SymbolicScope classkeras.SymbolicScope()
Scope to indicate the symbolic stage.
StatelessScope classkeras.StatelessScope(
state_mapping=None, collect_losses=False, initialize_variables=True
)
Scope to prevent any update to Keras Variables.
The values of variables to be used inside the scope
should be passed via the state_mapping argument, a
list of tuples (k, v) where k is a Variable
and v is the intended value for this variable
(a backend tensor).
Updated values can be collected on scope exit via
value = scope.get_current_value(variable). No updates
will be applied in-place to any variables for the duration
of the scope.
Example
state_mapping = [(k, ops.ones(k.shape, k.dtype)) for k in model.weights]
with keras.StatelessScope(state_mapping) as scope:
outputs = model.some_function(inputs)
# All model variables remain unchanged. Their new values can be
# collected via:
for k in model.weights:
new_value = scope.get_current_value(k)
print(f"New value for {k}: {new_value})