Keras api. Keras is: Simple – but not simplistic.

Keras api. It is written in Python and is used to make the implementation of neural networks easy. Keras：简介指南可帮助您入门。 对于初学者，如需了解有关使用 tf. 快速瞭解 Keras 指南可協助你快速上手。 如需 tf. The Functional API, which is an easy-to-use, fully-featured API that supports arbitrary model architectures. Pour une présentation détaillée de l'API, consultez les guides suivants qui contiennent tout ce que vous devez savoir en tant qu'utilisateur expérimenté de TensorFlow Keras : Guide de l'API fonctionnelle Keras; Guide d'entraînement et d'évaluation; Guide de création de couches et de modèles avec la sous-classification Mar 14, 2017 · The new Keras 2 API is our first long-term-support API: codebases written in Keras 2 next month should still run many years from now, on up-to-date software. Want to learn more about Keras 3 and its capabilities? See the Keras 3 launch announcement. Keras 函数式 API 是定义复杂模型（如多输出模型、有向无环图或具有共享层的模型）的方法。 这部分文档假设你已经对 Sequential 顺序模型比较熟悉。 让我们先从一些简单的示例开始。 Keras は TensorFlow プラットフォームの高レベル API です。機械学習（ML）問題を解決するためのアプローチしやすく生産性の高いインターフェースを、最新のディープラーニングに焦点を当てて提供しています。 Getting started Developer guides The Functional API The Sequential model Making new layers & models via subclassing Training & evaluation with the built-in methods Customizing `fit()` with JAX Customizing `fit()` with TensorFlow Customizing `fit()` with PyTorch Writing a custom training loop in JAX Writing a custom training loop in TensorFlow Models API Layers API Callbacks API Ops API Optimizers Metrics Losses Data loading Built-in small datasets Keras Applications Xception EfficientNet B0 to B7 EfficientNetV2 B0 to B3 and S, M, L ConvNeXt Tiny, Small, Base, Large, XLarge VGG16 and VGG19 ResNet and ResNetV2 MobileNet, MobileNetV2, and MobileNetV3 DenseNet NasNetLarge and Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, PyTorch, and OpenVINO (for inference-only). io Keras遵循减少认知困难的最佳实践：Keras提供一致而简洁的API， 能够极大减少一般应用下用户的工作量，同时，Keras提供清晰和具有实践意义的bug反馈。 模块性：模型可理解为一个层的序列或数据的运算图，完全可配置的模块可以用最少的代价自由组合在一起。 기능적 API를 사용하는 경우. keras 機器學習的入門介紹，請參閱這套新手教學課程。 如要進一步瞭解這個 API，請參閱下列這套指南，其中包含 TensorFlow Keras 進階使用者需要瞭解的知識： Keras Functional API 指南; 訓練與評估的指南 tf. Layers are the basic building blocks of neural networks in Keras. keras, ve este conjunto de tutoriales para principiantes. 0 将会是最后一个多后端 Keras 主版本。多后端 Keras 已被 tf. API에 관해 자세히 알아보려면 TensorFlow Keras 고급 사용자로서 알아야 할 사항을 다루는 다음 가이드 세트를 참조하세요. 3. Keras reduces developer cognitive load to free you to focus on the parts of the problem that really m Para una introduccion amigable a principiantes sobre aprendizaje maquina con tf. A layer consists of a tensor-in tensor-out computation function (the layer's call method) and some state, held in TensorFlow variables (the layer's weights). keras Models API. Keras layers API. They are usually generated from Jupyter notebooks. It was developed to enable fast experimentation and iteration, and it lowers the barrier to entry for working with deep learning. 2. . Are you looking for tutorials showing Keras in action across a wide range of use cases? 深度学习与Keras：位于导航栏最下方的该模块翻译了来自Keras作者博客keras. Effortlessly build and train models for computer vision, natural language processing, audio processing, timeseries forecasting, recommender systems, etc. The full Keras API, available for JAX, TensorFlow, and PyTorch. py file that follows a specific format. Mar 9, 2023 · Keras is a high-level, user-friendly API used for building and training neural networks. To make this possible, we have extensively redesigned the API with this release, preempting most future issues. It also supports multiple backend neural network computation. keras를 사용한 머신러닝에 관한 초보자 맞춤형 소개는 이 초보자 가이드 세트를 참조하세요. May 13, 2024 · Keras is a powerful API built on top of deep learning libraries like TensorFlow and PyTorch. For most Keras 提供了许多其他用于深度学习的 API 和工具，包括. Keras is a deep learning API written in Python and capable of running on top of either JAX, TensorFlow, or PyTorch. * API 的 Keras 版本。它是最后一个仅支持 TensorFlow 1（以及 Theano 和 CNTK）的版本。 Keras 的当前版本是 2. Keras Functional API は、tf. 0 的支持。2. See the tutobooks documentation for more details. Are you looking for detailed guides covering in-depth usage of different parts of the Keras API? Read our Keras developer guides. keras module in TensorFlow, including its functions, classes, and usage for building and training machine learning models. Keras 3 implements the full Keras API and makes it available with TensorFlow, JAX, and PyTorch — over a hundred layers, dozens of metrics, loss functions, optimizers, and callbacks, the Keras training and evaluation loops, and the Keras saving & serialization infrastructure. keras 进行机器学习开发的知识，请参阅这一系列新手入门教程。 如需深入了解该 API，请参阅下方所列的一系列指南，其中介绍了您作为 TensorFlow Keras 高级用户需要了解的知识： Keras 函数式 API 指南 앞서 구현한 선형, 로지스틱, 소프트맥스 회귀 모델들과 케라스 훑어보기 실습에서 배운 케라스의 모델 설계 방식은 Sequential API을 사용한 것입니다. It offers code elegance, debugging speed, maintainability, and deployability for ML developers. Keras is a deep learning API designed for human beings, not machines. 优化器; 指标; 损失; 数据加载实用程序; 有关可用 API 的完整列表，请参阅 Keras API 参考。要了解有关其他 Keras 项目和计划的更多信息，请参阅 Keras 生态系统。 下一步. The Layers API is a key component of Keras, allowing you to stack predefined layers or create custom layers for your model. When you choose Keras, your codebase is smaller, more readable, easier to iterate on. Sequential API よりも柔軟なモデルの作成が可能で、非線形トポロジー、共有レイヤー、さらには複数の入力または出力を持つモデル処理することができます。 New examples are added via Pull Requests to the keras. It is an open-source library built in Python that runs on top of TensorFlow. If you would like to convert a Keras 2 example to Keras 3, please open a Pull Request to the keras. They must be submitted as a . Keras 함수형 API 가이드; 학습 및 평가 가이드. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability. 언제 Keras 함수형 API를 사용하여 새 모델을 작성하거나 Model 클래스를 직접 하위 클래스화해야 할까요? 일반적으로, 함수형 API는 고수준의 쉽고 안전하며, 하위 클래스화되지 않은 모델에서 지원하지 않는 많은 특성을 가지고 있습니다. Keras is a deep learning API that supports JAX, TensorFlow, and PyTorch backends. Para profundizar mas en la API, consulta el siguiente conjunto de guías que cubren lo siguiente que necesitas saber como super usuario de TensorFlow Keras: Guía de la API funcional de Keras Getting started Developer guides Code examples Keras 3 API documentation Keras 2 API documentation Models API Layers API Callbacks API Optimizers Metrics Losses Data loading Built-in small datasets Keras Applications Mixed precision Utilities KerasTuner: Hyperparam Tuning KerasHub: Pretrained Models KerasRS 开始使用 Keras 函数式 API. io和其他Keras相关博客的文章，该栏目的文章提供了对深度学习的理解和大量使用Keras的例子，您也可以向这个栏目投稿。 所有的文章均在醒目位置标志标明来源与作者，本文档对该栏目 Keras is an open-source library that provides a Python interface for artificial neural networks. 0，它对 API 做了重大的调整，并且添加了 TensorFlow 2. 要开始使用 Keras 与 TensorFlow，请查看 Provides comprehensive documentation for the tf. Structured data preprocessing utilities Tensor utilities Python & NumPy utilities Scikit-Learn API wrappers Keras configuration utilities Keras 3 API documentation Models API Layers API Callbacks API Ops API Optimizers Metrics Losses Data loading Built-in small datasets Keras Applications Mixed precision Multi-device distribution RNG API See full list on tensorflow. io repository. Keras 2. 5 是最后一个实现 2. Keras is: Simple – but not simplistic. org May 19, 2025 · Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, PyTorch, and OpenVINO (for inference-only). There are three ways to create Keras models: The Sequential model, which is very straightforward (a simple list of layers), but is limited to single-input, single-output stacks of layers (as the name gives away). keras. . Keras was first independent software, then integrated into the About Keras 3. 5 days ago · Keras is a high-level, deep learning API developed by Google for implementing neural networks. edloylrbw hsirpj aprtf iasfz baqw wnnzhz ikg ljvx wwkflxvgc gndtwz