RNNs with layer normalization
Project description
keras-layernorm-rnn
Table of Contents
Installation
The keras-layernorm-rnn
git repo is available as PyPi package
pip install keras-layernorm-rnn
pip install git+ssh://git@github.com/kmedian/keras-layernorm-rnn.git
Usage
Check the examples folder for notebooks.
import tensorflow as tf
from keras_layernorm_rnn import LayernormLSTM3
model = tf.keras.Sequential([
LayernormLSTM3(units=8, return_sequences=False), # Many-to-One
tf.keras.layers.Dense(1, activation='linear')
])
Commands
Install a virtual environment
python3 -m venv .venv
source .venv/bin/activate
pip3 install --upgrade pip
pip3 install -r requirements.txt
(If your git repo is stored in a folder with whitespaces, then don't use the subfolder .venv
. Use an absolute path without whitespaces.)
Python commands
- Jupyter for the examples:
jupyter lab
- Check syntax:
flake8 --ignore=F401 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')
- Run Unit Test:
python keras_layernorm_rnn/layernorm_simplernn_test.py
- Upload to PyPi with twine:
python setup.py sdist && twine upload -r pypi dist/*
Clean up
find . -type f -name "*.pyc" | xargs rm
find . -type d -name "__pycache__" | xargs rm -r
rm -r .pytest_cache
rm -r .venv
Support
Please open an issue for support.
Contributing
Please contribute using Github Flow. Create a branch, add commits, and open a pull request.
Project details
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Source Distribution
keras-layernorm-rnn-0.2.0.tar.gz
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