Abstract
Machines see pictures utilizing pixels. Pixels in pictures are generally related. A convolution increases a grid of pixels with a channel framework or ‘part’ and summarizes the duplication esteems. Convolutional Neural Network is one of the most sought over concepts of technology for Image classification. Implementing them through Keras took a step forward and initiated fast processing. In this research paper, the authors intend to explore the different architectures of the convolutional neural networks and the inline layers of the network and understand the influence of training batches and epochs on it. To make things significantly simpler to decipher, we will utilize the ‘accuracy’ metric to see the ac- curacy score to comprehend the likelihood of cross-entropy.
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Cherukuri, P.A.A., Linh, N.T.D., Vududala, S.K., Cherukuri, R. (2021). Keras Convolutional Neural Network Model in Sequential Build of 4 and 6 Layer Architectures. In: Tran, DT., Jeon, G., Nguyen, T.D.L., Lu, J., Xuan, TD. (eds) Intelligent Systems and Networks . ICISN 2021. Lecture Notes in Networks and Systems, vol 243. Springer, Singapore. https://doi.org/10.1007/978-981-16-2094-2_56
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