Merge pull request #1461 from rstudio/retether-3.4.0

Retether 3.4.0

.tether/man/InputLayer.txt

@@ -1,7 +1,7 @@
 Help on class InputLayer in module keras.src.layers.core.input_layer:
 
 class InputLayer(keras.src.layers.layer.Layer)
- |  InputLayer(shape=None, batch_size=None, dtype=None, sparse=None, batch_shape=None, input_tensor=None, name=None, **kwargs)
+ |  InputLayer(shape=None, batch_size=None, dtype=None, sparse=None, batch_shape=None, input_tensor=None, optional=False, name=None, **kwargs)
  |
  |  Method resolution order:
  |      InputLayer
@@ -24,6 +24,7 @@ class InputLayer(keras.src.layers.layer.Layer)
  |    sparse=None,
  |    batch_shape=None,
  |    input_tensor=None,
+ |    optional=False,
  |    name=None,
  |    **kwargs
  |  )

.tether/man/Layer.txt

@@ -462,6 +462,14 @@ class Layer(keras.src.backend.tensorflow.layer.TFLayer, keras.src.ops.operation.
  |      training. Unlike, `layer.non_trainable_variables` this excludes metric
  |      state and random seeds.
  |
+ |  path
+ |      The path of the layer.
+ |
+ |      If the layer has not been built yet, it will be `None`.
+ |
+ |  quantization_mode
+ |      The quantization mode of this layer, `None` if not quantized.
+ |
  |  trainable_variables
  |      List of all trainable layer state.
  |

.tether/man/application_convnext_base.txt

@@ -1,14 +1,14 @@
 __signature__
 keras.applications.ConvNeXtBase(
-  model_name='convnext_base',
   include_top=True,
   include_preprocessing=True,
   weights='imagenet',
   input_tensor=None,
   input_shape=None,
   pooling=None,
   classes=1000,
-  classifier_activation='softmax'
+  classifier_activation='softmax',
+  name='convnext_base'
 )
 __doc__
 Instantiates the ConvNeXtBase architecture.
@@ -73,6 +73,8 @@ Args:
         Defaults to `"softmax"`.
         When loading pretrained weights, `classifier_activation` can only
         be `None` or `"softmax"`.
+    name: The name of the model (string).
 
 Returns:
     A model instance.
+

.tether/man/application_convnext_large.txt

@@ -1,14 +1,14 @@
 __signature__
 keras.applications.ConvNeXtLarge(
-  model_name='convnext_large',
   include_top=True,
   include_preprocessing=True,
   weights='imagenet',
   input_tensor=None,
   input_shape=None,
   pooling=None,
   classes=1000,
-  classifier_activation='softmax'
+  classifier_activation='softmax',
+  name='convnext_large'
 )
 __doc__
 Instantiates the ConvNeXtLarge architecture.
@@ -73,6 +73,8 @@ Args:
         Defaults to `"softmax"`.
         When loading pretrained weights, `classifier_activation` can only
         be `None` or `"softmax"`.
+    name: The name of the model (string).
 
 Returns:
     A model instance.
+

.tether/man/application_convnext_small.txt

@@ -1,14 +1,14 @@
 __signature__
 keras.applications.ConvNeXtSmall(
-  model_name='convnext_small',
   include_top=True,
   include_preprocessing=True,
   weights='imagenet',
   input_tensor=None,
   input_shape=None,
   pooling=None,
   classes=1000,
-  classifier_activation='softmax'
+  classifier_activation='softmax',
+  name='convnext_small'
 )
 __doc__
 Instantiates the ConvNeXtSmall architecture.
@@ -73,6 +73,8 @@ Args:
         Defaults to `"softmax"`.
         When loading pretrained weights, `classifier_activation` can only
         be `None` or `"softmax"`.
+    name: The name of the model (string).
 
 Returns:
     A model instance.
+

.tether/man/application_convnext_tiny.txt

@@ -1,14 +1,14 @@
 __signature__
 keras.applications.ConvNeXtTiny(
-  model_name='convnext_tiny',
   include_top=True,
   include_preprocessing=True,
   weights='imagenet',
   input_tensor=None,
   input_shape=None,
   pooling=None,
   classes=1000,
-  classifier_activation='softmax'
+  classifier_activation='softmax',
+  name='convnext_tiny'
 )
 __doc__
 Instantiates the ConvNeXtTiny architecture.
@@ -73,6 +73,8 @@ Args:
         Defaults to `"softmax"`.
         When loading pretrained weights, `classifier_activation` can only
         be `None` or `"softmax"`.
+    name: The name of the model (string).
 
 Returns:
     A model instance.
+

.tether/man/application_convnext_xlarge.txt

@@ -1,14 +1,14 @@
 __signature__
 keras.applications.ConvNeXtXLarge(
-  model_name='convnext_xlarge',
   include_top=True,
   include_preprocessing=True,
   weights='imagenet',
   input_tensor=None,
   input_shape=None,
   pooling=None,
   classes=1000,
-  classifier_activation='softmax'
+  classifier_activation='softmax',
+  name='convnext_xlarge'
 )
 __doc__
 Instantiates the ConvNeXtXLarge architecture.
@@ -73,6 +73,8 @@ Args:
         Defaults to `"softmax"`.
         When loading pretrained weights, `classifier_activation` can only
         be `None` or `"softmax"`.
+    name: The name of the model (string).
 
 Returns:
     A model instance.
+

.tether/man/application_densenet121.txt

@@ -6,7 +6,8 @@ keras.applications.DenseNet121(
   input_shape=None,
   pooling=None,
   classes=1000,
-  classifier_activation='softmax'
+  classifier_activation='softmax',
+  name='densenet121'
 )
 __doc__
 Instantiates the Densenet121 architecture.
@@ -25,40 +26,42 @@ on your inputs before passing them to the model.
 
 Args:
     include_top: whether to include the fully-connected
-    layer at the top of the network.
+        layer at the top of the network.
     weights: one of `None` (random initialization),
-    `"imagenet"` (pre-training on ImageNet),
-    or the path to the weights file to be loaded.
-    input_tensor: optional Keras tensor
-    (i.e. output of `layers.Input()`)
-    to use as image input for the model.
+        `"imagenet"` (pre-training on ImageNet),
+        or the path to the weights file to be loaded.
+        input_tensor: optional Keras tensor
+        (i.e. output of `layers.Input()`)
+        to use as image input for the model.
     input_shape: optional shape tuple, only to be specified
-    if `include_top` is False (otherwise the input shape
-    has to be `(224, 224, 3)` (with `'channels_last'` data format)
-    or `(3, 224, 224)` (with `'channels_first'` data format).
-    It should have exactly 3 inputs channels,
-    and width and height should be no smaller than 32.
-    E.g. `(200, 200, 3)` would be one valid value.
+        if `include_top` is False (otherwise the input shape
+        has to be `(224, 224, 3)` (with `'channels_last'` data format)
+        or `(3, 224, 224)` (with `'channels_first'` data format).
+        It should have exactly 3 inputs channels,
+        and width and height should be no smaller than 32.
+        E.g. `(200, 200, 3)` would be one valid value.
     pooling: Optional pooling mode for feature extraction
-    when `include_top` is `False`.
-    - `None` means that the output of the model will be
-        the 4D tensor output of the
-        last convolutional block.
-    - `avg` means that global average pooling
-        will be applied to the output of the
-        last convolutional block, and thus
-        the output of the model will be a 2D tensor.
-    - `max` means that global max pooling will
-        be applied.
+        when `include_top` is `False`.
+        - `None` means that the output of the model will be
+            the 4D tensor output of the
+            last convolutional block.
+        - `avg` means that global average pooling
+            will be applied to the output of the
+            last convolutional block, and thus
+            the output of the model will be a 2D tensor.
+        - `max` means that global max pooling will
+            be applied.
     classes: optional number of classes to classify images
-    into, only to be specified if `include_top` is `True`, and
-    if no `weights` argument is specified.
+        into, only to be specified if `include_top` is `True`, and
+        if no `weights` argument is specified.
     classifier_activation: A `str` or callable.
-    The activation function to use
-    on the "top" layer. Ignored unless `include_top=True`. Set
-    `classifier_activation=None` to return the logits
-    of the "top" layer. When loading pretrained weights,
-    `classifier_activation` can only be `None` or `"softmax"`.
+        The activation function to use
+        on the "top" layer. Ignored unless `include_top=True`. Set
+        `classifier_activation=None` to return the logits
+        of the "top" layer. When loading pretrained weights,
+        `classifier_activation` can only be `None` or `"softmax"`.
+    name: The name of the model (string).
 
 Returns:
     A Keras model instance.
+

.tether/man/application_densenet169.txt

@@ -6,7 +6,8 @@ keras.applications.DenseNet169(
   input_shape=None,
   pooling=None,
   classes=1000,
-  classifier_activation='softmax'
+  classifier_activation='softmax',
+  name='densenet169'
 )
 __doc__
 Instantiates the Densenet169 architecture.
@@ -25,40 +26,42 @@ on your inputs before passing them to the model.
 
 Args:
     include_top: whether to include the fully-connected
-    layer at the top of the network.
+        layer at the top of the network.
     weights: one of `None` (random initialization),
-    `"imagenet"` (pre-training on ImageNet),
-    or the path to the weights file to be loaded.
-    input_tensor: optional Keras tensor
-    (i.e. output of `layers.Input()`)
-    to use as image input for the model.
+        `"imagenet"` (pre-training on ImageNet),
+        or the path to the weights file to be loaded.
+        input_tensor: optional Keras tensor
+        (i.e. output of `layers.Input()`)
+        to use as image input for the model.
     input_shape: optional shape tuple, only to be specified
-    if `include_top` is False (otherwise the input shape
-    has to be `(224, 224, 3)` (with `'channels_last'` data format)
-    or `(3, 224, 224)` (with `'channels_first'` data format).
-    It should have exactly 3 inputs channels,
-    and width and height should be no smaller than 32.
-    E.g. `(200, 200, 3)` would be one valid value.
+        if `include_top` is False (otherwise the input shape
+        has to be `(224, 224, 3)` (with `'channels_last'` data format)
+        or `(3, 224, 224)` (with `'channels_first'` data format).
+        It should have exactly 3 inputs channels,
+        and width and height should be no smaller than 32.
+        E.g. `(200, 200, 3)` would be one valid value.
     pooling: Optional pooling mode for feature extraction
-    when `include_top` is `False`.
-    - `None` means that the output of the model will be
-        the 4D tensor output of the
-        last convolutional block.
-    - `avg` means that global average pooling
-        will be applied to the output of the
-        last convolutional block, and thus
-        the output of the model will be a 2D tensor.
-    - `max` means that global max pooling will
-        be applied.
+        when `include_top` is `False`.
+        - `None` means that the output of the model will be
+            the 4D tensor output of the
+            last convolutional block.
+        - `avg` means that global average pooling
+            will be applied to the output of the
+            last convolutional block, and thus
+            the output of the model will be a 2D tensor.
+        - `max` means that global max pooling will
+            be applied.
     classes: optional number of classes to classify images
-    into, only to be specified if `include_top` is `True`, and
-    if no `weights` argument is specified.
+        into, only to be specified if `include_top` is `True`, and
+        if no `weights` argument is specified.
     classifier_activation: A `str` or callable.
-    The activation function to use
-    on the "top" layer. Ignored unless `include_top=True`. Set
-    `classifier_activation=None` to return the logits
-    of the "top" layer. When loading pretrained weights,
-    `classifier_activation` can only be `None` or `"softmax"`.
+        The activation function to use
+        on the "top" layer. Ignored unless `include_top=True`. Set
+        `classifier_activation=None` to return the logits
+        of the "top" layer. When loading pretrained weights,
+        `classifier_activation` can only be `None` or `"softmax"`.
+    name: The name of the model (string).
 
 Returns:
     A Keras model instance.
+