# application_vgg

##### 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

##### 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*