def get_config(self):
config = {
'units':
self.units,
'kl_weight':
self.kl_weight,
'kl_use_exact':
self.kl_use_exact,
'activation':
tf.keras.activations.serialize(self.activation),
'use_bias':
self.use_bias,
'activity_regularizer':
tf.keras.regularizers.serialize(self.activity_regularizer),
}
function_keys = [
'make_posterior_fn',
'make_prior_fn',
]
for function_key in function_keys:
function = getattr(self, function_key)
if function is None:
function_name = None
function_type = None
else:
function_name, function_type = tfp_layers_util.serialize_function(
function)
config[function_key] = function_name
config[function_key + '_type'] = function_type
base_config = super(DenseVariational, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
"""Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the
configuration of a layer. The same layer can be reinstantiated later
(without its trained weights) from this configuration.
Returns:
config: A Python dictionary of class keyword arguments and their
serialized values.
"""
config = {
"filters": self.filters,
"kernel_size": self.kernel_size,
"strides": self.strides,
"padding": self.padding,
"data_format": self.data_format,
"dilation_rate": self.dilation_rate,
"activation": (
tf.keras.activations.serialize(self.activation)
if self.activation
else None
),
"activity_regularizer": tf.keras.initializers.serialize(
self.activity_regularizer
),
}
function_keys = [
"kernel_posterior_fn",
"kernel_posterior_tensor_fn",
"kernel_prior_fn",
"kernel_divergence_fn",
"bias_posterior_fn",
"bias_posterior_tensor_fn",
"bias_prior_fn",
"bias_divergence_fn",
]
for function_key in function_keys:
function = getattr(self, function_key)
if function is None:
function_name = None
function_type = None
else:
function_name, function_type = tfp_layers_util.serialize_function(
function
)
config[function_key] = function_name
config[function_key + "_type"] = function_type
base_config = super(_ConvVariational, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
"""Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the
configuration of a layer. The same layer can be reinstantiated later
(without its trained weights) from this configuration.
Returns:
config: A Python dictionary of class keyword arguments and their
serialized values.
"""
config = {
'units':
self.units,
'activation': (tf.keras.activations.serialize(self.activation)
if self.activation else None),
'activity_regularizer':
tf.keras.initializers.serialize(self.activity_regularizer),
}
function_keys = [
'kernel_posterior_fn',
'kernel_posterior_tensor_fn',
'kernel_prior_fn',
'kernel_divergence_fn',
'bias_posterior_fn',
'bias_posterior_tensor_fn',
'bias_prior_fn',
'bias_divergence_fn',
]
for function_key in function_keys:
function = getattr(self, function_key)
if function is None:
function_name = None
function_type = None
else:
function_name, function_type = tfp_layers_util.serialize_function(
function)
config[function_key] = function_name
config[function_key + '_type'] = function_type
base_config = super(_DenseVariational, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
"""Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the
configuration of a layer. The same layer can be reinstantiated later
(without its trained weights) from this configuration.
Returns:
config: A Python dictionary of class keyword arguments and their
serialized values.
"""
config = {
'units': self.units,
'activation': (tf.keras.activations.serialize(self.activation)
if self.activation else None),
'activity_regularizer':
tf.keras.initializers.serialize(self.activity_regularizer),
}
function_keys = [
'kernel_posterior_fn',
'kernel_posterior_tensor_fn',
'kernel_prior_fn',
'kernel_divergence_fn',
'bias_posterior_fn',
'bias_posterior_tensor_fn',
'bias_prior_fn',
'bias_divergence_fn',
]
for function_key in function_keys:
function = getattr(self, function_key)
if function is None:
function_name = None
function_type = None
else:
function_name, function_type = tfp_layers_util.serialize_function(
function)
config[function_key] = function_name
config[function_key + '_type'] = function_type
base_config = super(_DenseVariational, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
