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Instantiates the Xception architecture.

Source: R/applications.R
application_xception.Rd

Instantiates the Xception architecture.

Usage

application_xception(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  name = "xception"
)

Arguments

include_top

whether to include the 3 fully-connected layers at the top of the network.

weights

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

input_tensor

optional Keras tensor (i.e. output of keras_input()) to use as image input for the model.

input_shape

optional shape tuple, 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.

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 block.

  • avg means that global average pooling will be applied to the output of the last convolutional block, 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.

classifier_activation

A str 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. When loading pretrained weights, classifier_activation can only be NULL or "softmax".

name

The name of the model (string).

Value

A model instance.

Reference

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.

Note

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

See also