ResNet50 model for Keras.

  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000



whether to include the fully-connected layer at the top of the network.


NULL (random initialization), imagenet (ImageNet weights), or the path to the weights file to be loaded.


optional Keras tensor to use as image input for the model.


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.


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.


optional number of classes to classify images into, only to be specified if include_top is TRUE, and if no weights argument is specified.


A Keras model instance.


Optionally loads weights pre-trained on ImageNet.

The imagenet_preprocess_input() function should be used for image preprocessing.


- Deep Residual Learning for Image Recognition


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]] }