Merge pull request #1468 from rstudio/retether-3.5.0

Retether-3.5.0

Author: t-kalinowski

.tether/man/InputLayer.txt

@@ -41,6 +41,8 @@ class InputLayer(keras.src.layers.layer.Layer)
  |  ----------------------------------------------------------------------
  |  Readonly properties defined here:
  |
+ |  batch_shape
+ |
  |  dtype
  |      Alias of `layer.variable_dtype`.
  |

.tether/man/Layer.txt

@@ -173,6 +173,9 @@ class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.
  |  )
  |      Call self as a function.
  |
+ |  __delattr__(self, name)
+ |      Implement delattr(self, name).
+ |
  |  __init__(
  |    self,
  |    *,
@@ -211,7 +214,11 @@ class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.
  |              return x
  |      ```
  |
- |  add_metric(self)
+ |  add_metric(
+ |    self,
+ |    *args,
+ |    **kwargs
+ |  )
  |
  |  add_variable(
  |    self,
@@ -347,7 +354,17 @@ class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.
  |      Args:
  |          store: Dict from which the state of the model will be loaded.
  |
- |  quantize(self, mode)
+ |  quantize(
+ |    self,
+ |    mode,
+ |    type_check=True
+ |  )
+ |
+ |  quantized_build(
+ |    self,
+ |    input_shape,
+ |    mode
+ |  )
  |
  |  quantized_call(
  |    self,

.tether/man/Loss.txt

@@ -5,6 +5,17 @@ class Loss(keras.src.saving.keras_saveable.KerasSaveable)
  |
  |  Loss base class.
  |
+ |  Args:
+ |      reduction: Type of reduction to apply to the loss. In almost all cases
+ |          this should be `"sum_over_batch_size"`.
+ |          Supported options are `"sum"`, `"sum_over_batch_size"` or `None`.
+ |      name: Optional name for the loss instance.
+ |      dtype: The dtype of the loss's computations. Defaults to `None`, which
+ |          means using `keras.backend.floatx()`. `keras.backend.floatx()` is a
+ |          `"float32"` unless set to different value
+ |          (via `keras.backend.set_floatx()`). If a `keras.DTypePolicy` is
+ |          provided, then the `compute_dtype` will be utilized.
+ |
  |  To be implemented by subclasses:
  |
  |  * `call()`: Contains the logic for loss calculation using `y_true`,
@@ -54,4 +65,9 @@ class Loss(keras.src.saving.keras_saveable.KerasSaveable)
  |
  |  from_config(config)
  |
+ |  ----------------------------------------------------------------------
+ |  Readonly properties defined here:
+ |
+ |  dtype
+ |
 

.tether/man/Metric.txt

@@ -6,8 +6,12 @@ class Metric(keras.src.saving.keras_saveable.KerasSaveable)
  |  Encapsulates metric logic and state.
  |
  |  Args:
- |      name: (Optional) string name of the metric instance.
- |      dtype: (Optional) data type of the metric result.
+ |      name: Optional name for the metric instance.
+ |      dtype: The dtype of the metric's computations. Defaults to `None`, which
+ |          means using `keras.backend.floatx()`. `keras.backend.floatx()` is a
+ |          `"float32"` unless set to different value
+ |          (via `keras.backend.set_floatx()`). If a `keras.DTypePolicy` is
+ |          provided, then the `compute_dtype` will be utilized.
  |
  |  Example:
  |

.tether/man/audio_dataset_from_directory.txt

@@ -73,8 +73,9 @@ Args:
         length of the longest sequence in the batch.
     ragged: Whether to return a Ragged dataset (where each sequence has its
         own length). Defaults to `False`.
-    shuffle: Whether to shuffle the data. Defaults to `True`.
+    shuffle: Whether to shuffle the data.
         If set to `False`, sorts the data in alphanumeric order.
+        Defaults to `True`.
     seed: Optional random seed for shuffling and transformations.
     validation_split: Optional float between 0 and 1, fraction of data to
         reserve for validation.

.tether/man/callback_backup_and_restore.txt

@@ -59,11 +59,11 @@ class BackupAndRestore(keras.src.callbacks.callback.Callback)
  |        When set to an integer, the callback saves the checkpoint every
  |        `save_freq` batches. Set `save_freq=False` only if using
  |        preemption checkpointing (i.e. with `save_before_preemption=True`).
- |      delete_checkpoint: Boolean, defaults to `True`. This `BackupAndRestore`
+ |      delete_checkpoint: Boolean. This `BackupAndRestore`
  |        callback works by saving a checkpoint to back up the training state.
  |        If `delete_checkpoint=True`, the checkpoint will be deleted after
  |        training is finished. Use `False` if you'd like to keep the checkpoint
- |        for future usage.
+ |        for future usage. Defaults to `True`.
  |
  |  Method resolution order:
  |      BackupAndRestore

.tether/man/image_dataset_from_directory.txt

@@ -68,15 +68,15 @@ Args:
         (must match names of subdirectories). Used to control the order
         of the classes (otherwise alphanumerical order is used).
     color_mode: One of `"grayscale"`, `"rgb"`, `"rgba"`.
-        Defaults to `"rgb"`. Whether the images will be converted to
-        have 1, 3, or 4 channels.
+        Whether the images will be converted to
+        have 1, 3, or 4 channels. Defaults to `"rgb"`.
     batch_size: Size of the batches of data. Defaults to 32.
         If `None`, the data will not be batched
         (the dataset will yield individual samples).
     image_size: Size to resize images to after they are read from disk,
-        specified as `(height, width)`. Defaults to `(256, 256)`.
+        specified as `(height, width)`.
         Since the pipeline processes batches of images that must all have
-        the same size, this must be provided.
+        the same size, this must be provided. Defaults to `(256, 256)`.
     shuffle: Whether to shuffle the data. Defaults to `True`.
         If set to `False`, sorts the data in alphanumeric order.
     seed: Optional random seed for shuffling and transformations.
@@ -88,9 +88,10 @@ Args:
         When `subset="both"`, the utility returns a tuple of two datasets
         (the training and validation datasets respectively).
     interpolation: String, the interpolation method used when
-        resizing images. Defaults to `"bilinear"`.
+        resizing images.
         Supports `"bilinear"`, `"nearest"`, `"bicubic"`, `"area"`,
         `"lanczos3"`, `"lanczos5"`, `"gaussian"`, `"mitchellcubic"`.
+        Defaults to `"bilinear"`.
     follow_links: Whether to visit subdirectories pointed to by symlinks.
         Defaults to `False`.
     crop_to_aspect_ratio: If `True`, resize the images without aspect

.tether/man/image_smart_resize.txt

@@ -56,9 +56,9 @@ Args:
         or `(batch_size, height, width, channels)`.
     size: Tuple of `(height, width)` integer. Target size.
     interpolation: String, interpolation to use for resizing.
-        Defaults to `'bilinear'`.
-        Supports `bilinear`, `nearest`, `bicubic`,
-        `lanczos3`, `lanczos5`.
+        Supports `"bilinear"`, `"nearest"`, `"bicubic"`,
+        `"lanczos3"`, `"lanczos5"`.
+        Defaults to `"bilinear"`.
     data_format: `"channels_last"` or `"channels_first"`.
     backend_module: Backend module to use (if different from the default
         backend).
@@ -68,3 +68,4 @@ Returns:
     If the input image was a NumPy array, the output is a NumPy array,
     and if it was a backend-native tensor,
     the output is a backend-native tensor.
+

.tether/man/keras.distribution.txt

@@ -1,4 +1,8 @@
-DataParallel(device_mesh=None, devices=None)
+DataParallel(
+  device_mesh=None,
+  devices=None,
+  auto_shard_dataset=True
+)
 DeviceMesh(
   shape,
   axis_names,

.tether/man/keras.layers.txt

@@ -1247,7 +1247,11 @@ UpSampling3D(
   **kwargs
 )
 Wrapper(layer, **kwargs)
-ZeroPadding1D(padding=1, **kwargs)
+ZeroPadding1D(
+  padding=1,
+  data_format=None,
+  **kwargs
+)
 ZeroPadding2D(
   padding=(1, 1),
   data_format=None,