tensorhub | TensorHub is a library built on top of TensorFlow 2.0 to provide simple, modular and repeatable abst | Machine Learning library

by nityansuman Python Version: 1.0.0a4 License: Apache-2.0

X-Ray Key Features Code Snippets(2)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | tensorhub Summary

tensorhub is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. tensorhub has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install tensorhub' or download it from GitHub, PyPI.

TensorHub is a deep learning API written in Python, running on top of the machine learning platform TensorFlow 2 to provide simple, modular and repeatable abstractions to accelerate deep learning research. TensorHub is designed to be simple to understand, easy to write and quick to change. Unlike many frameworks TensorHub is extremely flexible about how to use modules. Modules are designed to be self contained and entirely decoupled from one another.

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Quality

Security

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Support

tensorhub has a low active ecosystem.

It has 48 star(s) with 10 fork(s). There are 7 watchers for this library.

It had no major release in the last 12 months.

tensorhub has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorhub is 1.0.0a4

Quality

tensorhub has 0 bugs and 0 code smells.

Security

tensorhub has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorhub code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tensorhub is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

It has 92 lines of code, 12 functions and 17 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed tensorhub and discovered the below as its top functions. This is intended to give you an instant insight into tensorhub implemented functionality, and help decide if they suit your requirements.

Squash of x
Inverse of tanh
Softplus function

Get all kandi verified functions for this library.

tensorhub Key Features

No Key Features are available at this moment for tensorhub.

tensorhub Examples and Code Snippets

Installation & Compatibility

Jupyter Notebook

Lines of Code : 1

License : Permissive (Apache-2.0)

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$ pip install tensorhub

Testing TF serving model fails with bytes as strings and strings as bytes confusion

Python

Lines of Code : 19

License : Strong Copyleft (CC BY-SA 4.0)

Copy

  def save_serving_model(self,estimator):
      feature_placeholder = {'Headline': tf.placeholder('string', [1], name='headline_placeholder'),
      'Description': tf.placeholder('string', [1], name='description_placeholder')}
      servin

Community Discussions

Trending Discussions on tensorhub

Where to get models for TransferLearning based on topics

QUESTION

Where to get models for TransferLearning based on topics

Asked 2020-Jun-02 at 10:46

Suppose you're searching for a pretrained model for e.g. human gender recognition, or age estimation (Transfer Learning). So, you'd want a net that is trained on, ideally, human faces and not on stuff like the ImageNet dataset.

I know that there are two big starting points for the search:

Keras applications
TensorHub

Now, the best I've found is to use the search tool of the TensorHub website, like here.

That gives me some models trained on the CelebA-HQ dataset, which is something I was searching for.

But, it didn't give any results for e.g. the keywords "sport", "food" or "gun".

So, what is a good way to find pretrained models for a desired "topic"?

...

ANSWER

Answered 2020-Jun-02 at 10:46

It's hard to find a model for each topic at a single place.

The general strategy could be searching in GitHub with the relevant tags ["tensorflow", "sport"].

You can generally find many models on model-zoo websites: https://modelzoo.co/

This is also useful: https://github.com/tensorflow/models

If you need code (probably with pre-trained weights): paperswithcode.com is a good place to search.

Source https://stackoverflow.com/questions/62149846

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

Vulnerabilities

No vulnerabilities reported

Install tensorhub

To use, simply install from PyPI via pip:.
Python 3.5–3.8
TensorFlow 2.3.0 or later
Ubuntu 16.04 or later
Windows 7 or later
macOS 10.12.6 (Sierra) or later.
The ideas behind deep learning are simple, so why should their implementation be painful?. TensorHub ships with a number of built in modules like pre-built models and advance layers that can be used easily.