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.github/ISSUE_TEMPLATE/00-bug-template.md

@@ -37,17 +37,17 @@ python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VER
 
 **Describe the problem**.
 
-Describe the problem clearly here. Be sure to convey here why it's a bug in Keras or why the requested feature is needed.
+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.
 
 **Describe the current behavior**.
 
 
 **Describe the expected behavior**.
 
-**[Contributing](https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md)**.
+**[Contributing](https://github.com/keras-team/tf-keras/blob/master/CONTRIBUTING.md)**.
 
 - Do you want to contribute a PR? (yes/no):
-- If yes, please read [this page](https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md) for instructions
+- If yes, please read [this page](https://github.com/keras-team/tf-keras/blob/master/CONTRIBUTING.md) for instructions
 - Briefly describe your candidate solution(if contributing):
 
 **Standalone code to reproduce the issue**.

.github/ISSUE_TEMPLATE/10-feature-request.md

@@ -30,8 +30,8 @@ Describe the feature clearly here. Be sure to convey here why the requested feat
 **Who will benefit from this feature?**
 
 
-**[Contributing](https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md)**
+**[Contributing](https://github.com/keras-team/tf-keras/blob/master/CONTRIBUTING.md)**
 
 - Do you want to contribute a PR? (yes/no):
-- If yes, please read [this page](https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md) for instructions
+- If yes, please read [this page](https://github.com/keras-team/tf-keras/blob/master/CONTRIBUTING.md) for instructions
 - Briefly describe your candidate solution(if contributing):

.github/ISSUE_TEMPLATE/20-documentation-issue.md

@@ -58,6 +58,6 @@ Are there currently visuals? If not, will it clarify the content?
 
 **Submit a pull request?**.
 
-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):
+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):
 
 

.github/workflows/format.yml

@@ -46,7 +46,7 @@ jobs:
             This is a pull request automatically created by a Github Action to format the code.
 
             If there is any conflict, click the run workflow button on
-            [this page](https://github.com/keras-team/keras/actions/workflows/format.yml).
+            [this page](https://github.com/keras-team/tf-keras/actions/workflows/format.yml).
           labels: |
             ready to pull
           draft: false

CONTRIBUTING.md

@@ -56,7 +56,7 @@ pull request. A team member will take care of the merging.
 
 ![Ready to pull](https://i.imgur.com/yCEqJsA.png)
 
-Here is an [example pull request](https://github.com/keras-team/keras/pull/15015)
+Here is an [example pull request](https://github.com/keras-team/tf-keras/pull/15015)
 for your reference.
 
 ## Setup environment
@@ -68,7 +68,7 @@ to setup a local environment by installing the dev tools needed.
 ### Option 1: Use a Docker container
 
 We provide a
-[Dockerfile](https://github.com/keras-team/keras/blob/master/.devcontainer/Dockerfile)
+[Dockerfile](https://github.com/keras-team/tf-keras/blob/master/.devcontainer/Dockerfile)
 to build the dev environment. You can build the Dockerfile into a Docker image
 named `keras-dev` with the following command at the root directory of your
 cloned repo.
@@ -79,18 +79,18 @@ docker build -t keras-dev .devcontainer
 
 You can launch a Docker container from the image with the following command. The
 `-it` option gives you an interactive shell of the container. The `-v
-path/to/repo/:/home/keras/` mounts your cloned repo to the container. Replace
+path/to/repo/:/home/tf_keras/` mounts your cloned repo to the container. Replace
 `path/to/repo` with the path to your cloned repo directory.
 
 ```shell
-docker run -it -v path/to/repo/:/home/keras/ keras-dev
+docker run -it -v path/to/repo/:/home/tf_keras/ keras-dev
 ```
 
 In the container shell, you need to install the latest dependencies with the
 following command.
 
 ```shell
-pip install -r /home/keras/requirements.txt && pip uninstall keras-nightly -y
+pip install -r /home/tf_keras/requirements.txt && pip uninstall keras-nightly -y
 ```
 
 Now, the environment setup is complete. You are ready to run the tests.
@@ -107,14 +107,14 @@ with setup instructions.
 
 To setup your local dev environment, you will need the following tools.
 
-1.  [Bazel](https://bazel.build/) is the tool to build and test Keras. See the
+1.  [Bazel](https://bazel.build/) is the tool to build and test TF-Keras. See the
     [installation guide](https://docs.bazel.build/versions/4.0.0/install.html)
     for how to install and config bazel for your local environment.
 2.  [git](https://github.com/) for code repository management.
-3.  [python](https://www.python.org/) to build and code in Keras.
+3.  [python](https://www.python.org/) to build and code in TF-Keras.
 
 The following commands check the tools above are successfully installed. Note
-that Keras requires at least Python 3.7 to run.
+that TF-Keras requires at least Python 3.7 to run.
 
 ```shell
 bazel --version
@@ -146,7 +146,7 @@ venv_dir\Scripts\activate.bat  # for Windows
 Clone your forked repo to your local machine. Go to the cloned directory to
 install the dependencies into the venv. Since `tf-nightly` uses `keras-nightly`
 as a dependency, we need to uninstall `keras-nightly` so that tests will run
-against Keras code in the local workspace.
+against TF-Keras code in the local workspace.
 
 ```shell
 git clone https://github.com/YOUR_GITHUB_USERNAME/keras.git
@@ -165,9 +165,9 @@ pip install --upgrade tf-nightly
 
 ## Code style
 
-The Keras uses [Black](https://black.readthedocs.io/en/stable/) and
+The TF-Keras uses [Black](https://black.readthedocs.io/en/stable/) and
 [isort](https://pycqa.github.io/isort/) to format the code. Please refer to
-[requirements.txt](https://github.com/keras-team/keras/blob/master/requirements.txt)
+[requirements.txt](https://github.com/keras-team/tf-keras/blob/master/requirements.txt)
 for the required versions. Run the following command **at the root directory of
 the repo** to format your code.
 
@@ -199,7 +199,7 @@ A **class docstring** may contain the following items:
     * Optional `Raises` section for possible errors.
 
 You can check out `MultiHeadAttention` as an example
-[(link)](https://github.com/keras-team/keras/blob/v2.12.0-rc1/keras/layers/attention/multi_head_attention.py#L131).
+[(link)](https://github.com/keras-team/tf-keras/blob/v2.12.0-rc1/tf_keras/layers/attention/multi_head_attention.py#L131).
 
 A **function docstring** may contain the following items:
 
@@ -211,7 +211,7 @@ A **function docstring** may contain the following items:
 * Optional `Raises` section for possible errors.
 
 You can check out `text_dataset_from_directory` as an example
-[(link)](https://github.com/keras-team/keras/blob/v2.12.0-rc1/keras/utils/text_dataset.py#L31).
+[(link)](https://github.com/keras-team/tf-keras/blob/v2.12.0-rc1/tf_keras/utils/text_dataset.py#L31).
 
 
 ## Run tests
@@ -267,22 +267,22 @@ command above.
   However, it may slow down the tests for not running in parallel
   and may cause the test to timeout.
 
-## Contributing to Keras applications
+## Contributing to TF-Keras applications
 
 Contributions to the
 [pre-trained application library](https://keras.io/api/applications/) are
-welcome. Code for Keras applications is located in Keras repository in
-[keras/applications](https://github.com/keras-team/keras/blob/master/keras/applications).
-When contributing to Keras applications, please keep following checklist in
+welcome. Code for TF-Keras applications is located in TF-Keras repository in
+[keras/applications](https://github.com/keras-team/tf-keras/blob/master/tf_keras/applications).
+When contributing to TF-Keras applications, please keep following checklist in
 mind.
 
--   Keras applications must implement an established and widely used model.
+-   TF-Keras applications must implement an established and widely used model.
     Applications should include a link to a paper describing the architecture of
     the model with at least 20 citations.
 -   Applications should be provided with pre-trained weights.
-    -   When submitting a pull request for a Keras application, these weights
+    -   When submitting a pull request for a TF-Keras application, these weights
         can be provided at any publically available URL (e.g. a personal Cloud
-        Storage bucket). The weights will be uploaded to a Keras storage bucket
+        Storage bucket). The weights will be uploaded to a TF-Keras storage bucket
         while merging the pull request.
     -   Weights should be downloaded with the
         [get_file()](https://keras.io/api/utils/python_utils/#getfile-function)
@@ -312,5 +312,5 @@ mind.
 
 ## Security vulnerability reports
 
-Since Keras is the high-level API of TensorFlow 2, Keras follows same security practices as TensorFlow.
+Since TF-Keras is the high-level API of TensorFlow 2, TF-Keras follows same security practices as TensorFlow.
 For details on guidelines on vulnerabilities and reporting them, you can refer [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md). 

ISSUE_TEMPLATE.md

@@ -8,7 +8,7 @@ If you open a GitHub issue, here is our policy:
     documentation (for small docs fixes please send a PR instead).
 2.  The form below must be filled out.
 
-**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.
+**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.
 
 ------------------------
 
@@ -35,7 +35,7 @@ python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VER
 ```
 
 ### Describe the problem
-Describe the problem clearly here. Be sure to convey here why it's a bug in Keras or why the requested feature is needed.
+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.
 
 ### Source code / logs
 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.

README.md

@@ -2,7 +2,7 @@
 
 ![Keras logo](https://s3.amazonaws.com/keras.io/img/keras-logo-2018-large-1200.png)
 
-This repository hosts the development of the Keras library.
+This repository hosts the development of the TF-Keras library.
 Read the documentation at [keras.io](https://keras.io/).
 
 ## About Keras
@@ -12,26 +12,26 @@ running on top of the machine learning platform [TensorFlow](https://github.com/
 It was developed with a focus on enabling fast experimentation and
 providing a delightful developer experience.
 
-**The purpose of Keras is to give an *unfair advantage* to any developer looking to ship ML-powered apps.**
+**The purpose of TF-Keras is to give an *unfair advantage* to any developer looking to ship ML-powered apps.**
 
 Keras is:
 
--   **Simple** -- but not simplistic. Keras reduces developer *cognitive load*
+-   **Simple** -- but not simplistic. TF-Keras reduces developer *cognitive load*
     to free you to focus on the parts of the problem that really matter.
-    Keras focuses on ease of use, debugging speed, code elegance & conciseness,
+    TF-Keras focuses on ease of use, debugging speed, code elegance & conciseness,
     maintainability, and deployability (via TFServing, TFLite, TF.js).
--   **Flexible** -- Keras adopts the principle of *progressive disclosure of
+-   **Flexible** -- TF-Keras adopts the principle of *progressive disclosure of
     complexity*: simple workflows should be quick and easy, while arbitrarily
     advanced workflows should be *possible* via a clear path that builds upon
     what you've already learned.
--   **Powerful** -- Keras provides industry-strength performance and
+-   **Powerful** -- TF-Keras provides industry-strength performance and
     scalability: it is used by organizations and companies including NASA,
     YouTube, and Waymo. That's right -- your YouTube recommendations are
     powered by Keras, and so is the world's most advanced driverless vehicle.
 
 ---
 
-## Keras & TensorFlow 2
+## TF-Keras & TensorFlow 2
 
 [TensorFlow 2](https://www.tensorflow.org/) is an end-to-end, open-source machine learning platform.
 You can think of it as an infrastructure layer for
@@ -49,14 +49,14 @@ with a focus on modern deep learning. It provides essential abstractions and bui
 and shipping machine learning solutions with high iteration velocity.
 
 Keras empowers engineers and researchers to take full advantage of the scalability
-and cross-platform capabilities of TensorFlow 2: you can run Keras on TPU or on large clusters of GPUs,
-and you can export your Keras models to run in the browser or on a mobile device.
+and cross-platform capabilities of TensorFlow 2: you can run TF-Keras on TPU or on large clusters of GPUs,
+and you can export your TF-Keras models to run in the browser or on a mobile device.
 
 ---
 
 ## First contact with Keras
 
-The core data structures of Keras are __layers__ and __models__.
+The core data structures of TF-Keras are __layers__ and __models__.
 The simplest type of model is the [`Sequential` model](https://keras.io/guides/sequential_model/), a linear stack of layers.
 For more complex architectures, you should use the [Keras functional API](https://keras.io/guides/functional_api/),
 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/).
@@ -86,7 +86,7 @@ model.compile(loss='categorical_crossentropy',
               metrics=['accuracy'])
 ```
 
-If you need to, you can further configure your optimizer. The Keras philosophy is to keep simple things simple,
+If you need to, you can further configure your optimizer. The TF-Keras philosophy is to keep simple things simple,
 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).
 
 ```python
@@ -114,16 +114,16 @@ Or generate predictions on new data:
 classes = model.predict(x_test, batch_size=128)
 ```
 
-What you just saw is the most elementary way to use Keras.
+What you just saw is the most elementary way to use TF-Keras.
 
-However, Keras is also a highly-flexible framework suitable to iterate on state-of-the-art research ideas.
+However, TF-Keras is also a highly-flexible framework suitable to iterate on state-of-the-art research ideas.
 Keras follows the principle of **progressive disclosure of complexity**: it makes it easy to get started,
 yet it makes it possible to handle arbitrarily advanced use cases,
 only requiring incremental learning at each step.
 
 In pretty much the same way that you were able to train & evaluate a simple neural network above in a few lines,
-you can use Keras to quickly develop new training procedures or exotic model architectures.
-Here's a low-level training loop example, combining Keras functionality with the TensorFlow `GradientTape`:
+you can use TF-Keras to quickly develop new training procedures or exotic model architectures.
+Here's a low-level training loop example, combining TF-Keras functionality with the TensorFlow `GradientTape`:
 
 ```python
 import tensorflow as tf
@@ -150,8 +150,8 @@ for inputs, targets in dataset:
 
 For more in-depth tutorials about Keras, you can check out:
 
--   [Introduction to Keras for engineers](https://keras.io/getting_started/intro_to_keras_for_engineers/)
--   [Introduction to Keras for researchers](https://keras.io/getting_started/intro_to_keras_for_researchers/)
+-   [Introduction to TF-Keras for engineers](https://keras.io/getting_started/intro_to_keras_for_engineers/)
+-   [Introduction to TF-Keras for researchers](https://keras.io/getting_started/intro_to_keras_for_researchers/)
 -   [Developer guides](https://keras.io/guides/)
 -   [Other learning resources](https://keras.io/getting_started/learning_resources/)
 
@@ -161,10 +161,10 @@ For more in-depth tutorials about Keras, you can check out:
 
 Keras comes packaged with TensorFlow 2 as `tensorflow.keras`.
 To start using Keras, simply [install TensorFlow 2](https://www.tensorflow.org/install).
-You can then import Keras as follows:
+You can then import TF-Keras as follows:
 
 ```python
-from tensorflow import keras
+from tensorflow import tf_keras as keras
 ```
 
 ---
@@ -173,7 +173,7 @@ from tensorflow import keras
 
 Keras has **nightly releases** (`keras-nightly` on PyPI)
 and **stable releases** (`keras` on PyPI).
-The nightly Keras releases are usually compatible with the corresponding version
+The nightly TF-Keras releases are usually compatible with the corresponding version
 of the `tf-nightly` releases
 (e.g. `keras-nightly==2.7.0.dev2021100607` should be
 used with `tf-nightly==2.7.0.dev2021100607`).
@@ -182,11 +182,11 @@ For stable releases, each Keras
 version maps to a specific stable version of TensorFlow.
 
 The table below shows the compatibility version mapping
-between TensorFlow versions and Keras versions.
+between TensorFlow versions and TF-Keras versions.
 
-All the release branches can be found on [GitHub](https://github.com/keras-team/keras/releases).
+All the release branches can be found on [GitHub](https://github.com/keras-team/tf-keras/releases).
 
-All the release binaries can be found on [Pypi](https://pypi.org/project/keras/#history).
+All the release binaries can be found on [Pypi](https://pypi.org/project/tf_keras/#history).
 
 ---
 ## Support
@@ -201,13 +201,13 @@ You can ask questions and join the development discussion:
 ## Opening an issue
 
 You can also post **bug reports and feature requests** (only)
-in [GitHub issues](https://github.com/keras-team/keras/issues).
+in [GitHub issues](https://github.com/keras-team/tf-keras/issues).
 
 
 ---
 
 ## Opening a PR
 
 We welcome contributions! Before opening a PR, please read
-[our contributor guide](https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md),
+[our contributor guide](https://github.com/keras-team/tf-keras/blob/master/CONTRIBUTING.md),
 and the [API design guideline](https://github.com/keras-team/governance/blob/master/keras_api_design_guidelines.md).