def model_from_config(config, custom_objects={}):
from Keras.utils.layer_utils import layer_from_config
if isinstance(config, list):
raise Exception(
'`model_fom_config` expects a dictionary, not a list. '
'Maybe you meant to use `Sequential.from_config(config)`?')
return layer_from_config(config, custom_objects=custom_objects)
def model_from_json(json_string, custom_objects={}):
'''Parses a JSON model configuration file
and returns a model instance.
'''
import json
from Keras.utils.layer_utils import layer_from_config
config = json.loads(json_string)
return layer_from_config(config, custom_objects=custom_objects)
def model_from_yaml(yaml_string, custom_objects={}):
'''Parses a yaml model configuration file
and returns a model instance.
'''
import yaml
from Keras.utils.layer_utils import layer_from_config
config = yaml.load(yaml_string)
return layer_from_config(config, custom_objects=custom_objects)
def from_config(cls, config, layer_cache=None):
'''Supports legacy formats
'''
from Keras.utils.layer_utils import layer_from_config
from Keras.layers import Merge
assert type(config) is list
if not layer_cache:
layer_cache = {}
def normalize_legacy_config(conf):
if 'class_name' not in conf:
class_name = conf['name']
name = conf.get('custom_name')
conf['name'] = name
new_config = {
'class_name': class_name,
'config': conf,
}
return new_config
return conf
# the model we will return
model = cls()
def get_or_create_layer(layer_data):
if layer_data['class_name'] == 'Sequential':
return Sequential.from_config(layer_data['config'],
layer_cache=layer_cache)
name = layer_data['config'].get('name')
if name in layer_cache:
return layer_cache[name]
layer = layer_from_config(layer_data)
layer_cache[name] = layer
return layer
first_layer = config[0]
first_layer = normalize_legacy_config(first_layer)
if first_layer['class_name'] == 'Merge':
merge_inputs = []
first_layer_config = first_layer['config']
for merge_input_config in first_layer_config.pop('layers'):
merge_input = layer_from_config(merge_input_config)
merge_inputs.append(merge_input)
first_layer_config['layers'] = merge_inputs
merge = Merge.from_config(first_layer_config)
model.add(merge)
else:
layer = get_or_create_layer(first_layer)
model.add(layer)
for conf in config[1:]:
conf = normalize_legacy_config(conf)
layer = get_or_create_layer(conf)
model.add(layer)
return model
def get_or_create_layer(layer_data):
if layer_data['class_name'] == 'Sequential':
return Sequential.from_config(layer_data['config'],
layer_cache=layer_cache)
name = layer_data['config'].get('name')
if name in layer_cache:
return layer_cache[name]
layer = layer_from_config(layer_data)
layer_cache[name] = layer
return layer
def from_config(cls, config):
# TODO: test legacy support
from Keras.utils.layer_utils import layer_from_config
def normalize_legacy_config(conf):
if 'class_name' not in conf:
class_name = conf['name']
name = conf.get('custom_name')
conf['name'] = name
new_config = {
'class_name': class_name,
'config': conf,
}
return new_config
return conf
graph = cls()
inputs = config.get('input_config')
for input in inputs:
graph.add_input(**input)
nodes = config.get('node_config')
for node in nodes:
layer_config = config['nodes'][node['name']]
layer_config = normalize_legacy_config(layer_config)
if 'layer' in node:
# for add_shared_node
node['layer'] = layer_from_config(node['layer'])
else:
layer = layer_from_config(layer_config)
node['layer'] = layer
node['create_output'] = False # outputs will be added below
if layer_config.get('shared'):
graph.add_shared_node(**node)
else:
graph.add_node(**node)
outputs = config.get('output_config')
for output in outputs:
graph.add_output(**output)
return graph
def from_config(cls, config):
from Keras.utils.layer_utils import layer_from_config
layer = layer_from_config(config.pop('layer'))
return cls(layer, **config)