A Novel Deep Neural Network Model for Image Classification
N.Karthika1, B.Janet2, Himanshu Shukla3
1N.Karthika*, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Trichy, Tamil Nadu, India.
2B.Janet, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Trichy, Tamil Nadu, India.
3Himanshu Shukla, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Trichy, Tamil Nadu, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 3241-3249 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8832088619/2019©BEIESP | DOI: 10.35940/ijeat.F8832.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this article, we have trained neural network based on deep learning architectures to classify images on standard Fashion-MNIST and CIFAR-10 dataset. The various CNN- based classification architecture and RNN-based classification architecture are trained as well as tested on those standard datasets. In CNN architecture, we include CNN with 1, 2 and 3 Convolutional Layer and in RNN architecture, we include Long- Short Term Memory (LSTM) with one and two LSTM layer. Our models show remarkable outcome on the standard benchmark dataset. The tested models like CNN1 show greater accuracy on the MNIST fashion dataset and CNN3, LSTM1 and LSTM2 performed better than other models on the CIFAR-10 dataset.
Keywords: Deep Neural Networks, CNN, LSTM, Multi-Class Classifications.