ABSTRACT

Increase in popularity of deep learning in various research areas leads to use it in resolving image classification problems. The objective of this research is to compare and to find learning algorithms which perform better for image classification task with small dataset. We have also tuned the hyperparameters associated with optimizers and models to improve performance. First, we performed several experiments using eight learning algorithms to come closer to optimal values of hyperparameters. Then, we executed twenty-four final experiments with near optimum values of hyperparameters to find the best learning algorithm. Experimental results showed that the AdaGrad learning algorithm achieves better accuracy, lesser training time, as well as fewer memory utilization compared to the rest of the learning algorithms.

摘要

深度学习在各个研究领域的普及导致将其用于解决图像分类问题。本研究的目的是比较并找到在小数据集的图像分类任务中表现更好的学习算法。我们还调整了与优化器和模型相关的超参数以提高性能。首先，我们使用八种学习算法进行了几次实验，以接近超参数的最佳值。然后，我们使用接近最优的超参数值执行了 24 次最终实验，以找到最佳学习算法。实验结果表明，与其他学习算法相比，AdaGrad 学习算法具有更好的准确性、更短的训练时间以及更少的内存占用。