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tensorflow.js is a |
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JavaScript library for training and deploying ML models in the browser and on |
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node.js. |
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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:
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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}`); } } }); |
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tensorflow.js via script tag
The below is the example of Tensorflow.js coding as a part of HTML documentation.
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<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> |
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Reference URL: https://js.tensorflow.org/