API Reference

Once the MLTK is installed into the Python environment, it may be imported into a python script using:

import mltk

Once the MLTK is imported, it’s various APIs may be accessed.

API Overview

The following provides a general overview of the MLTK Python API:

Name	Description
Model Operations	Modeling operations such as profiling and training
MLTK Model	Provides the root object of a model specification
Tensorflow-Lite Model	Enables reading/writing .tflite model flatbuffer
Tensorflow-Lite Micro Model	Enables running .tflite models in the Tensorflow-Lite Micro interpreter
Keras Model	The model object used by Tensorflow during model training
Data Preprocessing	Dataset preprocessing utilities
Utilities	Common utilities
Reference Models	Pre-trained reference models
Reference Datasets	Datasets used by reference models

Package Directory Structure

The MLTK Python package has the following structure:

Name	Description
mltk	The root of the MLTK package
mltk.core	Core modeling utilities, see the Model Operations docs for more details
mltk.core.model	Provides the root object of a model specification, more details in the MLTK Model docs
mltk.core.preprocess	Data pre-processing utilities, see the Data Preprocessing docs for more info
mltk.core.tflite_model	Enables reading/writing .tflite model flatbuffers, more details in the TfliteModel docs
mltk.core.tflite_model_parameters	Enables read/writing custom parameters in a .tflite model flatbuffer
mltk.core.tflite_micro	Enables running .tflite models in the Tensorflow-Lite Micro interpreter, more details in the Tensorflow-Lite Micro Wrapper docs
mltk.core.keras	Helper scripts for the Keras API
mltk.utils	Common utility scripts, more details in the utilities docs
mltk.cli	MLTK Command-Line Interface (CLI) scripts
mltk.models	Reference models, more details in the Reference models docs
mltk.datasets	Reference datasets, more details in the Reference datasets docs