
    0h                     $   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                  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          y)zResNet v2 models for TF-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.ResNet50V2Nc                 H    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    ]/var/www/html/engine/venv/lib/python3.12/site-packages/tf_keras/src/applications/resnet_v2.pystack_fnzResNet50V2.<locals>.stack_fn,   sU    MM!R1MM!S!'2MM!S!'2}}QQ@@    T
resnet50v2classifier_activationr   ResNetinclude_topweightsinput_tensorinput_shapepoolingclassesr!   r   s           r   
ResNet50V2r+      s<    A ==3 r   z(keras.applications.resnet_v2.ResNet101V2zkeras.applications.ResNet101V2c                 H    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   U    MM!R1MM!S!'2MM!S"73}}QQ@@r   Tresnet101v2r    r"   r$   s           r   ResNet101V2r1   A   <    A ==3 r   z(keras.applications.resnet_v2.ResNet152V2zkeras.applications.ResNet152V2c                 H    d }t        j                  |ddd| ||||||      S )z*Instantiates the ResNet152V2 architecture.c                     t        j                  | ddd      } t        j                  | ddd      } t        j                  | dd	d
      } t        j                  | dddd      S )Nr   r	   r
   r   r      r   r   $   r   r   r   r   r   r   r   s    r   r   zResNet152V2.<locals>.stack_fnr   r/   r   Tresnet152v2r    r"   r$   s           r   ResNet152V2r8   d   r2   r   z-keras.applications.resnet_v2.preprocess_inputc                 2    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_predictionsc                 0    t        j                  | |      S )N)top)r   decode_predictions)predsr?   s     r   r@   r@      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 TF-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 TF-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__)TimagenetNNNi  softmax)N)   )rE   tf_keras.src.applicationsr   r    tensorflow.python.util.tf_exportr   r+   r1   r8   r=   r@   PREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_ERROR_DOCDOCsetattr r   r   <module>rR      s    5 , : -/N #@ .0P #@ .0P #@ => ? ?@= A= *>>EE	22
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