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Scope to prevent any update to Keras Variables.
tf.keras.StatelessScope(
state_mapping=None, collect_losses=False, initialize_variables=True
)
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 KerasVariable
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})
Methods
add_loss
add_loss(
loss
)
add_update
add_update(
update
)
get_current_value
get_current_value(
variable
)
__enter__
__enter__()
__exit__
__exit__(
*args, **kwargs
)