![]() Export the trained Keras DNN model for Core ML.Validate the performance of the trained DNN against the test data using learning curve and confusion matrix.Train the deep neural network for human activity recognition data.Define a deep neural network model in Keras which can later be processed by Apple’s Core ML.Split up the data set into training, validation, and test set.Reshape the multi-dimensional tabular data so that it is accepted by Keras.Convert and reformat accelerometer data into a time-sliced representation.Load accelerometer data from the WISDM data set.This article walks you through the following steps: The approach presented in this article should work well for any other sensor data that you might come across within the Internet of Things (IOT). We will use a WISDM data set for this tutorial ( WISDM). To be more specific, we will train a deep neural network (DNN) to recognize the type of movement (Walking, Running, Jogging, etc.) based on a given set of accelerometer data from a mobile device carried around a person’s waist. Instead, you will learn how to process time-sliced, multi-dimensional sensor data. We will go beyond this widely covered machine learning example. Most other tutorials focus on the popular MNIST data set for image recognition. Keras and Apple’s Core ML are a very powerful toolset if you want to quickly deploy a neural network on any iOS device.
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