application_vgg16 | R Documentation |
Instantiates the VGG16 model.
application_vgg16(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax"
)
include_top |
whether to include the 3 fully-connected layers at the top of the network. |
weights |
one of |
input_tensor |
optional Keras tensor
(i.e. output of |
input_shape |
optional shape tuple, only to be specified
if |
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 |
A model instance.
For image classification use cases, see this page for detailed examples.
For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
The default input size for this model is 224x224.
Each Keras Application expects a specific kind of input preprocessing.
For VGG16, call application_preprocess_inputs()
on your
inputs before passing them to the model.
application_preprocess_inputs()
will convert the input images from RGB to BGR,
then will zero-center each color channel with respect to the ImageNet
dataset, without scaling.
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