hypersearch | Hyperparameter optimization for PyTorch | Machine Learning library

 by   kevinzakka Python Version: Current License: MIT

kandi X-RAY | hypersearch Summary

kandi X-RAY | hypersearch Summary

hypersearch is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. hypersearch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Tune the hyperparameters of your PyTorch models with HyperSearch.
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            kandi-support Support

              hypersearch has a low active ecosystem.
              It has 391 star(s) with 36 fork(s). There are 16 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 3 open issues and 2 have been closed. On average issues are closed in 3 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of hypersearch is current.

            kandi-Quality Quality

              hypersearch has 0 bugs and 0 code smells.

            kandi-Security Security

              hypersearch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              hypersearch code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              hypersearch is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              hypersearch releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed hypersearch and discovered the below as its top functions. This is intended to give you an instant insight into hypersearch implemented functionality, and help decide if they suit your requirements.
            • Tune configs
            • Find a key in params
            • Generate random configuration
            • Sample from a random distribution
            • Returns a base model
            • Save results to json file
            • Prepare directories
            • Add an argument group to the parser
            • Parse known arguments
            Get all kandi verified functions for this library.

            hypersearch Key Features

            No Key Features are available at this moment for hypersearch.

            hypersearch Examples and Code Snippets

            No Code Snippets are available at this moment for hypersearch.

            Community Discussions

            QUESTION

            How to reference widgets inside widgets using IDs in Kivy, Kivy Lang, Kivy MD
            Asked 2021-May-17 at 12:58

            okay, THIS HAS BEEN DRIVING ME INSANE FOR LIKE THE PAST TWO DAYS. and its really annoying, so i am building an application using kivy, after getting used "easy as pie" tkinter I thought it was time to learn kivy (as it has mobile compatibility).

            #I wanted to learn to reference different widgets# I searched YouTube and google for hours but to no avail I keep getting an error message

            my code:

            .py

            ...

            ANSWER

            Answered 2021-May-17 at 10:04

            Let's give you a simple example of how to use the magic id attribute of kivy !

            Source https://stackoverflow.com/questions/67567320

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install hypersearch

            You can download it from GitHub.
            You can use hypersearch like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            [x] Activation [x] all [x] per layer[x] L1/L2 regularization (weights & biases) [x] all [x] per layer[x] Add Batch Norm [x] sandwiched between every layer[x] Add Dropout [x] sandwiched between every layer[ ] Add Layers [ ] conv Layers [ ] fc Layers[ ] Change Layer Params [x] change fc output size [ ] change conv params[x] Optimization [x] batch size [x] learning rate [x] optimizer (adam, sgd)
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          • HTTPS

            https://github.com/kevinzakka/hypersearch.git

          • CLI

            gh repo clone kevinzakka/hypersearch

          • sshUrl

            git@github.com:kevinzakka/hypersearch.git

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