ross-pfahler/neocortex

Run trained deep neural networks in the browser or node.js

Run trained deep neural networks in the browser or node.js. Currently supports serialization from trained Keras models.

PROJECT PAGE AND EXAMPLES

Background

Training deep neural networks on any meaningful dataset requires massive computational resources and lots and lots of time. However, the forward pass prediction phase is relatively cheap - typically there is no backpropagation, computational graphs, loss functions, or optimization algorithms to worry about.

What do you do when you have a trained deep neural network and now wish to use it to power a part of your client-facing web application? Traditionally, you would deploy your model on a server and call it from your web application through an API. But what if you can deploy it in the browser alongside the rest of your webapp? Computation would be offloaded entirely to your end-user!

Perhaps most users will not be able to run billion-parameter networks in their browsers quite yet, but smaller networks are certainly within the realm of possibility.

The goal of this project is to provide a lightweight javascript library that can take a serialized Keras, Caffe, Torch or [insert other deep learning framework here] model, together with pre-trained weights, pack it in your webapp, and be up and running. Currently supports serialization from trained Keras models.

Examples

• MNIST multi-layer perceptron / src / demo

• CIFAR-10 VGGNet-like convolutional neural network / src / demo

• LSTM recurrent neural network for classifying astronomical object names / src / demo

You can also run the examples on your local machine at http://localhost:8000:

$ npm run examples-server

Usage

See the source code of the examples above. In particular, the CIFAR-10 example demonstrates a multi-threaded implementation using Web Workers.

In the browser:

<script src="neocortex.min.js"></script>
<script>
  // use neural network here
</script>

In node.js:

$ npm install neocortex-js

import NeuralNet from 'neocortex-js';

The core steps involve:

1. Instantiate neural network class

let nn = new NeuralNet({
  // relative URL in browser/webworker, absolute path in node.js
  modelFilePath: 'model.json',
  arrayType: 'float64', // float64 or float32
});

2. Load the model JSON file, then once loaded, feed input data into neural network

nn.init().then(() => {
  let predictions = nn.predict(input);
  // make use of predictions
});

Build

To build the project yourself, for both the browser (outputs to build/neocortex.min.js) and node.js (outputs to dist/):

$ npm run build

To build just for the browser:

$ npm run build-browser

Frameworks

Keras

A script to serialize a trained Keras model together with its hdf5 formatted weights is located in the utils/ folder here. It currently only supports sequential models with layers in the API section below. Implementation of graph models is planned.

API

Functions and layers currently implemented are listed below. More forthcoming.

Activation functions

• linear

• relu

• sigmoid

• hard_sigmoid

• tanh

• softmax

Advanced activation layers

• leakyReLULayer

• parametricReLULayer

• parametricSoftplusLayer

• thresholdedLinearLayer

• thresholdedReLuLayer

Basic layers

• denseLayer

• flattenLayer

Recurrent layers

• rGRULayer (gated-recurrent unit or GRU)

• rLSTMLayer (long short-term memory or LSTM)

• rJZS1Layer, rJZS2Layer, rJZS3Layer (mutated GRUs - JZS1, JZS2, JZS3 - from Jozefowicz et al. 2015)

Convolutional layers

• convolution2DLayer

• maxPooling2DLayer

• convolution1DLayer

• maxPooling1DLayer

Embedding layers

• embeddingLayer - maps indices to corresponding embedding vectors

Normalization layers

• batchNormalizationLayer - see Ioffe and Szegedy 2015

Todo

• implement merge and graph structures from keras

• implement additional keras layers such as TimeDistributedDense, etc.

Tests

$ npm test

Browser testing is planned.

Credits

Thanks to @halmos for the logo.

License

Apache 2.0