tf.keras.layers.AdaptiveMaxPooling1D

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

Inherits From: Layer, Operation

tf.keras.layers.AdaptiveMaxPooling1D(
    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 length specified by output_size, regardless of the input length. The kernel size and stride are automatically computed to achieve the target output size.

Args
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)

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.