tfm.utils.get_activation
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Maps an identifier to a Python function, e.g., "relu" => tf.nn.relu.
tfm.utils.get_activation(
identifier, use_keras_layer=False, **kwargs
)
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', 'mish', 'leaky_relu', and 'gelu'.
Args | |
|---|---|
identifier
|
String name of the activation function or callable. |
use_keras_layer
|
If True, use keras layer if identifier is allow-listed. |
**kwargs
|
Keyword arguments to use to instantiate an activation function. Available only for 'leaky_relu' and 'gelu' when using keras layers. For example: get_activation('leaky_relu', use_keras_layer=True, alpha=0.1) |
Returns | |
|---|---|
| A Python function corresponding to the activation function or a keras activation layer when use_keras_layer=True. |