Function init_scope

tensorflow/python/framework/ops.py:5613–5713

A context manager that lifts ops out of control-flow scopes and function-building graphs. There is often a need to lift variable initialization ops out of control-flow scopes, function-building graphs, and gradient tapes. Entering an `init_scope` is a mechanism for satisfying these desiderata

()

@tf_export("init_scope")
@tf_contextlib.contextmanager
def init_scope():
  """A context manager that lifts ops out of control-flow scopes and function-building graphs.

  There is often a need to lift variable initialization ops out of control-flow
  scopes, function-building graphs, and gradient tapes. Entering an
  `init_scope` is a mechanism for satisfying these desiderata. In particular,
  entering an `init_scope` has three effects:

    (1) All control dependencies are cleared the moment the scope is entered;
        this is equivalent to entering the context manager returned from
        `control_dependencies(None)`, which has the side-effect of exiting
        control-flow scopes like `tf.cond` and `tf.while_loop`.

    (2) All operations that are created while the scope is active are lifted
        into the lowest context on the `context_stack` that is not building a
        graph function. Here, a context is defined as either a graph or an eager
        context. Every context switch, i.e., every installation of a graph as
        the default graph and every switch into eager mode, is logged in a
        thread-local stack called `context_switches`; the log entry for a
        context switch is popped from the stack when the context is exited.
        Entering an `init_scope` is equivalent to crawling up
        `context_switches`, finding the first context that is not building a
        graph function, and entering it. A caveat is that if graph mode is
        enabled but the default graph stack is empty, then entering an
        `init_scope` will simply install a fresh graph as the default one.

    (3) The gradient tape is paused while the scope is active.

  When eager execution is enabled, code inside an init_scope block runs with
  eager execution enabled even when defining graph functions via
  tf.contrib.eager.defun. For example:

  ```python
  tf.compat.v1.enable_eager_execution()

  @tf.contrib.eager.defun
  def func():
    # A defun-decorated function constructs TensorFlow graphs,
    # it does not execute eagerly.
    assert not tf.executing_eagerly()
    with tf.init_scope():
      # Initialization runs with eager execution enabled
      assert tf.executing_eagerly()
  ```

  Raises:
    RuntimeError: if graph state is incompatible with this initialization.
  """
  # pylint: enable=g-doc-return-or-yield,line-too-long

  if context.executing_eagerly():
    # Fastpath.
    with tape.stop_recording():
      yield
  else:
    # Retrieve the active name scope: entering an `init_scope` preserves
    # the name scope of the current context.
    scope = get_default_graph().get_name_scope()