Source code for objax.optimizer.adam

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

__all__ = ['Adam']

from typing import List, Optional

from jax import numpy as jn

from objax import functional
from objax.module import Module, ModuleList
from objax.typing import JaxArray
from objax.util import class_name
from objax.variable import TrainRef, StateVar, TrainVar, VarCollection

[docs]class Adam(Module): """Adam optimizer."""
[docs] def __init__(self, vc: VarCollection, beta1: float = 0.9, beta2: float = 0.999, eps: float = 1e-8): """Constructor for Adam optimizer class. Args: vc: collection of variables to optimize. beta1: value of Adam's beta1 hyperparameter. Defaults to 0.9. beta2: value of Adam's beta2 hyperparameter. Defaults to 0.999. eps: value of Adam's epsilon hyperparameter. Defaults to 1e-8. """ self.beta1 = beta1 self.beta2 = beta2 self.eps = eps self.step = StateVar(jn.array(0, jn.uint32), reduce=lambda x: x[0]) 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) self.v = ModuleList(StateVar(jn.zeros_like(x.value)) for x in self.train_vars)
[docs] def __call__(self, lr: float, grads: List[JaxArray], beta1: Optional[float] = None, beta2: Optional[float] = None): """Updates variables and other state based on Adam algorithm. Args: lr: the learning rate. grads: the gradients to apply. beta1: optional, override the default beta1. beta2: optional, override the default beta2. """ assert len(grads) == len(self.train_vars), 'Expecting as many gradients as trainable variables' if beta1 is None: beta1 = self.beta1 if beta2 is None: beta2 = self.beta2 self.step.value += 1 lr *= jn.sqrt(1 - beta2 ** self.step.value) / (1 - beta1 ** self.step.value) for g, p, m, v in zip(grads, self.train_vars, self.m, self.v): m.value += (1 - beta1) * (g - m.value) v.value += (1 - beta2) * (g ** 2 - v.value) p.value -= lr * m.value * functional.rsqrt(v.value + self.eps)
def __repr__(self): return f'{class_name(self)}(beta1={self.beta1}, beta2={self.beta2}, eps={self.eps})'