
    ~Wh                     ,   d Z ddlmZ ddlmZ ddlmZ  edd          	 	 	 	 	 	 	 dd            Z edd          	 	 	 	 	 	 	 dd            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Z eedej         ez               eedej         ez               eedej         ez              d	S )zResNet v2 models for Keras.

Reference:
  - [Identity Mappings in Deep Residual Networks](
      https://arxiv.org/abs/1603.05027) (CVPR 2016)
    )imagenet_utils)resnet)keras_exportz'keras.applications.resnet_v2.ResNet50V2zkeras.applications.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   stack2xs    b/var/www/html/movieo_spanner_bot/venv/lib/python3.11/site-packages/keras/applications/resnet_v2.pystack_fnzResNet50V2.<locals>.stack_fn,   se    M!R111M!S!'222M!S!'222}QQ@@@@    T
resnet50v2classifier_activationr   ResNetinclude_topweightsinput_tensorinput_shapepoolingclassesr$   r    s           r   
ResNet50V2r.      sP    A A A =3   r!   z(keras.applications.resnet_v2.ResNet101V2zkeras.applications.ResNet101V2c                 F    d }t          j        |ddd| ||||||          S )z*Instantiates the ResNet101V2 architecture.c                     t          j        | ddd          } t          j        | ddd          } t          j        | dd	d
          } t          j        | dddd          S )Nr   r   r   r   r   r   r   r      r   r   r   r   r   r   r   s    r   r    zResNet101V2.<locals>.stack_fnO   e    M!R111M!S!'222M!S"7333}QQ@@@@r!   Tresnet101v2r#   r%   r'   s           r   ResNet101V2r4   A   P    A A A =3   r!   z(keras.applications.resnet_v2.ResNet152V2zkeras.applications.ResNet152V2c                 F    d }t          j        |ddd| ||||||          S )z*Instantiates the ResNet152V2 architecture.c                     t          j        | ddd          } t          j        | ddd          } t          j        | dd	d
          } t          j        | dddd          S )Nr   r   r   r   r      r   r   $   r   r   r   r   r   r   r   s    r   r    zResNet152V2.<locals>.stack_fnr   r2   r!   Tresnet152v2r#   r%   r'   s           r   ResNet152V2r;   d   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 ResNetV2, call `tf.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 `keras.Model` instance.
__doc__)Tr   NNNr   r   )N)rA   )rI   keras.applicationsr   r    tensorflow.python.util.tf_exportr   r.   r4   r;   r@   rD   PREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_ERROR_DOCDOCsetattr r!   r   <module>rS      s1     . - - - - - % % % % % % : 9 9 9 9 9 -/N  #   @ .0P  #   @ .0P  #   @ =>>   ?> ?@@= = = A@= *>EE	2
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