VGG16 and VGG19 models for Keras.
application_vgg16(include_top = TRUE, weights = "imagenet", input_tensor = NULL, input_shape = NULL, pooling = NULL, classes = 1000) application_vgg19(include_top = TRUE, weights = "imagenet", input_tensor = NULL, input_shape = NULL, pooling = NULL, classes = 1000)
include_top  whether to include the 3 fullyconnected layers at the top of the network. 

weights 

input_tensor  optional Keras tensor to use as image input for the model. 
input_shape  optional shape list, 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 
Keras model instance.
Optionally loads weights pretrained on ImageNet.
The imagenet_preprocess_input()
function should be used for image preprocessing.
 Very Deep Convolutional Networks for LargeScale ImageRecognition
# NOT RUN { 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) # }