semvec/pypi-packages huggingface dataset:
keras-layernorm-rnn 0.2.1
RNNs with layer normalization
License: MIT
Requires Python: >=3.6

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# keras-layernorm-rnn


## Table of Contents
* [Installation](#installation)
* [Usage](#usage)
* [Commands](#commands)
* [Support](#support)
* [Contributing](#contributing)


## Installation
The `keras-layernorm-rnn` [git repo](http://github.com/kmedian/keras-layernorm-rnn) is available as [PyPi package](https://pypi.org/project/keras-layernorm-rnn)

```
pip install keras-layernorm-rnn
pip install git+ssh://git@github.com/kmedian/keras-layernorm-rnn.git
```


## Usage
Check the [examples](https://github.com/kmedian/keras-layernorm-rnn/blob/master/examples/How%20to%20use%20LayernormSimpleRNN.ipynb) 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](https://github.com/kmedian/keras-layernorm-rnn/issues/new) for support.


## Contributing
Please contribute using [Github Flow](https://guides.github.com/introduction/flow/). Create a branch, add commits, and [open a pull request](https://github.com/kmedian/keras-layernorm-rnn/compare/).