import tensorflow as tf
from gmmvi.models.gmm_wrapper import GmmWrapper
[docs]class WeightStepsizeAdaptation:
""" This class provides a common interface for adapting the stepsize for the weight update.
There are currently three options for weight stepsize adpatation:
1. The :py:class:`FixedWeightStepsizeAdaptation` is a dummy-class, that does not do anything.
2. The :py:class:`DecayingWeightStepsizeAdaptation` uses exponential decay.
3. The :py:class:`ImprovementBasedWeightStepsizeAdaptation` uses a procedure similar to VIPS :cite:p:`Arenz2020` to
increase the stepsize if the mixture improved during the last updates, and to decrease it otherwise.
Parameters:
initial_stepsize: float
The initial stepsize for the weight update.
"""
def __init__(self, initial_stepsize: tf.float32):
self.stepsize = tf.Variable(initial_stepsize, dtype=tf.float32)
[docs] @staticmethod
def build_from_config(config, gmm_wrapper):
"""This static method provides a convenient way to create a :py:class:`FixedWeightStepsizeAdaptation`,
:py:class:`DecayingWeightStepsizeAdaptation` or :py:class:`ImprovementBasedWeightStepsizeAdaptation`
depending on the provided config.
Parameters:
config: dict
The dictionary is typically read from YAML a file, and holds all hyperparameters.
gmm_wrapper: :py:class:`GmmWrapper<gmmvi.models.gmm_wrapper.GmmWrapper>`
The wrapped model.
"""
if config["weight_stepsize_adapter_type"] == "fixed":
return FixedWeightStepsizeAdaptation(**config['weight_stepsize_adapter_config'])
elif config["weight_stepsize_adapter_type"] == "decaying":
return DecayingWeightStepsizeAdaptation(**config['weight_stepsize_adapter_config'])
elif config["weight_stepsize_adapter_type"] == "improvement_based":
return ImprovementBasedWeightStepsizeAdaptation(gmm_wrapper, **config['weight_stepsize_adapter_config'])
else:
raise ValueError(
f"config['weight_stepsize_adapter_type'] is '{config['weight_stepsize_adapter_type']}' "
f"which is an unknown type")
def _update_stepsize(self):
pass
[docs] def update_stepsize(self):
"""
Update the stepsizes, according to the chosen procedure.
Returns:
float: the updated stepsize.
"""
self._update_stepsize()
return self.stepsize
[docs]class FixedWeightStepsizeAdaptation(WeightStepsizeAdaptation):
""" This class is a dummy class, that can be used when we want to keep the stepsize for the weight update constant.
Parameters:
initial_stepsize: float
The initial stepsize for the weight update.
"""
def __init__(self, initial_stepsize: tf.float32):
super(FixedWeightStepsizeAdaptation, self).__init__(initial_stepsize)
[docs]class DecayingWeightStepsizeAdaptation(WeightStepsizeAdaptation):
""" This class implements an exponentially decaying stepsize schedule.
See :cite:p:`Khan2018a`.
Parameters:
gmm_wrapper: :py:class:`GmmWrapper<gmmvi.models.gmm_wrapper.GmmWrapper>`
The wrapped.
annealing_exponent: float
controls how fast the stepsize decays
initial_stepsize: float
The initial stepsize for the weight update.
"""
def __init__(self, initial_stepsize: tf.float32, annealing_exponent: tf.float32):
super(DecayingWeightStepsizeAdaptation, self).__init__(initial_stepsize)
self.initial_stepsize = tf.constant(initial_stepsize)
self.annealing_exponent = annealing_exponent
self.num_weight_updates = tf.Variable(0, dtype=tf.float32)
def _update_stepsize(self):
""" Updates the stepsize using exponential decay. More specifially, the new stepsize is given by
:math:`\\frac{\\text{initial\\_stepsize}}{1 + \\text{num\\_iterations}^\\text{annealing\\_exponent}}`.
Returns:
float: the updated stepsize.
"""
self.stepsize.assign(self.initial_stepsize / (1. + tf.math.pow(self.num_weight_updates,
self.annealing_exponent)))
self.num_weight_updates.assign_add(1.)
[docs]class ImprovementBasedWeightStepsizeAdaptation(WeightStepsizeAdaptation):
""" Increases the stepsize if the last weight update increased its reward, decreases it otherwise.
Parameters:
gmm_wrapper: :py:class:`GmmWrapper<gmmvi.models.gmm_wrapper.GmmWrapper>`
The wrapped model.
initial_stepsize: float
The initial stepsize for the weight update.
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
"""
def __init__(self, model: GmmWrapper, initial_stepsize: tf.float32, min_stepsize: tf.float32,
max_stepsize: tf.float32, stepsize_inc_factor: tf.float32, stepsize_dec_factor: tf.float32):
super(ImprovementBasedWeightStepsizeAdaptation, self).__init__(initial_stepsize)
self.initial_stepsize = tf.constant(initial_stepsize)
self.model = model
self.min_stepsize = min_stepsize
self.max_stepsize = max_stepsize
self.stepsize_inc_factor = stepsize_inc_factor
self.stepsize_dec_factor = stepsize_dec_factor
self.elbo_history = tf.Variable([tf.float32.min], shape=[None], dtype=tf.float32)
def _update_stepsize(self):
""" Updates the stepsize of each component based on the mixture model improvement.
Returns:
float: the updated stepsize.
"""
elbo = tf.reduce_sum(self.model.weights * self.model.reward_history[:,-1]) - tf.reduce_sum(self.model.weights * self.model.log_weights)
self.elbo_history.assign(tf.concat((self.elbo_history, tf.expand_dims(elbo,0)), axis=0))
if self.elbo_history[-1] > self.elbo_history[-2]:
self.stepsize.assign(tf.math.minimum(
self.stepsize_inc_factor * self.stepsize,
self.max_stepsize))
else:
self.stepsize.assign(tf.math.maximum(
self.stepsize_dec_factor * self.stepsize,
self.min_stepsize))