application_mobilenet_v2 | R Documentation |
MobileNetV2 model architecture
application_mobilenet_v2(
input_shape = NULL,
alpha = 1,
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
pooling = NULL,
classes = 1000,
classifier_activation = "softmax",
...
)
mobilenet_v2_preprocess_input(x)
mobilenet_v2_decode_predictions(preds, top = 5)
mobilenet_v2_load_model_hdf5(filepath)
input_shape |
optional shape list, only to be specified if |
alpha |
controls the width of the network.
|
include_top |
whether to include the fully-connected 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 top-guesses to return. |
filepath |
File path |
application_mobilenet_v2()
and mobilenet_v2_load_model_hdf5()
return a
Keras model instance. mobilenet_v2_preprocess_input()
returns image input
suitable for feeding into a mobilenet v2 model. mobilenet_v2_decode_predictions()
returns a list of data frames with variables class_name
, class_description
,
and score
(one data frame per sample in batch input).
application_mobilenet
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