|TensorFlow 1 version||View source on GitHub|
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,
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
(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.
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 tracing a
tf.function. For example:
tf.compat.v1.enable_eager_execution() @tf.function def func(): # A 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()
RuntimeError: if graph state is incompatible with this initialization.