keras (version 2.2.4)

application_vgg: VGG16 and VGG19 models for Keras.

Description

VGG16 and VGG19 models for Keras.

Usage

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

NULL (random initialization), imagenet (ImageNet weights), or the path to the weights file to be loaded.

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 32. 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.

Reference

- Very Deep Convolutional Networks for Large-Scale Image Recognition

Details

Optionally loads weights pre-trained on ImageNet.

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

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

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