Git Product home page Git Product logo

iterative_training's Introduction

Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization

This repository contains the source code for the paper: https://arxiv.org/abs/2111.07046.

Requirements

  • GPU
  • Python 3
  • PyTorch 1.9
    • Earlier version may work, but untested.
  • pip install -r requirements.txt
  • If running ResNet-21 or ImageNet experiments, first download and prepare the ImageNet 2012 dataset with bin/imagenet_prep.sh script.

Running

For non-ImageNet experiments, the main python file is main.py. To see its arguments:

python main.py --help

Running for the first time can take a little longer due to automatic downloading of the MNIST and Cifar-10 dataset from the Internet.

For ImageNet experiments, the main python files are main_imagenet_float.py and main_imagenet_binary.py. Too see their arguments:

python main_imagenet_float.py --help

and

python main_imagenet_binary.py --help

The ImageNet dataset must be already downloaded and prepared. Please see the requirements section for details.

Scripts

The main python file has many options. The following scripts runs training with hyper-parameters given in the paper. Output includes a run-log text file and tensorboard files. These files are saved to ./logs and reused for subsequent runs.

300-100-10

Sensitivity Pre-training

# Layer 1. Learning rate 0.1.
./scripts/mnist/300/sensitivity/layer.sh sensitivity forward 0.1 0
# Layer 2. Learning rate 0.1.
./scripts/mnist/300/sensitivity/layer.sh sensitivity 231 0.1 0
# Layer 3. Learning rate 0.1.
./scripts/mnist/300/sensitivity/layer.sh sensitivity reverse 0.1 0

Output files and run-log are written to ./logs/mnist/val/sensitivity/.

Hyperparam search

For floating-point training:

# Learning rate 0.1.
./scripts/mnist/300/val/float.sh hyperparam 0.1 0

For full binary training:

# Learning rate 0.1.
./scripts/mnist/300/val/binary.sh hyperparam 0.1 0

For iterative training:

# Forward order. Learning rate 0.1.
./scripts/mnist/300/val/layer.sh hyperparam forward 0.1 0
# Reverse order. Learning rate 0.1.
./scripts/mnist/300/val/layer.sh hyperparam reverse 0.1 0
# 1, 3, 2 order. Learning rate 0.1.
./scripts/mnist/300/val/layer.sh hyperparam 132 0.1 0
# 2, 1, 3 order. Learning rate 0.1.
./scripts/mnist/300/val/layer.sh hyperparam 213 0.1 0
# 2, 3, 1 order. Learning rate 0.1.
./scripts/mnist/300/val/layer.sh hyperparam 231 0.1 0
# 3, 1, 2 order. Learning rate 0.1.
./scripts/mnist/300/val/layer.sh hyperparam 312 0.1 0

Output files and run-log are written to ./logs/mnist/val/hyperparam/.

Full Training

For floating-point training:

# Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/float.sh full 0.1 316 0

For full binary training:

# Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/binary.sh full 0.1 316 0

For iterative training:

# Forward order. Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/layer.sh full forward 0.1 316 0
# Reverse order. Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/layer.sh full reverse 0.1 316 0
# 1, 3, 2 order. Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/layer.sh full 132 0.1 316 0
# 2, 1, 3 order. Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/layer.sh full 213 0.1 316 0
# 2, 3, 1 order. Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/layer.sh full 231 0.1 316 0
# 3, 1, 2 order. Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/layer.sh full 312 0.1 316 0

Output files and run-log are written to ./logs/mnist/run/full/.

784-100-10

Sensitivity Pre-training

# Layer 1. Learning rate 0.1.
./scripts/mnist/784/sensitivity/layer.sh sensitivity forward 0.1 0
# Layer 2. Learning rate 0.1.
./scripts/mnist/784/sensitivity/layer.sh sensitivity 231 0.1 0
# Layer 3. Learning rate 0.1.
./scripts/mnist/784/sensitivity/layer.sh sensitivity reverse 0.1 0

Output files and run-log are written to ./logs/mnist/val/sensitivity/.

Hyperparam search

For floating-point training:

# Learning rate 0.1.
./scripts/mnist/784/val/float.sh hyperparam 0.1 0

For full binary training:

# Learning rate 0.1.
./scripts/mnist/784/val/binary.sh hyperparam 0.1 0

For iterative training:

# Forward order. Learning rate 0.1.
./scripts/mnist/784/val/layer.sh hyperparam forward 0.1 0
# Reverse order. Learning rate 0.1.
./scripts/mnist/784/val/layer.sh hyperparam reverse 0.1 0
# 1, 3, 2 order. Learning rate 0.1.
./scripts/mnist/784/val/layer.sh hyperparam 132 0.1 0
# 2, 1, 3 order. Learning rate 0.1.
./scripts/mnist/784/val/layer.sh hyperparam 213 0.1 0
# 2, 3, 1 order. Learning rate 0.1.
./scripts/mnist/784/val/layer.sh hyperparam 231 0.1 0
# 3, 1, 2 order. Learning rate 0.1.
./scripts/mnist/784/val/layer.sh hyperparam 312 0.1 0

Output files and run-log are written to ./logs/mnist/val/hyperparam/.

Full Training

For floating-point training:

# Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/float.sh full 0.1 316 0

For full binary training:

# Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/binary.sh full 0.1 316 0

For iterative training:

# Forward order. Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/layer.sh full forward 0.1 316 0
# Reverse order. Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/layer.sh full reverse 0.1 316 0
# 1, 3, 2 order. Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/layer.sh full 132 0.1 316 0
# 2, 1, 3 order. Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/layer.sh full 213 0.1 316 0
# 2, 3, 1 order. Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/layer.sh full 231 0.1 316 0
# 3, 1, 2 order. Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/layer.sh full 312 0.1 316 0

Output files and run-log are written to ./logs/mnist/run/full/.

Vgg-5

Sensitivity Pre-training

# Layer 1. Learning rate 0.1.
./scripts/cifar10/vgg5/sensitivity/layer.sh sensitivity 1 0.1 0
# Layer 2. Learning rate 0.1.
./scripts/cifar10/vgg5/sensitivity/layer.sh sensitivity 2 0.1 0
# Layer 5. Learning rate 0.1.
./scripts/cifar10/vgg5/sensitivity/layer.sh sensitivity 5 0.1 0

Output files and run-log are written to ./logs/cifar10/val/sensitivity/.

Hyperparam Search

For floating-point training:

# Learning rate 0.1.
./scripts/cifar10/vgg5/val/float.sh hyperparam 0.1 0

For full binary training:

# Learning rate 0.1.
./scripts/cifar10/vgg5/val/binary.sh hyperparam 0.1 0

For iterative training:

# Forward order. Learning rate 0.1.
./scripts/cifar10/vgg5/val/layer.sh hyperparam forward 0.1 0
# Ascend order. Learning rate 0.1.
./scripts/cifar10/vgg5/val/layer.sh hyperparam ascend 0.1 0
# Reverse order. Learning rate 0.1.
./scripts/cifar10/vgg5/val/layer.sh hyperparam reverse 0.1 0
# Descend order. Learning rate 0.1.
./scripts/cifar10/vgg5/val/layer.sh hyperparam descend 0.1 0
# Random order. Learning rate 0.1.
./scripts/cifar10/vgg5/val/layer.sh hyperparam random 0.1 0

Output files and run-log are written to ./logs/cifar10/val/hyperparam/.

Full Training

For floating-point training:

# Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/float.sh full 0.1 316 0

For full binary training:

# Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/binary.sh full 0.1 316 0

For iterative training:

# Forward order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/layer.sh full forward 0.1 316 0
# Ascend order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/layer.sh full ascend 0.1 316 0
# Reverse order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/layer.sh full reverse 0.1 316 0
# Descend order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/layer.sh full descend 0.1 316 0
# Random order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/layer.sh full random 0.1 316 0

Output files and run-log are written to ./logs/cifar10/run/full/.

Vgg-9

Sensitivity Pre-training

# Layer 1. Learning rate 0.1.
./scripts/cifar10/vgg9/sensitivity/layer.sh sensitivity 1 0.1 0
# Layer 2. Learning rate 0.1.
./scripts/cifar10/vgg9/sensitivity/layer.sh sensitivity 2 0.1 0
# Layer 5. Learning rate 0.1.
./scripts/cifar10/vgg9/sensitivity/layer.sh sensitivity 5 0.1 0

Output files and run-log are written to ./logs/cifar10/val/sensitivity/.

Hyperparam Search

For floating-point training:

# Learning rate 0.1.
./scripts/cifar10/vgg9/val/float.sh hyperparam 0.1 0

For full binary training:

# Learning rate 0.1.
./scripts/cifar10/vgg9/val/binary.sh hyperparam 0.1 0

For iterative training:

# Forward order. Learning rate 0.1.
./scripts/cifar10/vgg9/val/layer.sh hyperparam forward 0.1 0
# Ascend order. Learning rate 0.1.
./scripts/cifar10/vgg9/val/layer.sh hyperparam ascend 0.1 0
# Reverse order. Learning rate 0.1.
./scripts/cifar10/vgg9/val/layer.sh hyperparam reverse 0.1 0
# Descend order. Learning rate 0.1.
./scripts/cifar10/vgg9/val/layer.sh hyperparam descend 0.1 0
# Random order. Learning rate 0.1.
./scripts/cifar10/vgg9/val/layer.sh hyperparam random 0.1 0

Output files and run-log are written to ./logs/cifar10/val/hyperparam/.

Full Training

For floating-point training:

# Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/float.sh full 0.1 316 0

For full binary training:

# Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/binary.sh full 0.1 316 0

For iterative training:

# Forward order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/layer.sh full forward 0.1 316 0
# Ascend order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/layer.sh full ascend 0.1 316 0
# Reverse order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/layer.sh full reverse 0.1 316 0
# Descend order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/layer.sh full descend 0.1 316 0
# Random order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/layer.sh full random 0.1 316 0

Output files and run-log are written to ./logs/cifar10/run/full/.

ResNet-20

Sensitivity Pre-training

# Layer 1. Learning rate 0.1.
./scripts/cifar10/resnet20/sensitivity/layer.sh sensitivity 1 0.1 0
# Layer 2. Learning rate 0.1.
./scripts/cifar10/resnet20/sensitivity/layer.sh sensitivity 2 0.1 0
# ...
# Layer 20. Learning rate 0.1.
./scripts/cifar10/resnet20/sensitivity/layer.sh sensitivity 20 0.1 0

Output files and run-log are written to ./logs/cifar10/val/sensitivity/.

Hyperparam Search

For floating-point training:

# Learning rate 0.1
./scripts/cifar10/resnet20/val/float.sh hyperparam 0.1 0

For full binary training:

# Learning rate 0.1
./scripts/cifar10/resnet20/val/binary.sh hyperparam 0.1 0

For iterative training:

# Forward order. Learning rate 0.1
./scripts/cifar10/resnet20/val/layer.sh hyperparam forward 0.1 0
# Ascend order. Learning rate 0.1
./scripts/cifar10/resnet20/val/layer.sh hyperparam ascend 0.1 0
# Reverse order. Learning rate 0.1
./scripts/cifar10/resnet20/val/layer.sh hyperparam reverse 0.1 0
# Descend order. Learning rate 0.1
./scripts/cifar10/resnet20/val/layer.sh hyperparam descend 0.1 0
# Random order. Learning rate 0.1
./scripts/cifar10/resnet20/val/layer.sh hyperparam random 0.1 0

Output files and run-log are written to ./logs/cifar10/val/hyperparam/.

Full Training

For floating-point training:

# Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/float.sh full 0.1 316 0

For full binary training:

# Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/binary.sh full 0.1 316 0

For iterative training:

# Forward order. Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/layer.sh full forward 0.1 316 0
# Ascend order. Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/layer.sh full ascend 0.1 316 0
# Reverse order. Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/layer.sh full reverse 0.1 316 0
# Descend order. Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/layer.sh full descend 0.1 316 0
# Random order. Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/layer.sh full random 0.1 316 0

Output files and run-log are written to ./logs/cifar10/run/full/.

ResNet-21

To run experiments for ResNet-21, first download and prepare the ImageNet dataset. See the requirements section at the beginning of this readme. We assume the dataset is prepared and is at ./imagenet.

Sensitivity Pre-training

# Layer 1. Learning rate 0.01.
./scripts/imagenet/layer.sh sensitivity ./imagenet 20 "[20]" 20 1 0.01
# Layer 2. Learning rate 0.01.
./scripts/imagenet/layer.sh sensitivity ./imagenet 20 "[20]" 20 2 0.01
# Layer 21. Learning rate 0.01.
./scripts/imagenet/layer.sh sensitivity ./imagenet 20 "[20]" 20 21 0.01

Output files and run-log are written to ./logs/imagenet/sensitivity/.

Full Training

For floating-point training:

# Learning rate 0.01.
./scripts/imagenet/float.sh full ./imagenet 67 "[42,57]" 0.01

For full binary training:

# Learning rate 0.01.
./scripts/imagenet/binary.sh full ./imagenet 67 "[42,57]" 0.01

For layer-by-layer training:

# Forward order
./scripts/imagenet/layer.sh full ./imagenet 67 "[42,57]" 2 forward 0.01
# Ascending order
./scripts/imagenet/layer.sh full ./imagenet 67 "[42,57]" 2 ascend 0.01

For all scripts, output files and run-log are written to ./logs/imagenet/full/.

License

See LICENSE

Contributing

See the contributing guide for details of how to participate in development of the module.

iterative_training's People

Contributors

rlan avatar

Watchers

avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.