Digital Inputs and Outputs from an IoT device using Neural Network
Using 8 digital inputs (Think Bits) and 4 digital outputs, say from an IoT device such as an internet connected Arduino,
Particle Photon (or the new Mesh devices ),
Raspberry Pi or similiar IoT sensor Actuator arrangement.
This example shows how to take a small set of sensor inputs and actuator outputs train a Neural Network and then
continuously monitor your IoT setup.
Note: This is a theoretical setup, in actual practice I am sure it would be much harder to train and fine tune. See my
Robotics Github with a long list of video tutorials.
Like the xOr example the inputs and outputs will be a series of ones and zeros, say for a smart garden
light 1 bit: 0 no light, 1 light
Rangefinder 1 bit: 0 no one, 1 entry detected
Air Moisture 1 bit: 0 low air moisture, 1 Ok level
Soil Moisture 1 bit: 0 too dry, 1 Ok
CO2 2 bit: 0,0 too low, 0,1 low OK, 1,0 high OK, 1,1 Too High
Temperature 2 bit: 0,0 too low, 0,1 low OK, 1,0 high OK, 1,1 Too High
pre-defined text 1 bit: 0 don't send, 1 send
Fan 1 bit: 0 off, 1 on
Heat Lamp 1 bit: 0 off, 1 on
Water 1 bit: 0 off, 1 on
Now lets try the above in a full Machine Learning Web App
Tensorflowjs Machine Learning Web App Template
As simple as possible template. Note that for simplicity this has minimal CSS