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ND-Adam

This repository contains the code for the paper Normalized Direction-preserving Adam. ND-Adam is a tailored version of Adam for training DNNs, which bridges the generalization gap between Adam and SGD.

The code is based on a TensorFlow implementation and a PyTorch implementation of wide residual networks.

Usage

PyTorch

CIFAR-10
## ND-Adam
main.py --depth 28 --width 10 --dataroot ../cifar10 --save ./logs/resnet_model --ngpu 1 --gpu_id 0
CIFAR-100
## ND-Adam
main.py --depth 28 --width 10 --dataset CIFAR100 --dataroot ../cifar100 --save ./logs/resnet_model --ngpu 1 --gpu_id 0

TensorFlow

CIFAR-10
# Training
## ND-Adam
resnet_main.py --bnsoftmax_scale 2.5 --train_data_path=cifar10/data_batch* --log_root=./resnet_model --train_dir=./resnet_model/train --dataset=cifar10 --num_gpus=1

## Adam
resnet_main.py --optimizer adam --bnsoftmax_scale 2.5 --train_data_path=cifar10/data_batch* --log_root=./resnet_model --train_dir=./resnet_model/train --dataset=cifar10 --num_gpus=1

## SGD with momentum
resnet_main.py --optimizer mom --init_learning_rate 0.1 --weight_decay 0.001 --bnsoftmax_scale 2.5 --train_data_path=cifar10/data_batch* --log_root=./resnet_model --train_dir=./resnet_model/train --dataset=cifar10 --num_gpus=1

# Evaluation
resnet_main.py --bnsoftmax_scale 2.5 --eval_data_path=cifar10/test_batch.bin --log_root=./resnet_model --eval_dir=./resnet_model/test --mode=eval --dataset=cifar10 --num_gpus=0
CIFAR-100

Change --train_data_path, --eval_data_path, and --dataset accordingly, and replace --bnsoftmax_scale 2.5 with --bnsoftmax_scale 1.