def learning_rate(self):
if self._learning_rate is None:
iterations = K.cast(self.iterations + 1, K.floatx())
learning_rate = K.minimum(1.0 / K.sqrt(iterations), 0.01)
if self.multiply_by_parameter_scale:
return learning_rate
else:
return learning_rate * 0.05
else:
if not hasattr(self, '__learning_rate'):
with K.name_scope(self.__class__.__name__):
self.__learning_rate = K.variable(self._learning_rate,
name='learning_rate')
return self.__learning_rate
def __init__(self, *args, **kwargs):
super(AdaFactorV1, self).__init__(*args, **kwargs)
with K.name_scope(self.__class__.__name__):
self.iterations = K.variable(0, dtype='int64', name='iterations')
