https://github.com/yoyolicoris/building-nn-with-numpy

numpy lenet implementation.
https://github.com/yoyolicoris/building-nn-with-numpy

convolutional-neural-networks deep-learning deep-neural-networks lenet-mnist mnist-classification numpy-tutorial scipy

Last synced: 20 days ago
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numpy lenet implementation.

• Host: GitHub
• URL: https://github.com/yoyolicoris/building-nn-with-numpy
• Owner: yoyolicoris
• Created: 2019-08-10T10:06:21.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-08-11T08:23:14.000Z (over 5 years ago)
• Last Synced: 2024-12-14T21:39:15.003Z (20 days ago)
• Topics: convolutional-neural-networks, deep-learning, deep-neural-networks, lenet-mnist, mnist-classification, numpy-tutorial, scipy
• Language: Python
• Homepage:
• Size: 42 KB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Building NN with NumPy

This repo provide the minimum necessary code to build and train a CNN model from scratch using NumPy solely (basically I just trying to mimic what PyTorch framework do).

3 layer and one loss function is implemented: **Conv2d**, **MaxPool2d**, **Linear** and **cross_entropy**. 

They are heavily relied on `numpy.lib.stride_tricks.as_stride` function (Conv2d, MaxPool2d) and `scipy.sparse` matrix (MaxPool2d), 

and have almost the same functionality as their PyTorch version.

You can check out in the file [nn](nn.py).

## Quick Start: MNIST classification with LeNet

Please download mnist dataset from [here](http://yann.lecun.com/exdb/mnist/).

```

python main.py /path/to/mnist/data

```

I got a 96.30% test accuracy, 97.61% training accuracy and final loss around 0.0772 on my laptop. 

It took about 20 minutes to complete.

![](loss_curve.png)

## Requirements

* NumPy

* SciPy (for MaxPool2d)

To run the mnist example, some extra package need to install:

* tqdm

* matplotlib

* Scikit-learn

## Notes

Because I focusing on the core part of understanding neural network (forward and gradient back propagation...), the module reusability is limited 

(ex: you can't forward a module twice before backward the loss).