def from_config(cls, config):
"""Creates a layer from its config.
This method is the reverse of `get_config`, capable of instantiating the
same layer from the config dictionary.
Args:
config: A Python dictionary, typically the output of `get_config`.
Returns:
layer: A layer instance.
"""
config = config.copy()
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:
serial = config[function_key]
function_type = config.pop(function_key + '_type')
if serial is not None:
config[function_key] = tfp_layers_util.deserialize_function(
serial, function_type=function_type)
return cls(**config)
def from_config(cls, config):
"""Creates a layer from its config.
This method is the reverse of `get_config`, capable of instantiating the
same layer from the config dictionary.
Args:
config: A Python dictionary, typically the output of `get_config`.
Returns:
layer: A layer instance.
"""
config = config.copy()
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:
serial = config[function_key]
function_type = config.pop(function_key + '_type')
if serial is not None:
config[function_key] = tfp_layers_util.deserialize_function(
serial,
function_type=function_type)
return cls(**config)
