application_mobilenet  R Documentation 
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)
input_shape 
optional shape list, only to be specified if 
alpha 
controls the width of the network.

depth_multiplier 
depth multiplier for depthwise convolution (also called the resolution multiplier) 
dropout 
dropout rate 
include_top 
whether to include the fullyconnected layer at the top of the network. 
weights 

input_tensor 
optional Keras tensor (i.e. output of 
pooling 
Optional pooling mode for feature extraction when

classes 
optional number of classes to classify images into, only to be
specified if 
classifier_activation 
A string or callable. The activation function to
use on the "top" layer. Ignored unless 
... 
For backwards and forwards compatibility 
x 
input tensor, 4D 
preds 
Tensor encoding a batch of predictions. 
top 
integer, how many topguesses 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()
.
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).
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