python - 如何在 AutoKeras 1.0 中保存/加载模型

Question

我正在使用 AutoKeras 1.0，我无法理解我应该如何保存和重新加载经过训练的模型(加上权重等)。

我可以使用类似于以下的代码轻松训练模型:

num_data = 500
train_x = common.generate_structured_data(num_data)
train_y = common.generate_one_hot_labels(num_instances=num_data, num_classes=3)
clf = ak.StructuredDataClassifier(
    column_names=common.COLUMN_NAMES_FROM_NUMPY,
    max_trials=1,
    seed=common.SEED)
clf.fit(train_x, train_y, epochs=4, validation_data=(train_x, train_y))
loss = clf.evaluate(train_x, train_y)
print(loss)

但是，我无法从文档中得知如何保存此模型并稍后在另一个程序中重用它。我试过找到“最佳”模型并保存它，如下所示:

preprocess_graph, best_model = clf.tuner.get_best_model()
best_model.save("testmodel.h5")

但是，当我尝试再次加载该模型时，我得到以下信息:

new_model = load_model("testmodel.h5")

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-12-bd01053bfeda> in <module>
----> 1 new_model = load_model("testmodel.h5")

/opt/conda/lib/python3.7/site-packages/keras/engine/saving.py in load_wrapper(*args, **kwargs)
    490                 os.remove(tmp_filepath)
    491             return res
--> 492         return load_function(*args, **kwargs)
    493 
    494     return load_wrapper

/opt/conda/lib/python3.7/site-packages/keras/engine/saving.py in load_model(filepath, custom_objects, compile)
    582     if H5Dict.is_supported_type(filepath):
    583         with H5Dict(filepath, mode='r') as h5dict:
--> 584             model = _deserialize_model(h5dict, custom_objects, compile)
    585     elif hasattr(filepath, 'write') and callable(filepath.write):
    586         def load_function(h5file):

/opt/conda/lib/python3.7/site-packages/keras/engine/saving.py in _deserialize_model(h5dict, custom_objects, compile)
    272         raise ValueError('No model found in config.')
    273     model_config = json.loads(model_config.decode('utf-8'))
--> 274     model = model_from_config(model_config, custom_objects=custom_objects)
    275     model_weights_group = h5dict['model_weights']
    276 

/opt/conda/lib/python3.7/site-packages/keras/engine/saving.py in model_from_config(config, custom_objects)
    625                         '`Sequential.from_config(config)`?')
    626     from ..layers import deserialize
--> 627     return deserialize(config, custom_objects=custom_objects)
    628 
    629 

/opt/conda/lib/python3.7/site-packages/keras/layers/__init__.py in deserialize(config, custom_objects)
    166                                     module_objects=globs,
    167                                     custom_objects=custom_objects,
--> 168                                     printable_module_name='layer')

/opt/conda/lib/python3.7/site-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    145                     config['config'],
    146                     custom_objects=dict(list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 147                                         list(custom_objects.items())))
    148             with CustomObjectScope(custom_objects):
    149                 return cls.from_config(config['config'])

/opt/conda/lib/python3.7/site-packages/keras/engine/network.py in from_config(cls, config, custom_objects)
   1054         # First, we create all layers and enqueue nodes to be processed
   1055         for layer_data in config['layers']:
-> 1056             process_layer(layer_data)
   1057 
   1058         # Then we process nodes in order of layer depth.

/opt/conda/lib/python3.7/site-packages/keras/engine/network.py in process_layer(layer_data)
   1040 
   1041             layer = deserialize_layer(layer_data,
-> 1042                                       custom_objects=custom_objects)
   1043             created_layers[layer_name] = layer
   1044 

/opt/conda/lib/python3.7/site-packages/keras/layers/__init__.py in deserialize(config, custom_objects)
    166                                     module_objects=globs,
    167                                     custom_objects=custom_objects,
--> 168                                     printable_module_name='layer')

/opt/conda/lib/python3.7/site-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    147                                         list(custom_objects.items())))
    148             with CustomObjectScope(custom_objects):
--> 149                 return cls.from_config(config['config'])
    150         else:
    151             # Then `cls` may be a function returning a class.

/opt/conda/lib/python3.7/site-packages/keras/engine/base_layer.py in from_config(cls, config)
   1177             A layer instance.
   1178         """
-> 1179         return cls(**config)
   1180 
   1181     def count_params(self):

/opt/conda/lib/python3.7/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

TypeError: __init__() got an unexpected keyword argument 'ragged'

我做错了吗还是有更好的方法？

Answer 1

你可以试试这个来加载保存的模型:

将tensorflow导入为tf
new_model = tf.keras.models.load_model('testmodel.h5')