MaxPooling3D layer

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MaxPooling3D class

tf_keras.layers.MaxPooling3D(
    pool_size=(2, 2, 2), strides=None, padding="valid", data_format=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: 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). When unspecified, uses image_data_format value found in your TF-Keras config file at ~/.keras/keras.json (if exists) else 'channels_last'. Defaults to '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.MaxPooling3D(pool_size=3)
outputs = layer(inputs)  # Shape: (batch_size, 10, 10, 10, 3)