In this post, we'll learn how to define custom model metrics in TensorFlow.js and Node.js.
Custom model metrics are functions that take in two arguments:
yTrue
: The ground truth values.yPred
: The predicted values.And return a single value that represents the metric.
For example, we might want to define a custom metric that calculates the mean squared error (MSE) between the ground truth values and the predicted values. We can do this by defining the following function:
function mse(yTrue, yPred) {
// ...
}
In TensorFlow.js, we can define custom model metrics by creating a CustomCallback
and passing it to the fit
method.
const model = tf.sequential();
// ...
model.compile({
// ...
metrics: ['mse']
});
model.fit(x, y, {
epochs: 10,
callbacks: tf.callbacks.customCallback(['mse'], (logs) => {
console.log(logs.mse);
})
});
In the code above, we've defined a CustomCallback
that calculates the mse
metric. We've then passed this callback to the fit
method, which tells the model to calculate the mse
metric during training.
In Node.js, we can define custom model metrics by creating a tf.Metrics
instance and passing it to the fit
method.
const model = tf.sequential();
// ...
model.compile({
// ...
metrics: ['mse']
});
const metrics = new tf.Metrics(['mse']);
model.fit(x, y, {
epochs: 10,
callbacks: {
onEpochEnd: (epoch, logs) => {
metrics.update(logs);
}
}
});
metrics.print();
In the code above, we've created a tf.Metrics
instance that calculates the mse
metric. We've then passed this instance to the fit
method, which tells the model to calculate the mse
metric during training.
In this post, we've learned how to define custom model metrics in TensorFlow.js and Node.js.