tf.keras.applications.Xception

TensorFlow 1 version View source on GitHub

Instantiates the Xception architecture.

tf.keras.applications.Xception(
    include_top=True, weights='imagenet', input_tensor=None, input_shape=None,
    pooling=None, classes=1000, classifier_activation='softmax'
)

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json. Note that the default input image size for this model is 299x299.

include_top whether to include the fully-connected layer at the top of the network.
weights one of None (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 layers.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.

  • None 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=None to return the logits of the "top" layer.

A keras.Model instance.

ValueError in case of invalid argument for weights, or invalid input shape.
ValueError if classifier_activation is not softmax or None when using a pretrained top layer.