`R/applications.R`

`application_inception_resnet_v2.Rd`

Inception-ResNet v2 model, with weights trained on ImageNet

```
application_inception_resnet_v2(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000,
classifier_activation = "softmax",
...
)
inception_resnet_v2_preprocess_input(x)
```

- include_top
Whether to include the fully-connected layer at the top of the network. Defaults to

`TRUE`

.- weights
One of

`NULL`

(random initialization),`'imagenet'`

(pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to`'imagenet'`

.- input_tensor
Optional Keras tensor (i.e. output of

`layer_input()`

) 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`(299, 299, 3)`

. It should have exactly 3 inputs channels, and width and height should be no smaller than 71. E.g.`(150, 150, 3)`

would be one valid value.- pooling
Optional pooling mode for feature extraction when

`include_top`

is`FALSE`

. Defaults to`NULL`

.`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. Defaults to 1000 (number of ImageNet classes).- classifier_activation
A string or callable. The activation function to use on the "top" layer. Ignored unless

`include_top = TRUE`

. Set`classifier_activation = NULL`

to return the logits of the "top" layer. Defaults to`'softmax'`

. When loading pretrained weights,`classifier_activation`

can only be`NULL`

or`"softmax"`

.- ...
For backwards and forwards compatibility

- x
`preprocess_input()`

takes an array or floating point tensor, 3D or 4D with 3 color channels, with values in the range`[0, 255]`

.

A Keras model instance.

Do note that the input image format for this model is different than for the VGG16 and ResNet models (299x299 instead of 224x224).

The `inception_resnet_v2_preprocess_input()`

function should be used for image
preprocessing.

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning(https://arxiv.org/abs/1512.00567)