tensorflow-java

This release upgrades the native component to TensorFlow 2.21, merges tensorflow-ndarray back into this project, and adds small fixes to the gradient system. Due to a lack of available builds from upstream TensorFlow it…

The project follows the TensorFlow API stability guarantees.

Starting from this version, the project will follow the TensorFlow API stability guarantees.

This is the second release candidate for TensorFlow Java 1.0.0, with added support for Linux Arm64 systems, improved in-code documentation (JavaDoc) and other minor fixes.

This is the first release candidate for TensorFlow Java 1.0.0, and includes a major refactoring of the source structure and the CI/CD pipelines (see https://github.com/tensorflow/java/pull/521). While most the API has…

TensorFlow Java is still only available as an alpha release and is not covered by the TensorFlow API stability guarantees

TensorFlow Java is still only available as an alpha release and is not covered by the TensorFlow API stability guarantees

TensorFlow Java is still only available as an alpha release and is not covered by the TensorFlow API stability guarantees

TensorFlow Java is still only available as an alpha release and is not covered by the TensorFlow API stability guarantees

Note: TensorFlow Java is still in alpha release and is not covered by TensorFlow API stability guarantees

Note: TensorFlow Java is still in alpha release and is not covered by TensorFlow API stability guarantees

Note: TensorFlow Java is still in alpha release and is not covered by TensorFlow API stability guarantees

在当今这个数据驱动的时代，机器学习和深度学习的应用已经渗透到各行各业。然而，许多开发者在使用TensorFlow时却面临着语言选择的困扰。Python虽然强大，但对于习惯使用Java的开发者来说，学习曲线陡峭，且在企业环境中，Java的稳定性和可维护性更具优势。此时，tensorflow-java应运而生，成为了一个解决方案。它不仅让Java开发者能够轻松地利用TensorFlow的强大功能，还能在企业级应用中保持一致性和高效性…