Method trainOnBatch

tfjs-layers/src/engine/training.ts:1819–1839

* Runs a single gradient update on a single batch of data. * * This method differs from `fit()` and `fitDataset()` in the following * regards: * - It operates on exactly one batch of data. * - It returns only the loss and metric values, instead of * returning the batch-by-b

(
      x: Tensor|Tensor[]|{[inputName: string]: Tensor},
      y: Tensor|Tensor[]|
      {[inputName: string]: Tensor})

   * @doc {heading: 'Models', subheading: 'Classes'}
   */
  async trainOnBatch(
      x: Tensor|Tensor[]|{[inputName: string]: Tensor},
      y: Tensor|Tensor[]|
      {[inputName: string]: Tensor}): Promise<number|number[]> {
    // TODO(cais): Support sampleWeight and classWeight.
    // TODO(cais): Support Dataset objects.
    const standardizeOut = await this.standardizeUserData(x, y);
    const inputs = standardizeOut[0];
    const targets = standardizeOut[1];
    const trainFunction = this.makeTrainFunction();
    const losses = trainFunction(inputs.concat(targets));
    const lossValues: number[] = [];
    for (const loss of losses) {
      const v = await loss.data();
      lossValues.push(v[0]);
    }
    tfc.dispose(losses);
    disposeNewTensors(standardizeOut[0], x);
    disposeNewTensors(standardizeOut[1], y);
    return singletonOrArray(lossValues);
  }

  /**
   * Extract weight values of the model.