Method __init__

tensorflow/python/ops/variables.py:1786–1895

Creates a new variable with value `initial_value`. The new variable is added to the graph collections listed in `collections`, which defaults to `[GraphKeys.GLOBAL_VARIABLES]`. If `trainable` is `True` the variable is also added to the graph collection `GraphKeys.TRAINABLE_VARIABLE

(
      self,  # pylint: disable=super-init-not-called
      initial_value=None,
      trainable=None,
      collections=None,
      validate_shape=True,
      caching_device=None,
      name=None,
      variable_def=None,
      dtype=None,
      expected_shape=None,
      import_scope=None,
      constraint=None,
      synchronization=None,
      aggregation=None,
      shape=None)

  """Ref-based implementation of variables."""

  def __init__(
      self,  # pylint: disable=super-init-not-called
      initial_value=None,
      trainable=None,
      collections=None,
      validate_shape=True,
      caching_device=None,
      name=None,
      variable_def=None,
      dtype=None,
      expected_shape=None,
      import_scope=None,
      constraint=None,
      synchronization=None,
      aggregation=None,
      shape=None):
    """Creates a new variable with value `initial_value`.

    The new variable is added to the graph collections listed in `collections`,
    which defaults to `[GraphKeys.GLOBAL_VARIABLES]`.

    If `trainable` is `True` the variable is also added to the graph collection
    `GraphKeys.TRAINABLE_VARIABLES`.

    This constructor creates both a `variable` Op and an `assign` Op to set the
    variable to its initial value.

    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. In
        that case, `dtype` must be specified. (Note that initializer functions
        from init_ops.py must first be bound to a shape before being used here.)
      trainable: If `True`, 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. Defaults to `True`,
        unless `synchronization` is set to `ON_READ`, in which case it defaults
        to `False`.
      collections: List of graph collections keys. The new variable is added to
        these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
      validate_shape: If `False`, allows the variable to be initialized with a
        value of unknown shape. If `True`, the default, the shape of
        `initial_value` must be known.
      caching_device: Optional device string 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.
      variable_def: `VariableDef` protocol buffer. If not `None`, recreates the
        Variable object with its contents, referencing the variable's nodes in
        the graph, which must already exist. The graph is not changed.
        `variable_def` and the other arguments are mutually exclusive.
      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 `convert_to_tensor` will decide.