Retether Keras to v3.11.3

.tether/man/Layer.txt

@@ -1,6 +1,6 @@
 Help on class Layer in module keras.src.layers.layer:
 
-class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.Operation, keras.src.saving.keras_saveable.KerasSaveable)
+class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.Operation)
  |  Layer(*args, **kwargs)
  |
  |  This is the class from which all layers inherit.
@@ -243,6 +243,7 @@ class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.
  |    regularizer=None,
  |    constraint=None,
  |    aggregation='none',
+ |    overwrite_with_gradient=False,
  |    name=None
  |  )
  |      Add a weight variable to the layer.
@@ -274,6 +275,9 @@ class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.
  |              the type of multi-replica aggregation to be used for this
  |              variable when writing custom data parallel training loops.
  |              Defaults to `"none"`.
+ |          overwrite_with_gradient: Boolean, whether to overwrite the variable
+ |              with the computed gradient. This is useful for float8 training.
+ |              Defaults to `False`.
  |          name: String name of the variable. Useful for debugging purposes.
  |
  |  build(self, input_shape)
@@ -448,6 +452,12 @@ class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.
  |          ref_var.assign(value)
  |      ```
  |
+ |  symbolic_call(
+ |    self,
+ |    *args,
+ |    **kwargs
+ |  )
+ |
  |  ----------------------------------------------------------------------
  |  Static methods defined here:
  |

.tether/man/activation_elu.txt

@@ -19,6 +19,7 @@ and the information that is propagated to the next layer.
 
 Args:
     x: Input tensor.
+    alpha: A scalar, slope of positive section. Defaults to `1.0`.
 
 Reference:
 

.tether/man/activation_sparse_sigmoid.txt

@@ -0,0 +1,18 @@
+__signature__
+keras.activations.sparse_sigmoid(x)
+__doc__
+Sparse sigmoid activation function.
+
+It is defined as
+
+`f(x) = 0` for `x <= -1`,
+`f(x) = 0.5 * (x + 1)` for `-1 < x < 1`,
+`f(x) = 1` for `x >= 1`.
+
+Args:
+    x: Input tensor.
+
+Reference:
+
+- [M. Blondel, A. F. T. Martins, V. Niculae, 2019](https://arxiv.org/pdf/1901.02324)
+

.tether/man/callback_early_stopping.txt

@@ -1,6 +1,6 @@
 Help on class EarlyStopping in module keras.src.callbacks.early_stopping:
 
-class EarlyStopping(keras.src.callbacks.callback.Callback)
+class EarlyStopping(keras.src.callbacks.monitor_callback.MonitorCallback)
  |  EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto', baseline=None, restore_best_weights=False, start_from_epoch=0)
  |
  |  Stop training when a monitored metric has stopped improving.
@@ -60,6 +60,7 @@ class EarlyStopping(keras.src.callbacks.callback.Callback)
  |
  |  Method resolution order:
  |      EarlyStopping
+ |      keras.src.callbacks.monitor_callback.MonitorCallback
  |      keras.src.callbacks.callback.Callback
  |      builtins.object
  |

.tether/man/callback_lambda.txt

@@ -13,8 +13,8 @@ class LambdaCallback(keras.src.callbacks.callback.Callback)
  |        `epoch`, `logs`
  |      - `on_train_begin` and `on_train_end` expect one positional argument:
  |        `logs`
- |      - `on_train_batch_begin` and `on_train_batch_end` expect two positional
- |        arguments: `batch`, `logs`
+ |      - `on_train_batch_begin` and `on_train_batch_end` expect a positional
+ |        argument `batch` and a keyword argument `logs`
  |      - See `Callback` class definition for the full list of functions and their
  |        expected arguments.
  |
@@ -79,3 +79,4 @@ class LambdaCallback(keras.src.callbacks.callback.Callback)
  |  )
  |      Initialize self.  See help(type(self)) for accurate signature.
  |
+

.tether/man/callback_model_checkpoint.txt

@@ -1,6 +1,6 @@
 Help on class ModelCheckpoint in module keras.src.callbacks.model_checkpoint:
 
-class ModelCheckpoint(keras.src.callbacks.callback.Callback)
+class ModelCheckpoint(keras.src.callbacks.monitor_callback.MonitorCallback)
  |  ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', save_freq='epoch', initial_value_threshold=None)
  |
  |  Callback to save the Keras model or model weights at some frequency.
@@ -95,9 +95,8 @@ class ModelCheckpoint(keras.src.callbacks.callback.Callback)
  |          decision to overwrite the current save file is made based on either
  |          the maximization or the minimization of the monitored quantity.
  |          For `val_acc`, this should be `"max"`, for `val_loss` this should be
- |          `"min"`, etc. In `"auto"` mode, the mode is set to `"max"` if the
- |          quantities monitored are `"acc"` or start with `"fmeasure"` and are
- |          set to `"min"` for the rest of the quantities.
+ |          `"min"`, etc. In `"auto"` mode, the direction is automatically
+ |          inferred from the name of the monitored quantity.
  |      save_weights_only: if `True`, then only the model's weights will be
  |          saved (`model.save_weights(filepath)`), else the full model is
  |          saved (`model.save(filepath)`).
@@ -116,6 +115,7 @@ class ModelCheckpoint(keras.src.callbacks.callback.Callback)
  |
  |  Method resolution order:
  |      ModelCheckpoint
+ |      keras.src.callbacks.monitor_callback.MonitorCallback
  |      keras.src.callbacks.callback.Callback
  |      builtins.object
  |

.tether/man/callback_reduce_lr_on_plateau.txt

@@ -1,6 +1,6 @@
 Help on class ReduceLROnPlateau in module keras.src.callbacks.reduce_lr_on_plateau:
 
-class ReduceLROnPlateau(keras.src.callbacks.callback.Callback)
+class ReduceLROnPlateau(keras.src.callbacks.monitor_callback.MonitorCallback)
  |  ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=0, mode='auto', min_delta=0.0001, cooldown=0, min_lr=0.0, **kwargs)
  |
  |  Reduce learning rate when a metric has stopped improving.
@@ -39,6 +39,7 @@ class ReduceLROnPlateau(keras.src.callbacks.callback.Callback)
  |
  |  Method resolution order:
  |      ReduceLROnPlateau
+ |      keras.src.callbacks.monitor_callback.MonitorCallback
  |      keras.src.callbacks.callback.Callback
  |      builtins.object
  |

.tether/man/config_is_nnx_enabled.txt

@@ -0,0 +1,9 @@
+__signature__
+keras.config.is_nnx_enabled()
+__doc__
+Checks whether NNX specific features are enabled for the JAX backend.
+
+Returns:
+    bool: `True` if NNX backend features are enabled, `False` otherwise.
+    Defaults to `False`.
+

.tether/man/config_max_epochs.txt

@@ -0,0 +1,13 @@
+__signature__
+keras.config.max_epochs()
+__doc__
+Get the maximum number of epochs for any call to fit.
+
+Retrieves the limit on the number of epochs set by
+`keras.config.set_max_epochs` or the `KERAS_MAX_EPOCHS` environment
+variable.
+
+Returns:
+    The integer limit on the number of epochs or `None`, if no limit has
+    been set.
+

.tether/man/config_max_steps_per_epoch.txt

@@ -0,0 +1,13 @@
+__signature__
+keras.config.max_steps_per_epoch()
+__doc__
+Get the maximum number of steps for any call to fit/evaluate/predict.
+
+Retrieves the limit on the number of epochs set by
+`keras.config.set_max_steps_per_epoch` or the `KERAS_MAX_STEPS_PER_EPOCH`
+environment variable.
+
+Args:
+    max_epochs: The integer limit on the number of epochs or `None`. If
+        `None`, no limit is applied.
+

.tether/man/config_set_max_epochs.txt

@@ -0,0 +1,13 @@
+__signature__
+keras.config.set_max_epochs(max_epochs)
+__doc__
+Limit the maximum number of epochs for any call to fit.
+
+This will cap the number of epochs for any training run using `model.fit()`.
+This is purely for debugging, and can also be set via the `KERAS_MAX_EPOCHS`
+environment variable to quickly run a script without modifying its source.
+
+Args:
+    max_epochs: The integer limit on the number of epochs or `None`. If
+        `None`, no limit is applied.
+

.tether/man/config_set_max_steps_per_epoch.txt

@@ -0,0 +1,14 @@
+__signature__
+keras.config.set_max_steps_per_epoch(max_steps_per_epoch)
+__doc__
+Limit the maximum number of steps for any call to fit/evaluate/predict.
+
+This will cap the number of steps for single epoch of a call to `fit()`,
+`evaluate()`, or `predict()`. This is purely for debugging, and can also be
+set via the `KERAS_MAX_STEPS_PER_EPOCH` environment variable to quickly run
+a scrip without modifying its source.
+
+Args:
+    max_epochs: The integer limit on the number of epochs or `None`. If
+        `None`, no limit is applied.
+

.tether/man/keras.activations.txt

@@ -31,6 +31,7 @@ softmax(x, axis=-1)
 softplus(x)
 softsign(x)
 sparse_plus(x)
+sparse_sigmoid(x)
 sparsemax(x, axis=-1)
 squareplus(x, b=4)
 swish(x)

.tether/man/keras.applications.txt

@@ -1,4 +1,4 @@
-convnext: Module(keras.api.applications.convnext)
+convnext: Module(keras.applications.convnext)
 ConvNeXtBase(
   include_top=True,
   include_preprocessing=True,
@@ -54,7 +54,7 @@ ConvNeXtXLarge(
   classifier_activation='softmax',
   name='convnext_xlarge'
 )
-densenet: Module(keras.api.applications.densenet)
+densenet: Module(keras.applications.densenet)
 DenseNet121(
   include_top=True,
   weights='imagenet',
@@ -85,8 +85,8 @@ DenseNet201(
   classifier_activation='softmax',
   name='densenet201'
 )
-efficientnet: Module(keras.api.applications.efficientnet)
-efficientnet_v2: Module(keras.api.applications.efficientnet_v2)
+efficientnet: Module(keras.applications.efficientnet)
+efficientnet_v2: Module(keras.applications.efficientnet_v2)
 EfficientNetB0(
   include_top=True,
   weights='imagenet',
@@ -244,9 +244,9 @@ EfficientNetV2S(
   include_preprocessing=True,
   name='efficientnetv2-s'
 )
-imagenet_utils: Module(keras.api.applications.imagenet_utils)
-inception_resnet_v2: Module(keras.api.applications.inception_resnet_v2)
-inception_v3: Module(keras.api.applications.inception_v3)
+imagenet_utils: Module(keras.applications.imagenet_utils)
+inception_resnet_v2: Module(keras.applications.inception_resnet_v2)
+inception_v3: Module(keras.applications.inception_v3)
 InceptionResNetV2(
   include_top=True,
   weights='imagenet',
@@ -267,7 +267,7 @@ InceptionV3(
   classifier_activation='softmax',
   name='inception_v3'
 )
-mobilenet: Module(keras.api.applications.mobilenet)
+mobilenet: Module(keras.applications.mobilenet)
 MobileNet(
   input_shape=None,
   alpha=1.0,
@@ -281,8 +281,8 @@ MobileNet(
   classifier_activation='softmax',
   name=None
 )
-mobilenet_v2: Module(keras.api.applications.mobilenet_v2)
-mobilenet_v3: Module(keras.api.applications.mobilenet_v3)
+mobilenet_v2: Module(keras.applications.mobilenet_v2)
+mobilenet_v3: Module(keras.applications.mobilenet_v3)
 MobileNetV2(
   input_shape=None,
   alpha=1.0,
@@ -322,7 +322,7 @@ MobileNetV3Small(
   include_preprocessing=True,
   name='MobileNetV3Small'
 )
-nasnet: Module(keras.api.applications.nasnet)
+nasnet: Module(keras.applications.nasnet)
 NASNetLarge(
   input_shape=None,
   include_top=True,
@@ -343,8 +343,8 @@ NASNetMobile(
   classifier_activation='softmax',
   name='nasnet_mobile'
 )
-resnet: Module(keras.api.applications.resnet)
-resnet_v2: Module(keras.api.applications.resnet_v2)
+resnet: Module(keras.applications.resnet)
+resnet_v2: Module(keras.applications.resnet_v2)
 ResNet101(
   include_top=True,
   weights='imagenet',
@@ -385,7 +385,7 @@ ResNet152V2(
   classifier_activation='softmax',
   name='resnet152v2'
 )
-resnet50: Module(keras.api.applications.resnet50)
+resnet50: Module(keras.applications.resnet50)
 ResNet50(
   include_top=True,
   weights='imagenet',
@@ -406,7 +406,7 @@ ResNet50V2(
   classifier_activation='softmax',
   name='resnet50v2'
 )
-vgg16: Module(keras.api.applications.vgg16)
+vgg16: Module(keras.applications.vgg16)
 VGG16(
   include_top=True,
   weights='imagenet',
@@ -417,7 +417,7 @@ VGG16(
   classifier_activation='softmax',
   name='vgg16'
 )
-vgg19: Module(keras.api.applications.vgg19)
+vgg19: Module(keras.applications.vgg19)
 VGG19(
   include_top=True,
   weights='imagenet',
@@ -428,7 +428,7 @@ VGG19(
   classifier_activation='softmax',
   name='vgg19'
 )
-xception: Module(keras.api.applications.xception)
+xception: Module(keras.applications.xception)
 Xception(
   include_top=True,
   weights='imagenet',

.tether/man/keras.config.txt

@@ -12,10 +12,15 @@ floatx()
 image_data_format()
 is_flash_attention_enabled()
 is_interactive_logging_enabled()
+is_nnx_enabled()
 is_traceback_filtering_enabled()
+max_epochs()
+max_steps_per_epoch()
 set_backend(backend)
 set_dtype_policy(policy)
 set_epsilon(value)
 set_floatx(value)
 set_image_data_format(data_format)
+set_max_epochs(max_epochs)
+set_max_steps_per_epoch(max_steps_per_epoch)
 

.tether/man/keras.datasets.txt

@@ -1,9 +1,9 @@
-boston_housing: Module(keras.api.datasets.boston_housing)
-california_housing: Module(keras.api.datasets.california_housing)
-cifar10: Module(keras.api.datasets.cifar10)
-cifar100: Module(keras.api.datasets.cifar100)
-fashion_mnist: Module(keras.api.datasets.fashion_mnist)
-imdb: Module(keras.api.datasets.imdb)
-mnist: Module(keras.api.datasets.mnist)
-reuters: Module(keras.api.datasets.reuters)
+boston_housing: Module(keras.datasets.boston_housing)
+california_housing: Module(keras.datasets.california_housing)
+cifar10: Module(keras.datasets.cifar10)
+cifar100: Module(keras.datasets.cifar100)
+fashion_mnist: Module(keras.datasets.fashion_mnist)
+imdb: Module(keras.datasets.imdb)
+mnist: Module(keras.datasets.mnist)
+reuters: Module(keras.datasets.reuters)
 

.tether/man/keras.distribution.txt

@@ -21,6 +21,7 @@ ModelParallel(
   *,
   layout_map=None,
   batch_dim_name=None,
+  auto_shard_dataset=True,
   **kwargs
 )
 set_distribution(value)