# ComponentStepsizeAdaptation

class gmmvi.optimization.gmmvi_modules.component_stepsize_adaptation.ComponentStepsizeAdaptation(gmm_wrapper: GmmWrapper, initial_stepsize: float)

  This class provides a common interface for adapting the individual stepsizes for the component updates.

  There are currently three options for component stepsize adpatation:

  1. The `FixedComponentStepsizeAdaptation` is a dummy-class, that does not do anything.

  2. The `DecayingComponentStepsizeAdaptation` uses exponential decay.

  3. The `ImprovementBasedComponentStepsizeAdaptation` uses the procedure of VIPS [AZN20] to increase the stepsize if a component improved during the last updates, and to decrease it otherwise.

  Parameters:

    - gmm_wrapper – `GmmWrapper` The wrapped model where we want to adapt the number of components.

    - initial_stepsize – float The stepsize used when the component receives its first update

  static build_from_config(config, gmm_wrapper)

    This static method provides a convenient way to create a `FixedComponentStepsizeAdaptation`, `DecayingComponentStepsizeAdaptation` or `ImprovementBasedComponentStepsizeAdaptation` depending on the provided config.

    Parameters:

      - config – dict The dictionary is typically read from YAML a file, and holds all hyperparameters.

      - gmm_wrapper – `GmmWrapper` The wrapped model.

  update_stepsize(current_stepsizes: Tensor) → Tensor

    Update the stepsizes, according to the chosen procedure.

    Parameters:

      current_stepsizes – tf.Tensor A tensor that contains the stepsize of each component

    Returns:

      a tensor of same size as current_stepsizes that contains the updates stepsizes.

    Return type:

      tf.Tensor

## DecayingComponentStepsizeAdaptation

class gmmvi.optimization.gmmvi_modules.component_stepsize_adaptation.DecayingComponentStepsizeAdaptation(gmm_wrapper: GmmWrapper, annealing_exponent: float, initial_stepsize: float)

  This class implements an exponentially decaying stepsize schedule. See [KNT+18].

  Parameters:

    - gmm_wrapper – `GmmWrapper` The wrapped model.

    - annealing_exponent – float controls how fast the stepsize decays

    - initial_stepsize – float The stepsize used for all component updates

  update_stepsize(current_stepsizes: Tensor) → Tensor

    Updates the stepsize using exponential decay. More specifially, the new stepsize is given by .

    Parameters:

      current_stepsizes – tf.Tensor A tensor that contains the stepsize of each component

    Returns:

      a tensor of same size as current_stepsizes that contains the updates stepsizes.

    Return type:

      tf.Tensor

## FixedComponentStepsizeAdaptation

class gmmvi.optimization.gmmvi_modules.component_stepsize_adaptation.FixedComponentStepsizeAdaptation(gmm_wrapper: GmmWrapper, initial_stepsize: float)

  This class is a dummy class, that can be used when we want to keep the stepsizes constant.

  Parameters:

    - gmm_wrapper – `GmmWrapper` The wrapped model where we want to adapt the number of components.

    - initial_stepsize – float The stepsize used for all component updates

  update_stepsize(current_stepsizes: Tensor) → Tensor

    This dummy function does nothing.

    Parameters:

      current_stepsizes – tf.Tensor A tensor that contains the stepsize of each component

    Returns:

      the same current_stepsizes tensor, that it was called with.

    Return type:

      tf.Tensor

## ImprovementBasedComponentStepsizeAdaptation

class gmmvi.optimization.gmmvi_modules.component_stepsize_adaptation.ImprovementBasedComponentStepsizeAdaptation(gmm_wrapper: GmmWrapper, initial_stepsize: float, min_stepsize: float, max_stepsize: float, stepsize_inc_factor: float, stepsize_dec_factor: float)

  Increases the stepsize if the last component update increased its reward, decreases it otherwise. See [AZN20].

  Parameters:

    - gmm_wrapper – `GmmWrapper` The wrapped model where we want to adapt the number of components.

    - initial_stepsize – float The stepsize used for all component updates

    - min_stepsize – float Do not not decrease the stepsize below this point

    - max_stepsize – float Do not increase the stepsize above this point

    - stepsize_inc_factor – float Factor (>1) for increasing the stepsize

    - stepsize_dec_factor – float Factor in the range [0, 1] for decreasing the stepsize

  update_stepsize(current_stepsizes: Tensor) → Tensor

    Updates the stepsize of each component based on previous reward improvements [AZN20].

    Parameters:

      current_stepsizes – tf.Tensor A tensor that contains the stepsize of each component

    Returns:

      a tensor of same size as current_stepsizes that contains the updates stepsizes.

    Return type:

      tf.Tensor