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Copy file name to clipboardExpand all lines: .github/ISSUE_TEMPLATE/20-documentation-issue.md
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**Submit a pull request?**.
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Are you planning to also submit a pull request to fix the issue? See the [docs contributor guide](https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md):
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Are you planning to also submit a pull request to fix the issue? See the [docs contributor guide](https://github.com/keras-team/tf-keras/blob/master/CONTRIBUTING.md):
Since Keras is the high-level API of TensorFlow 2, Keras follows same security practices as TensorFlow.
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Since TF-Keras is the high-level API of TensorFlow 2, TF-Keras follows same security practices as TensorFlow.
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For details on guidelines on vulnerabilities and reporting them, you can refer [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md).
Copy file name to clipboardExpand all lines: ISSUE_TEMPLATE.md
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documentation (for small docs fixes please send a PR instead).
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2. The form below must be filled out.
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**Here's why we have that policy**: Keras developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
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**Here's why we have that policy**: TF-Keras developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
Describe the problem clearly here. Be sure to convey here why it's a bug in Keras or why the requested feature is needed.
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Describe the problem clearly here. Be sure to convey here why it's a bug in TF-Keras or why the requested feature is needed.
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### Source code / logs
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Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.
This repository hosts the development of the Keras library.
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This repository hosts the development of the TF-Keras library.
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Read the documentation at [keras.io](https://keras.io/).
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## About Keras
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It was developed with a focus on enabling fast experimentation and
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providing a delightful developer experience.
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**The purpose of Keras is to give an *unfair advantage* to any developer looking to ship ML-powered apps.**
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**The purpose of TF-Keras is to give an *unfair advantage* to any developer looking to ship ML-powered apps.**
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Keras is:
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-**Simple** -- but not simplistic. Keras reduces developer *cognitive load*
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-**Simple** -- but not simplistic. TF-Keras reduces developer *cognitive load*
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to free you to focus on the parts of the problem that really matter.
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Keras focuses on ease of use, debugging speed, code elegance & conciseness,
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TF-Keras focuses on ease of use, debugging speed, code elegance & conciseness,
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maintainability, and deployability (via TFServing, TFLite, TF.js).
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-**Flexible** -- Keras adopts the principle of *progressive disclosure of
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-**Flexible** -- TF-Keras adopts the principle of *progressive disclosure of
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complexity*: simple workflows should be quick and easy, while arbitrarily
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advanced workflows should be *possible* via a clear path that builds upon
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what you've already learned.
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-**Powerful** -- Keras provides industry-strength performance and
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-**Powerful** -- TF-Keras provides industry-strength performance and
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scalability: it is used by organizations and companies including NASA,
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YouTube, and Waymo. That's right -- your YouTube recommendations are
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powered by Keras, and so is the world's most advanced driverless vehicle.
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---
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## Keras & TensorFlow 2
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## TF-Keras & TensorFlow 2
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[TensorFlow 2](https://www.tensorflow.org/) is an end-to-end, open-source machine learning platform.
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You can think of it as an infrastructure layer for
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and shipping machine learning solutions with high iteration velocity.
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Keras empowers engineers and researchers to take full advantage of the scalability
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and cross-platform capabilities of TensorFlow 2: you can run Keras on TPU or on large clusters of GPUs,
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and you can export your Keras models to run in the browser or on a mobile device.
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and cross-platform capabilities of TensorFlow 2: you can run TF-Keras on TPU or on large clusters of GPUs,
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and you can export your TF-Keras models to run in the browser or on a mobile device.
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---
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## First contact with Keras
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The core data structures of Keras are __layers__ and __models__.
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The core data structures of TF-Keras are __layers__ and __models__.
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The simplest type of model is the [`Sequential` model](https://keras.io/guides/sequential_model/), a linear stack of layers.
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For more complex architectures, you should use the [Keras functional API](https://keras.io/guides/functional_api/),
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which allows you to build arbitrary graphs of layers or [write models entirely from scratch via subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/).
If you need to, you can further configure your optimizer. The Keras philosophy is to keep simple things simple,
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If you need to, you can further configure your optimizer. The TF-Keras philosophy is to keep simple things simple,
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while allowing the user to be fully in control when they need to be (the ultimate control being the easy extensibility of the source code via subclassing).
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```python
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classes = model.predict(x_test, batch_size=128)
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```
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What you just saw is the most elementary way to use Keras.
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What you just saw is the most elementary way to use TF-Keras.
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However, Keras is also a highly-flexible framework suitable to iterate on state-of-the-art research ideas.
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However, TF-Keras is also a highly-flexible framework suitable to iterate on state-of-the-art research ideas.
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Keras follows the principle of **progressive disclosure of complexity**: it makes it easy to get started,
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yet it makes it possible to handle arbitrarily advanced use cases,
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only requiring incremental learning at each step.
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In pretty much the same way that you were able to train & evaluate a simple neural network above in a few lines,
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you can use Keras to quickly develop new training procedures or exotic model architectures.
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Here's a low-level training loop example, combining Keras functionality with the TensorFlow `GradientTape`:
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you can use TF-Keras to quickly develop new training procedures or exotic model architectures.
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Here's a low-level training loop example, combining TF-Keras functionality with the TensorFlow `GradientTape`:
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```python
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import tensorflow as tf
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For more in-depth tutorials about Keras, you can check out:
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-[Introduction to Keras for engineers](https://keras.io/getting_started/intro_to_keras_for_engineers/)
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-[Introduction to Keras for researchers](https://keras.io/getting_started/intro_to_keras_for_researchers/)
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-[Introduction to TF-Keras for engineers](https://keras.io/getting_started/intro_to_keras_for_engineers/)
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-[Introduction to TF-Keras for researchers](https://keras.io/getting_started/intro_to_keras_for_researchers/)
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