def cloneLayerFromLayer(pLayer):
if isinstance(pLayer, Convolution1D):
return Convolution1D.from_config(pLayer.get_config())
elif isinstance(pLayer, Convolution2D):
return Convolution2D.from_config(pLayer.get_config())
elif isinstance(pLayer, Convolution3D):
return Convolution3D.from_config(pLayer.get_config())
# Max-Pooling:
elif isinstance(pLayer, MaxPooling1D):
return MaxPooling2D.from_config(pLayer.get_config())
elif isinstance(pLayer, MaxPooling2D):
return MaxPooling2D.from_config(pLayer.get_config())
elif isinstance(pLayer, MaxPooling3D):
return MaxPooling3D.from_config(pLayer.get_config())
# Average-Pooling
elif isinstance(pLayer, AveragePooling1D):
return AveragePooling1D.from_config(pLayer.get_config())
elif isinstance(pLayer, AveragePooling2D):
return AveragePooling2D.from_config(pLayer.get_config())
elif isinstance(pLayer, AveragePooling3D):
return AveragePooling3D.from_config(pLayer.get_config())
#
elif isinstance(pLayer, Flatten):
return Flatten.from_config(pLayer.get_config())
elif isinstance(pLayer, Merge):
return Merge.from_config(pLayer.get_config())
elif isinstance(pLayer, Activation):
return Activation.from_config(pLayer.get_config())
elif isinstance(pLayer, Dropout):
return Dropout.from_config(pLayer.get_config())
#
elif isinstance(pLayer, Dense):
return Dense.from_config(pLayer.get_config())
return None
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 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 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 from_config(cls, config):
'''Supports legacy formats
'''
from keras.utils.layer_utils import layer_from_config
from keras.layers import Merge
assert type(config) is list
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
model = cls()
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 = layer_from_config(first_layer)
model.add(layer)
for conf in config[1:]:
conf = normalize_legacy_config(conf)
layer = layer_from_config(conf)
model.add(layer)
return model
def from_config(cls, config):
'''Supports legacy formats
'''
from keras.utils.layer_utils import layer_from_config
from keras.layers import Merge
assert type(config) is list
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
model = cls()
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 = layer_from_config(first_layer)
model.add(layer)
for conf in config[1:]:
conf = normalize_legacy_config(conf)
layer = layer_from_config(conf)
model.add(layer)
return model
