Multiple Keras Layers for Sensor Model

General Discussions
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@brian_plancher
@marcozennaro

Perhaps someone here can make a suggestion, for something I have posted on the EdgeImpulse forum to: generic-sensor-raw-data-to-keras-classification

My high school students would never add a different layer to a Keras model, but they would duplicate or delete a layer to improve an already working model. So I want to make a Keras model with lots of different layers for the students to optimise.

Can anyone think of a model that uses lots of Keras layers that is reasonably sensible for multiple sensor inputs and classification outputs.

My students sensor data might range from

  1. 5 flexible sensors.
  2. Pixy2 Camera https://pixycam.com/pixy2/ with data: shade, x, y, width, height and a few other data points.
  3. temperature, humidity, light, wind speed…
  4. 2-4 various air quality sensors such as CO2, VOC, Ozone
  5. motion (x, y only) with several touch sensors
  6. GPS with Acceleration

Here is a possible suggestion from https://machinelearningmastery.com/cnn-models-for-human-activity-recognition-time-series-classification/

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Anyone got any other ideas? I am presently testing the above without the MaxPooling. Any suggestions for how to fit in a 2D Convolution layer for regular sensor data?

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Two thoughts:

  1. If you know good model architectures for each sensor independently – you could consider having effectively separate model heads that combine into a unified tail – then you can use the model heads that you know work well – I haven’t done this but know its possible.
  2. To fit a Conv2D you could reduce the number of filters because that is effectively the size of another dimension in terms of the number of parameters
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And I recognize this is like a year later but for some reason the UI just gave me the notification lol – sooo what did you do to fix this? Curious what worked!