Optimizing Hyperparameters for MINIST Dataset Classification using Deep Learning Techniques

Abstract

The potential of deep learning to improve the performance of image classification has been demonstrated. In this paper, we present a study on the optimization of the hyperparameters for the classification of the MNIST dataset. We performed a comparison between a standard two-layer perceptron model and a CNN model with different techniques. The optimized hyperparameters for CNN are based on the number of filters, kernel size, and convolutional layers. The optimized CNN model performed better than the default model on the classification task of the MNIST dataset. The various hyperparameters included the learning rate, batch size, the number of hidden layers, the dropout rate, the activation function, and the optimizer. The optimized CNN model was able to achieve an accuracy of 99% on a test, which is significantly better than the 96% accuracy of the default model. The difference between the two models is that the former takes longer to train and has a slightly longer time per image. The study demonstrates the importance of optimizing the hyperparameters in deep learning-focused classification tasks. The findings show how CNN architectures perform well in these applications, and it shows how optimizing these components can yield superior results. These recommendations can help further develop efficient and accurate models for this field.