Merge pull request #1468 from rstudio/retether-3.5.0

Retether-3.5.0

.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,

.tether/man/keras.models.txt

@@ -18,7 +18,7 @@ save_model(
   model,
   filepath,
   overwrite=True,
-  zipped=True,
+  zipped=None,
   **kwargs
 )
 Sequential(*args, **kwargs)

.tether/man/keras.ops.txt

@@ -56,6 +56,12 @@ argpartition(
 )
 argsort(x, axis=-1)
 array(x, dtype=None)
+associative_scan(
+  f,
+  elems,
+  reverse=False,
+  axis=0
+)
 average(
   x,
   axis=None,
@@ -480,6 +486,11 @@ scatter_update(
   indices,
   updates
 )
+searchsorted(
+  sorted_sequence,
+  values,
+  side='left'
+)
 segment_max(
   data,
   segment_ids,

.tether/man/keras.optimizers.txt

@@ -124,6 +124,23 @@ Ftrl(
   **kwargs
 )
 get(identifier)
+Lamb(
+  learning_rate=0.001,
+  beta_1=0.9,
+  beta_2=0.999,
+  epsilon=1e-07,
+  weight_decay=None,
+  clipnorm=None,
+  clipvalue=None,
+  global_clipnorm=None,
+  use_ema=False,
+  ema_momentum=0.99,
+  ema_overwrite_frequency=None,
+  loss_scale_factor=None,
+  gradient_accumulation_steps=None,
+  name='lamb',
+  **kwargs
+)
 legacy: Module(keras.api.optimizers.legacy)
 Lion(
   learning_rate=0.001,

.tether/man/keras.quantizers.txt

@@ -3,7 +3,8 @@ abs_max_quantize(
   axis,
   value_range=(-127, 127),
   dtype='int8',
-  epsilon=1e-07
+  epsilon=1e-07,
+  to_numpy=False
 )
 AbsMaxQuantizer(
   axis,

.tether/man/keras.saving.txt

@@ -30,7 +30,7 @@ save_model(
   model,
   filepath,
   overwrite=True,
-  zipped=True,
+  zipped=None,
   **kwargs
 )
 save_weights(

.tether/man/keras.txt

@@ -78,6 +78,7 @@ StatelessScope(
   collect_losses=False,
   initialize_variables=True
 )
+SymbolicScope()
 tree: Module(keras.api.tree)
 utils: Module(keras.api.utils)
 Variable(

.tether/man/keras_input.txt

@@ -35,6 +35,8 @@ Args:
         be passed into the input - they will be densified with a default
         value of 0. This feature is only supported with the TensorFlow
         backend. Defaults to `False`.
+    batch_shape: Optional shape tuple (tuple of integers or `None` objects),
+        including the batch size.
     name: Optional name string for the layer.
         Should be unique in a model (do not reuse the same name twice).
         It will be autogenerated if it isn't provided.

.tether/man/keras_model.txt

@@ -231,7 +231,11 @@ class Model(keras.src.backend.tensorflow.trainer.TensorFlowTrainer, keras.src.tr
  |              there is a mismatch in the number of weights, or a mismatch in
  |              the shape of the weights.
  |
- |  quantize(self, mode)
+ |  quantize(
+ |    self,
+ |    mode,
+ |    **kwargs
+ |  )
  |      Quantize the weights of the model.
  |
  |      Note that the model must be built first before calling this method.
@@ -246,7 +250,7 @@ class Model(keras.src.backend.tensorflow.trainer.TensorFlowTrainer, keras.src.tr
  |    self,
  |    filepath,
  |    overwrite=True,
- |    zipped=True,
+ |    zipped=None,
  |    **kwargs
  |  )
  |      Saves a model as a `.keras` file.
@@ -260,7 +264,9 @@ class Model(keras.src.backend.tensorflow.trainer.TensorFlowTrainer, keras.src.tr
  |              the target location, or instead ask the user via
  |              an interactive prompt.
  |          zipped: Whether to save the model as a zipped `.keras`
- |              archive (default), or as an unzipped directory.
+ |              archive (default when saving locally), or as an
+ |              unzipped directory (default when saving on the
+ |              Hugging Face Hub).
  |
  |      Example:
  |

.tether/man/layer_conv_2d.txt

@@ -6,10 +6,10 @@ class Conv2D(keras.src.layers.convolutional.base_conv.BaseConv)
  |  2D convolution layer.
  |
  |  This layer creates a convolution kernel that is convolved with the layer
- |  input over a single spatial (or temporal) dimension to produce a tensor of
- |  outputs. If `use_bias` is True, a bias vector is created and added to the
- |  outputs. Finally, if `activation` is not `None`, it is applied to the
- |  outputs as well.
+ |  input over a 2D spatial (or temporal) dimension (height and width) to
+ |  produce a tensor of outputs. If `use_bias` is True, a bias vector is created
+ |  and added to the outputs. Finally, if `activation` is not `None`, it is
+ |  applied to the outputs as well.
  |
  |  Args:
  |      filters: int, the dimension of the output space (the number of filters

.tether/man/layer_conv_3d.txt

@@ -6,10 +6,10 @@ class Conv3D(keras.src.layers.convolutional.base_conv.BaseConv)
  |  3D convolution layer.
  |
  |  This layer creates a convolution kernel that is convolved with the layer
- |  input over a single spatial (or temporal) dimension to produce a tensor of
- |  outputs. If `use_bias` is True, a bias vector is created and added to the
- |  outputs. Finally, if `activation` is not `None`, it is applied to the
- |  outputs as well.
+ |  input over a 3D spatial (or temporal) dimension (width,height and depth) to
+ |  produce a tensor of outputs. If `use_bias` is True, a bias vector is created
+ |  and added to the outputs. Finally, if `activation` is not `None`, it is
+ |  applied to the outputs as well.
  |
  |  Args:
  |      filters: int, the dimension of the output space (the number of filters

.tether/man/layer_dense.txt

@@ -119,20 +119,18 @@ class Dense(keras.src.layers.layer.Layer)
  |      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,
- |    inputs,
- |    training=None
- |  )
- |
  |  save_own_variables(self, store)
  |      Saves the state of the layer.
  |