Method testLoop

tfjs-layers/src/engine/training.ts:1226–1269

* Loop over some test data in batches. * @param f A Function returning a list of tensors. * @param ins Array of tensors to be fed to `f`. * @param batchSize Integer batch size or `null` / `undefined`. * @param verbose verbosity mode. * @param steps Total number of steps (batches of sa

(
      f: (data: Tensor[]) => Scalar[], ins: Tensor[], batchSize?: number,
      verbose = 0, steps?: number)

   * @returns Array of Scalars.
   */
  private testLoop(
      f: (data: Tensor[]) => Scalar[], ins: Tensor[], batchSize?: number,
      verbose = 0, steps?: number): Scalar[] {
    return tfc.tidy(() => {
      const numSamples = this.checkNumSamples(ins, batchSize, steps, 'steps');
      const outs: Scalar[] = [];
      if (verbose > 0) {
        throw new NotImplementedError('Verbose mode is not implemented yet.');
      }
      // TODO(cais): Use `indicesForConversionToDense' to prevent slow down.
      if (steps != null) {
        throw new NotImplementedError(
            'steps mode in testLoop() is not implemented yet');
      } else {
        const batches = makeBatches(numSamples, batchSize);
        const indexArray = tensor1d(range(0, numSamples));
        for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
          const batchStart = batches[batchIndex][0];
          const batchEnd = batches[batchIndex][1];
          const batchIds =
              K.sliceAlongFirstAxis(
                  indexArray, batchStart, batchEnd - batchStart) as Tensor1D;
          // TODO(cais): In ins, train flag can be a number, instead of an
          //   Tensor? Do we need to handle this in tfjs-layers?
          const insBatch = sliceArraysByIndices(ins, batchIds) as Scalar[];
          const batchOuts = f(insBatch);
          if (batchIndex === 0) {
            for (let i = 0; i < batchOuts.length; ++i) {
              outs.push(scalar(0));
            }
          }
          for (let i = 0; i < batchOuts.length; ++i) {
            const batchOut = batchOuts[i];
            outs[i] =
                tfc.add(outs[i], tfc.mul(batchEnd - batchStart, batchOut));
          }
        }
        for (let i = 0; i < outs.length; ++i) {
          outs[i] = tfc.div(outs[i], numSamples);
        }
      }
      return outs;
    });
  }

  protected getDedupedMetricsNames(): string[] {
    const outLabels = this.metricsNames;