keras-data-generator | Template for data generator in Keras | Machine Learning library

by afshinea Python Version: Current License: No License

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

kandi X-RAY | keras-data-generator Summary

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

Template for data generator in Keras

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Quality

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Support

keras-data-generator has a low active ecosystem.

It has 260 star(s) with 146 fork(s). There are 14 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of keras-data-generator is current.

Quality

keras-data-generator has 0 bugs and 0 code smells.

Security

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

keras-data-generator code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-data-generator 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

keras-data-generator 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.

keras-data-generator saves you 18 person hours of effort in developing the same functionality from scratch.

It has 50 lines of code, 5 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Generate training data .
Return the data for the given index .
Initializer .
Initialize the indexes .
Returns the number of items in the list .

Get all kandi verified functions for this library.

keras-data-generator Key Features

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

keras-data-generator Examples and Code Snippets

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

Community Discussions

Trending Discussions on keras-data-generator

How do I fit tensorflow ImageDataGenerator

QUESTION

How do I fit tensorflow ImageDataGenerator

Asked 2020-Dec-04 at 02:14

I've build my model, but do not know how to fit it. Could anyone give me some tip so I can use ImageDataGenerator in my models while working with images, or it is better to use other ways like using Dataset?

...

ANSWER

Answered 2020-Dec-03 at 19:06

history = model.fit(train_generator,
                    validation_data=validation_generator,
                    steps_per_epoch=100,
                    epochs=15,
                    validation_steps=50,
                    verbose=2)

Source https://stackoverflow.com/questions/65131908

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

Vulnerabilities

No vulnerabilities reported

Install keras-data-generator

You can download it from GitHub.
You can use keras-data-generator 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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