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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'.
Args | |
---|---|
identifier
|
String name of the activation function or callable. |
use_keras_layer
|
If True, use keras layer if identifier is allow-listed. |
Returns | |
---|---|
A Python function corresponding to the activation function or a keras activation layer when use_keras_layer=True. |