Pruned Cascade Neural Network Image Classification
G.D.Praveenkumar1, M.Dharmalingam2
1G.D.Praveenkumar, Research Scholar , Department of Computer Science, Bharathiar University Arts and Science College-Modakkurichi, Erode ,India.
2Dr.M.Dharmalingam, Assistant Professor, Department of Computer Science, Bharathiar University Arts and Science College-Modakkurichi, Erode ,India.
Manuscript received on 07 August 2019. | Revised Manuscript received on 14 August 2019. | Manuscript published on 30 September 2019. | PP: 6454-6457 | Volume-8 Issue-3 September 2019 | Retrieval Number: F2929037619/2019©BEIESP | DOI: 10.35940/ijrte.F2929.098319
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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 paper we propose a new model of deep neural network to build in deeper network. The convoluational neural network is one of the leading Image classification problem. The vanishing gradient problem requires us to use small learning rate with gradient descent which needs many small steps to converge and its take long time to proceed . By using GPU we can process more than one dataset (CIFAR-100) in a particular session. To overcome vanishing gradient problem by using the prune cascade correlation neural network learning algorithm compared to the deep cascade learning in CNN architecture. We improve the filter size, to reduce to the problem by training algorithm that trains in the network from bottom to top approach and its performing attain the task for better image classification in Google Net. We reduce the time complexity (training time ), storage capacity can be used pre training algorithm.
Index Terms: Deep Learning, Vanishing gradient, Google Net, CNN, Image Classification.
Scope of the Article: Classification.