keras/keras_core/activations/__init__.py

import types

from keras_core.activations.activations import elu
from keras_core.activations.activations import exponential
from keras_core.activations.activations import gelu
from keras_core.activations.activations import hard_sigmoid
from keras_core.activations.activations import leaky_relu
from keras_core.activations.activations import linear
from keras_core.activations.activations import log_softmax
from keras_core.activations.activations import mish
from keras_core.activations.activations import relu
from keras_core.activations.activations import relu6
from keras_core.activations.activations import selu
from keras_core.activations.activations import sigmoid
from keras_core.activations.activations import silu
from keras_core.activations.activations import softmax
from keras_core.activations.activations import softplus
from keras_core.activations.activations import softsign
from keras_core.activations.activations import tanh
from keras_core.api_export import keras_core_export
from keras_core.saving import object_registration
from keras_core.saving import serialization_lib

ALL_OBJECTS = {
    relu,
    leaky_relu,
    relu6,
    softmax,
    elu,
    selu,
    softplus,
    softsign,
    silu,
    gelu,
    tanh,
    sigmoid,
    exponential,
    hard_sigmoid,
    linear,
    mish,
    log_softmax,
}

ALL_OBJECTS_DICT = {fn.__name__: fn for fn in ALL_OBJECTS}


@keras_core_export("keras_core.activations.serialize")
def serialize(activation):
    fn_config = serialization_lib.serialize_keras_object(activation)
    if "config" not in fn_config:
        raise ValueError(
            f"Unknown activation function '{activation}' cannot be "
            "serialized due to invalid function name. Make sure to use "
            "an activation name that matches the references defined in "
            "activations.py or use "
            "`@keras_core.saving.register_keras_serializable()`"
            "to register any custom activations. "
            f"config={fn_config}"
        )
    if not isinstance(activation, types.FunctionType):
        # Case for additional custom activations represented by objects
        return fn_config
    if (
        isinstance(fn_config["config"], str)
        and fn_config["config"] not in globals()
    ):
        # Case for custom activation functions from external activations modules
        fn_config["config"] = object_registration.get_registered_name(
            activation
        )
        return fn_config
    # Case for keras.activations builtins (simply return name)
    return fn_config["config"]


@keras_core_export("keras_core.activations.deserialize")
def deserialize(config, custom_objects=None):
    """Return a Keras activation function via its config."""
    return serialization_lib.deserialize_keras_object(
        config,
        module_objects=ALL_OBJECTS_DICT,
        custom_objects=custom_objects,
    )


@keras_core_export("keras_core.activations.get")
def get(identifier):
    """Retrieve a Keras activation function via an identifier."""
    if identifier is None:
        return linear
    if isinstance(identifier, (str, dict)):
        return deserialize(identifier)
    elif callable(identifier):
        return identifier
    raise TypeError(
        f"Could not interpret activation function identifier: {identifier}"
    )