https://github.com/3outeille/cnnumpy

A Numpy implementation of a Convolutional Neural Network: slow & fast (im2col/col2im).
https://github.com/3outeille/cnnumpy

cnn col2im convolutional-neural-networks im2col numpy python python3

Last synced: 25 days ago
JSON representation

A Numpy implementation of a Convolutional Neural Network: slow & fast (im2col/col2im).

• Host: GitHub
• URL: https://github.com/3outeille/cnnumpy
• Owner: 3outeille
• Created: 2019-12-05T13:22:30.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2023-07-06T22:00:25.000Z (over 1 year ago)
• Last Synced: 2023-10-20T15:46:36.254Z (about 1 year ago)
• Topics: cnn, col2im, convolutional-neural-networks, im2col, numpy, python, python3
• Language: Python
• Homepage: https://3outeille.github.io/deep-learning/
• Size: 32.8 MB
• Stars: 43
• Watchers: 3
• Forks: 10
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        


## Introduction

- **CNNumpy** is a Convolutional Neural Network written in pure Numpy (educational purpose only).

- There are 2 implementation versions:

    - Slow: The naive version with nested for loops.

    - Fast: The im2col/col2im version.

- The [slow implementation][slow-implementation] takes around **4 hours for 1 epoch** where the [fast implementation][fast-implementation] takes only **6 min for 1 epoch**.

- For your information, with the same architecture using **Pytorch**, it will take around **1 min for 1 epoch**.

- **For more details, here are my blog posts explaining in depth what's going on under the hood for each implementation ([slow][slow-blog] and [fast][fast-blog]).**

- In the [`demo-cnnumpy-fast.ipynb`][demo-notebook] notebook, the im2col/col2im implementation can achieve an accuracy up to  **97.2% in 1 epoch (~6 min)**. Here are some results:





## Installation

- Create a virtual environment in the root folder using [virtualenv][virtualenv] and activate it.

```bash

# On Linux terminal, using virtualenv.

virtualenv myenv

# Activate it.

source myenv/bin/activate

```

- Install **requirements.txt**.

```bash

pip install -r requirements.txt

# Tidy up the root folder.

python3 setup.py clean

```

## Usage of demo notebooks

To play with the `demo-notebooks/` files, you need to make sure jupyter notebook can select your virtual environnment as a kernel.

- Follow **"Installation"** instructions first and make sure your virtual environment is still activated.

- Run the following line in the terminal.

```bash

python -m ipykernel install --user--name=myenv

```

- Run the notebook file **only from `demo_notebooks/`** and then select **Kernel > Switch Kernel > myenv**. You are now ready to go !

[slow-implementation]: https://github.com/3outeille/CNNumpy/tree/master/src/slow

[fast-implementation]: https://github.com/3outeille/CNNumpy/tree/master/src/fast

[slow-blog]: https://hackmd.io/@machine-learning/blog-post-cnnumpy-slow

[fast-blog]: https://hackmd.io/@machine-learning/blog-post-cnnumpy-fast

[demo-notebook]: https://github.com/3outeille/CNNumpy/blob/master/src/demo_notebooks/demo-cnnumpy-fast.ipynb

[virtualenv]: https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/