safe-drive | SAFE Drive : access SAFE Network

 by   happybeing JavaScript Version: Current License: GPL-3.0

kandi X-RAY | safe-drive Summary

kandi X-RAY | safe-drive Summary

safe-drive is a JavaScript library typically used in Manufacturing, Utilities, Automotive, macOS applications. safe-drive has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

The SAFE Network is a truly autonomous, decentralised internet. This Secure Access For Everyone Network (SAFE) tackles the increasing risks to individuals, business and nation states arising from over centralisation: domination by commercial monopolies, security risks from malware, hacking, surveillance and so on. It's a new and truly open internet aligned with the original vision held by its creators and early users, with security, net neutrality and unmediated open access baked in.
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              safe-drive has a low active ecosystem.
              It has 12 star(s) with 2 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 11 open issues and 1 have been closed. There are 11 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of safe-drive is current.

            kandi-Quality Quality

              safe-drive has no bugs reported.

            kandi-Security Security

              safe-drive has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              safe-drive is licensed under the GPL-3.0 License. This license is Strong Copyleft.
              Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

            kandi-Reuse Reuse

              safe-drive releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of safe-drive
            Get all kandi verified functions for this library.

            safe-drive Key Features

            No Key Features are available at this moment for safe-drive.

            safe-drive Examples and Code Snippets

            No Code Snippets are available at this moment for safe-drive.

            Community Discussions

            Trending Discussions on safe-drive

            QUESTION

            Keras Denoising Autoencoder (tabular data)
            Asked 2018-Dec-17 at 08:18

            I have a project where I am doing a regression with Gradient Boosted Trees using tabular data. I want to see if using a denoising autoencoder on my data can find a better representation of my original data and improve my original GBT scores. Inspiration is taken from the popular Kaggle winner here.

            AFAIK I have two main choices for extracting the activation's of the DAE - creating a bottleneck structure and taking the single middle layer activations or concatenating every layer's activation's as the representation.

            Let's assume I want all layer activations from the 3x 512 node layers below:

            ...

            ANSWER

            Answered 2018-Apr-25 at 07:46

            Taking the activations of the above will give me a new representation of x_train, right? Should I repeat this process for x_test? I need both to train my GBT model.

            Of course, you need to have the denoised representation for both training and testing data, because the GBT model that you train later only accepts the denoised feature.

            How can I do inference? Each new data point will need to be "converted" into this new representation format. How can I do that with Keras?

            If you want to use the denoised/reconstructed feature, you can directly use autoencoder.predict( X_feat ) to extract features. If you want to use the middle layer, you need to build a new model encoder_only=Model(inputs, encoded) first and use it for feature extraction.

            Do I actually need to provide validation_data= to .fit in this situation?

            You'd better separate some training data for validation to prevent overfitting. However, you can always train multiple models, e.g. in a leave-one-out way to fully use all data in an ensemble way.

            Additional remarks:

            • 512 hidden neurons seems to be too many for your task
            • consider to use DropOut
            • be careful about tabular data, especially when data in different columns are of different dynamic ranges (i.e. MSE does not fairly quantize the reconstruction errors of different columns).

            Source https://stackoverflow.com/questions/50012105

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

            Vulnerabilities

            No vulnerabilities reported

            Install safe-drive

            NOTE: you only need one of either libfuse-dev or libfuse2, and one may fail to install depending on which linux distro you have. So as long as one of them is installed your're good.

            Support

            Pull requests are welcome for outstanding issues and feature requests. Please note that contributions are subject to the LICENSE (see below). IMPORTANT: By submitting a pull request, you will be offering code under the LICENSE (below).
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/happybeing/safe-drive.git

          • CLI

            gh repo clone happybeing/safe-drive

          • sshUrl

            git@github.com:happybeing/safe-drive.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Consider Popular JavaScript Libraries

            freeCodeCamp

            by freeCodeCamp

            vue

            by vuejs

            react

            by facebook

            bootstrap

            by twbs

            Try Top Libraries by happybeing

            safepress

            by happybeingJavaScript

            visualisation-lab

            by happybeingJavaScript

            tauri-svelte-template

            by happybeingJavaScript

            d3-fdg-svelte

            by happybeingJavaScript

            safenetwork-webapi

            by happybeingJavaScript