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► Keras 3 API documentation / Layers API / Pooling layers / AdaptiveMaxPooling3D layer

AdaptiveMaxPooling3D layer

[source]

AdaptiveMaxPooling3D class

keras.layers.AdaptiveMaxPooling3D(output_size, data_format=None, **kwargs)

Adaptive max pooling operation for 3D volumetric data.

This layer applies an adaptive max pooling operation, which pools the input such that the output has a target spatial size specified by output_size, regardless of the input spatial size. The kernel size and stride are automatically computed to achieve the target output size.

Arguments

  • output_size: Integer or tuple of 3 integers specifying the target output size. If an integer, the same value is used for depth, height, and width.
  • data_format: string, either "channels_last" or "channels_first". "channels_last" corresponds to inputs with shape (batch, depth, height, width, channels). "channels_first" corresponds to inputs with shape (batch, channels, depth, height, width). Defaults to the value found in your Keras config file at ~/.keras/keras.json. If never set, "channels_last" is used.

Input shape

  • If data_format="channels_last": 5D tensor (batch_size, depth, height, width, channels)
  • If data_format="channels_first": 5D tensor (batch_size, channels, depth, height, width)

Output shape

  • If data_format="channels_last": (batch_size, output_depth, output_height, output_width, channels)
  • If data_format="channels_first": (batch_size, channels, output_depth, output_height, output_width)

Examples

import numpy as np input_vol = np.random.rand(1, 32, 32, 32, 3) layer = AdaptiveMaxPooling3D(output_size=16) output_vol = layer(input_vol) output_vol.shape (1, 16, 16, 16, 3)

AdaptiveMaxPooling3D layer
AdaptiveMaxPooling3D class