Handwriting Recognition (MNIST) in C++

This repository provides a comprehensive implementation of Neural Network in C++ for the task of Handwriting recognition i.e. identify the the handwritten digits in the images.

Code Structure

The repository is organized as follows:

• src/Layers: Contains the implementation of differnet layers in our network.
  

1. "activations.cpp" : Implementations of forward and backward pass of 2 activation functions namely - ReLU and Softmax
   
2. "fullyconnected.cpp" : Implementation of forward and backward pass of fully connected layers of the network
   

• src/Optimization: Contains implementation of network optimization methods
  

1. "loss.cpp" : Implementation of forward and backward pass of cross-entropy loss
   
2. "sgd.cpp" : Implementation of forward and backward pass of Stochastic Gradient Descent with Momentum
   

• src/: Includes implementation of network optimization methods
  

1. "eval.cpp" : Implementation of evaluation metric "Accuracy" to measure the model performance
   
2. "mnist_data_reader.cpp" : Implementation of module for reading the data i.e. reading images and labels
   
3. "network.cpp" : Contains the code to run the forward and backward pass of the full network architecture, which implements Fully connected and activation layers
   

• src/mnist.cpp: It has the main function which triggers the project.
  

• src/test_image_reader.cpp: Function to test if the images are read correctly.
  

• src/test_label_reader.cpp: Function to test if the labels are read correctly.
  

Running the pipeline

We can test the implementation by running the following shell scripts with our own (and not publicly available) datasets:

• build.sh: Contains all necessary code to prepare/build your executable(s)
• read_dataset_images.sh: Checks if you can successfully read in, convert and write an MNIST dataset image to file.
• read_dataset_labels.sh: Checks if you can successfully read in and write an MNIST dataset label to file.
• mnist.sh: Triggers the training and testing of your neural network implementation.