MobileNet model architecture.

```
application_mobilenet(
input_shape = NULL,
alpha = 1,
depth_multiplier = 1L,
dropout = 0.001,
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
...
)
```mobilenet_preprocess_input(x)

mobilenet_decode_predictions(preds, top = 5)

mobilenet_load_model_hdf5(filepath)

`application_mobilenet()`

and `mobilenet_load_model_hdf5()`

return a
Keras model instance. `mobilenet_preprocess_input()`

returns image input
suitable for feeding into a mobilenet model. `mobilenet_decode_predictions()`

returns a list of data frames with variables `class_name`

, `class_description`

,
and `score`

(one data frame per sample in batch input).

- input_shape
optional shape list, 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
`NULL`

(random initialization),`imagenet`

(ImageNet weights), or the path to the weights file to be loaded.- input_tensor
optional Keras tensor (i.e. output of

`layer_input()`

) to use as image input for the model.- pooling
Optional pooling mode for feature extraction when

`include_top`

is`FALSE`

. -`NULL`

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.- classifier_activation
A string or callable. The activation function to use on the "top" layer. Ignored unless

`include_top = TRUE`

. Set`classifier_activation = NULL`

to return the logits of the "top" layer. Defaults to`'softmax'`

. When loading pretrained weights,`classifier_activation`

can only be`NULL`

or`"softmax"`

.- ...
For backwards and forwards compatibility

- x
input tensor, 4D

- preds
Tensor encoding a batch of predictions.

- top
integer, how many top-guesses to return.

- filepath
File path

The `mobilenet_preprocess_input()`

function should be used for image
preprocessing. To load a saved instance of a MobileNet model use
the `mobilenet_load_model_hdf5()`

function. To prepare image input
for MobileNet use `mobilenet_preprocess_input()`

. To decode
predictions use `mobilenet_decode_predictions()`

.