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// To see the callback live, uncomment the following lines for and try the
// code out with ts-node.
// (async function() {
//   const model = tfl.sequential({layers: [
//     tfl.layers.dense({units: 100, inputShape: [20], activation: 'relu'}),
//     tfl.layers.dense({units: 1})
//   ]});
//   model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

//   const xs = tfc.ones([500, 20]);

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import time

import tensorflow as tf

tf.enable_eager_execution()

# with tf.device('gpu:0'):
def model(xs):
  ys = tf.keras.layers.Conv2D(8, [2, 8], activation='relu')(xs)
  ys = tf.keras.layers.MaxPool2D([2, 2], strides=[2, 2])(ys)

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const tf = require('@tensorflow/tfjs');
require('@tensorflow/tfjs-node-gpu');

(async function() {
    const inputShape = [43, 232, 1];
    const numClasses = 10;

    const model = tf.sequential();
    model.add(tf.layers.conv2d({
        filters: 8, kernelSize: [2, 8],

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import keras
import tensorflowjs as tfjs

model = keras.applications.mobilenet.MobileNet(alpha=0.25)

tfjs.converters.save_keras_model(model, '/tmp/mobilnet_0.25')

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Custom Layers in TensorFlow.js (Stateful, Configurable, and Serializable)

A Pen by Shanqing Cai on CodePen.

License.

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1. Model-loading function name changes

Rationale: Indicate type of model being loaded more explicitly in code.

For models converted from Keras and models saved from TensorFlow.js itself:

// Before: 
await tf.loadModel('http://server/model.json');

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