Function fitDataset

tfjs-layers/src/engine/training_dataset.ts:301–500

(
    // Type `model` as `any` here to avoid circular dependency w/
    // training.ts.
    // tslint:disable-next-line:no-any
    model: any, dataset: Dataset<T>,
    args: ModelFitDatasetArgs<T>)

}

export async function fitDataset<T>(
    // Type `model` as `any` here to avoid circular dependency w/
    // training.ts.
    // tslint:disable-next-line:no-any
    model: any, dataset: Dataset<T>,
    args: ModelFitDatasetArgs<T>): Promise<History> {
  const hasBatchesPerEpoch = args.batchesPerEpoch != null;
  tfc.util.assert(
      model.optimizer != null,
      () => 'You must compile a model before training/testing. Use ' +
          'LayersModel.compile(modelCompileConfig).');

  tfc.util.assert(
      args != null,
      () => `For fitDataset(), the 2nd argument (config) is required, ` +
          `but it is not provided in this call.`);
  tfc.util.assert(
      args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs),
      () => `For fitDataset(), config.epochs is expected to be a positive ` +
          `integer, but got ${args.epochs}`);
  tfc.util.assert(
      !hasBatchesPerEpoch ||
          (args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch)),
      () => `For fitDataset(), config.batchesPerEpoch is expected to be a ` +
          `positive integer if specified, but got ${args.batchesPerEpoch}`);
  tfc.util.assert(
      // tslint:disable-next-line:no-any
      (args as any)['validationSplit'] == null,
      () => '`validationSplit` is not supported by `fitDataset()`. ' +
          'Use validationData instead.');

  if (model.isTraining) {
    throw new Error(
        'Cannot start training because another fit() call is ongoing.');
  }
  model.isTraining = true;

  try {
    const doValidation = args.validationData != null;
    let valXs: tfc.Tensor|tfc.Tensor[];
    let valYs: tfc.Tensor|tfc.Tensor[];
    if (doValidation) {
      if (isDatasetObject(args.validationData)) {
        tfc.util.assert(
            args.validationBatches == null ||
                (args.validationBatches > 0 &&
                 Number.isInteger(args.validationBatches)),
            () => `For fitDataset() with dataset-based validation, ` +
                `config.validationBatches is expected not to be provided, ` +
                `or to be a positive integer, ` +
                `but got ${args.validationBatches}`);
      } else {
        const validationData = standardizeTensorValidationData(
            args.validationData as
                    [tfc.Tensor | tfc.Tensor[], tfc.Tensor | tfc.Tensor[]] |
            [
              tfc.Tensor | tfc.Tensor[], tfc.Tensor | tfc.Tensor[],
              tfc.Tensor | tfc.Tensor[]