AReLU

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

(★ 63)

AReLU: Attention-based-Rectified-Linear-Unit

AReLU 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# AReLU: Attention-based-Rectified-Linear-Unit

Activation function player with PyTorch on supervised/transfer/meta learning.

![teaser](https://raw.githubusercontent.com/densechen/AReLU/master/pictures/teaser.png)

## Introduction

This repository contains the implementation of paper [AReLU: Attention-based-Rectified-Linear-Unit](https://arxiv.org/pdf/2006.13858.pdf).

Any contribution is welcome! If you have found some new activations, please open a new issue and I will add it into this project ASAP.

## Install

### From PyPi

```shell
pip install activations

# check installation
python -c "import activations; print(activations.__version__)"
```

activations package only contains different activation functions under `activations`.
If you want to do full experiments, please use the following way.

### From GitHub

```shell
git clone https://github.com/densechen/AReLU
cd AReLU
pip install -r requirements.txt
# or `python setup.py install` for basic usage of package `activations`.
```

**with PAU**: PAU is only CUDA supported. You have to compile it manaully:

```shell
pip install airspeed==0.5.14

cd activations/pau/cuda
python setup.py install
```

The code of PAU is directly token from [PAU](https://github.com/ml-research/pau.git), if you occur any problems while compiling, please refer to the original repository.

## Classification

```shell
python -m visdom.server & # start visdom
python main.py # run with default parameters
```

Click [here](https://localhost:8097/) to check your training process.

```shell
python main_mnist.py -h
    usage: main_mnist.py [-h] [--batch_size BATCH_SIZE] [--lr LR] [--lr_aux LR_AUX]
                [--epochs EPOCHS] [--epochs_aux EPOCHS_AUX] [--times TIMES]
                [--data_root DATA_ROOT]
                [--dataset {MNIST,SVHN,EMNIST,KMNIST,QMNIST,FashionMNIST}]
                [--dataset_aux {MNIST,SVHN,EMNIST,KMNIST,QMNIST,FashionMNIST}]
                [--num_workers NUM_WORKERS]
                [--net {BaseModel,ConvMNIST,LinearMNIST}] [--resume RESUME]
                [--af {APL,AReLU,GELU,Maxout,Mixture,SLAF,Swish,ReLU,ReLU6,Sigmoid,LeakyReLU,ELU,PReLU,SELU,Tanh,RReLU,CELU,Softplus,PAU,all}]
                [--optim {SGD,Adam}] [--cpu] [--exname {AFS,TransferLearning}]
                [--silent]

Activation Function Player with PyTorch.

optional arguments:
    -h, --help            show this help message and exit
    --batch_size BATCH_SIZE
                            batch size for training
    --lr LR               learning rate
    --lr_aux LR_AUX       learning rate of finetune. only used while transfer
                            learning.
    --epochs EPOCHS       training epochs
    --epochs_aux EPOCHS_AUX
                            training epochs. only used while transfer learning.
    --times TIMES         repeat runing times
    --data_root DATA_ROOT
                            the path to dataset
    --dataset {MNIST,SVHN,EMNIST,KMNIST,QMNIST,FashionMNIST}
                            the dataset to play with.
    --dataset_aux {MNIST,SVHN,EMNIST,KMNIST,QMNIST,FashionMNIST}
                            the dataset to play with. only used while transfer
                            learning.
    --num_workers NUM_WORKERS
                            number of workers to load data
    --net {BaseModel,ConvMNIST,LinearMNIST}
                            network architecture for experiments. you can add new
                            models in ./models.
    --resume RESUME       pretrained path to resume
    --af {APL,AReLU,GELU,Maxout,Mixture,SLAF,Swish,ReLU,ReLU6,Sigmoid,LeakyReLU,ELU,PReLU,SELU,Tanh,RReLU,CELU,Softplus,PAU,all}
                            the activation function used in experiments. you can
                            specify an activation function by name, or try with
                            all activation functions by `all`
    --optim {SGD,Adam}    optimizer used in training.
    --cpu                 with cuda training. this would be much faster.
    --exname {AFS,TransferLearning}
                            experiment name of visdom.
    --silent              if True, shut down the visdom visualizer.
```

Or:

```shell
nohup ./main_mnist.sh > main_mnist.log &
```

![result](https://raw.githubusercontent.com/densechen/AReLU/master/pictures/result.png)

## Meta Learning

```shell
python meta_mnist.py --help
    usage: meta_mnist.py [-h] [--ways N] [--shots N] [-tps N] [-fas N]
                         [--iterations N] [--lr LR] [--maml-lr LR] [--no-cuda]
                         [--seed S] [--download-location S]
                         [--afs {APL,AReLU,GELU,Maxout,Mixture,SLAF,Swish,ReLU,ReLU6,Sigmoid,LeakyReLU,ELU,PReLU,SELU,Tanh,RReLU,CELU,Softplus,PAU}]

Learn2Learn MNIST Example

optional arguments:
      -h, --help            show this help message and exit
      --ways N              number of ways (default: 5)
      --shots N             number of shots (default: 1)
      -tps N, --tasks-per-step N
                            tasks per step (default: 32)
      -fas N, --fast-adaption-steps N
                            steps per fast adaption (default: 5)
      --iterations N        number of iterations (default: 1000)
      --lr LR               learning rate (default: 0.005)
      --maml-lr LR          learning rate for MAML (default: 0.01)
      --no-cuda             disables CUDA training
      --seed S              random seed (default: 1)
      --download-location S
                            download location for train data (default : data
      --afs {APL,AReLU,GELU,Maxout,Mixture,SLAF,Swish,ReLU,ReLU6,Sigmoid,LeakyReLU,ELU,PReLU,SELU,Tanh,RReLU,CELU,Softplus,PAU}
                            activation function used to meta learning.
```

Or:

```shell
nohup ./meta_mnist.sh > meta_mnist.log &
```

## ELSA

See `ELSA.ipynb` for more details.

## Citation

If you use this code, please cite the following paper:

```shell
@misc{AReLU,
Author = {Dengsheng Chen and Kai Xu},
Title = {AReLU: Attention-based Rectified Linear Unit},
Year = {2020},
Eprint = {arXiv:2006.13858},
}
```

AReLU

AReLU 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

AReLU

AReLU 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README