application_xception: Instantiates the Xception architecture

In keras: R Interface to 'Keras'

Instantiates the Xception architecture

Description

Instantiates the Xception architecture

Usage

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

xception_preprocess_input(x)

Arguments

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 ⁠(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. 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).
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".
...	For backwards and forwards compatibility
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

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.

Reference

• Xception: Deep Learning with Depthwise Separable Convolutions (CVPR 2017)

Note

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

• https://www.tensorflow.org/api_docs/python/tf/keras/applications/xception/Xception

• https://keras.io/api/applications/

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