Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Show HN: TensorFire

tenso.rs

Post reply on HN
Print view View original

11–20 of 91 posts

Re: Show HN: TensorFire

#12

Post by nametube »

"running networks in the browser with TensorFire can be faster than running it natively with TensorFlow."

could you elaborate on this statement ?. What kinds of architectures does this hold true for ?.

Re: Show HN: TensorFire

#14

Post by arnioxux »

Is the end goal to allow people to donate computing power for training? (a la Folding@home or SETI@home except just by visiting a webpage)

If so that's amazingly clever!

Re: Show HN: TensorFire

#16

Post by nametube »

arnioxux wrote: post #14

Is the end goal to allow people to donate computing power for training? (a la Folding@home or SETI@home except just by visiting a webpage) If so that's amazingly clever!

I think the goal is to allow people to develop webapps with models built using neural network libraries like keras and tensorflow. This would greatly improve the distribution of applications that are powered by deep learning because you wont have to install a bunch of dependencies in order to use the app.

Re: Show HN: TensorFire

#17

Post by binarymax »

narrator wrote: post #15

I guess WebGL is now the "good enough" cross-platform vendor neutral replacement for CUDA. Tensorflow should add a WebGL backend that runs in NodeJS.

Not quite. Training is not really supported in WebGL. For running a trained model this is cutting edge, and still has varying browser quirks.

Re: Show HN: TensorFire

#19

Post by dordoka »

motoboi wrote: post #11

Could someone explains whats is going on here? What are the steps? Why those colorful artifacts appear before the final result?

It´s just a demo of an upcoming open source API that allows running deep neural network models on the browser.

Steps (disclaimer: I´m not related to the creators, so this is just what I understand it does)

1.- You upload your image

2.- Select an image to be the origin of the style

3.- Downloading Model: downloads a trained (on style transfering) deep neural net

4.- Colorful artifacts: the model is applied to your image. Probably the artifacts are a visualization of the network weights being transformed to WebGL shaders, or just a simple visualization of the internal hidden steps of the transformation

5.- You get your image with the style applied

Post reply on HN