from keras.models import load_model
 
# 创建HDF5文件'my_model.h5'，保存模型参数
model.save('my_model.h5')
 
# 加载模型参数
load_model('my_model.h5')

from keras.models import load_model
 
# 假设你的模型包含一个 AttentionLayer 类的实例
model = load_model('my_model.h5', custom_objects={'AttentionLayer': AttentionLayer})

或者，你可以使用 自定义对象作用域：

from keras.utils import CustomObjectScope
with CustomObjectScope({'AttentionLayer': AttentionLayer}):
  model = load_model('my_model.h5')

from keras.models import load_model
 
model.save('my_model.h5') # creates a HDF5 file 'my_model.h5'
del model # deletes the existing model
 
# returns a compiled model
# identical to the previous one
model = load_model('my_model.h5')

# save as JSON
json_string = model.to_json()
 
# save as YAML
yaml_string = model.to_yaml()

# model reconstruction from JSON:
from keras.models import model_from_json
model = model_from_json(json_string)
 
# model reconstruction from YAML
model = model_from_yaml(yaml_string)

"""
假如原模型为：
  model = Sequential()
  model.add(Dense(2, input_dim=3, name="dense_1"))
  model.add(Dense(3, name="dense_2"))
  ...
  model.save_weights(fname)
"""
# new model
model = Sequential()
model.add(Dense(2, input_dim=3, name="dense_1")) # will be loaded
model.add(Dense(10, name="new_dense")) # will not be loaded
 
# load weights from first model; will only affect the first layer, dense_1.
model.load_weights(fname, by_name=True)