# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # pylint: disable=invalid-name """Learning rate scheduler.""" import math import logging class LearningRateScheduler(object): """Base class of learning rate scheduler.""" def __init__(self): self.base_lr = 0.01 def __call__(self, iteration): """ Call to schedule current learning rate. Parameters ---------- iteration: int Current iteration count. """ raise NotImplementedError("must override this") class FactorScheduler(LearningRateScheduler): """Reduce learning rate in factor. Parameters ---------- step: int Schedule learning rate after every round. factor: float Reduce learning rate factor. """ def __init__(self, step, factor=0.1): super(FactorScheduler, self).__init__() if step < 1: raise ValueError("Schedule step must be greater or equal than 1 round") if factor >= 1.0: raise ValueError("Factor must be less than 1 to make lr reduce") self.step = step self.factor = factor self.old_lr = self.base_lr self.init = False def __call__(self, iteration): """ Call to schedule current learning rate. Parameters ---------- iteration: int Current iteration count. """ if not self.init: self.init = True self.old_lr = self.base_lr lr = self.base_lr * math.pow(self.factor, int(iteration / self.step)) if lr != self.old_lr: self.old_lr = lr logging.info("At Iteration [%d]: Swith to new learning rate %.5f", iteration, lr) return lr