tf.keras.applications.Xception

Aliases:

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

Defined in tensorflow/python/keras/applications/xception.py.

Instantiates the Xception architecture.

Optionally loads weights pre-trained on ImageNet. This model is available for TensorFlow only, and can only be used with inputs following the TensorFlow data format (width, height, channels). You should set image_data_format='channels_last' in your Keras config located at ~/.keras/keras.json.

Note that the default input image size for this model is 299x299.

Arguments:

  • 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 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.

Returns:

A Keras model instance.

Raises:

  • ValueError: in case of invalid argument for weights, or invalid input shape.
  • RuntimeError: If attempting to run this model with a backend that does not support separable convolutions.