Abstract
Convolutional Neural Networks (CNNs) are widely used in image classification tasks and have demonstrated promising classification accuracy results. Designing a CNN architecture requires a manual adjustment of parameters through a series of experiments as well as sufficient knowledge both in the problem domain and CNN architecture design. Therefore, it is extremely difficult for users without prior experience to design a promising CNN for their purposes. To solve this issue, various solutions on the automatic construction of CNN architectures are proposed, including but not limited to a genetic algorithm AE-CNN 1 for automatically evolving CNN design using ResNet and DenseNet blocks. In this paper, a significant improvement of the afore-mentioned solution by the addition of Inception blocks is introduced. Performance of the algorithm is assessed on the CINIC-10 benchmark dataset without and with the usage of Inception blocks. As it can be observed from the outcome of the experiment, the addition of Inception blocks positively affects the final classification accuracy. The proposed algorithm does not only improve the current solution but also keeps the advantages of automatic CNN without requesting any manual interventions.