API doc fixit for MobileNetV2.
PiperOrigin-RevId: 292967941
Change-Id: Ie6ea25c1013dae26cbcfe99fdccac144682f17af
diff --git a/tensorflow/python/keras/applications/mobilenet_v2.py b/tensorflow/python/keras/applications/mobilenet_v2.py
index bcb0b2e..186b6e3 100644
--- a/tensorflow/python/keras/applications/mobilenet_v2.py
+++ b/tensorflow/python/keras/applications/mobilenet_v2.py
@@ -69,9 +69,6 @@
| [mobilenet_v2_0.35_128] | 20 | 1.66 | 50.8 | 75.0 |
| [mobilenet_v2_0.35_96] | 11 | 1.66 | 45.5 | 70.4 |
-Reference paper:
- - [MobileNetV2: Inverted Residuals and Linear Bottlenecks]
- (https://arxiv.org/abs/1801.04381) (CVPR 2018)
"""
from __future__ import absolute_import
from __future__ import division
@@ -105,9 +102,15 @@
**kwargs):
"""Instantiates the MobileNetV2 architecture.
+ Reference paper:
+ - [MobileNetV2: Inverted Residuals and Linear Bottlenecks]
+ (https://arxiv.org/abs/1801.04381) (CVPR 2018)
+
+ Optionally loads weights pre-trained on ImageNet.
+
Arguments:
- input_shape: optional shape tuple, to be specified if you would
- like to use a model with an input img resolution that is not
+ input_shape: Optional shape tuple, to be specified if you would
+ like to use a model with an input image resolution that is not
(224, 224, 3).
It should have exactly 3 inputs channels (224, 224, 3).
You can also omit this option if you would like
@@ -116,24 +119,25 @@
input_shape will be used if they match, if the shapes
do not match then we will throw an error.
E.g. `(160, 160, 3)` would be one valid value.
- alpha: controls the width of the network. This is known as the
- width multiplier in the MobileNetV2 paper, but the name is kept for
- consistency with MobileNetV1 in Keras.
+ alpha: Float between 0 and 1. controls the width of the network.
+ This is known as the width multiplier in the MobileNetV2 paper,
+ but the name is kept for consistency with `applications.MobileNetV1`
+ model in Keras.
- 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.
- include_top: whether to include the fully-connected
- 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
+ are used at each layer.
+ include_top: Boolean, whether to include the fully-connected
+ layer at the top of the network. Defaults to `True`.
+ weights: String, 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.
- pooling: Optional pooling mode for feature extraction
+ pooling: String, 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
@@ -145,13 +149,13 @@
2D tensor.
- `max` means that global max pooling will
be applied.
- classes: optional number of classes to classify images
+ classes: Integer, optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
**kwargs: For backwards compatibility only.
Returns:
- A Keras model instance.
+ A `keras.Model` instance.
Raises:
ValueError: in case of invalid argument for `weights`,
@@ -481,9 +485,11 @@
@keras_export('keras.applications.mobilenet_v2.preprocess_input')
def preprocess_input(x, data_format=None):
+ """Preprocesses the input (encoding a batch of images) for the model."""
return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')
@keras_export('keras.applications.mobilenet_v2.decode_predictions')
def decode_predictions(preds, top=5):
+ """Decodes the prediction result from the model."""
return imagenet_utils.decode_predictions(preds, top=top)
