decodeSequence(inputSeq) {
// Encode the inputs state vectors.
let statesValue = this.encoderModel.predict(inputSeq);
// Generate empty target sequence of length 1.
let targetSeq = tf.buffer([1, 1, this.numDecoderTokens]);
// Populate the first character of the target sequence with the start
// character.
targetSeq.set(1, 0, 0, this.targetTokenIndex['\t']);
// Sample loop for a batch of sequences.
// (to simplify, here we assume that a batch of size 1).
let stopCondition = false;
let decodedSentence = '';
while (!stopCondition) {
const predictOutputs =
this.decoderModel.predict([targetSeq.toTensor()].concat(statesValue));
const outputTokens = predictOutputs[0];
const h = predictOutputs[1];
const c = predictOutputs[2];
// Sample a token.
// We know that outputTokens.shape is [1, 1, n], so no need for slicing.
const logits = outputTokens.reshape([outputTokens.shape[2]]);
const sampledTokenIndex = logits.argMax().dataSync()[0];
const sampledChar = this.reverseTargetCharIndex[sampledTokenIndex];
decodedSentence += sampledChar;
// Exit condition: either hit max length or find stop character.
if (sampledChar === '\n' ||
decodedSentence.length > this.maxDecoderSeqLength) {
stopCondition = true;
}
// Update the target sequence (of length 1).
targetSeq = tf.buffer([1, 1, this.numDecoderTokens]);
targetSeq.set(1, 0, 0, sampledTokenIndex);
// Update states.
statesValue = [h, c];
}
return decodedSentence;
}
/**
* Encode a string (e.g., a sentence) as a Tensor3D that can be fed directly
* into the TensorFlow.js model.
*/
encodeString(str) {
const strLen = str.length;
const encoded =
tf.buffer([1, this.maxEncoderSeqLength, this.numEncoderTokens]);
for (let i = 0; i < strLen; ++i) {
if (i >= this.maxEncoderSeqLength) {
console.error(
'Input sentence exceeds maximum encoder sequence length: ' +
this.maxEncoderSeqLength);
}
const tokenIndex = this.inputTokenIndex[str[i]];
if (tokenIndex == null) {
console.error(
'Character not found in input token index: "' + tokenIndex + '"');
}
encoded.set(1, 0, i, tokenIndex);
}
return encoded.toTensor();
}