tf.contrib.keras.applications.Xception

tf.contrib.keras.applications.Xception

tf.contrib.keras.applications.xception.Xception

Xception(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000
)

Defined in tensorflow/contrib/keras/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)
    or "imagenet" (pre-training on ImageNet).
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.