Update applications.md

docs/templates/applications.md

@@ -14,10 +14,12 @@ Weights are downloaded automatically when instantiating a model. They are stored
 - [VGG19](#vgg19)
 - [ResNet50](#resnet50)
 - [InceptionV3](#inceptionv3)
+- [MobileNet](#mobilenet)
 
-All of these architectures (except Xception) are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image data format set in your Keras configuration file at `~/.keras/keras.json`. For instance, if you have set `image_data_format=channels_last`, then any model loaded from this repository will get built according to the TensorFlow data format convention, "Width-Height-Depth".
+All of these architectures (except Xception and MobileNet) are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image data format set in your Keras configuration file at `~/.keras/keras.json`. For instance, if you have set `image_data_format=channels_last`, then any model loaded from this repository will get built according to the TensorFlow data format convention, "Width-Height-Depth".
 
 The Xception model is only available for TensorFlow, due to its reliance on `SeparableConvolution` layers.
+The MobileNet model is only available for TensorFlow, due to its reliance on `DepthwiseConvolution` layers.
 
 -----
 
@@ -168,6 +170,7 @@ model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=T
 - [VGG19](#vgg19)
 - [ResNet50](#resnet50)
 - [InceptionV3](#inceptionv3)
+- [MobileNet](#mobilenet)
 
 -----
 
@@ -176,7 +179,7 @@ model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=T
 
 
 ```python
-keras.applications.xception.Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)
+keras.applications.xception.Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
 ```
 
 Xception V1 model, with weights pre-trained on ImageNet.
@@ -235,7 +238,7 @@ These weights are trained by ourselves and are released under the MIT license.
 ## VGG16
 
 ```python
-keras.applications.vgg16.VGG16(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)
+keras.applications.vgg16.VGG16(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
 ```
 
 VGG16 model, with weights pre-trained on ImageNet.
@@ -290,7 +293,7 @@ These weights are ported from the ones [released by VGG at Oxford](http://www.ro
 
 
 ```python
-keras.applications.vgg19.VGG19(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)
+keras.applications.vgg19.VGG19(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
 ```
 
 
@@ -347,7 +350,7 @@ These weights are ported from the ones [released by VGG at Oxford](http://www.ro
 
 
 ```python
-keras.applications.resnet50.ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)
+keras.applications.resnet50.ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
 ```
 
 
@@ -404,7 +407,7 @@ These weights are ported from the ones [released by Kaiming He](https://github.c
 
 
 ```python
-keras.applications.inception_v3.InceptionV3(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)
+keras.applications.inception_v3.InceptionV3(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
 ```
 
 Inception V3 model, with weights pre-trained on ImageNet.
@@ -453,3 +456,84 @@ A Keras model instance.
 ### License
 
 These weights are released under [the Apache License](https://github.com/tensorflow/models/blob/master/LICENSE).
+
+-----
+
+## MobileNet
+
+
+```python
+keras.applications.mobilenet.MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000)
+```
+
+MobileNet model, with weights pre-trained on ImageNet.
+
+Note that only TensorFlow is supported for now,
+therefore it only works with the data format
+`image_data_format='channels_last'` in your Keras config at `~/.keras/keras.json`.
+To load a MobileNet model via `load_model`, import the custom objects `relu6` and `DepthwiseConv2D` and pass them to the `custom_objects` parameter.
+
+E.g.
+
+```python
+model = load_model('mobilenet.h5', custom_objects={
+                   'relu6': mobilenet.relu6,
+                   'DepthwiseConv2D': mobilenet.DepthwiseConv2D})
+```
+
+
+The default input size for this model is 224x224.
+
+### Arguments
+
+- input_shape: optional shape tuple, only to be specified
+    if `include_top` is False (otherwise the input shape
+    has to be `(224, 224, 3)` (with `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.
+- alpha: controls the width of the network.
+    - 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.
+- depth_multiplier: depth multiplier for depthwise convolution
+    (also called the resolution multiplier)
+- dropout: dropout rate
+- include_top: whether to include the fully-connected
+    layer at the top of the network.
+- weights: `None` (random initialization) or
+    `imagenet` (ImageNet weights)
+- 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
+    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
+        will be applied to the output of the
+        last convolutional layer, and thus
+        the output of the model will be a
+        2D tensor.
+    - `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.
+
+### Returns
+
+A Keras model instance.
+
+### References
+
+- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/pdf/1704.04861.pdf)
+
+### License
+
+These weights are released under [the Apache License](https://github.com/tensorflow/models/blob/master/LICENSE).