In this post, we'll be looking at how to build Long Short-Term Memory (LSTM) networks in TensorFlow.js and Node.js.
LSTMs are a type of recurrent neural network that are well-suited for working with sequential data. Sequential data is data where each element is dependent on the previous element, such as time series data.
LSTMs are able to learn long-term dependencies by using a "memory" cell that can remember information for long periods of time. LSTMs are also able to handle input elements that are not in the same order as the output elements, which is important for working with time series data.
We'll start by looking at how to build an LSTM network in TensorFlow.js.
First, we need to install TensorFlow.js. We can do this using the Node.js package manager, npm:
npm install @tensorflow/tfjs
Next, we need to create a new file called lstm.js
and import TensorFlow.js:
const tf = require('@tensorflow/tfjs');
Now we're ready to start building our LSTM network.
We'll start by defining the model:
const model = tf.sequential();
Next, we'll add an LSTM layer to the model. The first argument to the tf.layers.lstm
function is the number of units, which is the number of memory cells in the LSTM layer. We'll use 16 units:
model.add(tf.layers.lstm({units: 16, inputShape: [1, 1]}));
The inputShape
argument is the shape of the input data. The first element is the number of time steps, and the second element is the number of features. In our case, we have one time step and one feature.
Next, we'll add a dense layer to the model. This is a standard fully-connected layer that will output a single value:
model.add(tf.layers.dense({units: 1}));
Now we need to compile the model. We need to specify the loss function and the optimizer. We'll use the mean squared error loss function and the Adam optimizer:
model.compile({loss: 'meanSquaredError', optimizer: 'adam'});
We're now ready to train the model. We'll use a tf.tensor2d
to specify the training data. The first argument is the data, and the second argument is the shape of the data. In our case, we have two training examples, each with one time step and one feature:
const xs = tf.tensor2d([[1], [2]]);
const ys = tf.tensor2d([[1], [2]]);
We'll train the model for 10 epochs, which is the number of times the model will see the training data:
model.fit(xs, ys, {epochs: 10});
Once the model has been trained, we can use it to make predictions. We'll use a tf.tensor2d
to specify the input data. In our case, we have one time step and one feature:
const xs = tf.tensor2d([[3]]);
We can then call the model.predict
method to make a prediction:
model.predict(xs).print();
This will output the prediction, which is a single value.
We'll now look at how to build an LSTM network in Node.js.
First, we need to install TensorFlow.js. We can do this using the Node.js package manager, npm:
npm install @tensorflow/tfjs
Next, we need to create a new file called lstm.js
and import TensorFlow.js:
const tf = require('@tensorflow/tfjs');
Now we're ready to start building our LSTM network.
We'll start by defining the model:
const model = tf.sequential();
Next, we'll add an LSTM layer to the model. The first argument to the tf.layers.lstmLayer
function is the number of units, which is the number of memory cells in the LSTM layer. We'll use 16 units:
model.add(tf.layers.lstmLayer({units: 16, inputShape: [1, 1]}));
The inputShape
argument is the shape of the input data. The first element is the number of time steps, and the second element is the number of features. In our case, we have one time step and one feature.
Next, we'll add a dense layer to the model. This is a standard fully-connected layer that will output a single value:
model.add(tf.layers.denseLayer({units: 1}));
Now we need to compile the model. We need to specify the loss function and the optimizer. We'll use the mean squared error loss function and the Adam optimizer:
model.compile({loss: 'meanSquaredError', optimizer: 'adam'});
We're now ready to train the model. We'll use a tf.tensor2d
to specify the training data. The first argument is the data, and the second argument is the shape of the data. In our case, we have two training examples, each with one time step and one feature:
const xs = tf.tensor2d([[1], [2]]);
const ys = tf.tensor2d([[1], [2]]);
We'll train the model for 10 epochs, which is the number of times the model will see the training data:
model.fit(xs, ys, {epochs: 10});
Once the model has been trained, we can use it to make predictions. We'll use a tf.tensor2d
to specify the input data. In our case, we have one time step and one feature:
const xs = tf.tensor2d([[3]]);
We can then call the model.predict
method to make a prediction:
model.predict(xs).print();
This will output the prediction, which is a single value.
In this post, we've seen how to build Long Short-Term Memory (LSTM) networks in TensorFlow.js and Node.js. LSTMs are a type of recurrent neural network that are well-suited for working with sequential data.
If you're interested in learning more about LSTMs, I recommend the following resources: