keras.applications.Xception( include_top=True, weights="imagenet", input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation="softmax", )
Instantiates the Xception architecture.
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
on your inputs before passing them to the model.
xception.preprocess_input will scale input pixels between -1 and 1.
"imagenet"(pre-training on ImageNet), or the path to the weights file to be loaded.
layers.Input()) to use as image input for the model.
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.
Nonemeans that the output of the model will be the 4D tensor output of the last convolutional block. -
avgmeans 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. -
maxmeans that global max pooling will be applied.
True, and if no
weightsargument is specified.
stror callable. The activation function to use on the "top" layer. Ignored unless
classifier_activation=Noneto return the logits of the "top" layer. When loading pretrained weights,
classifier_activationcan only be
A model instance.