VGG16 and VGG19 models for Keras.

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)

Arguments

include_top	whether to include the 3 fully-connected layers at the top of the network.
weights	one of NULL (random initialization) or "imagenet" (pre-training on ImageNet).
input_tensor	optional Keras tensor to use as image input for the model.
input_shape	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 48. E.g. (200, 200, 3) would be one valid value.
pooling	Optional pooling mode for feature extraction when include_top is FALSE. 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.
classes	optional number of classes to classify images into, only to be specified if include_top is TRUE, and if no weights argument is specified.

Value

Keras model instance.

Details

Optionally loads weights pre-trained on ImageNet.

The imagenet_preprocess_input() function should be used for image preprocessing.

Reference

- Very Deep Convolutional Networks for Large-Scale ImageRecognition

Examples

# 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)
dim(x) <- c(1, dim(x))
x <- imagenet_preprocess_input(x)

features <- model %>% predict(x)
# }