tf.keras.layers.MaxPool3D

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

Inherits From: Layer, Operation

tf.keras.layers.MaxPool3D(
    pool_size=(2, 2, 2),
    strides=None,
    padding='valid',
    data_format=None,
    name=None,
    **kwargs
)

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.

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)

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 a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

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

Returns
A layer instance.

symbolic_call

View source

symbolic_call(
    *args, **kwargs
)