Abstract:
This repository contains the CNN model capable of recognising hand-written digits from the MNIST Dataset.
Environment Requirements:
Python 3.0
Google Collab
GPU support
Keras-backend
numpy
split_folders
Dataset Links:
Part 1:
(trainPart1.zip): https://www.dropbox.com/s/pan6mutc5xj5kj0/trainPart1.zip
Part 2:
MNIST.zip: provided in this repository
Part 3:
mnistTask3.zip: https://www.dropbox.com/s/otc12z2w7f7xm8z/mnistTask3.zip
Observations:
TRAINING DATASET:
A final accuracy of ~98.30% is found in part 1, ~92.75% in part 2 and ~90% in part 3.
TESTING DATASET:
Part 1: '4' was predicted as 'K' (Sample021) with a probability of 1.0
Part 2: '7' was predicted as '7' (Sample008) with a probability of 1.0
Part 3: '6' was predicted as '5' with a probability of 1.0
ISSUES:
The model always gives a prediction probability of 1.0 on the testing data for most of the cases.
Author:
Shirshakk Purkayastha