
    2Vh5	                     X    d dl mZ d dlmZ d dlmZ  eddg       G d de             Zy)	    )ops)keras_export)BaseGlobalPoolingzkeras.layers.GlobalMaxPooling1Dzkeras.layers.GlobalMaxPool1Dc                   *     e Zd ZdZd fd	Zd Z xZS )GlobalMaxPooling1Dal  Global max pooling operation for temporal data.

    Args:
        data_format: string, either `"channels_last"` or `"channels_first"`.
            The ordering of the dimensions in the inputs. `"channels_last"`
            corresponds to inputs with shape `(batch, steps, features)`
            while `"channels_first"` corresponds to inputs with shape
            `(batch, features, steps)`. It defaults to the `image_data_format`
            value found in your Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be `"channels_last"`.
        keepdims: A boolean, whether to keep the temporal dimension or not.
            If `keepdims` is `False` (default), the rank of the tensor is
            reduced for spatial dimensions. If `keepdims` is `True`, the
            temporal dimension are retained with length 1.
            The behavior is the same as for `tf.reduce_mean` or `np.mean`.

    Input shape:

    - If `data_format='channels_last'`:
        3D tensor with shape:
        `(batch_size, steps, features)`
    - If `data_format='channels_first'`:
        3D tensor with shape:
        `(batch_size, features, steps)`

    Output shape:

    - If `keepdims=False`:
        2D tensor with shape `(batch_size, features)`.
    - If `keepdims=True`:
        - If `data_format="channels_last"`:
            3D tensor with shape `(batch_size, 1, features)`
        - If `data_format="channels_first"`:
            3D tensor with shape `(batch_size, features, 1)`

    Example:

    >>> x = np.random.rand(2, 3, 4)
    >>> y = keras.layers.GlobalMaxPooling1D()(x)
    >>> y.shape
    (2, 4)
    c                 ,    t        |   dd||d| y )N   )pool_dimensionsdata_formatkeepdims )super__init__)selfr   r   kwargs	__class__s       ]/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/layers/pooling/global_max_pooling1d.pyr   zGlobalMaxPooling1D.__init__8   s(     	
#	
 		
    c                 l    | j                   dk(  rdnd}t        j                  ||| j                        S )Nchannels_lastr	      )axisr   )r   r   maxr   )r   inputs
steps_axiss      r   callzGlobalMaxPooling1D.call@   s-    **o=Q1
wwvJGGr   )NF)__name__
__module____qualname____doc__r   r   __classcell__)r   s   @r   r   r      s    )V
Hr   r   N)	keras.srcr   keras.src.api_exportr   ,keras.src.layers.pooling.base_global_poolingr   r   r   r   r   <module>r%      s=     - J )&6H* 6H6Hr   