Multiple Keras Layers for Sensor Model


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 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

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?