application_xception: Instantiates the Xception architecture

View source: R/applications.R

application_xceptionR Documentation

Instantiates the Xception architecture


Instantiates the Xception architecture


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




Whether to include the fully-connected layer at the top of the network. Defaults to TRUE.


One of NULL (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to 'imagenet'.


Optional Keras tensor (i.e. output of layer_input()) 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 ⁠(299, 299, 3)⁠. It should have exactly 3 inputs channels, and width and height should be no smaller than 71. E.g. ⁠(150, 150, 3)⁠ would be one valid value.


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.


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).


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".


For backwards and forwards compatibility


preprocess_input() takes an array or floating point tensor, 3D or 4D with 3 color channels, with values in the range ⁠[0, 255]⁠.


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.

The default input image size for this model is 299x299.



Each Keras Application typically expects a specific kind of input preprocessing. For Xception, call xception_preprocess_input() on your inputs before passing them to the model. xception_preprocess_input() will scale input pixels between -1 and 1.

See Also

keras documentation built on May 29, 2024, 3:20 a.m.