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|	d0dd1d2}	| rtjd3d2|	}	t|| tj||d4d5|	}	n|d6krt |	}	n|d7krt |	}	|d	urt|}n|}t||	|d2}|dkr| rd8}t j|t| d9d:d;}nd<}t j|t| d9d=d;}|| |S |d	ur|| |S )>a  Instantiates the Inception-ResNet v2 architecture.

    Reference:
    - [Inception-v4, Inception-ResNet and the Impact of
       Residual Connections on Learning](https://arxiv.org/abs/1602.07261)
      (AAAI 2017)

    This function returns a Keras image classification model,
    optionally loaded with weights pre-trained on ImageNet.

    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 InceptionResNetV2, call
    `keras.applications.inception_resnet_v2.preprocess_input`
    on your inputs before passing them to the model.
    `inception_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 `(299, 299, 3)`
            (with `'channels_last'` data format)
            or `(3, 299, 299)` (with `'channels_first'` data format).
            It should have exactly 3 inputs channels,
            and width and height should be no smaller than 75.
            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"`.
        name: The name of the model (string).

    Returns:
        A 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   zbIf using `weights="imagenet"` with `include_top=True`, `classes` should be 1000. Received classes=i+  K   )default_sizemin_sizedata_formatrequire_flattenweightsN)shape)tensorr             valid)stridespadding)r   @   )r   P         `   0      samechannels_firstmixed_5baxisname   g(\?block35)scale
block_type	block_idxi     mixed_6a   g?block17i   i@  mixed_7a
   g?block8g      ?)r+   
activationr,   r-   i   conv_7br(   avg_poolpredictions)r5   r(   avgmaxz9inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5models e693bd0210a403b3192acc6073ad2e96)cache_subdir	file_hashz?inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5 d19885ff4a710c122648d3b5c3b684e4)r	   exists
ValueErrorr   obtain_input_shaper   image_data_formatr   Inputis_keras_tensor	conv2d_bnMaxPooling2DAveragePooling2DConcatenaterangeinception_resnet_blockGlobalAveragePooling2Dvalidate_activationDenseGlobalMaxPooling2Dr   get_source_inputsr   get_fileBASE_WEIGHT_URLload_weights)include_topr   input_tensorinput_shapepoolingclassesclassifier_activationr(   	img_inputxbranch_0branch_1branch_2branch_poolbrancheschannel_axisr-   inputsmodelfnameweights_path rg   e/var/www/html/chatgem/venv/lib/python3.10/site-packages/keras/src/applications/inception_resnet_v2.pyInceptionResNetV2   s   Q	











ri   r   r#   reluFc                 C   s   t j||||||d| } |s-t dkrdnd}|du rdn|d }	t j|d|	d| } |durD|du r7dn|d	 }
t j||
d
| } | S )a2  Utility function to apply conv + BN.

    Args:
        x: input tensor.
        filters: filters in `Conv2D`.
        kernel_size: kernel size as in `Conv2D`.
        strides: strides in `Conv2D`.
        padding: padding mode in `Conv2D`.
        activation: activation in `Conv2D`.
        use_bias: whether to use a bias in `Conv2D`.
        name: name of the ops; will become `name + '_ac'`
            for the activation and `name + '_bn'` for the batch norm layer.

    Returns:
        Output tensor after applying `Conv2D` and `BatchNormalization`.
    )r   r   use_biasr(   r$   r   r   N_bnF)r'   r+   r(   _acr7   )r   Conv2Dr   rD   BatchNormalization
Activation)r\   filterskernel_sizer   r   r5   rk   r(   bn_axisbn_nameac_namerg   rg   rh   rG      s(   rG   c                       s0   e Zd Z fddZ fddZdd Z  ZS )CustomScaleLayerc                    s   t  jdi | || _d S )Nrg   )super__init__r+   )selfr+   kwargs	__class__rg   rh   rx   '  s   
zCustomScaleLayer.__init__c                    s   t   }|d| ji |S )Nr+   )rw   
get_configupdater+   )ry   configr{   rg   rh   r}   +  s   
zCustomScaleLayer.get_configc                 C   s   |d |d | j   S )Nr   r   )r+   )ry   rc   rg   rg   rh   call0  s   zCustomScaleLayer.call)__name__
__module____qualname__rx   r}   r   __classcell__rg   rg   r{   rh   rv   &  s    rv   c                 C   s  |dkr.t | dd}t | dd}t |dd}t | dd}t |dd}t |dd}|||g}nR|dkrSt | dd}t | d	d}t |d
ddg}t |dddg}||g}n-|dkrxt | dd}t | dd}t |dddg}t |dddg}||g}ntdt| |d t| }	t dkrdnd}
tj|
|	d d|}t || j|
 ddd|	d d}t|| |g} |durtj	||	d d| } | S )a  Adds an Inception-ResNet block.

    Args:
        x: input tensor.
        scale: scaling factor to scale the residuals
            (i.e., the output of passing `x` through an inception module)
            before adding them to the shortcut
            branch. Let `r` be the output from the residual branch,
            the output of this block will be `x + scale * r`.
        block_type: `'block35'`, `'block17'` or `'block8'`,
            determines the network structure in the residual branch.
        block_idx: an `int` used for generating layer names.
            The Inception-ResNet blocks are repeated many times
            in this network. We use `block_idx` to identify each
            of the repetitions. For example, the first
            Inception-ResNet-A block will have
            `block_type='block35', block_idx=0`, and the layer names
            will have a common prefix `'block35_0'`.
        activation: activation function to use at the end of the block.

    Returns:
        Output tensor for the block.
    r*   r   r   r   r!   r   r1   r            r4      r.   zXUnknown Inception-ResNet block type. Expects "block35", "block17" or "block8", but got: _r$   _mixedr&   NT_conv)r5   rk   r(   rm   r7   )
rG   rB   strr   rD   r   rJ   r   rv   rp   )r\   r+   r,   r-   r5   r]   r^   r_   ra   
block_namerb   mixeduprg   rg   rh   rL   4  sT   

	rL   z7keras.applications.inception_resnet_v2.preprocess_inputc                 C   s   t j| |ddS )Ntf)r   mode)r   preprocess_input)r\   r   rg   rg   rh   r   {  s   r   z9keras.applications.inception_resnet_v2.decode_predictionsr"   c                 C   s   t j| |dS )N)top)r   decode_predictions)predsr   rg   rg   rh   r     s   r    )r   reterror)Tr
   NNNr   r   r   )r   r#   rj   FN)rj   )N)r"   )	keras.srcr   r   keras.src.api_exportr   keras.src.applicationsr   keras.src.layers.layerr   keras.src.modelsr   keras.src.opsr   keras.src.utilsr	   rS   ri   rG   rv   rL   r   r   PREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_ERROR_DOC__doc__rg   rg   rg   rh   <module>   sV     g
.
G