R/applications.R

application_resnet

Instantiates the ResNet architecture

Description

Instantiates the ResNet architecture

Usage

 
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)

Arguments

Arguments Description
include_top Whether to include the fully-connected layer at the top of the network. Defaults to TRUE.
weights One of NULL (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to 'imagenet'.
input_tensor Optional Keras tensor (i.e. output of layer_input()) 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 c(224, 224, 3) (with 'channels_last' data format) or c(3, 224, 224) (with 'channels_first' data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. c(200, 200, 3) would be one valid value.
pooling Optional pooling mode for feature extraction when include_top is FALSE. Defaults to NULL.
- 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. Defaults to 1000 (number of ImageNet classes).
… For backwards and forwards compatibility
classifier_activation A string or callable. The activation function to use on the “top” layer. Ignored unless include_top = TRUE. Set classifier_activation = NULL to return the logits of the “top” layer. Defaults to 'softmax'. When loading pretrained weights, classifier_activation can only be NULL or "softmax".
x preprocess_input() takes an array or floating point tensor, 3D or 4D with 3 color channels, with values in the range [0, 255].

Details

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 & fine-tuning. 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 zero-center each color channel with respect to the ImageNet dataset, without scaling.

Examples

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]]

See Also