&Vf `ddlmZddlmZeddgGddeZdS)) keras_export) BasePoolingzkeras.layers.MaxPooling3Dzkeras.layers.MaxPool3Dc.eZdZdZ dfd ZxZS) MaxPooling3Da Max pooling operation for 3D data (spatial or spatio-temporal). Downsamples the input along its spatial dimensions (depth, height, and width) by taking the maximum value over an input window (of size defined by `pool_size`) for each channel of the input. The window is shifted by `strides` along each dimension. Args: pool_size: int or tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). If only one integer is specified, the same window length will be used for all dimensions. strides: int or tuple of 3 integers, or None. Strides values. If None, it will default to `pool_size`. If only one int is specified, the same stride size will be used for all dimensions. padding: string, either `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `"channels_first"` corresponds to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`. 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"`. Input shape: - If `data_format="channels_last"`: 5D tensor with shape: `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)` - If `data_format="channels_first"`: 5D tensor with shape: `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)` Output shape: - If `data_format="channels_last"`: 5D tensor with shape: `(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)` - If `data_format="channels_first"`: 5D tensor with shape: `(batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)` Example: ```python depth = 30 height = 30 width = 30 channels = 3 inputs = keras.layers.Input(shape=(depth, height, width, channels)) layer = keras.layers.MaxPooling3D(pool_size=3) outputs = layer(inputs) # Shape: (batch_size, 10, 10, 10, 3) ``` rrNvalidc Jtj||fdd|||d|dS)Nmax)pool_dimensions pool_modepadding data_formatname)super__init__)self pool_sizestridesrrrkwargs __class__s c/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/layers/pooling/max_pooling3d.pyrzMaxPooling3D.__init__AsU    #  )rNr NN)__name__ __module__ __qualname____doc__r __classcell__)rs@rrrsY88x            rrN)keras.src.api_exportr%keras.src.layers.pooling.base_poolingrrrrr#s------======*,DEFFM M M M M ;M M GFM M M r