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keras

application_resnet50

ResNet50 model for Keras.

Description

ResNet50 model for Keras.

Usage

application_resnet50(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000
)

Arguments

include_top

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

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

optional Keras tensor 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 (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.
pooling

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.

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.

Details

Optionally loads weights pre-trained on ImageNet.

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

Value

A Keras model instance.

Reference

Examples

## 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)
keras

R Interface to 'Keras'

v2.4.0
MIT + file LICENSE
Authors
Daniel Falbel [ctb, cph, cre], JJ Allaire [aut, cph], François Chollet [aut, cph], RStudio [ctb, cph, fnd], Google [ctb, cph, fnd], Yuan Tang [ctb, cph] (<https://orcid.org/0000-0001-5243-233X>), Wouter Van Der Bijl [ctb, cph], Martin Studer [ctb, cph], Sigrid Keydana [ctb]
Initial release

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