API Reference

Keras implementation

class autopool.keras.AutoPool1D(axis=0, kernel_initializer='zeros', kernel_constraint=None, kernel_regularizer=None, **kwargs)

Automatically tuned soft-max pooling. (Keras implementation)

This layer automatically adapts the pooling behavior to interpolate between mean- and max-pooling for each dimension.

__init__(axis=0, kernel_initializer='zeros', kernel_constraint=None, kernel_regularizer=None, **kwargs)

Parameters:

axis : int

Axis along which to perform the pooling. By default 0 (should be time).

kernel_initializer: Initializer for the weights matrix

kernel_regularizer: Regularizer function applied to the weights matrix

kernel_constraint: Constraint function applied to the weights matrix

kwargs

class autopool.keras.SoftMaxPool1D(axis=0, **kwargs)

Keras softmax pooling layer.

__init__(axis=0, **kwargs)

Parameters:

axis : int

Axis along which to perform the pooling. By default 0 (should be time).

kwargs

Tensorflow implementation

class autopool.tf.AutoPool1D(axis=0, kernel_initializer='zeros', kernel_constraint=None, kernel_regularizer=None, **kwargs)

Automatically tuned soft-max pooling. (tensorflow.keras implementation)

This layer automatically adapts the pooling behavior to interpolate between mean- and max-pooling for each dimension.

__init__(axis=0, kernel_initializer='zeros', kernel_constraint=None, kernel_regularizer=None, **kwargs)

Parameters:

axis : int

Axis along which to perform the pooling. By default 0 (should be time).

kernel_initializer: Initializer for the weights matrix

kernel_regularizer: Regularizer function applied to the weights matrix

kernel_constraint: Constraint function applied to the weights matrix

kwargs

class autopool.tf.SoftMaxPool1D(axis=0, **kwargs)

Tensorflow-keras softmax pooling layer.

__init__(axis=0, **kwargs)

Parameters:

axis : int

Axis along which to perform the pooling. By default 0 (should be time).

kwargs