Machine Learning by By Audrey Tam & Matthijs Hollemans & By Matthijs Hollemans & By Chris LaPollo

Author:By Audrey Tam & Matthijs Hollemans & By Matthijs Hollemans & By Chris LaPollo
Language: eng
Format: epub
Publisher: Ray Wenderlich

When you run this code, coremltools goes through the Keras model layer-by-layer and prints its progress.

You can also supply metadata, which can be helpful for the users of your model, especially the descriptions of the inputs and outputs:

coreml_model.author = "Your Name Here" coreml_model.license = "Public Domain" coreml_model.short_description = "Image classifier for 20 different types of snacks" coreml_model.input_description["image"] = "Input image" coreml_model.output_description["labelProbability"]= "Prediction probabilities" coreml_model.output_description["label"]= "Class label of top prediction"

At this point, it’s useful to write print(coreml_model) to make sure that everything is correct. The input should be of type imageType, not multiArrayType, and there should be two outputs: one a dictionaryType and the other a stringType.

Finally, save the model to an .mlmodel file:

coreml_model.save("MultiSnacks.mlmodel")

If you weren’t on your Mac already, then download this .mlmodel file to your Mac.

Double-click the file to open it in Xcode:

Download

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