TypeScript deeplearn.oneHot函数代码示例

  await dl.tidy(async () => {
    const forgetBias = dl.scalar(1.0);
    const lstm1 = (data: dl.Tensor2D, c: dl.Tensor2D, h: dl.Tensor2D) =>
        dl.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
    const lstm2 = (data: dl.Tensor2D, c: dl.Tensor2D, h: dl.Tensor2D) =>
        dl.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);

    let c: dl.Tensor2D[] = [
      dl.zeros([1, lstmBias1.shape[0] / 4]),
      dl.zeros([1, lstmBias2.shape[0] / 4])
    ];
    let h: dl.Tensor2D[] = [
      dl.zeros([1, lstmBias1.shape[0] / 4]),
      dl.zeros([1, lstmBias2.shape[0] / 4])
    ];

    let input = primerData;
    for (let i = 0; i < expected.length; i++) {
      const onehot = dl.oneHot(dl.tensor1d([input]), 10);

      const output = dl.multiRNNCell([lstm1, lstm2], onehot, c, h);

      c = output[0];
      h = output[1];

      const outputH = h[1];
      const logits =
          outputH.matMul(fullyConnectedWeights).add(fullyConnectedBiases);

      const result = await dl.argMax(logits).val();
      results.push(result);
      input = result;
    }
  });

  await dl.tidy(async () => {
    const lstm1 = (data: dl.Tensor2D, c: dl.Tensor2D, h: dl.Tensor2D) =>
        dl.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
    const lstm2 = (data: dl.Tensor2D, c: dl.Tensor2D, h: dl.Tensor2D) =>
        dl.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
    const lstm3 = (data: dl.Tensor2D, c: dl.Tensor2D, h: dl.Tensor2D) =>
        dl.basicLSTMCell(forgetBias, lstmKernel3, lstmBias3, data, c, h);

    const outputs: dl.Scalar[] = [];
    // Generate some notes.
    for (let i = 0; i < STEPS_PER_GENERATE_CALL; i++) {
      // Use last sampled output as the next input.
      const eventInput = dl.oneHot(lastSample.as1D(), EVENT_SIZE).as1D();
      // Dispose the last sample from the previous generate call, since we
      // kept it.
      if (i === 0) {
        lastSample.dispose();
      }
      const conditioning = getConditioning();
      const axis = 0;
      const input = conditioning.concat(eventInput, axis);
      const output =
          dl.multiRNNCell([lstm1, lstm2, lstm3], input.as2D(1, -1), c, h);
      c = output[0];
      h = output[1];

      const outputH = h[2];
      const logits = outputH.matMul(fcW).add(fcB);

      const softmax = logits.as1D().softmax();
      const sampledOutput = dl.multinomial(softmax, 1).asScalar();

      outputs.push(sampledOutput);
      dl.keep(sampledOutput);
      lastSample = sampledOutput;
    }

    c.forEach(val => dl.keep(val));
    h.forEach(val => dl.keep(val));

    await outputs[outputs.length - 1].data();

    for (let i = 0; i < outputs.length; i++) {
      playOutput(await outputs[i].val());
    }

    if (piano.now() - currentPianoTimeSec > MAX_GENERATION_LAG_SECONDS) {
      console.warn(
          `Generation is ${
              piano.now() - currentPianoTimeSec} seconds behind, ` +
          `which is over ${MAX_NOTE_DURATION_SECONDS}. Resetting time!`);
      currentPianoTimeSec = piano.now();
    }
    const delta = Math.max(
        0, currentPianoTimeSec - piano.now() - GENERATION_BUFFER_SECONDS);
    setTimeout(() => generateStep(loopId), delta * 1000);
  });

 return dl.tidy(() => {
   if (!conditioned) {
     // TODO(nsthorat): figure out why we have to cast these shapes to numbers.
     // The linter is complaining, though VSCode can infer the types.
     const size = 1 + (noteDensityEncoding.shape[0] as number) +
         (pitchHistogramEncoding.shape[0] as number);
     const conditioning: dl.Tensor1D =
         dl.oneHot(dl.tensor1d([0]), size).as1D();
     return conditioning;
   } else {
     const axis = 0;
     const conditioningValues =
         noteDensityEncoding.concat(pitchHistogramEncoding, axis);
     return dl.tensor1d([0]).concat(conditioningValues, axis);
   }
 });

function updateConditioningParams() {
  const pitchHistogram = pitchHistogramElements.map(e => {
    return parseInt(e.value, 10) || 0;
  });
  updateDisplayHistogram(pitchHistogram);

  if (noteDensityEncoding != null) {
    noteDensityEncoding.dispose();
    noteDensityEncoding = null;
  }

  window.location.assign(
      '#' + densityControl.value + '|' + pitchHistogram.join(',') + '|' +
      preset1.join(',') + '|' + preset2.join(',') + '|' +
      (conditioned ? 'true' : 'false'));

  const noteDensityIdx = parseInt(densityControl.value, 10) || 0;
  const noteDensity = DENSITY_BIN_RANGES[noteDensityIdx];
  densityDisplay.innerHTML = noteDensity.toString();
  noteDensityEncoding =
      dl.oneHot(
            dl.tensor1d([noteDensityIdx + 1]), DENSITY_BIN_RANGES.length + 1)
          .as1D();

  if (pitchHistogramEncoding != null) {
    pitchHistogramEncoding.dispose();
    pitchHistogramEncoding = null;
  }
  const buffer = dl.buffer<dl.Rank.R1>([PITCH_HISTOGRAM_SIZE], 'float32');
  const pitchHistogramTotal = pitchHistogram.reduce((prev, val) => {
    return prev + val;
  });
  for (let i = 0; i < PITCH_HISTOGRAM_SIZE; i++) {
    buffer.set(pitchHistogram[i] / pitchHistogramTotal, i);
  }
  pitchHistogramEncoding = buffer.toTensor();
}