lib/deeplearning.mjs
import { TensorScriptModelInterface, } from '@tensorscript/core';
/**
* Deep Learning with Tensorflow
* @class BaseNeuralNetwork
* @implements {TensorScriptModelInterface}
*/
export class BaseNeuralNetwork extends TensorScriptModelInterface {
/**
* @param {{layers:Array<Object>,compile:Object,fit:Object}} options - neural network configuration and tensorflow model hyperparameters
* @param {{model:Object,tf:Object,}} properties - extra instance properties
*/
constructor(options = {}, properties) {
const config = Object.assign({
layers: [],
compile: {
loss: 'meanSquaredError',
optimizer: 'adam',
},
fit: {
epochs: 100,
batchSize: 5,
},
}, options);
super(config, properties);
return this;
}
/**
* Adds dense layers to tensorflow model
* @abstract
* @param {Array<Array<number>>} x_matrix - independent variables
* @param {Array<Array<number>>} y_matrix - dependent variables
* @param {Array<Object>} layers - model dense layer parameters
*/
generateLayers(x_matrix, y_matrix, layers) {
throw new ReferenceError('generateLayers method is not implemented');
}
/**
* Asynchronously trains tensorflow model
* @override
* @param {Array<Array<number>>} x_matrix - independent variables
* @param {Array<Array<number>>} y_matrix - dependent variables
* @param {Array<Object>} layers - array of model dense layer parameters
* @param {Array<Array<number>>} x_text - validation data independent variables
* @param {Array<Array<number>>} y_text - validation data dependent variables
* @return {Object} returns trained tensorflow model
*/
async train(x_matrix, y_matrix, layers, x_test, y_test) {
const xShape = this.getInputShape(x_matrix);
const yShape = this.getInputShape(y_matrix);
const xs = this.tf.tensor(x_matrix, xShape);
const ys = this.tf.tensor(y_matrix, yShape);
this.xShape = xShape;
this.yShape = yShape;
this.model = this.tf.sequential();
this.generateLayers.call(this, x_matrix, y_matrix, layers || this.layers, x_test, y_test);
this.model.compile(this.settings.compile);
await this.model.fit(xs, ys, this.settings.fit);
xs.dispose();
ys.dispose();
return this.model;
}
/**
* Predicts new dependent variables
* @override
* @param {Array<Array<number>>|Array<number>} matrix - new test independent variables
* @return {{data: Promise}} returns tensorflow prediction
*/
calculate(input_matrix) {
if (!input_matrix || Array.isArray(input_matrix)===false) throw new Error('invalid input matrix');
const predictionInput = (Array.isArray(input_matrix[ 0 ]))
? input_matrix
: [
input_matrix,
];
const predictionTensor = this.tf.tensor(predictionInput);
const prediction = this.model.predict(predictionTensor);
predictionTensor.dispose();
return prediction;
}
}