Hands-On Deep Learning for IoT

Use case one

We consider a stacked LSTM, which is a popular DL model for sequence prediction, including time series problems. A stacked LSTM architecture consists of two or more LSTM layers. We implemented the HAR for use case one, using a two-layered stacked LSTM architecture. The following diagram presents a two-layered LSTM, where the first layer provides a sequence of outputs instead of a single value output to the second LSTM layer:

We can train and test the model by running the LSTM -HAR.py code, available in the use-case-1 folder (after making the necessary changes to your setup, such as the data directory):

python LSTM-HAR.py