tfm.utils.get_activation

Maps a identifier to a Python function, e.g., "relu" => tf.nn.relu.

tfm.utils.get_activation(
    identifier, use_keras_layer=False
)

It checks string first and if it is one of customized activation not in TF, the corresponding activation will be returned. For non-customized activation names and callable identifiers, always fallback to tf.keras.activations.get.

Prefers using keras layers when use_keras_layer=True. Now it only supports 'relu', 'linear', 'identity', 'swish'.

identifier String name of the activation function or callable.
use_keras_layer If True, use keras layer if identifier is allow-listed.

A Python function corresponding to the activation function or a keras activation layer when use_keras_layer=True.