Vivekanandan B 1, Hemadarshini M V 2
1AVIN Systems Private Limited Bangalore, India
2Department of Mathematics Mount Carmel College Bangalore, India
There are a number of machine learning software in use. In this paper, we look at the implementation of artificial neural networks using TensorFlow – A machine learning software developed by Google. TensorFlow has been used to solve two image classification problems – Classification of digits of MNIST database and classification of traffic signs of GTSRB database. Classification of MNIST digits could be done with 99% accuracy after 20000 iterations with every 500 iterations taking about 25 seconds. Classification of GTSRB traffic signs could be done with about 70% accuracy with every iteration taking about 9 minutes. Both these neural networks have been trained and tested on a CPU at 2.3GHz and 4 GB RAM. It has been noted that CPUs are fast enough to implement neural networks on a small scale. For real world applications, neural networks should have considerably more parameters which can conveniently be trained and implemented on GPUs. It has been noted that TensorFlow is capable of handling convolutional neural networks (CNNs) in an efficient manner.
Keywords: TensorFlow, Artificial Intelligence, Neural Networks, Machine Learning, Image Classification