Dog Breed Prediction using Convolutional Neural Network 
Sneha I. Kadari1, Shubhada S. Kulkarni2, Sharada G. Kulkarni3
1Sneha I. Kadari*, Computer Science and Engineering, KLS Gogte Institute of Technology, Belagavi, India.
2Shubhada S. Kulkarni, Professor Computer Science and Engineering, KLS Gogte Institute of Technology, India.
3Sharada G. Kulkarni, Professor Computer Science and Engineering, KLS Gogte Institute of Technology, Belagavi, India.
Manuscript received on April 11, 2020. | Revised Manuscript received on May 15, 2020. | Manuscript published on June 30, 2020. | PP: 318-322 | Volume-9 Issue-5, June 2020. | Retrieval Number: D8058049420/2020©BEIESP | DOI: A1432109119/2020©BEIESP
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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: Deep learning gives the strength on the way to train algorithms model that can handle the difficulties of info classification also prediction grounded on totally on arising information as of raw information. Convolutional Neural Networks (CNNs) gives single often used method for image classification and detection. In this exertion, we define a CNNbased approach for spotting dogs in per chance complex images and due to this fact reflect inconsideration on the identification of the one of kinds of dog breed. The experimental outcome analysis supported the standard metrics and thus the graphical representation confirms that the algorithm (CNN) gives good analysis accuracy for all the tested datasets.
Keywords: Machine Learning, Tensorflow, Classification, CNN, Deep Learning