Fix URLs to tensorflow.org guides.

In the process of renaming `keras` to `tf_keras`, the URLs to tensorflow.org were modified too. Revert them so that they actually point to the guides. PiperOrigin-RevId: 592303600
keras-team · Dec 19, 2023 · f458edc · f458edc
1 parent 3c9a51b
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diff --git a/tf_keras/backend_config.py b/tf_keras/backend_config.py
@@ -88,7 +88,7 @@ def set_floatx(value):
     is using a mix of float16 and float32, can be used by calling
     `tf.keras.mixed_precision.set_global_policy('mixed_float16')`. See the
     [mixed precision guide](
-      https://www.tensorflow.org/guide/tf_keras/mixed_precision) for details.
+      https://www.tensorflow.org/guide/keras/mixed_precision) for details.
 
     Args:
         value: String; `'float16'`, `'float32'`, or `'float64'`.

diff --git a/tf_keras/engine/base_layer.py b/tf_keras/engine/base_layer.py
@@ -190,11 +190,11 @@ class Layer(tf.Module, version_utils.LayerVersionSelector):
       Two reserved keyword arguments you can optionally use in `call()` are:
         - `training` (boolean, whether the call is in inference mode or training
           mode). See more details in [the layer/model subclassing guide](
-          https://www.tensorflow.org/guide/tf_keras/custom_layers_and_models#privileged_training_argument_in_the_call_method)
+          https://www.tensorflow.org/guide/keras/custom_layers_and_models#privileged_training_argument_in_the_call_method)
         - `mask` (boolean tensor encoding masked timesteps in the input, used
           in RNN layers). See more details in
           [the layer/model subclassing guide](
-          https://www.tensorflow.org/guide/tf_keras/custom_layers_and_models#privileged_mask_argument_in_the_call_method)
+          https://www.tensorflow.org/guide/keras/custom_layers_and_models#privileged_mask_argument_in_the_call_method)
       A typical signature for this method is `call(self, inputs)`, and user
       could optionally add `training` and `mask` if the layer need them. `*args`
       and `**kwargs` is only useful for future extension when more input
@@ -300,7 +300,7 @@ def call(self, inputs):
 
     For more information about creating layers, see the guide
     [Making new Layers and Models via subclassing](
-      https://www.tensorflow.org/guide/tf_keras/custom_layers_and_models)
+      https://www.tensorflow.org/guide/keras/custom_layers_and_models)
     """
 
     @tf.__internal__.tracking.no_automatic_dependency_tracking

diff --git a/tf_keras/engine/training.py b/tf_keras/engine/training.py
@@ -606,7 +606,7 @@ def call(self, inputs, training=None, mask=None):
               run the `Network` in training mode or inference mode.
             mask: A mask or list of masks. A mask can be either a boolean tensor
               or None (no mask). For more details, check the guide
-              [here](https://www.tensorflow.org/guide/tf_keras/masking_and_padding).
+              [here](https://www.tensorflow.org/guide/keras/masking_and_padding).
 
         Returns:
             A tensor if there is a single output, or
@@ -1121,7 +1121,7 @@ def train_step(self, data):
         This method can be overridden to support custom training logic.
         For concrete examples of how to override this method see
         [Customizing what happens in fit](
-        https://www.tensorflow.org/guide/tf_keras/customizing_what_happens_in_fit).
+        https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit).
         This method is called by `Model.make_train_function`.
 
         This method should contain the mathematical logic for one step of

diff --git a/tf_keras/layers/core/lambda_layer.py b/tf_keras/layers/core/lambda_layer.py
@@ -42,7 +42,7 @@ class Lambda(Layer):
     and Functional API models. `Lambda` layers are best suited for simple
     operations or quick experimentation. For more advanced use cases, follow
     [this guide](
-    https://www.tensorflow.org/guide/tf_keras/custom_layers_and_models)
+    https://www.tensorflow.org/guide/keras/custom_layers_and_models)
     for subclassing `tf.keras.layers.Layer`.
 
     WARNING: `tf.keras.layers.Lambda` layers have (de)serialization limitations!

diff --git a/tf_keras/layers/core/masking.py b/tf_keras/layers/core/masking.py
@@ -60,7 +60,7 @@ class Masking(Layer):
     ```
 
     See [the masking and padding guide](
-      https://www.tensorflow.org/guide/tf_keras/masking_and_padding)
+      https://www.tensorflow.org/guide/keras/masking_and_padding)
     for more details.
     """
 

diff --git a/tf_keras/layers/preprocessing/category_encoding.py b/tf_keras/layers/preprocessing/category_encoding.py
@@ -46,7 +46,7 @@ class CategoryEncoding(base_layer.Layer):
     use `tf.keras.layers.IntegerLookup` instead.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Examples:
 

diff --git a/tf_keras/layers/preprocessing/discretization.py b/tf_keras/layers/preprocessing/discretization.py
@@ -145,7 +145,7 @@ class Discretization(base_preprocessing_layer.PreprocessingLayer):
     element was placed in.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Input shape:
       Any `tf.Tensor` or `tf.RaggedTensor` of dimension 2 or higher.

diff --git a/tf_keras/layers/preprocessing/hashed_crossing.py b/tf_keras/layers/preprocessing/hashed_crossing.py
@@ -46,7 +46,7 @@ class HashedCrossing(base_layer.Layer):
     `()`.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Args:
         num_bins: Number of hash bins.

diff --git a/tf_keras/layers/preprocessing/hashing.py b/tf_keras/layers/preprocessing/hashing.py
@@ -53,7 +53,7 @@ class Hashing(base_layer.Layer):
     the `salt` value serving as additional input to the hash function.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     **Example (FarmHash64)**
 

diff --git a/tf_keras/layers/preprocessing/image_preprocessing.py b/tf_keras/layers/preprocessing/image_preprocessing.py
@@ -58,7 +58,7 @@ class Resizing(base_layer.Layer):
     size.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Args:
         height: Integer, the height of the output shape.
@@ -162,7 +162,7 @@ class CenterCrop(base_layer.Layer):
     By default, the layer will output floats.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Input shape:
         3D (unbatched) or 4D (batched) tensor with shape:
@@ -247,7 +247,7 @@ class RandomCrop(base_layer.BaseRandomLayer):
     floats.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Input shape:
         3D (unbatched) or 4D (batched) tensor with shape:
@@ -340,7 +340,7 @@ class Rescaling(base_layer.Layer):
     floats.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Input shape:
         Arbitrary.
@@ -399,7 +399,7 @@ class RandomFlip(base_layer.BaseRandomLayer):
     By default, the layer will output floats.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Input shape:
         3D (unbatched) or 4D (batched) tensor with shape:
@@ -500,7 +500,7 @@ class RandomTranslation(base_layer.BaseRandomLayer):
     floats.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Args:
       height_factor: a float represented as fraction of value, or a tuple of
@@ -872,7 +872,7 @@ class RandomRotation(base_layer.BaseRandomLayer):
     By default, the layer will output floats.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Input shape:
         3D (unbatched) or 4D (batched) tensor with shape:
@@ -1014,7 +1014,7 @@ class RandomZoom(base_layer.BaseRandomLayer):
     By default, the layer will output floats.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Args:
         height_factor: a float represented as fraction of value,
@@ -1254,7 +1254,7 @@ class RandomContrast(base_layer.BaseRandomLayer):
     range of RGB colors.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Input shape:
         3D (unbatched) or 4D (batched) tensor with shape:
@@ -1340,7 +1340,7 @@ class RandomBrightness(base_layer.BaseRandomLayer):
     will be apply to each the images in the batch.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Args:
         factor: Float or a list/tuple of 2 floats between -1.0 and 1.0. The
@@ -1497,7 +1497,7 @@ class RandomHeight(base_layer.BaseRandomLayer):
     By default, this layer is inactive during inference.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Args:
         factor: A positive float (fraction of original height),
@@ -1616,7 +1616,7 @@ class RandomWidth(base_layer.BaseRandomLayer):
     By default, this layer is inactive during inference.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Args:
         factor: A positive float (fraction of original width),

diff --git a/tf_keras/layers/preprocessing/integer_lookup.py b/tf_keras/layers/preprocessing/integer_lookup.py
@@ -63,7 +63,7 @@ class IntegerLookup(index_lookup.IndexLookup):
     begin with OOV indices and instances of the mask token will be dropped.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Args:
       max_tokens: Maximum size of the vocabulary for this layer. This should

diff --git a/tf_keras/layers/preprocessing/normalization.py b/tf_keras/layers/preprocessing/normalization.py
@@ -44,7 +44,7 @@ class Normalization(base_preprocessing_layer.PreprocessingLayer):
     be called before `fit()`, `evaluate()`, or `predict()`.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Args:
         axis: Integer, tuple of integers, or None. The axis or axes that should

diff --git a/tf_keras/layers/preprocessing/string_lookup.py b/tf_keras/layers/preprocessing/string_lookup.py
@@ -60,7 +60,7 @@ class StringLookup(index_lookup.IndexLookup):
     OOV indices and instances of the mask token will be dropped.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Args:
       max_tokens: Maximum size of the vocabulary for this layer. This should

diff --git a/tf_keras/layers/preprocessing/text_vectorization.py b/tf_keras/layers/preprocessing/text_vectorization.py
@@ -101,7 +101,7 @@ class TextVectorization(base_preprocessing_layer.PreprocessingLayer):
        site natively compatible with `tf.strings.split()`.
 
     For an overview and full list of preprocessing layers, see the preprocessing
-    [guide](https://www.tensorflow.org/guide/tf_keras/preprocessing_layers).
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
 
     Args:
       max_tokens: Maximum size of the vocabulary for this layer. This should

diff --git a/tf_keras/layers/rnn/abstract_rnn_cell.py b/tf_keras/layers/rnn/abstract_rnn_cell.py
@@ -27,7 +27,7 @@ class AbstractRNNCell(base_layer.Layer):
     """Abstract object representing an RNN cell.
 
     See
-    [the TF-Keras RNN API guide](https://www.tensorflow.org/guide/tf_keras/rnn)
+    [the TF-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.

diff --git a/tf_keras/layers/rnn/base_rnn.py b/tf_keras/layers/rnn/base_rnn.py
@@ -40,7 +40,7 @@ class RNN(base_layer.Layer):
     """Base class for recurrent layers.
 
     See
-    [the TF-Keras RNN API guide](https://www.tensorflow.org/guide/tf_keras/rnn)
+    [the TF-Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
     for details about the usage of RNN API.
 
     Args:

diff --git a/tf_keras/layers/rnn/gru.py b/tf_keras/layers/rnn/gru.py
@@ -47,7 +47,7 @@ class GRUCell(DropoutRNNCellMixin, base_layer.BaseRandomLayer):
     """Cell class for the GRU layer.
 
     See
-    [the TF-Keras RNN API guide](https://www.tensorflow.org/guide/tf_keras/rnn)
+    [the TF-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
@@ -397,7 +397,7 @@ class GRU(DropoutRNNCellMixin, RNN, base_layer.BaseRandomLayer):
     """Gated Recurrent Unit - Cho et al. 2014.
 
     See
-    [the TF-Keras RNN API guide](https://www.tensorflow.org/guide/tf_keras/rnn)
+    [the TF-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

diff --git a/tf_keras/layers/rnn/lstm.py b/tf_keras/layers/rnn/lstm.py
@@ -47,7 +47,7 @@ class LSTMCell(DropoutRNNCellMixin, base_layer.BaseRandomLayer):
     """Cell class for the LSTM layer.
 
     See
-    [the TF-Keras RNN API guide](https://www.tensorflow.org/guide/tf_keras/rnn)
+    [the TF-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
@@ -385,7 +385,7 @@ class LSTM(DropoutRNNCellMixin, RNN, base_layer.BaseRandomLayer):
     """Long Short-Term Memory layer - Hochreiter 1997.
 
     See
-    [the TF-Keras RNN API guide](https://www.tensorflow.org/guide/tf_keras/rnn)
+    [the TF-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

diff --git a/tf_keras/layers/rnn/simple_rnn.py b/tf_keras/layers/rnn/simple_rnn.py
@@ -39,7 +39,7 @@ class SimpleRNNCell(DropoutRNNCellMixin, base_layer.BaseRandomLayer):
     """Cell class for SimpleRNN.
 
     See
-    [the TF-Keras RNN API guide](https://www.tensorflow.org/guide/tf_keras/rnn)
+    [the TF-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
@@ -256,7 +256,7 @@ class SimpleRNN(RNN):
     """Fully-connected RNN where the output is to be fed back to input.
 
     See
-    [the TF-Keras RNN API guide](https://www.tensorflow.org/guide/tf_keras/rnn)
+    [the TF-Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
     for details about the usage of RNN API.
 
     Args:

diff --git a/tf_keras/legacy_tf_layers/convolutional.py b/tf_keras/legacy_tf_layers/convolutional.py
@@ -263,7 +263,7 @@ def conv1d(
     After:
 
     To migrate code using TF1 functional layers use the [Keras Functional API]
-    (https://www.tensorflow.org/guide/tf_keras/functional):
+    (https://www.tensorflow.org/guide/keras/functional):
 
     ```python
      x = tf.keras.Input((28, 28, 1))
@@ -551,7 +551,7 @@ def conv2d(
     After:
 
     To migrate code using TF1 functional layers use the [Keras Functional API]
-    (https://www.tensorflow.org/guide/tf_keras/functional):
+    (https://www.tensorflow.org/guide/keras/functional):
 
     ```python
      x = tf.keras.Input((28, 28, 1))
@@ -841,7 +841,7 @@ def conv3d(
     After:
 
     To migrate code using TF1 functional layers use the [Keras Functional API]
-    (https://www.tensorflow.org/guide/tf_keras/functional):
+    (https://www.tensorflow.org/guide/keras/functional):
 
     ```python
      x = tf.keras.Input((28, 28, 1))
@@ -1304,7 +1304,7 @@ def separable_conv1d(
     After:
 
     To migrate code using TF1 functional layers use the [Keras Functional API]
-    (https://www.tensorflow.org/guide/tf_keras/functional):
+    (https://www.tensorflow.org/guide/keras/functional):
 
     ```python
      x = tf.keras.Input((28, 28, 1))
@@ -1475,7 +1475,7 @@ def separable_conv2d(
     After:
 
     To migrate code using TF1 functional layers use the [Keras Functional API]
-    (https://www.tensorflow.org/guide/tf_keras/functional):
+    (https://www.tensorflow.org/guide/keras/functional):
 
     ```python
      x = tf.keras.Input((28, 28, 1))
@@ -1746,7 +1746,7 @@ def conv2d_transpose(
     After:
 
     To migrate code using TF1 functional layers use the [Keras Functional API]
-    (https://www.tensorflow.org/guide/tf_keras/functional):
+    (https://www.tensorflow.org/guide/keras/functional):
 
     ```python
      x = tf.keras.Input((28, 28, 1))
@@ -2002,7 +2002,7 @@ def conv3d_transpose(
     After:
 
     To migrate code using TF1 functional layers use the [Keras Functional API]
-    (https://www.tensorflow.org/guide/tf_keras/functional):
+    (https://www.tensorflow.org/guide/keras/functional):
 
     ```python
      x = tf.keras.Input((28, 28, 1))