import {TensorScriptModelInterface} from '@tensorscript/core/lib/core.mjs'

public class | source

TensorScriptModelInterface

Base class for tensorscript models

Static Method Summary

Static Public Methods
public static	getInputShape(matrix: Array<Array<number>>): Array<number> Returns the shape of an input matrix
public static	reshape(array: Array<number>, shape: Array<number>): Array<Array<number>> Reshapes an array

Constructor Summary

Public Constructor
public	constructor(options: Object, customTF: Object, properties: *)

Member Summary

Public Members
public	getInputShape: Function
public	model: Object
public	reshape: Function
public	settings: Object
public	tf: Object

Method Summary

Public Methods
public abstract	calculate(matrix: Array<Array<number>>\|Array<number>): {data: Promise} Predicts new dependent variables
public	async loadModel(options: Object): Object Loads a saved tensoflow / keras model
public	async predict(input_matrix: Array<Array<number>>\|Array<number>): Array<number>\|Array<Array<number>> Returns prediction values from tensorflow model
public abstract	train(x_matrix: Array<Array<number>>, y_matrix: Array<Array<number>>): Object Asynchronously trains tensorflow model, must be implemented by tensorscript class

Static Public Methods

public static getInputShape(matrix: Array<Array<number>>): Array<number> source

Returns the shape of an input matrix

Params:

Name	Type	Attribute	Description
matrix	Array<Array<number>>		input matrix

Return:

Array<number>

returns the shape of a matrix (e.g. [2,2])

Example:

const input = [
  [ 0, 1, ],
  [ 1, 0, ],
];
TensorScriptModelInterface.getInputShape(input) // => [2,2]

public static reshape(array: Array<number>, shape: Array<number>): Array<Array<number>> source

Reshapes an array

Params:

Name	Type	Attribute	Description
array	Array<number>		input array
shape	Array<number>		shape array

Return:

Array<Array<number>>

returns a matrix with the defined shape

Example:

const array = [ 0, 1, 1, 0, ];
const shape = [2,2];
TensorScriptModelInterface.reshape(array,shape) // => 
[
  [ 0, 1, ],
  [ 1, 0, ],
];

Public Constructors

public constructor(options: Object, customTF: Object, properties: *) source

Params:

Name	Type	Attribute	Description
options	Object		tensorflow model hyperparameters
customTF	Object		custom, overridale tensorflow / tensorflow-node / tensorflow-node-gpu
properties	*		extra instance properties

Public Members

public getInputShape: Function source

public model: Object source

public reshape: Function source

public settings: Object source

public tf: Object source

Public Methods

public abstract calculate(matrix: Array<Array<number>>|Array<number>): {data: Promise} source

Predicts new dependent variables

Params:

Name	Type	Attribute	Description
matrix	Array<Array<number>>\|Array<number>		new test independent variables

Return:

{data: Promise}

returns tensorflow prediction

public async loadModel(options: Object): Object source

Loads a saved tensoflow / keras model

Params:

Name	Type	Attribute	Description
options	Object		tensorflow load model options

Return:

Object

tensorflow model

public async predict(input_matrix: Array<Array<number>>|Array<number>): Array<number>|Array<Array<number>> source

Returns prediction values from tensorflow model

Params:

Name	Type	Attribute	Description
input_matrix	Array<Array<number>>\|Array<number>		new test independent variables
options.json	Boolean	optional default: true	return object instead of typed array
options.probability	Boolean	optional default: true	return real values instead of integers

Return:

Array<number>|Array<Array<number>>

predicted model values

public abstract train(x_matrix: Array<Array<number>>, y_matrix: Array<Array<number>>): Object source

Asynchronously trains tensorflow model, must be implemented by tensorscript class

Params:

Name	Type	Attribute	Description
x_matrix	Array<Array<number>>		independent variables
y_matrix	Array<Array<number>>		dependent variables

Return:

Object

returns trained tensorflow model