tf.keras.StatelessScope

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

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add_loss(     loss )

add_update

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add_update(     update )

get_current_value

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get_current_value(     variable )

__enter__

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__enter__()

__exit__

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__exit__(     *args, **kwargs )