Backpropagation

This is a simple implementation of backpropagation in Python. It is intended to be used as a learning tool, not as a production-ready neural network library.

Demo task: classify digits in the image.

Requirements

• Python 3.6+

• Install requirements.txt

   pip install -r requirements.txt

Backpropagation Implementation

See model.py __call__ method for the implementation of backpropagation.

Usage

• python train.py to train a network on the digits recognition task. Model will be saved to model.pkl file.

• python test.py to test the network on the images in the ./examples directory.

Dataloader

• The dataloader module contains a class for randomly generating data for the digits recognition task.

• Each batch contain batch_size images of shape (25 x 25).

• The images are generated by randomly placing a digit from 0 to 9 on a 25 x 25 random noise image.

• The labels are vectors of length batch_size.

Test Examples

0.png	1.png	2.png	3.png	4.png

python test.py output:

loading model from: ./model.pkl
image: examples/0.png, prediction: [2]
image: examples/1.png, prediction: [6]
image: examples/2.png, prediction: [0]
image: examples/3.png, prediction: [7]
image: examples/4.png, prediction: [4]