This is the code written for the final project (#3) for the course FYS-STK4155, code by Betina, Ingrid, Polina, Mona and Henrik
In this project we study and attempt to make an comparison between our own NumPy-based CNN, Keras CNN, TensorFlow CNN and support vector machine model of MNIST database. We find that to make a computationally fast CNN is quite hard. However, prediction accuracy for NumPy-based CNN, Keras CNN,TensorFlow CNN and support vector machine models are 95.3%, 98.9%, 99.0% and 96.9%, respectively. Due to computational difficulties of NumPy-based CNN we had to resort to a smaller database of digits, 8 x 8 database, to obtain such a good accuracy. Another aspects that we could emphasize in case of NumPy-based, TensorFlow and Keras CNNs is that employed structure of CNN model and the training has an immense impact on the final prediction accuracy.