
    ~Wh$(                        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 )zVGG19 model for Keras.

Reference:
  - [Very Deep Convolutional Networks for Large-Scale Image Recognition](
      https://arxiv.org/abs/1409.1556) (ICLR 2015)
    N)backend)imagenet_utils)training)VersionAwareLayers)
data_utils)layer_utils)keras_exportznhttps://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels.h5zthttps://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5zkeras.applications.vgg19.VGG19zkeras.applications.VGG19Timagenet  softmaxc                 :   |dv s7t           j        j                            |          st	          d| d          |dk    r| r|dk    rt	          d| d          t          j        |ddt          j                    | |	          }|t          
                    |          }n3t          j        |          st          
                    ||          }n|}t                              ddddd          |          }t                              ddddd          |          }t                              ddd          |          }t                              ddddd          |          }t                              ddddd          |          }t                              ddd          |          }t                              ddddd          |          }t                              ddddd          |          }t                              ddddd          |          }t                              ddddd          |          }t                              ddd           |          }t                              d!dddd"          |          }t                              d!dddd#          |          }t                              d!dddd$          |          }t                              d!dddd%          |          }t                              ddd&          |          }t                              d!dddd'          |          }t                              d!dddd(          |          }t                              d!dddd)          |          }t                              d!dddd*          |          }t                              ddd+          |          }| rt                              d,-          |          }t                              d.dd/0          |          }t                              d.dd10          |          }t          j        ||           t                              ||d20          |          }nO|d3k    r"t                                          |          }n'|d4k    r!t                                          |          }|t'          j        |          }	n|}	t+          j        |	|d5-          }
|dk    rS| rt/          j        d6t2          d7d89          }nt/          j        d:t4          d7d;9          }|
                    |           n||
                    |           |
S )<a%  Instantiates the VGG19 architecture.

    Reference:
    - [Very Deep Convolutional Networks for Large-Scale Image Recognition](
        https://arxiv.org/abs/1409.1556) (ICLR 2015)

    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 size for this model is 224x224.

    Note: each Keras Application expects a specific kind of input preprocessing.
    For VGG19, call `tf.keras.applications.vgg19.preprocess_input` on your
    inputs before passing them to the model.
    `vgg19.preprocess_input` will convert the input images from RGB to BGR,
    then will zero-center each color channel with respect to the ImageNet
    dataset, without scaling.

    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 `(224, 224, 3)`
        (with `channels_last` data format)
        or `(3, 224, 224)` (with `channels_first` data format).
        It should have exactly 3 inputs channels,
        and width and height should be no smaller than 32.
        E.g. `(200, 200, 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.  Received: `weights=z.`r
   r   zmIf using `weights` as `"imagenet"` with `include_top` as true, `classes` should be 1000.  Received: `classes=       )default_sizemin_sizedata_formatrequire_flattenweightsN)shape)tensorr   @   )   r   relusameblock1_conv1)
activationpaddingnameblock1_conv2)   r    block1_pool)stridesr      block2_conv1block2_conv2block2_pool   block3_conv1block3_conv2block3_conv3block3_conv4block3_pooli   block4_conv1block4_conv2block4_conv3block4_conv4block4_poolblock5_conv1block5_conv2block5_conv3block5_conv4block5_poolflatten)r   i   fc1)r   r   fc2predictionsavgmaxvgg19z+vgg19_weights_tf_dim_ordering_tf_kernels.h5models cbe5617147190e668d6c5d5026f83318)cache_subdir	file_hashz1vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5 253f8cb515780f3b799900260a226db6)tfiogfileexists
ValueErrorr   obtain_input_shaper   image_data_formatlayersInputis_keras_tensorConv2DMaxPooling2DFlattenDensevalidate_activationGlobalAveragePooling2DGlobalMaxPooling2Dr   get_source_inputsr   Modelr   get_fileWEIGHTS_PATHWEIGHTS_PATH_NO_TOPload_weights)include_topr   input_tensorinput_shapepoolingclassesclassifier_activation	img_inputxinputsmodelweights_paths               ^/var/www/html/movieo_spanner_bot/venv/lib/python3.11/site-packages/keras/applications/vgg19.pyVGG19rf   0   s   R )))RU[-?-?-H-H). #*	. . .
 
 	
 *D."). . .
 
 	
 !3-//#  K LL{L33		&|44 	%LLLII$I
FvvN 	 	 		 	A 	
FvvN 	 	 			 		A 	FFGGJJA 	V^ 	 	 			 		A 	V^ 	 	 			 		A 	FFGGJJA 	V^ 	 	 			 		A 	V^ 	 	 			 		A 	V^ 	 	 			 		A 	V^ 	 	 			 		A 	FFGGJJA 	V^ 	 	 			 		A 	V^ 	 	 			 		A 	V^ 	 	 			 		A 	V^ 	 	 			 		A 	FFGGJJA 	V^ 	 	 			 		A 	V^ 	 	 			 		A 	V^ 	 	 			 		A 	V^ 	 	 			 		A 	FFGGJJA /NN	N**1--LL&uL==a@@LL&uL==a@@*+@'JJJLL 5M  
 

  e--//22AA))++A..A .|<<N617333E * 	%.=%<	  LL &.C#%<	  L 	<((((		7###L    z)keras.applications.vgg19.preprocess_inputc                 0    t          j        | |d          S )Ncaffe)r   mode)r   preprocess_input)ra   r   s     re   rk   rk     s#    *	{   rg   z+keras.applications.vgg19.decode_predictions   c                 .    t          j        | |          S )N)top)r   decode_predictions)predsrn   s     re   ro   ro     s    ,U<<<<rg    )rj   reterror)Tr
   NNNr   r   )N)rl   )__doc__tensorflow.compat.v2compatv2rC   kerasr   keras.applicationsr   keras.enginer   keras.layersr   keras.utilsr   r    tensorflow.python.util.tf_exportr	   rW   rX   rJ   rf   rk   ro   PREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_CAFFEPREPROCESS_INPUT_ERROR_DOC rg   re   <module>r      s     " ! ! ! ! ! ! ! !       - - - - - - ! ! ! ! ! ! + + + + + + " " " " " " # # # # # # : 9 9 9 9 98 
8  
			 .0JKK#S S S LKSl 9::   ;: ;<<= = = =<= *>EE	5

3 F    
 ,>F    rg   