Function saveModelAndRandomInputs

e2e/integration_tests/create_save_predict.js:47–73

* Generate random input(s), get predict() output(s), and save them along with * the model. * * @param model The `tf.LayersModel` instance in question. It may have one or * more inputs and one or more outputs. It is assumed that for each input, only * the first dimension (i.e., the batch dimen

(
    model, exportPathprefix, inputIntegerMax)

 *   tensors. Used for models that take integer tensors as inputs.
 */
async function saveModelAndRandomInputs(
    model, exportPathprefix, inputIntegerMax) {
  await model.save(tfjsNode.io.fileSystem(exportPathprefix));

  const xs = [];
  const xsData = [];
  const xsShapes = [];
  for (const inputTensor of model.inputs) {
    const inputShape = inputTensor.shape;
    inputShape[0] = 1;
    if (inputShape.indexOf(null) !== -1) {
      throw new Error(
          `It is assumed that the only the first dimension of the tensor ` +
          `is undetermined, but the assumption is not satisfied for ` +
          `input shape ${JSON.stringify(inputTensor.shape)}`);
    }
    const xTensor = inputIntegerMax == null ?
        tfc.randomNormal(inputShape) :
        tfc.floor(tfc.randomUniform(inputShape, 0, inputIntegerMax));
    xs.push(xTensor);
    xsData.push(Array.from(xTensor.dataSync()));
    xsShapes.push(xTensor.shape);
  }
  fs.writeFileSync(exportPathprefix + '.xs-data.json', JSON.stringify(xsData));
  fs.writeFileSync(
      exportPathprefix + '.xs-shapes.json', JSON.stringify(xsShapes));
}

// Multi-layer perceptron (MLP).
async function exportMLPModel(exportPath) {