py-ml | The goal of this project is to assess wikipedia article | Machine Learning library

by rbinrais Python Version: Current License: No License

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

kandi X-RAY | py-ml Summary

py-ml is a Python library typically used in Artificial Intelligence, Machine Learning applications. py-ml has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

The goal of this project is to assess wikipedia article comments and label them as "semantics" or "syntax". For this purpose we have built the following components:. NOTE: This project is not intented to be used in Production and comes without any warranty. This is suppose to be learning playground to understand building blocks of a common ML project.

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Support

py-ml has a low active ecosystem.

It has 4 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

py-ml has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of py-ml is current.

Quality

py-ml has no bugs reported.

Security

py-ml has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

py-ml does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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

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

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

Top functions reviewed by kandi - BETA

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

Captures the wiki stream
Get the title and comment from the wiki
Get title and comment
Check the conditions of the text
Generates a classification model
Uploads file to Azure Cloud Storage
Compute word2vec
Creates a BlockBlockBlobService object for sending
Propagate labels to new labels
Decode labels
Label feature propagation
Get the new labels from the dataframe
Creates labels for a given folder
Label each of the dataframe
Takes a pandas dataframe
Generate predictions
Pad the features in the data file
Save data to file

Get all kandi verified functions for this library.

py-ml Key Features

No Key Features are available at this moment for py-ml.

py-ml Examples and Code Snippets

No Code Snippets are available at this moment for py-ml.

Community Discussions

Trending Discussions on py-ml

How do I export a graph to Tensorflow Serving so that the input is b64?

QUESTION

How do I export a graph to Tensorflow Serving so that the input is b64?

Asked 2019-Apr-29 at 16:09

I have a Keras graph with a float32 tensor of shape (?, 224, 224, 3) that I want to export to Tensorflow Serving, in order to make predictions with RESTful. Problem is that I cannot input tensors, but encoded b64 strings, as that is a limitation of the REST API. That means that when exporting the graph, the input needs to be a string that needs to be decoded.

How can I "inject" the new input to be converted to the old tensor, without retraining the graph itself? I have tried several examples [1][2].

I currently have the following code for exporting:

...

ANSWER

Answered 2018-Aug-07 at 14:51

You can use tf.decode_base64:

Source https://stackoverflow.com/questions/51728513

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

Vulnerabilities

No vulnerabilities reported

Install py-ml

Fastest way to setup and test everything is by using the Docker Linux containers. Technically, this also means you should able to test it on any platform of your choice (Windows/Linux/Mac). Howerver, following instructions are only tested on macOS (mostly Mojave). The order in which containers are going to run is important. Please follow the instructions in the same sequence as they are provided.

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 .

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