semvec/pypi-packages huggingface dataset:
banquet 0.0.1
CLI tool for working with OpenAPI and Lambda functions
License: MIT
Requires Python: >=3.8,<4.0
Keywords: OpenAPI,HTTP,Deverlopment Server,AWS Lambda

# Banquet

> Development server for OpenAPI/Lambda Projects

NB This project is very early in development - milage may vary!

## Installing

Install and update using pip:

`pip install -U banquet`
sweet-perceptron 1.0.1
A simple neural network library
License: MIT
Requires Python: >=3.8

# sweet-perceptron
![Python](https://img.shields.io/badge/Python-%3E%3D3.8-brightgreen)
![NumPy](https://img.shields.io/badge/NumPy-%3E%3D1.15-blue)
![GitHub](https://img.shields.io/github/license/SweetBubaleXXX/sweet-perceptron)

This is a simple neural network library.

The model of NN is configurable multilayer Perceptron (MLP).

It was built only using [NumPy](https://numpy.org/).

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install [sweet-perceptron](https://pypi.org/project/sweet-perceptron).

```bash
pip install sweet-perceptron
```

## Usage

```python
from perceptron import NeuralNetwork
from perceptron.Neuron import functions

# Initialize network
nw = NeuralNetwork((2, 4, 1))

# Change activation functions
nw.activation_funcs = functions.relu, functions.tanh

# Initialize weights
nw.initialize_weights()

# Train netwotk and get list with losses
loss = nw.train(50, ['train inputs set'], ['train outputs set'])

# Get output of forward propagation
output = nw.forward(['input'])
```

## License

[MIT License](https://github.com/SweetBubaleXXX/sweet-perceptron/blob/main/LICENSE)