Xception
functionkeras.applications.Xception(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
)
Instantiates the Xception architecture.
Reference
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 keras.applications.xception.preprocess_input
on your inputs before passing them to the model.
xception.preprocess_input
will scale input pixels between -1 and 1.
Arguments
None
(random initialization),
"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.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.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.include_top
is True
, and
if no weights
argument is specified.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. When loading pretrained weights, classifier_activation
can
only be None
or "softmax"
.Returns
A model instance.