Distributed Neural Network with TensorFlow on Human Activity Recognition Over Multicore TPU

Abstract

There have been increasing interests and success of applying deep learning neural networks to their big data platforms and workflows, say Distributed Deep Learning. In this paper, we present distributed long short-term memory (dLSTM) neural network model using TensorFlow over multicore Tensor Processing Unit (TPU) on Google Cloud. LSTM is a variant of the recurrent neural network (RNN), which is more suitable for processing temporal sequences. This model could extract human activity features automatically and classify them with a few model parameters. In the proposed model, the raw data collected by mobile sensors was fed into distributed multi-layer LSTM layers. Human activity recognition data from UCI machine-learning library have been applied to the proposed distributed LSTM (dLSTM) model to compare the efficiency of TensorFlow over CPU and TPU based on execution time, and evaluation metrics: accuracy, precision, recall and F1 score along with the use of Google Colab Notebook.