The data set that we are using is a collection of accelerometer data taken from a smartphone that various people carried with them while conducting six different exercises (Downstairs, Jogging, Sitting, Standing, Upstairs, Walking). There are multiple TensorFlow APIs while trying to use the more convenient estimator API (which is also recommended by the TensorFlow team - you can find more information here) I ran into compilation issues when converting the trained estimator to Core MLīefore we walk through the different steps in Python and Xcode, let’s take a brief look at the problem statement and our solution approach.Keras is very simple to learn and has a modern, more intuitive API than TensorFlow while still leveraging the capabilities of TensorFlow in the backend.You might wonder why Keras was chosen for this article over other frameworks, namely TensorFlow. Out of scope for this article: The creation of the perfect machine learning model with the highest possible performance for this type of problem statement is not the focus of this walkthrough. Prerequisites in order to conduct all steps explained in this article (including the version number that the code was tested with): Use Apple’s Core ML library in order to predict the outcomes for a given data set using Swift.Create a playground in Xcode and import the already trained Keras model.Ensure that the Core ML model was exported correctly by conducting a sample prediction in Python.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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