Implementing an LSTM model for HAR

The overall algorithm (HumanAR.scala) has the following workflow:

• Loading the data
• Defining hyperparameters
• Setting up the LSTM model using imperative programming and the hyperparameters
• Applying batch wise training, that is, picking batch size data, feeding it to the model, then at some iterations evaluating the model and printing the batch loss and the accuracy
• Output the chart for the training and test errors

The preceding steps can be followed and constructed by way of a pipeline:

Figure 10: MXNet pre-built binary generated

Now let's start the implementation step-by-step. Make sure that you understand each line of code then import the given project in Eclipse or SBT.

Step 1 - Importing necessary libraries and packages

Let's start coding now. We start from the very beginning, that is, by importing libraries and packages:

package com.packt.ScalaML.HAR 

import ml.dmlc.mxnet.Context 
import LSTMNetworkConstructor.LSTMModel 
import scala.collection.mutable.ArrayBuffer...