tf.keras.layers.AveragePooling3D

Average pooling operation for 3D data (spatial or spatio-temporal).

Inherits From: Layer, Module

tf.keras.layers.AveragePooling3D(
    pool_size=(2, 2, 2), strides=None, padding='valid', data_format=None,
    **kwargs
)

Downsamples the input along its spatial dimensions (depth, height, and width) by taking the average 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.

pool_size tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). (2, 2, 2) will halve the size of the 3D input in each dimension.
strides tuple of 3 integers, or None. Strides values.
padding One of "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 A string, one of channels_last (default) 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
input_channels = 3

inputs = tf.keras.Input(shape=(depth, height, width, input_channels))
layer = tf.keras.layers.AveragePooling3D(pool_size=3)
outputs = layer(inputs)  # Shape: (batch_size, 10, 10, 10, 3)