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dsmilkov avatar dsmilkov commented on May 18, 2024

Hi @sgserg,

Can you share more info about the error you are seeing? Thanks!

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tafsiri avatar tafsiri commented on May 18, 2024

Also, if you are trying to run the examples locally you should have node version 8.9.0 or greater. This is currently documented in the package.json files for each project.

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sgserg avatar sgserg commented on May 18, 2024

@tafsiri yes, trying to run them locally, seeing:

$ yarn
yarn install v1.5.1
warning You are using Node "7.8.0" which is not supported and may encounter bugs or unexpected behavior. Yarn supports the following semver range: "^4.8.0 || ^5.7.0 || ^6.2.2 || >=8.0.0"
warning ../../package.json: No license field
[1/5] 🔍  Validating package.json...
error [email protected]: The engine "node" is incompatible with this module. Expected version ">=8.9.0".
error An unexpected error occurred: "Found incompatible module".
info If you think this is a bug, please open a bug report with the information provided in "/Users/serg/mom/tf/tfjs-examples/polynomial-regression-core/yarn-error.log".
info Visit https://yarnpkg.com/en/docs/cli/install for documentation about this command.

Last time I tried updating node it failed too, so it could be Mavericks limitation.

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sgserg avatar sgserg commented on May 18, 2024

@dsmilkov the iOS screenshot is from iPad mini 2 with latest iOS installed. Same results in stock Safari and Firefox.

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tafsiri avatar tafsiri commented on May 18, 2024

@sgserg You could look at instructions at https://nodejs.org/en/ for how to get a more recent node.js version installed.

To your question about examples on iOS it could be that your device doesn't support webgl float textures, this results in lower precision and may need more training time in order to converge. The red line that renders shows that the example runs, it just didn't learn the function effectively. Once you get it running locally, you can try tweaking the number of iterations and learning rate to see how that affects performance on that device. I've found that on a recent iphone the model learns the correct parameters fairly quickly.

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