Merge branch 'master' into patch-5

Author: MarkDaoust

OWNERS_METADATA

@@ -0,0 +1,50 @@
+# TensorFlow docs
+
+These are the source files for the core TensorFlow
+[guide](https://www.tensorflow.org/guide),
+[tutorials](https://www.tensorflow.org/tutorials), and other technical docs.
+Please read the
+[contributor guide](https://www.tensorflow.org/community/contribute)
+to submit patches to the TensorFlow documentation and code.
+
+## TensorFlow ecosystem projects
+
+In addition to the core TensorFlow docs,
+[tensorflow.org](https://www.tensorflow.org) hosts documentation for many
+[libraries and extensions](https://www.tensorflow.org/resources/libraries-extensions).
+These docs are sourced from separate project repos and where pull requests can
+be sent. The following is a list of TensorFlow documentation projects published
+on the website and a link to their source files:
+
+tensorflow.org project | GitHub docs location
+-----------------------|---------------------
+[/addons](https://www.tensorflow.org/addons) | https://github.com/tensorflow/addons/tree/master/docs
+[/agents](https://www.tensorflow.org/agents) | https://github.com/tensorflow/agents/tree/master/docs
+[/cloud](https://www.tensorflow.org/cloud) | https://github.com/tensorflow/cloud/tree/master/g3doc
+[/datasets](https://www.tensorflow.org/datasets) | https://github.com/tensorflow/datasets/tree/master/docs
+[/decision_forests](https://www.tensorflow.org/decision_forests) | https://github.com/tensorflow/decision-forests/tree/main/documentation
+[/federated](https://www.tensorflow.org/federated) | https://github.com/tensorflow/federated/tree/main/docs
+[/graphics](https://www.tensorflow.org/graphics) | https://github.com/tensorflow/graphics/tree/master/tensorflow_graphics/g3doc
+[/hub](https://www.tensorflow.org/hub) | https://github.com/tensorflow/hub/tree/master/docs
+[/io](https://www.tensorflow.org/io) | https://github.com/tensorflow/io/tree/master/docs/
+[/js](https://www.tensorflow.org/js) | https://github.com/tensorflow/tfjs-website/tree/master/docs
+[/jvm](https://www.tensorflow.org/jvm) | https://github.com/tensorflow/java/tree/master/docs
+[/lattice](https://www.tensorflow.org/lattice) | https://github.com/tensorflow/lattice/tree/master/docs
+[/lite](https://www.tensorflow.org/lite) | https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/g3doc
+[/mlir](https://www.tensorflow.org/mlir) | https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/mlir/g3doc
+[/model_optimization](https://www.tensorflow.org/model_optimization) | https://github.com/tensorflow/model-optimization/tree/master/tensorflow_model_optimization/g3doc
+[/neural_structured_learning](https://www.tensorflow.org/neural_structured_learning) | https://github.com/tensorflow/neural-structured-learning/tree/master/g3doc
+[/probability](https://www.tensorflow.org/probability) | https://github.com/tensorflow/probability/tree/main/tensorflow_probability/g3doc
+[/quantum](https://www.tensorflow.org/quantum) | https://github.com/tensorflow/quantum/tree/master/docs
+[/ranking](https://www.tensorflow.org/ranking) | https://github.com/tensorflow/ranking/tree/master/docs
+[/recommenders](https://www.tensorflow.org/recommenders) | https://github.com/tensorflow/recommenders/tree/main/docs
+[/responsible_ai/fairness_indicators](https://www.tensorflow.org/responsible_ai/fairness_indicators/guide) | https://github.com/tensorflow/fairness-indicators/tree/master/g3doc
+[/responsible_ai/model_card_toolkit](https://www.tensorflow.org/responsible_ai/model_card_toolkit/guide) | https://github.com/tensorflow/model-card-toolkit/tree/master/model_card_toolkit/documentation
+[/responsible_ai/model_remediation](https://www.tensorflow.org/responsible_ai/model_remediation) | https://github.com/tensorflow/model-remediation/tree/master/docs
+[/responsible_ai/privacy](https://www.tensorflow.org/responsible_ai/privacy/guide) | https://github.com/tensorflow/privacy/tree/master/g3doc
+[/tensorboard](https://www.tensorflow.org/tensorboard) | https://github.com/tensorflow/tensorboard/tree/master/docs
+[/guide/keras](https://www.tensorflow.org/guide/keras/) | https://github.com/keras-team/keras-io/tree/master/guides
+[/text](https://www.tensorflow.org/text) | https://github.com/tensorflow/text/tree/master/docs
+[/tfx](https://www.tensorflow.org/tfx) | https://github.com/tensorflow/tfx/tree/master/docs
+[/tfx/guide/serving](https://www.tensorflow.org/tfx/guide/serving) | https://github.com/tensorflow/serving/tree/master/tensorflow_serving/g3doc
+[/xla](https://www.tensorflow.org/xla) | https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/xla/g3doc

site/en/README.md

@@ -70,7 +70,7 @@
       "source": [
         "In the previous guides, you have learned about [tensors](./tensor.ipynb), [variables](./variable.ipynb), [gradient tape](autodiff.ipynb), and [modules](./intro_to_modules.ipynb).  In this guide, you will fit these all together to train models.\n",
         "\n",
-        "TensorFlow also includes the [tf.Keras API](keras/overview.ipynb), a high-level neural network API that provides useful abstractions to reduce boilerplate.  However, in this guide, you will use basic classes."
+        "TensorFlow also includes the [tf.Keras API](https://www.tensorflow.org/guide/keras/overview), a high-level neural network API that provides useful abstractions to reduce boilerplate.  However, in this guide, you will use basic classes."
       ]
     },
     {
@@ -523,7 +523,7 @@
         "\n",
         "This is, however, an extremely simple problem. For a more practical introduction, see [Custom training walkthrough](../tutorials/customization/custom_training_walkthrough.ipynb).\n",
         "\n",
-        "For more on using built-in Keras training loops, see [this guide](keras/train_and_evaluate.ipynb).  For more on training loops and Keras, see [this guide](keras/writing_a_training_loop_from_scratch.ipynb).  For writing custom distributed training loops, see [this guide](distributed_training.ipynb#using_tfdistributestrategy_with_basic_training_loops_loops)."
+        "For more on using built-in Keras training loops, see [this guide](https://www.tensorflow.org/guide/keras/train_and_evaluate).  For more on training loops and Keras, see [this guide](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch).  For writing custom distributed training loops, see [this guide](distributed_training.ipynb#using_tfdistributestrategy_with_basic_training_loops_loops)."
       ]
     }
   ],

site/en/addons/README.md

@@ -78,7 +78,7 @@
         "* Provide good performance out of the box.\n",
         "* Easy switching between strategies.\n",
         "\n",
-        "You can distribute training using `tf.distribute.Strategy` with a high-level API like Keras `Model.fit`, as well as [custom training loops](keras/writing_a_training_loop_from_scratch.ipynb) (and, in general, any computation using TensorFlow).\n",
+        "You can distribute training using `tf.distribute.Strategy` with a high-level API like Keras `Model.fit`, as well as [custom training loops](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch) (and, in general, any computation using TensorFlow).\n",
         "\n",
         "In TensorFlow 2.x, you can execute your programs eagerly, or in a graph using [`tf.function`](function.ipynb). `tf.distribute.Strategy` intends to support both these modes of execution, but works best with `tf.function`. Eager mode is only recommended for debugging purposes and not supported for `tf.distribute.TPUStrategy`. Although training is the focus of this guide, this API can also be used for distributing evaluation and prediction on different platforms.\n",
         "\n",

site/en/agents/README.md

@@ -1783,7 +1783,7 @@
         "    shape = property(lambda self: self.values.shape)\n",
         "    dtype = property(lambda self: self.values.dtype)\n",
         "\n",
-        "    def with_shape(self):  # TODO(edloper): Show where this is useful?\n",
+        "    def with_shape(self):\n",
         "      return MaskedTensor.Spec(tf.TensorSpec(shape, self.values.dtype),\n",
         "                               tf.TensorSpec(shape, self.mask.dtype))"
       ]