Function standardizeDataIteratorOutput

tfjs-layers/src/engine/training_dataset.ts:202–260

* Standardize the output of a dataset iterator for use by * LayersModel.fitDataset(). * * @param model: A `tf.LayersModel` object. * @param iteratorOut The output of a dataset iterator. It is required to be * an object of the form `{xs: TensorOrArrayOrMap, ys: * TensorOrArrayOrMap}`, where `

(
    // Type `model` as `any` here to avoid circular dependency w/
    // training.ts.
    // tslint:disable-next-line:no-any
    model: any, iteratorOut: {})

 *   and `outputNames` of the model.
 */
function standardizeDataIteratorOutput(
    // Type `model` as `any` here to avoid circular dependency w/
    // training.ts.
    // tslint:disable-next-line:no-any
    model: any, iteratorOut: {}): {xs: tfc.Tensor[], ys: tfc.Tensor[]} {
  let xs: TensorOrArrayOrMap;
  let ys: TensorOrArrayOrMap;

  const iteratorOutObj = iteratorOut as FitDatasetElement;
  xs = iteratorOutObj['xs'];
  ys = iteratorOutObj['ys'];
  tfc.util.assert(
      xs != null && ys != null,
      () => 'A Dataset iterator for fitDataset() is expected to generate ' +
          'objects of the form `{xs: xVal, ys: yVal}`, where the two ' +
          'values may be `tf.Tensor`, an array of Tensors, or a map of ' +
          'string to Tensor.  The provided Dataset instead generates ' +
          `${iteratorOut}`);

  const flattenedXs: tfc.Tensor[] =
      flattenTensorOrArrayOrMap('input', model.inputNames, xs);
  const flattenedYs: tfc.Tensor[] =
      flattenTensorOrArrayOrMap('output', model.outputNames, ys);

  const batchSize: number = flattenedXs[0].shape[0];

  tfc.util.assert(
      flattenedXs.length === model.inputs.length,
      () => `LayersModel has ${model.inputs.length} inputs, but the dataset ` +
          `provides ${flattenedXs.length} inputs.  (Expected input keys: ` +
          `${JSON.stringify(model.inputNames)})`);

  tfc.util.assert(
      flattenedYs.length === model.outputs.length,
      () =>
          `LayersModel has ${model.outputs.length} outputs, but the dataset ` +
          `provides ${flattenedYs.length} outputs.  (Expected output keys: ` +
          `${JSON.stringify(model.outputNames)})`);

  for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {
    tfc.util.assert(
        flattenedXs[xIndex].shape[0] === batchSize,
        () => `Batch size mismatch: input ` +
            `${model.inputNames[xIndex]} has ${
                  flattenedXs[xIndex].shape[0]}; ` +
            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);
  }

  for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {
    tfc.util.assert(
        flattenedYs[yIndex].shape[0] === batchSize,
        () => `Batch size mismatch: output ` +
            `${model.outputNames[yIndex]} has ${
                  flattenedYs[yIndex].shape[0]}; ` +
            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);
  }

  return {xs: flattenedXs, ys: flattenedYs};