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 / AdaptiveMaxPooling1D layer

AdaptiveMaxPooling1D layer

[source]

AdaptiveMaxPooling1D class

keras.layers.AdaptiveMaxPooling1D(output_size, data_format=None, **kwargs)

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

This layer applies an adaptive max 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 = AdaptiveMaxPooling1D(output_size=32) output_seq = layer(input_seq) output_seq.shape (1, 32, 3)

AdaptiveMaxPooling1D layer
AdaptiveMaxPooling1D class