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TensorFlow for Mobile and IoT and TensorFlow.js
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The next section will explain how to use pretrained models in Node.js.
Node.js
In this section we will give an overview of how to use TensorFlow with Node.js. Let's
start:
The CPU package is imported with the following line of code, which will work for all
Mac, Linux, and Windows platforms:
import * as tf from '@tensorflow/tfjs-node'
The GPU package is imported with the following line of code (as of November 2019
this will work only on a GPU in a CUDA environment):
import * as tf from '@tensorflow/tfjs-node-gpu'
An example of Node.js code for defining and compiling a simple dense model is
reported below. The code is self-explanatory:
const model = tf.sequential();
model.add(tf.layers.dense({ units: 1, inputShape: [400] }));
model.compile({
loss: 'meanSquaredError',
optimizer: 'sgd',
metrics: ['MAE']
});
Training can then start with the typical Node.js asynchronous invocation:
const xs = tf.randomUniform([10000, 400]);
const ys = tf.randomUniform([10000, 1]);
const valXs = tf.randomUniform([1000, 400]);
const valYs = tf.randomUniform([1000, 1]);
async function train() {
await model.fit(xs, ys, {
epochs: 100,
validationData: [valXs, valYs],
});
}
train();
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