Practical overview
When it fits
Use it when you want a compact code example for studying or experimenting with attention in a Keras recurrent network on date translation, especially when you want to inspect attention maps alongside model behavior.
Who it is for
- Learners exploring attention mechanisms in recurrent neural networks
- Developers studying a Keras date-translation reference implementation
- Researchers or practitioners running small experiments with date inputs and attention visualizations
Documented uses
- Generate example date-translation training and validation datasets.
- Train the example recurrent network with configurable epochs, GPU, padding, data paths and batch size.
- Visualize attention maps using supplied weights, vocabularies and example inputs.
Requirements & limits
The public GitHub repository is listed under the AGPL-3.0 license. The README describes it as reference code only and says no further updates are expected.
- The example is specifically about translating dates, rather than a general-purpose attention toolkit.
- GPU use is recommended because CPU training may be prohibitively slow; a separate requirements file is provided for local prototyping without a GPU.
- The visualization script’s padding must correspond to the model’s padding, and requires weights plus an example input.
- The README includes an example of an incorrect translation, so outputs should be assessed rather than assumed correct.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up the date-translation example
Prepare the repository dependencies and generate the example training and validation data.
Before you start
- Python 3.4 or later
- A local clone of the repository
- GPU recommended for training; the README also provides a non-GPU requirements option
Steps
Clone the repository with the command below.
git clone https://github.com/datalogue/keras-attention.gitInstall dependencies with the command below for GPU use, or the command below for local prototyping without a GPU.
pip install -r requirements-gpu.txtpip install -r requirements.txtChange into the
datadirectory and run the command below.python generate.py
Expected outcome
The data directory receives training.csv, validation.csv, human_vocab.json and machine_vocab.json for the example.
Guide limitations
- The README warns that training without a GPU may be prohibitively slow.
- This setup recipe covers dependency installation and dataset generation; it does not establish successful model training on a particular system.
Visualize attention with the supplied example
Use the included example materials to produce an attention-map visualization.
Before you start
- The repository and its Python requirements installed
- The provided sample weights and vocabularies
- An example string or an example text file;
examples.txtis provided
Steps
From the repository, run the command below to inspect the script’s accepted arguments.
python visualize.py -hProvide the weights path and an example string or text-file path to the visualization script.
If changing padding, keep it consistent with the padding used by
run.py.
Expected outcome
The script is intended to visualize an attention map for the supplied example or examples.
Guide limitations
- The README requires a weights path and example input.
- The guidance describes the script and included example assets; it does not verify execution on a particular environment.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- datalogue/keras-attention · Official homepage title · retained in its source language