Unofficial Tensorflow-Keras implementation of Fastformer based on paper [Fastformer: Additive Attention Can Be All You Need](https://arxiv.org/abs/2108.09084).

Related tags

Deep Learning Fastformer-Keras
Overview

Fastformer-Keras

Unofficial Tensorflow-Keras implementation of Fastformer based on paper Fastformer: Additive Attention Can Be All You Need.

Network Architecture image from the paper

Tensorflow-keras port of the following repositories:

- https://github.com/wilile26811249/Fastformer-PyTorch

- https://github.com/cheesama/stock-transformer

I just cleaned up and translated their work, All credits whatsoever goes to them! :)

Usage :

from fastformer import Fastformer
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Concatenate, GlobalAveragePooling1D, Dropout, Dense

in_seq = Input(shape=(128, 64))
x = Fastformer(64)(in_seq)
x = GlobalAveragePooling1D(data_format='channels_first')(x)
x = Dense(64, activation = 'relu')(x)
out = Dense(1, activation = 'linear')(x)
model = Model(inputs = in_seq, outputs = out)
model.compile(loss = 'mse', optimizer = 'adam', metrics = ['mae', 'mape'])

Citation :

@misc{wu2021fastformer,
    title={Fastformer: Additive Attention Can Be All You Need},
    author={Chuhan Wu, Fangzhao Wu, Tao Qi and Yongfeng Huang},
    year={2021},
    eprint={2108.09084v2},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

If this implement have any problem please let me know, thank you.

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