
    ёi	                    x    S SK Jr  S SKJr  S SKrS SKJr  \(       a  S SKJr  / r " S S\R                  5      r	g)    )annotations)TYPE_CHECKINGN)nn)Tensorc                  J   ^  \ rS rSr% SrS\S'   SS	U 4S jjjrS
S jrSrU =r	$ )LeNet   ah  LeNet model from
`"Gradient-based learning applied to document recognition" <https://ieeexplore.ieee.org/document/726791>`_.

Args:
    num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer
        will not be defined. Default: 10.

Returns:
    :ref:`api_paddle_nn_Layer`. An instance of LeNet model.

Examples:
    .. code-block:: pycon

        >>> import paddle
        >>> from paddle.vision.models import LeNet

        >>> model = LeNet()

        >>> x = paddle.rand([1, 1, 28, 28])
        >>> out = model(x)

        >>> print(out.shape)
        paddle.Size([1, 10])
intnum_classesc                ,  > [         TU ]  5         Xl        [        R                  " [        R
                  " SSSSSS9[        R                  " 5       [        R                  " SS5      [        R
                  " SSSSSS9[        R                  " 5       [        R                  " SS5      5      U l        US:  a]  [        R                  " [        R                  " S	S
5      [        R                  " S
S5      [        R                  " SU5      5      U l
        g g )N         )stridepadding         r   i  x   T   )super__init__r   r   
SequentialConv2DReLU	MaxPool2DfeaturesLinearfc)selfr   	__class__s     Z/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/vision/models/lenet.pyr   LeNet.__init__:   s    &IIaAa3GGILLAIIaQq!4GGILLA
 ?mm		#s#		#r"		"k*DG     c                    U R                  U5      nU R                  S:  a(  [        R                  " US5      nU R	                  U5      nU$ )Nr   r   )r   r   paddleflattenr   )r    inputsxs      r"   forwardLeNet.forwardM   s@    MM&!aq!$A
Ar$   )r   r   r   )
   )r   r
   returnNone)r(   r   r-   r   )
__name__
__module____qualname____firstlineno____doc____annotations__r   r*   __static_attributes____classcell__)r!   s   @r"   r   r      s$    2  & r$   r   )

__future__r   typingr   r&   r   r   __all__Layerr    r$   r"   <module>r<      s2    #  
5BHH 5r$   