Instantiates the ResNet architecture

```
application_resnet50(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000,
...
)
application_resnet101(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000,
...
)
application_resnet152(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000,
...
)
application_resnet50_v2(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000,
classifier_activation = "softmax",
...
)
application_resnet101_v2(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000,
classifier_activation = "softmax",
...
)
application_resnet152_v2(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000,
classifier_activation = "softmax",
...
)
resnet_preprocess_input(x)
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`c(224, 224, 3)`

(with`'channels_last'`

data format) or`c(3, 224, 224)`

(with`'channels_first'`

data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g.`c(200, 200, 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).- ...
For backwards and forwards compatibility

- 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"`

.- x
`preprocess_input()`

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

.

Reference:

Deep Residual Learning for Image Recognition (CVPR 2015)

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing.
For ResNet, call `tf.keras.applications.resnet.preprocess_input`

on your
inputs before passing them to the model.
`resnet.preprocess_input`

will convert the input images from RGB to BGR,
then will zero-center each color channel with respect to the ImageNet dataset,
without scaling.

https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet50/ResNet50

https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet/ResNet101

https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet/ResNet152

https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet_v2/ResNet50V2

https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet_v2/ResNet101V2

https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet_v2/ResNet152V2

```
if (FALSE) {
library(keras)
# instantiate the model
model <- application_resnet50(weights = 'imagenet')
# load the image
img_path <- "elephant.jpg"
img <- image_load(img_path, target_size = c(224,224))
x <- image_to_array(img)
# ensure we have a 4d tensor with single element in the batch dimension,
# the preprocess the input for prediction using resnet50
x <- array_reshape(x, c(1, dim(x)))
x <- imagenet_preprocess_input(x)
# make predictions then decode and print them
preds <- model %>% predict(x)
imagenet_decode_predictions(preds, top = 3)[[1]]
}
```