ResNet50 model for Keras.

ResNet50 model for Keras.

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.

Value

A Keras model instance.

Details

Optionally loads weights pre-trained on ImageNet.

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

Reference

- Deep Residual Learning for Image Recognition

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]]
# }