tsong.me | would appreciate it if you provide a link | Static Site Generator library

 by   jiamings HTML Version: Current License: MIT

kandi X-RAY | tsong.me Summary

kandi X-RAY | tsong.me Summary

tsong.me is a HTML library typically used in Web Site, Static Site Generator, Jekyll applications. tsong.me has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

This site is available at (with decreasing priority).
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              tsong.me has a low active ecosystem.
              It has 12 star(s) with 6 fork(s). There are 5 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              tsong.me has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of tsong.me is current.

            kandi-Quality Quality

              tsong.me has no bugs reported.

            kandi-Security Security

              tsong.me has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              tsong.me 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

              tsong.me releases are not available. You will need to build from source code and install.

            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 tsong.me
            Get all kandi verified functions for this library.

            tsong.me Key Features

            No Key Features are available at this moment for tsong.me.

            tsong.me Examples and Code Snippets

            No Code Snippets are available at this moment for tsong.me.

            Community Discussions

            QUESTION

            Implementing piecewise convolutional neural networks / piecewise max pooling
            Asked 2018-Apr-27 at 08:59

            I'm currently trying to implement a piecewise max pooling operation in Tensorflow, as described here. Given a sentence, I want to divide it to three different portions and max pool all of those portions separately, so that I'd end up with 3 different values instead of 1.

            More concretely, I have a tensor training of shape [batch_len, 1, sentence_len, feature_len]. I also have another tensor splits of shape [batch_len, 2], where the first element of any row is the index to split off the first portion, and the second element is the index to split off the last portion. I want to index the training tensor in a way that divides it into tree parts based on the value index values provided in the splits tensor.

            We cannot simply index the training tensor using the other tensor, as we have different lengths for the first, second, and third portions for different examples. I could loop through all the training data and do it that way, but that would be horribly inefficient. I want to make this as efficient as possible.

            Note: since they will be max pooled, I'm fine with having 3 different tensors of shape [batch_len, 1, sentence_len, feature_len], where in the first tensor, only the elements in the first portion of each sentence has values, and the others have zero. The second tensor would only have values in the middle part, and so on.

            ...

            ANSWER

            Answered 2018-Apr-27 at 08:59

            Exploring and implementing PCNN model leads me to the same problem: splitting borders (positions of entities) may vary in input.

            To implement piecewise max pooling, the combination of tf.split calls for obtaining three parts and tf.padcall for each part were used. Then we apply tf.nn.max_pool to perform max pooling for each padded part.

            Here is a tensorflow implementation of PCNN model as an application for sentiment classification. Here is an exact position of the network description in code.

            Source https://stackoverflow.com/questions/47337618

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

            Vulnerabilities

            No vulnerabilities reported

            Install tsong.me

            You can download it from GitHub.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            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/jiamings/tsong.me.git

          • CLI

            gh repo clone jiamings/tsong.me

          • sshUrl

            git@github.com:jiamings/tsong.me.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 Static Site Generator Libraries

            hugo

            by gohugoio

            gatsby

            by gatsbyjs

            jekyll

            by jekyll

            mkdocs

            by mkdocs

            eleventy

            by 11ty

            Try Top Libraries by jiamings

            wgan

            by jiamingsPython

            fast-weights

            by jiamingsPython

            cramer-gan

            by jiamingsPython

            d2c

            by jiamingsPython

            ais

            by jiamingsPython