Docs: Updated links in docstrings for TF2

PiperOrigin-RevId: 272905147

tensorflow/api_template_v1.__init__.py

@@ -39,7 +39,7 @@ if "dev" in __version__:   # pylint: disable=undefined-variable
   TensorFlow's `tf-nightly` package will soon be updated to TensorFlow 2.0.
 
   Please upgrade your code to TensorFlow 2.0:
-    * https://www.tensorflow.org/beta/guide/migration_guide
+    * https://www.tensorflow.org/guide/migrate
 
   Or install the latest stable TensorFlow 1.X release:
     * `pip install -U "tensorflow==1.*"`

tensorflow/examples/tutorials/deepdream/README.md

@@ -2,5 +2,5 @@
 
 This example has moved.
 
-[A TensorFlow 2 version is available](https://tensorflow.org/en/beta/tutorials/generative/deepdream.ipynb)
+[A TensorFlow 2 version is available](https://tensorflow.org/tutorials/generative/deepdream)
 [The original is in the TensorFlow examples Repository](https://github.com/tensorflow/examples/tree/master/community/en/r1/deepdream.ipynb)

tensorflow/examples/tutorials/deepdream/deepdream.ipynb

@@ -19,7 +19,7 @@
       "source": [
         "This example has moved.\n",
         "\n",
-        "* [TensorFlow 2.0 version](https://tensorflow.org/en/beta/tutorials/generative/deepdream.ipynb)\n",
+        "* [TensorFlow 2.0 version](https://tensorflow.org/tutorials/generative/deepdream)\n",
         "* [The Original](https://github.com/tensorflow/examples/tree/master/community/en/r1/deepdream.ipynb)"
       ]
     }

tensorflow/python/autograph/g3doc/reference/index.md

@@ -16,7 +16,7 @@ graph.
 
 For more information on AutoGraph, see the following articles:
 
-*   [AutoGraph tutorial](https://www.tensorflow.org/alpha/beta/autograph)
+*   [AutoGraph guide](https://www.tensorflow.org/guide/function)
-*   [Eager tutorial](https://www.tensorflow.org/alpha/guide/eager)
+*   [tf.function tutorial](https://www.tensorflow.org/tutorials/customization/performance)
-*   [TensorFlow 2.0 Alpha](https://www.tensorflow.org/alpha)
+*   [Eager guide](https://www.tensorflow.org/guide/eager)
 *   [AutoGraph blog post](https://medium.com/tensorflow/autograph-converts-python-into-tensorflow-graphs-b2a871f87ec7)

tensorflow/python/autograph/impl/api.py

@@ -601,7 +601,7 @@ def to_graph(entity, recursive=True, experimental_optional_features=None):
   argument called `self`.
 
   For a tutorial, see the
-  [tf.function and AutoGraph guide](https://www.tensorflow.org/beta/guide/autograph).
+  [tf.function and AutoGraph guide](https://www.tensorflow.org/guide/function).
   For more detailed information, see the
   [AutoGraph reference documentation](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/g3doc/reference/index.md).
 

tensorflow/python/distribute/distribute_lib.py

@@ -15,8 +15,8 @@
 """Library for running a computation across multiple devices.
 
 See the guide for overview and examples:
-[TensorFlow v1.x](https://www.tensorflow.org/guide/distribute_strategy),
+[TensorFlow v2.x](https://www.tensorflow.org/guide/distributed_training),
-[TensorFlow v2.x](https://www.tensorflow.org/alpha/guide/distribute_strategy).
+[TensorFlow v1.x](https://github.com/tensorflow/docs/blob/master/site/en/r1/guide/distribute_strategy.ipynb).  # pylint: disable=line-too-long
 
 The intent of this library is that you can write an algorithm in a stylized way
 and it will be usable with a variety of different `tf.distribute.Strategy`
@@ -416,18 +416,18 @@ class InputContext(object):
 class Strategy(object):
   """A state & compute distribution policy on a list of devices.
 
-  See [the guide](https://www.tensorflow.org/alpha/guide/distribute_strategy)
+  See [the guide](https://www.tensorflow.org/guide/distributed_training)
   for overview and examples.
 
   In short:
 
   * To use it with Keras `compile`/`fit`,
     [please
-    read](https://www.tensorflow.org/alpha/guide/distribute_strategy#using_tfdistributestrategy_with_keras).
+    read](https://www.tensorflow.org/guide/distributed_training#using_tfdistributestrategy_with_keras).
   * You may pass descendant of `tf.distribute.Strategy` to
     `tf.estimator.RunConfig` to specify how a `tf.estimator.Estimator`
     should distribute its computation. See
-    [guide](https://www.tensorflow.org/alpha/guide/distribute_strategy#using_tfdistributestrategy_with_estimator).
+    [guide](https://www.tensorflow.org/guide/distributed_training#using_tfdistributestrategy_with_estimator_limited_support).
   * Otherwise, use `tf.distribute.Strategy.scope` to specify that a
     strategy should be used when building an executing your model.
     (This puts you in the "cross-replica context" for this strategy, which
@@ -435,7 +435,7 @@ class Strategy(object):
   * If you are writing a custom training loop, you will need to call a few more
     methods,
     [see the
-    guide](https://www.tensorflow.org/alpha/guide/distribute_strategy#using_tfdistributestrategy_with_custom_training_loops):
+    guide](https://www.tensorflow.org/guide/distributed_training#using_tfdistributestrategy_with_custom_training_loops):
 
       * Start by either creating a `tf.data.Dataset` normally or using
         `tf.distribute.experimental_make_numpy_dataset` to make a dataset out of
@@ -491,7 +491,7 @@ class Strategy(object):
 
   See the
   [custom training loop
-  tutorial](https://www.tensorflow.org/alpha/tutorials/distribute/training_loops)
+  tutorial](https://www.tensorflow.org/tutorials/distribute/custom_training)
   for a more detailed example.
 
   Note: `tf.distribute.Strategy` currently does not support TensorFlow's

tensorflow/python/eager/def_function.py

@@ -565,7 +565,7 @@ class Function(object):
           "due to passing python objects instead of tensors. Also, tf.function "
           "has experimental_relax_shapes=True option that relaxes argument "
           "shapes that can avoid unnecessary retracing. Please refer to "
-          "https://www.tensorflow.org/beta/tutorials/eager/tf_function#python_or_tensor_args"
+          "https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args"
           " and https://www.tensorflow.org/api_docs/python/tf/function for more "
           "details.".format(recent_tracing_count, self._call_counter.call_count,
                             self._python_function))
@@ -1112,7 +1112,7 @@ def function(func=None,
     autograph: Whether autograph should be applied on `func` before tracing a
       graph. Data-dependent control flow requires `autograph=True`. For more
       information, see the [tf.function and AutoGraph guide](
-      https://www.tensorflow.org/beta/guide/autograph).
+      https://www.tensorflow.org/guide/function).
     experimental_implements: If provided, contains a name of a "known" function
       this implements. For example "mycompany.my_recurrent_cell".
       This is stored as an attribute in inference function,

tensorflow/python/keras/engine/network.py

@@ -1025,7 +1025,7 @@ class Network(base_layer.Layer):
     means saving a `tf.keras.Model` using `save_weights` and loading into a
     `tf.train.Checkpoint` with a `Model` attached (or vice versa) will not match
     the `Model`'s variables. See the [guide to training
-    checkpoints](https://www.tensorflow.org/alpha/guide/checkpoints) for details
+    checkpoints](https://www.tensorflow.org/guide/checkpoint) for details
     on the TensorFlow format.
 
     Arguments:

tensorflow/python/keras/layers/core.py

@@ -89,7 +89,7 @@ class Masking(Layer):
   ```
 
   See [the masking and padding
-  guide](https://www.tensorflow.org/beta/guide/keras/masking_and_padding)
+  guide](https://www.tensorflow.org/guide/keras/masking_and_padding)
   for more details.
   """
 

tensorflow/python/keras/layers/recurrent.py

@@ -189,7 +189,7 @@ class StackedRNNCells(Layer):
 class RNN(Layer):
   """Base class for recurrent layers.
 
-  See [the Keras RNN API guide](https://www.tensorflow.org/beta/guide/keras/rnn)
+  See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
   for details about the usage of RNN API.
 
   Arguments:
@@ -982,7 +982,7 @@ class RNN(Layer):
 class AbstractRNNCell(Layer):
   """Abstract object representing an RNN cell.
 
-  See [the Keras RNN API guide](https://www.tensorflow.org/beta/guide/keras/rnn)
+  See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
   for details about the usage of RNN API.
 
   This is the base class for implementing RNN cells with custom behavior.
@@ -1202,7 +1202,7 @@ class DropoutRNNCellMixin(object):
 class SimpleRNNCell(DropoutRNNCellMixin, Layer):
   """Cell class for SimpleRNN.
 
-  See [the Keras RNN API guide](https://www.tensorflow.org/beta/guide/keras/rnn)
+  See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
   for details about the usage of RNN API.
 
   This class processes one step within the whole time sequence input, whereas
@@ -1393,7 +1393,7 @@ class SimpleRNNCell(DropoutRNNCellMixin, Layer):
 class SimpleRNN(RNN):
   """Fully-connected RNN where the output is to be fed back to input.
 
-  See [the Keras RNN API guide](https://www.tensorflow.org/beta/guide/keras/rnn)
+  See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
   for details about the usage of RNN API.
 
   Arguments:

tensorflow/python/keras/layers/recurrent_v2.py

@@ -57,7 +57,7 @@ _RUNTIME_GPU = 2
 class GRUCell(recurrent.GRUCell):
   """Cell class for the GRU layer.
 
-  See [the Keras RNN API guide](https://www.tensorflow.org/beta/guide/keras/rnn)
+  See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
   for details about the usage of RNN API.
 
   This class processes one step within the whole time sequence input, whereas
@@ -177,7 +177,7 @@ class GRUCell(recurrent.GRUCell):
 class GRU(recurrent.DropoutRNNCellMixin, recurrent.GRU):
   """Gated Recurrent Unit - Cho et al. 2014.
 
-  See [the Keras RNN API guide](https://www.tensorflow.org/beta/guide/keras/rnn)
+  See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
   for details about the usage of RNN API.
 
   Based on available runtime hardware and constraints, this layer
@@ -763,7 +763,7 @@ def gru_with_backend_selection(inputs, init_h, kernel, recurrent_kernel, bias,
 class LSTMCell(recurrent.LSTMCell):
   """Cell class for the LSTM layer.
 
-  See [the Keras RNN API guide](https://www.tensorflow.org/beta/guide/keras/rnn)
+  See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
   for details about the usage of RNN API.
 
   This class processes one step within the whole time sequence input, whereas
@@ -884,7 +884,7 @@ class LSTMCell(recurrent.LSTMCell):
 class LSTM(recurrent.DropoutRNNCellMixin, recurrent.LSTM):
   """Long Short-Term Memory layer - Hochreiter 1997.
 
-  See [the Keras RNN API guide](https://www.tensorflow.org/beta/guide/keras/rnn)
+  See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
   for details about the usage of RNN API.
 
   Based on available runtime hardware and constraints, this layer

tensorflow/python/keras/losses.py

@@ -62,7 +62,7 @@ class Loss(object):
   'SUM_OVER_BATCH_SIZE' will raise an error.
 
   Please see
-  https://www.tensorflow.org/alpha/tutorials/distribute/training_loops for more
+  https://www.tensorflow.org/tutorials/distribute/custom_training for more
   details on this.
 
   You can implement 'SUM_OVER_BATCH_SIZE' using global batch size like:
@@ -83,7 +83,7 @@ class Loss(object):
       When used with `tf.distribute.Strategy`, outside of built-in training
       loops such as `tf.keras` `compile` and `fit`, using `AUTO` or
       `SUM_OVER_BATCH_SIZE` will raise an error. Please see
-      https://www.tensorflow.org/alpha/tutorials/distribute/training_loops
+      https://www.tensorflow.org/tutorials/distribute/custom_training
       for more details on this.
     name: Optional name for the op.
   """
@@ -169,7 +169,7 @@ class Loss(object):
           'reduction=tf.keras.losses.Reduction.NONE)\n....\n'
           '    loss = tf.reduce_sum(loss_obj(labels, predictions)) * '
           '(1. / global_batch_size)\n```\nPlease see '
-          'https://www.tensorflow.org/alpha/tutorials/distribute/training_loops'
+          'https://www.tensorflow.org/tutorials/distribute/custom_training'
           ' for more details.')
 
     if self.reduction == losses_utils.ReductionV2.AUTO:
@@ -190,7 +190,7 @@ class LossFunctionWrapper(Loss):
       When used with `tf.distribute.Strategy`, outside of built-in training
       loops such as `tf.keras` `compile` and `fit`, using `AUTO` or
       `SUM_OVER_BATCH_SIZE` will raise an error. Please see
-      https://www.tensorflow.org/alpha/tutorials/distribute/training_loops
+      https://www.tensorflow.org/tutorials/distribute/custom_training
       for more details on this.
     name: (Optional) name for the loss.
     **kwargs: The keyword arguments that are passed on to `fn`.
@@ -387,7 +387,7 @@ class BinaryCrossentropy(LossFunctionWrapper):
       When used with `tf.distribute.Strategy`, outside of built-in training
       loops such as `tf.keras` `compile` and `fit`, using `AUTO` or
       `SUM_OVER_BATCH_SIZE` will raise an error. Please see
-      https://www.tensorflow.org/alpha/tutorials/distribute/training_loops
+      https://www.tensorflow.org/tutorials/distribute/custom_training
       for more details on this.
     name: (Optional) Name for the op.
   """
@@ -451,7 +451,7 @@ class CategoricalCrossentropy(LossFunctionWrapper):
       When used with `tf.distribute.Strategy`, outside of built-in training
       loops such as `tf.keras` `compile` and `fit`, using `AUTO` or
       `SUM_OVER_BATCH_SIZE` will raise an error. Please see
-      https://www.tensorflow.org/alpha/tutorials/distribute/training_loops
+      https://www.tensorflow.org/tutorials/distribute/custom_training
       for more details on this.
     name: Optional name for the op.
   """
@@ -512,7 +512,7 @@ class SparseCategoricalCrossentropy(LossFunctionWrapper):
       When used with `tf.distribute.Strategy`, outside of built-in training
       loops such as `tf.keras` `compile` and `fit`, using `AUTO` or
       `SUM_OVER_BATCH_SIZE` will raise an error. Please see
-      https://www.tensorflow.org/alpha/tutorials/distribute/training_loops
+      https://www.tensorflow.org/tutorials/distribute/custom_training
       for more details on this.
     name: Optional name for the op.
   """
@@ -746,7 +746,7 @@ class Huber(LossFunctionWrapper):
       When used with `tf.distribute.Strategy`, outside of built-in training
       loops such as `tf.keras` `compile` and `fit`, using `AUTO` or
       `SUM_OVER_BATCH_SIZE` will raise an error. Please see
-      https://www.tensorflow.org/alpha/tutorials/distribute/training_loops
+      https://www.tensorflow.org/tutorials/distribute/custom_training
       for more details on this.
     name: Optional name for the op.
   """
@@ -1128,7 +1128,7 @@ class CosineSimilarity(LossFunctionWrapper):
       When used with `tf.distribute.Strategy`, outside of built-in training
       loops such as `tf.keras` `compile` and `fit`, using `AUTO` or
       `SUM_OVER_BATCH_SIZE` will raise an error. Please see
-      https://www.tensorflow.org/alpha/tutorials/distribute/training_loops
+      https://www.tensorflow.org/tutorials/distribute/custom_training
       for more details on this.
     name: Optional name for the op.
   """

tensorflow/python/keras/saving/saved_model/README.md

@@ -15,8 +15,8 @@ object.
 
 Please see the links below for more details:
 
-- [Saved Model Guide](https://www.tensorflow.org/beta/guide/saved_model)
+- [Saved Model Guide](https://www.tensorflow.org/guide/saved_model)
-- [Checkpoint Guide](https://www.tensorflow.org/beta/guide/checkpoints)
+- [Checkpoint Guide](https://www.tensorflow.org/guide/checkpoint)
 
 ## Keras SavedModel implementation
 

tensorflow/python/ops/losses/loss_reduction.py

@@ -48,9 +48,9 @@ class ReductionV2(object):
            (1. / global_batch_size)
      ```
 
-     Please see
+  Please see the
-     https://www.tensorflow.org/alpha/tutorials/distribute/training_loops for
+  [custom training guide](https://www.tensorflow.org/tutorials/distribute/custom_training)  # pylint: disable=line-too-long
-     more details on this.
+  for more details on this.
   """
 
   AUTO = 'auto'

tensorflow/python/ops/variables.py

@@ -264,7 +264,7 @@ class VariableMetaclass(type):
 
 @tf_export("Variable", v1=[])
 class Variable(six.with_metaclass(VariableMetaclass, trackable.Trackable)):
-  """See the [Variables Guide](https://tensorflow.org/beta/guide/variables).
+  """See the [variable guide](https://tensorflow.org/guide/variable).
 
   A variable maintains shared, persistent state manipulated by a program.
 
@@ -322,9 +322,9 @@ class Variable(six.with_metaclass(VariableMetaclass, trackable.Trackable)):
   >>> m.trainable_variables
   (<tf.Variable ... shape=(1,) ... numpy=array([1.], dtype=float32)>,)
 
-  This tracking then allows saving variable values to [training
+  This tracking then allows saving variable values to
-  checkpoints](https://www.tensorflow.org/beta/guide/checkpoints), or to
+  [training checkpoints](https://www.tensorflow.org/guide/checkpoint), or to
-  [SavedModels](https://www.tensorflow.org/beta/guide/saved_model) which include
+  [SavedModels](https://www.tensorflow.org/guide/saved_model) which include
   serialized TensorFlow graphs.
 
   Variables are often captured and manipulated by `tf.function`s. This works the

tensorflow/python/training/tracking/util.py

@@ -155,7 +155,7 @@ class _CheckpointRestoreCoordinatorDeleter(object):
           "load status object, e.g. "
           "tf.train.Checkpoint.restore(...).expect_partial(), to silence these "
           "warnings, or use assert_consumed() to make the check explicit. See "
-          "https://www.tensorflow.org/alpha/guide/checkpoints#loading_mechanics"
+          "https://www.tensorflow.org/guide/checkpoint#loading_mechanics"
           " for details.")
 
 
@@ -1412,7 +1412,7 @@ class CheckpointV1(tracking.AutoTrackable):
   `save_weights` and loading into a `tf.train.Checkpoint` with a `Model`
   attached (or vice versa) will not match the `Model`'s variables. See the
   [guide to training
-  checkpoints](https://www.tensorflow.org/alpha/guide/checkpoints) for
+  checkpoints](https://www.tensorflow.org/guide/checkpoint) for
   details. Prefer `tf.train.Checkpoint` over `tf.keras.Model.save_weights` for
   training checkpoints.
 
@@ -1749,7 +1749,7 @@ class Checkpoint(tracking.AutoTrackable):
   `save_weights` and loading into a `tf.train.Checkpoint` with a `Model`
   attached (or vice versa) will not match the `Model`'s variables. See the
   [guide to training
-  checkpoints](https://www.tensorflow.org/alpha/guide/checkpoints) for
+  checkpoints](https://www.tensorflow.org/guide/checkpoint) for
   details. Prefer `tf.train.Checkpoint` over `tf.keras.Model.save_weights` for
   training checkpoints.
 

tensorflow/tools/docs/generate2.py

@@ -162,7 +162,7 @@ if LooseVersion(tf.__version__) < LooseVersion('2'):
     other projects like [`tensorflow_io`](https://github.com/tensorflow/io), or
     [`tensorflow_addons`](https://github.com/tensorflow/addons). For instructions
     on how to upgrade see the
-    [Migration guide](https://www.tensorflow.org/beta/guide/migration_guide).
+    [Migration guide](https://www.tensorflow.org/guide/migrate).
     """
 else:
   tf.raw_ops.__doc__ += _raw_ops_doc