Merge pull request #17954 from SamuelMarks:keras.applications-default…

…s-to PiperOrigin-RevId: 535295013
keras-team · May 25, 2023 · a8d4a7f · a8d4a7f
2 parents f4331f7 + 5879670
commit a8d4a7f
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Showing 4 changed files with 41 additions and 39 deletions.
diff --git a/keras/applications/convnext.py b/keras/applications/convnext.py
@@ -124,7 +124,7 @@
 
   Args:
     include_top: Whether to include the fully-connected
-      layer at the top of the network. Defaults to True.
+      layer at the top of the network. Defaults to `True`.
     weights: One of `None` (random initialization),
       `"imagenet"` (pre-training on ImageNet-1k), or the path to the weights
       file to be loaded. Defaults to `"imagenet"`.
@@ -135,7 +135,7 @@
       if `include_top` is False.
       It should have exactly 3 inputs channels.
     pooling: Optional pooling mode for feature extraction
-      when `include_top` is `False`. Defaults to None.
+      when `include_top` is `False`.
       - `None` means that the output of the model will be
         the 4D tensor output of the last convolutional layer.
       - `avg` means that global average pooling
@@ -144,16 +144,16 @@
         the output of the model will be a 2D tensor.
       - `max` means that global max pooling will
         be applied.
+      Defaults to `None`.
     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. Defaults to 1000 (number of
-      ImageNet classes).
+      if no `weights` argument is specified. 1000 is how many
+      ImageNet classes there are. Defaults to `1000`.
     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.
-      Defaults to `"softmax"`.
       When loading pretrained weights, `classifier_activation` can only
-      be `None` or `"softmax"`.
+      be `None` or `"softmax"`. Defaults to `"softmax"`.
 
   Returns:
     A `keras.Model` instance.
@@ -754,10 +754,10 @@ def preprocess_input(x, data_format=None):
 
     Args:
       x: A floating point `numpy.array` or a `tf.Tensor`.
-      data_format: Optional data format of the image tensor/array. Defaults to
-        None, in which case the global setting
-        `tf.keras.backend.image_data_format()` is used (unless you changed it,
-        it defaults to "channels_last").{mode}
+      data_format: Optional data format of the image tensor/array. `None` means
+        the global setting `tf.keras.backend.image_data_format()` is used
+        (unless you changed it, it uses "channels_last").
+        Defaults to `None`.
 
     Returns:
       Unchanged `numpy.array` or `tf.Tensor`.

diff --git a/keras/applications/efficientnet_v2.py b/keras/applications/efficientnet_v2.py
@@ -574,7 +574,7 @@
 
   Args:
     include_top: Boolean, whether to include the fully-connected
-      layer at the top of the network. Defaults to True.
+      layer at the top of the network. Defaults to `True`.
     weights: One of `None` (random initialization),
       `"imagenet"` (pre-training on ImageNet),
       or the path to the weights file to be loaded. Defaults to `"imagenet"`.
@@ -585,7 +585,7 @@
       if `include_top` is False.
       It should have exactly 3 inputs channels.
     pooling: Optional pooling mode for feature extraction
-      when `include_top` is `False`. Defaults to None.
+      when `include_top` is `False`.
       - `None` means that the output of the model will be
           the 4D tensor output of the
           last convolutional layer.
@@ -595,16 +595,17 @@
           the output of the model will be a 2D tensor.
       - `"max"` means that global max pooling will
           be applied.
+      Defaults to `None`.
     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. Defaults to 1000 (number of
-      ImageNet classes).
+      if no `weights` argument is specified. 1000 is how many
+      ImageNet classes there are. Defaults to `1000`.
     classifier_activation: A string 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.
-      Defaults to `"softmax"`.
       When loading pretrained weights, `classifier_activation` can only
       be `None` or `"softmax"`.
+      Defaults to `"softmax"`.
 
   Returns:
     A `keras.Model` instance.
@@ -1342,10 +1343,10 @@ def preprocess_input(x, data_format=None):
 
     Args:
       x: A floating point `numpy.array` or a `tf.Tensor`.
-      data_format: Optional data format of the image tensor/array. Defaults to
-        None, in which case the global setting
-        `tf.keras.backend.image_data_format()` is used (unless you changed it,
-        it defaults to "channels_last").{mode}
+      data_format: Optional data format of the image tensor/array. `None` means
+        the global setting `tf.keras.backend.image_data_format()` is used
+        (unless you changed it, it uses "channels_last").
+        Defaults to `None`.
 
     Returns:
       Unchanged `numpy.array` or `tf.Tensor`.

diff --git a/keras/applications/regnet.py b/keras/applications/regnet.py
@@ -342,7 +342,7 @@
 
   Args:
     include_top: Whether to include the fully-connected
-        layer at the top of the network. Defaults to True.
+        layer at the top of the network. Defaults to `True`.
     weights: One of `None` (random initialization),
           `"imagenet"` (pre-training on ImageNet), or the path to the weights
           file to be loaded. Defaults to `"imagenet"`.
@@ -353,7 +353,7 @@
         if `include_top` is False.
         It should have exactly 3 inputs channels.
     pooling: Optional pooling mode for feature extraction
-        when `include_top` is `False`. Defaults to None.
+        when `include_top` is `False`.
         - `None` means that the output of the model will be
             the 4D tensor output of the
             last convolutional layer.
@@ -363,16 +363,16 @@
             the output of the model will be a 2D tensor.
         - `max` means that global max pooling will
             be applied.
+        Defaults to `None`.
     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. Defaults to 1000 (number of
-        ImageNet classes).
+        if no `weights` argument is specified. 1000 is how many
+        ImageNet classes there are. Defaults to `1000`.
     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.
-        Defaults to `"softmax"`.
         When loading pretrained weights, `classifier_activation` can only
-        be `None` or `"softmax"`.
+        be `None` or `"softmax"`. Defaults to `"softmax"`.
 
   Returns:
     A `keras.Model` instance.
@@ -1819,10 +1819,10 @@ def preprocess_input(x, data_format=None):
 
     Args:
       x: A floating point `numpy.array` or a `tf.Tensor`.
-      data_format: Optional data format of the image tensor/array. Defaults to
-        None, in which case the global setting
-        `tf.keras.backend.image_data_format()` is used (unless you changed it,
-        it defaults to "channels_last").{mode}
+      data_format: Optional data format of the image tensor/array. `None` means
+        the global setting `tf.keras.backend.image_data_format()` is used
+        (unless you changed it, it uses "channels_last").
+        Defaults to `None`.
 
     Returns:
       Unchanged `numpy.array` or `tf.Tensor`.

diff --git a/keras/applications/resnet_rs.py b/keras/applications/resnet_rs.py
@@ -196,9 +196,9 @@
             `classifier_activation=None` to return the logits of the "top"
             layer.
         include_preprocessing: Boolean, whether to include the preprocessing
-            layer (`Rescaling`) at the bottom of the network. Defaults to
-            `True`.  Note: Input image is normalized by ImageNet mean and
-            standard deviation.
+            layer (`Rescaling`) at the bottom of the network. Note: Input image
+            is normalized by ImageNet mean and standard deviation.
+            Defaults to `True`.
 
     Returns:
         A `keras.Model` instance.
@@ -582,9 +582,10 @@ def ResNetRS(
           use on the "top" layer. Ignored unless `include_top=True`. Set
           `classifier_activation=None` to return the logits of the "top" layer.
         include_preprocessing: Boolean, whether to include the preprocessing
-          layer (`Rescaling`) at the bottom of the network. Defaults to `True`.
-          Note- Input image is normalized by ImageNet mean and standard
-          deviation.
+          layer (`Rescaling`) at the bottom of the network. Note - Input image
+          is normalized by ImageNet mean and standard deviation.
+          Defaults to `True`.
+
 
     Returns:
         A `tf.keras.Model` instance.
@@ -958,10 +959,10 @@ def preprocess_input(x, data_format=None):
 
     Args:
       x: A floating point `numpy.array` or a `tf.Tensor`.
-      data_format: Optional data format of the image tensor/array. Defaults to
-        None, in which case the global setting
-        `tf.keras.backend.image_data_format()` is used (unless you changed it,
-        it defaults to "channels_last").{mode}
+      data_format: Optional data format of the image tensor/array. `None` means
+        the global setting `tf.keras.backend.image_data_format()` is used
+        (unless you changed it, it uses "channels_last").
+        Defaults to `None`.
 
     Returns:
       Unchanged `numpy.array` or `tf.Tensor`.