instantiates the xception architecture reference xception deep learning with depthwise separable convolutions https arxiv orgabs1610 02357 cvpr 2017 for image classification use cases see this page for detailed examples https keras ioapiapplicationsusageexamplesforimageclassificationmodels for transfer learning use cases make sure to read the guide to transfer learning finetuning https keras ioguidestransferlearning 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 preprocessinput on your inputs before passing them to the model xception preprocessinput will scale input pixels between 1 and 1 args includetop whether to include the 3 fullyconnected layers at the top of the network weights one of none random initialization imagenet pretraining on imagenet or the path to the weights file to be loaded inputtensor optional keras tensor i e output of layers input to use as image input for the model inputshape optional shape tuple only to be specified if includetop 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 includetop 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 includetop is true and if no weights argument is specified classifieractivation a str or callable the activation function to use on the top layer ignored unless includetoptrue set classifieractivationnone to return the logits of the top layer when loading pretrained weights classifieractivation can only be none or softmax returns a model instance determine proper input shape ensure that the model takes into account any potential predecessors of inputtensor create model load weights instantiates the xception architecture reference xception deep learning with depthwise separable convolutions https arxiv org abs 1610 02357 cvpr 2017 for image classification use cases see this page for detailed examples https keras io api applications usage examples for image classification models for transfer learning use cases make sure to read the guide to transfer learning fine tuning https keras io guides transfer_learning 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 args include_top whether to include the 3 fully connected layers 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 when loading pretrained weights classifier_activation can only be none or softmax returns a model instance determine proper input shape ensure that the model takes into account any potential predecessors of input_tensor create model load weights
from keras import backend
from keras import layers
from keras.api_export import keras_export
from keras.applications import imagenet_utils
from keras.models import Functional
from keras.ops import operation_utils
from keras.utils import file_utils

WEIGHTS_PATH = (
    "https://storage.googleapis.com/tensorflow/keras-applications/"
    "xception/xception_weights_tf_dim_ordering_tf_kernels.h5"
)
WEIGHTS_PATH_NO_TOP = (
    "https://storage.googleapis.com/tensorflow/keras-applications/"
    "xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5"
)


@keras_export(
    [
        "keras.applications.xception.Xception",
        "keras.applications.Xception",
    ]
)
def Xception(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
):
    
    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
        raise ValueError(
            "The `weights` argument should be either "
            "`None` (random initialization), 'imagenet' "
            "(pre-training on ImageNet), "
            "or the path to the weights file to be loaded."
        )

    if weights == "imagenet" and include_top and classes != 1000:
        raise ValueError(
            "If using `weights='imagenet'` with `include_top=True`, "
            "`classes` should be 1000.  "
            f"Received classes={classes}"
        )

    
    input_shape = imagenet_utils.obtain_input_shape(
        input_shape,
        default_size=299,
        min_size=71,
        data_format=backend.image_data_format(),
        require_flatten=include_top,
        weights=weights,
    )

    if input_tensor is None:
        img_input = layers.Input(shape=input_shape)
    else:
        if not backend.is_keras_tensor(input_tensor):
            img_input = layers.Input(tensor=input_tensor, shape=input_shape)
        else:
            img_input = input_tensor

    channel_axis = 1 if backend.image_data_format() == "channels_first" else -1

    x = layers.Conv2D(
        32, (3, 3), strides=(2, 2), use_bias=False, name="block1_conv1"
    )(img_input)
    x = layers.BatchNormalization(axis=channel_axis, name="block1_conv1_bn")(x)
    x = layers.Activation("relu", name="block1_conv1_act")(x)
    x = layers.Conv2D(64, (3, 3), use_bias=False, name="block1_conv2")(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block1_conv2_bn")(x)
    x = layers.Activation("relu", name="block1_conv2_act")(x)

    residual = layers.Conv2D(
        128, (1, 1), strides=(2, 2), padding="same", use_bias=False
    )(x)
    residual = layers.BatchNormalization(axis=channel_axis)(residual)

    x = layers.SeparableConv2D(
        128, (3, 3), padding="same", use_bias=False, name="block2_sepconv1"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block2_sepconv1_bn")(
        x
    )
    x = layers.Activation("relu", name="block2_sepconv2_act")(x)
    x = layers.SeparableConv2D(
        128, (3, 3), padding="same", use_bias=False, name="block2_sepconv2"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block2_sepconv2_bn")(
        x
    )

    x = layers.MaxPooling2D(
        (3, 3), strides=(2, 2), padding="same", name="block2_pool"
    )(x)
    x = layers.add([x, residual])

    residual = layers.Conv2D(
        256, (1, 1), strides=(2, 2), padding="same", use_bias=False
    )(x)
    residual = layers.BatchNormalization(axis=channel_axis)(residual)

    x = layers.Activation("relu", name="block3_sepconv1_act")(x)
    x = layers.SeparableConv2D(
        256, (3, 3), padding="same", use_bias=False, name="block3_sepconv1"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block3_sepconv1_bn")(
        x
    )
    x = layers.Activation("relu", name="block3_sepconv2_act")(x)
    x = layers.SeparableConv2D(
        256, (3, 3), padding="same", use_bias=False, name="block3_sepconv2"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block3_sepconv2_bn")(
        x
    )

    x = layers.MaxPooling2D(
        (3, 3), strides=(2, 2), padding="same", name="block3_pool"
    )(x)
    x = layers.add([x, residual])

    residual = layers.Conv2D(
        728, (1, 1), strides=(2, 2), padding="same", use_bias=False
    )(x)
    residual = layers.BatchNormalization(axis=channel_axis)(residual)

    x = layers.Activation("relu", name="block4_sepconv1_act")(x)
    x = layers.SeparableConv2D(
        728, (3, 3), padding="same", use_bias=False, name="block4_sepconv1"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block4_sepconv1_bn")(
        x
    )
    x = layers.Activation("relu", name="block4_sepconv2_act")(x)
    x = layers.SeparableConv2D(
        728, (3, 3), padding="same", use_bias=False, name="block4_sepconv2"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block4_sepconv2_bn")(
        x
    )

    x = layers.MaxPooling2D(
        (3, 3), strides=(2, 2), padding="same", name="block4_pool"
    )(x)
    x = layers.add([x, residual])

    for i in range(8):
        residual = x
        prefix = "block" + str(i + 5)

        x = layers.Activation("relu", name=prefix + "_sepconv1_act")(x)
        x = layers.SeparableConv2D(
            728,
            (3, 3),
            padding="same",
            use_bias=False,
            name=prefix + "_sepconv1",
        )(x)
        x = layers.BatchNormalization(
            axis=channel_axis, name=prefix + "_sepconv1_bn"
        )(x)
        x = layers.Activation("relu", name=prefix + "_sepconv2_act")(x)
        x = layers.SeparableConv2D(
            728,
            (3, 3),
            padding="same",
            use_bias=False,
            name=prefix + "_sepconv2",
        )(x)
        x = layers.BatchNormalization(
            axis=channel_axis, name=prefix + "_sepconv2_bn"
        )(x)
        x = layers.Activation("relu", name=prefix + "_sepconv3_act")(x)
        x = layers.SeparableConv2D(
            728,
            (3, 3),
            padding="same",
            use_bias=False,
            name=prefix + "_sepconv3",
        )(x)
        x = layers.BatchNormalization(
            axis=channel_axis, name=prefix + "_sepconv3_bn"
        )(x)

        x = layers.add([x, residual])

    residual = layers.Conv2D(
        1024, (1, 1), strides=(2, 2), padding="same", use_bias=False
    )(x)
    residual = layers.BatchNormalization(axis=channel_axis)(residual)

    x = layers.Activation("relu", name="block13_sepconv1_act")(x)
    x = layers.SeparableConv2D(
        728, (3, 3), padding="same", use_bias=False, name="block13_sepconv1"
    )(x)
    x = layers.BatchNormalization(
        axis=channel_axis, name="block13_sepconv1_bn"
    )(x)
    x = layers.Activation("relu", name="block13_sepconv2_act")(x)
    x = layers.SeparableConv2D(
        1024, (3, 3), padding="same", use_bias=False, name="block13_sepconv2"
    )(x)
    x = layers.BatchNormalization(
        axis=channel_axis, name="block13_sepconv2_bn"
    )(x)

    x = layers.MaxPooling2D(
        (3, 3), strides=(2, 2), padding="same", name="block13_pool"
    )(x)
    x = layers.add([x, residual])

    x = layers.SeparableConv2D(
        1536, (3, 3), padding="same", use_bias=False, name="block14_sepconv1"
    )(x)
    x = layers.BatchNormalization(
        axis=channel_axis, name="block14_sepconv1_bn"
    )(x)
    x = layers.Activation("relu", name="block14_sepconv1_act")(x)

    x = layers.SeparableConv2D(
        2048, (3, 3), padding="same", use_bias=False, name="block14_sepconv2"
    )(x)
    x = layers.BatchNormalization(
        axis=channel_axis, name="block14_sepconv2_bn"
    )(x)
    x = layers.Activation("relu", name="block14_sepconv2_act")(x)

    if include_top:
        x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
        imagenet_utils.validate_activation(classifier_activation, weights)
        x = layers.Dense(
            classes, activation=classifier_activation, name="predictions"
        )(x)
    else:
        if pooling == "avg":
            x = layers.GlobalAveragePooling2D()(x)
        elif pooling == "max":
            x = layers.GlobalMaxPooling2D()(x)

    
    
    if input_tensor is not None:
        inputs = operation_utils.get_source_inputs(input_tensor)
    else:
        inputs = img_input
    
    model = Functional(inputs, x, name="xception")

    
    if weights == "imagenet":
        if include_top:
            weights_path = file_utils.get_file(
                "xception_weights_tf_dim_ordering_tf_kernels.h5",
                WEIGHTS_PATH,
                cache_subdir="models",
                file_hash="0a58e3b7378bc2990ea3b43d5981f1f6",
            )
        else:
            weights_path = file_utils.get_file(
                "xception_weights_tf_dim_ordering_tf_kernels_notop.h5",
                WEIGHTS_PATH_NO_TOP,
                cache_subdir="models",
                file_hash="b0042744bf5b25fce3cb969f33bebb97",
            )
        model.load_weights(weights_path)
    elif weights is not None:
        model.load_weights(weights)

    return model


@keras_export("keras.applications.xception.preprocess_input")
def preprocess_input(x, data_format=None):
    return imagenet_utils.preprocess_input(
        x, data_format=data_format, mode="tf"
    )


@keras_export("keras.applications.xception.decode_predictions")
def decode_predictions(preds, top=5):
    return imagenet_utils.decode_predictions(preds, top=top)


preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
    mode="",
    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__