In this post, we'll learn how to use TensorFlow.js and Node.js for hyperparameter tuning.
Hyperparameter tuning is the process of optimizing a machine learning model by choosing the best values for the model's hyperparameters. The goal of hyperparameter tuning is to find the hyperparameter values that result in the best performance of the model on unseen data.
There are a few different ways to approach hyperparameter tuning. One popular method is grid search, which involves exhaustively searching for the best values of the hyperparameters by trying all possible combinations of values.
Another popular method is Bayesian optimization, which uses a probabilistic model to guide the search for the best values of the hyperparameters.
In this post, we'll use TensorFlow.js and Node.js to implement a simple hyperparameter tuning experiment using Bayesian optimization.
Before we get started, there are a few things you'll need to have in order to follow along:
We'll start by creating a new Node.js project. Create a new directory for your project and initialize it with npm:
mkdir hyperparameter-tuning
cd hyperparameter-tuning
npm init -y
Next, we'll install the dependencies we'll need for our project. We'll need the BayesOptimization and TensorFlow.js packages:
npm install --save bayesopt @tensorflow/tfjs
With our dependencies installed, we can now create our project files. Create a file named index.js
in your project directory and add the following code:
const tf = require('@tensorflow/tfjs');
const BayesOptimization = require('bayesopt');
// TODO: implement our hyperparameter tuning experiment
We'll start by requiring the dependencies we installed. We'll also create a TODO comment to remind us where we need to implement our hyperparameter tuning experiment.
Now that we have our project set up, we can start implementing our hyperparameter tuning experiment.
We'll start by defining the parameters we want to optimize. For this experiment, we'll optimize the learning rate and batch size for a simple linear regression model. We'll also define the range of values we want to search for each parameter:
const params = {
learningRate: {
type: 'float',
min: 0.0001,
max: 0.1
},
batchSize: {
type: 'int',
min: 1,
max: 100
}
};
Next, we'll define our objective function. This is the function that we'll be optimizing. In this case, we want to find the values of the hyperparameters that result in the lowest error on the validation set:
const objective = (params, done) => {
// TODO: implement our objective function
};
We'll implement our objective function in a bit. For now, we just need to define it.
With our parameters and objective function defined, we can now create our Bayesian optimization object:
const bo = new BayesOptimization(params, objective);
We can now start optimizing our objective function. We'll run our optimization for 20 iterations and use the default acquisition function (expected improvement):
bo.optimize(20, (err, result) => {
if (err) {
console.error(err);
} else {
console.log(result);
}
});
We can now implement our objective function. We'll start by loading the Boston housing dataset and splitting it into training and validation sets:
const loadData = () =>
tf.data.csv('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data', {
columnConfigs: {
medv: {
isLabel: true
}
}
});
const splitData = (data) =>
data.shuffle().splitBatch(0.2);
Next, we'll define our linear regression model:
const createModel = () =>
tf.sequential({
layers: [
tf.layers.dense({ inputShape: [13], units: 1 })
]
});
Now we can implement our objective function. We'll start by getting the values of the hyperparameters from the params
object:
const objective = (params, done) => {
const learningRate = params.learningRate;
const batchSize = params.batchSize;
// TODO: implement our objective function
};
Next, we'll load the data and split it into training and validation sets:
const objective = (params, done) => {
const learningRate = params.learningRate;
const batchSize = params.batchSize;
loadData()
.then(splitData)
.then(([train, val]) => {
// TODO: implement our objective function
});
};
Now we can create our model and compile it with the hyperparameters we're optimizing:
const objective = (params, done) => {
const learningRate = params.learningRate;
const batchSize = params.batchSize;
loadData()
.then(splitData)
.then(([train, val]) => {
const model = createModel();
model.compile({
loss: 'meanSquaredError',
optimizer: tf.train.sgd(learningRate)
});
// TODO: implement our objective function
});
};
Finally, we can train our model and evaluate it on the validation set. We'll compute the mean squared error on the validation set and return it as the value of our objective function:
const objective = (params, done) => {
const learningRate = params.learningRate;
const batchSize = params.batchSize;
loadData()
.then(splitData)
.then(([train, val]) => {
const model = createModel();
model.compile({
loss: 'meanSquaredError',
optimizer: tf.train.sgd(learningRate)
});
model.fit(train, {
epochs: 10,
batchSize: batchSize,
validationData: val,
callbacks: {
onEpochEnd: (epoch, logs) => {
console.log(`Epoch ${epoch}: loss = ${logs.loss}`);
}
}
})
.then(() => {
const valLoss = model.evaluate(val);
console.log(`Validation loss: ${valLoss}`);
done(null, valLoss);
});
});
};
That's it! We've now implemented a simple hyperparameter tuning experiment using TensorFlow.js and Node.js.
In this post, we've learned how to use TensorFlow.js and Node.js for hyperparameter tuning. We've implemented a simple hyperparameter tuning experiment using Bayesian optimization.
There's a lot more to hyperparameter tuning than what we've covered in this post. If you're interested in learning more, I recommend checking out the following resources: