Facilitating Operation of a Machine Learning Environment
Machine learning systems are represented as directed acyclic graphs, where the nodes represent functional modules in the system and edges represent input/output relations between the functional modules. A machine learning environment can then be created to facilitate the training and operation of these machine learning systems.
1 . A computer-implemented method for facilitating operation of a machine learning environment, the environment comprising functional modules that can be configured and linked in different ways to define different machine learning instances, the method comprising:
receiving a directed acyclic graph defining a machine learning instance, the directed acyclic graph containing nodes and edges connecting the nodes, the nodes identifying functional modules, the edges entering a node representing inputs to the functional module and the edges exiting a node representing outputs of the functional module; and
executing the machine learning instance defined by the acyclic graph.
2 . The method of claim 1 further comprising:
saving a final output of the machine learning instance.
3 . The method of claim 1 further comprising:
saving an interim output of the machine learning instance.
4 . The method of claim 1 wherein the step of executing the machine learning instance comprises:
identifying that an output of a component of the machine learning instance has been previously saved; and
retrieving the saved output rather than re-executing the component.
5 . The method of claim 1 wherein the step of executing the machine learning instance comprises:
linking output of one functional module in the machine learning instance to input of a next functional module of the machine learning instance at run-time.
6 . The method of claim 1 wherein the functional modules communicate through a shared file system.
7 . The method of claim 1 wherein the nodes identify functional modules and at least one attribute for at least one functional module.
8 . The method of claim 7 wherein the at least one attribute is a version number for a software code for the functional module.
9 . The method of claim 7 wherein the functional module contains numerical, categorical, or structural parameters determining by supervised learning, and the at least one attribute identifies values for the numerical parameters.
10 . The method of claim 1 wherein at least one functional module is a sensor module that provides initial data as input to other functional modules for processing.
11 . The method of claim 1 wherein at least one functional module is a teacher module that receives input data and provides corresponding training outputs, the input data and corresponding training outputs forming a training set for training a parameterized model implemented by other functional modules.
12 . The method of claim 1 wherein at least one functional module is a learning module that receives a training set as input and undergoes learning of a parameterized model based on the training set.
13 . The method of claim 12 wherein the learning module outputs numerical, categorical, or structural parameters determined by learning for a parameterized model.
14 . The method of claim 1 wherein at least one functional module is a perceiver module that receives data as input and applies a parameterized model to produce corresponding outputs.
15 . The method of claim 14 wherein the perceiver module further receives numerical parameters for the parameterized model as input.
16 . The method of claim 15 wherein at least one functional module is a tester module that receives inputs from the perceiver model and evaluates an accuracy of the perceiver module.
17 . The method of claim 1 wherein the machine learning environment contains sufficient functional modules to define a machine learning instance that implements emotion detection from facial images.
18 . The method of claim 17 wherein at least one of the modules is a face detection module that identifies face location within facial images.
19 . The method of claim 17 wherein at least one of the modules is a facial landmark detection module that identifies locations of facial landmarks within an identified face.
20 . The method of claim 17 wherein at least one of the modules is an emotion detection module that outputs an indication of emotion based on identified facial landmarks within a face.
21 . The method of claim 1 wherein the machine learning environment contains sufficient functional modules to define a machine learning instance that implements smile detection from facial images.
22 . The method of claim 21 wherein at least one of the modules is a smile detection module that outputs an estimate of whether a smile is present based on identified facial landmarks within a facial image.
23 . The method of claim 1 wherein the step of receiving the directed acyclic graph comprises receiving a text string representing the directed acyclic graph.
24 . The method of claim 1 wherein the step of receiving the directed acyclic graph comprises receiving a graphical representation of the directed acyclic graph.
25 . A tangible computer readable medium containing instructions that, when executed by a processor, execute a method for facilitating operation of a machine learning environment, the environment comprising functional modules that can be configured and linked in different ways to define different machine learning instances, the method comprising:
receiving a directed acyclic graph defining a machine learning instance, the directed acyclic graph containing nodes and edges connecting the nodes, the nodes identifying functional modules, the edges entering a node representing inputs to the functional module and the edges exiting a node representing outputs of the functional module; and
executing the machine learning instance defined by the acyclic graph.
26 . A tool for facilitating operation of a machine learning environment, the environment comprising functional modules that can be configured and linked in different ways to define different machine learning instances, the method comprising:
means for receiving a directed acyclic graph defining a machine learning instance, the directed acyclic graph containing nodes and edges connecting the nodes, the nodes identifying functional modules, the edges entering a node representing inputs to the functional module and the edges exiting a node representing outputs of the functional module; and
means for executing the machine learning instance defined by the acyclic graph.