| Excerpt |
|---|
tensorflow.js is a |
...
JavaScript library for training and deploying ML models in the browser and on node.js. |
tensorflow.js via node.js on Mac
In order to run tensorflow.js in node.js on Mac, you should install tensorflow.js as following:
| Code Block |
|---|
$ npm install @tensorflow/tfjs-node |
If your system NVIDIA GPU, it will be recommended to install below package as well.
| Code Block |
|---|
$ npm install @tensorflow/tfjs-node-gpu |
The below is the example of tensorflow.js based on node.js
| Code Block |
|---|
|
const tf = require('@tensorflow/tfjs');
// Load the binding:
require('@tensorflow/tfjs-node'); // Use '@tensorflow/tfjs-node-gpu' if running with GPU.
// Train a simple model:
const model = tf.sequential();
model.add(tf.layers.dense({units: 100, activation: 'relu', inputShape: [10]}));
model.add(tf.layers.dense({units: 1, activation: 'linear'}));
model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
const xs = tf.randomNormal([100, 10]);
const ys = tf.randomNormal([100, 1]);
model.fit(xs, ys, {
epochs: 100,
callbacks: {
onEpochEnd: async (epoch, log) => {
console.log(`Epoch ${epoch}: loss = ${log.loss}`);
}
}
});
|
tensorflow.js via script tag
The below is the example of Tensorflow.js coding as a part of HTML documentation.
| Code Block |
|---|
| language | js |
|---|
| title | tensorflow-example.html |
|---|
|
<html>
<head>
<!-- Load TensorFlow.js -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.13.3/dist/tf.min.js"> </script>
<!-- Place your code in the script tag below. You can also use an external .js file -->
<script>
// Notice there is no 'import' statement. 'tf' is available on the index-page
// because of the script tag above.
// Define a model for linear regression.
const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));
// Prepare the model for training: Specify the loss and the optimizer.
model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
// Generate some synthetic data for training.
const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);
// Train the model using the data.
model.fit(xs, ys, {epochs: 10}).then(() => {
// Use the model to do inference on a data point the model hasn't seen before:
// Open the browser devtools to see the output
model.predict(tf.tensor2d([5], [1, 1])).print();
});
</script>
</head>
<body>
</body>
</html> |
on Node.js.Reference URL: https://js.tensorflow.org/