Improve Keras Applications docstrings.

PiperOrigin-RevId: 330552839
Change-Id: Ie6533dfed5bb16abdb454731763e7144c1dc0037

tensorflow/python/keras/applications/densenet.py

@@ -145,8 +145,9 @@ def DenseNet(
   Note that the data format convention used by the model is
   the one specified in your Keras config at `~/.keras/keras.json`.
 
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.densenet.preprocess_input` for an example.
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For DenseNet, call `tf.keras.applications.densenet.preprocess_input` on your
+  inputs before passing them to the model.
 
   Arguments:
     blocks: numbers of building blocks for the four dense layers.
@@ -382,9 +383,10 @@ DOC = """
   Optionally loads weights pre-trained on ImageNet.
   Note that the data format convention used by the model is
   the one specified in your Keras config at `~/.keras/keras.json`.
-  
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.densenet.preprocess_input` for an example.
+
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For DenseNet, call `tf.keras.applications.densenet.preprocess_input` on your
+  inputs before passing them to the model.
 
   Arguments:
     include_top: whether to include the fully-connected

tensorflow/python/keras/applications/inception_resnet_v2.py

@@ -61,8 +61,10 @@ def InceptionResNetV2(include_top=True,
   Note that the data format convention used by the model is
   the one specified in your Keras config at `~/.keras/keras.json`.
 
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.inception_resnet_v2.preprocess_input` for an example.
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For InceptionResNetV2, call
+  `tf.keras.applications.inception_resnet_v2.preprocess_input`
+  on your inputs before passing them to the model.
 
   Arguments:
     include_top: whether to include the fully-connected

tensorflow/python/keras/applications/inception_v3.py

@@ -63,8 +63,9 @@ def InceptionV3(
   Note that the data format convention used by the model is
   the one specified in the `tf.keras.backend.image_data_format()`.
 
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.inception_v3.preprocess_input` for an example.
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For InceptionV3, call `tf.keras.applications.inception_v3.preprocess_input`
+  on your inputs before passing them to the model.
 
   Arguments:
     include_top: Boolean, whether to include the fully-connected

tensorflow/python/keras/applications/mobilenet.py

@@ -104,8 +104,9 @@ def MobileNet(input_shape=None,
   Note that the data format convention used by the model is
   the one specified in the `tf.keras.backend.image_data_format()`.
 
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.mobilenet.preprocess_input` for an example.
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For MobileNet, call `tf.keras.applications.mobilenet.preprocess_input`
+  on your inputs before passing them to the model.
 
   Arguments:
     input_shape: Optional shape tuple, only to be specified if `include_top`

tensorflow/python/keras/applications/mobilenet_v2.py

@@ -111,8 +111,9 @@ def MobileNetV2(input_shape=None,
 
   Optionally loads weights pre-trained on ImageNet.
 
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.mobilenet_v2.preprocess_input` for an example.
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For MobileNetV2, call `tf.keras.applications.mobilenet_v2.preprocess_input`
+  on your inputs before passing them to the model.
 
   Arguments:
     input_shape: Optional shape tuple, to be specified if you would

tensorflow/python/keras/applications/mobilenet_v3.py

@@ -76,8 +76,10 @@ BASE_DOCSTRING = """Instantiates the {name} architecture.
 
   Optionally loads weights pre-trained on ImageNet.
 
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.mobilenet_v3.preprocess_input` for an example.
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For MobileNetV3, call
+  `tf.keras.applications.mobilenet_v3.preprocess_input` on your
+  inputs before passing them to the model.
 
   Arguments:
     input_shape: Optional shape tuple, to be specified if you would

tensorflow/python/keras/applications/nasnet.py

@@ -86,9 +86,6 @@ def NASNet(
   Note that the data format convention used by the model is
   the one specified in your Keras config at `~/.keras/keras.json`.
 
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.nasnet.preprocess_input` for an example.
-
   Arguments:
     input_shape: Optional shape tuple, the input shape
       is by default `(331, 331, 3)` for NASNetLarge and
@@ -331,7 +328,7 @@ def NASNetMobile(input_shape=None,
                  pooling=None,
                  classes=1000):
   """Instantiates a Mobile NASNet model in ImageNet mode.
-  
+
   Reference:
   - [Learning Transferable Architectures for Scalable Image Recognition](
       https://arxiv.org/abs/1707.07012) (CVPR 2018)
@@ -339,9 +336,10 @@ def NASNetMobile(input_shape=None,
   Optionally loads weights pre-trained on ImageNet.
   Note that the data format convention used by the model is
   the one specified in your Keras config at `~/.keras/keras.json`.
-  
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.nasnet.preprocess_input` for an example.
+
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For NASNet, call `tf.keras.applications.nasnet.preprocess_input` on your
+  inputs before passing them to the model.
 
   Arguments:
       input_shape: Optional shape tuple, only to be specified
@@ -407,7 +405,7 @@ def NASNetLarge(input_shape=None,
                 pooling=None,
                 classes=1000):
   """Instantiates a NASNet model in ImageNet mode.
-  
+
   Reference:
   - [Learning Transferable Architectures for Scalable Image Recognition](
       https://arxiv.org/abs/1707.07012) (CVPR 2018)
@@ -415,9 +413,10 @@ def NASNetLarge(input_shape=None,
   Optionally loads weights pre-trained on ImageNet.
   Note that the data format convention used by the model is
   the one specified in your Keras config at `~/.keras/keras.json`.
-  
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.nasnet.preprocess_input` for an example.
+
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For NASNet, call `tf.keras.applications.nasnet.preprocess_input` on your
+  inputs before passing them to the model.
 
   Arguments:
       input_shape: Optional shape tuple, only to be specified

tensorflow/python/keras/applications/resnet.py

@@ -79,9 +79,6 @@ def ResNet(stack_fn,
   Note that the data format convention used by the model is
   the one specified in your Keras config at `~/.keras/keras.json`.
 
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.resnet.preprocess_input` for an example.
-
   Arguments:
     stack_fn: a function that returns output tensor for the
       stacked residual blocks.
@@ -545,9 +542,10 @@ DOC = """
   Optionally loads weights pre-trained on ImageNet.
   Note that the data format convention used by the model is
   the one specified in your Keras config at `~/.keras/keras.json`.
-  
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.resnet.preprocess_input` for an example.
+
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For ResNet, call `tf.keras.applications.resnet.preprocess_input` on your
+  inputs before passing them to the model.
 
   Arguments:
     include_top: whether to include the fully-connected

tensorflow/python/keras/applications/resnet_v2.py

@@ -148,8 +148,9 @@ DOC = """
   Note that the data format convention used by the model is
   the one specified in your Keras config at `~/.keras/keras.json`.
 
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.resnet_v2.preprocess_input` for an example.
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For ResNetV2, call `tf.keras.applications.resnet_v2.preprocess_input` on your
+  inputs before passing them to the model.
 
   Arguments:
     include_top: whether to include the fully-connected

tensorflow/python/keras/applications/vgg16.py

@@ -66,8 +66,9 @@ def VGG16(
 
   The default input size for this model is 224x224.
 
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.vgg16.preprocess_input` for an example.
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For VGG16, call `tf.keras.applications.vgg16.preprocess_input` on your
+  inputs before passing them to the model.
 
   Arguments:
       include_top: whether to include the 3 fully-connected

tensorflow/python/keras/applications/vgg19.py

@@ -66,8 +66,9 @@ def VGG19(
 
   The default input size for this model is 224x224.
 
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.vgg19.preprocess_input` for an example.
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For VGG19, call `tf.keras.applications.vgg19.preprocess_input` on your
+  inputs before passing them to the model.
 
   Arguments:
     include_top: whether to include the 3 fully-connected

tensorflow/python/keras/applications/xception.py

@@ -68,8 +68,9 @@ def Xception(
   the one specified in your Keras config at `~/.keras/keras.json`.
   Note that the default input image size for this model is 299x299.
 
-  Caution: Be sure to properly pre-process your inputs to the application.
-  Please see `applications.xception.preprocess_input` for an example.
+  Note: each Keras Application expects a specific kind of input preprocessing.
+  For Xception, call `tf.keras.applications.xception.preprocess_input` on your
+  inputs before passing them to the model.
 
   Arguments:
     include_top: whether to include the fully-connected