lib/regression.mjs
import { BaseNeuralNetwork, } from './deep_learning';
/**
* Deep Learning Regression with Tensorflow
* @class DeepLearningRegression
* @implements {BaseNeuralNetwork}
*/
export class DeepLearningRegression extends BaseNeuralNetwork {
/**
* @param {{layers:Array<Object>,compile:Object,fit:Object,layerPreference:String}} 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: [],
layerPreference:'deep',
compile: {
loss: 'meanSquaredError',
optimizer: 'adam',
},
fit: {
epochs: 100,
batchSize: 5,
},
}, options);
super(config, properties);
return this;
}
/**
* Adds dense layers to tensorflow regression model
* @override
* @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) {
const xShape = this.getInputShape(x_matrix);
const yShape = this.getInputShape(y_matrix);
const denseLayers = [];
if (layers) {
denseLayers.push(...layers);
} else if(this.settings.layerPreference==='deep') {
denseLayers.push({ units: xShape[ 1 ], inputShape: [xShape[1],], kernelInitializer: 'randomNormal', activation: 'relu', });
denseLayers.push({ units: parseInt(Math.ceil(xShape[ 1 ] / 2), 10), kernelInitializer: 'randomNormal', activation: 'relu', });
denseLayers.push({ units: yShape[ 1 ], kernelInitializer: 'randomNormal', });
} else {
denseLayers.push({ units: (xShape[ 1 ] * 2), inputShape: [xShape[1],], kernelInitializer: 'randomNormal', activation: 'relu', });
denseLayers.push({ units: yShape[ 1 ], kernelInitializer: 'randomNormal', });
}
this.layers = denseLayers;
denseLayers.forEach(layer => {
this.model.add(this.tf.layers.dense(layer));
});
}
}