Keras 3 API documentation
Models API Layers API The base Layer class Layer activations Layer weight initializers Layer weight regularizers Layer weight constraints Core layers Convolution layers Pooling layers Recurrent layers Preprocessing layers Normalization layers Regularization layers Attention layers Reshaping layers Merging layers Activation layers Backend-specific layers Callbacks API Ops API Optimizers Metrics Losses Data loading Built-in small datasets Keras Applications Mixed precision Multi-device distribution RNG API Quantizers Scope Rematerialization Utilities
Keras 2 API documentation
► Keras 3 API documentation / Layers API / Pooling layers / AdaptiveAveragePooling2D layer

AdaptiveAveragePooling2D layer

[source]

AdaptiveAveragePooling2D class

keras.layers.AdaptiveAveragePooling2D(output_size, data_format=None, **kwargs)

Adaptive average pooling operation for 2D spatial data.

This layer applies an adaptive average 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 2 integers specifying the target output size. If an integer, the same value is used for both height and width.
  • data_format: string, either "channels_last" or "channels_first". "channels_last" corresponds to inputs with shape (batch, height, width, channels). "channels_first" corresponds to inputs with shape (batch, channels, 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": 4D tensor (batch_size, height, width, channels)
  • If data_format="channels_first": 4D tensor (batch_size, channels, height, width)

Output shape

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

Examples

import numpy as np input_img = np.random.rand(1, 64, 64, 3) layer = AdaptiveAveragePooling2D(output_size=32) output_img = layer(input_img) output_img.shape (1, 32, 32, 3)

AdaptiveAveragePooling2D layer
AdaptiveAveragePooling2D class