Prediction of training time for deep neural networks in TensorFlow

Abstract: Machine learning has gained a lot of interest over the past years and is now used extensively in various areas. Google has developed a framework called TensorFlow which simplifies the usage of machine learning without compromising the end result. However, it does not resolve the issue of neural network training being time consuming. The purpose of this thesis is to investigate with what accuracy training times can be predicted using TensorFlow. Essentially, how effectively one neural network in TensorFlow can be used to predict the training times of other neural networks, also in TensorFlow. In order to do this, training times for training different neural networks was collected. This data was used to create a neural network for prediction. The resulting neural network is capable of predicting training times with an average accuracy of 93.017%.