Function fitModel

polynomial-regression/index.js:165–189

(xyData, epochs, learningRate)

//   yMean: Arithmetic mean of y.
//   yStddev: Standard deviation of y.
async function fitModel(xyData, epochs, learningRate) {
  const batchSize = xyData.length;
  const outputs = toNormalizedTensors(xyData, order);
  const xPowerMeans = outputs[0];
  const xPowerStddevs = outputs[1];
  const xData = outputs[2];
  const yMean = outputs[3];
  const yStddev = outputs[4];
  const yData = outputs[5];
  const input = tf.input({shape: [order + 1]});
  const linearLayer =
      tf.layers.dense({units: 1, kernelInitializer: 'Zeros', useBias: false});
  const output = linearLayer.apply(input);
  const model = tf.model({inputs: input, outputs: output});
  const sgd = tf.train.sgd(learningRate);
  model.compile({optimizer: sgd, loss: 'meanSquaredError'});
  await model.fit(xData, yData, {
    batchSize: batchSize,
    epochs: epochs,
  });
  console.log(
      'Model weights (normalized):',
      model.trainableWeights[0].read().dataSync());
  return [model, xPowerMeans, xPowerStddevs, yMean, yStddev];
}

// Render the predictions made by the model.
function renderModelPredictions(