semvec/pypi-packages huggingface dataset:
keras-position-wise-feed-forward 0.8.0
Feed forward layer implemented in Keras
License: MIT

# Keras Position-Wise Feed Forward

[![Version](https://img.shields.io/pypi/v/keras-position-wise-feed-forward.svg)](https://pypi.org/project/keras-position-wise-feed-forward/)
![License](https://img.shields.io/pypi/l/keras-position-wise-feed-forward.svg)

Implementation of position-wise feed forward layer in the paper: [Attention is All You Need](https://arxiv.org/pdf/1706.03762.pdf)

## Install

```bash
pip install keras-position-wise-feed-forward
```

## Usage

```python
from tensorflow import keras
from keras_position_wise_feed_forward import FeedForward

input_layer = keras.layers.Input(shape=(None, 32))
feed_forward_layer = FeedForward(units=128)(input_layer)
model = keras.models.Model(inputs=input_layer, outputs=feed_forward_layer)
model.compile(optimizer='adam', loss='mse')
model.summary()
```
python-bsc 0.0.3a0
A python based balanced scorecard implementation for managing objectives, tracking KPIs and improving organisational performance
License: MIT
Keywords: Balanced Scorecard,KPI's,Performance management

# python-bsc
This is a python package containing a basic implementation of the Balanced Scorecard as designed by Kaplan and Norton. It is currently geared for agriculture but can be altered to fit any organisational need

## Implementation
The current implementation is a mix of _sqlalchemy_ on the database side and _flask_ mixed with some bootstrap and _wtf_ _forms_. 
The database can be spun up with a few commands as explained in more detail lower down, and the UI generated via _flask_ can be used 
to populate the tables within the database. 

Additions are relatively straight forward and so this package can be altered to suit a multitude of organisational needs
with some adjustments, even if the stack is the only thing used. 

## Features
* sqlalchemy back end
* Example implementation for an agrilcultural company
* Implements the Balanced Scorecard with objectives and measures
* Can be run in a dockerised environment

## Requirements
* Linux or Mac (windows not yet supported)
* Docker
* Postgresql client or just a psql docker container

To run and use this code base you need database backend where the tables can be built. 
I suggest running a postgresql docker container, but any standard sql contianer should work with some minor adjustments. 
See the section on starting a docker container with postgresql.

## Getting started
Create a virtual environment
```
python3 virtualenv <name-your-env>
source <name-your-env>/bin/activate
```

Install the package
```commandline
pip install python-bsc
```



## Running a postgresql docker container

```commandline
docker run -d --name my-bsc-db -e POSTGRES_HOST_AUTH_METHOD=trust -v my_dbdata:/var/lib/postgresql/bsc-data -p 5432:5432 postgres:12
```
## Contributing
Clone the repo
```commandline
git clone 
```
## Issues
I am super amped to improve this package and learn from the community. If you a suggestions or an improvement
let me know or send those requests, I will do my best.
keras-embed-sim 0.10.0
Calculate similarity with embedding
License: MIT

# Keras Embedding Similarity

[![Version](https://img.shields.io/pypi/v/keras-embed-sim.svg)](https://pypi.org/project/keras-embed-sim/)
![License](https://img.shields.io/pypi/l/keras-embed-sim.svg)

\[[中文](https://github.com/CyberZHG/keras-embed-sim/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-embed-sim/blob/master/README.md)\]

Compute the similarity between the outputs and the embeddings.

## Install

```bash
pip install keras-embed-sim
```

## Usage

```python
from tensorflow import keras
from keras_embed_sim import EmbeddingRet, EmbeddingSim

input_layer = keras.layers.Input(shape=(None,), name='Input')

embed, embed_weights = EmbeddingRet(
    input_dim=20,
    output_dim=100,
    mask_zero=True,
)(input_layer)

output_layer = EmbeddingSim()([embed, embed_weights])
```