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A context manager that lifts ops out of control-flow scopes and function-building graphs.
@tf_contextlib.contextmanager
tf.init_scope()
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 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()
Raises | |
---|---|
RuntimeError
|
if graph state is incompatible with this initialization. |