Move Defaults to to end of arg docstring and standardise values

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").{mode}.
+        Defaults to `None`.
 
     Returns:
       Unchanged `numpy.array` or `tf.Tensor`.

keras/applications/efficientnet.py

@@ -192,7 +192,7 @@
 
   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'.
@@ -203,7 +203,7 @@
         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`. Defaults to `None`.
         - `None` means that the output of the model will be
             the 4D tensor output of the
             last convolutional layer.
@@ -215,8 +215,8 @@
             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. 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.
@@ -852,10 +852,10 @@ 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").{mode}.
+        Defaults to `None`.
 
     Returns:
       Unchanged `numpy.array` or `tf.Tensor`.

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").{mode}.
+        Defaults to `None`.
 
     Returns:
       Unchanged `numpy.array` or `tf.Tensor`.

keras/applications/imagenet_utils.py

@@ -56,10 +56,10 @@
       The preprocessed data are written over the input data
       if the data types are compatible. To avoid this
       behaviour, `numpy.copy(x)` can be used.
-    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").{mode}
+      Defaults to `None`.
 
   Returns:
       Preprocessed `numpy.array` or a `tf.Tensor` with type `float32`.
@@ -70,7 +70,7 @@
   """
 
 PREPROCESS_INPUT_MODE_DOC = """
-    mode: One of "caffe", "tf" or "torch". Defaults to "caffe".
+    mode: One of "caffe", "tf" or "torch".
       - caffe: will convert the images from RGB to BGR,
           then will zero-center each color channel with
           respect to the ImageNet dataset,
@@ -80,6 +80,7 @@
       - torch: will scale pixels between 0 and 1 and then
           will normalize each channel with respect to the
           ImageNet dataset.
+      Defaults to "caffe".
   """
 
 PREPROCESS_INPUT_DEFAULT_ERROR_DOC = """

keras/applications/inception_v3.py

@@ -82,13 +82,13 @@ def InceptionV3(
 
     Args:
       include_top: Boolean, whether to include the fully-connected
-        layer at the top, as the last layer of the network. Default to `True`.
+        layer at the top, as the last layer 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. Default to `imagenet`.
+        or the path to the weights file to be loaded. Defaults to `imagenet`.
       input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`)
         to use as image input for the model. `input_tensor` is useful for
-        sharing inputs between multiple different networks. Default to None.
+        sharing inputs between multiple different networks. Defaults to `None`.
       input_shape: Optional shape tuple, only to be specified
         if `include_top` is False (otherwise the input shape
         has to be `(299, 299, 3)` (with `channels_last` data format)
@@ -108,7 +108,7 @@ def InceptionV3(
         - `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. Default to 1000.
+        if no `weights` argument is specified. 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.

keras/applications/mobilenet.py

@@ -124,25 +124,26 @@ def MobileNet(
         `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. Default to `None`.
+        valid value. Defaults to `None`.
         `input_shape` will be ignored if the `input_tensor` is provided.
       alpha: Controls the width of the network. This is known as the width
         multiplier in the MobileNet paper. - If `alpha` < 1.0, proportionally
         decreases the number of filters in each layer. - If `alpha` > 1.0,
         proportionally increases the number of filters in each layer. - If
         `alpha` = 1, default number of filters from the paper are used at each
-        layer. Default to 1.0.
+        layer. Defaults to `1.0`.
       depth_multiplier: Depth multiplier for depthwise convolution. This is
-        called the resolution multiplier in the MobileNet paper. Default to 1.0.
-      dropout: Dropout rate. Default to 0.001.
+        called the resolution multiplier in the MobileNet paper.
+        Defaults to `1.0`.
+      dropout: Dropout rate. Defaults to `0.001`.
       include_top: Boolean, whether to include the fully-connected layer at the
-        top of the network. Default to `True`.
+        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. Default to
+        on ImageNet), or the path to the weights file to be loaded. Defaults to
         `imagenet`.
       input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`) to
         use as image input for the model. `input_tensor` is useful for sharing
-        inputs between multiple different networks. Default to None.
+        inputs between multiple different networks. Defaults to `None`.
       pooling: Optional pooling mode for feature extraction when `include_top`
         is `False`.
         - `None` (default) means that the output of the model will be
@@ -154,7 +155,7 @@ def MobileNet(
         - `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. Defaults to 1000.
+        specified. 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.

keras/applications/mobilenet_v3.py

@@ -679,10 +679,10 @@ 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").{mode}.
+        Defaults to `None`.
 
     Returns:
       Unchanged `numpy.array` or `tf.Tensor`.

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").{mode}.
+        Defaults to `None`.
 
     Returns:
       Unchanged `numpy.array` or `tf.Tensor`.

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").{mode}.
+        Defaults to `None`.
 
     Returns:
       Unchanged `numpy.array` or `tf.Tensor`.

keras/backend.py

@@ -1901,8 +1901,8 @@ class RandomGenerator(tf.__internal__.tracking.AutoTrackable):
         When `rng_type` is "legacy_stateful", the seed will be passed down to
         stateful random ops.
       rng_type: Type of RNG to use, one of "stateful", "stateless",
-        "legacy_stateful". It defaults to "stateful" if
-        `enable_tf_random_generator` has been activated, or to
+        "legacy_stateful". When `None` it uses "stateful" if
+        `enable_tf_random_generator` has been activated, or
         "legacy_stateful" otherwise.
         - When using "stateless", the random ops outputs are constant (the same
           inputs result in the same outputs).
@@ -1913,6 +1913,7 @@ class RandomGenerator(tf.__internal__.tracking.AutoTrackable):
         - "legacy_stateful" is backed by TF1 stateful RNG ops
           (e.g. `tf.random.uniform`), while "stateful"
           is backed by TF2 APIs (e.g. `tf.random.Generator.uniform`).
+        Defaults to `None`.
     """
 
     RNG_STATELESS = "stateless"
@@ -6898,11 +6899,11 @@ def random_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
     Args:
         shape: A tuple of integers, the shape of tensor to create.
         mean: A float, the mean value of the normal distribution to draw
-          samples. Default to 0.0.
+          samples. Defaults to `0.0`.
         stddev: A float, the standard deviation of the normal distribution
-          to draw samples. Default to 1.0.
-        dtype: `tf.dtypes.DType`, dtype of returned tensor. Default to use Keras
-          backend dtype which is float32.
+          to draw samples. Defaults to `1.0`.
+        dtype: `tf.dtypes.DType`, dtype of returned tensor. None uses Keras
+          backend dtype which is float32. Defaults to `None`.
         seed: Integer, random seed. Will use a random numpy integer when not
           specified.