Merge pull request #1437 from rstudio/retether-3.3.2

Retether 3.3.2

.tether/man/Layer.txt

@@ -442,6 +442,9 @@ class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.
  |  losses
  |      List of scalar losses from `add_loss`, regularizers and sublayers.
  |
+ |  metrics
+ |      List of all metrics.
+ |
  |  metrics_variables
  |      List of all metric variables.
  |
@@ -488,6 +491,8 @@ class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.
  |  ----------------------------------------------------------------------
  |  Data descriptors defined here:
  |
+ |  dtype_policy
+ |
  |  input_spec
  |
  |  supports_masking

.tether/man/clear_session.txt

@@ -1,5 +1,5 @@
 __signature__
-keras.utils.clear_session()
+keras.utils.clear_session(free_memory=True)
 __doc__
 Resets all state generated by Keras.
 
@@ -11,6 +11,14 @@ an increasing amount of memory over time, and you may want to clear it.
 Calling `clear_session()` releases the global state: this helps avoid
 clutter from old models and layers, especially when memory is limited.
 
+Args:
+    free_memory: Whether to call Python garbage collection.
+        It's usually a good practice to call it to make sure
+        memory used by deleted objects is immediately freed.
+        However, it may take a few seconds to execute, so
+        when using `clear_session()` in a short loop,
+        you may want to skip it.
+
 Example 1: calling `clear_session()` when creating models in a loop
 
 ```python
@@ -39,3 +47,4 @@ dense_10
 >>> new_layer = keras.layers.Dense(10)
 >>> print(new_layer.name)
 dense
+

.tether/man/keras.applications.txt

@@ -1,4 +1,4 @@
-convnext: Module(keras.applications.convnext)
+convnext: Module(keras.api.applications.convnext)
 ConvNeXtBase(
   model_name='convnext_base',
   include_top=True,
@@ -54,7 +54,7 @@ ConvNeXtXLarge(
   classes=1000,
   classifier_activation='softmax'
 )
-densenet: Module(keras.applications.densenet)
+densenet: Module(keras.api.applications.densenet)
 DenseNet121(
   include_top=True,
   weights='imagenet',
@@ -82,8 +82,8 @@ DenseNet201(
   classes=1000,
   classifier_activation='softmax'
 )
-efficientnet: Module(keras.applications.efficientnet)
-efficientnet_v2: Module(keras.applications.efficientnet_v2)
+efficientnet: Module(keras.api.applications.efficientnet)
+efficientnet_v2: Module(keras.api.applications.efficientnet_v2)
 EfficientNetB0(
   include_top=True,
   weights='imagenet',
@@ -234,9 +234,9 @@ EfficientNetV2S(
   classifier_activation='softmax',
   include_preprocessing=True
 )
-imagenet_utils: Module(keras.applications.imagenet_utils)
-inception_resnet_v2: Module(keras.applications.inception_resnet_v2)
-inception_v3: Module(keras.applications.inception_v3)
+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)
 InceptionResNetV2(
   include_top=True,
   weights='imagenet',
@@ -255,7 +255,7 @@ InceptionV3(
   classes=1000,
   classifier_activation='softmax'
 )
-mobilenet: Module(keras.applications.mobilenet)
+mobilenet: Module(keras.api.applications.mobilenet)
 MobileNet(
   input_shape=None,
   alpha=1.0,
@@ -268,8 +268,8 @@ MobileNet(
   classes=1000,
   classifier_activation='softmax'
 )
-mobilenet_v2: Module(keras.applications.mobilenet_v2)
-mobilenet_v3: Module(keras.applications.mobilenet_v3)
+mobilenet_v2: Module(keras.api.applications.mobilenet_v2)
+mobilenet_v3: Module(keras.api.applications.mobilenet_v3)
 MobileNetV2(
   input_shape=None,
   alpha=1.0,
@@ -306,7 +306,7 @@ MobileNetV3Small(
   classifier_activation='softmax',
   include_preprocessing=True
 )
-nasnet: Module(keras.applications.nasnet)
+nasnet: Module(keras.api.applications.nasnet)
 NASNetLarge(
   input_shape=None,
   include_top=True,
@@ -325,8 +325,8 @@ NASNetMobile(
   classes=1000,
   classifier_activation='softmax'
 )
-resnet: Module(keras.applications.resnet)
-resnet_v2: Module(keras.applications.resnet_v2)
+resnet: Module(keras.api.applications.resnet)
+resnet_v2: Module(keras.api.applications.resnet_v2)
 ResNet101(
   include_top=True,
   weights='imagenet',
@@ -363,7 +363,7 @@ ResNet152V2(
   classes=1000,
   classifier_activation='softmax'
 )
-resnet50: Module(keras.applications.resnet50)
+resnet50: Module(keras.api.applications.resnet50)
 ResNet50(
   include_top=True,
   weights='imagenet',
@@ -382,7 +382,7 @@ ResNet50V2(
   classes=1000,
   classifier_activation='softmax'
 )
-vgg16: Module(keras.applications.vgg16)
+vgg16: Module(keras.api.applications.vgg16)
 VGG16(
   include_top=True,
   weights='imagenet',
@@ -392,7 +392,7 @@ VGG16(
   classes=1000,
   classifier_activation='softmax'
 )
-vgg19: Module(keras.applications.vgg19)
+vgg19: Module(keras.api.applications.vgg19)
 VGG19(
   include_top=True,
   weights='imagenet',
@@ -402,7 +402,7 @@ VGG19(
   classes=1000,
   classifier_activation='softmax'
 )
-xception: Module(keras.applications.xception)
+xception: Module(keras.api.applications.xception)
 Xception(
   include_top=True,
   weights='imagenet',

.tether/man/keras.backend.txt

@@ -1,5 +1,5 @@
 backend()
-clear_session()
+clear_session(free_memory=True)
 epsilon()
 floatx()
 get_uid(prefix='')

.tether/man/keras.datasets.txt

@@ -1,9 +1,9 @@
-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)
+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)
 

.tether/man/keras.dtype_policies.txt

@@ -1,4 +1,12 @@
-DTypePolicy(name)
+deserialize(config, custom_objects=None)
+DTypePolicy(
+  name,
+  *args,
+  **kwargs
+)
 FloatDTypePolicy(name)
+get(identifier)
 QuantizedDTypePolicy(name)
+QuantizedFloat8DTypePolicy(name, amax_history_length=1024)
+serialize(dtype_policy)
 

.tether/man/keras.layers.txt

@@ -537,6 +537,7 @@ Embedding(
   embeddings_regularizer=None,
   embeddings_constraint=None,
   mask_zero=False,
+  weights=None,
   lora_rank=None,
   **kwargs
 )
@@ -1007,6 +1008,9 @@ Resizing(
   width,
   interpolation='bilinear',
   crop_to_aspect_ratio=False,
+  pad_to_aspect_ratio=False,
+  fill_mode='constant',
+  fill_value=0.0,
   data_format=None,
   **kwargs
 )

.tether/man/keras.legacy.txt

@@ -1,2 +1,2 @@
-saving: Module(keras.legacy.saving)
+saving: Module(keras.api.legacy.saving)
 

.tether/man/keras.losses.txt

@@ -129,4 +129,16 @@ SparseCategoricalCrossentropy(
 )
 squared_hinge(y_true, y_pred)
 SquaredHinge(reduction='sum_over_batch_size', name='squared_hinge')
+tversky(
+  y_true,
+  y_pred,
+  alpha=0.5,
+  beta=0.5
+)
+Tversky(
+  alpha=0.5,
+  beta=0.5,
+  reduction='sum_over_batch_size',
+  name='tversky'
+)
 

.tether/man/keras.mixed_precision.txt

@@ -1,13 +1,21 @@
 dtype_policy()
-DTypePolicy(name)
+DTypePolicy(
+  name,
+  *args,
+  **kwargs
+)
 global_policy()
 LossScaleOptimizer(
   inner_optimizer,
   initial_scale=32768.0,
   dynamic_growth_steps=2000,
   **kwargs
 )
-Policy(name)
+Policy(
+  name,
+  *args,
+  **kwargs
+)
 set_dtype_policy(policy)
 set_global_policy(policy)
 

.tether/man/keras.ops.image.txt

@@ -44,6 +44,11 @@ resize(
   size,
   interpolation='bilinear',
   antialias=False,
+  crop_to_aspect_ratio=False,
+  pad_to_aspect_ratio=False,
+  fill_mode='constant',
+  fill_value=0.0,
   data_format='channels_last'
 )
+rgb_to_grayscale(image, data_format='channels_last')
 

.tether/man/keras.ops.nn.txt

@@ -42,6 +42,15 @@ conv_transpose(
   data_format=None,
   dilation_rate=1
 )
+ctc_decode(
+  inputs,
+  sequence_lengths,
+  strategy,
+  beam_width=100,
+  top_paths=1,
+  merge_repeated=True,
+  mask_index=None
+)
 ctc_loss(
   target,
   output,

.tether/man/keras.ops.numpy.txt

@@ -39,8 +39,16 @@ arcsinh(x)
 arctan(x)
 arctan2(x1, x2)
 arctanh(x)
-argmax(x, axis=None)
-argmin(x, axis=None)
+argmax(
+  x,
+  axis=None,
+  keepdims=False
+)
+argmin(
+  x,
+  axis=None,
+  keepdims=False
+)
 argsort(x, axis=-1)
 array(x, dtype=None)
 average(
@@ -204,7 +212,12 @@ moveaxis(
   destination
 )
 multiply(x1, x2)
-nan_to_num(x)
+nan_to_num(
+  x,
+  nan=0.0,
+  posinf=None,
+  neginf=None
+)
 ndim(x)
 negative(x)
 nonzero(x)
@@ -247,6 +260,11 @@ roll(
   axis=None
 )
 round(x, decimals=0)
+select(
+  condlist,
+  choicelist,
+  default=0
+)
 sign(x)
 sin(x)
 sinh(x)
@@ -317,6 +335,12 @@ var(
   keepdims=False
 )
 vdot(x1, x2)
+vectorize(
+  pyfunc,
+  *,
+  excluded=None,
+  signature=None
+)
 vstack(xs)
 where(
   condition,