Keras
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
tf.keras 是 TensorFlow 的高階 API，用於建構及訓練深度學習模型。這個 API 可用於快速原型設計、尖端研究及生產環境，且具備三大優點：
- 容易使用
Keras 的介面經過特別設計，適合用於常見用途，既簡單又具有一致性。此外，Keras 還能針對錯誤，為使用者提供清楚實用的意見回饋。
- 模組化且可組合
Keras 模型是由可組合的構成要素連接而成，幾乎沒有框架限制。
- 易於擴充
撰寫自訂的構成要素，來表達對研究的新想法。建立新的層、指標、損失函式，並開發最先進的模型。
快速瞭解 Keras 指南可協助你快速上手。
如需 tf.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：
觀看 YouTube 上的 Inside TensorFlow 影片，深入瞭解構成 Keras 的程式碼：
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上次更新時間：2020-06-05 (世界標準時間)。
[null,null,["上次更新時間：2020-06-05 (世界標準時間)。"],[],[],null,["# Keras: The high-level API for TensorFlow\n\n\u003cbr /\u003e\n\nKeras is the high-level API of the TensorFlow platform. It provides an\napproachable, highly-productive interface for solving machine learning (ML)\nproblems, with a focus on modern deep learning. Keras covers every step of the\nmachine learning workflow, from data processing to hyperparameter tuning to\ndeployment. It was developed with a focus on enabling fast experimentation.\n\nWith Keras, you have full access to the scalability and cross-platform\ncapabilities of TensorFlow. You can run Keras on a TPU Pod or large clusters of\nGPUs, and you can export Keras models to run in the browser or on mobile\ndevices. You can also serve Keras models via a web API.\n\nKeras is designed to reduce cognitive load by achieving the following goals:\n\n- Offer simple, consistent interfaces.\n- Minimize the number of actions required for common use cases.\n- Provide clear, actionable error messages.\n- Follow the principle of progressive disclosure of complexity: It's easy to get started, and you can complete advanced workflows by learning as you go.\n- Help you write concise, readable code.\n\nWho should use Keras\n--------------------\n\nThe short answer is that every TensorFlow user should use the Keras APIs by\ndefault. Whether you're an engineer, a researcher, or an ML practitioner, you\nshould start with Keras.\n\nThere are a few use cases (for example, building tools on top of TensorFlow or\ndeveloping your own high-performance platform) that require the low-level\n[TensorFlow Core APIs](https://www.tensorflow.org/guide/core). But if your use\ncase doesn't fall into one\nof the\n[Core API applications](https://www.tensorflow.org/guide/core#core_api_applications),\nyou should prefer Keras.\n\nKeras API components\n--------------------\n\nThe core data structures of Keras are [layers](https://keras.io/api/layers/) and\n[models](https://keras.io/api/models/). A layer is a simple input/output\ntransformation, and a model is a directed acyclic graph (DAG) of layers.\n\n### Layers\n\nThe `tf.keras.layers.Layer` class is the fundamental abstraction in Keras. A\n`Layer` encapsulates a state (weights) and some computation (defined in the\n`tf.keras.layers.Layer.call` method).\n\nWeights created by layers can be trainable or non-trainable. Layers are\nrecursively composable: If you assign a layer instance as an attribute of\nanother layer, the outer layer will start tracking the weights created by the\ninner layer.\n\nYou can also use layers to handle data preprocessing tasks like normalization\nand text vectorization. Preprocessing layers can be included directly into a\nmodel, either during or after training, which makes the model portable.\n\n### Models\n\nA model is an object that groups layers together and that can be trained on\ndata.\n\nThe simplest type of model is the\n[`Sequential` model](https://www.tensorflow.org/guide/keras/sequential_model),\nwhich is a linear stack of layers. For more complex architectures, you can\neither use the\n[Keras functional API](https://www.tensorflow.org/guide/keras/functional_api),\nwhich lets you build arbitrary graphs of layers, or\n[use subclassing to write models from scratch](https://www.tensorflow.org/guide/keras/making_new_layers_and_models_via_subclassing).\n\nThe `tf.keras.Model` class features built-in training and evaluation methods:\n\n- `tf.keras.Model.fit`: Trains the model for a fixed number of epochs.\n- `tf.keras.Model.predict`: Generates output predictions for the input samples.\n- `tf.keras.Model.evaluate`: Returns the loss and metrics values for the model; configured via the `tf.keras.Model.compile` method.\n\nThese methods give you access to the following built-in training features:\n\n- [Callbacks](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks). You can leverage built-in callbacks for early stopping, model checkpointing, and [TensorBoard](https://www.tensorflow.org/tensorboard) monitoring. You can also [implement custom callbacks](https://www.tensorflow.org/guide/keras/writing_your_own_callbacks).\n- [Distributed training](https://www.tensorflow.org/guide/keras/distributed_training). You can easily scale up your training to multiple GPUs, TPUs, or devices.\n- Step fusing. With the `steps_per_execution` argument in `tf.keras.Model.compile`, you can process multiple batches in a single `tf.function` call, which greatly improves device utilization on TPUs.\n\nFor a detailed overview of how to use `fit`, see the\n[training and evaluation guide](https://www.tensorflow.org/guide/keras/training_with_built_in_methods).\nTo learn how to customize the built-in training and evaluation loops, see\n[Customizing what happens in `fit()`](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit).\n\n### Other APIs and tools\n\nKeras provides many other APIs and tools for deep learning, including:\n\n- [Optimizers](https://keras.io/api/optimizers/)\n- [Metrics](https://keras.io/api/metrics/)\n- [Losses](https://keras.io/api/losses/)\n- [Data loading utilities](https://keras.io/api/data_loading/)\n\nFor a full list of available APIs, see the\n[Keras API reference](https://keras.io/api/). To learn more about other Keras\nprojects and initiatives, see\n[The Keras ecosystem](https://keras.io/getting_started/ecosystem/).\n\nNext steps\n----------\n\nTo get started using Keras with TensorFlow, check out the following topics:\n\n- [The Sequential model](https://www.tensorflow.org/guide/keras/sequential_model)\n- [The Functional API](https://www.tensorflow.org/guide/keras/functional)\n- [Training \\& evaluation with the built-in methods](https://www.tensorflow.org/guide/keras/training_with_built_in_methods)\n- [Making new layers and models via subclassing](https://www.tensorflow.org/guide/keras/custom_layers_and_models)\n- [Serialization and saving](https://www.tensorflow.org/guide/keras/save_and_serialize)\n- [Working with preprocessing layers](https://www.tensorflow.org/guide/keras/preprocessing_layers)\n- [Customizing what happens in fit()](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit)\n- [Writing a training loop from scratch](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch)\n- [Working with RNNs](https://www.tensorflow.org/guide/keras/rnn)\n- [Understanding masking \\& padding](https://www.tensorflow.org/guide/keras/masking_and_padding)\n- [Writing your own callbacks](https://www.tensorflow.org/guide/keras/custom_callback)\n- [Transfer learning \\& fine-tuning](https://www.tensorflow.org/guide/keras/transfer_learning)\n- [Multi-GPU and distributed training](https://www.tensorflow.org/guide/keras/distributed_training)\n\nTo learn more about Keras, see the following topics at\n[keras.io](http://keras.io):\n\n- [About Keras](https://keras.io/about/)\n- [Introduction to Keras for Engineers](https://keras.io/getting_started/intro_to_keras_for_engineers/)\n- [Introduction to Keras for Researchers](https://keras.io/getting_started/intro_to_keras_for_researchers/)\n- [Keras API reference](https://keras.io/api/)\n- [The Keras ecosystem](https://keras.io/getting_started/ecosystem/)"]]
