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项目作者： hasan-kamal

项目描述 ：
A vectorized implementation of fully-connected neural network(s) in NumPy
高级语言： Python
项目地址: git://github.com/hasan-kamal/Neural-Network-Implementation.git


Neural-Network-Implementation

Introduction

  • This is a working implementation of a vectorized fully-connected neural network in NumPy
  • Backpropagation algorithm is implemented in a full-vectorized fashion over a given minibatch
  • This enables us to take advantage of powerful built-in NumPy APIs (and avoid clumsy nested loops!), consequently improving training speed
  • Backpropagation code lies in the method take_gradient_step_on_minibatch of class NeuralNetwork (see src/neural_network.py)
  • Refer to in-code documentation and comments for description of how the code is working

Repository structure

  • Directory src/ contains the implementation of neural networks
    1. src/neural_network.py contains the actual implementation of the NeuralNetwork class (including vectorized backpropagation code)
    2. src/activations.py and src/losses.py contain implementations of activation functions and losses, respectively
    3. src/utils.py contains code to display confusion matrix
  • main.py contains driver code that trains an example neural network configuration using the NeuralNetwork class

Installation

  • To download MNIST data, install python-mnist through git clone method (run the script to download data; ensure python-mnist directory exists inside the root directory of this project)

Contents

  1. Implement class NeuralNetwork
  2. Implement common activation and loss functions
  3. Test implementation on MNIST data

Results

  • 1 hidden-layer (256 dimensional) with sigmoid activation on MNIST data
    Accuracy Plot
    Confusion Matrix

References

  1. “How the backpropagation algorithm works” by Michael Nielsen