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Docs: Updated links in docstrings for TF2

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Billy Lamberta 2019-10-04 10:32:59 -07:00 committed by TensorFlower Gardener
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2
tensorflow/api_template_v1.__init__.py
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@ -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.*"`

2
tensorflow/examples/tutorials/deepdream/README.md
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@ -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)

2
tensorflow/examples/tutorials/deepdream/deepdream.ipynb
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@ -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)"
]
}

6
tensorflow/python/autograph/g3doc/reference/index.md
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@ -16,7 +16,7 @@ graph.
For more information on AutoGraph, see the following articles:
* [AutoGraph tutorial](https://www.tensorflow.org/alpha/beta/autograph)
* [Eager tutorial](https://www.tensorflow.org/alpha/guide/eager)
* [TensorFlow 2.0 Alpha](https://www.tensorflow.org/alpha)
* [AutoGraph guide](https://www.tensorflow.org/guide/function)
* [tf.function tutorial](https://www.tensorflow.org/tutorials/customization/performance)
* [Eager guide](https://www.tensorflow.org/guide/eager)
* [AutoGraph blog post](https://medium.com/tensorflow/autograph-converts-python-into-tensorflow-graphs-b2a871f87ec7)

2
tensorflow/python/autograph/impl/api.py
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@ -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).

14
tensorflow/python/distribute/distribute_lib.py
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@ -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/alpha/guide/distribute_strategy).
[TensorFlow v2.x](https://www.tensorflow.org/guide/distributed_training),
[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

4
tensorflow/python/eager/def_function.py
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@ -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,

2
tensorflow/python/keras/engine/network.py
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@ -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:

2
tensorflow/python/keras/layers/core.py
View File

@ -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.
"""

8
tensorflow/python/keras/layers/recurrent.py
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@ -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:

8
tensorflow/python/keras/layers/recurrent_v2.py
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@ -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

18
tensorflow/python/keras/losses.py
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@ -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.
"""

4
tensorflow/python/keras/saving/saved_model/README.md
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@ -15,8 +15,8 @@ object.
Please see the links below for more details:
- [Saved Model Guide](https://www.tensorflow.org/beta/guide/saved_model)
- [Checkpoint Guide](https://www.tensorflow.org/beta/guide/checkpoints)
- [Saved Model Guide](https://www.tensorflow.org/guide/saved_model)
- [Checkpoint Guide](https://www.tensorflow.org/guide/checkpoint)
## Keras SavedModel implementation

6
tensorflow/python/ops/losses/loss_reduction.py
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@ -48,9 +48,9 @@ class ReductionV2(object):
(1. / global_batch_size)
```
Please see
https://www.tensorflow.org/alpha/tutorials/distribute/training_loops for
more details on this.
Please see the
[custom training guide](https://www.tensorflow.org/tutorials/distribute/custom_training) # pylint: disable=line-too-long
for more details on this.
"""
AUTO = 'auto'

8
tensorflow/python/ops/variables.py
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@ -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
checkpoints](https://www.tensorflow.org/beta/guide/checkpoints), or to
[SavedModels](https://www.tensorflow.org/beta/guide/saved_model) which include
This tracking then allows saving variable values to
[training checkpoints](https://www.tensorflow.org/guide/checkpoint), or to
[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

6
tensorflow/python/training/tracking/util.py
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@ -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.

2
tensorflow/tools/docs/generate2.py
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@ -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