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Artificial Intelligence","یادگیری ماشین","Deep Learning","Drone Frames","Tools","人工智能","Uncategorized"],"readme":"# Keras 3: Deep Learning for Humans\n\nKeras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, PyTorch, and OpenVINO (for inference-only).\nEffortlessly build and train models for computer vision, natural language processing, audio processing,\ntimeseries forecasting, recommender systems, etc.\n\n- **Accelerated model development**: Ship deep learning solutions faster thanks to the high-level UX of Keras\nand the availability of easy-to-debug runtimes like PyTorch or JAX eager execution.\n- **State-of-the-art performance**: By picking the backend that is the fastest for your model architecture (often JAX!),\nleverage speedups ranging from 20% to 350% compared to other frameworks. [Benchmark here](https://keras.io/getting_started/benchmarks/).\n- **Datacenter-scale training**: Scale confidently from your laptop to large clusters of GPUs or TPUs.\n\nJoin nearly three million developers, from burgeoning startups to global enterprises, in harnessing the power of Keras 3.\n\n\n## Installation\n\n### Install with pip\n\nKeras 3 is available on PyPI as `keras`. Note that Keras 2 remains available as the `tf-keras` package.\n\n1. Install `keras`:\n\n```\npip install keras --upgrade\n```\n\n2. Install backend package(s).\n\nTo use `keras`, you should also install the backend of choice: `tensorflow`, `jax`, or `torch`. Additionally,\nThe `openvino` backend is available with support for model inference only.\n\n### Local installation\n\n#### Minimal installation\n\nKeras 3 is compatible with Linux and macOS systems. For Windows users, we recommend using WSL2 to run Keras.\nTo install a local development version:\n\n1. Install dependencies:\n\n```\npip install -r requirements.txt\n```\n\n2. Run installation command from the root directory.\n\n```\npython pip_build.py --install\n```\n\n3. Run API generation script when creating PRs that update `keras_export` public APIs:\n\n```\n./shell/api_gen.sh\n```\n\n## Backend Compatibility Table\n\nThe following table lists the minimum supported versions of each backend for the latest stable release of Keras (v3.x):\n\n| Backend    | Minimum Supported Version |\n|------------|---------------------------|\n| TensorFlow | 2.16.1                    |\n| JAX        | 0.4.20                    |\n| PyTorch    | 2.1.0                     |\n| OpenVINO   | 2025.3.0                  |\n\n#### Adding GPU support\n\nThe `requirements.txt` file will install a CPU-only version of TensorFlow, JAX, and PyTorch. For GPU support, we also\nprovide a separate `requirements-{backend}-cuda.txt` for TensorFlow, JAX, and PyTorch. These install all CUDA\ndependencies via `pip` and expect a NVIDIA driver to be pre-installed. We recommend a clean Python environment for each\nbackend to avoid CUDA version mismatches. As an example, here is how to create a JAX GPU environment with `conda`:\n\n```shell\nconda create -y -n keras-jax python=3.10\nconda activate keras-jax\npip install -r requirements-jax-cuda.txt\npython pip_build.py --install\n```\n\n## Configuring your backend\n\nYou can export the environment variable `KERAS_BACKEND` or you can edit your local config file at `~/.keras/keras.json`\nto configure your backend. Available backend options are: `\"tensorflow\"`, `\"jax\"`, `\"torch\"`, `\"openvino\"`. Example:\n\n```\nexport KERAS_BACKEND=\"jax\"\n```\n\nIn Colab, you can do:\n\n```python\nimport os\nos.environ[\"KERAS_BACKEND\"] = \"jax\"\n\nimport keras\n```\n\n**Note:** The backend must be configured before importing `keras`, and the backend cannot be changed after\nthe package has been imported.\n\n**Note:** The OpenVINO backend is an inference-only backend, meaning it is designed only for running model\npredictions using `model.predict()` method.\n\n## Backwards compatibility\n\nKeras 3 is intended to work as a drop-in replacement for `tf.keras` (when using the TensorFlow backend). Just take your\nexisting `tf.keras` code, make sure that your calls to `model.save()` are using the up-to-date `.keras` format, and you're\ndone.\n\nIf your `tf.keras` model does not include custom components, you can start running it on top of JAX or PyTorch immediately.\n\nIf it does include custom components (e.g. custom layers or a custom `train_step()`), it is usually possible to convert it\nto a backend-agnostic implementation in just a few minutes.\n\nIn addition, Keras models can consume datasets in any format, regardless of the backend you're using:\nyou can train your models with your existing `tf.data.Dataset` pipelines or PyTorch `DataLoaders`.\n\n## Why use Keras 3?\n\n- Run your high-level Keras workflows on top of any framework -- benefiting at will from the advantages of each framework,\ne.g. the scalability and performance of JAX or the production ecosystem options of TensorFlow.\n- Write custom components (e.g. layers, models, metrics) that you can use in low-level workflows in any framework.\n    - You can take a Keras model and train it in a training loop written from scratch in native TF, JAX, or PyTorch.\n    - You can take a Keras model and use it as part of a PyTorch-native `Module` or as part of a JAX-native model function.\n- Make your ML code future-proof by avoiding framework lock-in.\n- As a PyTorch user: get access to power and usability of Keras, at last!\n- As a JAX user: get access to a fully-featured, battle-tested, well-documented modeling and training library.\n\n\nRead more in the [Keras 3 release announcement](https://keras.io/keras_3/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkeras-team%2Fkeras","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkeras-team%2Fkeras","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkeras-team%2Fkeras/lists"}