https://github.com/sdobber/FluxArchitectures.jl

Complex neural network examples for Flux.jl
https://github.com/sdobber/FluxArchitectures.jl

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Complex neural network examples for Flux.jl

• Host: GitHub
• URL: https://github.com/sdobber/FluxArchitectures.jl
• Owner: sdobber
• License: mit
• Created: 2020-04-05T16:50:26.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-11-22T17:13:39.000Z (10 months ago)
• Last Synced: 2024-07-22T07:09:27.451Z (about 2 months ago)
• Language: Julia
• Size: 44.3 MB
• Stars: 122
• Watchers: 12
• Forks: 15
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# FluxArchitectures

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Complex neural network examples for Flux.jl.

This package contains a loose collection of (slightly) more advanced neural network architectures, mostly centered around time series forecasting.

## Installation

To install FluxArchitectures, type `]` to activate the package manager, and type

```julia

add FluxArchitectures

```

for installation. After `using FluxArchitectures`, the following functions are exported:

* `prepare_data`

* `get_data`

* `DARNN`

* `DSANet` 

* `LSTnet`

* `TPALSTM`

See their docstrings, the [documentation]((https://sdobber.github.io/FluxArchitectures.jl/stable)), and the `examples` folder for details.

## Models

* **LSTnet**: This "Long- and Short-term Time-series network" follows the paper by [Lai et. al.](https://arxiv.org/abs/1703.07015).

* **DARNN**: The "Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction" is based on the paper by [Qin et. al.](https://arxiv.org/abs/1704.02971).

* **TPA-LSTM**: The Temporal Pattern Attention LSTM network is based on the paper "Temporal Pattern Attention for Multivariate Time Series Forecasting" by [Shih et. al.](https://arxiv.org/pdf/1809.04206v2.pdf).

* **DSANet**: The "Dual Self-Attention Network for Multivariate Time Series Forecasting" is based on the paper by [Siteng Huang et. al.](https://kyonhuang.top/files/Huang-DSANet.pdf)

## Quickstart

Activate the package and load some sample-data:

```julia

using FluxArchitectures

poollength = 10; horizon = 15; datalength = 1000;

input, target = get_data(:exchange_rate, poollength, datalength, horizon) 

```

Define a model and a loss function:

```julia

model = LSTnet(size(input, 1), 2, 3, poollength, 120)

loss(x, y) = Flux.mse(model(x), y')

```

Train the model:

```julia

Flux.train!(loss, Flux.params(model),Iterators.repeated((input, target), 20), Adam(0.01))

```