Run Keras models in the browser, with GPU support using WebGL

Last update: Dec 29, 2022

Overview

**This project is no longer active. Please check out TensorFlow.js.**
The Keras.js demos still work but is no longer updated.

Run Keras models in the browser, with GPU support using WebGL

Interactive Demos | Documentation

Run Keras models in the browser, with GPU support provided by WebGL 2. Models can be run in Node.js as well, but only in CPU mode. Because Keras abstracts away a number of frameworks as backends, the models can be trained in any backend, including TensorFlow, CNTK, etc.

Library version compatibility: Keras 2.1.2

Interactive Demos

Check out the demos/ directory for real examples running Keras.js in VueJS.

Basic Convnet for MNIST
Convolutional Variational Autoencoder, trained on MNIST
Auxiliary Classifier Generative Adversarial Networks (AC-GAN) on MNIST
50-layer Residual Network, trained on ImageNet
Inception v3, trained on ImageNet
DenseNet-121, trained on ImageNet
SqueezeNet v1.1, trained on ImageNet
Bidirectional LSTM for IMDB sentiment classification

Documentation

MIT License

Comments

Error: [Model] error loading weights.

I'm running the latest Keras JS on a trained Keras model. Following the instructions given on the README, I tested loading my model before training (random initialized weights) and it works.

However, after I perform training and use the exact same way to called model.save_weights, Keras JS is unable to read the model file. When trying to parse the metadata, it gives [Model] error loading weights.

Any insight into why this would happen?

opened by calclavia 10
Multi-dimensional Sequential Inputs / unflattened float32 arrays

This library appears to be focused on the Model structure, which is okay, but most of my trained networks are in Sequential form. Specifically LSTM Sequentials.

LSTM Sequentials generally expect a three-dimensional input (batchSize, X,Y). However this repo requires there to be only one flattened input for Sequential, named "input". Has anyone found a good way to convert Sequentials to a Model or a way around this?

I would like to just do prediction in javascript. That means the model shouldn't even need to be compiled, so maybe I can just hack something together myself.

opened by LRonHubs 9

Error loading demo model ("RangeError: Invalid typed array length")

Hi, I am trying to load the mnist_vae model weights demo with the following html:

<html>
<head>
  <meta charset="utf-8">
  <script language="javascript" type="text/javascript" src="scripts/lib/keras.js"></script>
</head>
<body>
  <script language="javascript" type="text/javascript">
	  const model = new KerasJS.Model({
	  filepaths: {
	    model: 'static/model/mnist_vae.json',
	    weights: 'static/model/mnist_vae_weights.buf',
	    metadata: 'static/model/mnist_vae_metadata.json'
	  },
	  gpu: false
	});
  </script>
</body>
</html>

This is the error I get in Chrome:

RangeError: Invalid typed array length
    at new Float32Array (native)
    at http://0.0.0.0:8000/scripts/lib/keras.js:4:18505
    at Array.map (native)
    at http://0.0.0.0:8000/scripts/lib/keras.js:4:18250
    at Array.forEach (native)
    at t.value (http://0.0.0.0:8000/scripts/lib/keras.js:4:16687)
    at http://0.0.0.0:8000/scripts/lib/keras.js:4:15092
From previous event:
    at t.value (http://0.0.0.0:8000/scripts/lib/keras.js:4:15067)
    at new t (http://0.0.0.0:8000/scripts/lib/keras.js:4:14730)
    at http://0.0.0.0:8000/keras.html:8:18

I get a similar error in Firefox that begins with:

Error: WebGL: Exceeded 16 live WebGL contexts for this principal, losing the least recently used one.
RangeError: attempting to construct out-of-bounds TypedArray on ArrayBuffer
Stack trace:
[...]

Any ideas what might be going wrong?

opened by mobeets 7

Weights have been permuted for Convolution1D

I noticed an error building Convolutional1D layer in js when the input has 1 feature layer. I added a failing test for it to this fork, so that it can be easily reproduced.

Here is the error reported at localhost:3000/test (all other tests pass)

Error: cwise: Arrays do not all have the same shape!
    at assign_cwise_thunk (eval at createThunk (eval at <anonymous> (http://localhost:3000/dist/keras.js:2858:1)), <anonymous>:8:71)
    at Object.assign_ndarrayops [as assign] (eval at makeOp (eval at <anonymous> (http://localhost:3000/dist/keras.js:108:1)), <anonymous>:3:43)
    at Convolution2D._w2row (eval at <anonymous> (http://localhost:3000/dist/keras.js:493:1), <anonymous>:274:30)
    at Convolution2D.setWeights (eval at <anonymous> (http://localhost:3000/dist/keras.js:493:1), <anonymous>:126:12)
    at Convolution1D.setWeights (eval at <anonymous> (http://localhost:3000/dist/keras.js:1355:1), <anonymous>:103:20)
    at r.<anonymous> (convolutional/Convolution1D.js:61:19)
    at r (https://cdnjs.cloudflare.com/ajax/libs/mocha/3.0.2/mocha.min.js:2:7852)
    at r.run (https://cdnjs.cloudflare.com/ajax/libs/mocha/3.0.2/mocha.min.js:2:8853)
    at i.runTest (https://cdnjs.cloudflare.com/ajax/libs/mocha/3.0.2/mocha.min.js:2:13553)
    at https://cdnjs.cloudflare.com/ajax/libs/mocha/3.0.2/mocha.min.js:2:14192
    at r (https://cdnjs.cloudflare.com/ajax/libs/mocha/3.0.2/mocha.min.js:2:13024)
    at https://cdnjs.cloudflare.com/ajax/libs/mocha/3.0.2/mocha.min.js:2:13000
    at n (https://cdnjs.cloudflare.com/ajax/libs/mocha/3.0.2/mocha.min.js:2:12791)
    at https://cdnjs.cloudflare.com/ajax/libs/mocha/3.0.2/mocha.min.js:2:12855
    at i (https://cdnjs.cloudflare.com/ajax/libs/mocha/3.0.2/mocha.min.js:1:559)

opened by wassname 7

Conv1D layer: check for legacy shape of weights

Keras changed the ordering of the weights (see https://github.com/fchollet/keras/commit/9f6acd960c0a0c699c79ca1d571783e1692568fb and https://github.com/fchollet/keras/commit/305b3bed747bb8dd358cf82d11bcb1aee5b6e517).

This will only transpose the weights if in legacy format. Fixes issue #22.

opened by stekaiser 6
Fira sans is broken in Chrome and Firefox on Linux

On Debian testing at least but probably on other distros as well. Could be also broken on Mac, the issue with Fira sans was revealed in habrahabr.ru redesign (worked fine on Windows but was totally invisible on Mac/Linux). It looks like this: Disabling font-family in body reveals the text. Please replace the font with the working version.

opened by rkfg 4
Demo is not working

webgl.js:56Uncaught TypeError: Cannot read property 'getShaderPrecisionFormat' of null keras.js:4 Uncaught TypeError: Cannot read property 'getParameter' of null bundle.js:15n @ bundle.js:1(anonymous function) @ bundle.js:1(anonymous function) @ bundle.js:1

opened by AlexanderKozhevin 4
Can not initiate model in nodejs with demo models
Hi

I am trying to predict on the IMDB bidrectional LSTM demo in NodeJS, so I have this code:

const KerasJS = require('keras-js'); const model = new KerasJS.Model({ filepaths: { model: 'imdb_bidirectional_lstm.json', weights: 'imdb_bidirectional_lstm_weights.buf', metadata: 'imdb_bidirectional_lstm_metadata.json' }, filesystem: true });

When I run this, it throws this error:

RangeError: Invalid typed array length at Float32Array (native) at D:\development\webserving\node_modules\core-js\modules_typed-array.js:416:15 at new Float32Array (D:\development\webserving\node_modules\core-js\modules\es6.typed.float32-array.js:3:12) at D:\development\webserving\node_modules\keras-js\lib\Model.js:465:30 at Array.map (native) at D:\development\webserving\node_modules\keras-js\lib\Model.js:433:37 at Array.forEach (native) at Model._createLayers (D:\development\webserving\node_modules\keras-js\lib\Model.js:352:19) at D:\development\webserving\node_modules\keras-js\lib\Model.js:195:15 at tryCatcher (D:\development\webserving\node_modules\bluebird\js\release\util.js:16:23) at Promise._settlePromiseFromHandler (D:\development\webserving\node_modules\bluebird\js\release\promise.js:512:31) at Promise._settlePromise (D:\development\webserving\node_modules\bluebird\js\release\promise.js:569:18) at Promise._settlePromise0 (D:\development\webserving\node_modules\bluebird\js\release\promise.js:614:10) at Promise._settlePromises (D:\development\webserving\node_modules\bluebird\js\release\promise.js:693:18) at Promise._fulfill (D:\development\webserving\node_modules\bluebird\js\release\promise.js:638:18) at PromiseArray._resolve (D:\development\webserving\node_modules\bluebird\js\release\promise_array.js:126:19)

Any idea what I am doing wrong here?

Thanks :-)
opened by spec2e 3

simply importing keras.js results in "Uncaught SyntaxError: Invalid regular expression"

Hi transcranial, I just wanted to write a simple web page to import keras-js. After cloning this repo, I create a file test.html containing the following codes:

<html>
<head>
<title>keras-js test</title>

<script src="dist/keras.js"></script>

<script type="text/javascript">

function periodic() {
  var d = document.getElementById('egdiv');
  d.innerHTML = 'Random number: ' + Math.random()
}

var net; // declared outside -> global variable in window scope
function start() {

  setInterval(periodic, 500);
}

}
</script>
</head>

<body onload="start()">

<div id="egdiv"></div>

</body>
</html>

Then I dragged this file into my Chrome browser, but in the console I received the following error:

Uncaught SyntaxError: Invalid regular expression: /[ÂªÂµÂºÃ€-Ã–Ã˜-Ã¶Ã¸-ËË†-Ë‘Ë -Ë¤Ë¬Ë®Í°-Í´Í¶Í·Íº-Í½Î†Îˆ-ÎŠÎŒÎŽ-Î¡Î£-ÏµÏ·-ÒÒŠ-Ô§Ô±-Õ–Õ™Õ¡-Ö‡×-×ª×°-×²Ø -ÙŠÙ®Ù¯Ù±-Û“Û•Û¥Û¦Û®Û¯Ûº-Û¼Û¿ÜÜ’-Ü¯Ý-Þ¥Þ±ßŠ-ßªß´ßµßºà €-à •à šà ¤à ¨à¡€-à¡˜à¢ à¢¢-à¢¬à¤„-à¤¹à¤½à¥à¥˜-à¥¡à¥±-à¥·à¥¹-à¥¿à¦…-à¦Œà¦à¦à¦“-à¦¨à¦ª-à¦°à¦²à¦¶-à¦¹à¦½à§Žà§œà§à§Ÿ-à§¡à§°à§±à¨…-à¨Šà¨à¨à¨“-à¨¨à¨ª-à¨°à¨²à¨³à¨µà¨¶à¨¸à¨¹à©™-à©œà©žà©²-à©´àª…-àªàª-àª‘àª“-àª¨àªª-àª°àª²àª³àªµ-àª¹àª½à«à« à«¡à¬…-à¬Œà¬à¬à¬“-à¬¨à¬ª-à¬°à¬²à¬³à¬µ-à¬¹à¬½àœààŸ-à¡à±à®ƒà®…-à®Šà®Ž-à®à®’-à®•à®™à®šà®œà®žà®Ÿà®£à®¤à®¨-à®ªà®®-à®¹à¯à°…-à°Œà°Ž-à°à°’-à°¨à°ª-à°³à°µ-à°¹à°½à±˜à±™à± à±¡à²…-à²Œà²Ž-à²à²’-à²¨à²ª-à²³à²µ-à²¹à²½à³žà³ à³¡à³±à³²à´…-à´Œà´Ž-à´à´’-à´ºà´½àµŽàµ àµ¡àµº-àµ¿à¶…-à¶–à¶š-à¶±à¶³-à¶»à¶½à·€-à·†à¸-à¸°à¸²à¸³à¹€-à¹†àºàº‚àº„àº‡àºˆàºŠàºàº”-àº—àº™-àºŸàº¡-àº£àº¥àº§àºªàº«àº-àº°àº²àº³àº½à»€-à»„à»†à»œ-à»Ÿà¼€à½€-à½‡à½‰-à½¬à¾ˆ-à¾Œá€€-á€ªá€¿á-á•áš-áá¡á¥á¦á®-á°áµ-á‚á‚Žá‚ -áƒ…áƒ‡áƒáƒ-áƒºáƒ¼-á‰ˆá‰Š-á‰á‰-á‰–á‰˜á‰š-á‰á‰ -áŠˆáŠŠ-áŠáŠ-áŠ°áŠ²-áŠµáŠ¸-áŠ¾á‹€á‹‚-á‹…á‹ˆ-á‹–á‹˜-áŒáŒ’-áŒ•áŒ˜-ášáŽ€-áŽáŽ -á´á-á™¬á™¯-á™¿áš-áššáš -á›ªá›®-á›°áœ€-áœŒáœŽ-áœ‘áœ -áœ±á€-á‘á -á¬á®-á°áž€-áž³áŸ—áŸœá  -á¡·á¢€-á¢¨á¢ªá¢°-á£µá¤€-á¤œá¥-á¥á¥°-á¥´á¦€-á¦«á§-á§‡á¨€-á¨–á¨ -á©”áª§á¬…-á¬³á…-á‹á®ƒ-á® á®®á®¯á®º-á¯¥á°€-á°£á±-á±á±š-á±½á³©-á³¬á³®-á³±á³µá³¶á´€-á¶¿á¸€-á¼•á¼˜-á¼á¼ -á½…á½ˆ-á½á½-á½—á½™á½›á½á½Ÿ-á½½á¾€-á¾´á¾¶-á¾¼á¾¾á¿‚-á¿„á¿†-á¿Œá¿-á¿“á¿–-á¿›á¿ -á¿¬á¿²-á¿´á¿¶-á¿¼â±â¿â‚-â‚œâ„‚â„‡â„Š-â„“â„•â„™-â„â„¤â„¦â„¨â„ª-â„â„¯-â„¹â„¼-â„¿â……-â…‰â…Žâ… -â†ˆâ°€-â°®â°°-â±žâ± -â³¤â³«-â³®â³²â³³â´€-â´¥â´§â´â´°-âµ§âµ¯â¶€-â¶–â¶ -â¶¦â¶¨-â¶®â¶°-â¶¶â¶¸-â¶¾â·€-â·†â·ˆ-â·Žâ·-â·–â·˜-â·žâ¸¯ã€…-ã€‡ã€¡-ã€©ã€±-ã€µã€¸-ã€¼ã-ã‚–ã‚-ã‚Ÿã‚¡-ãƒºãƒ¼-ãƒ¿ã„…-ã„ã„±-ã†Žã† -ã†ºã‡°-ã‡¿ã€-ä¶µä¸€-é¿Œê€€-ê’Œê“-ê“½ê”€-ê˜Œê˜-ê˜Ÿê˜ªê˜«ê™€-ê™®ê™¿-êš—êš -ê›¯êœ—-êœŸêœ¢-êžˆêž‹-êžŽêž-êž“êž -êžªêŸ¸-ê ê ƒ-ê …ê ‡-ê Šê Œ-ê ¢ê¡€-ê¡³ê¢‚-ê¢³ê£²-ê£·ê£»ê¤Š-ê¤¥ê¤°-ê¥†ê¥ -ê¥¼ê¦„-ê¦²ê§ê¨€-ê¨¨ê©€-ê©‚ê©„-ê©‹ê© -ê©¶ê©ºêª€-êª¯êª±êªµêª¶êª¹-êª½ê«€ê«‚ê«›-ê«ê« -ê«ªê«²-ê«´ê¬-ê¬†ê¬‰-ê¬Žê¬‘-ê¬–ê¬ -ê¬¦ê¬¨-ê¬®ê¯€-ê¯¢ê°€-íž£íž°-íŸ†íŸ‹-íŸ»ï¤€-ï©ï©°-ï«™ï¬€-ï¬†ï¬“-ï¬—ï¬ï¬Ÿ-ï¬¨ï¬ª-ï¬¶ï¬¸-ï¬¼ï¬¾ï€ïïƒï„ï†-ï®±ï¯“-ï´½ïµ-ï¶ï¶’-ï·‡ï·°-ï·»ï¹°-ï¹´ï¹¶-ï»¼ï¼¡-ï¼ºï½-ï½šï½¦-ï¾¾ï¿‚-ï¿‡ï¿Š-ï¿ï¿’-ï¿—ï¿š-ï¿œ]/: Range out of order in character class
    at new RegExp (<anonymous>)
    at Object.<anonymous> (keras.js:31478)
    at keras.js:31221
    at Object.e.read.s (keras.js:31221)
    at e (keras.js:1)
    at Object.<anonymous> (keras.js:31221)
    at Object.o (keras.js:31221)
    at e (keras.js:1)
    at Object.<anonymous> (keras.js:1)
    at e (keras.js:1)
    at Object.<anonymous> (keras.js:1)
    at e (keras.js:1)
    at Object.t.__esModule.default (keras.js:10644)
    at e (keras.js:1)
    at Object.t.__esModule.default (keras.js:10644)
    at e (keras.js:1)

Can you tell me why this happened and how to deal with it?

Best wishes, Yiming

opened by yiminglin-ai 3

Error when call model.predict

It's saying that there are a token |= but my code only has one file index.html and it has no such symbol. What is the cause here?

SyntaxError: Unexpected token |=
    at new Function (<anonymous>)
    at u (https://off99555.github.io/HoNNeT/keras-js/keras.js:13:8807)
    at assigns_cwise_thunk (eval at r (https://off99555.github.io/HoNNeT/keras-js/keras.js:13:10619), <anonymous>:7:29)
    at Object.assigns_ndarrayops [as assigns] (eval at i (https://off99555.github.io/HoNNeT/keras-js/keras.js:1:6744), <anonymous>:3:44)
    at e.value (https://off99555.github.io/HoNNeT/keras-js/keras.js:7:19739)
    at e.value (https://off99555.github.io/HoNNeT/keras-js/keras.js:7:21879)
    at t.value (https://off99555.github.io/HoNNeT/keras-js/keras.js:4:15902)
    at t.<anonymous> (https://off99555.github.io/HoNNeT/keras-js/keras.js:4:16786)
    at o (https://off99555.github.io/HoNNeT/keras-js/keras.js:16:7592)
    at Generator._invoke (https://off99555.github.io/HoNNeT/keras-js/keras.js:16:9001)
    at Generator.t.(anonymous function) [as next] (https://off99555.github.io/HoNNeT/keras-js/keras.js:16:7771)
    at Generator.i (https://off99555.github.io/HoNNeT/keras-js/keras.js:11:1532)
    at u._promiseFulfilled (https://off99555.github.io/HoNNeT/keras-js/keras.js:9:22799)
    at t.<anonymous> (https://off99555.github.io/HoNNeT/keras-js/keras.js:9:24615)
From previous event:
    at t (https://off99555.github.io/HoNNeT/keras-js/keras.js:4:15972)
    at t.<anonymous> (https://off99555.github.io/HoNNeT/keras-js/keras.js:4:18474)
    at o (https://off99555.github.io/HoNNeT/keras-js/keras.js:16:7592)
    at Generator._invoke (https://off99555.github.io/HoNNeT/keras-js/keras.js:16:9001)
    at Generator.t.(anonymous function) [as next] (https://off99555.github.io/HoNNeT/keras-js/keras.js:16:7771)
From previous event:
    at t (https://off99555.github.io/HoNNeT/keras-js/keras.js:4:17225)
    at model.ready.then (https://off99555.github.io/HoNNeT/:165:16)
From previous event:
    at https://off99555.github.io/HoNNeT/:158:20

Here's the javascript part of that index.html file:

     const N_AVAIL_HEROES = 260
     const HERO_INPUT = new Array(2 * N_AVAIL_HEROES).fill(0)
     const vm = new Vue({
         el: '#app',
         data() {
             return {
                 model: null,
                 modelReady: false
             }
         },
         watch: {
             modelReady: function() {
                 if (this.modelReady)
                     console.log('the model is ready now')
             }
         }
     })
     const model = new KerasJS.Model({
         filepaths: {
             model: 'honnet_brain_architecture.json',
             weights: 'honnet_brain_weights_weights.buf',
             metadata: 'honnet_brain_weights_metadata.json'
         }
     })
     function getInputData(legion, hellbourne) {
        console.log('predicting with data')
        const inputData = {
            'hero_input': new Float32Array(HERO_INPUT)
        }
        return inputData
     }
     model.ready().then(() => {
         vm.model = model
         vm.modelReady = true

         // make predictions
         // outputData is an object keyed by names of the output layers
         // or `output` for Sequential models
         model.predict(getInputData([], [])).then(function(outputData) { // <====== error happens here!
             console.log('output')
             console.log(outputData)
         }).catch(err => {
             // handle error
             console.log('pred error: ')
             console.log(err)
         })
     }).catch(err => {
         // handle error
         console.log('ready error: ')
         console.log(err)
     })

opened by off99555 3

[Model] path to protobuf-serialized model definition file is missing

Update from master 11/10/2017

Code builds but fails in browser, see error below. What changed?

Error: [Model] path to protobuf-serialized model definition file is missing. new Model ./src/Model.js:34 31 | } = config 32 | 33 | if (!filepath) {

34 | throw new Error('[Model] path to protobuf-serialized model definition file is missing.') 35 | } 36 | this.filepath = filepath 37 |

opened by dparnellmitek 2
Purpose of wrapping the prediction inference call in a setTimeout() and also passing in exactly 10 milliseconds

I have a small question, could anyone please explain to me what's the purpose of wrapping the prediction inference call in a setTimeout() and also passing in exactly 10 milliseconds (and not something like 5 or 15 milliseconds)? Thanks!

https://github.com/transcranial/keras-js/blob/7713c554f062a77b6897892c2d10209e72576868/demos/src/components/common/Imagenet.vue#L274-L276

opened by jackylu0124 0
Can i use CRF models on keras-js ?

Hello ! I have created a Named Entity Recognition model with tensorflow/keras/keras_contrib. I would like run this model on my clients browser with Keras.js but i doesn't found anything for this. When i load my model, i use keras_contrib class like this in python :

self.model = load_model( dir_path + '/v1/tools/TextAnalyzerData/model.h5', custom_objects={ 'CRF':CRF, 'crf_loss':crf_loss, 'crf_viterbi_accuracy':crf_viterbi_accuracy } )

Any alternative or tips for javascript size ? Thanks

opened by sandro971 0
model.predict() always outputs same invalid results after deployment

Problem Statment:

Goal: Use keras-js (https://github.com/transcranial/keras-js) to do Sentiment Analysis for input summary(NLP).

Developing environment: React 16.6.3

Related Dependencies: keras-js, imdb_bidirectional_lstm.bin(trained model, located in public folder), imdb_dataset_word_index_top20000.json

Problem: The model.predict() function in keras-js always output the same result, regardless what the the actual input is, the output is always 50% after deployment.

It works fine on my machine, I’m using Mac OS. But if we deployed it to Linux server, the problem occurred. We can load all dependencies in Linux server, no error messages, but the prediction results are totally different from that from local machine even we used completely same code. The prediction results in deployed version are always 50%, which is not valid.

opened by zjtisme 0

TypeError: layer.layer is undefined

I want to run a keras model on a browser using keras.js , but keras.js predict is not working. while loading i got the following error

TypeError: layer.layer is undefined here is the js code:

const model = new KerasJS.Model(
     {
     filepath: 'example.bin',
     gpu : true,
     filesystem : true

     })

       model.ready().then(() => model.predict(
       {
         input: new Float32Array(samples),
       })) .then(({output}) => 
       {
         alert(output['output']);
       })
       .catch((error) => 
       {
         alert(error);
      });

opened by hmhwe 0

Error: predict() must take an object where the keys are the named inputs of the model:

I have been trying to run a keras model in browser using keras.js. But whenever i try to predict I got the following error

Error: predict() must take an object where the keys are the named inputs of the model: Here is js code

handlePredict()
  {
    const model = new KerasJS.Model({
    filepaths: {
    model: 'model.json',
    weights: 'model_weights.buf',
    metadata: 'model_metadata.json'
  },

  gpu : true,
  filesystem : true

})

  model.ready().then(() =>{
    const inputData = {
      'input_1': new Float32Array(samples)
    }
    alert('Converted text = ' + inputData['input_1'])
    return model.predict(inputData['input_1'])
  })
  .then(output =>
    {
    alert('Predicted value : ' + output['fc1000']);
    })
  .catch(err =>
    {
    alert(err);
    })

  }

I am using keras-js 0.3.0 and keras 2.2.4 . Any help would be appreciated.

opened by hmhwe 0

Releases(v0.3.0)

v0.3.0(May 20, 2017)
Added back legacy Merge, MaxoutDense, Highway layers

Fixed model creation when Model class contains branches of Sequential class

Added SqueezeNet v1.1 and Auxiliary Classifier Generative Adversarial Network (AC-GAN) demos

Source code(tar.gz)
Source code(zip)
v0.2.0(May 20, 2017)

Updated library to be compatible with Keras v2 (tested on 2.0.4 with TF 1.1.0).
Source code(tar.gz)
Source code(zip)
v0.1.0(May 20, 2017)

Add as npm package for Node.js in addition to UMD browser build.
Source code(tar.gz)
Source code(zip)

Owner

Leon Chen

Co-founder, MD.ai. Software/ML engineer, doctor, electronic musician.

GitHub Repository https://transcranial.github.io/keras-js

Feedback is important: response-aware feedback mechanism for background based conversation

RFM The code for the paper: "Feedback is important: response-aware feedback mechanism for background based conversation." Requirements python 3.7 pyto

2 Sep 29, 2022

PyTorch code for 'Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning'

Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning This repository is for EMSRDPN introduced in the foll

7 Feb 10, 2022

(3DV 2021 Oral) Filtering by Cluster Consistency for Large-Scale Multi-Image Matching

Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching (3DV 2021 Oral Presentation) Filtering by Cluster Consistency (FCC) is a very

11 Sep 28, 2022

masscan + nmap + Finger

说明个人根据使用习惯修改masnmap而来的一个小工具。调用masscan做全端口扫描，再调用nmap做服务识别，最后调用Finger做Web指纹识别。工具使用场景适合风险探测排查、众测等。使用方法安装依赖 pip3 install -r requirements.txt -i https:/

3 Mar 25, 2022

Official implementation of YOGO for Point-Cloud Processing

You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module By Chenfeng Xu, Bohan Zhai, Bichen Wu, T

67 Dec 20, 2022

YolactEdge: Real-time Instance Segmentation on the Edge

YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7

1.1k Jan 06, 2023

[CVPR 2021] Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

Scan2Cap: Context-aware Dense Captioning in RGB-D Scans Introduction We introduce the task of dense captioning in 3D scans from commodity RGB-D sensor

79 Nov 07, 2022

🛰️ List of earth observation companies and job sites

Earth Observation Companies & Jobs source Portals & Jobs Geospatial Geospatial jobs newsletter: ~biweekly newsletter with geospatial jobs by Ali Ahmad

64 Dec 27, 2022

Easily Process a Batch of Cox Models

ezcox: Easily Process a Batch of Cox Models The goal of ezcox is to operate a batch of univariate or multivariate Cox models and return tidy result. ⏬

15 May 23, 2022

Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.

VQGAN-CLIP-GENERATOR Overview This is a package (with available notebook) for running VQGAN+CLIP locally, with a focus on ease of use, good documentat

98 Dec 30, 2022

Run Keras models in the browser, with GPU support using WebGL

Related tags

Overview

Interactive Demos | Documentation

Comments

Releases(v0.3.0)

v0.3.0(May 20, 2017)

v0.2.0(May 20, 2017)

v0.1.0(May 20, 2017)

Owner

Leon Chen

Feedback is important: response-aware feedback mechanism for background based conversation

PyTorch code for 'Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning'

(3DV 2021 Oral) Filtering by Cluster Consistency for Large-Scale Multi-Image Matching

masscan + nmap + Finger

Official implementation of YOGO for Point-Cloud Processing

YolactEdge: Real-time Instance Segmentation on the Edge

[CVPR 2021] Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

🛰️ List of earth observation companies and job sites

Easily Process a Batch of Cox Models

Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.

Code for database and frontend of webpage for Neural Fields in Visual Computing and Beyond.

The fastai deep learning library

:hot_pepper: R²SQL: "Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing." (AAAI 2021)

CLIP + VQGAN / PixelDraw

[ICCV 2021 Oral] SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer

[ICCV 2021] FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces

Learning Correspondence from the Cycle-consistency of Time (CVPR 2019)

The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue.

Train neural network for semantic segmentation (deep lab V3) with pytorch in less then 50 lines of code