# Copyright 2020 The Flax Authors.
#
# 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
#
# 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.
# Lint as: python3
from .. import struct
import jax.numpy as jnp
import numpy as onp
from .base import OptimizerDef
@struct.dataclass
class _MomentumHyperParams:
learning_rate: onp.ndarray
beta: onp.ndarray
weight_decay: onp.ndarray
nesterov: bool
@struct.dataclass
class _MomentumParamState:
momentum: onp.ndarray
[docs]class Momentum(OptimizerDef):
"""Momentum optimizer."""
def __init__(self, learning_rate=None, beta=0.9, weight_decay=0,
nesterov=False):
"""Constructor for the Momentum optimizer.
Args:
learning_rate: the step size used to update the parameters.
beta: the coefficient used for the moving average of the
gradient (default: 0.9).
weight_decay: weight decay coefficient to apply (default: 0).
nesterov: whether to use Nesterov momentum (default: False).
"""
hyper_params = _MomentumHyperParams(
learning_rate, beta, weight_decay, nesterov)
super().__init__(hyper_params)
[docs] def init_param_state(self, param):
return _MomentumParamState(jnp.zeros_like(param))
[docs] def apply_param_gradient(self, step, hyper_params, param, state, grad):
del step
assert hyper_params.learning_rate is not None, 'no learning rate provided.'
if hyper_params.weight_decay != 0:
grad += hyper_params.weight_decay * param
momentum = state.momentum
new_momentum = hyper_params.beta * momentum + grad
if hyper_params.nesterov:
d_p = grad + hyper_params.beta * new_momentum
else:
d_p = new_momentum
new_param = param - hyper_params.learning_rate * d_p
new_state = _MomentumParamState(new_momentum)
return new_param, new_state