def test_serialization_v2(self):
activation_map = {nn.softmax_v2: '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 from_config(cls, config, custom_objects=None):
"""Creates a RNNModel from its config.
Args:
config: A Python dictionary, typically the output of `get_config`.
custom_objects: Optional dictionary mapping names (strings) to custom
classes or functions to be considered during deserialization.
Returns:
A RNNModel.
"""
rnn_layer = keras_layers.deserialize(config.pop('rnn_layer'),
custom_objects=custom_objects)
sequence_feature_columns = fc.deserialize_feature_columns(
config.pop('sequence_feature_columns'),
custom_objects=custom_objects)
context_feature_columns = config.pop('context_feature_columns', None)
if context_feature_columns:
context_feature_columns = fc.deserialize_feature_columns(
context_feature_columns, custom_objects=custom_objects)
activation = activations.deserialize(config.pop('activation', None),
custom_objects=custom_objects)
return cls(rnn_layer=rnn_layer,
sequence_feature_columns=sequence_feature_columns,
context_feature_columns=context_feature_columns,
activation=activation,
**config)
def from_config(cls, config):
config = config.copy()
# use_bias is not an argument of this class, as explained by
# comment in __init__.
config.pop('use_bias')
config['post_activation'] = activations.deserialize(
config['post_activation'])
return cls(**config)
def test_serialization(self):
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 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 _from_config(cls_initializer, config):
"""All shared from_config logic for fused layers."""
config = config.copy()
# use_bias is not an argument of this class, as explained by
# comment in __init__.
config.pop('use_bias')
is_advanced_activation = 'class_name' in config['post_activation']
if is_advanced_activation:
config['post_activation'] = deserialize_layer(config['post_activation'])
else:
config['post_activation'] = activations.deserialize(
config['post_activation'])
return cls_initializer(**config)
def from_config(cls, config):
return activations.deserialize(config['activation'])
