tf.keras.layers.AdaptiveMaxPooling3D

Adaptive max pooling operation for 3D volumetric data.

Inherits From: Layer, Operation

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

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.

Args
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)

Attributes
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

View source

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

Args
config	A Python dictionary, typically the output of get_config.

Returns
An operation instance.