API Reference¶
Keras implementation¶
- class autopool.keras.AutoPool1D(axis=0, kernel_initializer='zeros', kernel_constraint=None, kernel_regularizer=None, **kwargs)[source]¶
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)[source]¶
- Parameters
- axisint
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
Tensorflow implementation¶
- class autopool.tf.AutoPool1D(axis=0, kernel_initializer='zeros', kernel_constraint=None, kernel_regularizer=None, **kwargs)[source]¶
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)[source]¶
- Parameters
- axisint
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