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);
});