Keras
Keras 3 API 문서
Layers API
Pooling layers
MaxPooling3D layer

MaxPooling3D layer

source

MaxPooling3D class 

keras.layers.MaxPooling3D(
    pool_size=(2, 2, 2),
    strides=None,
    padding="valid",
    data_format=None,
    name=None,
    **kwargs
)

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.

Arguments

  • 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

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)