def test_serialization_v2(self):
activation_map = {tf.math.softmax: 'softmax'}
for fn_v2_key in activation_map:
fn_v2 = activations.get(fn_v2_key)
config = activations.serialize(fn_v2)
fn = activations.deserialize(config)
assert fn.__name__ == activation_map[fn_v2_key]
def get(identifier: Union[str, Callable]) -> Callable:
try:
return keras_get(identifier)
except ValueError:
if isinstance(identifier, str):
return deserialize(identifier, custom_objects=globals())
else:
raise ValueError('Could not interpret:', identifier)
def from_config(cls, config, custom_objects=None):
linear_config = config.pop('linear_model')
linear_model = layer_module.deserialize(linear_config, custom_objects)
dnn_config = config.pop('dnn_model')
dnn_model = layer_module.deserialize(dnn_config, custom_objects)
activation = activations.deserialize(config.pop('activation', None),
custom_objects=custom_objects)
return cls(linear_model=linear_model,
dnn_model=dnn_model,
activation=activation,
**config)
def test_serialization():
all_activations = ['softmax', 'relu', 'elu', 'tanh',
'sigmoid', 'hard_sigmoid', 'linear',
'softplus', 'softsign', 'selu']
for name in all_activations:
fn = activations.get(name)
ref_fn = getattr(activations, name)
assert fn == ref_fn
config = activations.serialize(fn)
fn = activations.deserialize(config)
assert fn == ref_fn
def test_serialization(self):
all_activations = [
"softmax",
"relu",
"elu",
"tanh",
"sigmoid",
"hard_sigmoid",
"linear",
"softplus",
"softsign",
"selu",
"gelu",
"relu6",
]
for name in all_activations:
fn = activations.get(name)
ref_fn = getattr(activations, name)
assert fn == ref_fn
config = activations.serialize(fn)
fn = activations.deserialize(config)
assert fn == ref_fn
