[keras/applications/mobilenet.py] Standardise docstring usage of "Default to"
diff --git a/keras/applications/mobilenet.py b/keras/applications/mobilenet.py
index 5e4daa174ec..e3a0cdd09e1 100644
--- a/keras/applications/mobilenet.py
+++ b/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.

This is one of many PRs. Discussion + request to split into multiple PRs @ #17748