Keras: The high-level API for TensorFlow

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data-title="New!" aria-hidden="true"></span></a></li> <li class="devsite-nav-item devsite-nav-heading"><div class="devsite-nav-title devsite-nav-title-no-path"> <span class="devsite-nav-text" tooltip>Accelerators</span> </div></li> <li class="devsite-nav-item"><a href="/guide/distributed_training" class="devsite-nav-title gc-analytics-event" data-category="Site-Wide Custom Events" data-label="Book nav link, pathname: /guide/distributed_training" track-type="bookNav" track-name="click" track-metadata-eventdetail="/guide/distributed_training" ><span class="devsite-nav-text" tooltip>Distributed training</span></a></li> <li class="devsite-nav-item"><a href="/guide/gpu" class="devsite-nav-title gc-analytics-event" data-category="Site-Wide Custom Events" data-label="Book nav link, pathname: /guide/gpu" track-type="bookNav" track-name="click" track-metadata-eventdetail="/guide/gpu" ><span class="devsite-nav-text" tooltip>GPU</span></a></li> <li class="devsite-nav-item"><a href="/guide/tpu" 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class="devsite-page-bookmark-tooltip nocontent" dismiss-button="true" id="devsite-collections-dropdown" dismiss-button-text="Dismiss" close-button-text="Got it"> <devsite-bookmark></devsite-bookmark> <span slot="popout-heading"> Stay organized with collections </span> <span slot="popout-contents"> Save and categorize content based on your preferences. </span> </devsite-feature-tooltip> <div class="devsite-page-title-meta"><devsite-view-release-notes></devsite-view-release-notes></div> <devsite-toc class="devsite-nav" depth="2" devsite-toc-embedded > </devsite-toc> <div class="devsite-article-body clearfix "> <p></p>  <p>Keras is the high-level API of the TensorFlow platform. It provides an approachable, highly-productive interface for solving machine learning (ML) problems, with a focus on modern deep learning. Keras covers every step of the machine learning workflow, from data processing to hyperparameter tuning to deployment. It was developed with a focus on enabling fast experimentation.</p> <p>With Keras, you have full access to the scalability and cross-platform capabilities of TensorFlow. You can run Keras on a TPU Pod or large clusters of GPUs, and you can export Keras models to run in the browser or on mobile devices. You can also serve Keras models via a web API.</p> <p>Keras is designed to reduce cognitive load by achieving the following goals:</p> <ul> <li>Offer simple, consistent interfaces.</li> <li>Minimize the number of actions required for common use cases.</li> <li>Provide clear, actionable error messages.</li> <li>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.</li> <li>Help you write concise, readable code.</li> </ul> <h2 id="who_should_use_keras" data-text="Who should use Keras" tabindex="-1">Who should use Keras</h2> <p>The short answer is that every TensorFlow user should use the Keras APIs by default. Whether you're an engineer, a researcher, or an ML practitioner, you should start with Keras.</p> <p>There are a few use cases (for example, building tools on top of TensorFlow or developing your own high-performance platform) that require the low-level <a href="https://www.tensorflow.org/guide/core">TensorFlow Core APIs</a>. But if your use case doesn't fall into one of the <a href="https://www.tensorflow.org/guide/core#core_api_applications">Core API applications</a>, you should prefer Keras.</p> <h2 id="keras_api_components" data-text="Keras API components" tabindex="-1">Keras API components</h2> <p>The core data structures of Keras are <a href="https://keras.io/api/layers/">layers</a> and <a href="https://keras.io/api/models/">models</a>. A layer is a simple input/output transformation, and a model is a directed acyclic graph (DAG) of layers.</p> <h3 id="layers" data-text="Layers" tabindex="-1">Layers</h3> <p>The <code translate="no" dir="ltr">tf.keras.layers.Layer</code> class is the fundamental abstraction in Keras. A <code translate="no" dir="ltr">Layer</code> encapsulates a state (weights) and some computation (defined in the <code translate="no" dir="ltr">tf.keras.layers.Layer.call</code> method).</p> <p>Weights created by layers can be trainable or non-trainable. Layers are recursively composable: If you assign a layer instance as an attribute of another layer, the outer layer will start tracking the weights created by the inner layer.</p> <p>You can also use layers to handle data preprocessing tasks like normalization and text vectorization. Preprocessing layers can be included directly into a model, either during or after training, which makes the model portable.</p> <h3 id="models" data-text="Models" tabindex="-1">Models</h3> <p>A model is an object that groups layers together and that can be trained on data.</p> <p>The simplest type of model is the <a href="https://www.tensorflow.org/guide/keras/sequential_model"><code translate="no" dir="ltr">Sequential</code> model</a>, which is a linear stack of layers. For more complex architectures, you can either use the <a href="https://www.tensorflow.org/guide/keras/functional_api">Keras functional API</a>, which lets you build arbitrary graphs of layers, or <a href="https://www.tensorflow.org/guide/keras/making_new_layers_and_models_via_subclassing">use subclassing to write models from scratch</a>.</p> <p>The <code translate="no" dir="ltr">tf.keras.Model</code> class features built-in training and evaluation methods:</p> <ul> <li><code translate="no" dir="ltr">tf.keras.Model.fit</code>: Trains the model for a fixed number of epochs.</li> <li><code translate="no" dir="ltr">tf.keras.Model.predict</code>: Generates output predictions for the input samples.</li> <li><code translate="no" dir="ltr">tf.keras.Model.evaluate</code>: Returns the loss and metrics values for the model; configured via the <code translate="no" dir="ltr">tf.keras.Model.compile</code> method.</li> </ul> <p>These methods give you access to the following built-in training features:</p> <ul> <li><a href="https://www.tensorflow.org/api_docs/python/tf/keras/callbacks">Callbacks</a>. You can leverage built-in callbacks for early stopping, model checkpointing, and <a href="https://www.tensorflow.org/tensorboard">TensorBoard</a> monitoring. You can also <a href="https://www.tensorflow.org/guide/keras/writing_your_own_callbacks">implement custom callbacks</a>.</li> <li><a href="https://www.tensorflow.org/guide/keras/distributed_training">Distributed training</a>. You can easily scale up your training to multiple GPUs, TPUs, or devices.</li> <li>Step fusing. With the <code translate="no" dir="ltr">steps_per_execution</code> argument in <code translate="no" dir="ltr">tf.keras.Model.compile</code>, you can process multiple batches in a single <code translate="no" dir="ltr">tf.function</code> call, which greatly improves device utilization on TPUs.</li> </ul> <p>For a detailed overview of how to use <code translate="no" dir="ltr">fit</code>, see the <a href="https://www.tensorflow.org/guide/keras/training_with_built_in_methods">training and evaluation guide</a>. To learn how to customize the built-in training and evaluation loops, see <a href="https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit">Customizing what happens in <code translate="no" dir="ltr">fit()</code></a>.</p> <h3 id="other_apis_and_tools" data-text="Other APIs and tools" tabindex="-1">Other APIs and tools</h3> <p>Keras provides many other APIs and tools for deep learning, including:</p> <ul> <li><a href="https://keras.io/api/optimizers/">Optimizers</a></li> <li><a href="https://keras.io/api/metrics/">Metrics</a></li> <li><a href="https://keras.io/api/losses/">Losses</a></li> <li><a href="https://keras.io/api/data_loading/">Data loading utilities</a></li> </ul> <p>For a full list of available APIs, see the <a href="https://keras.io/api/">Keras API reference</a>. To learn more about other Keras projects and initiatives, see <a href="https://keras.io/getting_started/ecosystem/">The Keras ecosystem</a>.</p> <h2 id="next_steps" data-text="Next steps" tabindex="-1">Next steps</h2> <p>To get started using Keras with TensorFlow, check out the following topics:</p> <ul> <li><a href="https://www.tensorflow.org/guide/keras/sequential_model">The Sequential model</a></li> <li><a href="https://www.tensorflow.org/guide/keras/functional">The Functional API</a></li> <li><a href="https://www.tensorflow.org/guide/keras/training_with_built_in_methods">Training & evaluation with the built-in methods</a></li> <li><a href="https://www.tensorflow.org/guide/keras/custom_layers_and_models">Making new layers and models via subclassing</a></li> <li><a href="https://www.tensorflow.org/guide/keras/save_and_serialize">Serialization and saving</a></li> <li><a href="https://www.tensorflow.org/guide/keras/preprocessing_layers">Working with preprocessing layers</a></li> <li><a href="https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit">Customizing what happens in fit()</a></li> <li><a href="https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch">Writing a training loop from scratch</a></li> <li><a href="https://www.tensorflow.org/guide/keras/rnn">Working with RNNs</a></li> <li><a href="https://www.tensorflow.org/guide/keras/masking_and_padding">Understanding masking & padding</a></li> <li><a href="https://www.tensorflow.org/guide/keras/custom_callback">Writing your own callbacks</a></li> <li><a href="https://www.tensorflow.org/guide/keras/transfer_learning">Transfer learning & fine-tuning</a></li> <li><a href="https://www.tensorflow.org/guide/keras/distributed_training">Multi-GPU and distributed training</a></li> </ul> <p>To learn more about Keras, see the following topics at <a href="http://keras.io">keras.io</a>:</p> <ul> <li><a href="https://keras.io/about/">About Keras</a></li> <li><a href="https://keras.io/getting_started/intro_to_keras_for_engineers/">Introduction to Keras for Engineers</a></li> <li><a href="https://keras.io/getting_started/intro_to_keras_for_researchers/">Introduction to Keras for Researchers</a></li> <li><a href="https://keras.io/api/">Keras API reference</a></li> <li><a href="https://keras.io/getting_started/ecosystem/">The Keras ecosystem</a></li> </ul> </div> <devsite-thumb-rating position="footer"> </devsite-thumb-rating> <div class="devsite-floating-action-buttons"> </div> </article> <devsite-content-footer class="nocontent"> <p>Except as otherwise noted, the content of this page is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 License</a>, and code samples are licensed under the <a href="https://www.apache.org/licenses/LICENSE-2.0">Apache 2.0 License</a>. 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