VGG16 and VGG19 models for Keras.
application_vgg16(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000,
classifier_activation = "softmax"
)
application_vgg19(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000,
classifier_activation = "softmax"
)
whether to include the 3 fully-connected layers at the top of the network.
One of NULL
(random initialization),
'imagenet'
(pre-training on ImageNet),
or the path to the weights file to be loaded. Defaults to 'imagenet'
.
Optional Keras tensor
(i.e. output of layer_input()
)
to use as image input for the model.
optional shape list, only to be specified if include_top
is FALSE (otherwise the input shape has to be (224, 224, 3)
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.
Optional pooling mode for feature extraction
when include_top
is FALSE
. Defaults to NULL
.
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.
Optional number of classes to classify images into, only to be
specified if include_top
is TRUE, and if no weights
argument is
specified. Defaults to 1000 (number of ImageNet classes).
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"
.
Keras model instance.
Optionally loads weights pre-trained on ImageNet.
The imagenet_preprocess_input()
function should be used for image preprocessing.
if (FALSE) {
library(keras)
model <- application_vgg16(weights = 'imagenet', include_top = FALSE)
img_path <- "elephant.jpg"
img <- image_load(img_path, target_size = c(224,224))
x <- image_to_array(img)
x <- array_reshape(x, c(1, dim(x)))
x <- imagenet_preprocess_input(x)
features <- model %>% predict(x)
}