Hyperparameters impact in a convolutional neural network

Abstract: Machine learning and image recognition is a big and growing subject in today's society. Therefore the aim of this thesis is to compare convolutional neural networks with different hyperparameter settings and see how the hyperparameters affect the networks test accuracy in identifying images of traffic signs. The reason why traffic signs are chosen as objects to evaluate hyperparameters is due to the author's previous experience in the domain. The object itself that is used for image recognition does not matter. Any dataset with images can be used to see the hyperparameters affect. Grid search is used to create a large amount of models with different width and depth, learning rate and momentum. Convolution layers, activation functions and batch size are all tested separately. These experiments make it possible to evaluate how the hyperparameters affect the networks in their performance of recognizing images of traffic signs. The models are created using Keras API and then trained and tested on the dataset Traffic Signs Preprocessed. The results show that hyperparameters affect test accuracy, some affect more than others. Configuring learning rate and momentum can in some cases result in disastrous results if they are set too high or too low. Activation function also show to be a crucial hyperparameter where it in some cases produce terrible results.