demo-load-pb-tensorflow | loading pb files in tensorflow | Machine Learning library

 by   kindlychung Python Version: Current License: No License

kandi X-RAY | demo-load-pb-tensorflow Summary

kandi X-RAY | demo-load-pb-tensorflow Summary

demo-load-pb-tensorflow is a Python library typically used in Artificial Intelligence, Machine Learning, Tensorflow applications. demo-load-pb-tensorflow has no bugs, it has no vulnerabilities and it has low support. However demo-load-pb-tensorflow build file is not available. You can download it from GitLab, GitHub.

Demo of loading pb files in tensorflow, and making inference
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            kandi-support Support

              demo-load-pb-tensorflow has a low active ecosystem.
              It has 49 star(s) with 19 fork(s). There are 2 watchers for this library.
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              It had no major release in the last 6 months.
              There are 1 open issues and 0 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of demo-load-pb-tensorflow is current.

            kandi-Quality Quality

              demo-load-pb-tensorflow has no bugs reported.

            kandi-Security Security

              demo-load-pb-tensorflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              demo-load-pb-tensorflow does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              demo-load-pb-tensorflow releases are not available. You will need to build from source code and install.
              demo-load-pb-tensorflow has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed demo-load-pb-tensorflow and discovered the below as its top functions. This is intended to give you an instant insight into demo-load-pb-tensorflow implemented functionality, and help decide if they suit your requirements.
            • Get a batch of examples from files .
            • Initialize the dataset .
            • Get a batch of images .
            • Return one - hot array for the given index .
            • Generate a random index .
            Get all kandi verified functions for this library.

            demo-load-pb-tensorflow Key Features

            No Key Features are available at this moment for demo-load-pb-tensorflow.

            demo-load-pb-tensorflow Examples and Code Snippets

            No Code Snippets are available at this moment for demo-load-pb-tensorflow.

            Community Discussions

            Trending Discussions on demo-load-pb-tensorflow

            QUESTION

            Tensorflow freezes on session run when reading data from tf records
            Asked 2017-Aug-17 at 11:04

            Here is the code:

            ...

            ANSWER

            Answered 2017-Aug-17 at 11:04

            So it freezes as you didn't initialize the local variables associated with the queue used in tf.train.shuffle_batch. Local variables are in general temporary variables created for operations such as enqueue and dequeue to keep track of elements.

            Source https://stackoverflow.com/questions/45732826

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

            Vulnerabilities

            No vulnerabilities reported

            Install demo-load-pb-tensorflow

            You can download it from GitLab, GitHub.
            You can use demo-load-pb-tensorflow 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.

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