Abstract
Deep learning (DL) is a process that consists of a set of methods which classifies the raw data into meaningful information fed into the machine. DL performs classification tasks directly from sound, text, and images. One of the famous algorithms for classification of images in DL is convolutional neural networks (CNN). In this research, we tested DL model for image recognition using TensorFlow from Dockers software. We received 99% accurate to identify the test image. The system configuration used for this research includes Ubuntu 16.04, Python 2.7, TensorFlow 1.9, and Google image set (Fatkun Batch Download Image: Google, Google, chrome.google.com/webstore/detail/fatkun-batch-download-ima/nnjjahlikiabnchcpehcpkdeckfgnohf).
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Acknowledgement
This work was supported by the AFRL Minority Leaders Research Collaboration Program, contract FA8650-13-C-5800. The authors greatly acknowledge AFRL/RY for their assistance in this work.
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Seetala, K., Birdsong, W., Reddy, Y.B. (2019). Image Classification Using TensorFlow. In: Latifi, S. (eds) 16th International Conference on Information Technology-New Generations (ITNG 2019). Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-14070-0_67
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DOI: https://doi.org/10.1007/978-3-030-14070-0_67
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