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n?|dZk(  r t        j$                         |
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n|d[k(  r t        j*                         |
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|t-        j.                  |      }n|}t1        ||
|      }|dk(  rP| rt        j2                  d\t4        d]d^_      }nt        j2                  d`t6        d]da_      }|j9                  |       |S ||j9                  |       |S )ba  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. Defaults to `1000`.
        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"`.
        name: The name of the model (string).

    Returns:
        A model instance.
    >   NimagenetzThe `weights` argument should be either `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.r     zcIf using `weights='imagenet'` with `include_top=True`, `classes` should be 1000.  Received classes=i+  G   )default_sizemin_sizedata_formatrequire_flattenweights)shape)tensorr   channels_first       )   r   )   r   Fblock1_conv1)stridesuse_biasnameblock1_conv1_bn)axisr   relublock1_conv1_act)r   @   block1_conv2)r   r   block1_conv2_bnblock1_conv2_act   )r   r   same)r   paddingr   )r    block2_sepconv1)r)   r   r   block2_sepconv1_bnblock2_sepconv2_actblock2_sepconv2block2_sepconv2_bnblock2_pool)r   r)   r      block3_sepconv1_actblock3_sepconv1block3_sepconv1_bnblock3_sepconv2_actblock3_sepconv2block3_sepconv2_bnblock3_pooli  block4_sepconv1_actblock4_sepconv1block4_sepconv1_bnblock4_sepconv2_actblock4_sepconv2block4_sepconv2_bnblock4_pool   block   _sepconv1_act	_sepconv1_sepconv1_bn_sepconv2_act	_sepconv2_sepconv2_bn_sepconv3_act	_sepconv3_sepconv3_bni   block13_sepconv1_actblock13_sepconv1block13_sepconv1_bnblock13_sepconv2_actblock13_sepconv2block13_sepconv2_bnblock13_pooli   block14_sepconv1block14_sepconv1_bnblock14_sepconv1_acti   block14_sepconv2block14_sepconv2_bnblock14_sepconv2_actavg_poolpredictions)
activationr   avgmaxz.xception_weights_tf_dim_ordering_tf_kernels.h5models 0a58e3b7378bc2990ea3b43d5981f1f6)cache_subdir	file_hashz4xception_weights_tf_dim_ordering_tf_kernels_notop.h5 b0042744bf5b25fce3cb969f33bebb97)r	   exists
ValueErrorr   obtain_input_shaper   image_data_formatr   Inputis_keras_tensorConv2DBatchNormalization
ActivationSeparableConv2DMaxPooling2DaddrangestrGlobalAveragePooling2Dvalidate_activationDenseGlobalMaxPooling2Dr   get_source_inputsr   get_fileWEIGHTS_PATHWEIGHTS_PATH_NO_TOPload_weights)include_topr   input_tensorinput_shapepoolingclassesclassifier_activationr   	img_inputchannel_axisxresidualiprefixinputsmodelweights_paths                    O/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/applications/xception.pyXceptionr      s
   X ))Z->->w-G<
 	
 *D  'y*
 	
 !33--/#K LL{3	&&|4LLI$I1137GG1RL	
FFU		A 	M!!|:KLQOA:&'9:1=AFb&5~FqIAL!!|:KLQOA:&'9:1=Av}}VVVe	H <v((l;HEH	VVe:K			A 	P!!|:NO		A 	>&'<=a@A	VVe:K			A 	P!!|:NO		A	]			A 	

Ax=!Av}}VVVe	H <v((l;HEH=&'<=a@A	VVe:K			A 	P!!|:NO		A 	>&'<=a@A	VVe:K			A 	P!!|:NO		A	]			A 	

Ax=!Av}}VVVe	H <v((l;HEH=&'<=a@A	VVe:K			A 	P!!|:NO		A 	>&'<=a@A	VVe:K			A 	P!!|:NO		A	]			A 	

Ax=!A1X &&3q1u:%DFf6O+CDQG
F""+%
 
F%%F^$;

 EFf6O+CDQG
F""+%
 
F%%F^$;

 EFf6O+CDQG
F""+%
 
F%%F^$;

 JJ8}%M&&Pv}}fffu	H <v((l;HEH>&'=>qAA	VVe:L			A	!! 5			A 	?&'=>qAA	ffu;M			A	!! 5			A	^			A 	

Ax=!A	ffu;M			A	!! 5			A 	?&'=>qAA	ffu;M			A	!! 5			A 	?&'=>qAA:F))z:1=**+@'J
FLL 5M

 e/--/2A+))+A.A  22<@vqt,E *%..@%<	L &..F#%<	L 	<( L 
	7#L    z,keras.applications.xception.preprocess_inputc                 2    t        j                  | |d      S )Ntf)r   mode)r   preprocess_input)r   r   s     r   r   r   R  s    **	{ r   z.keras.applications.xception.decode_predictionsc                 0    t        j                  | |      S )N)top)r   decode_predictions)predsr   s     r   r   r   Y  s    ,,U<<r    )r   reterror)Tr   NNNr   softmaxxception)N)rA   )	keras.srcr   r   keras.src.api_exportr   keras.src.applicationsr   keras.src.modelsr   keras.src.opsr   keras.src.utilsr	   rv   rw   r   r   r   PREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_ERROR_DOC__doc__ r   r   <module>r      s      - 1 ' ) &> 
D  .% #	vvr	 <= > >?= @= *>>EE	22

3
3 F   
 ,>>FF  r   