
    &Vf                     .   d dl mZ d dlmZ d dlmZ  eddg          	 	 	 	 	 	 	 dd            Z eddg          	 	 	 	 	 	 	 dd            Z eddg          	 	 	 	 	 	 	 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Z eedej        ez               eedej        ez               eedej        ez              dS )    )keras_export)imagenet_utils)resnetzkeras.applications.ResNet50V2z'keras.applications.resnet_v2.ResNet50V2TimagenetN  softmaxc                 F    d }t          j        |ddd| ||||||          S )z)Instantiates the ResNet50V2 architecture.c                     t          j        | ddd          } t          j        | ddd          } t          j        | dd	d
          } t          j        | dddd          S )N@      conv2name      conv3      conv4      conv5stride1r   r   stack_residual_blocks_v2xs    ]/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/applications/resnet_v2.pystack_fnzResNet50V2.<locals>.stack_fn   sr    +Ar17CCC+AsAGDDD+AsAGDDD.sAqw
 
 
 	
    T
resnet50v2classifier_activationr   ResNetinclude_topweightsinput_tensorinput_shapepoolingclassesr$   r    s           r   
ResNet50V2r.      sM    "
 
 
 =3   r!   zkeras.applications.ResNet101V2z(keras.applications.resnet_v2.ResNet101V2c                 F    d }t          j        |ddd| ||||||          S )z*Instantiates the ResNet101V2 architecture.c                     t          j        | ddd          } t          j        | ddd          } t          j        | dd	d
          } t          j        | dddd          S )Nr   r   r   r   r   r   r   r      r   r   r   r   r   r   r   s    r   r    zResNet101V2.<locals>.stack_fn?   r    +Ar17CCC+AsAGDDD+AsBWEEE.sAqw
 
 
 	
r!   Tresnet101v2r#   r%   r'   s           r   ResNet101V2r4   .   M    "
 
 
 =3   r!   zkeras.applications.ResNet152V2z(keras.applications.resnet_v2.ResNet152V2c                 F    d }t          j        |ddd| ||||||          S )z*Instantiates the ResNet152V2 architecture.c                     t          j        | ddd          } t          j        | ddd          } t          j        | dd	d
          } t          j        | dddd          S )Nr   r   r   r   r      r   r   $   r   r   r   r   r   r   r   s    r   r    zResNet152V2.<locals>.stack_fng   r2   r!   Tresnet152v2r#   r%   r'   s           r   ResNet152V2r;   V   r5   r!   z-keras.applications.resnet_v2.preprocess_inputc                 0    t          j        | |d          S )Ntf)data_formatmode)r   preprocess_input)r   r>   s     r   r@   r@   ~   s#    *	{   r!   z/keras.applications.resnet_v2.decode_predictions   c                 .    t          j        | |          S )N)top)r   decode_predictions)predsrC   s     r   rD   rD      s    ,U<<<<r!    )r?   reterrora	  

Reference:
- [Identity Mappings in Deep Residual Networks](
    https://arxiv.org/abs/1603.05027) (CVPR 2016)

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/).

Note: each Keras Application expects a specific kind of input preprocessing.
For ResNet, call `keras.applications.resnet_v2.preprocess_input` on your
inputs before passing them to the model. `resnet_v2.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 `(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 Model instance.
__doc__)Tr   NNNr   r   )N)rA   )keras.src.api_exportr   keras.src.applicationsr   r   r.   r4   r;   r@   rD   PREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_ERROR_DOCrI   DOCsetattr r!   r   <module>rS      s2   - - - - - - 1 1 1 1 1 1 ) ) ) ) ) ) '1  #   D (2  #   D (2  #   D =>>   ?> ?@@= = = A@= *>EE	2

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 ,>F  3j 
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