ResNet50 model for Keras.
application_resnet50(include_top = TRUE, weights = "imagenet", input_tensor = NULL, input_shape = NULL, pooling = NULL, classes = 1000)
include_top  whether to include the fullyconnected layer at the top of the network. 

weights 

input_tensor  optional Keras tensor to use as image input for the model. 
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 
A Keras model instance.
Optionally loads weights pretrained on ImageNet.
The imagenet_preprocess_input()
function should be used for image
preprocessing.
 Deep Residual Learning for ImageRecognition
# 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]] # }