
    ~Wh5                        d Z ddlmc mZ ddlmZ ddlmZ ddl	m
Z
 ddlmZ ddlmZ ddlmZ dd	lmZ d
ZdZ e            Z edd          	 	 	 	 	 	 	 dd            Z ed          dd            Z ed          dd            Zej                            dej        ej                  e_         ej        j         e_         dS )a  Xception V1 model for Keras.

On ImageNet, this model gets to a top-1 validation accuracy of 0.790
and a top-5 validation accuracy of 0.945.

Reference:
  - [Xception: Deep Learning with Depthwise Separable Convolutions](
      https://arxiv.org/abs/1610.02357) (CVPR 2017)
    N)backend)imagenet_utils)training)VersionAwareLayers)
data_utils)layer_utils)keras_exportzthttps://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels.h5zzhttps://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5z$keras.applications.xception.Xceptionzkeras.applications.XceptionTimagenet  softmaxc           
      V   |dv s3t           j        j                            |          st	          d          |dk    r| r|dk    rt	          d          t          j        |ddt          j                    | |          }|t          
                    |
          }n3t          j        |          st          
                    ||          }n|}t          j                    dk    rdnd}t                              ddddd          |          }	t                              |d          |	          }	t                              dd          |	          }	t                              dddd          |	          }	t                              |d          |	          }	t                              dd          |	          }	t                              dd dd!d"          |	          }
t                              |#          |
          }
t                              ddd!dd$%          |	          }	t                              |d&          |	          }	t                              dd'          |	          }	t                              ddd!dd(%          |	          }	t                              |d)          |	          }	t                              ddd!d*+          |	          }	t                              |	|
g          }	t                              d,d dd!d"          |	          }
t                              |#          |
          }
t                              dd-          |	          }	t                              d,dd!dd.%          |	          }	t                              |d/          |	          }	t                              dd0          |	          }	t                              d,dd!dd1%          |	          }	t                              |d2          |	          }	t                              ddd!d3+          |	          }	t                              |	|
g          }	t                              d4d dd!d"          |	          }
t                              |#          |
          }
t                              dd5          |	          }	t                              d4dd!dd6%          |	          }	t                              |d7          |	          }	t                              dd8          |	          }	t                              d4dd!dd9%          |	          }	t                              |d:          |	          }	t                              ddd!d;+          |	          }	t                              |	|
g          }	t%          d<          D ]}|	}
d=t'          |d>z             z   }t                              d|d?z             |	          }	t                              d4dd!d|d@z   %          |	          }	t                              ||dAz             |	          }	t                              d|dBz             |	          }	t                              d4dd!d|dCz   %          |	          }	t                              ||dDz             |	          }	t                              d|dEz             |	          }	t                              d4dd!d|dFz   %          |	          }	t                              ||dGz             |	          }	t                              |	|
g          }	t                              dHd dd!d"          |	          }
t                              |#          |
          }
t                              ddI          |	          }	t                              d4dd!ddJ%          |	          }	t                              |dK          |	          }	t                              ddL          |	          }	t                              dHdd!ddM%          |	          }	t                              |dN          |	          }	t                              ddd!dO+          |	          }	t                              |	|
g          }	t                              dPdd!ddQ%          |	          }	t                              |dR          |	          }	t                              ddS          |	          }	t                              dTdd!ddU%          |	          }	t                              |dV          |	          }	t                              ddW          |	          }	| r^t                              dX          |	          }	t          j        ||           t                              ||dYZ          |	          }	nO|d[k    r"t                                          |	          }	n'|d\k    r!t                                          |	          }	|t1          j        |          }n|}t5          j        ||	d]          }|dk    rS| rt9          j        d^t<          d_d`a          }nt9          j        dbt>          d_dca          }|                     |           n||                     |           |S )daU
  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 `tf.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 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 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 `keras.Model` instance.
    >   Nr
   zThe `weights` argument should be either `None` (random initialization), `imagenet` (pre-training on ImageNet), or the path to the weights file to be loaded.r
   r   zWIf using `weights` as `"imagenet"` with `include_top` as true, `classes` should be 1000i+  G   )default_sizemin_sizedata_formatrequire_flattenweightsN)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maxxceptionz.xception_weights_tf_dim_ordering_tf_kernels.h5models 0a58e3b7378bc2990ea3b43d5981f1f6)cache_subdir	file_hashz4xception_weights_tf_dim_ordering_tf_kernels_notop.h5 b0042744bf5b25fce3cb969f33bebb97)!tfiogfileexists
ValueErrorr   obtain_input_shaper   image_data_formatlayersInputis_keras_tensorConv2DBatchNormalization
ActivationSeparableConv2DMaxPooling2DaddrangestrGlobalAveragePooling2Dvalidate_activationDenseGlobalMaxPooling2Dr   get_source_inputsr   Modelr   get_fileTF_WEIGHTS_PATHTF_WEIGHTS_PATH_NO_TOPload_weights)include_topr   input_tensorinput_shapepoolingclassesclassifier_activation	img_inputchannel_axisxresidualiprefixinputsmodelweights_paths                   a/var/www/html/movieo_spanner_bot/venv/lib/python3.11/site-packages/keras/applications/xception.pyXceptionr   2   s   N )))RU[-?-?-H-H)<
 
 	
 *D1
 
 	
 !3-//#  K LL{L33		&|44 	%LLLII$I1337GGG11RL
FFU 	 	 		 	A 	!!|:K!LLQOOA&'9::1==Ab&5~FFqIIA!!|:K!LLQOOA&'9::1==A}}VVVe   	 	H ((l(;;HEEHVVe:K 	 	 			 		A 	!!|:N!OO		 	A 	&'<==a@@AVVe:K 	 	 			 		A 	!!|:N!OO		 	A 	] 	 	 			 		A 	

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

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

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

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

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

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

Ax=!!Affu;M 	 	 			 		A 	!! 5 	" 	 			 		A 	&'=>>qAAAffu;M 	 	 			 		A 	!! 5 	" 	 			 		A 	&'=>>qAAA 
/))z)::1==*+@'JJJLL 5M  
 

  e--//22AA))++A..A .|<<N61:666E * 	%.@%<	  LL &.F&%<	  L 	<((((		7###L    z,keras.applications.xception.preprocess_inputc                 0    t          j        | |d          S )Nrd   )r   mode)r   preprocess_input)r   r   s     r   r   r   k  s#    *	{   r   z.keras.applications.xception.decode_predictionsrB   c                 .    t          j        | |          S )N)top)r   decode_predictions)predsr   s     r   r   r   r  s    ,U<<<<r    )r   reterror)Tr
   NNNr   r   )N)rB   )__doc__tensorflow.compat.v2compatv2rd   kerasr   keras.applicationsr   keras.enginer   keras.layersr   keras.utilsr   r    tensorflow.python.util.tf_exportr	   r}   r~   rk   r   r   r   PREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_ERROR_DOC r   r   <module>r      s     " ! ! ! ! ! ! ! !       - - - - - - ! ! ! ! ! ! + + + + + + " " " " " " # # # # # # : 9 9 9 9 9> 
D 
 
			 *,I  #s s s sl	 <==   >= >??= = = @?= *>EE	2

3 F    
 ,>F    r   