# Copyright 2020 Google LLC
#
# Licensed 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
#
# https://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.
__all__ = ['Momentum']
from typing import List
from jax import numpy as jn
from objax.module import Module, ModuleList
from objax.variable import TrainRef, StateVar, TrainVar, VarCollection
[docs]class Momentum(Module):
"""Momentum optimizer."""
[docs] def __init__(self, vc: VarCollection, momentum: float = 0.9, nesterov: bool = False):
"""Constructor for momentum optimizer class.
Args:
vc: collection of variables to optimize.
momentum: the momentum hyperparameter.
nesterov: bool indicating whether to use the Nesterov method.
"""
self.momentum = momentum
self.nesterov = nesterov
self.train_vars = ModuleList(TrainRef(x) for x in vc.subset(TrainVar))
self.m = ModuleList(StateVar(jn.zeros_like(x.value)) for x in self.train_vars)
[docs] def __call__(self, lr: float, grads: List[jn.ndarray]):
"""Updates variables and other state based on momentum (or Nesterov) SGD.
Args:
lr: the learning rate.
grads: the gradients to apply.
"""
assert len(grads) == len(self.train_vars), 'Expecting as many gradients as trainable variables'
if self.nesterov:
for g, p, m in zip(grads, self.train_vars, self.m):
m.value = g + self.momentum * m.value
p.value -= lr * (g + self.momentum * m.value)
else:
for g, p, m in zip(grads, self.train_vars, self.m):
m.value = g + self.momentum * m.value
p.value -= lr * m.value