Hello,
see
https://www.mbs-plugins.com/archive/2019-12-17/Train_machine_learning_models_/monkeybreadsoftware_blog_filemaker
<https://www.mbs-plugins.com/archive/2019-12-17/Train_machine_learning_models_/monkeybreadsoftware_blog_filemaker>
For next MBS FileMaker Plugin we add a new CoreML functions to update the model
on device. If you have an updatable model for CoreML, the Apple framework to
use machine learning on Mac and iOS devices (including iPad), then you can now
use our CoreML.Update function to pass new training data to the plugin and we
update the model.
You can load a model with CoreML.OpenModel and you get the description with
CoreML.Description, you now see there a new entry
trainingInputDescriptionsByName in the JSON. e.g.
"trainingInputDescriptionsByName" : {
"drawing" : {
"optional" : false,
"type" : "Image",
"imageConstraint" : {
"pixelsWide" : 28,
"pixelFormatTypeName" : "OneComponent8",
"pixelFormatType" : 1278226488,
"pixelFormatTypeDescription" : "8 bit one component, black is zero",
"pixelsHigh" : 28
},
"name" : "drawing"
},
"label" : {
"optional" : false,
"type" : "String",
"name" : "label"
}
}
This shows you there is a drawing parameter for the picture with 28 by 28 pixel
resolution in grayscale. The other parameter is the label with the correct
output for this image. In a sample call to CoreML.Updatewe pass input and
output paths for the model files and pass the training data as JSON:
MBS( "CoreML.Update";
"/Users/cs/Desktop/UpdatableDrawingClassifier.mlmodelc";
"/Users/cs/Desktop/UpdatableDrawingClassifier2.mlmodelc";
"[{\"drawing\": \"/Users/cs/Desktop/mbslogo.png\", \"label\": \"MBS\"}]" )
In the JSON we expect an array of objects. Each object contains the pairs of
input and output parameters. Values are passed as numbers, text or objects. For
images we decided to allow you to pass the image file as native file path and
then the plugin adjust images as needed.
You can build solutions which come with a pre-calculated machine learning
model, which is then adjusted on device (e.g. iPad) while the user takes new
data and provides correct answers. On the server you can take a basic model to
recognize some data and then adjust with all the records you have in your
database.
If you are interested to use this functions, please try the 9.6pr3 release or
newer. This functionality is available on MacOS 10.15 or iOS 13. Calculation
happens on device using GPU if available.
See also Presentation about a Core ML database for image detection.
Sincerely
Christian
--
Read our blog about news on our plugins:
http://www.mbsplugins.de/ <http://www.mbsplugins.de/>
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