tf-end-to-end | TensorFlow code to perform end | Machine Learning library

by OMR-Research Python Version: Current License: MIT

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

kandi X-RAY | tf-end-to-end Summary

tf-end-to-end is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Neural Network applications. tf-end-to-end has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However tf-end-to-end build file is not available. You can download it from GitHub.

TensorFlow code to perform end-to-end Optical Music Recognition on monophonic scores through Convolutional Recurrent Neural Networks and CTC-based training.

Support

Quality

Security

License

Reuse

Support

tf-end-to-end has a low active ecosystem.

It has 111 star(s) with 54 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 10 have been closed. On average issues are closed in 22 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tf-end-to-end is current.

Quality

tf-end-to-end has 0 bugs and 0 code smells.

Security

tf-end-to-end has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tf-end-to-end code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tf-end-to-end is licensed under the MIT License. This license is Permissive.

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

Reuse

tf-end-to-end releases are not available. You will need to build from source code and install.

tf-end-to-end has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

tf-end-to-end saves you 159 person hours of effort in developing the same functionality from scratch.

It has 395 lines of code, 15 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Generate next batch of images
Resize an image
Normalize an image
Return a word separator
CTC CNN
LeakyRelu activation
Convert input text to cc format
Construct a sparse tuple from a sequence of sequences
Compute the edit distance between two strings
Compute the Levenshtein distance between a and b
Get validation images
Convert a sparse tensor to a list of strings
Return default model parameters
Construct a sparse matrix from a sequence of sequences

Get all kandi verified functions for this library.

tf-end-to-end Key Features

No Key Features are available at this moment for tf-end-to-end.

tf-end-to-end Examples and Code Snippets

No Code Snippets are available at this moment for tf-end-to-end.

Community Discussions

Trending Discussions on tf-end-to-end

How do I convert a .meta .index and .data file into SavedModel (.pb) format without losing metagraphdef?

QUESTION

How do I convert a .meta .index and .data file into SavedModel (.pb) format without losing metagraphdef?

Asked 2021-Jan-11 at 22:02

I'm trying to convert these three files of a pre-trained model:

semantic_model.data-00000-of-00001
semantic_model.index
semantic_model.meta

into a Saved Model format, so that I can later convert it into TFLite format for Inference. Searching StackOverflow, I'd come across this code, which properly generates the Saved_model.pb, however as noted in some comments, doing it in this way doesn't keep the Meta Graph Definitions, which causes an error when I later try to convert it into TFlite format or freeze it.

...

ANSWER

Answered 2021-Jan-11 at 22:02

Refer to these steps for converting checkpoints to a TFLite model: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/r1/convert/python_api.md#convert-checkpoints-

Source https://stackoverflow.com/questions/65572476

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

Vulnerabilities

No vulnerabilities reported

Install tf-end-to-end

You can download it from GitHub.
You can use tf-end-to-end 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.