application_vgg

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VGG16 and VGG19 models for Keras.

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.

Details

Optionally loads weights pre-trained on ImageNet.

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

Value

Keras model instance.

Reference

- Very Deep Convolutional Networks for Large-Scale Image Recognition

Aliases
  • application_vgg
  • application_vgg16
  • application_vgg19
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)
# }
Documentation reproduced from package keras, version 2.3.0.0, License: MIT + file LICENSE

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