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► Keras 3 API documentation / Layers API / Pooling layers / AdaptiveAveragePooling1D layer

AdaptiveAveragePooling1D layer

[source]

AdaptiveAveragePooling1D class

keras.layers.AdaptiveAveragePooling1D(output_size, data_format=None, **kwargs)

Adaptive average pooling operation for 1D temporal or spatial data.

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

Arguments

  • output_size: Integer specifying the target output length.
  • data_format: string, either "channels_last" or "channels_first". "channels_last" corresponds to inputs with shape (batch, length, channels). "channels_first" corresponds to inputs with shape (batch, channels, length). 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": 3D tensor (batch_size, length, channels)
  • If data_format="channels_first": 3D tensor (batch_size, channels, length)

Output shape

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

Examples

import numpy as np input_seq = np.random.rand(1, 64, 3) layer = AdaptiveAveragePooling1D(output_size=32) output_seq = layer(input_seq) output_seq.shape (1, 32, 3)

AdaptiveAveragePooling1D layer
AdaptiveAveragePooling1D class