Code for "Aggregated Momentum: Stability Through Passive Damping", Lucas et al. 2018
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README.md
aggmo.py

README.md

Aggregated Momentum

This repository contains code to reproduce the experiments from "Aggregated Momentum: Stability Through Passive Damping".

Both pytorch and tensorflow implementations of the AggMo optimizer are included.

AggMo Optimizer

Pytorch

The aggmo.py file provides a pytorch implementation of AggMo. The optimizer can be constructed as follows:

optimizer = aggmo.AggMo(model.parameters(), lr, betas=[0, 0.9, 0.99])

The AggMo class also has an "exponential form" constructor. In this case the damping vector is specified by two hyparameters, K - the number of beta values, and a - the exponential scale factor. For i=0...K-1 , each beta_i = 1 - a^i . The following is equivalent to using the beta values [0, 0.9, 0.99]:

optimizer = aggmo.AggMo.from_exp_form(model.parameters(), lr, a=0.1, k=3)

Tensorflow

There is also a tensorflow implementation within the tensorflow folder. This version has not been carefully tested.

optimizer = aggmo.AggMo(lr, betas=[0, 0.9, 0.99])

Or using the exponential form:

optimizer = aggmo.AggMo.from_exp_form(lr, a=0.1, k=3)

Running Experiments

Code to run experiments can be found in the src directory. Each task and optimizer has their own config file which can be easily overridden from the command line.

The first argument points to the task configuration. The optimizer is specified with --optim <optimizer_name>. Additional config overrides can be given after -o in the format e.g. -o optim.lr_schedule.lr_decay=0.5.

The optimizer configs do not provide optimal hyperparameters for every task.

Autoencoders

From the src directory:

python main.py configs/ae.json --optim aggmo

Classification

From the src directory:

python main.py configs/cifar-10.json --optim aggmo
python main.py configs/cifar-100.json --optim aggmo

LSTMs

The LSTM code is not directly included here. We made direct use of the official code from "Regularizing and Optimizing LSTM Language Models". You can run these experiments by using the AggMo optimizer within this repository. The model hyperparameters used are detailed in the appendix.