application_resnet  R Documentation 
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 fullyconnected
layer at the top of the network. Defaults to 
weights 
One of 
input_tensor 
Optional Keras tensor
(i.e. output of 
input_shape 
optional shape list, only to be specified
if 
pooling 
Optional pooling mode for feature extraction
when

classes 
Optional number of classes to classify images into, only to be
specified if 
... 
For backwards and forwards compatibility 
classifier_activation 
A string or callable. The activation function to
use on the "top" layer. Ignored unless 
x 

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 & finetuning.
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 zerocenter 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
## Not run: 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]] ## End(Not run)
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