MobileNet model architecture.
application_mobilenet(input_shape = NULL, alpha = 1, depth_multiplier = 1, dropout = 0.001, include_top = TRUE, weights = "imagenet", input_tensor = NULL, pooling = NULL, classes = 1000) 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 
x  input tensor, 4D 
preds  Tensor encoding a batch of predictions. 
top  integer, how many topguesses to return. 
filepath  File path 
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).
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()
.
MobileNet is currently only supported with the TensorFlow backend.