Merge pull request #1429 from rstudio/retether-3.2.1

Retether 3.2.1

Author: t-kalinowski

.tether/man/InputLayer.txt

@@ -42,9 +42,4 @@ class InputLayer(keras.src.layers.layer.Layer)
  |  dtype
  |      Alias of `layer.variable_dtype`.
  |
- |  ----------------------------------------------------------------------
- |  Data and other attributes defined here:
- |
- |  __annotations__ = {}
- |
 

.tether/man/Layer.txt

@@ -218,6 +218,7 @@ class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.
  |    initializer,
  |    dtype=None,
  |    trainable=True,
+ |    autocast=True,
  |    regularizer=None,
  |    constraint=None,
  |    name=None
@@ -232,36 +233,41 @@ class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.
  |    initializer=None,
  |    dtype=None,
  |    trainable=True,
+ |    autocast=True,
  |    regularizer=None,
  |    constraint=None,
+ |    aggregation='mean',
  |    name=None
  |  )
  |      Add a weight variable to the layer.
  |
  |      Args:
- |          shape: Shape tuple for the variable.
- |              Must be fully-defined (no `None` entries).
- |              Defaults to `()` (scalar) if unspecified.
- |          initializer: Initializer object to use to
- |              populate the initial variable value,
- |              or string name of a built-in initializer
- |              (e.g. `"random_normal"`). If unspecified,
- |              defaults to `"glorot_uniform"`
- |              for floating-point variables and to `"zeros"`
+ |          shape: Shape tuple for the variable. Must be fully-defined
+ |              (no `None` entries). Defaults to `()` (scalar) if unspecified.
+ |          initializer: Initializer object to use to populate the initial
+ |              variable value, or string name of a built-in initializer
+ |              (e.g. `"random_normal"`). If unspecified, defaults to
+ |              `"glorot_uniform"` for floating-point variables and to `"zeros"`
  |              for all other types (e.g. int, bool).
- |          dtype: Dtype of the variable to create,
- |              e.g. `"float32"`. If unspecified,
- |              defaults to the layer's
- |              variable dtype (which itself defaults to
- |              `"float32"` if unspecified).
- |          trainable: Boolean, whether the variable should
- |              be trainable via backprop or whether its
- |              updates are managed manually.
- |          constraint: Contrainst object to call on the
- |              variable after any optimizer update,
- |              or string name of a built-in constraint.
- |          name: String name of the variable. Useful
- |              for debugging purposes.
+ |          dtype: Dtype of the variable to create, e.g. `"float32"`. If
+ |              unspecified, defaults to the layer's variable dtype
+ |              (which itself defaults to `"float32"` if unspecified).
+ |          trainable: Boolean, whether the variable should be trainable via
+ |              backprop or whether its updates are managed manually. Defaults
+ |              to `True`.
+ |          autocast: Boolean, whether to autocast layers variables when
+ |              accessing them. Defaults to `True`.
+ |          regularizer: Regularizer object to call to apply penalty on the
+ |              weight. These penalties are summed into the loss function
+ |              during optimization. Defaults to `None`.
+ |          constraint: Contrainst object to call on the variable after any
+ |              optimizer update, or string name of a built-in constraint.
+ |              Defaults to `None`.
+ |          aggregation: String, one of `'mean'`, `'sum'`,
+ |              `'only_first_replica'`. Annotates the variable with the type
+ |              of multi-replica aggregation to be used for this variable
+ |              when writing custom data parallel training loops.
+ |          name: String name of the variable. Useful for debugging purposes.
  |
  |  build(self, input_shape)
  |
@@ -490,9 +496,4 @@ class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.
  |  trainable
  |      Settable boolean, whether this layer should be trainable or not.
  |
- |  ----------------------------------------------------------------------
- |  Data and other attributes defined here:
- |
- |  __annotations__ = {}
- |
 

.tether/man/Metric.txt

@@ -9,7 +9,7 @@ class Metric(builtins.object)
  |      name: (Optional) string name of the metric instance.
  |      dtype: (Optional) data type of the metric result.
  |
- |  Standalone usage:
+ |  Example:
  |
  |  ```python
  |  m = SomeMetric(...)
@@ -111,6 +111,7 @@ class Metric(builtins.object)
  |    shape,
  |    initializer,
  |    dtype=None,
+ |    aggregation='sum',
  |    name=None
  |  )
  |

.tether/man/activation_hard_sigmoid.txt

@@ -5,9 +5,9 @@ Hard sigmoid activation function.
 
 The hard sigmoid activation is defined as:
 
-- `0` if `if x < -2.5`
-- `1` if `x > 2.5`
-- `0.2 * x + 0.5` if `-2.5 <= x <= 2.5`
+- `0` if `if x <= -3`
+- `1` if `x >= 3`
+- `(x/6) + 0.5` if `-3 < x < 3`
 
 It's a faster, piecewise linear approximation
 of the sigmoid activation.
@@ -18,3 +18,4 @@ Args:
 Reference:
 
 - [Wikipedia "Hard sigmoid"](https://en.wikipedia.org/wiki/Hard_sigmoid)
+

.tether/man/callback_remote_monitor.txt

@@ -18,7 +18,7 @@ class RemoteMonitor(keras.src.callbacks.callback.Callback)
  |      path: String; path relative to `root` to which the events will be sent.
  |      field: String; JSON field under which the data will be stored.
  |          The field is used only if the payload is sent within a form
- |          (i.e. send_as_json is set to False).
+ |          (i.e. when `send_as_json=False`).
  |      headers: Dictionary; optional custom HTTP headers.
  |      send_as_json: Boolean; whether the request should be
  |          sent as `"application/json"`.
@@ -58,3 +58,4 @@ class RemoteMonitor(keras.src.callbacks.callback.Callback)
  |            the `Model`'s metrics are returned. Example:
  |            `{'loss': 0.2, 'accuracy': 0.7}`.
  |
+

.tether/man/callback_tensorboard.txt

@@ -77,8 +77,6 @@ class TensorBoard(keras.src.callbacks.callback.Callback)
  |
  |  Examples:
  |
- |  Basic usage:
- |
  |  ```python
  |  tensorboard_callback = keras.callbacks.TensorBoard(log_dir="./logs")
  |  model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])
@@ -266,3 +264,4 @@ class TensorBoard(keras.src.callbacks.callback.Callback)
  |
  |  summary
  |
+

.tether/man/export_savedmodel.keras.src.models.model.Model.txt

@@ -5,10 +5,10 @@ keras.Model.export(
   format='tf_saved_model'
 )
 __doc__
-[TF backend only]* Create a TF SavedModel artifact for inference
-(e.g. via TF-Serving).
+Create a TF SavedModel artifact for inference.
 
-**Note:** This can currently only be used with the TF backend.
+**Note:** This can currently only be used with
+the TensorFlow or JAX backends.
 
 This method lets you export a model to a lightweight SavedModel artifact
 that contains the model's forward pass only (its `call()` method)

.tether/man/image_from_array.txt

@@ -8,7 +8,7 @@ keras.utils.array_to_img(
 __doc__
 Converts a 3D NumPy array to a PIL Image instance.
 
-Usage:
+Example:
 
 ```python
 from PIL import Image
@@ -30,3 +30,4 @@ Args:
 
 Returns:
     A PIL Image instance.
+

.tether/man/image_load.txt

@@ -9,7 +9,7 @@ keras.utils.load_img(
 __doc__
 Loads an image into PIL format.
 
-Usage:
+Example:
 
 ```python
 image = keras.utils.load_img(image_path)
@@ -37,3 +37,4 @@ Args:
 
 Returns:
     A PIL Image instance.
+

.tether/man/image_to_array.txt

@@ -7,7 +7,7 @@ keras.utils.img_to_array(
 __doc__
 Converts a PIL Image instance to a NumPy array.
 
-Usage:
+Example:
 
 ```python
 from PIL import Image
@@ -28,3 +28,4 @@ Args:
 
 Returns:
     A 3D NumPy array.
+