@modelx/model
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Deep Learning Classification, LSTM Time Series, Regression and Multi-Layered Perceptrons with Tensorflow
54 lines (53 loc) • 1.92 kB
JavaScript
import { BaseNeuralNetwork, } from './base_neural_network';
/**
* Deep Learning Classification with Tensorflow
* @class DeepLearningClassification
* @implements {BaseNeuralNetwork}
*/
export class DeepLearningClassification extends BaseNeuralNetwork {
/**
* @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: 'categoricalCrossentropy',
optimizer: 'adam',
},
fit: {
epochs: 100,
batchSize: 5,
},
}, options);
super(config, properties);
this.type = 'DeepLearningClassification';
return this;
}
/**
* Adds dense layers to tensorflow classification 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);
this.yShape = yShape;
this.xShape = xShape;
const denseLayers = [];
if (layers) {
denseLayers.push(...layers);
}
else {
denseLayers.push({ units: (xShape[1] * 2), inputDim: xShape[1], activation: 'relu', });
denseLayers.push({ units: yShape[1], activation: 'softmax', });
}
this.layers = denseLayers;
denseLayers.forEach(layer => {
this.model.add(this.tf.layers.dense(layer));
});
}
}