In this post, we'll learn how to generate text using TensorFlow.js and Node.js. We'll be using a char-rnn model, which is a type of recurrent neural network (RNN) that can learn to predict the next character in a sequence of text.
A char-rnn is a type of RNN that can learn to predict the next character in a sequence of text. The char-rnn model we'll be using is based on the one described in the paper "Character-Aware Neural Language Models" (https://arxiv.org/abs/1508.06615).
TensorFlow.js is a JavaScript library for training and deploying machine learning models in the browser and in Node.js.
Node.js is a JavaScript runtime built on Chrome's V8 JavaScript engine.
First, we need to install TensorFlow.js and Node.js. We'll be using the TensorFlow.js char-rnn model, which is a Node.js module.
npm install @tensorflow/tfjs-node
npm install @tensorflow/tfjs-node-charrnn
Next, we need to download the training data. For this example, we'll use a dataset of Shakespeare's works (https://www.kaggle.com/kinguistics/shakespeare-plays).
Once we have the training data, we can start training the char-rnn model. The training process can take a few minutes to complete.
const tf = require('@tensorflow/tfjs-node');
const charRNN = require('@tensorflow/tfjs-node-charrnn');
const model = charRNN.create(
'https://storage.googleapis.com/tfjs-models/tfjs/charrnn/data/shakespeare_input.txt',
{
rnnType: 'lstm',
embeddingSize: 128,
rnnUnits: 128,
batchSize: 64,
seqLength: 128,
temperature: 0.8,
numEpochs: 20
});
model.fit().then(() => {
// The model is trained!
});
Once the model is trained, we can use it to generate text.
model.generate('The').then((text) => {
console.log(text);
});