keras-lambda | Porting Keras Tensorflow on AWS Lambda for Inference | Machine Learning library

by sunilmallya Python Version: Current License: MIT

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kandi X-RAY | keras-lambda Summary

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

Porting Keras Tensorflow on AWS Lambda for Inference

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Security

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Support

keras-lambda has a highly active ecosystem.

It has 22 star(s) with 6 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 708 days. There are no pull requests.

It has a positive sentiment in the developer community.

The latest version of keras-lambda is current.

Quality

keras-lambda has 0 bugs and 0 code smells.

Security

keras-lambda has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-lambda code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-lambda is licensed under the MIT License. This license is Permissive.

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

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

keras-lambda 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.

keras-lambda saves you 720725 person hours of effort in developing the same functionality from scratch.

It has 352070 lines of code, 22052 functions and 1575 files.

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

Top functions reviewed by kandi - BETA

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

Generate a table from a text file .
Analyze a group .
Apply the given op_type .
Inception V3 layer .
Base function for inceptionv2 .
Compute a histogram of an array .
Pad an array .
Analyze a block .
Batch norm .
Produce a raw RNN cell .

Get all kandi verified functions for this library.

keras-lambda Key Features

No Key Features are available at this moment for keras-lambda.

keras-lambda Examples and Code Snippets

No Code Snippets are available at this moment for keras-lambda.

Community Discussions

Trending Discussions on keras-lambda

NameError when opening Keras model that uses Tensorflow Backend

QUESTION

NameError when opening Keras model that uses Tensorflow Backend

Asked 2017-Jun-20 at 00:14

I wanted to resize my input image in my first Keras layer so I followed this SO question. Solution worked great until I saved my model, and then tried to use it in another file and it throws

...

ANSWER

Answered 2017-Jun-20 at 00:14

Solution was the workaround as described, which was to import backend as 'k':

train.py:

Source https://stackoverflow.com/questions/44620582

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

Vulnerabilities

No vulnerabilities reported

Install keras-lambda

You can download it from GitHub.
You can use keras-lambda 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.

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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