def get_config(self):
config = {
'ensemble_activation':
tf.keras.activations.serialize(self.ensemble_activation),
'use_ensemble_bias':
self.use_ensemble_bias,
'alpha_initializer':
initializers.serialize(self.alpha_initializer),
'gamma_initializer':
initializers.serialize(self.gamma_initializer),
'ensemble_bias_initializer':
initializers.serialize(self.ensemble_bias_initializer),
'alpha_regularizer':
regularizers.serialize(self.alpha_regularizer),
'gamma_regularizer':
regularizers.serialize(self.gamma_regularizer),
'ensemble_bias_regularizer':
regularizers.serialize(self.ensemble_bias_regularizer),
'alpha_constraint':
constraints.serialize(self.alpha_constraint),
'gamma_constraint':
constraints.serialize(self.gamma_constraint),
'ensemble_bias_constraint':
constraints.serialize(self.ensemble_bias_constraint),
'ensemble_size':
self.ensemble_size,
}
new_config = super().get_config()
new_config.update(config)
return new_config
def get_config(self):
config = {
'activation': tf.keras.activations.serialize(self.activation),
'use_bias': self.use_bias,
'alpha_initializer':
initializers.serialize(self.alpha_initializer),
'gamma_initializer':
initializers.serialize(self.gamma_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'alpha_regularizer':
regularizers.serialize(self.alpha_regularizer),
'gamma_regularizer':
regularizers.serialize(self.gamma_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'alpha_constraint': constraints.serialize(self.alpha_constraint),
'gamma_constraint': constraints.serialize(self.gamma_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'use_additive_perturbation': self.use_additive_perturbation,
'ensemble_size': self.ensemble_size,
}
base_config = super().get_config()
dense_config = self.dense.get_config()
return dict(
list(base_config.items()) + list(dense_config.items()) +
list(config.items()))
def get_config(self):
return {
'mean_initializer': serialize(self.mean_initializer),
'stddev_initializer': serialize(self.stddev_initializer),
'mean_regularizer': regularizers.serialize(self.mean_regularizer),
'stddev_regularizer': regularizers.serialize(self.stddev_regularizer),
'mean_constraint': constraints.serialize(self.mean_constraint),
'stddev_constraint': constraints.serialize(self.stddev_constraint),
'seed': self.seed,
}
def get_config(self):
return {
'loc_initializer': serialize(self.loc_initializer),
'scale_initializer': serialize(self.scale_initializer),
'loc_regularizer': regularizers.serialize(self.loc_regularizer),
'scale_regularizer': regularizers.serialize(self.scale_regularizer),
'loc_constraint': constraints.serialize(self.loc_constraint),
'scale_constraint': constraints.serialize(self.scale_constraint),
'seed': self.seed,
}
def get_config(self):
return {
'num_components': self.num_components,
'loc_initializer': serialize(self.loc_initializer),
'loc_regularizer': regularizers.serialize(self.loc_regularizer),
'loc_constraint': constraints.serialize(self.loc_constraint),
'seed': self.seed,
}
def get_config(self):
return {
'loc': self.loc,
'stddev_initializer':
tf.keras.initializers.serialize(self.stddev_initializer),
'stddev_regularizer': serialize(self.stddev_regularizer),
'stddev_constraint': constraints.serialize(self.stddev_constraint),
'scale_factor': self.scale_factor,
'seed': self.seed,
}
def get_config(self):
config = {
'units':
self.units,
'lambda_key_to_index':
self.lambda_key_to_index,
'rank':
self.dense.rank,
'ensemble_size':
self.ensemble_size,
'activation':
tf.keras.activations.serialize(self.activation),
'use_bias':
self.use_bias,
'alpha_initializer':
initializers.serialize(self.alpha_initializer),
'gamma_initializer':
initializers.serialize(self.gamma_initializer),
'kernel_initializer':
initializers.serialize(self.dense.kernel_initializer),
'bias_initializer':
initializers.serialize(self.bias_initializer),
'kernel_regularizer':
regularizers.serialize(self.dense.kernel_regularizer),
'bias_regularizer':
regularizers.serialize(self.dense.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint':
constraints.serialize(self.dense.kernel_constraint),
'bias_constraint':
constraints.serialize(self.dense.bias_constraint),
'regularize_fast_weights':
self.regularize_fast_weights,
'fast_weights_eq_contraint':
self.fast_weights_eq_contraint
}
new_config = super().get_config()
new_config.update(config)
return new_config
def get_config(self):
config = {
'ensemble_size': self.ensemble_size,
'alpha_initializer': initializers.serialize(self.alpha_initializer),
'gamma_initializer': initializers.serialize(self.gamma_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'bias_constraint': constraints.serialize(self.bias_constraint),
'activation': tf.keras.activations.serialize(self.activation),
'use_bias': self.use_bias,
}
new_config = super(DepthwiseConv2DBatchEnsemble, self).get_config()
new_config.update(self.conv2d.get_config())
new_config.update(config)
return new_config
def get_config(self):
"""Returns the configuration for the layer."""
config = {
'units': self.units,
'activation': tf.keras.activations.serialize(self.activation),
'recurrent_activation': tf.keras.activations.serialize(
self.recurrent_activation),
'use_bias': self.use_bias,
'alpha_initializer': initializers.serialize(self.alpha_initializer),
'gamma_initializer': initializers.serialize(self.gamma_initializer),
'kernel_initializer': initializers.serialize(
self.kernel_initializer),
'recurrent_alpha_initializer': initializers.serialize(
self.recurrent_alpha_initializer),
'recurrent_gamma_initializer': initializers.serialize(
self.recurrent_gamma_initializer),
'recurrent_initializer': initializers.serialize(
self.recurrent_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'unit_forget_bias': self.unit_forget_bias,
'alpha_regularizer': regularizers.serialize(self.alpha_regularizer),
'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
'kernel_regularizer': regularizers.serialize(
self.kernel_regularizer),
'recurrent_alpha_regularizer': regularizers.serialize(
self.recurrent_alpha_regularizer),
'recurrent_gamma_regularizer': regularizers.serialize(
self.recurrent_gamma_regularizer),
'recurrent_regularizer': regularizers.serialize(
self.recurrent_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'alpha_constraint': constraints.serialize(self.alpha_constraint),
'gamma_constraint': constraints.serialize(self.gamma_constraint),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'recurrent_alpha_constraint': constraints.serialize(
self.recurrent_alpha_constraint),
'recurrent_gamma_constraint': constraints.serialize(
self.recurrent_gamma_constraint),
'recurrent_constraint': constraints.serialize(
self.recurrent_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'dropout': self.dropout,
'recurrent_dropout': self.recurrent_dropout,
'implementation': self.implementation,
'use_additive_perturbation': self.use_additive_perturbation,
'ensemble_size': self.ensemble_size,
}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
