.convert_to_tensor(initial_value)
    if dtype is None:
      assert initial_value is not None, ("Trying to create a resource variable "
                                         "with no dtype or initial value. At"
                                         " least one of these must be set.")
      dtype = initial_value.dtype
    elif initial_value is not None:
      initial_value = math_ops.cast(initial_value, dtype)
    if shape is None:
      if initial_value is not None:
        shape = initial_value.get_shape().as_proto()
      else:
        shape = tensor_shape.unknown_shape()
    else:
      shape = tensor_shape.as_shape(shape)

    self._dtype = dtype
    with ops.name_scope(name, "Variable", [initial_value]) as name:
      self._handle = gen_resource_variable_ops.var_handle_op(shared_name=name,
                                                             name=name,
                                                             dtype=dtype,
                                                             shape=shape)

      with ops.name_scope("IsInitialized"):
        self._is_initialized_op = (
            gen_resource_variable_ops.var_is_initialized_op(self._handle))
      if initial_value is not None:
        with ops.name_scope("Create"):
          self._initialize_op = gen_resource_variable_ops.assign_variable_op(
              self._handle, initial_value)
        resources.register_resource(self._handle,
                                    self._initialize_op,
                                    self._is_initialized_op)

      with ops.name_scope("Read"):
        if caching_device is not None:
          with ops.device(caching_device):
            self._value = gen_resource_variable_ops.read_variable_op(
                self._handle, dtype=self._dtype)
        else:
          self._value = gen_resource_variable_ops.read_variable_op(
              self._handle, dtype=self._dtype)
        # TODO(apassos) this is terrible
        self._value.initializer = self._initialize_op
      _register_variable_read(
          self._value, trainable=trainable, collections=collections)