https://github.com/tapishr/numpy-rnn

Implementation of an RNN using numpy library in python
https://github.com/tapishr/numpy-rnn

numpy python recursive-neural-network rnn

Last synced: 11 days ago
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Implementation of an RNN using numpy library in python

• Host: GitHub
• URL: https://github.com/tapishr/numpy-rnn
• Owner: tapishr
• License: mit
• Created: 2018-01-28T00:18:34.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-02-01T02:54:30.000Z (almost 7 years ago)
• Last Synced: 2024-10-24T22:13:52.239Z (about 2 months ago)
• Topics: numpy, python, recursive-neural-network, rnn
• Language: Jupyter Notebook
• Size: 271 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# numpy-RNN

This project contains the implementation of a Recursive Neural Network (RNN) in Python using only numpy and no other high level machine learning/neural network APIs or libraries. It is the first in a series of projects, and is developed with an aim to gain a deeper understanding of the fundamental concepts involved in designing and training RNNs.

In this project, a RNN is trained on a corpus of characters to enable it to predict the next character given previous characters.

## Dependencies

Only 3 dependecies for the code - 

- Python 2.7

- numpy

- jupyter

## Instructions

Install Jupyter notebooks, navigate to the directory containing `numpy-RNN.ipynb` in the command terminal and run the notebook by typing - 

`$ jupyter notebook numpy-RNN.ipynb`

This will open the notebook in a browser. Execute each cell in the notebook one by one.

## Usage

This code was written using [Andrej Karpathy's](https://gist.github.com/karpathy/d4dee566867f8291f086) code, and his [blog post](http://karpathy.github.io/2015/05/21/rnn-effectiveness/).

## Licence

Copyright (c) 2016, Damien Henry

All rights reserved.

Redistribution and use in source and binary forms, with or without

modification, are permitted provided that the following conditions are met:

- Redistributions of source code must retain the above copyright notice, this

  list of conditions and the following disclaimer.

- Redistributions in binary form must reproduce the above copyright notice,

  this list of conditions and the following disclaimer in the documentation

  and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"

AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE

IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE

DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE

FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL

DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR

SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER

CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,

OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE

OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.